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IPCC:AR6/WGI/Chapter-12

2026-05-13T14:00:45Z

172.18.0.1: /* Chapter 12: Climate Change Information for Regional Impact and for Risk Assessment */

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= Chapter 12: Climate Change Information for Regional Impact and for Risk Assessment =

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'''Coordinating Lead Authors:'''

Roshanka Ranasinghe (The Netherlands/Sri Lanka, Australia), Alex C. Ruane (United States of America), Robert Vautard (France)

'''Lead Authors:'''

Nigel Arnell (United Kingdom), Erika Coppola (Italy), Faye Abigail Cruz (Philippines), Suraje Dessai (United Kingdom/Portugal), A.K.M. Saiful Islam (Bangladesh), Mohammad Rahimi (Iran), Daniel Ruiz Carrascal (United States of America/Colombia), Jana Sillmann (Norway/Germany), Mouhamadou Bamba Sylla (Rwanda/Senegal), Claudia Tebaldi (United States of America), Wen Wang (China), Rashyd Zaaboul (United Arab Emirates/Morocco)

'''Contributing Authors:'''

Carley E. Iles (Norway, France/United Kingdom), Jérôme Servonnat (France), Guðfinna Aðalgeirsdóttir (Iceland), Rita Adrian (Germany), Roxana Bojariu (Romania), Laurent Bopp (France), Audrey Brouillet (France), Carlo Buontempo (United Kingdom/Italy), Winston Chow (Singapore), Augustin Colette (France), Cecilia Conde (Mexico), Leticia Cotrim da Cunha (Brazil), Claudine Dereczynski (Brazil), Alejandro Di Luca (Australia, Canada/Argentina), Fabio Di Sante (Italy), Arona Diedhiou (Côte d’Ivoire/Senegal), Aida Diongue-Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Pariva Dobriyal (India), Sybren S. Drijfhout (The Netherlands), John P. Dunne (United States of America), Tamsin L. Edwards (United Kingdom), Aidan D. Farrell (Trinidad and Tobago/Ireland), Erich Fischer (Switzerland), John C. Fyfe (Canada), Alexander Gelfan (Russian Federation), Subimal Ghosh (India), Irina V. Gorodetskaya (Portugal/Belgium, Russian Federation), Michael Grose (Australia), José Manuel Gutiérrez (Spain), David S. Gutzler (United States of America), Rebecca Harris (Australia), Matthias Hauser (Switzerland), Mark Hemer (Australia), Kevin Hennessy (Australia), Helene T. Hewitt (United Kingdom), Masao Ishii (Japan), Maialen Iturbide (Spain), Christopher D. Jack (South Africa), Richard G. Jones (United Kingdom), Nikolay Kadygrov (France/Russian Federation), Ebru Kirezci (Australia/Turkey), Nana Ama Browne Klutse (Ghana), Robert E. Kopp (United States of America), James Kossin (United States of America), Charles Koven (United States of America), Svitlana Krakovska (Ukraine), Gerhard Krinner (France/Germany, France), Benjamin L. Lamptey (Niger, Ghana/Ghana), Christopher Lennard (South Africa), Xianfu Lu (United Kingdom/China), Douglas Maraun (Austria/Germany), Simon McGree (Australia/Fiji, Australia), Glenn McGregor (United Kingdom/New Zealand, United Kingdom), Kathleen L. McInnes (Australia), Dirk Notz (Germany), Brian O’Neill (United States of America), Ben Orlove (United States of America), Friederike Otto (United Kingdom/Germany), Carlos Pérez García-Pando (Spain), Franz Prettenthaler (Austria), Francesca Raffaele (Italy), Srivatsan Raghavan (Singapore/India), Christophe F. Randin (Switzerland/France, Switzerland), Johan Reyns (The Netherlands/Belgium), Lucas Ruiz (Argentina), Fahad Saeed (Germany/Pakistan), Jean-Baptiste Sallee (France), Marit Sandstad (Norway), Clemens Schwingshackl (Norway, Germany/Italy), Sonia I. Seneviratne (Switzerland), Aimée B.A. Slangen (The Netherlands), Tannecia S. Stephenson (Jamaica), Anna Steynor (South Africa), Markus Stoffel (Switzerland), Benjamin Sultan (France), William V. Sweet (United States of America), Sophie Szopa (France), Izuru Takayabu (Japan), Moustapha Tall (Rwanda/Senegal), N’Datchoh Evelyne Touré N’Datchoh (Cote d’Ivoire), Bart van den Hurk (The Netherlands), Sergio M. Vicente-Serrano (Spain), Michalis Vousdoukas (Italy, Greece/Greece), Morgan Wairiu (Fiji), Prodromos Zanis (Greece), Xuebin Zhang (Canada)

'''Review Editors:'''

Edvin Aldrian (Indonesia), David Karoly (Australia), Murat Türkeş (Turkey)

'''Chapter Scientists:'''

Carley E. Iles (Norway, France/United Kingdom), Jérôme Servonnat (France)

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'''This chapter should be cited as:'''

Ranasinghe, R., A.C. Ruane, R. Vautard, N. Arnell, E. Coppola, F.A. Cruz, S. Dessai, A.S. Islam, M. Rahimi, D. Ruiz Carrascal, J. Sillmann, M.B. Sylla, C. Tebaldi, W. Wang, and R. Zaaboul, 2021: Climate Change Information for Regional Impact and for Risk Assessment. In ''Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change'' [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi: [https://doi.org/10.1017/9781009157896.014 10.1017/9781009157896.014] .

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== Executive Summary ==

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'''Climate change information is increasingly available and robust at regional scale for impacts and risk assessment.''' Climate services and vulnerability, impacts and adaptation studies require regional scale multi-decadal climate observations and projections. Since the IPCC Fifth Assessment Report (AR5), the increased availability of coordinated ensemble regional climate model projections and improvements in the level of sophistication and resolution of global and regional climate models, completed by attribution and sectoral vulnerability studies, have enabled the investigation of past and future evolution of a range of climatic quantities that are relevant to socio-economic sectors and natural systems. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] consolidates core physical knowledge from preceding AR6 Working Group I (WGI) chapters and post-AR5 climate impact assessment literature to assess the spatio-temporal evolution of the climatic conditions that may lead to regional scale impacts and risks (following the sectoral classes adopted by AR6 WGII) in the world’s regions (presented in Chapter 1). {12.1}

'''The climatic impact-driver (CID) framework adopted in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] allows for assessment of changing climate conditions that are relevant for regional impacts and risk assessment.''' CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems and are thus a priority for climate information provision. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral or a mixture of each across interacting system elements, regions and society sectors. Each sector is affected by multiple CIDs and each CID affects multiple sectors. A CID can be measured by indices to represent related tolerance thresholds. {12.1–12.3}

'''The current climate in most regions is already different from the climate of the early or mid-20th century with respect to several CIDs. Climate change has already altered CID profiles and resulted in shifts in the magnitude, frequency, duration, seasonality and spatial extent of associated indices''' ( ''high confidence'' ''').''' Changes in temperature-related CIDs such as mean temperatures, growing season length, extreme heat and frost have already occurred and many of these changes have been attributed to human activities ( ''medium confidence'' ). Mean temperatures and heat extremes have emerged above natural variability in all land regions ( ''high confidence'' ). In tropical regions, recent past temperature distributions have already shifted to a range different to that of the early 20th century ( ''high confidence'' ). Ocean acidification and deoxygenation have already emerged over most of the global open ocean, as has reduction in Arctic sea ice ( ''high confidence'' ). Using CID index distributions and event probabilities accurately in both current and future risk assessments requires accounting for the climate change-induced shifts in distributions that have already occurred. {12.4, 12.5}

'''Several impact-relevant changes have not yet emerged from the natural variability but will emerge sooner or later in this century depending on the emissions scenario''' ( ''high confidence'' ''').''' Increasing precipitation is projected to emerge before the middle of the century in the high latitudes of the Northern Hemisphere ( ''high confidence'' ). Decreasing precipitation will emerge in a very few regions (Mediterranean, Southern Africa, south-western Australia) ( ''medium confidence'' ) by mid-century ( ''medium confidence'' ). The anthropogenic forced signal in near-coast relative sea level rise will emerge by mid-century RCP8.5 in all regions with coasts, except in the West Antarctic region where emergence is projected to occur before 2100 ( ''medium confidence'' ). The signal of ocean acidification in the surface ocean is projected to emerge before 2050 in every ocean basin ( ''high confidence'' ). However, there is ''limited evidence'' of drought trends emerging above natural variability in the 21st century. {12.5}

'''Every region of the world will experience concurrent changes in multiple CIDs by mid-century''' ( ''high confidence'' '''), challenging the resilience and adaptation capacity of the region.''' Changes in heat, cold, snow and ice, coastal, oceanic, and CO <sub>2</sub> at surface CIDs are projected with ''high confidence'' in most regions, indicating worldwide challenges, while additional region-specific changes are projected in other CIDs that may lead to more regional challenges. ''High confidence'' increases in some of the drought, aridity and fire weather CIDs will challenge, for example, agriculture, forestry, water systems, health and ecosystems in Southern Africa, the Mediterranean, North Central America, Western North America, the Amazon regions, South-Western South America, and Australia. ''High confidence'' changes in snow, ice and pluvial or river flooding will pose challenges for, for example, energy production, river transportation, ecosystems, infrastructure and winter tourism in North America, Arctic regions, Andes regions, Europe, Siberia, Central, South and East Asia, Southern Australia and New Zealand. Only a few CIDs are projected to change with ''high confidence'' in the Sahara, Madagascar, Arabian Peninsula, Western Africa and Small Islands; however, the ''lower confidence'' levels for CID changes in these regions can originate from knowledge gaps or model uncertainties, and does not necessarily mean that these regions have relatively low risk. {12.5}

'''Worldwide changes in heat, cold, snow and ice, coastal, oceanic and CO''' <sub>2</sub> '''-related CIDs will continue over the 21st century, albeit with regionally varying rates of change, regardless of the climate scenario''' ( ''high confidence'' ''').''' In all regions there is ''high confidence'' that, by 2050, mean temperature and heat extremes will increase, and there is ''high confidence'' that sea surface temperature will increase in all oceanic regions except the North Atlantic. Apart from a few regions with substantial land uplift, relative sea level rise is ''very likely'' to ''virtually certain'' (depending on the region) to continue in the 21st century, contributing to increased coastal flooding in most low-lying coastal areas ( ''high confidence'' ) and coastal erosion along most sandy coasts ( ''high confidence'' ), while ocean acidification is ''virtually certain'' to increase. It is ''virtually certain'' that atmospheric CO <sub>2</sub> at the surface will increase in all emissions scenarios until net zero emissions are achieved. Glaciers will continue to shrink and permafrost to thaw in all regions where they are present ( ''high confidence'' ). These changes will lead to climate states with no recent analogue that are of particular importance for specific regions such as tropical forests or biodiversity hotspots. {12.4}

'''A wide range of region-specific CID changes relative to recent past are expected with''' ''high'' '''or''' ''medium confidence'' ''', by 2050 and beyond.''' Most of these changes concern CIDs related to the water cycle and storms. Agricultural and ecological drought changes are generally of ''higher confidence'' than hydrological drought changes, with increases projected in North and Southern Africa, Madagascar, Southern and Eastern Australia, some regions of Central and South America, Mediterranean Europe, Western North America and North Central America ( ''medium'' to ''high confidence'' ). Fire weather conditions will increase by 2050 under RCP4.5 or above in several regions in Africa, Australia, several regions of South America, Mediterranean Europe, and North America ( ''medium'' to ''high confidence'' ). Extreme precipitation and pluvial flooding will increase in many regions around the world ( ''high confidence'' ). Increases in river flooding are also expected in Western and Central Europe and in polar regions ( ''high confidence'' ), most of Asia, Australasia, North America, the South American Monsoon region and South-Eastern South America ( ''medium confidence'' ). Mean winds are projected to slightly decrease by 2050 over much of Europe, Asia and Western North America, and increase in many parts of South America except Patagonia, West and South Africa and the eastern Mediterranean ( ''medium confidence'' ). Extratropical storms are expected to have a decreasing frequency but increasing intensity over the Mediterranean, increasing intensity over most of North America, and an increase in both intensity and frequency over most of Europe ( ''medium confidence'' ). Enhanced convective conditions are expected in North America ( ''medium confidence'' ). Tropical cyclones are expected to increase in intensity despite a decrease in frequency in most tropical regions ( ''medium confidence'' ). Climate change will modify multiple CIDs over Small Islands in all ocean basins, most notably those related to heat, aridity and droughts, tropical cyclones and coastal impacts. {12.4}

'''The level of confidence in the projected direction of change in CIDs and the intensity of the signal depend on mitigation efforts over the 21st century, as reflected by the differences between end-century projections for different climate scenarios.''' Dangerous humid heat thresholds, such as the US National Oceanic and Atmospheric Administration Heat Index (NOAA HI) of 41°C, will be exceeded much more frequently under the SSP5-8.5 scenario than under SSP1-2.6 and will affect many more regions ( ''high confidence'' ). In many tropical regions, the number of days per year where an HI of 41°C is exceeded will increase by more than 100 days relative to the recent past under SSP5-8.5, while this increase will be limited to less than 50 days under SSP1-2.6 ( ''high confidence'' ). The number of days per year where temperature exceeds 35°C will increase by more than 150 days in many tropical areas, such as the Amazon basin and South East Asia under SSP5-8.5, while it is expected to increase by less than two months in these areas under SSP1-2.6 (except for the Amazon Basin). There is ''high confidence'' that sandy shorelines will retreat in most regions of the world, in the absence of additional sediment sources or physical barriers to shoreline retreat. The total length of sandy shorelines around the world that are projected to retreat by more than 100 m by the end of the century is about 35% greater under RCP8.5 (about 130,000 km) compared to that under RCP 4.5 (about 95,000 km). The frequency of the present-day 1-in-100 year extreme sea level event (represented here by extreme total water level) is also projected to increase substantially in most regions ( ''high confidence'' ). In a globally averaged sense, the 1-in-100 year extreme sea level is projected to become an event that occurs multiple times per year under RCP8.5, while under RCP4.5 it is projected to become a one-in-five-year event, representing at least a five fold increase from RCP4.5 to RCP8.5. {12.4, 12.5}

'''There is''' ''low confidence'' '''in past and future changes of several CIDs.''' In nearly all regions there is ''low confidence'' in changes in hail, ice storms, severe storms, dust storms, heavy snowfall and avalanches, although this does not indicate that these CIDs will not be affected by climate change. For such CIDs, observations are short term or lack homogeneity, and models often do not have sufficient resolution or accurate parametrization to adequately simulate them over climate change time scales. {12.4}

'''Many global- and regional-scale CIDs have a direct relation to global warming levels (GWLs) and can thus inform the hazard component of ‘Representative Key Risks’ and ‘Reasons for Concern’ assessed by AR6 WGII.''' These include both mean and extreme heat, cold, wet and dry hazards; cryospheric hazards (snow cover, ice extent, permafrost); and oceanic hazards (marine heatwaves) ( ''high confidence'' ) ''.'' For some of these, a quantitative relation can be drawn ( ''high confidence'' ). For example, with each degree of global surface air temperature (GSAT) warming, the magnitude and intensity of many heat extremes show a linear change, while some changes in frequency of threshold exceedances are exponential: Arctic temperatures warm about twice as fast as GSAT; global sea surface temperatures increase by about 80% of GSAT change; Northern Hemisphere spring snow cover decreases by about 8% <sup></sup> per 1°C. For other hazards (e.g., ice-sheet behaviour, glacier mass loss, global mean sea level rise, coastal floods and coastal erosion) the time and/or scenario dimensions remain critical and a simple relation with GWLs cannot be drawn ( ''high confidence'' ), but still quantitative estimates assuming specific time frames and/or stabilized GWLs can be derived ( ''medium confidence'' ). Model uncertainty challenges the link between specific GWLs and tipping points and irreversible behaviour, but their occurrence cannot be excluded and their chances increase with warming levels ( ''medium confidence'' ). {CCB 12.1}

'''Since AR5, climate change information produced in climate service contexts has increased significantly due to scientific, technological advancements and growing user demand''' ( ''very high confidence'' ''').''' Climate services involve the provision of climate information in such a way as to assist decision-making. These services include appropriate engagement from users and providers, are based on scientifically credible information and expertise, have an effective access mechanism, and respond to user needs. Climate services are being developed across regions, sectors, time scales and target users. {12.6}

'''Climate services are growing rapidly and are highly diverse in their practices and products''' ( ''very high confidence'' ''').''' The decision-making context, level of user engagement and co-production between scientists, practitioners and intended users are important determinants of the type of climate service developed and its utility supporting adaptation, mitigation and risk management decisions. User needs and decision-making contexts are very diverse and there is no universal approach to climate services. {12.6}

'''Realization of the full potential of climate services is often hindered by limited resources for the co-design and c''' '''o-product''' '''ion process, including sustained engagement between scientists, service providers and users''' ( ''high confidence'' ''').''' Further challenges relate to climate services development, provision of climate services, generation of climate service products, communication with users, and evaluation of the quality and socio-economic value of climate services. The development of climate services often uncovers and presents new research challenges to the scientific community. {12.6}

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== 12.1 Framing ==

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Climate change is already resulting in significant societal and environmental impacts and will induce major socio-economic damages in the future (AR5 WGII). The society, at large, benefits from information related to climate change risks, which enables the development of options to protect lives, preserve nature, build resilience and prevent avoidable loss and damage. Climate change can also lead to beneficial conditions that can be taken into account in adaptation strategies.

'''This chapter assesses climate change information relevant for regional impact and for risk assessment. It complements other WGI chapters that focus on the physical processes determining changes in the climate system and on methods for estimating regional changes.'''

Impacts of climate change are driven not only by changes in climate conditions, but also by changes in exposure and vulnerability (Cross-Chapter Box 1.3). This chapter concentrates on drivers of impacts that are of climatic origin (see also the IPCC Special Report on Global Warming of 1.5°C (SR1.5, [[#IPCC--2018|IPCC, 2018]] ), and [[IPCC:Wg1:Chapter:Chapter-1#1.3.2|Section 1.3.2]] in this Report), referred to in WGI as ‘climatic impact-drivers’ (CIDs). CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions. However, this chapter largely focuses on drivers commonly connected to hazards, and adopts the IPCC risk framework (Cross-Chapter Box 1.3) since the main objective of the United Nations Framework Convention on Climate Change (UNFCCC) is to ‘prevent dangerous anthropogenic interference with the climate system’ (Article 2).

In some cases, risk assessments may require climate information beyond the CIDs identified in this chapter, with further impacts or risk modelling often driven by historical climate forcing datasets (e.g., [[#Ruane--2021|Ruane et al., 2021]] ) and full climate scenario time series (e.g., [[#Lange--2019|Lange, 2019]] ) produced using methods described in Chapter 10. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] focuses on the assessment of a finite number of drivers and how they are projected to evolve with climate change, in order to inform impact and risk assessments.

This chapter is new in IPCC WGI assessment reports, in that it represents a contribution to the ‘IPCC risk framework’. Within this framework, climate-related impacts and risks are determined through an interplay between the occurrence of climate hazards and their consequences depending on the exposure of the affected human or natural system and its vulnerability to the hazardous conditions. In Chapter 12, we are assessing climatic impact-drivers that could lead to hazards or to opportunities, from the literature and model results since AR5. This will particularly support the assessment of key risks related to climate change by WGII (Chapter 16). Despite the fact that impacts may also be induced by climate adaptation and mitigation policies themselves, as well as by socio-economic trends, changes in vulnerability or exposure, and external geophysical hazards such as volcanoes, the focus here is only on climatic impacts and risks induced by shifts in physical climate phenomena that directly influence human and ecological systems (Cross-chapter Box 1.3).

This chapter follows the terminology associated with the framing introduced in [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] (Cross-Chapter Box 1.2) and as found in Annex VII: Glossary. The highlighted terms below are introduced and used extensively in this chapter:

* '''Indices for climatic impact-drivers:''' numerically computable indices using one or a combination of climate variables designed to measure the intensity of the climatic impact-driver, or the probability of exceedance of a threshold. For instance, an index of heat inducing human health stress is the Heat Index (HI) that combines temperature and relative humidity (e.g., [[#Burkart--2011|Burkart et al., 2011]] ; [[#Lin--2012|Lin et al., 2012]] ; [[#Kent--2014|Kent et al., 2014]] ) and is used by the NOAA for issuing heat warnings.
* '''Thresholds for climatic impact-drivers:''' an identified index value beyond which a climatic impact-driver interacts with vulnerability or exposure to create, increase or reduce an impact, risk or opportunity. Thresholds can be used to measure various aspects of the climatic impact-driver (magnitude or intensity, duration, frequency, timing, and spatial extent of threshold exceedance). For instance, a threshold of daily maximum temperature above 35°C is considered critical for maize pollination and production (e.g., [[#Schauberger--2017|Schauberger et al., 2017]] ; [[#Tesfaye--2017|Tesfaye et al., 2017]] ).

The approach adopted here is consistent with the UN Sendai Framework for Disaster Risk Reduction 2015–2030, which aims to face disaster consequences (including but not limited to climate disasters) and reduce risks in natural, managed and built environments ( [[#Aitsi-Selmi--2015|Aitsi-Selmi et al., 2015]] ; [[#UNISDR--2015|UNISDR, 2015]] ). The classification of climatic impact-drivers in this chapter is largely consistent with the classification of hazards used in the Sendai Framework. However, the UNISDR hazard list spans a wider range of hazards inducing damage to society, including hazards that are not directly related to climate (such as volcanoes and earthquakes), which are excluded from the assessment herein. Furthermore, the UNISDR classification of hazards does not include mean climatic conditions, which are also discussed as climatic impact-drivers in this chapter. The first priority mentioned in the Sendai Framework is understanding disaster risk as a necessary step for action. Facilitating such an understanding is a clear goal of this chapter.

The chapter adopts a regional perspective (continental regions as defined in [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] and used in WGII; see Figure 1.18) on climatic impact-drivers to support decision-making across a wide audience of global and regional stakeholders in addition to governments (e.g., civil society organizations, public and private sectors, academia). While the focus here is on future changes, it also describes current levels and observed trends of CIDs as an important point of reference for informing adaptation strategies.

Figure 12.1 summarizes the rationale behind ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] as the linkage (also referred to as a ‘handshake’) between WGI and WGII, illustrating how the changing profile of risk may be informed by an assessment of climatic impact-drivers, aligning WGI findings on physical climate change with WGII needs. The implementation of mitigation policy shifts may modulate hazard probability changes (i.e., by reducing emissions to limit global warming) as well as regional vulnerability and exposure. The assessment herein is organized around regional climatic impact-drivers, but also relates key indices and thresholds to increasing global drivers (such as mean surface warming) as a contribution to the assessment of ‘Reasons for Concern’ in WGII ( [[#O’Neill--2017|O’Neill et al., 2017]] ).

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'''Figure 1''' '''2.1 |''' '''Schematic diagram showing the use of climate change information (AR6 WGI chapters) for typical impacts or risk assessment (AR6 WGII chapters) and the role of Chapter 12, via an illustration of the assessment of property damage or loss in a particular region when extreme sea level exceeds dike height.'''

The narrative of ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] is illustrated in Figure 12.2. First, [[#12.2|Section 12.2]] defines a range of climatic impact-driver categories that are relevant for regional and sectoral impacts. Next, [[#12.3|Section 12.3]] identifies climatic impact-drivers and their relevant indices that are frequently used in the context of climate impacts in the WGII focus sectors (AR6 WGII Chapters 2–8). The assessment of changes in regional-scale climatic impact-drivers is then developed within [[#12.4|Section 12.4]] by continent, following the structure of the WGII assessment report regional chapters (AR6 WGII Chapters 9–15), and adding the polar regions, open/deep ocean and other specific zones corresponding to the WGII Cross-Chapter Papers. [[#12.5|Section 12.5]] then presents a global perspective (both bottom-up and top-down) on the change of regional climatic impact-drivers, including an assessment of the ‘emergence’ of climatic impact-drivers. [[#12.6|Section 12.6]] discusses how climate information is used in ‘climate services’, which encompasses a range of activities bridging climate science and its use for adaptation and mitigation decision-making (see also AR6 WGII Chapter 17). The chapter concludes with final remarks in [[#12.7|Section 12.7]] .

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'''Figure 1''' '''2.2 |''' '''Visual guide to Chapter 12.'''

The chapter includes two Cross-Chapter Boxes. Cross-Chapter Box 12.1 connects climatic impact-drivers to global climate drivers and levels of warming as an element of the ‘Reasons for Concern’ framework (AR6 WGII Chapter 16). An additional Cross-Chapter Box, including three case studies from Europe, Asia and Africa, describes how climate services draw upon and apply regional climate information to support stakeholder decisions (Cross-Chapter Box 12.2).

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== 12.2 Methodological Approach ==

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This section details the methodological approach followed in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] and discusses the underlying rationale for the assessments presented herein. Scientific literature on vulnerability, impacts, and adaptation (as typically asssessed in IPCC WGII) is examined to identify relevant climatic impact-drivers (CIDs) that contribute to sectoral risks and opportunities. Projected changes in corresponding CID indices are then derived from existing literature on changes in the physical climate system, results of other AR6 WGI chapters, and direct calculations based on climate projections from several model ensembles.

The classification of climatic impact-drivers, the ways that they change (e.g., their magnitude or intensity, duration, frequency, timing and spatial extent) is described in this section. It is emphasized that this chapter assesses literature relating only to physical climatic impact-drivers, not their impacts on human systems or the environment. Thus, here we do not consider indicators including exposure or vulnerability as assessed by WGII, although the selection of climatic impact-drivers is informed by literature feeding into WGII.

( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] assesses climate change information relevant for regional impact and for risk assessment in the seven main sectors corresponding to Chapters 2–8 of the WGII Assessment Report:

* Terrestrial and freshwater ecosystems and their services (WGII Chapter 2);
* Ocean and coastal ecosystems and their services (WGII Chapter 3);
* Water (WGII Chapter 4);
* Food, fibre and other ecosystem products (WGII Chapter 5);
* Cities, settlements and key infrastructure (WGII Chapter 6);
* Health, well-being and the changing structure of communities (WGII Chapter 7);
* Poverty, livelihoods and sustainable development (WGII Chapter 8).

Many of these sectors also include assets affected by climate change that are important for recreation and tourism, including elements of ecosystems services, health and well-being, communities, livelihoods and sustainable development (see also [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] on the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), and the IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SRCCL; [[#Hurlbert--2019|Hurlbert et al., 2019]] ; [[#IPCC--2019c|IPCC, 2019c]] )).

CIDs can be captured in seven main types: heat and cold; wet and dry; wind; snow and ice; coastal; oceanic and other. Table 12.1 provides an overview of the seven CID types and the CID categories associated with each type. The type ‘Other’ comprises additional CIDs that are not encompassed within the other six CID types, including air pollution weather (e.g., meteorological conditions that favour high concentrations of surface ozone, particulate matter or other air pollutants), near-surface atmospheric CO <sub>2</sub> concentrations, and mean radiation forcing at the surface (which are, for example, relevant for plant growth). Icebergs, fog and lightning are also noted in this chapter but are not broadly assessed across all subsections. In addition, there can be changes in impacts associated with earthquakes that interact with climate variables and climate change, such as liquefaction (e.g., [[#Yasuhara--2012|Yasuhara et al., 2012]] ) during earthquakes, or earthquakes caused by snow and water changes ( [[#Amos--2014|Amos et al., 2014]] ; [[#Johnson--2017|Johnson et al., 2017]] ), which are secondary effects on geophysical hazards that are not further assessed in this chapter. The characteristics and physical description of the climate phenomena or essential climate variables associated with each of these CID categories are assessed and described in previous Chapters 2–11 or ( [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] directly as indicated in Table 12.1. The CID categories are further mapped on to different sectors in [[#12.3|Section 12.3]] (Table 12.2).

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'''Table 12.1''' '''|''' '''Overview of the main climatic impact-driver (CID) types and related CID categories with a short description and their link to other chapters where the underlying climatic phenomenon and its associated essential climate variables are assessed and described.'''

[[File:b7672d886a8cc3f775d9a4e026e8703d IPCC_AR6_WGI_Chapter12_Table_12_1_1.jpg]] [[File:4149f4573e388d02820d3e8757901264 IPCC_AR6_WGI_Chapter12_Table_12_1_2.jpg]]

Potential changes in the seasonality of CIDs or the length and characteristics of seasons (e.g., changes in growing season length or pollen season) are also important as they may shift the timing of many CIDs with broad implications for sectors and regional stakeholders ( [[#Wanders--2015|Wanders and Wada, 2015]] ; [[#Cassou--2016|Cassou and Cattiaux, 2016]] ; [[#Hansen--2016|Hansen and Sato, 2016]] ; [[#Brönnimann--2018|Brönnimann et al., 2018]] ; [[#Marelle--2018|Marelle et al., 2018]] ; [[#Unterberger--2018|Unterberger et al., 2018]] ; [[#Kuriqi--2020|Kuriqi et al., 2020]] ). Episodic CIDs characterize impact-relevant conditions persisting from short to long time frames but eventually returning to normal conditions.

In some situations, phenomena causing severe impacts go well beyond a single extreme event or a single climate variable, and can include interaction of climatic conditions, such as sea level rise and storm surge ( [[#Wahl--2015|Wahl et al., 2015]] ), precipitation in combination with strong winds ( [[#Martius--2016|Martius et al., 2016]] ) or flooding quickly followed by a heatwave (S.S.-Y. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ; see also [[IPCC:Wg1:Chapter:Chapter-10#10.5.2|Section 10.5.2.4]] ). Such compound events, particularly in the context of climate extremes, are assessed in [[IPCC:Wg1:Chapter:Chapter-11#11.8|Section 11.8]] . A combination of non-extreme climatic impact-drivers in time or space can also lead to severe impacts ( [[#Cutter--2018|Cutter, 2018]] ).

Several climatic impact-drivers are reliant on many factors beyond their associated primary climatic phenomenon. For example, river flooding is heavily dependent on river management and engineering and could also be affected by tidal water levels due to sea level rise and/or storm surge. Coastal flooding could be affected by coastal protection structures, port and harbour structures, as well as river flows (on inlet-interrupted coasts). Coastal erosion could be influenced by coastal protection measures as well as fluvial sediment supply to the coast. Furthermore, air pollution weather is not the only or dominant driver, for instance, of surface ozone pollution, but precursor emissions from anthropogenic sources can play a significant role (Section 6.5). [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] focuses only on the influence of the atmospheric, land and oceanic conditions associated with the climatic impact-drivers and the confidence in the direction of CID changes given here does not take into account existing or potential future adaptation measures, unless otherwise stated.

For each CID category there can be a range of indices that capture the sector- or application-relevant characteristics of a climatic impact-driver as described in Sections 12.3 and 12.4. Indices for climatic impact-drivers that are based on absolute or percentile thresholds (e.g., daily maximum temperature above 35°C) can be affected by biases in climate model simulations, such as local or regional deviations of a simulated climate variable from observed values ( [[#Sillmann--2014|Sillmann et al., 2014]] ; [[#Dosio--2016|Dosio, 2016]] ). Where sensible (i.e., where reliable observational data are available and a climate model that fits for the desired purpose), the output of climate model simulations can be bias-adjusted, potentially involving advanced methods to account for multiple variables and extreme value statistics as assessed in detail in Cross-Chapter Box 10.2. Yet, there is no general agreement about which bias adjustment methods to apply, as artefacts can arise both from the climate model and from the bias adjustment method, and the number of available methods has considerably grown in recent years (for a detailed discussion of available methods and their performance see Sections 10.3.1.3.2 and 10.3.3.7.2, and Cross-Chapter Box 10.2). The WGI Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] illustrates original and bias-adjusted CIDs (see Atlas.1.4.5).

A global perspective on climatic impact-drivers is provided in [[#12.5.1|Section 12.5.1]] . [[#12.5.2|Section 12.5.2]] focuses on assessing evidence for the emergence ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.2.2|Section 1.4.2.2]] ) of an anthropogenic climate change signal on the change in CIDs beyond natural climate variability, based on the literature assessed in other chapters and additional literature, at both global and regional scales. The process of generating user-relevant regional climate information in the context of co-production and climate services is assessed in Sections 10.5, 12.6, Box 10.2 and Cross-Chapter Boxes 10.3 and 12.2. Cross-Chapter Box 12.1 provides a global perspective on climatic impact-drivers related to their evolution for different GWLs ( [[IPCC:Wg1:Chapter:Chapter-1#1.6|Section 1.6]] ).

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<span id="climatic-impact-drivers-for-sectors"></span>
== 12.3 Climatic Impact-drivers for Sectors ==

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Climate change becomes relevant for regional impact management and for risk assessment when changes in mean conditions or episodic events affect natural and societal assets (system components with socio-economic, cultural or intrinsic value) positively or negatively (Table 12.2). Decision makers, policymakers, risk managers and engineers therefore benefit from climate information that tracks key trends and exceedance of thresholds that represent crucial challenges for natural and human systems. While useful indices can vary widely for a given sector and precise tolerance threshold values are often unknown, common metrics, categories and progressions of threshold levels allow experts to recognize coherent messages concerning altered regional impacts and risk profiles under climate change.

This section surveys the links between CIDs and affected sectors; not to perform specific climate change impact or risk assessments (see AR6 WGII), but to describe key indices (among many) that quantify these links as guidance for stakeholders seeking applicable climate information. This survey builds on the work of the World Meteorological Organization Expert Team on Sector-Specific Climate Indices (ET-SCI) and previous IPCC assessments, notably AR5 WGII ( [[#Birkmann--2014|Birkmann et al., 2014]] ; [[#IPCC--2014a|IPCC, 2014a]] ) and IPCC Special Reports ( [[#IPCC--2018|IPCC, 2018]] , 2019b, c) that have assessed climate hazards affecting sectors but is organized from a CID perspective drawing also upon recent summaries of sectoral hazards ( [[#Mora--2018|Mora et al., 2018]] ; [[#ICOMOS--2019|ICOMOS, 2019]] ; [[#Yokohata--2019|Yokohata et al., 2019]] ). Impacts, risks and opportunities are rarely attributable to a single CID index or threshold, but climate shifts that push conditions outside of expected conditions and beyond tolerance levels are indicative of impact, risk or benefit given vulnerability and exposure. Focus is on direct sectoral connections of a CID ( [[#Hallegatte--2010|Hallegatte and Przyluski, 2010]] ) rather than cascading or secondary effects (e.g., water-borne diseases following a flood, mental health challenges following a severe storm, or the effects of drought on poverty), as these are strongly affected by exposure, vulnerability and response, as discussed in the WGII Report.

Table 12.2 presents a summary of ( [[#12.3|Section 12.3]] connections between CIDs as defined in Table 12.1 and key sectoral assets, utilizing the WGII organization of sectors (corresponding to WGII Chapters 2–8). Colours are shown for connections with at least ''medium confidence'' as assessed from sectoral impacts and risk literature, with relevance assessed according to the prominence of that specific CID/asset connection in analyses of current and future impacts and risk. Within each sector there is a multitude of specific sectoral systems that may be affected by CID increases and decreases, with consequences further distinguished by region, background climate and socio-economic or ecological context of the affected asset. Our aim is therefore to recognize important drivers and the common attributes of change within each CID that scientists and practitioners monitor to understand current and future challenges for important asset groups, thereby pointing to the climate information that needs to be tailored and analysed for impacts and for risk assessment ( [[#12.6|Section 12.6]] ). Additional effects whereby CIDs affect each other (across Table 12.2 columns) are discussed as climatic phenomena within WGI. The ways sectoral assets affect each other (across Table 12.2 rows) are described throughout WGII, for example with information about the suitability of future climate zones and climate velocity challenges for a given asset potentially drawing from multiple CIDs and associated system tolerance thresholds ( [[#Hamann--2015|Hamann et al., 2015]] ). Some broad connections indicated as ''low confidence'' may be under-represented in the literature or could be acute under specific circumstances.

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'''Table 12.2''' '''|''' '''Relevance of key climatic impact-drivers (and their respective changes in intensity, frequency, duration, timing and spatial extent) for major categories of sectoral assets, as assessed with at least''' ''medium confidence'' '''in [[#12.3|Section 12.3]] across many studies and applications.''' ‘High relevance’ indicates climatic impact-drivers that are most prominent and widely studied for their direct connection to assets, while lower relevance indicates weaker linkages and less commonly-studied driving behaviours. Specific levels of risk and opportunity depend on the changing character of regional hazards, vulnerability and exposure as assessed in WGII.

[[File:026a146d497cb21f8daeb5d94d20d213 IPCC_AR6_WGI_Chapter12_Table_12_2a.jpg]] [[File:5af8b391dfb0485caf32b5b47bfd920d IPCC_AR6_WGI_Chapter12_Table_12_2b.jpg]]

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<span id="heat-and-cold"></span>
=== 12.3.1 Heat and Cold ===

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<span id="mean-air-temperature"></span>
==== 12.3.1.1 Mean Air Temperature ====

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Information about increasing mean annual and seasonal air temperature is relevant in the determination of suitable species range for terrestrial, freshwater and intertidal species ( [[#Thomas--2004|Thomas et al., 2004]] ; [[#Elith--2010|Elith et al., 2010]] ; [[#Hincapie--2013|Hincapie and Caicedo, 2013]] ; [[#Cooper--2014|Cooper, 2014]] ; [[#Krist--2014|Krist et al., 2014]] ; [[#Lindner--2014|Lindner et al., 2014]] ; [[#Saintilan--2014|Saintilan et al., 2014]] ; [[#Lenoir--2015|Lenoir and Svenning, 2015]] ; [[#Myers-Smith--2015|Myers-Smith et al., 2015]] ; [[#Urban--2015|Urban, 2015]] ; [[#Thorne--2017|Thorne et al., 2017]] ). Ocean ecosystems are affected by the ocean temperature CID (described in [[#12.3.6.1|Section 12.3.6.1]] ). Species redistribution and extinction studies also need information about climate velocity, a comparison of the pace of warming to geographical temperature gradients that indicates the rate at which a species would have to move to maintain its climatological temperature ( [[#Thomas--2004|Thomas et al., 2004]] ; [[#Loarie--2009|Loarie et al., 2009]] ; [[#Dobrowski--2013|Dobrowski et al., 2013]] ; [[#Burrows--2014|Burrows et al., 2014]] ; [[#Dobrowski--2016|Dobrowski and Parks, 2016]] ; [[#Sittaro--2017|Sittaro et al., 2017]] ) with some studies incorporating additional variables beyond temperature ( [[#Hamann--2015|Hamann et al., 2015]] ). Many freshwater ecosystems are strongly constrained by stream and lake temperatures ( [[#Scheurer--2009|Scheurer et al., 2009]] ; [[#Comte--2013|Comte and Grenouillet, 2013]] ; [[#Contador--2014|Contador et al., 2014]] ; [[#Knouft--2017|Knouft and Ficklin, 2017]] ). Warmer and more stratified lake temperatures are more conducive to cyanobacteria blooms with implications for ecosystem health and water resource quality ( [[#Whitehead--2009|Whitehead et al., 2009]] ; [[#Moss--2011|Moss et al., 2011]] ; [[#Jones--2014|Jones and Brett, 2014]] ; [[#Chapra--2017|Chapra et al., 2017]] ; [[#Shatwell--2019|Shatwell et al., 2019]] ). Consideration of nighttime and daytime temperature trends also elucidates different biophysical effects on vegetation ( [[#Peng--2013|Peng et al., 2013]] ). Changes in the seasonal timing caused by warming trends are critical to species ranges and ecosystem function ( [[#Pearce-Higgins--2015|Pearce-Higgins et al., 2015]] ; [[#Hughes--2017b|Hughes et al., 2017b]] ), and indices that characterize the onset of spring shed light on plant emergence and development ( [[#Ault--2015|Ault et al., 2015]] ).

Mean air temperature dictates many aspects of crop cultivation, livestock production, agroforestry and output from freshwater aquaculture and fisheries, as well as the potential for food contamination. Mean warming alters suitable cultivation zones for crop species ( [[#Bragança--2016|Bragança et al., 2016]] ; [[#Gendron%20St-Marseille--2019|Gendron St-Marseille et al., 2019]] ; [[#IPCC--2019c|IPCC, 2019c]] ) and tree species ( [[#Hanewinkel--2013|Hanewinkel et al., 2013]] ; [[#Fei--2017|Fei et al., 2017]] ). Crop and ecosystem service productivity often responds directly to mean temperatures, although this is dependent on farming systems ( [[#Bassu--2014|Bassu et al., 2014]] ; [[#Challinor--2014|Challinor et al., 2014]] ; [[#Lobell--2014|Lobell and Tebaldi, 2014]] ; [[#Rosenzweig--2014|Rosenzweig et al., 2014]] ; [[#Asseng--2015|Asseng et al., 2015]] ; [[#Li--2015|Li et al., 2015]] ; [[#Fleisher--2017|Fleisher et al., 2017]] ; [[#Zhao--2017|Zhao et al., 2017]] ; [[#Smith--2019|Smith and Fazil, 2019]] ). Many studies relate plant development (phenology), insect generation cycles and pest outbreaks to growing degree days, an aggregation of daily thermal units above a threshold (e.g., T <sub>mean</sub> >5°C) that accelerates with warmer conditions ( [[#Hof--2016|Hof and Svahlin, 2016]] ; [[#Ruosteenoja--2016|Ruosteenoja et al., 2016]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ). Many plants respond to changes in nighttime temperatures that affect respiration and transpiration rates (Narayanan et al., 2015; X. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ), and warming of the soil column is also relevant to determine plant sprouting ( [[#Grotjahn--2021|Grotjahn, 2021]] ). A number of indices have been developed to represent the length of the viable local growing season, including a count of days where T <sub>max</sub> >5°C ( [[#Mueller--2015|Mueller et al., 2015]] ) or the period between a year’s first and last set of five consecutive days with a weighted T <sub>mean</sub> ≥10°C (G. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). Warmer conditions and altered seasonality modify the range and metabolism of some pollinators, pests, diseases and weeds ( [[#Wolfe--2008|Wolfe et al., 2008]] ; [[#Bebber--2015|Bebber, 2015]] ; [[#Aljaryian--2016|Aljaryian and Kumar, 2016]] ; IPBES, 2016; [[#Ramesh--2017|Ramesh et al., 2017]] ; [[#Deutsch--2018|Deutsch et al., 2018]] ; [[#Nyangiwe--2018|Nyangiwe et al., 2018]] ) and may reduce the effectiveness of winter storage for farmers and caching species ( [[#Sutton--2016|Sutton et al., 2016]] ).

Warming raises accumulated seasonal heat indices used in livestock production, especially when humidity is high ( [[#Key--2014|Key et al., 2014]] ; [[#Lallo--2018|Lallo et al., 2018]] ), determines aquaculture suitability and is important for wild fish species migration ( [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Brander--2017|Brander et al., 2017]] ). Agricultural planners may also calculate how overall warming trends alter the accumulation of vernalization units or chilling hours for agricultural or horticultural crops (often accumulated temperature deficit below a given daily or hourly threshold; [[#Dennis--2009|Dennis and Peacock, 2009]] ; [[#Luedeling--2012|Luedeling, 2012]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). Warming in the post-harvest is also important for the determination of spoilage and waste ( [[#Stathers--2013|Stathers et al., 2013]] ) as well as food-borne diseases ( [[#Kovats--2004|Kovats et al., 2004]] ; [[#Mbow--2019|Mbow et al., 2019]] ).

Warming affects road degradation rates ( [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ; [[#Espinet--2016|Espinet et al., 2016]] ) and warming rates inform designs for long-term energy efficiency of buildings ( [[#Kalvelage--2014|Kalvelage et al., 2014]] ). Mean temperature drives seasonal energy demand, often expressed using winter heating degree days (the accumulated deficit of daily temperatures below a ‘comfortable’ indoor temperature, e.g., 15.5°C) and summer cooling degree days (the accumulated excess of temperature above a ‘comfortable’ level, e.g., 18°C; [[#Spinoni--2015|Spinoni et al., 2015]] ; [[#Arnell--2019|Arnell et al., 2019]] ). Energy resources may also need information on warming trends to determine suitable zones and overall productivity for biofuels and solar panels, the efficiency of which decreases with higher temperatures ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Wild--2015|Wild et al., 2015]] ; [[#Solaun--2019|Solaun and Cerdá, 2019]] ).

Health impacts and risk studies compare seasonal temperature conditions to limiting thresholds to understand range shifts and incubation rates for pathogens, disease vectors and zoonotic hosts (e.g., mosquitoes, ticks; [[#Caminade--2012|Caminade et al., 2012]] , 2014; [[#Eisen--2013|Eisen and Moore, 2013]] ; [[#Lima--2016|Lima et al., 2016]] ; [[#Ogden--2017|Ogden, 2017]] ; [[#Monaghan--2018|Monaghan et al., 2018]] ) and warming of surface ocean and lake waters conducive to bacterial outbreaks ( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Jacobs--2015|Jacobs et al., 2015]] ; [[#Vezzulli--2015|Vezzulli et al., 2015]] ). Warmer conditions can also affect tourism ( [[#Kovács--2017|Kovács et al., 2017]] ) and impact human health by lengthening the allergy season and increasing pollen concentration ( [[#Hamaoui-Laguel--2015|Hamaoui-Laguel et al., 2015]] ; [[#Kinney--2015a|Kinney et al., 2015a]] ; [[#Lake--2017|Lake et al., 2017]] ; [[#Upperman--2017|Upperman et al., 2017]] ; [[#Sapkota--2019|Sapkota et al., 2019]] ; [[#Ziska--2019|Ziska et al., 2019]] ).

<div id="12.3.1.2" class="h3-container"></div>

<span id="extreme-heat"></span>
==== 12.3.1.2 Extreme Heat ====

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Impacts and risk assessments utilize a large variety of indices and approaches tailored to evaluate heat impacts on human health ( [[#Sanderson--2017|Sanderson et al., 2017]] ; [[#Gao--2018|]] [[#Gao--2018|C. Gao et al., 2018]] ; [[#McGregor--2018|McGregor and Vanos, 2018]] ; [[#Staiger--2019|Staiger et al., 2019]] ; J. [[#Zhu--2019|]] [[#Zhu--2019|Zhu et al., 2019]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). A mixture of simple and complex heat stress indices often combine extreme temperatures and high humidity to capture human health challenges ( [[#Aström--2013|Aström et al., 2013]] ; [[#Chow--2016|Chow et al., 2016]] ; [[#Dahl--2017a|Dahl et al., 2017a]] ; [[#Im--2017|Im et al., 2017]] ; [[#Coffel--2018|Coffel et al., 2018]] ; J. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Vanos--2020|Vanos et al., 2020]] ). Different optimum temperatures and extreme heat thresholds based on local distributions are needed to reflect acclimation of different locations and populations ( [[#Hajat--2014|Hajat et al., 2014]] ; [[#WHO--2014|WHO, 2014]] ; [[#Kinney--2015b|Kinney et al., 2015b]] ; [[#Russo--2015|Russo et al., 2015]] ; [[#Petitti--2016|Petitti et al., 2016]] ; [[#Dosio--2017|Dosio, 2017]] ; [[#Cheng--2018|Cheng et al., 2018]] ; [[#Lay--2018|Lay et al., 2018]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). Hot and humid heat episodes can be deadly ( [[#Mora--2017|Mora et al., 2017]] ), are associated with elevated hospital intake ( [[#Goldie--2017|Goldie et al., 2017]] ) and lower safety and productivity of outdoor labourers ( [[#Dunne--2013|Dunne et al., 2013]] ; [[#Graff%20Zivin--2014|Graff Zivin and Neidell, 2014]] ; [[#Kjellstrom--2016|Kjellstrom et al., 2016]] ; [[#Pal--2016|Pal and Eltahir, 2016]] ; Y. [[#Zhao--2016|]] [[#Zhao--2016|Zhao et al., 2016]] ; [[#Mora--2017|Mora et al., 2017]] ; [[#Watts--2018|Watts et al., 2018]] ; [[#Orlov--2019|Orlov et al., 2019]] ). Elevated nighttime temperatures prevent the human body from experiencing relief from heat stress ( [[#Zhang--2012|Zhang et al., 2012]] ) and can be tracked over extended periods of sequential day and night heat extremes ( [[#Murage--2017|Murage et al., 2017]] ; [[#Mukherjee--2018|Mukherjee and Mishra, 2018]] ). Extreme heat also exacerbates asthma, respiratory difficulties and response to airborne allergens such as hay fever ( [[#Upperman--2017|Upperman et al., 2017]] ). Extreme heat affects outdoor exercise such as the use of bike-share facilities ( [[#Heaney--2019|Heaney et al., 2019]] ; [[#Vanos--2020|Vanos et al., 2020]] ). Large-scale recreational and sporting events such as marathons and tennis tournaments monitor heat extremes when determining the viability of host cities ( [[#Smith--2016|Smith et al., 2016]] , 2018).

Short-term exposure of crops to temperatures beyond a critical temperature threshold can lead to lower yields and above a limiting temperature threshold, crops may fail altogether ( [[#Schlenker--2009|Schlenker and Roberts, 2009]] ; [[#Lobell--2012|Lobell et al., 2012]] , 2013; [[#Gourdji--2013|Gourdji et al., 2013]] ; [[#Deryng--2014|Deryng et al., 2014]] ; [[#Schauberger--2017|Schauberger et al., 2017]] ; [[#Tesfaye--2017|Tesfaye et al., 2017]] ; [[#Vogel--2019|Vogel et al., 2019]] ). The exact level of these thresholds depends on species, cultivar and farm management ( [[#Hatfield--2015|Hatfield and Prueger, 2015]] ; [[#Hatfield--2015|Hatfield et al., 2015]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). The timing of heatwaves is particularly important, as extreme heat is more damaging during critical phenological stages ( [[#Teixeira--2013|Teixeira et al., 2013]] ; [[#Eyshi%20Rezaei--2015|Eyshi Rezaei et al., 2015]] ; [[#Fontana--2015|Fontana et al., 2015]] ; [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|B. Wang et al., 2017]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ). Extreme canopy temperatures, rather than 2 m air temperatures, may be a more robust biophysical indicator of heat impacts on crop production ( [[#Siebert--2017|Siebert et al., 2017]] ). Heat stress indices based upon temperature and humidity determine livestock productivity as well as conception and mortality rates ( [[#Key--2014|Key et al., 2014]] ; [[#Dash--2016|Dash et al., 2016]] ; [[#Pragna--2016|Pragna et al., 2016]] ; [[#Rojas-Downing--2017|Rojas-Downing et al., 2017]] ).

Heat extremes factor in mortality, morbidity and the range of some thermally sensitive ecosystem species ( [[#Smith--2015|Smith and Nagy, 2015]] ; [[#Ratnayake--2019|Ratnayake et al., 2019]] ; [[#Thomsen--2019|Thomsen et al., 2019]] ). Combined heat and drought stress can reduce forest and grassland primary productivity ( [[#Ciais--2005|Ciais et al., 2005]] ; [[#De%20Boeck--2018|De Boeck et al., 2018]] ) and even cause tree mortality at higher extremes ( [[#Teskey--2015|Teskey et al., 2015]] ).

Extreme heat events raise temperatures in buildings and cities already warmed by the urban heat island effect ( [[#Gaffin--2012|Gaffin et al., 2012]] ; [[#Oleson--2018|Oleson et al., 2018]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Mauree--2019|Mauree et al., 2019]] ; Box 10.3) and can induce disruptions in critical infrastructure networks ( [[#Chapman--2013|Chapman et al., 2013]] ). Heat affects transportation infrastructure by warping roads and airport runways ( [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ) or buckling railways ( [[#Dobney--2010|Dobney et al., 2010]] ; [[#Dépoues--2017|Dépoues, 2017]] ; [[#Chinowsky--2019|Chinowsky et al., 2019]] ), and high temperatures reduce air density leading to aircraft take-off weight restrictions ( [[#Coffel--2017|Coffel et al., 2017]] ; [[#Palko--2017|Palko, 2017]] ; T. [[#Zhou--2018|]] [[#Zhou--2018|Zhou et al., 2018]] ). Heat extremes increase peak cooling demand and challenge transmission and transformer capacity ( [[#Sathaye--2013|Sathaye et al., 2013]] ; [[#Russo--2016|Russo et al., 2016]] ; [[#Craig--2018|Craig et al., 2018]] ; X. [[#Gao--2018|]] [[#Gao--2018|Gao et al., 2018]] ) and may cause transmission lines to sag or fail ( [[#Gupta--2012|Gupta et al., 2012]] ). Thermal and nuclear electricity plants may be challenged when using warmer river waters for cooling or when mixing waste waters back into waterways without causing ecosystem impacts ( [[#Kopytko--2011|Kopytko and Perkins, 2011]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ; [[#Tobin--2018|Tobin et al., 2018]] ). Extreme temperature can also reduce photovoltaic panel efficiency ( [[#Jerez--2015|Jerez et al., 2015]] ).

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<span id="cold-spells"></span>
==== 12.3.1.3 Cold Spells ====

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The magnitude and timing (relative to developmental stages) of cold extremes (such as the typical coldest day of the year) set limits in the range of species habitat for ecosystems as well as for agricultural and forest pests ( [[#Osland--2013|Osland et al., 2013]] ; [[#Cavanaugh--2014|Cavanaugh et al., 2014]] ; [[#Parker--2016|Parker and Abatzoglou, 2016]] ; [[#Brunner--2018|Brunner et al., 2018]] ; [[#Unterberger--2018|Unterberger et al., 2018]] ). Cold air outbreaks can lead to chilling injuries for crops (even above 0°C) and may kill outdoor livestock (particularly young animals; [[#Mader--2010|Mader et al., 2010]] ; [[#Liu--2013|Liu et al., 2013]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ), but are often necessary for crop chill requirements ( [[#Dennis--2009|Dennis and Peacock, 2009]] ).

Increases in human mortality can occur on exceptionally cold days (e.g., <1st percentile of temperatures in winter) although thresholds and human-perceived temperatures linked to wind speed (i.e., ‘wind chill’) vary geographically due to acclimatization ( [[#Li--2013|Li et al., 2013]] ; [[#Gao--2015|Gao et al., 2015]] ; J. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; J. [[#Zhu--2019|]] [[#Zhu--2019|Zhu et al., 2019]] ). The timing of ‘unseasonal’ cold spells also affect human health ( [[#Kinney--2015b|Kinney et al., 2015b]] ). Extreme cold can increase heat and electricity demand ( [[#Stuivenvolt-Allen--2019|Stuivenvolt-Allen and Wang, 2019]] ), cause water pipes to burst, and mechanically alter roads, railroads and buildings ( [[#Underwood--2017|Underwood et al., 2017]] ).

<div id="12.3.1.4" class="h3-container"></div>

<span id="frost"></span>
==== 12.3.1.4 Frost ====

<div id="h3-4-siblings" class="h3-siblings"></div>

Frost (T <sub>min</sub> <0°C) is a natural and fundamental aspect of many ecosystems, with more extreme conditions defined as ice (or icing) days (T <sub>max</sub> <0°C) ( [[#Vincent--2018|L.A. Vincent et al., 2018]] ). Agricultural systems planning (e.g., planting calendars, seed selection or the opportunity to double-crop) requires information about the start and end of the frost-free season ( [[#Wypych--2017|Wypych et al., 2017]] ; [[#Wolfe--2018|Wolfe et al., 2018]] ). Crops and wild plants can be directly damaged by frost, but hard or killing frosts (at a threshold several degrees below freezing) can kill crops or lower harvest quality depending on duration (which relates to soil temperature penetration) and plant developmental stage ( [[#Crimp--2016a|Crimp et al., 2016a]] ; [[#Cradock-Henry--2017|Cradock-Henry, 2017]] ; G. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). Earlier disappearance of snow cover reduces natural insulation that protects plants and burrowing animals from hard frost damages ( [[#Trnka--2014|Trnka et al., 2014]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ). In some cases an early season warm spell may reduce plant hardiness or induce fruit tree flowering that exposes plants to devastating subsequent frost impacts ( [[#Hufkens--2012|Hufkens et al., 2012]] ; [[#Hatfield--2014|Hatfield et al., 2014]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Brunner--2018|Brunner et al., 2018]] ; [[#DeGaetano--2018|DeGaetano, 2018]] ; [[#Unterberger--2018|Unterberger et al., 2018]] ; [[#Wolfe--2018|Wolfe et al., 2018]] ). Shifts in the seasonality of frozen soils also affect groundwater recharge and surface streamflow for water resource applications, particularly when peak precipitation is shifted to a season that no longer has frozen soils ( [[#Jyrkama--2007|Jyrkama and Sykes, 2007]] ).

Regional information about the spring and autumn seasonal periods in which freeze-thaw cycles are common (such as the dates of first spring thaw and last spring frost, or the number of days where T <sub>max</sub> >0°C and T <sub>min</sub> <0°C) are particularly useful in estimating the rate of potential road and building damage or determining seasonal truck weight restrictions ( [[#Kvande--2009|Kvande and Lisø, 2009]] ; [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ; [[#Palko--2017|Palko, 2017]] ; [[#Daniel--2018|Daniel et al., 2018]] ). The altitude of the freezing level also identifies portions of mountain slopes where freeze/thaw transitions or changes in snowpack condition can influence landslide and snow avalanche hazards ( [[#Coe--2018|Coe et al., 2018]] ). The geographical distribution of frost is also a determining factor in the range of vectors for human diseases such as malaria (X. [[#Zhao--2016|]] [[#Zhao--2016|Zhao et al., 2016]] ; [[#Smith--2020|Smith et al., 2020]] ).

Figure 12.3 illustrates how successive heat and cold hazards can potentially affect important natural and human systems, with climatic pressures reaching new sectoral assets or becoming increasingly severe as conditions become more extreme. While the precise value of any CID threshold may depend strongly on local environmental and system characteristics, there are common patterns and interdependencies in the types of thresholds encountered. Changes in the regional profile of CIDs can thus substantially alter threshold exceedance likelihoods.

<div id="_idContainer026" class="Basic-Text-Frame"></div>

[[File:1fbbe02ba84f8df0d3124b2557ff7990 IPCC_AR6_WGI_Figure_12_3.png]]

'''Figure 12.3''' '''|''' '''Conceptual illustration of representative climatic impact-driver thresholds showing how graduating thresholds affect successive sectoral assets and lead to potentially more acute hazards as conditions become more extreme (exact values are not shown as these must be tailored to reflect diverse vulnerabilities of regional assets).''' Representative threshold definitions (T = instantaneous temperature; T '''''' = mean temperature): '''Cities and Infrastructures:''' T <sub>trans</sub> = temperature at which energy transmission lines efficiency reduced; T <sub>aircraft</sub> = temperature at which aircraft become weight-restricted for takeoff; T <sub>hotroads</sub> = temperature above which roads begin to warp; T <sub>stream</sub> = temperature at which streams are not capable of adequately cooling thermal plants; CDD <sub>min</sub> = minimum temperature for calculating cooling degree days; HDD <sub>max</sub> = maximum temperature for calculating heating degree days; T <sub>ice</sub> = temperature at which ice threatens transportation; T '''''' <sub>permafrost</sub> = mean seasonal temperature above which permafrost thaws at critical depths; T <sub>coldroads</sub> = temperature below which road asphalt performance suffers. '''Health:''' T <sub>deadly</sub> = temperature above which prolonged exposure may be deadly (often combined with humidity for heat indices); T <sub>severe</sub> = temperature above which prolonged exposure may cause elevated morbidity; T '''''' <sub>blooms</sub> = mean temperature for harmful algal or cyanobacteria blooms; T <sub>danger</sub> = level of dangerous cold temperatures (often combined with wind for chill indices); T <sub>overwinter</sub> = temperature below which disease vector species cannot survive winter. '''Ecosystems''' (CID indices for air and ocean temperature): T <sub>hotlim</sub> and T <sub>coldlim</sub> = limiting hot and cold temperatures for a given species range; T <sub>frost</sub> = frost threshold; T '''''' <sub>max</sub> and T '''''' <sub>min</sub> = maximum and minimum suitable annual mean temperatures for a given species; T <sub>crit</sub> = critical temperature above which a given species is stressed. '''Agriculture:''' T <sub>hotlim</sub> = temperature above which a crop or livestock species dies; T <sub>hotpest</sub> = maximum (or ‘lethal’) temperature above which an agricultural pest/disease/weed cannot survive; T <sub>crit</sub> = temperature at which productivity for a given crop is depressed; T '''''' <sub>opt</sub> = optimal mean temperature for a given plant’s productivity; GDD <sub>min</sub> = threshold temperature for growing degree days determining plant development; T <sub>chill</sub> = temperature below which chilling units are accumulated; T <sub>frost</sub> = temperature below which frost occurs; T <sub>hfrost</sub> = temperature below which a hard frost threatens crops or livestock; T <sub>coldpest</sub> = minimum winter temperature below which a given agricultural pest cannot survive; T <sub>coldlim</sub> = minimum temperature below which a given crop cannot survive.

<div id="12.3.2" class="h2-container"></div>

<span id="wet-and-dry"></span>
=== 12.3.2 Wet and Dry ===

<div id="h2-2-siblings" class="h2-siblings"></div>

<div id="12.3.2.1" class="h3-container"></div>

<span id="mean-precipitation"></span>
==== 12.3.2.1 Mean Precipitation ====

<div id="h3-5-siblings" class="h3-siblings"></div>

Changes in mean precipitation alter total water resources and long-term surface, snowpack and groundwater reservoirs ( [[#Schewe--2014|Schewe et al., 2014]] ). Annual and seasonal wet trends can alter the suitable geographic range of species, with implications for biodiversity and vector-borne diseases ( [[#Knouft--2017|Knouft and Ficklin, 2017]] ; [[#Smith--2020|Smith et al., 2020]] ). The rate at which higher total streamflow increases river erosion and changes sediment loading is relevant for fish breeding ( [[#Scheurer--2009|Scheurer et al., 2009]] ), the location of riverine salt fronts that affect coastal agriculture and ecosystems ( [[#Chun--2018|Chun et al., 2018]] ; [[#Vu--2018|Vu et al., 2018]] ), coastal freshwater stratification ( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Bell--2013|Bell et al., 2013]] ), and the accretion of sediment in estuaries and beaches ( [[#Syvitski--2007|Syvitski and Milliman, 2007]] ). Wetter conditions may shift tourist appeal ( [[#Kovács--2017|Kovács et al., 2017]] ) and alter the pace of degradation for paved and especially unpaved roads ( [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ).

Many agricultural systems require minimum rainfall totals or rely upon irrigation ( [[#Mbow--2019|Mbow et al., 2019]] ). The length of the wet season helps determine the potential for multiple cropping seasons, but inconsistency of wet season arrival times poses challenges for farm management ( [[#Waha--2020|Waha et al., 2020]] ). Wetter growing season conditions increase the chance of waterlogging, which can delay planting or damage planted seeds ( [[#Rosenzweig--2002|Rosenzweig et al., 2002]] ; [[#Ben-Ari--2018|Ben-Ari et al., 2018]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#Wolfe--2018|Wolfe et al., 2018]] ; [[#Kolberg--2019|Kolberg et al., 2019]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). [[#Tomasek--2017|Tomasek et al. (2017)]] calculated ‘workable days’ for agricultural machinery around planting and harvest time set in part by limits in soil moisture saturation below which farmers can utilize critical machinery with less rutting or soil compaction. Wetter conditions may also increase canopy moisture that is conducive to crop pathogens ( [[#Garrett--2006|Garrett et al., 2006]] ; [[#Kilroy--2015|Kilroy, 2015]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ).

<div id="12.3.2.2" class="h3-container"></div>

<span id="river-flood"></span>
==== 12.3.2.2 River Flood ====

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A large variety of climate indices and models are utilized to understand how river flooding affects both natural or built environments with highly variable hazard thresholds, given unique local topography and engineered defences such as dams and polders ( [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Ekström--2018|Ekström et al., 2018]] ). Key transportation routes, built infrastructure and agricultural lands are threatened when floods exceed design standards commonly based around flood magnitudes of a given historic return period (e.g., 1-in-100-year flood event), an annual exceedance probability or precipitation intensity-duration-frequency relationships with key indices (e.g., 10-day cumulative precipitation) related to catchment size and properties ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Arnell--2014|Arnell and Lloyd-Hughes, 2014]] ; [[#Kundzewicz--2014|Kundzewicz et al., 2014]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Dikanski--2016|Dikanski et al., 2016]] ; [[#Gosling--2016|Gosling and Arnell, 2016]] ; [[#Forzieri--2017|Forzieri et al., 2017]] ; [[#Fluixá-Sanmartín--2018|Fluixá-Sanmartín et al., 2018]] ; [[#Koks--2019|Koks et al., 2019]] ). Floods and high-flow events can scour river beds and elevate silt loads, reducing water quality and accelerating deposition in estuaries and reservoirs ( [[#Khan--2018|Khan et al., 2018]] ; [[#Parasiewicz--2019|Parasiewicz et al., 2019]] ). Floods can knock down, drown or wash away crops and livestock, and partially submerged plants can have yield reduction depending on water turbidity and their development stage ( [[#Ruane--2013|Ruane et al., 2013]] ; [[#Shrestha--2019|Shrestha et al., 2019]] ). Basin snowpack properties may also be important during heavy rain events, as rain-on-snow events can lead to rapid acceleration of flood stages that threaten wildlife and society ( [[#Hansen--2014|Hansen et al., 2014]] ).

<div id="12.3.2.3" class="h3-container"></div>

<span id="heavy-precipitation-and-pluvial-flood"></span>
==== 12.3.2.3 Heavy Precipitation and Pluvial Flood ====

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Heavy downpours can lead to pluvial flooding in cities, roadways, farmland, subway tunnels and buildings (particularly those with basements; [[#Grahn--2017|Grahn and Nyberg, 2017]] ; [[#Palko--2017|Palko, 2017]] ; [[#Pregnolato--2017|Pregnolato et al., 2017]] ; [[#Orr--2018|Orr et al., 2018]] ). Heavy precipitation may overwhelm city transportation and storm water drainage systems, which are typically designed using intensity-duration-frequency information such as the return periods for 1-, 6- or 24-hour rainfall totals ( [[#Kermanshah--2017|Kermanshah et al., 2017]] ; [[#Depietri--2018|Depietri and McPhearson, 2018]] ; [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ; [[#Courty--2019|Courty et al., 2019]] ). Heavy rain events can directly cause leaf loss and damage, or knock over crops, also driving pollutant entrainment and erosion hazards in terrestrial ecosystems and farmland, with downstream ramifications for water quality ( [[#Hatfield--2014|Hatfield et al., 2014]] ; [[#Segura--2014|Segura et al., 2014]] ; [[#Li--2016|Li and Fang, 2016]] ; [[#Chhetri--2019|Chhetri et al., 2019]] ). The proportion of total precipitation that falls in heavy events also affects the percentage that is retained in the soil column, altering groundwater recharge and deep soil moisture content for agricultural use ( [[#Fishman--2016|Fishman, 2016]] ; [[#Lesk--2020|Lesk et al., 2020]] ).

<div id="12.3.2.4" class="h3-container"></div>

<span id="landslide"></span>
==== 12.3.2.4 Landslide ====

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Landslides, mudslides, rockfalls and other mass movements can lead to fatalities, destroy infrastructure and housing stock, and block critical transportation routes. Climate models cannot resolve these complex slope failure processes (nor triggering mechanisms such as earthquakes), so most studies rely on proxies or conditions conducive to slope failure ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Ho--2017|Ho et al., 2017]] ). Common indices include precipitation intensity-duration thresholds ( [[#Brunetti--2010|Brunetti et al., 2010]] ; [[#Khan--2012|Khan et al., 2012]] ; [[#Melchiorre--2012|Melchiorre and Frattini, 2012]] ) and thresholds related to antecedent wet periods and extreme rainfall intensities ( [[#Alvioli--2018|Alvioli et al., 2018]] ; [[#Monsieurs--2019|Monsieurs et al., 2019]] ). Landslides and rockfalls may also be exacerbated by permafrost thaw and receding glaciers in polar and mountain areas ( [[#Cook--2016|Cook et al., 2016]] ; [[#Haeberli--2017|Haeberli et al., 2017]] ; [[#Patton--2019|Patton et al., 2019]] ).

<div id="12.3.2.5" class="h3-container"></div>

<span id="aridity"></span>
==== 12.3.2.5 Aridity ====

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Aridity indices may track long-term changes in precipitation, evapotranspiration demand, surface water, groundwater or soil moisture ( [[#Sherwood--2014|Sherwood and Fu, 2014]] ; [[#Herrera-Pantoja--2015|Herrera-Pantoja and Hiscock, 2015]] ; B.I. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ). Changes in soil moisture and surface water can shift the rate of carbon uptake by ecosystems ( [[#Humphrey--2018|Humphrey et al., 2018]] ) and alter suitable climate zones for wild species and agricultural cultivation ( [[#Feng--2013|Feng and Fu, 2013]] ; [[#Garcia--2014|Garcia et al., 2014]] ; [[#Huang--2016a|Huang et al., 2016a]] ; [[#Schlaepfer--2017|Schlaepfer et al., 2017]] ; [[#Fatemi--2018|Fatemi et al., 2018]] ; [[#IPCC--2019c|IPCC, 2019c]] ) as well as the prevalence of related pests and pathogen-carrying vectors ( [[#Paritsis--2011|Paritsis and Veblen, 2011]] ; [[#Smith--2020|Smith et al., 2020]] ). Water table depth, in relation to rooting depth, is also important for farms and forests under dry conditions ( [[#Feng--2006|Feng et al., 2006]] ). A reduction in water availability (via aridity or hydrological drought) challenges water supplies needed for for municipal, industrial, agriculture and hydropower use ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Arnell--2014|Arnell and Lloyd-Hughes, 2014]] ; [[#Schewe--2014|Schewe et al., 2014]] ; [[#Gosling--2016|Gosling and Arnell, 2016]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ).

<div id="12.3.2.6" class="h3-container"></div>

<span id="hydrological-drought"></span>
==== 12.3.2.6 Hydrological Drought ====

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Water managers often utilize a variety of hydrological drought indices and hydrological models to characterize water resources, low flow conditions and the potential for irrigation ( [[#Wanders--2015|Wanders and Wada, 2015]] ; [[#Mukherjee--2018|Mukherjee et al., 2018]] ). Low flow volume and intermittency thresholds can indicate reductions in dissolved oxygen, more concentrated pollutants, and higher stream temperatures relevant for ecosystems, water resource quality and thermal power plant cooling ( [[#Feeley--2008|Feeley et al., 2008]] ; [[#Döll--2012|Döll and Schmied, 2012]] ; [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Prudhomme--2014|Prudhomme et al., 2014]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ). Low water levels may also restrict waterway navigation for commerce and recreation ( [[#Forzieri--2018|Forzieri et al., 2018]] ).

<div id="12.3.2.7" class="h3-container"></div>

<span id="agricultural-and-ecological-drought"></span>
==== 12.3.2.7 Agricultural and Ecological Drought ====

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Agricultural and ecological drought indices relate to the ability of plants to meet growth and transpiration needs (Table 11.3; [[#Zargar--2011|Zargar et al., 2011]] ; [[#Lobell--2015|Lobell et al., 2015]] ; [[#Pedro-Monzonís--2015|Pedro-Monzonís et al., 2015]] ; [[#Bachmair--2016|Bachmair et al., 2016]] ; [[#Wehner--2017|Wehner et al., 2017]] ; [[#Naumann--2018|Naumann et al., 2018]] ) and the timing and duration of droughts can lead to substantially different impacts ( [[#Peña-Gallardo--2019|Peña-Gallardo et al., 2019]] ). Drought stress for agriculture and ecosystems is difficult to directly observe, and therefore scientists use a variety of drought indices (Table 11.3), proxy information about changes in precipitation supply and reference evapotranspiration demand, the ratio of actual/potential evapotranspiration or a deficit in available soil water content, particularly at rooting level ( [[#Park%20Williams--2013|Park Williams et al., 2013]] ; [[#Trnka--2014|Trnka et al., 2014]] ; C.D. [[#Allen--2015|]] [[#Allen--2015|Allen et al., 2015]] ; [[#Svoboda--2017|Svoboda and Fuchs, 2017]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#Otkin--2018|Otkin et al., 2018]] ). Severe water stress can lead to crop failure, in particular when droughts persist for an extended period or occur during key plant developmental stages ( [[#Hatfield--2014|Hatfield et al., 2014]] ; [[#Jolly--2015|Jolly et al., 2015]] ; [[#Leng--2019|Leng and Hall, 2019]] ). Projections of high wind speed and low humidity (even for just a portion of the day) can also inform studies examining fruit desiccation and rice cracking ( [[#Grotjahn--2021|Grotjahn, 2021]] ). Drought also raises disease infection rates for West Nile virus ( [[#Paull--2017|Paull et al., 2017]] ), and the alternation of dry and wet spells induces swelling and shrinkage of clay soils that can lead to sinkholes and destabilize buildings ( [[#Hadji--2014|Hadji et al., 2014]] ).

<div id="12.3.2.8" class="h3-container"></div>

<span id="fire-weather"></span>
==== 12.3.2.8 Fire Weather ====

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Complex fire weather indices shed light on conditions that increase the likelihood of wildfire and shifts in the fire season ( [[#Flannigan--2013|Flannigan et al., 2013]] ; [[#Bedia--2015|Bedia et al., 2015]] ; [[#Jolly--2015|Jolly et al., 2015]] ; [[#Harvey--2016|Harvey, 2016]] ; [[#Littell--2016|Littell et al., 2016]] ; [[#Westerling--2016|Westerling, 2016]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ), which pose particularly acute challenges for indigenous communities ( [[#Christianson--2019|Christianson and McGee, 2019]] ). Projection of future lightning frequency provides information on an important natural triggering mechanism, particularly when coupled with long-term warming and drying trends ( [[#Romps--2014|Romps et al., 2014]] ; [[#Jin--2015|Jin et al., 2015]] ; [[#Veraverbeke--2017|Veraverbeke et al., 2017]] ). Fuel aridity metrics also help determine vegetative fuel desiccation and therefore the ignitability, flammability and spread of fires when they occur ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ). The presence of snow cover can influence the length of the fire season and the penetration of fire danger into new portions of the Arctic tundra ( [[#Young--2017|Young et al., 2017]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). Data on the changing characteristics of local wind circulations like the Santa Ana in California shed light on future intensity and spread patterns for fires ( [[#Jin--2015|Jin et al., 2015]] ). Fires also produce smoke plumes that reduce air and water quality (via deposition), adversely affecting health, visibility and water resources both near and far downwind ( [[#Dennekamp--2011|Dennekamp and Abramson, 2011]] ; [[#McKenzie--2014|McKenzie et al., 2014]] ; [[#Dreessen--2016|Dreessen et al., 2016]] ; [[#Liu--2016|Liu et al., 2016]] ; [[#Martin--2016|Martin, 2016]] ).

<div id="12.3.3" class="h2-container"></div>

<span id="wind"></span>
=== 12.3.3 Wind ===

<div id="h2-3-siblings" class="h2-siblings"></div>

<div id="12.3.3.1" class="h3-container"></div>

<span id="mean-wind-speed"></span>
==== 12.3.3.1 Mean Wind Speed ====

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Changes in the speed and direction of prevailing winds can alter the profile of seed dispersal, windblown pest and disease vectors, animal activities, and dust or pollen dispersal affecting ecosystems, agriculture and human health ( [[#Reid--2009|Reid and Gamble, 2009]] ; [[#Bullock--2012|Bullock et al., 2012]] ; [[#Hellberg--2016|Hellberg and Chu, 2016]] ; [[#Nourani--2017|Nourani et al., 2017]] ). Seasonal winds influence algal blooms, ecosystems and fisheries via lake mixing, ocean currents and coastal upwelling ( [[#Bakun--2015|Bakun et al., 2015]] ; [[#Townhill--2018|Townhill et al., 2018]] ; [[#Woolway--2020|Woolway et al., 2020]] ). Changes to wind density also modify a region’s wind and wave renewable energy endowment ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Sierra--2017|Sierra et al., 2017]] ; [[#Craig--2018|Craig et al., 2018]] ; [[#Devis--2018|Devis et al., 2018]] ; [[#Tobin--2018|Tobin et al., 2018]] ; [[#Yalew--2020|Yalew et al., 2020]] ). D. [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|Li et al. (2020)]] and [[#Karnauskas--2018a|Karnauskas et al. (2018a)]] evaluated wind thresholds at turbine height (about 80–100 m above ground) including periods outside of cut-in (2.5–3 m s <sup>–1</sup> ) and cut-out (about 25 m s <sup>–1</sup> ) levels beyond which given turbines could not operate.

<div id="12.3.3.2" class="h3-container"></div>

<span id="severe-wind-storm"></span>
==== 12.3.3.2 Severe Wind Storm ====

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High winds associated with severe storms can destroy trees and houses, break plant stems and knock fruits, nuts and grains to the ground, with tolerance thresholds depending on crop species and developmental stage ( [[#Seidl--2017|Seidl et al., 2017]] ; [[#Lai--2018|Lai, 2018]] ; [[#Elsner--2019|Elsner et al., 2019]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). Severe storms particularly threaten energy infrastructure, with maximum wind speed associated with treefall and breaking of above-ground electrical transmission lines ( [[#Ward--2013|Ward, 2013]] ; [[#Nik--2020|Nik et al., 2020]] ). The profile of heavy wind gusts is also required in the design of skyscrapers (C.-H. [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ) and bridges ( [[#Mondoro--2018|Mondoro et al., 2018]] ). Severe storms are difficult to simulate at the relatively coarse spatial scales of Earth system models, thus scientists often project changes by noting areas with increased convective available potential energy (CAPE) and strong low-level wind shear as these are conducive to tornado formation ( [[#Diffenbaugh--2013|Diffenbaugh et al., 2013]] ; [[#Tippett--2016|Tippett et al., 2016]] ; [[#Glazer--2021|Glazer et al., 2021]] ).

<div id="12.3.3.3" class="h3-container"></div>

<span id="tropical-cyclone"></span>
==== 12.3.3.3 Tropical Cyclone ====

<div id="h3-15-siblings" class="h3-siblings"></div>

Tropical cyclones and severe coastal storms can deliver wind, water and coastal hazards with the potential for widespread mortality and damages to cities, housing, transportation and energy infrastructure, ecosystems and agricultural lands ( [[#Burkett--2011|Burkett, 2011]] ; [[#NASEM--2012|NASEM, 2012]] ; [[#Bell--2013|Bell et al., 2013]] ; [[#Wehof--2014|Wehof et al., 2014]] ; [[#Ward--2016|Ward et al., 2016]] ; [[#Cheal--2017|Cheal et al., 2017]] ; [[#Godoi--2018|Godoi et al., 2018]] ; [[#Koks--2019|Koks et al., 2019]] ; [[#Pinnegar--2019|Pinnegar et al., 2019]] ). Storm planning is often tied to the Saffir –Simpson scale related to peak sustained wind speed ( [[#Izaguirre--2021|Izaguirre et al., 2021]] ), with several indices focusing on storms’ overall power and energy, size and translation speed to anticipate destructive potential ( [[#Knutson--2015|Knutson et al., 2015]] ; [[#Wang--2016|Wang and Toumi, 2016]] ; [[#Parker--2018|Parker et al., 2018]] ; [[#Hassanzadeh--2020|Hassanzadeh et al., 2020]] ).

<div id="12.3.3.4" class="h3-container"></div>

<span id="sand-and-dust-storm"></span>
==== 12.3.3.4 Sand and Dust Storm ====

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Sand and dust storms erode soils, damage crops and induce problems for health, transportation, mechanical equipment and built infrastructure corresponding to the magnitude and duration of high winds and particulate matter concentrations ( [[#Goudie--2014|Goudie, 2014]] ; [[#O’Loingsigh--2014|O’Loingsigh et al., 2014]] ; [[#Crooks--2016|Crooks et al., 2016]] ; [[#Barreau--2017|Barreau et al., 2017]] ; [[#Bhattachan--2018|Bhattachan et al., 2018]] ; [[#Al%20Ameri--2019|Al Ameri et al., 2019]] ; [[#Middleton--2019|Middleton et al., 2019]] ). Dust events may be represented as the number of dust hours per year and by particulate matter (PM) concentrations ( [[#Leys--2011|Leys et al., 2011]] ; [[#Spickett--2011|Spickett et al., 2011]] ; [[#Hand--2016|Hand et al., 2016]] ). Photovoltaic panels can lose energy production efficiency with dust accumulation ( [[#Patt--2013|Patt et al., 2013]] ; [[#Javed--2017|Javed et al., 2017]] ). It is also useful to track dust storm deposition of nutrients necessary for coral and tropical forest systems, but they may also feed algal blooms harming lake and coastal ecosystems, health and recreation ( [[#Jickells--2005|Jickells et al., 2005]] ; [[#Hallegraeff--2014|Hallegraeff et al., 2014]] ; [[#Gabric--2016|Gabric et al., 2016]] ). Dust storms also cause air pollution and redistribute the soil-based fungus associated with Valley fever ( [[#Barreau--2017|Barreau et al., 2017]] ; [[#Coopersmith--2017|Coopersmith et al., 2017]] ; [[#Tong--2017|Tong et al., 2017]] ; [[#Gorris--2018|Gorris et al., 2018]] ).

<div id="12.3.4" class="h2-container"></div>

<span id="snow-and-ice"></span>
=== 12.3.4 Snow and Ice ===

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Cryospheric changes are a focus of ( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and were central to the recent IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; [[#IPCC--2019b|IPCC, 2019b]] ). Here we focus on the ways that scientists use snow and ice CIDs to understand current and future societal impacts and risks.

<div id="12.3.4.1" class="h3-container"></div>

<span id="snow-glacier-and-ice-sheet"></span>
==== 12.3.4.1 Snow, Glacier and Ice Sheet ====

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A large number of indices have been used in water resource and ecosystem studies to track changes in snow under current and future climate conditions, including measurements of the snow water equivalent at key seasonal dates, the fraction of precipitation falling as snow, the first and last days of snow cover, and cold season temperatures ( [[#Mills--2013|Mills et al., 2013]] ; [[#Pierce--2013|Pierce and Cayan, 2013]] ; [[#Berghuijs--2014|Berghuijs et al., 2014]] ; [[#Klos--2014|Klos et al., 2014]] ; [[#Musselman--2017|Musselman et al., 2017]] ; [[#Rhoades--2018|Rhoades et al., 2018]] ). Impact studies also examine shifts in seasonal streamflow for snow-fed river basins ( [[#Mote--2005|Mote et al., 2005]] ; [[#Pederson--2011|Pederson et al., 2011]] ; [[#Beniston--2014|Beniston and Stoffel, 2014]] ; [[#Coppola--2014b|Coppola et al., 2014b]] , 2018; [[#Fyfe--2017|Fyfe et al., 2017]] ; [[#Islam--2017|Islam et al., 2017]] ; [[#Knouft--2017|Knouft and Ficklin, 2017]] ) as well as the geographic extent of snow cover and the depth of frosts when snow cover’s natural insulation is absent ( [[#Scheurer--2009|Scheurer et al., 2009]] ; [[#Millar--2015|Millar and Stephenson, 2015]] ). Studies examining the impact of snow changes on winter recreation and transportation have used thresholds of about 30 cm snow depth or snow water equivalent >10 cm to determine the length of the season for alpine and cross-country skiing and snowmobiling ( [[#Damm--2017|Damm et al., 2017]] ; [[#Wobus--2017b|Wobus et al., 2017b]] ; [[#Spandre--2019|Spandre et al., 2019]] ; [[#Steiger--2019|Steiger et al., 2019]] ; [[#Abegg--2021|Abegg et al., 2021]] ). Changes in snow quality also affect recreational activities ( [[#Rutty--2017|Rutty et al., 2017]] ), and artificial snowmaking can augment recreational snowpack depending on the number of suitable snowmaking hours (e.g., where wet bulb globe temperature (WBGT) <–2.2°C; [[#Wobus--2017b|Wobus et al., 2017b]] ). Local detail may also be provided by tracking the seasonal rain–snow transition line across space and elevation ( [[#Berghuijs--2014|Berghuijs et al., 2014]] ) ( [[#Pierce--2013|Pierce and Cayan, 2013]] ; [[#Berghuijs--2014|Berghuijs et al., 2014]] ; [[#Klos--2014|Klos et al., 2014]] ; [[#Musselman--2017|Musselman et al., 2017]] ).

Change in ice sheet and glacier spatial extent and surface mass balance is relevant for polar and high mountain ecosystems and downstream assets that rely on glacial water resources (J.R. [[#Lee--2017|]] [[#Lee--2017|Lee et al., 2017]] ; [[#Milner--2017|Milner et al., 2017]] ; [[#Huss--2018|Huss and Hock, 2018]] ; [[#Schaefli--2019|Schaefli et al., 2019]] ). The loss of glaciers reduces the thermal consistency of cold streams suitable for some freshwater species ( [[#Giersch--2017|Giersch et al., 2017]] ), and parks and recreation areas may lose appeal as glaciers and seasonal snow cover retreat ( [[#Gonzalez--2018|Gonzalez et al., 2018]] ; [[#Wang--2019|Wang and Zhou, 2019]] ). Rapid glacial retreat can lead to glacial lakes and outburst floods that endanger downstream communities ( [[#Carrivick--2016|Carrivick and Tweed, 2016]] ; [[#Cook--2016|Cook et al., 2016]] ; [[#Harrison--2018|Harrison et al., 2018]] ).

<div id="12.3.4.2" class="h3-container"></div>

<span id="permafrost"></span>
==== 12.3.4.2 Permafrost ====

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Changes in permafrost temperature, extent and active layer thickness are metrics that track how permafrost thaw below, for example, roads, airstrips, rails and building foundations in high-latitude and mountain regions may destabilize settlements and critical infrastructure ( [[#Pendakur--2016|Pendakur, 2016]] ; [[#Derksen--2018|Derksen et al., 2018]] ; [[#Duvillard--2019|Duvillard et al., 2019]] ; [[#Olsson--2019|Olsson et al., 2019]] ; [[#Streletskiy--2019|Streletskiy et al., 2019]] ). Warmer conditions can also affect ecosystems, built infrastructure and water resources through thawing of especially ice-rich permafrost (≥20% ice content) and by thawing of ice wedges ( [[#Shiklomanov--2017|Shiklomanov et al., 2017]] ; [[#Hjort--2018|Hjort et al., 2018]] ), creation of thermokarst ponds and increased subsurface drainage for polar and high-mountain wetlands ( [[#Walvoord--2016|Walvoord and Kurylyk, 2016]] ; [[#Farquharson--2019|Farquharson et al., 2019]] ) and the release of water pollutants such as mercury ( [[#Burkett--2011|Burkett, 2011]] ; [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Schuster--2018|Schuster et al., 2018]] ).

<div id="12.3.4.3" class="h3-container"></div>

<span id="lake-river-and-sea-ice"></span>
==== 12.3.4.3 Lake, River and Sea Ice ====

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Reductions in the duration of thick sea, lake and river ice influence ecosystems as well as ice fishing, hunting, dog sledding and snowmobiling, which are recreation activities for some but vital aspects of many traditional indigenous communities ( [[#Durkalec--2015|Durkalec et al., 2015]] ; [[#AMAP--2017|AMAP, 2017]] ; [[#Baztan--2017|Baztan et al., 2017]] ; [[#Arp--2018|Arp et al., 2018]] ; [[#Rokaya--2018|Rokaya et al., 2018]] ; [[#Knoll--2019|Knoll et al., 2019]] ; [[#Meredith--2019|Meredith et al., 2019]] ; [[#Sharma--2019|Sharma et al., 2019]] ). The seasonal extent of thin ice and iceberg density also determines the viability of shipping lanes and seasonal roads ( [[#Valsson--2011|Valsson and Ulfarsson, 2011]] ; [[#Pizzolato--2016|Pizzolato et al., 2016]] ; [[#AMAP--2017|AMAP, 2017]] ; [[#Mullan--2017|Mullan et al., 2017]] ; [[#Sturm--2017|Sturm et al., 2017]] ), oil and gas exploration timing ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ) and the seasonality of phytoplankton blooms ( [[#Oziel--2017|Oziel et al., 2017]] ). Sea ice is a critical aspect of some ecosystems and fisheries ( [[#Massom--2010|Massom and Stammerjohn, 2010]] ; [[#Jenouvrier--2014|Jenouvrier et al., 2014]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ; [[#Meredith--2019|Meredith et al., 2019]] ). Various definitions of ‘ice free’ Arctic Ocean conditions can be tailored to represent transportation needs, including thresholds of ice coverage (<5% or <30% or <1 million km <sup>2</sup> ) in September or over a four-month period ( [[#Laliberté--2016|Laliberté et al., 2016]] ; [[#Jahn--2018|Jahn, 2018]] ).

<div id="12.3.4.4" class="h3-container"></div>

<span id="heavy-snowfall-and-ice-storm"></span>
==== 12.3.4.4 Heavy Snowfall and Ice Storm ====

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Heavy snowfall is a substantial concern for cities, settlements and key transportation and energy infrastructure ( [[#Ward--2013|Ward, 2013]] ; [[#Palko--2017|Palko, 2017]] ; [[#Janoski--2018|Janoski et al., 2018]] ; [[#Collins--2019|Collins et al., 2019]] ). Heavy snowfall can interfere with transportation ( [[#Herring--2018|Herring et al., 2018]] ) and cause a loss of both work and school days depending on local snow removal infrastructure. Freezing rain and ice storms can be treacherous for road and air travel ( [[#Tamerius--2016|Tamerius et al., 2016]] ), and can knock down power and telecommunication lines if ice accumulation is high ( [[#Degelia--2016|Degelia et al., 2016]] ). Rain-on-snow events can create a solid barrier that hinders wildlife and livestock grazing that is important to indigenous communities ( [[#Forbes--2016|Forbes et al., 2016]] ). Shifts in the frequency, seasonal timing and regions susceptible to ice storms alter risks for agriculture and infrastructure ( [[#Lambert--2011|Lambert and Hansen, 2011]] ; [[#Klima--2015|Klima and Morgan, 2015]] ; [[#Ning--2015|Ning and Bradley, 2015]] ; [[#Groisman--2016|Groisman et al., 2016]] ).

<div id="12.3.4.5" class="h3-container"></div>

<span id="hail"></span>
==== 12.3.4.5 Hail ====

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Information on the changing frequency and size distribution of hail can help stakeholders build resilience for agriculture, vehicles, transportation infrastructure and buildings, solar panels and wild species that see critical damage at particular hail size thresholds ( [[#Dessens--2007|Dessens et al., 2007]] ; [[#Webb--2009|Webb et al., 2009]] ; [[#Patt--2013|Patt et al., 2013]] ; [[#Fiss--2019|Fiss et al., 2019]] ). Most climate models do not directly resolve hail and therefore studies often examine proxies associated with severe mesoscale storms ( [[#Tippett--2015|Tippett et al., 2015]] ; [[#Prein--2018|Prein and Holland, 2018]] ), although some regional studies now utilize hail-resolving models ( [[#Mahoney--2012|Mahoney et al., 2012]] ; [[#Brimelow--2017|Brimelow et al., 2017]] ).

<div id="12.3.4.6" class="h3-container"></div>

<span id="snow-avalanche"></span>
==== 12.3.4.6 Snow Avalanche ====

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Information about the changing frequency and seasonal timing of snow avalanches is important to assess threats to transportation routes, infrastructure, recreational skiing and people living in alpine communities ( [[#Lazar--2008|Lazar and Williams, 2008]] ; [[#Mock--2017|Mock et al., 2017]] ; [[#Ballesteros-Cánovas--2018|Ballesteros-Cánovas et al., 2018]] ; [[#Hock--2019|Hock et al., 2019]] ). Like landslides and other mass movements, snow avalanches are not directly resolved by climate models and are thus tracked using proxy climate information describing snow avalanche susceptibility, particularly the snow water equivalent, and triggering mechanisms such as warm spells, high winds, rain-on-snow and heavy precipitation ( [[#Hock--2019|Hock et al., 2019]] ). The quality of snow also provides insight into avalanche hazards ( [[#Mock--2017|Mock et al., 2017]] ), with the seasonal altitude of wet snowpack (>0.5% liquid water by volume) particularly important in determining characteristics of potential avalanches ( [[#Castebrunet--2014|Castebrunet et al., 2014]] ).

<div id="12.3.5" class="h2-container"></div>

<span id="coastal"></span>
=== 12.3.5 Coastal ===

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The SROCC included in-depth discussions of threats facing the world’s coastlines ( [[#IPCC--2019b|IPCC, 2019b]] ) and [[IPCC:Wg1:Chapter:Chapter-9#9.6|Section 9.6]] provides further discussion on coastal processes. Here we note major connections between coastal CIDs and ecosystem and societal assets near coastlines.

<div id="12.3.5.1" class="h3-container"></div>

<span id="relative-sea-level"></span>
==== 12.3.5.1 Relative Sea Level ====

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Sea level rise hazards for coastal ecosystems, infrastructure, farmland, cities and settlements in a particular region are often driven by regional changes in relative sea level (RSL) that account for land uplift or subsidence and thus represent local asset vulnerability better than global mean sea level (Box 9.1; [[#Hallegatte--2013|Hallegatte et al., 2013]] ; [[#Hinkel--2013|Hinkel et al., 2013]] ; [[#McInnes--2016|McInnes et al., 2016]] ; [[#Weatherdon--2016|Weatherdon et al., 2016]] ; [[#Brown--2018|Brown et al., 2018]] ; [[#IPCC--2019b|IPCC, 2019b]] ; [[#Rasoulkhani--2020|Rasoulkhani et al., 2020]] ). Vertical land motion (i.e., land subsidence) caused by local fluid (gas or groundwater) extraction can also have a large influence on relative sea levels ( [[#Minderhoud--2020|Minderhoud et al., 2020]] ). Several indices have been suggested to signify coastal inundation, including a threshold when the local land elevation falls below the local mean higher high water (MHHW) that is close to the ‘high tide’ level ( [[#Kulp--2019|Kulp and Strauss, 2019]] ) or a threshold when flooding occurs about once every two weeks ( [[#Sweet--2014|Sweet and Park, 2014]] ; [[#Dahl--2017b|Dahl et al., 2017b]] ). RSL rise (or RSLR) can drive increased inland penetration of above-ground and subterranean salt water fronts (i.e., salinity intrusion) affecting coastal ecosystems, agriculture and water resources ( [[#Ferguson--2012|Ferguson and Gleeson, 2012]] ; [[#Kirwan--2013|Kirwan and Megonigal, 2013]] ; [[#Rotzoll--2013|Rotzoll and Fletcher, 2013]] ; [[#Chen--2016|Chen et al., 2016]] ; [[#Colombani--2016|Colombani et al., 2016]] ; [[#Holding--2016|Holding et al., 2016]] ; [[#Sawyer--2016|Sawyer et al., 2016]] ; [[#Mohammed--2018|Mohammed and Scholz, 2018]] ). The rate of RSLR can determine the survival and net pressure on niche coastal ecosystems such as mangroves, tidal flats, sea grasses and coral reefs ( [[#Hubbard--2008|Hubbard et al., 2008]] ; [[#Craft--2009|Craft et al., 2009]] ; [[#Bell--2013|Bell et al., 2013]] ; [[#Kirwan--2013|Kirwan and Megonigal, 2013]] ; [[#Alongi--2015|Alongi, 2015]] ; [[#Ellison--2015|Ellison, 2015]] ; [[#Lovelock--2015|Lovelock et al., 2015]] ; [[#Ward--2016|Ward et al., 2016]] ; [[#Lee--2018|Lee et al., 2018]] ).

<div id="12.3.5.2" class="h3-container"></div>

<span id="coastal-flood"></span>
==== 12.3.5.2 Coastal Flood ====

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Episodic coastal flooding of coastal communities, farmland, buildings, transportation routes, industry and other infrastructure is caused by extreme total water levels (ETWL), which is the combination of RSL, tides, storm surge and high wave setup at the shoreline ( [[#Vitousek--2017|Vitousek et al., 2017]] ; [[#Melet--2018|Melet et al., 2018]] ; [[#Vousdoukas--2018|Vousdoukas et al., 2018]] , 2020a; [[#Koks--2019|Koks et al., 2019]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Coastal settlement and infrastructure design often uses coastal flooding metrics such as the ETWL frequency distribution or the 100-year average return interval storm tide (storm surge + high tide) level ( [[#McInnes--2016|McInnes et al., 2016]] ; [[#Mills--2016|Mills et al., 2016]] ; [[#Walsh--2016b|Walsh et al., 2016b]] ; [[#Zheng--2017|Zheng et al., 2017]] ). The duration of floods that overtop coastal protection, due to extreme coastal water levels (ECWL), is important for port and harbour operations and coastal energy infrastructure thresholds ( [[#Bilskie--2016|Bilskie et al., 2016]] ; [[#Camus--2017|Camus et al., 2017]] ). Frequent inundation by salt water can also have significant impacts on water resources, crops, aquaculture and transportation systems due to corrosion and undercutting of coastal roads, bridges and rails ( [[#Zimmerman--2010|Zimmerman and Faris, 2010]] ; N. [[#Ahmed--2019|]] [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Gopalakrishnan--2019|Gopalakrishnan et al., 2019]] ).

<div id="12.3.5.3" class="h3-container"></div>

<span id="coastal-erosion"></span>
==== 12.3.5.3 Coastal Erosion ====

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Effective management of coastal ecosystems, cities, settlements, beaches and infrastructure requires information about coastal erosion driven by storm surge, waves and sea level rise ( [[#Dawson--2009|Dawson et al., 2009]] ; [[#Hinkel--2013|Hinkel et al., 2013]] ; [[#Harley--2017|Harley et al., 2017]] ; [[#Mentaschi--2017|Mentaschi et al., 2017]] ). Coastal erosion is generally accompanied by shoreline retreat, which can occur as a gradual process (e.g., due to sea level rise) or as an episodic event due to storm surge and/or extreme waves, especially when combined with high tide ( [[#Ranasinghe--2016|Ranasinghe, 2016]] ). The most commonly used shoreline retreat index is the magnitude of shoreline retreat by a pre-determined planning horizon such as 50 or 100 years into the future. Commonly used metrics for episodic coastal erosion include the beach erosion volume due to the 100-year recurrence storm wave height, the full exceedance probability distribution of coastal erosion volume ( [[#Li--2014a|Li et al., 2014a]] ; [[#Pender--2015|Pender et al., 2015]] ; [[#Ranasinghe--2017|Ranasinghe and Callaghan, 2017]] ) and the cumulative storm energy and storm power index ( [[#Godoi--2018|Godoi et al., 2018]] ). The destruction or overtopping of barrier islands may lead to irreversible changes in the physical system as well as in coastal ecosystems ( [[#Carrasco--2016|Carrasco et al., 2016]] ; [[#Zinnert--2019|Zinnert et al., 2019]] ). Shoreline position change rates along inlet-interrupted coasts may also be affected by changes in river flows and fluvial sediment supply ( [[#Hinkel--2013|Hinkel et al., 2013]] ; [[#Bamunawala--2018|Bamunawala et al., 2018]] ; [[#Ranasinghe--2019|Ranasinghe et al., 2019]] ). Permafrost thaw and Arctic sea ice decline also reduce natural coastal protection from wave erosion for communities and industry ( [[#Forbes--2011|Forbes, 2011]] ; [[#Melvin--2017|Melvin et al., 2017]] ).

<div id="12.3.6" class="h2-container"></div>

<span id="oceanic"></span>
=== 12.3.6 Oceanic ===

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Oceanic changes and impacts were a substantial focus of SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ). [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] of this Report assesses changes in ocean processes, and here we note major connections used by scientists to understand how oceanic CIDs affect ecosystems and society.

<div id="12.3.6.1" class="h3-container"></div>

<span id="mean-ocean-temperature"></span>
==== 12.3.6.1 Mean Ocean Temperature ====

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Shifts in thermal zones affect the suitability of fisheries and marine and coastal species habitat and migration routes ( [[#Hoegh-Guldberg--2010|Hoegh-Guldberg and Bruno, 2010]] ; [[#Doney--2012|Doney et al., 2012]] ; [[#Burrows--2014|Burrows et al., 2014]] ; [[#Urban--2015|Urban, 2015]] ; [[#Hixson--2016|Hixson and Arts, 2016]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; N. [[#Ahmed--2019|]] [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ). Intertidal species are particularly dependent on suitable conditions for both air and sea surface temperatures ( [[#Monaco--2019|Monaco and McQuaid, 2019]] ). The structure of ocean warming also affects the intensity of upper-ocean stratification and the timing and strength of coastal upwelling (driven also by mean wind changes), which alters the vertical transport of oxygen- and nutrient-rich waters affecting fishery and marine ecosystem productivity (D. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ).

<div id="12.3.6.2" class="h3-container"></div>

<span id="marine-heatwave"></span>
==== 12.3.6.2 Marine Heatwave ====

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Marine heatwaves (MHW) push water temperatures above key thresholds and have been associated with coral bleaching episodes, species shifts and harmful algal blooms that can disrupt ecosystems, tourism and human health (Box 9.2; [[#Wernberg--2016|Wernberg et al., 2016]] ; [[#Arias-Ortiz--2018|Arias-Ortiz et al., 2018]] ; [[#Oliver--2018|Oliver et al., 2018]] ; [[#Frölicher--2019|Frölicher, 2019]] ; [[#Smale--2019|Smale et al., 2019]] ; [[#Sully--2019|Sully et al., 2019]] ). The duration and return period of marine heatwaves provide insight into aggregate stresses on marine species, fisheries and ecosystems, with various indices gauging cumulative intensity or the number of days, weeks or months exceeding critical thresholds ( [[#Frieler--2013|Frieler et al., 2013]] ; [[#Frölicher--2018|Frölicher et al., 2018]] ; [[#Hughes--2018b|Hughes et al., 2018b]] ; [[#Cheung--2020|Cheung and Frölicher, 2020]] ). [[#Hobday--2016|Hobday et al. (2016)]] defined marine heatwaves as the exceedance of the 90th percentile of the sea surface temperature (SST) distribution on a given Julian day during five or more consecutive days, while Box 9.2, Figure 1 shows MHW as an exceedance of 99th-percentile 11-day de-seasonalized SSTs. The return period of marine heatwaves is also critical in determining a coral system’s ability to recover before the next event ( [[#Hughes--2018a|Hughes et al., 2018a]] ).

<div id="12.3.6.3" class="h3-container"></div>

<span id="ocean-acidity"></span>
==== 12.3.6.3 Ocean Acidity ====

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Uptake of atmospheric CO <sub>2</sub> and subsequent increases in dissolved CO <sub>2</sub> lowers ocean pH and can reduce carbonate ion concentrations below critical calcium carbonate saturation thresholds for marine and aquatic organisms growth, reproduction and/or survival, with extended implications for marine ecosystems including fisheries ( [[#Bell--2013|Bell et al., 2013]] ; [[#Kroeker--2013|Kroeker et al., 2013]] ; [[#Barange--2014|Barange et al., 2014]] ; [[#Dutkiewicz--2015|Dutkiewicz et al., 2015]] ; [[#Ekstrom--2015|Ekstrom et al., 2015]] ; [[#Gattuso--2015|Gattuso et al., 2015]] ; [[#Mathis--2015a|Mathis et al., 2015a]] ; [[#Nagelkerken--2015|Nagelkerken and Connell, 2015]] ; [[#Behrenfeld--2016|Behrenfeld et al., 2016]] ; [[#Nagelkerken--2016|Nagelkerken and Munday, 2016]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Jiang--2018|Jiang et al., 2018]] ; [[#Weiss--2018|Weiss et al., 2018]] ; N. [[#Ahmed--2019|]] [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ). Lower pH may provide more favourable conditions for toxic algal blooms ( [[#Riebesell--2018|Riebesell et al., 2018]] ) and can interact with hypoxic zones to impact ecosystems ( [[#Gobler--2016|Gobler and Baumann, 2016]] ; [[#Cai--2017|Cai et al., 2017]] ).

<div id="12.3.6.4" class="h3-container"></div>

<span id="ocean-salinity"></span>
==== 12.3.6.4 Ocean Salinity ====

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Changes in currents, sea ice brine rejection and net freshwater flux in the ocean can alter salinity with effects on mixed layer structure, density stratification and the vertical movement of nutrients and marine organisms ( [[#Freeland--2013|Freeland, 2013]] ; [[#Haumann--2016|Haumann et al., 2016]] ).

<div id="12.3.6.5" class="h3-container"></div>

<span id="dissolved-oxygen"></span>
==== 12.3.6.5 Dissolved Oxygen ====

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Ocean warming and increased stratification decrease the oxygen content of the ocean ( [[#Griffiths--2017|Griffiths et al., 2017]] ; [[#Schmidtko--2017|Schmidtko et al., 2017]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ), lead to an expansion of oxygen minimum zones in the open ocean ( [[#Stramma--2012|Stramma et al., 2012]] ; [[#Zhang--2013|Zhang et al., 2013]] ) and exacerbate the creation of anoxic ‘dead zones’ in the coastal oceans ( [[#Breitburg--2018|Breitburg et al., 2018]] ). Such a decline (characterized by successive dissolved oxygen concentration thresholds) could affect a wide range of marine organisms and reduce marine habitats ( [[#Chan--2008|Chan et al., 2008]] ; [[#Vaquer-Sunyer--2008|Vaquer-Sunyer and Duarte, 2008]] ; [[#Hoegh-Guldberg--2010|Hoegh-Guldberg and Bruno, 2010]] ; [[#Altieri--2015|Altieri and Gedan, 2015]] ; [[#Breitburg--2018|Breitburg et al., 2018]] ) and can also lead to further local acidification ( [[#Zhang--2016|Zhang and Gao, 2016]] ; [[#Laurent--2017|Laurent et al., 2017]] ).

<div id="12.3.7" class="h2-container"></div>

<span id="other-climatic-impact-drivers"></span>
=== 12.3.7 Other Climatic Impact-drivers ===

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<div id="12.3.7.1" class="h3-container"></div>

<span id="air-pollution-weather"></span>
==== 12.3.7.1 Air Pollution Weather ====

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Although future air pollution will be strongly driven by air quality policies, anthropogenically-driven changes to temperature, humidity, precipitation and synoptic patterns have the potential to affect the emissions, production, concentration and transport of particulate matter (e.g., from dust, fires, pollen) and gaseous pollutants such as sulphur dioxide, tropospheric ozone and nitrogen dioxide (Section 6.5) with resulting impacts on human health, agriculture and ecosystems ( [[#Ren--2011|Ren et al., 2011]] ; [[#Fiore--2015|Fiore et al., 2015]] ; [[#Kinney--2015a|Kinney et al., 2015a]] ; [[#Tian--2016|Tian et al., 2016]] ; [[#Orru--2017|Orru et al., 2017]] ; [[#Emberson--2018|Emberson et al., 2018]] ; [[#Hayes--2020|Hayes et al., 2020]] ). Information about conditions leading to poor air quality is also important for visibility in natural parks and tourist locations ( [[#Yue--2013|Yue et al., 2013]] ; [[#Val%20Martin--2015|Val Martin et al., 2015]] ), as well as the efficiency of solar photovoltaic panels ( [[#Sweerts--2019|Sweerts et al., 2019]] ). Relevant information about conditions favouring air pollution includes tracking warmer conditions that accelerate ozone formation ( [[#Peel--2013|Peel et al., 2013]] ; [[#Schnell--2016|Schnell et al., 2016]] ) and the frequency and duration of stagnant air events ( [[#Horton--2014|Horton et al., 2014]] ; [[#Fann--2015|Fann et al., 2015]] ; [[#Lelieveld--2015|Lelieveld et al., 2015]] ; [[#Vautard--2018|Vautard et al., 2018]] ), although no regional index has proven sufficient to capture regional changes or acute events ( [[#Kerr--2018|Kerr and Waugh, 2018]] ; [[#Schnell--2018|Schnell et al., 2018]] ). By contrast, precipitation and moister air tend to reduce pollution (Section 6.5).

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==== 12.3.7.2 Atmospheric Carbon Dioxide at Surface ====

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Carbon dioxide (CO <sub>2</sub> ) is a well-mixed greenhouse gas with global repercussions on Earth’s energy balance; however, atmospheric CO <sub>2</sub> concentration changes at the land surface also affect plant functions within ecosystems and agriculture (see also Chapter 5). High CO <sub>2</sub> concentration can increase photosynthesis rates and primary production within natural ecosystems ( [[#Norby--2010|Norby et al., 2010]] ; [[#Ratliff--2015|Ratliff et al., 2015]] ; [[#Zhu--2016|Zhu et al., 2016]] ) and agricultural crops ( [[#Hatfield--2011|Hatfield et al., 2011]] ; [[#Leakey--2012|Leakey et al., 2012]] ; [[#Bell--2013|Bell et al., 2013]] ; [[#Glenn--2014|Glenn et al., 2014]] ; [[#Nagelkerken--2015|Nagelkerken and Connell, 2015]] ; [[#Behrenfeld--2016|Behrenfeld et al., 2016]] ; [[#Deryng--2016|Deryng et al., 2016]] ; [[#Kimball--2016|Kimball, 2016]] ). High CO <sub>2</sub> concentration affects total biomass and plant sugar content important to bioenergy production ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ), but also helps some pests and weeds flourish ( [[#Hamilton--2005|Hamilton et al., 2005]] ; [[#Wolfe--2008|Wolfe et al., 2008]] ; [[#Valerio--2013|Valerio et al., 2013]] ; [[#Korres--2016|Korres et al., 2016]] ; [[#Stinson--2016|Stinson et al., 2016]] ; [[#Ramesh--2017|Ramesh et al., 2017]] ), while potentially shifting the effectiveness of herbicides ( [[#Varanasi--2016|Varanasi et al., 2016]] ; [[#Refatti--2019|Refatti et al., 2019]] ). Higher CO <sub>2</sub> concentration reduces transpiration losses during drought conditions ( [[#Cammarano--2016|Cammarano et al., 2016]] ; [[#Deryng--2016|Deryng et al., 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Durand--2018|Durand et al., 2018]] ), which also changes the energy balance within the plant canopy ( [[#Webber--2017|Webber et al., 2017]] ). Higher CO <sub>2</sub> reduces the nutritional density of crops and forage lands ( [[#Loladze--2014|Loladze, 2014]] ; [[#Müller--2014|Müller et al., 2014]] ; [[#Myers--2014|Myers et al., 2014]] , 2017; X. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Lee--2017|]] [[#Lee--2017|M.A. Lee et al., 2017]] ; [[#Smith--2018|Smith and Myers, 2018]] ; [[#Zhu--2018|Zhu et al., 2018]] ; [[#Beach--2019|Beach et al., 2019]] ) and can increase the production of toxins ( [[#Ziska--2007|Ziska et al., 2007]] ) and allergenic pollen ( [[#Schmidt--2016|Schmidt, 2016]] ).

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<span id="radiation-at-surface"></span>
==== 12.3.7.3 Radiation at Surface ====

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Changes in surface solar and longwave radiation fluxes alter photosynthesis rates and potential evapotranspiration for natural ecosystems and food, fibre and energy crops ( [[#Mäkinen--2018|Mäkinen et al., 2018]] ); changes in radiation fluxes can also shift solar energy resources ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Jerez--2015|Jerez et al., 2015]] ; [[#Wild--2015|Wild et al., 2015]] ; [[#Fant--2016|Fant et al., 2016]] ; [[#Craig--2018|Craig et al., 2018]] ). Plants and aquatic systems particularly respond to changes in photosynthetically active radiation (PAR) and the fraction of diffuse radiation ( [[#Proctor--2018|Proctor et al., 2018]] ; [[#Ren--2018|Ren et al., 2018]] ; [[#Ryu--2018|Ryu et al., 2018]] ). Increases in ultraviolet radiation can also detrimentally affect ecosystems and human health ( [[#Barnes--2019|Barnes et al., 2019]] ).

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<span id="additional-relevant-climatic-impact-drivers"></span>
==== 12.3.7.4 Additional Relevant Climatic Impact-drivers ====

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Additional CIDs may be relevant for regional studies but are not the focus of assessment in this Report. For example, information about changes in the frequency and seasonal timing of fog helps anticipate airport delays and cool beach days, and is also important for water delivery and retention in coastal ecological and agricultural systems ( [[#Torregrosa--2014|Torregrosa et al., 2014]] ).

Threats to many sectoral assets and associated systems may also be compounded when multiple hazards occur simultaneously in the same place, affect multiple regions at the same time, or occur in a sequence that may amplify overall impact ( [[IPCC:Wg1:Chapter:Chapter-11#11.8|Section 11.8]] ; [[#IPCC--2012|IPCC, 2012]] ; [[#Clarke--2018|Clarke et al., 2018]] ; [[#Zscheischler--2018|Zscheischler et al., 2018]] ; [[#Raymond--2020|Raymond et al., 2020]] ). There is emerging literature on many connected extremes and their associated hazards (e.g., climatic conditions that could drive multi-breadbasket failures; [[#Trnka--2019|Trnka et al., 2019]] ; [[#Kornhuber--2020|Kornhuber et al., 2020]] ), but a full accounting is not practical here especially considering the many possible CID combinations and the need to assess how exposed systems would be vulnerable to compound CIDs (assessed in WGII). Table 12.2 is once again instructive here in considering hazard-related storylines, as the multiple CIDs affecting a given sectoral asset (assessing across a row of Table 12.2) point to potentially dangerous hazard combinations. Similarly, change in a single CID has the potential to affect multiple sectoral assets (assessing down a column of Table 12.2) in a manner with broader systemic implications (AR6 WGII).

'''Recent literature defines CID indices to represent trends and thresholds that influence sectoral assets, albeit with considerable variation owing to the unique characteristics of regional and sectoral assets. Indices include direct information about the CID’s profile (magnitude, frequency, duration, timing, spatial extent) or utilize atmospheric conditions as a proxy for CIDs that are more difficult to directly observe or simulate. Each sector is affected by multiple CIDs, and each CID affects multiple sectors. Assets within the same sector may require different or tailored indices even for the same CID. These indices may be defined to capture graduated thresholds associated with tipping points or inflection points in a particular sectoral vulnerability, with commonalities in the types of processes these thresholds represent even as their precise magnitude may vary by specific sectoral system and asset.'''

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== 12.4 Regional Information on Changing Climate ==

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This section describes the historical and projected changes in commonly used indices and thresholds associated with the main climatic impact-drivers (Sections 12.2 and 12.3) at the scale of AR6 regions described in Figure 1.18a. The section is organised by continents (Sections 12.4.1–12.4.6) with a specific assessment for Small Islands ( [[#12.4.7|Section 12.4.7]] ), open and deep ocean ( [[#12.4.8|Section 12.4.8]] ), and polar regions ( [[#12.4.9|Section 12.4.9]] ) as defined in [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] (Figure 1.18c). In addition, CID indices and thresholds relevant to ‘specific zones’ '','' as defined in AR6 WGII Cross-Chapter Papers, are assessed in [[#12.4.10|Section 12.4.10]] except for the Mediterranean, which is addressed both under Africa and Europe (Sections 12.4.1 and 12.4.5) and is a focus in [[IPCC:Wg1:Chapter:Chapter-10#10.6.4|Section 10.6.4]] .

'''Regional assessment method and tables:''' In each section herein (Sections 12.4.1–12.4.10), we assess changes in sector-relevant CIDs following the main CID categories defined in [[#12.2|Section 12.2]] through commonly used indices and thresholds relevant for sectors described in 12.3. Sections 12.4.1–12.4.9 each include a summary qualitative CID assessment table (Tables 12.3–12.11) showing the confidence levels associated with the direction of projected CID changes (i.e., increasing or decreasing) for the mid-century period (2041–2060) relative to the recent past, for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above (RCP6.0, RCP8.5, SSP3-7.0, SSP5-8.5, SRES A2), which approximately encompasses GWLs of 2.0°C to 2.4°C (as best estimate; see Chapter 4, Table 4.5). For scenarios RCP2.6, SSP1-2.6 or SSP1-1.9, the signal may have lower confidence levels in some cases due to smaller overall changes, embedded in a similar internal variability, and to the availability of relatively few studies that account for these scenarios. Nevertheless, CID changes under these lower emissions scenarios are included in the text whenever information is available. For each cell in Tables 12.3–12.11, literature is assessed, aided by global Figure 12.4 or regional Figures 12.5–12.10. Confidence in projections is established considering evidence emerging from observations, attribution and projections, as explained in Cross-Chapter Box 10.3 while considering the amount of evidence and agreement across models and studies and model generations.

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[[File:f9a8282fd23fe8ee1705990a4f92af30 IPCC_AR6_WGI_Figure_12_4.png]]

'''Figure 12.4''' '''|''' '''Median projected changes in selected climatic impact-driver indices based on CMIP6 models.''' '''(d–f)''' mean number of days per year with the NOAA Heat Index (HI) exceeding 41°C; '''(g–i)''' number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index; '''(j–l)''' mean soil moisture (%) and '''(m–o)''' mean wind speed (%). '''(p–r)''' shows change in extreme sea level (1-in-100-year return period total water level from [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] ’s CMIP5 based dataset; metres). Left-hand column is for SSP1-2.6, 2081–2100; middle column is for SSP5-8.5 2041–2060; and right-hand column SSP5-8.5, 2081–2100, all expressed as changes relative to 1995–2014. Exception is extreme total water level which is for (p) RCP4.5 2100, (q) RCP8.5 2050 and (r) RCP8.5 2100, each relative to 1980–2014. Bias correction is applied to daily maximum temperature and HI data (Atlas.1.4.5). Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign (direction) of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. See ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. Figures 12.SM.1–12.SM.6 show regionally averaged values of these indices for the AR6 WGI Reference Regions for various model ensembles, scenarios, time horizons and global warming levels. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

The confidence levels associated with the directions of projected CID changes are synthesized assessments based on literature that may utilize different indices and baseline periods or projections by GWLs. For extreme heat, cold spell, heavy precipitation and drought CIDs that are assessed in Chapter 11, here we draw projections from the 2°C GWL tables in [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] . In some cases, more details are needed in order to emphasize one aspect of projected CID change. For instance, the change in a CID may be different for intensity, duration, frequency; or there can be strong sub-regional or seasonal signals; or different CID indices may have conflicting signals. A footnote is added in such cases, but a confidence level for a direction of projected change is given based on the [[#12.3|Section 12.3]] assessment of aspects of regional CID change that are most relevant for impacts and for risks. As an example, tropical cyclones are increasing in intensity but decreasing in frequency in some regions. Here, in assessing the confidence of the direction of projected change in the Tropical cyclone CID (i.e., the colour of the table cell), we assign more weight to the ‘intensity’ rather than the ‘frequency’, corresponding to the higher relevance of the intensity of major tropical cyclones for risk assessment. Low confidence of changes, arising from lack of evidence, strong spatial or seasonal heterogeneity, or lack of agreement, are represented by colour-less cells, and, for the sake of simplicity, only two categories of confidence are given: ''medium confidence'' and ''high confidence'' (and higher). In addition, CID assessment tables also indicate observed or projected emergence of the CID change signal from the natural interannual variability if assessed with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] , using as a basis a criterion of S/N > 1, noise being defined as the interannual variability. The time of emergence (ToE) is given as either: (i) already emerged in the historical period, or (ii) emerging by 2050 at least for RCP8.5/SSP5-8.5, or (iii) emerging after 2050 but before 2100 at least for scenarios RCP8.5 or SSP5-8.5. Table cells that do not include emergence information are indicative of ‘ ''low confidence'' of emergence in the 21st century’, which includes situations where assessment indicates emergence will not occur before 2100 or that evidence is not available or insufficient for a confidence assessment of time of emergence.

'''Figures:''' The assessment of changes in CIDs is based on literature, physical understanding (Chapters 2–11), and global and regional climate projections of indices and thresholds presented in the Atlas, as well as in the global and regional figures in [[#12.4|Section 12.4]] (Figures 12.4–12.10) showing the future evolution of nine key CID indices/thresholds used in this assessment (see also Cross-Chapter Box 10.3). The figure indices and impact-relevant thresholds are described in Annex VI: Climatic Impact-drivers and Extreme Indices.

Figure 12.4 shows changes in six CID indices. These global maps are derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for different time periods and scenarios (except for extreme total water level where CMIP Phase 5 (CMIP5) is used). The uncertainty due to climate models, time, scenarios and regional downscaling is illustrated in Supplementary Material SM.12.1 to SM.12.6, which show the distribution of the spatial average of the index among models over each land region for CMIP5, CMIP6 and Coordinated Regional Climate Downscaling Experiment (CORDEX) ensembles for the recent past, mid- and end-21st century, and for GWLs of +1.5°C, +2°C and +4°C. The hatching in the figure covers areas where less than 80% of models agree on the sign of change.

Further regional detail is provided for the remaining indices in each continental section in the form of continental maps accompanied by regional box plots displaying changes calculated for AR6 region averages, and the associated regionally averaged uncertainty.

'''Climatic impact-drivers changing in a globally coherent way:''' For the sake of conciseness, assessments pertaining to ocean acidity and the ‘Other’ CID type in Sections 12.2 and 12.3 are not provided per region in Sections 12.4.1–12.4.9 but are summarized here, given the globally coherent way in which they change.

'''Ocean acidity:''' Observations show increasing ocean acidification ( ''robust evidence'' , ''high agreement'' ), and it is ''virtually certain'' that future ocean acidification will increase given future increases in greenhouse gases ( [[IPCC:Wg1:Chapter:Chapter-5#5.4|Section 5.4]] ). Areas below calcium carbonate saturation thresholds expanded from the 1990s to 2010 and [[#Meredith--2019|Meredith et al. (2019)]] indicated that both the Southern and Arctic Oceans will experience year-round under-saturation conditions by 2100 under RCP8.5. The vertical level of the aragonite saturation horizon off the Pacific coast of North America has risen towards the surface by 30–50 m since pre-industrial times ( [[#Mathis--2015b|Mathis et al., 2015b]] ; [[#Feely--2016|Feely et al., 2016]] ). In a study of US coastlines, [[#Ekstrom--2015|Ekstrom et al. (2015)]] mapped out the projected year when aragonite saturation state drops below 1.5 (a sublethal threshold for bivalve mollusc larvae), finding hazardous conditions before 2030 from northern Oregon to Alaska and before 2100 for the Pacific coast and Atlantic coastline north of New Jersey. [[#Mathis--2015a|Mathis et al. (2015a)]] found that surface waters in the Beaufort Sea have already dropped below aragonite saturation thresholds, projecting further declines and the Chukchi Sea also dropping below saturation by about 2030.

'''Air pollution weather:''' The effect of climate change on air quality is assessed in Section 6.5 with limitations for local planning explained in Section 6.1.3, and only a brief summary is given here. Section 6.5 notes that climate change will have a small burden on particulate matter (PM) pollution ( ''medium confidence'' ) while the main controlling factor in determining future concentrations will be future emissions policy for PM and their precursors ( ''high confidence'' ). Surface ozone is sensitive to temperature and water vapour changes, but future levels depend on precursor emissions. Although there is ''low confidence'' in precise regional changes (Section 6.5), climate change will generally introduce a surface ozone (O <sub>3</sub> ) penalty (increasing concentrations with increasing warming levels) over regions with high anthropogenic and/or natural ozone precursor emissions, while in less polluted regions higher temperatures and humidity favour destruction of ozone ( [[#Schnell--2016|Schnell et al., 2016]] ). There is ''low confidence'' in changes to future stagnation events given the lack of robust projections of related atmospheric conditions, such as future atmospheric blocking events (Sections 3.3.3 and 8.4.2). The response of regional air pollution to climate change will also be affected by other CIDs like fire weather, as well as by ecosystem responses such as shifts in emissions by vegetation ( [[#Fiore--2015|Fiore et al., 2015]] ). Section 6.5 assessed ''medium confidence'' that climate-driven changes to meteorological conditions generally favour extreme air pollution episodes in heavily polluted environments, but noted strong regional and metric dependencies. Given the dominant influence of future air quality policies, uncertainties around stagnation or blocking events, and the potential contrasting regional changes of conditions favouring ozone and PM formation, accumulation and destruction, cells in Tables 12.3–12.11 for air pollution weather are marked as ''low confidence'' , and the reader is referred to Section 6.5 for further details.

'''Atmospheric CO''' <sub>2</sub> <sub></sub> '''at surface:''' Observations show rising atmospheric CO <sub>2</sub> concentrations at the surface over all Earth regions ( ''robust evidence'' , ''high agreement'' ) (Sections 2.2 and 5.1.1), and it is ''virtually certain'' that surface atmospheric CO <sub>2</sub> concentrations will continue to increase without substantial changes to emissions ( [[IPCC:Wg1:Chapter:Chapter-5#5.4|Section 5.4]] ).

'''Radiation at surface:''' Radiation has undergone decadal variations in past observations, which are mostly responding to the so-called dimming and brightening phenomenon driven by the increase and decrease of aerosols. Over the last two decades or so, brightening continues in Europe and North America and dimming stabilizes over South and East Asia and increases in some other areas ( [[IPCC:Wg1:Chapter:Chapter-7#7.2.2.3|Section 7.2.2.3]] ). Future regional shortwave radiation projections depend mostly on cloud trends, aerosol and water vapour trends, and stratospheric ozone when considering UV radiation. Over Africa in 2050 and beyond, there is ''medium confidence'' that radiation will increase in North and South Africa and decrease over the Sahara, North Eastern Africa and Western Africa ( [[#Wild--2015|Wild et al., 2015]] , 2017; [[#Soares--2019|Soares et al., 2019]] ; [[#Tang--2019|]] [[#Tang--2019|C. Tang et al., 2019]] ; [[#Sawadogo--2020|Sawadogo et al., 2020]] , 2021). Over Asia, the CMIP5 multi-model mean response shows that solar radiation will decrease in South Asia and increase in East Asia ( ''medium confidence'' ) by the mid-century RCP8.5 ( [[#Wild--2015|Wild et al., 2015]] , 2017; [[#Ruosteenoja--2019b|Ruosteenoja et al., 2019b]] ). Projected solar resources show an increasing trend throughout the 21st century in East Asia under RCP2.6 and RCP8.5 scenarios in CMIP5 simulations ( ''medium confidence'' ) ( [[#Wild--2015|Wild et al., 2015]] ; F. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#Shiogama--2020|Shiogama et al., 2020]] ). More sunshine is projected over Australia in winter and spring by the end of the century ( ''medium confidence'' ) with the increases in Southern Australia exceeding 10% (CSIRO and BOM, 2015; [[#Wild--2015|Wild et al., 2015]] ). In Central and South America, there is ''medium confidence'' of increasing solar radiation over the Amazon basin and the northern part of South America ( ''medium confidence'' ) ( [[#Wild--2015|Wild et al., 2015]] , 2017; [[#de%20Jong--2019|de Jong et al., 2019]] ). There is ''low confidence'' for an increase in surface radiation in central Europe, owing in particular to disagreement in cloud cover across global and regional models ( [[#Jerez--2015|Jerez et al., 2015]] ; [[#Bartók--2017|Bartók et al., 2017]] ; [[#Craig--2018|Craig et al., 2018]] ), as well as water vapour. The treatment of aerosol appears to be key in explaining these differences ( [[#Boé--2016|Boé, 2016]] ; [[#Undorf--2018|Undorf et al., 2018]] ; [[#Boé--2020|Boé et al., 2020]] ; [[#Gutiérrez--2020|Gutiérrez et al., 2020]] ). Regional and global studies, however, indicate that there is ''medium confidence'' in increasing radiation over southern Europe and decreasing radiation over Northern Europe. Increasing radiation trends are also found over southern and eastern USA, and decreasing trends over North-Western North America ( [[#Wild--2015|Wild et al., 2015]] ; [[#Losada%20Carreño--2020|Losada Carreño et al., 2020]] ), despite large differences between responses from regional climate models (RCMs) and general circulation models (GCMs) over southern and eastern USA ( ''low confidence'' ), where, as for Central Europe, the role of aerosols appears important ( [[#Chen--2021|Chen, 2021]] ). Over polar regions there is ''medium confidence'' of a decrease in radiation due to increasing moisture in the atmosphere and clouds ( [[#Wild--2015|Wild et al., 2015]] ).

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<span id="africa"></span>
=== 12.4.1 Africa ===

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Previous IPCC assessments results are summarized in Atlas.4.1.1 For the purpose of this assessment the Africa region has been divided in nine sub-regions of which eight – Sahara (SAH), Western Africa (WAF), Central Africa (CAF), North Eastern Africa (NEAF), South Eastern Africa (SEAF), West Southern Africa (WSAF), East Southern Africa (ESAF) and Madagascar (MDG) – are the official AR6 regions (Figure Atlas.2) and one – North Africa – is used in this assessment to indicate the African portion of the Mediterranean region.

Quite a large body of new literature is now available for the African climate as a result of regionally downscaled CORDEX Africa outputs, in particular, providing projections of both the mean climate ( [[#Mariotti--2014|Mariotti et al., 2014]] ; [[#Nikulin--2018|Nikulin et al., 2018]] ; [[#Dosio--2019|Dosio et al., 2019]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ) and extreme climate phenomena ( [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Nikulin--2018|Nikulin et al., 2018]] ; [[#Dosio--2019|Dosio et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ). CORDEX Africa simulations are assessed in the Atlas, which finds reasonable skill in mean temperature and precipitation as well as important features of regional climate (e.g., timing of monsoon onset in West Africa) although lower performance in Central Africa.

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==== 12.4.1.1 Heat and Cold ====

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'''Mean air temperature:''' The African continent has experienced increased warming since the beginning of the 20th century in regions where measurements allow a sufficient homogeneous observation coverage to estimate trends ( ''high confidence'' ) (Figure Atlas.11). This warming is ''very likely'' attributable to human influence ( [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] and Atlas.4.2) at continental scale. Mean annual temperatures have increased at a high rate since the mid-20th century, reaching 0.2°C–0.5°C per decade in some regions such as north, north-eastern, west and south-western Africa (high confidence) (Atlas.4.2 and Figure Atlas.11).

It is ''very likely'' that temperatures will increase in all future emissions scenarios and all regions of Africa (Atlas.4.4). By the end of the century under RCP8.5 or SSP5-8.5, all African regions will ''very likely'' experience a warming larger than 3°C except Central Africa, where warming is ''very likely'' expected above 2.5°C, while under RCP2.6 or SSP1-2.6, the warming remains ''very likely'' limited to below 2°C (Figure Atlas.12). A ''very likely'' warming with ranges between 0.5°C and 2.5°C is projected by the mid-century for all scenarios depending on the region ( ''high confidence'' ). Mean temperatures for all regions are projected to increase with increasing global warming ( ''virtually certain'' ) (Figure Atlas.12).

'''Extreme heat:''' Warm extremes have increased in most of the regions ( ''high confidence'' ), NEAF, SEAF and MDG ( ''medium confidence'' ) and with ''low confidence'' in CAF (Table 11.4). Despite the increasing mean temperature, there is ''low confidence'' ( ''limited evidence'' ) that Africa has experienced increased extreme heat stress trend for agriculture or human health in the last two decades of the 20th century in a few regions such as West Africa, South Africa and North Africa considering the period from 1973 to 2012 ( [[#Knutson--2016|Knutson and Ploshay, 2016]] ).

A substantial increase in heatwave magnitude and frequency over most of the Africa domain is projected for even 2°C global warming ( ''high confidence'' ) (Sections 11.3 and 11.9, and Table 11.4), with potential effects on health and agriculture. The number of days with maximum temperature exceeding 35°C is projected to increase ( [[#Coppola--2021b|Coppola et al., 2021b]] ) in the range of 50–100 days by 2050 under SSP5-8.5 in WAF, ESAF and WSAF and NEAF ( ''high confidence'' ) (Figure 12.4b). Under SSP1-2.6, the change in the number of exceedance days remains limited to about 40–50 days per year at the end of the century in these regions, while it increases by 150 days or more in WAF, CAF for SSP5-8.5 (Figure 12.4a,c; Figure 12.SM.1).

Mortality-related heat stress levels and deadly temperatures are ''very'' ''likely'' to become more frequent in the future in RCP8.5/SSP5-8.5 and RCP4.5/SSP2-4.5 and for a 2°C global warming ( [[#Mora--2017|Mora et al., 2017]] ; [[#Nangombe--2018|Nangombe et al., 2018]] ; [[#Sylla--2018a|Sylla et al., 2018a]] ; [[#Rohat--2019|Rohat et al., 2019]] ; Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ). In particular the equatorial regions where heat is combined with higher humidity levels, but also North Africa, the Sahel and Southern Africa (Figure 12.4d–f) are among the regions with the largest increases of heat stress ( [[#Zhao--2015|Zhao et al., 2015]] ; [[#Ahmadalipour--2018|Ahmadalipour and Moradkhani, 2018]] ; [[#Coffel--2018|Coffel et al., 2018]] ). Mitigation scenarios make a large difference in frequency of exceedance of high heat stress indices thresholds (e.g., HI > 41°C) by the end of the century (Figure 12.4d–f; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). In West Africa and Central Africa, under SSP5-8.5, the expected number of days per year with HI > 41°C will increase by around 200 days while in SSP1-2.6 such exceedances are expected to increase by less than 50 days per year (Figure 12.4; Figure 12.SM.2).

'''Cold spell and frost:''' Africa experiences cold events and frost days that can affect agriculture, infrastructure, health and ecosystems, especially in Southern and North Africa, which have marked cold seasons and mountainous areas. Cold spells have ''likely'' decreased in frequency over subtropical areas. In particular, in North and Southern Africa, the frequency of cold events has ''likely'' decreased in the last few decades (Sections 11.3 and 11.9). There is a ''high confidence'' that cold spells and low target temperatures will decrease in future climates under all scenarios in West, Central and East Africa. Heating degree days will have a substantial decrease by the end of the century for up to about one month under RCP8.5 in North and Southern Africa ( ''high confidence'' ) ( [[#Coppola--2021b|Coppola et al., 2021b]] ).

'''There is''' high confidence '''that extreme heat has increased in frequency and intensity in most African regions. Heatwaves and deadly heat stress and the frequency of exceedance of hot temperature thresholds (e.g., 35°C) will drastically increase by the end of the century''' ( high confidence ''') under SSP5-8.5, but limited increases are expected in SSP1-2.6. Dangerous heat stress thresholds (HI > 41°C) are projected to be crossed more than 200 days more in West and Central Africa under SSP5-8.5, while this increase remains limited to a few tens of days more for SSP1-2.6. Cold spells and frost days are projected to occur less frequently in all scenarios.'''

<div id="_idContainer031" class="_idGenObjectStyleOverride-1"></div>

[[File:ec270bb71e166b03dbc08e4cca546973 IPCC_AR6_WGI_Figure_12_5.png]]

'''Figure 12.5''' '''|''' '''Projected changes in selected climatic impact-driver indices for Africa.''' Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) from CORDEX-Africa models for 2041–2060 relative to 1995–2014 for RCP8.5. '''(b)''' Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . '''(c)''' Bar plots for Q100 (m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (GWLs, defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. '''(d)''' Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

<div id="12.4.1.2" class="h3-container"></div>

<span id="wet-and-dry-1"></span>
==== 12.4.1.2 Wet and Dry ====

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'''Mean precipitation:''' Since the mid-20th century, precipitation trends have varied in Africa but notable drying trends are found in eastern, central and north-eastern parts of Southern Africa, Central Africa and in the Horn of Africa (Atlas.4.2).

There is ''high confidence'' in projected mean precipitation decreases in North Africa and West Southern Africa and ''medium confidence'' in East Southern Africa by the end of the 21st century ( [[#Dosio--2019|Dosio et al., 2019]] ; [[#Gebrechorkos--2019|Gebrechorkos et al., 2019]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ; Atlas.4.5). The Western Africa region features a gradient in which precipitation decreases in the west and increases in the east and increase is also projected over Eastern Africa ( ''medium confidence'' ) (Atlas.4.5), with trends in Western Africa affecting the boreal summer monsoon ( [[#Chen--2020|Chen et al., 2020]] ). Increasing precipitation for 1.5°C and 2°C GWLs are found in central and eastern Sahel with ''low confidence'' and the wet signal is getting stronger and more extended for a 3°C and 4°C warmer world (Atlas.4.4).

A change in monsoon seasonality is also reported in Western Africa and Sahel ( ''low confidence'' ) with a forward shift in time (later onset and end; [[IPCC:Wg1:Chapter:Chapter-8#8.2|Section 8.2]] ; [[#Mariotti--2011|Mariotti et al., 2011]] ; [[#Seth--2013|Seth et al., 2013]] ; [[#Ashfaq--2021|Ashfaq et al., 2021]] ). This shift has been associated with a precipitation decrease during the monsoon season attributed to a decrease of African easterly wave activity in the 6–9-day regime ( [[#Mariotti--2014|Mariotti et al., 2014]] ) and a soil precipitation feedback reported in [[#Mariotti--2011|Mariotti et al. (2011)]] .

'''River flood:''' Generally in Africa from 1990 through 2014, annual flood frequencies have fluctuated and there is ''medium confidence'' in an upward trend in flood events occurrences (C.-J. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ). In particular, over Western Africa, upward trends in hydrological extremes such as maximum peak discharge have ''likely'' occurred during the last few decades (i.e., after 1980) and have caused increased flood events in riparian countries of rivers such as Niger, Senegal and Volta ( ''high confidence'' ) ( [[#Nka--2015|Nka et al., 2015]] ; [[#Aich--2016a|Aich et al., 2016a]] ; [[#Wilcox--2018|Wilcox et al., 2018]] ; [[#Tramblay--2020|Tramblay et al., 2020]] ). In Southern Africa, trends in flood occurrences were decreasing prior to 1980 and increasing afterwards ( ''medium confidence'' ) ( [[#Tramblay--2020|Tramblay et al., 2020]] ).

Under future climate scenarios, the extreme river discharge as characterized by the 30-year return period of 5-day average peak flow is projected to increase by the end of the century for RCP8.5 (more than 10% relative to the 1971–2000 period) for most of the tropical African river basins ( [[#Dankers--2014|Dankers et al., 2014]] ) and a consistent increase of flood magnitude is projected across humid tropical Africa by 2050 for the A1B scenario ( ''medium confidence'' ) (Figure 12.5; [[#Arnell--2013|Arnell and Gosling, 2013]] ). Specifically, in Western Africa there is not a univocal pattern of change for future projections ( [[#Roudier--2014|Roudier et al., 2014]] ); However, under RCP8.5, there is ''medium confidence'' of a projected increase of 20-year flood magnitudes by 2050 in countries within the Niger River basin ( [[#Aich--2016b|Aich et al., 2016b]] ) and ''low confidence'' ( ''limited evidence'' ) of an increase in extreme peak flows and their duration in countries of the Volta River basin by 2050 and 2090 ( [[#Jin--2018|Jin et al., 2018]] ). A significant median change of flood magnitude for the Gambia River (–4.5%) and for the Sessandra (+14.4%) and Niger (+6.1%) are projected under several scenarios between mid- and end-of-century ( [[#Roudier--2014|Roudier et al., 2014]] ). In East Africa, extreme flows are projected to increase for regions within the Blue Nile basin with ''low confidence'' ( ''limited evidence'' ) ( [[#Aich--2014|Aich et al., 2014]] ). However, uncertainty due to the climate scenario dominates the projection of extreme flows ( [[#Aich--2014|Aich et al., 2014]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ) for the Blue Nile and Niger River basins. Averaged over the African continent for different levels of global warming, the present-day 100-year return period flood levels will have a return period of 40 years in 1.5°C and 2°C ( [[#Alfieri--2017|Alfieri et al., 2017]] ) and 21 years for 4°C warmer climate ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ).

'''Heavy precipitation and pluvial flood:''' [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] found that heavy precipitation intensity and frequency has ''likely'' increased over West and East Southern Africa but there is no evidence due to a lack of studies that any significant trend is observed in any other region. In addition, East Africa has experienced strong precipitation variability and intense wet spells leading to widespread pluvial flooding events hitting most countries including Ethiopia, Somalia, Kenya and Tanzania ( ''medium confidence'' ). Finally, with respect to Southern Africa, heavy precipitations events have increased in frequency ( ''medium confidence'' ).

In West Africa and Central Africa, there is ''high confidence'' that the intensity of extreme precipitation will increase in a future climate under both RCP4.5 and RCP8.5 scenarios and 1.5°C and 2°C GWLs threatening widespread flood occurrences before, during and after the mature monsoon season (Chapter 11). Extreme precipitation intensity is also increasing in several other regions, such as SAH, NEAF, SEAF, ESAF and MDG ( ''high confidence'' ) for 2°C GWL and higher (Chapter 11).

'''Landslides:''' There is an increase in reported landslides in WAF, CAF, NEAF and SEAF in the past decades but with ''limited evidence'' of significant trends ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Haque--2019|Haque et al., 2019]] ). There is ''low confidence'' ( ''limited evidence'' ) of a future increase in landslides in central-eastern Africa, and literature is largely missing to assess this important hazard ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ).

'''Aridity:''' Section 11.9 assesses ''medium confidence'' in observed long-term declines of soil moisture and aridity indices in several African regions (NAF, WAF). Trends in East Africa are not definitive given uncertain balances between precipitation and potential evaporation ( [[#Kew--2021|Kew et al., 2021]] ). Projected declines in precipitation and soil moisture trends indicate ''high confidence'' in increased aridity over the 21st century in NAF, WSAF and ESAF but ''low confidence'' elsewhere in Africa ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ; see also Figure 12.4j–l; [[#Gizaw--2017|Gizaw and Gan, 2017]] ). A growing number of studies provide further regional context on expanding aridity in several places in East and West Africa, respectively ( [[#Sylla--2016a|Sylla et al., 2016a]] ; [[#Liu--2018b|Liu et al., 2018b]] ; [[#Haile--2020|Haile et al., 2020]] ).

'''Hydrological drought:''' Section 11.9 noted observed decreases in hydrological drought over the Mediterranean ( ''high confidence'' ) and diminished summer river flows in West Africa ( ''medium confidence'' ). Recent regional modelling studies project substantial increases in hydrological drought affecting major West African river basins under 1.5°C and 2°C GWLs and RCP4.5 and RCP8.5 scenarios (Oguntunde et al. 2018, 2020; [[#Sylla--2018b|Sylla et al., 2018b]] ); however, there remains ''low confidence'' in future projections given disagreement with global model runoff projections (e.g., B.I. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ). There is ''high confidence'' that a 2°C GWL would see an increase in hydrological droughts in the Mediterranean region, and ''medium confidence'' in increasing hydrological drought conditions in the Southern Africa regions ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

'''Agricultural and ecological drought:''' Farmers and food security experts in East Africa have noted spatial extensions in seasonal agricultural droughts in recent decades ( [[#Elagib--2014|Elagib, 2014]] ), but it is difficult to disentangle these trends from climate variability. In Ethiopia, past severe agricultural drought conditions in the northern regions are moderately common events in recent years ( [[#Zeleke--2017|Zeleke et al., 2017]] ). In Southern Africa, the number of ‘flash’ droughts (with rapid onset and durations from a few days to couple of months) have increased by 220% between 1961 and 2016 as a result of anthropogenic warming ( [[#Yuan--2018|Yuan et al., 2018]] ). [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] notes ''medium confidence'' increases in agricultural and ecological drought trends in North, Western and Central Africa as well as both Southern Africa regions. The most striking drought is the Western Cape drought in 2015–2018, a prolonged drought that resulted in acute water shortages ( [[#Wolski--2018|Wolski, 2018]] ; [[#Burls--2019|Burls et al., 2019]] ; [[IPCC:Wg1:Chapter:Chapter-10#10.6.2|Section 10.6.2]] ). Anthropogenic climate change caused a threefold increase in the probability of such a drought to occur (Chapters 10 and 11; [[#Botai--2017|Botai et al., 2017]] ; [[#Otto--2018|Otto et al., 2018]] ). [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] assesses increases in agricultural and ecological drought at 2°C GWL for North Africa and West Southern Africa ( ''high confidence'' ) and for East Southern Africa and Madagascar ( ''medium confidence'' ), with confidence generally rising for higher emissions scenarios ( [[#Sylla--2016b|Sylla et al., 2016b]] ; [[#Zhao--2017|Zhao and Dai, 2017]] ; [[#Diedhiou--2018|Diedhiou et al., 2018]] ; [[#Abiodun--2019|Abiodun et al., 2019]] ; [[#Todzo--2020|Todzo et al., 2020]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ). [[#Liu--2018b|Liu et al. (2018b)]] identified the Southern Africa region as the drought ‘hottest spot’ in Africa in 1.5°C and 2°C global warming scenarios.

'''Fire weather:''' There is ''low confidence'' ( ''low agreement'' ) in recent reductions in fire activity given soil moisture increases in some regions and substantial land use changes ( [[#Andela--2017|Andela et al., 2017]] ; [[#Forkel--2019|Forkel et al., 2019]] ; [[#Zubkova--2019|Zubkova et al., 2019]] ). Days prone to fire conditions are going to increase in all extratropical Africa until the end of the century and fire weather indices are projected to largely increase in North and Southern Africa, where increasing aridity trends occur ( ''high confidence'' ), with an emerging signal well before the middle of the century where drought and heat increase will combine (Chapter 11; [[#Engelbrecht--2015|Engelbrecht et al., 2015]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). There is ''low confidence'' ( ''limited evidence'' ) of fire weather changes for other African regions.

'''Total precipitation is projected to decrease in the northernmost''' ( high confidence ''') and southernmost regions of Africa''' ( medium confidence '''), with West and East Africa regions each having a west-to-east pattern of decreasing-to-increasing precipitation''' ( medium confidence '''). Most African regions will undergo an increase in heavy precipitation that can lead to pluvial floods''' ( high confidence '''), even as increasing dry climatic impact-drivers (aridity, hydrological, agricultural and ecological droughts, fire weather) are generally projected in the North Africa and Southern African regions''' ( high confidence ''') and western portions of West Africa''' ( medium confidence ''').'''

<div id="12.4.1.3" class="h3-container"></div>

<span id="wind-1"></span>
==== 12.4.1.3 Wind ====

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'''Mean wind speed:''' Decreasing trends in wind speeds have occurred in many parts of Africa ( ''low confidence'' due to observations with limited homogeneity) ( [[#McVicar--2012|McVicar et al., 2012]] ; AR5 WGI). There is ''high confidence'' in climate change-induced future decreasing mean wind, wind energy potential and strong winds in North Africa and Mediterranean regions as a consequence of the poleward shift of the Hadley cell ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Sivakumar--2018|Sivakumar and Lucio, 2018]] ; [[#Tobin--2018|Tobin et al., 2018]] ; [[#Jung--2019|Jung and Schindler, 2019]] ) in the RCP4.5 and RCP8.5 scenarios by the middle of the century or beyond, and for a GWL of 2°C or higher. Over Western Africa and Southern Africa a future significant increase in wind speeds and wind energy potential is expected ( ''medium confidence'' ) (Figure 12.4m–o; [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Jung--2019|Jung and Schindler, 2019]] ).

'''Severe wind storm:''' A limited number of studies allow an assessment of past trends in wind storms. In West Africa and specifically in the Sahel band, more intense storms have occurred since the 1980s ( ''low confidence, limited evidence'' ). A persistent and large increase of frequency of Sahelian mesoscale convective storms has been found in several studies ( [[#Panthou--2014|Panthou et al., 2014]] ; C.M. [[#Taylor--2017|]] [[#Taylor--2017|Taylor et al., 2017]] ), with consequences for extreme rainfalls, and potentially extreme winds ( ''low confidence, limited evidence'' ). There is ''low confidence'' of a general increasing trend in extreme winds across Western, Central, Eastern and Southern Africa in a majority of regions by the middle of the century even in high-end scenarios. The frequency of Mediterranean wind storms reaching North Africa, including Medicanes, is projected to decrease, but their intensities are projected to increase, by the mid-century and beyond under SRES A1B, SRES A2 and RCP8.5 ( ''medium confidence'' ) (Chapter 11; [[#Cavicchia--2014|Cavicchia et al., 2014]] ; [[#Walsh--2014|Walsh et al., 2014]] ; [[#Tous--2016|Tous et al., 2016]] ; [[#Romera--2017|Romera et al., 2017]] ; [[#Romero--2017|Romero and Emanuel, 2017]] ; [[#González-Alemán--2019|González-Alemán et al., 2019]] ).

'''Tropical cyclone:''' In the South Indian Ocean, an increase in Category 5 cyclones has been observed in recent decades ( [[#Fitchett--2018|Fitchett, 2018]] ) as in other basins ( [[IPCC:Wg1:Chapter:Chapter-11#11.7|Section 11.7]] ). However, there is a projected decrease in the frequency of tropical cyclones making landfall over Madagascar, South Eastern Africa and East Southern Africa in a 1°C, 2°C and 3°C warmer world ( ''medium confidence'' ) ( [[#Malherbe--2013|Malherbe et al., 2013]] ; [[#Roberts--2015|Roberts et al., 2015]] , 2020; [[#Muthige--2018|Muthige et al., 2018]] ; [[#Knutson--2020|Knutson et al., 2020]] ). There is ''medium confidence'' in general increasing intensities for cyclones in such studies for African regions.

'''Sand and dust storm:''' North Africa and the Sahel, and to a lesser extent Southern Africa, are prone to dust storms, having consequences on health ( [[#Querol--2019|Querol et al., 2019]] ), transmission of infectious diseases ( [[#Agier--2013|Agier et al., 2013]] ; [[#Wu--2016|Wu et al., 2016]] ), and solar power generation and related maintenance costs. There is ''limited evidence'' and ''low agreement'' of secular 20th century trends in wind speeds or dust emissions (limited length of data records, large variability). Dust variations are controlled by changes in surface winds, precipitation and vegetation, which in turn are modulated at multiple time scales by dominant modes of internal climate variability (Chapter 10). In North Africa, wind variability explains both the observed high concentrations between the 1970s and 1980s and lower concentrations thereafter ( [[#Ridley--2014|Ridley et al., 2014]] ; [[#Evan--2016|Evan et al., 2016]] ). Yet, the effect of vegetation changes may not be negligible ( [[#Pu--2017|Pu and Ginoux, 2017]] , 2018).

Changes to the frequency and intensity of dust storms also remain largely uncertain due to uncertainty in future regional wind and precipitation as the climate warms, CO <sub>2</sub> fertilization effects on vegetation ( [[#Huang--2017|Huang et al., 2017]] ), and anthropogenic land use and land-cover change due to land management and invasive species ( [[#Ginoux--2012|Ginoux et al., 2012]] ; [[#Webb--2018|Webb and Pierre, 2018]] ). Dust loadings and related air pollution hazards (from fine particles that affect health) are projected to generally decrease in many regions of the Sahara and Sahel due to the changing winds ( [[#Evan--2016|Evan et al., 2016]] ) and slightly increase over the Guinea coast and West Africa ( ''low confidence'' ) ( [[#Ji--2018|Ji et al., 2018]] ).

'''In summary, there is''' high confidence '''of a decrease in mean wind speed and wind energy potential in North Africa and''' medium confidence '''of an increase in Southern and Western Africa, by the middle of the century regardless of climate scenario or global warming level equal or superior to 2°C,''' high confidence '''of a decrease in frequency of cyclones landing in SEAF, ESAF and MDG, and''' low confidence '''of a general increase in wind storms in most African regions located south of the Sahel. The evolution of dust storms remains largely uncertain.'''

<div id="12.4.1.4" class="h3-container"></div>

<span id="snow-and-ice-1"></span>
==== 12.4.1.4 Snow and Ice ====

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'''Snow and glacier:''' African glaciers are located in East Africa and more specifically on Mount Kenya, the Rwenzori Mountains and Mount Kilimanjaro, with glaciers reducing substantially in each region ( ''high confidence'' ) ( [[#Taylor--2006|Taylor et al., 2006]] ; [[#Cullen--2013|Cullen et al., 2013]] ; [[#Chen--2018|Chen et al., 2018]] ; [[#Prinz--2018|Prinz et al., 2018]] ; [[#Wang--2019|Wang and Zhou, 2019]] ). Observation and future projection of African glacier mass changes are assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.5.1%20|Section 9.5.1]] within the low-latitude glacier region, which is one of the regions with the largest mass loss even under low-emissions scenarios (assessment of this region is dominated by glaciers in the South American Andes, however) ( ''high confidence'' ). Glaciers in the low-latitude region will lose 67 ± 42%, 86 ± 24% and 94 ± 13% of their mass by the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios respectively ( [[#Marzeion--2020|Marzeion et al., 2020]] ). [[#Cullen--2013|Cullen et al. (2013)]] calculated that even imbalances between the Mount Kilimanjaro glaciers and present-day climate would be enough to eliminate the mountain’s glaciers by 2060. Snow water equivalent and snow cover season duration also decline in the East African mountains, Ethiopian Highlands and [[IPCC:Wg1:Chapter:Atlas|Atlas]] Mountains with climate change ( ''high confidence'' ) ( [[#López-Moreno--2017|López-Moreno et al., 2017]] ).

'''In conclusion, there is''' high confidence '''that African snow and glaciers have very significantly decreased in the last decades and that this trend will continue over the 21st century.'''

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<span id="coastal-and-oceanic"></span>
==== 12.4.1.5 Coastal and Oceanic ====

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'''Relative sea level:''' Around Africa, from 1900 to 2018, a new tide gauge-based reconstruction finds a regional mean RSL change of 2.07 [1.36 to 2.77] mm yr <sup>–1</sup> in the South Atlantic and 1.33 [0.80 to 1.86] mm yr <sup>–1</sup> in the Indian Ocean ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). For the period 1993–2018, these RSLR rates, based on satellite altimetry, increased to 3.45 [3.04 to 3.86] mm yr <sup>–1</sup> and 3.65 [3.23 to 4.08] mm yr <sup>–1</sup> respectively ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5).

Relative sea level rise is ''virtually certain'' to continue in the oceans around Africa. Regional mean RSLR projections for the oceans around Africa range from 0.4–0.5 m under SSP1-2.6 to 0.8–0.9 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), which is within the range of projected GMSL change ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.3|Section 9.6.3.3]] ). These RSLR projections may, however, be underestimated due to potential partial representation of land subsidence in their assessment ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ).

'''Coastal flood:''' The present-day 1-in-100-year extreme total water level is between 0.1 and 1.2 m around Africa, with values around 1 m or above along the south-west, south-east and central east coasts ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ).

Extreme total water level (ETWL) magnitude and occurrence frequency are expected to increase throughout the region ( ''high confidence'' ) (Figure 12.4p–r and Figure 12.SM.6). Across the continent, the 5th–95th percentile range of the 1-in-100-year ETWL is projected to increase (relative to 1980–2014) by 7–36 cm and by 14–42 cm by 2050 under RCP4.5 and RCP8.5 respectively. By 2100, this range is projected to be 28–86 cm and 43–190 cm under RCP4.5 and RCP8.5 respectively ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). In terms of ETWL occurrence frequencies, the present-day 1-in-100-year ETWL is projected to have median return periods of around 1-in-10-years to 1-in-20-years by 2050 and 1-in-1-year to 1-in-5-years by 2100 in southern and North Africa and occur more than once per year by 2050 and 2100 in most of East and West Africa under RCP4.5 ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ). The present-day 1-in-50-year ETWL is projected to occur around three times a year by 2100 with an SLR of 1 m in Africa ( [[#Vitousek--2017|Vitousek et al., 2017]] ).

'''Coastal erosion:''' Shoreline retreat rates up to 1 m yr <sup>–1</sup> have been observed around the continent during 1984–2015, except in ESAF, which has experienced a shoreline progradation rate of 0.1 m/r over the same period ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). [[#Mentaschi--2018|Mentaschi et al. (2018)]] report a coastal area losses of 160 km <sup>2</sup> and 460 km <sup>2</sup> over a 30-year period (1984–2015) along the Atlantic and Indian Ocean coasts of the continent. At the more regional level, in Ghana along the Gulf of Guinea about 79% of the shoreline was found to be retreating while 21% was found to be stable or prograding over the period 1974–1996 ( [[#Addo--2016|Addo and Addo, 2016]] ).

Projections indicate that a vast majority of sandy coasts in the region will experience shoreline retreat throughout the 21st century ( ''high confidence'' ), while parts of the ESAF and western MDG coastline are projected to prograde over the 21st century, if present ambient trends continue. Median shoreline change projections (CMIP5), relative to 2010, presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] show that, under RCP4.5, shorelines in Africa will retreat by between 30 m (SAH, NEAF, WSAF, ESAF, MDG) and 55 m (WAF, CAF), by mid-century. By the same period but under RCP8.5, the median shoreline retreat is projected to be between 35 m (SAH, NEAF, WSAF, ESAF) and 65 m (WAF, CAF). By 2100, more than 100 m of median retreat is projected in WAF, CAF and SEAF under RCP4.5, while under RCP8.5, more than 100 m of shoreline retreat is projected in all regions except NEAF and WSAF. Under RCP8.5 especially, the projected retreat by 2100 is greater than 150 m in WAF and CAF. The total length of sandy coasts in Africa that is projected to retreat by more than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 13,000 km and 17,000 km respectively, an increase of approximately 33%.

'''Marine heatwave (MHW):''' From 1982 to 2016, the coastal oceans of Africa have experienced on average 2–3 MHWs per year, with the coastal oceans around the southern half of the continent experiencing on average 2.5–3 MHWs per year. The average duration was between 5 and 15 days ( [[#Oliver--2018|Oliver et al., 2018]] ). Changes over the 20th century, derived from MHW proxies, show an increase in frequency between 0.5 and 2 MHWs per decade over the region, especially off the Horn of Africa; an increase in intensity per event around Southern Africa; and an increase in MHW duration along the North African coastlines ( [[#Oliver--2018|Oliver et al., 2018]] ).

There is ''high confidence'' that MHWs will increase around Africa. Mean SST, a common proxy for MHWs, is projected to increase by 1°C (2°C) around Africa by 2100, with a hotspot of around 2°C (5°C) along the coastlines of South Africa under RCP4.5 (RCP8.5; Interactive Atlas). Under global warming conditions, MHW intensity and duration will increase in the coastal zones of all sub-regions of Africa ( [[#Frölicher--2018|Frölicher et al., 2018]] ). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around Australasia by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).

'''In general, there is''' high confidence '''that most coastal- and ocean-related hazards in Africa will increase over the 21st century. Relative sea level rise is''' virtually certain '''to continue around Africa, contributing to increased coastal flooding in low-lying areas''' ( high confidence ''') and shoreline retreat along most sandy coasts''' ( high confidence '''). Marine heatwaves are also expected to increase around the region over the 21st century''' ( high confidence ''').'''

The assessed direction of change in CIDs for Africa and associated confidence levels are illustrated in Table 12.3. No relevant literature could be found for permafrost and hail, although these phenomena may be relevant in parts of the continent.

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'''Table 12.3''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in Africa, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:cd304a9910c93ec3527dd226ee411456 IPCC_AR6_WGI_Chapter12_Table_12_3.jpg]]

<div id="12.4.2" class="h2-container"></div>

<span id="asia"></span>
=== 12.4.2 Asia ===

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According to the region definitions given in Chapter 1, Asia is divided into 11 regions: the Arabian Peninsula (ARP), Western Central Asia (WCA), West Siberia (WSB), East Siberia (ESB), the Russian Far East (RFE), East Asia (EAS), East Central Asia (ECA), the Tibetan Plateau (TIB), South Asia (SAS), South East Asia (SEA) and the Russian Arctic Region (RAR). CID changes in RAR are assessed in the Polar Region section ( [[#12.4.9|Section 12.4.9]] ). As assessed in previous IPCC Reports, major concerns in Asia are associated particularly with droughts and floods in all regions, heat extremes in SAS and EAS, sand-dust storms in WCA, tropical cyclones in SEA and EAS, snow cover and glacier changes in ECA and the Hindu Kush Himalaya (HKH) region, and sea ice and permafrost thawing in northern Asia.

Since AR5, a large body of new literature is now available relevant to climate change in Asia, which includes projections of both mean climate and extreme climate phenomena from global and regional ensembles of climate simulations such as CMIP6 and CORDEX ( [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] and the Atlas). Literature has also considerably grown on several climate topics relevant to Asia such as the mountain climate ( [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] of the SROCC), and the novel regional assessments such as the Hindu Kush Himalaya Assessment ( [[#Wester--2019|Wester et al., 2019]] ). Figure 12.6 shows the regional changes in indices related to floods, and coastal erosion over Asia, which are assessed on a regional basis along with other climatic impact-driver indices below.

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[[File:ceaaf766f0a386cf4d807136bb2b937f IPCC_AR6_WGI_Figure_12_6.png]]

'''Figure 12.6''' '''|''' '''Projected changes in selected climatic impact-driver indices for Asia.''' '''(a)''' Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) from CORDEX models for the West, South East and East Asia domains for 2041–2060 relative to 1995–2014 for RCP8.5. '''(b)''' Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . '''(c)''' Bar plots for Q100 (m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. '''(d)''' Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

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<span id="heat-and-cold-2"></span>
==== 12.4.2.1 Heat and Cold ====

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'''Mean air temperature:''' A long-term warming trend in annual mean surface temperature has been observed across Asia during 1960–2015, and the warming accelerated after the 1970s ( ''high confidence'' ) ( [[#Davi--2015|Davi et al., 2015]] ; [[#Aich--2017|Aich et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ; S. [[#Dong--2018|]] [[#Dong--2018|Dong et al., 2018]] ; [[#IPCC--2018|IPCC, 2018]] ; [[#Krishnan--2019|Krishnan et al., 2019]] ; M. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ). Records also indicate a higher rate of warming in minimum temperatures than maximum temperatures in Asia, leading to more frequent warm nights and warm days, and less frequent cold days and cold nights ( ''high confidence'' ) ( [[#Supari--2017|Supari et al., 2017]] ; [[#Akperov--2018|Akperov et al., 2018]] ; [[#Cheong--2018|Cheong et al., 2018]] ; [[#Rahimi--2018|Rahimi et al., 2018]] ; [[#Khan--2019a|Khan et al., 2019a]] ; L. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; M. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ).

Projections show continued warming over Asia in the future with contrasted regional patterns across the continent ( ''high confidence'' ) (Figure 4.19). For RCP8.5/SSP5-8.5 at the end of the century, the mean estimated warming exceeds 5°C in WSB, ESB and RFE and 7°C in some parts ( ''high confidence'' ). In most areas of ARP and WCA, 5°C is exceeded ( [[#Ozturk--2017|Ozturk et al., 2017]] ), but EAS, SAS and SEA have a lower projected warming of less than 5°C ( [[#Basha--2017|Basha et al., 2017]] ; [[#Lu--2019|]] [[#Lu--2019|C. Lu et al., 2019]] ; [[#Almazroui--2020|Almazroui et al., 2020]] ; Atlas.5). Under SSP1-2.6, the warming remains limited to 2°C in most areas except Arctic regions, where it exceeds 2°C (Figure 4.19).

'''Extreme heat:''' There is increased evidence and ''high confidence'' of more frequent heat extremes in the recent decades than in previous ones in most of Asia ( [[#Acar%20Deniz--2015|Acar Deniz and Gönençgil, 2015]] ; [[#Rohini--2016|Rohini et al., 2016]] ; [[#Mishra--2017|Mishra et al., 2017]] ; [[#You--2017|You et al., 2017]] ; [[#Imada--2018|Imada et al., 2018]] ; [[#Khan--2019b|Khan et al., 2019b]] ; [[#Krishnan--2019|Krishnan et al., 2019]] ; [[#Rahimi--2019|Rahimi et al., 2019]] ; [[#Yin--2019|Yin et al., 2019]] ; Chapter 11) due to the effects of anthropogenic global warming, El Niño and urbanization ( [[#Luo--2017|Luo and Lau, 2017]] ; [[#Thirumalai--2017|Thirumalai et al., 2017]] ; [[#Imada--2019|Imada et al., 2019]] ; Y. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Zhou--2019|Zhou et al., 2019]] ). But there is ''medium confidence'' of heat extremes increasing in frequency in many parts of India ( [[#Rohini--2016|Rohini et al., 2016]] ; [[#Mazdiyasni--2017|Mazdiyasni et al., 2017]] ; [[#van%20Oldenborgh--2018|van Oldenborgh et al., 2018]] ; [[#Sen%20Roy--2019|Sen Roy, 2019]] ; [[#Kumar--2020|Kumar et al., 2020]] ) partly due to the alleviation of anthropogenic warming by increased air pollution with aerosols and expanding irrigation ( [[#van%20Oldenborgh--2018|van Oldenborgh et al., 2018]] ; [[#Thiery--2020|Thiery et al., 2020]] ).

Extreme heat events are ''very likely'' to become more intense and/or more frequent in SAS, WCA, ARP, EAS, and SEA by the end of 21st century, especially under RCP6.0 and RCP8.5 (Figure 12.4a–c and Chapter 11; [[#Lelieveld--2016|Lelieveld et al., 2016]] ; [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Guo--2017|Guo et al., 2017]] ; [[#Mishra--2017|Mishra et al., 2017]] ; [[#Dosio--2018|Dosio et al., 2018]] ; [[#Lin--2018|Lin et al., 2018]] ; [[#Nasim--2018|Nasim et al., 2018]] ; [[#Shin--2018|Shin et al., 2018]] ; [[#Hong--2019|Hong et al., 2019]] ; [[#Su--2019|Su and Dong, 2019]] ; [[#Khan--2020|Khan et al., 2020]] ; [[#Kumar--2020|Kumar et al., 2020]] ). The exceedance of the dangerous heat stress 41°C threshold of the HI is expected to increase by about 250 days in SEA and by 50–150 days in SAS, WCA, ARP and EAS for SSP5-8.5 at the end of the century (Figure 12.4d–f and Figure 12.SM.2). Under SSP1-2.6, the increase would be restricted to less than 30 days in many of these regions except SEA, where the number of exceedance days increases by about 100 days in some areas. Such increases are already present in the middle of the century (Figure 12.4d–f; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). In these regions, the increase in number of days with exceedance of 35°C of high heat stress is also expected to increase substantially for the mid-century under SSP5-8.5 (typically by 10–50 days except in Arctic and Siberian regions), and by more than 60 days in areas of SEA, and a large difference is found between low- and high-end scenarios in the end of the century ( ''high confidence'' ) (Figure 12.4b). Over WSB, ESB and RFE also, an increase of extreme heat durations and frequency is expected in all scenarios ( ''high confidence'' ) ( [[#Kattsov--2017|Kattsov et al., 2017]] ; [[#Khlebnikova--2019b|Khlebnikova et al., 2019b]] ).

'''Cold spell and frost:''' Cold spells intensity and frequency, as well as the number of frost days, in most Asian regions have been decreasing since the beginning of the 20th century ( ''high confidence'' ) (Chapter 11; [[#Sheikh--2015|Sheikh et al., 2015]] ; [[#Donat--2016|Donat et al., 2016]] ; [[#Erlat--2016|Erlat and Türkeş, 2016]] ; S. [[#Dong--2018|]] [[#Dong--2018|Dong et al., 2018]] ; [[#Liao--2018|Liao et al., 2018]] , 2020; [[#Lu--2018|Lu et al., 2018]] ; [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ), except for the central Eurasian regions, where there was a cooling trend during 1995–2014, which is linked to sea ice loss in the Barents–Kara Seas ( ''medium confidence'' ) (Atlas.5.2; [[#Wegmann--2018|Wegmann et al., 2018]] ; [[#Blackport--2019|Blackport et al., 2019]] ; [[#Mori--2019|Mori et al., 2019]] ).

It is ''very likely'' that cold spells will have a decreasing frequency in all future scenarios across Asian regions (J. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ; [[#Sui--2018|Sui et al., 2018]] ; L. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ), as well as frost days (L. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Fallah-Ghalhari--2019|Fallah-Ghalhari et al., 2019]] ) except in tropical Asia (Chapter 11).

'''In Asia, temperatures have warmed during the last century''' ( high confidence '''). Extreme heat episodes have become more frequent in most regions''' ( high confidence '''), and are''' very likely '''to increase in all regions of Asia under all warming scenarios during this century. Dangerous heat stress thresholds such as HI > 41°C will be crossed much more often (typically 5''' '''0–1''' '''50 days per year more than the recent past) in many southern Asia regions at the end of the century under SSP5-8.5 while these numbers should remain limited to a few tens under SSP1-2.6''' ( high confidence '''). It is''' very likely '''that cold spells and frost days will decrease in frequency in all future scenarios across Asian regions during the century.'''

<div id="12.4.2.2" class="h3-container"></div>

<span id="wet-and-dry-2"></span>
==== 12.4.2.2 Wet and Dry ====

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'''Mean precipitation:''' The most prominent features about changes in precipitation over Asia (1901–2010) are the increasing precipitation trends across higher latitudes, along with some scattered smaller regions of detectable increases and decreases ( [[#Knutson--2018|Knutson and Zeng, 2018]] ); however, spatial variability remains high (W. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#Limsakul--2016|Limsakul and Singhruck, 2016]] ; [[#Supari--2017|Supari et al., 2017]] ; [[#Rahimi--2018|Rahimi et al., 2018]] , 2019; [[#Sein--2018|Sein et al., 2018]] ; [[#Kumar--2019|Kumar et al., 2019]] ; H. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ; see Atlas.5) ( ''medium confidence'' ).

Mean precipitation is ''likely'' to increase in most areas of northern (WSB, ESB, RFE), southern (ECA, TIB, SAS) and East Asia (EAS) in different scenarios ( ''high confidence'' ) ( [[#Huang--2014|Huang et al., 2014]] ; [[#Xu--2017|Xu et al., 2017]] ; [[#Kusunoki--2018|Kusunoki, 2018]] ; [[#Mandapaka--2018|Mandapaka and Lo, 2018]] ; [[#Luo--2019|Luo et al., 2019]] ; [[#Wu--2019|Wu et al., 2019]] ; X. [[#Zhu--2019|]] [[#Zhu--2019|Zhu et al., 2019]] ; [[#Almazroui--2020|Almazroui et al., 2020]] ; [[#Jiang--2020|Jiang et al., 2020]] ; [[#Rai--2020|Rai et al., 2020]] ; see Atlas.5). Monsoon circulation will also increase seasonal contrasts, with SAS seeing wetter wet seasons and drier dry seasons (Atlas.5.3). Higher uncertainty between CMIP5 and CMIP6 as well as spatial differences lend ''low confidence'' to model projections in ARP and WCA (Atlas.5.5), with large seasonal differences ( [[#Zhu--2020|Zhu et al., 2020]] ) and some models projecting decreases in precipitation in Central Asia ( [[#Ozturk--2017|Ozturk et al., 2017]] ), Pakistan ( [[#Nabeel--2020|Nabeel and Athar, 2020]] ) and SEA (Supari et al., 2020).

'''River flood:''' Flood risk has grown in many places in China from 1961 to 2017 ( [[#Kundzewicz--2019|Kundzewicz et al., 2019]] ) ( ''low confidence'' ). In SAS, the numbers of flood events and human fatalities have increased in India during 1978–2006 ( [[#Singh--2013|Singh and Kumar, 2013]] ), whereas the average country-wide inundation depth has been decreasing during 2002–2010 in Bangladesh, attributed to improved flood management ( ''low confidence'' ) ( [[#Sciance--2018|Sciance and Nooner, 2018]] ).

Given the increase of heavy precipitation in most Asian regions, the river flood frequency and intensities will change consequently in Asia. Over China floods will increase with different levels under different warming scenarios ( ''medium confidence'' ) ( [[#Lin--2018|Lin et al., 2018]] ; [[#Kundzewicz--2019|Kundzewicz et al., 2019]] ; [[#Liang--2019|Liang et al., 2019]] ; [[#Gu--2020|Gu et al., 2020]] ). Monsoon floods will be more intense in SAS ( ''medium confidence'' ) ( [[#Nowreen--2015|Nowreen et al., 2015]] ; [[#Babur--2016|Babur et al., 2016]] ; [[#Mohammed--2018|Mohammed et al., 2018]] ). The total flood damage will increase greatly in river basins in SEA countries under the conditions of climate change and rapid urbanization in the near future ( [[#Dahal--2018|Dahal et al., 2018]] ; [[#Kefi--2020|Kefi et al., 2020]] ). A changing snowmelt regime in the mountains may contribute to a shift of spring floods to earlier periods in Central Asia in future ( ''medium confidence'' ) ( [[#Reyer--2017b|Reyer et al., 2017b]] ). The annual maximum river discharge can almost double by the mid-21st century in major Siberian rivers, and annual maximum flood area is projected to increase across Siberia mostly by 2–5% relative to the baseline period (1990–1999) under RCP8.5 scenario ( ''medium confidence'' ) ( [[#Shkolnik--2018|Shkolnik et al., 2018]] ).

'''Heavy precipitation and pluvial flood:''' Pluvial floods are driven by extreme precipitation and land use. Observed changes in extreme precipitation vary considerably by region (Chapter 11). Heavy precipitation is ''very'' ''likely'' to become more intense and frequent in all areas of Asia except in ARP ( ''medium confidence'' ) for a 2°C GWL or higher (Chapter 11).

'''Landslide:''' The majority of non-seismic fatal landslide events were triggered by rainfall, and Asia is the dominant geographical area of landslide distribution ( [[#Froude--2018|Froude and Petley, 2018]] ). Floods and landslides are the most frequently occurring natural hazards in the eastern Himalayas and hilly regions, particularly caused by torrential rain during the monsoon season ( [[#Gaire--2015|Gaire et al., 2015]] ; [[#Syed--2016|Syed and Al Amin, 2016]] ). They accounted for nearly half of the events recorded in the countries of the HKH region ( [[#Vaidya--2019|Vaidya et al., 2019]] ). Intense monsoon rainfall in northern India and western Nepal in 2013, which led to landslides and one of the worst floods in history, has been linked to increased loading of GHG and aerosols ( [[#Cho--2016|Cho et al., 2016]] ). Due to an increase of heavy precipitation and permafrost thawing, an increase in landslides is expected in some areas of Asia, such as northern Taiwan (China), some South Korean mountains, Himalayan mountains, and permafrost territories of Siberia, and the increase is expected to be the greatest over areas covered by current glaciers and glacial lakes ( ''medium confidence'' , ''medium evidence'' ) ( [[#Kim--2015|Kim et al., 2015]] ; [[#Kharuk--2016|Kharuk et al., 2016]] ; C.-W. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ; [[#Kirschbaum--2020|Kirschbaum et al., 2020]] ).

'''Aridity:''' Aridity in West Central Asia and parts of South Asia increased in recent decades ( ''medium confidence'' ), as documented in Afghanistan ( [[#Qutbudin--2019|Qutbudin et al., 2019]] ), Iran ( [[#Zarei--2016|Zarei et al., 2016]] ; [[#Zolfaghari--2016|Zolfaghari et al., 2016]] ; [[#Pour--2020|Pour et al., 2020]] ), most parts of Pakistan (K. [[#Ahmed--2018|Ahmed et al., 2018]] , 2019), and many parts of India ( [[#Roxy--2015|Roxy et al., 2015]] ; [[#Mallya--2016|Mallya et al., 2016]] ; [[#Matin--2017|Matin and Behera, 2017]] ; [[#Ramarao--2019|Ramarao et al., 2019]] ). Some spatial and seasonal differences within these regions remain, with [[#Ambika--2020|Ambika and]] [[#Mishra--2020|Mishra (2020)]] noting significant aridity declines over the Indo–Gangetic Plain in India during 1979–2018 due in part to the effect of irrigation, and [[#Araghi--2018|Araghi et al. (2018)]] found that many parts of Iran show no significant trends in aridity. There was a drying tendency in the dry season and significant wetting in the wet season in the Philippines during 1951–2010 ( [[#Villafuerte--2014|Villafuerte et al., 2014]] ), and slight wetting in Vietnam during 1980–2017 ( [[#Stojanovic--2020|Stojanovic et al., 2020]] ) ( ''low confidence'' ). In EAS there is ''low confidence'' of broad aridity changes, as the frequency of droughts have increased (especially in spring) along a strip extending from south-west China to the western part of north-east China; however, there is no evidence of a significant increase in drought severity over China as a whole and many parts in the arid north-west China got wetter during 1961–2012 (W. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#Zhai--2017|Zhai et al., 2017]] ; H. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ; [[#Zhang--2019|Zhang and Shen, 2019]] ). In Siberia, the number of dry days has decreased for much of the region, but increased in its southern parts ( [[#Khlebnikova--2019a|Khlebnikova et al., 2019a]] ).

The counteracting factors of projected increases in precipitation and temperature across most of Asia ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] and Atlas.5) leads to ''low confidence'' ( ''limited evidence'' , inconsistent trends) for broad, long-term aridity changes with ''medium confidence'' only for aridity increases in West Central Asia and East Asia. A growing number of studies highlight the potential for more localized aridity trends, including projection ensembles indicating significant increase in aridity and more frequent and intense droughts in most parts of China (Y. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Yao--2020|Yao et al., 2020]] ) and India under RCP4.5 and RCP8.5 for the 2020–2100 period ( [[#Gupta--2018|Gupta and Jain, 2018]] ; [[#Bisht--2019|Bisht et al., 2019]] ; [[#Preethi--2019|Preethi et al., 2019]] ).

'''Hydrological drought:''' Section 11.9 indicates that ''limited evidence'' and inconsistent regional trends gives ''low confidence'' to observed and projected changes in hydrological drought in all Asian regions at a 2°C GWL (approximately mid-century), although West Central Asia hydrological droughts increase at the 4°C GWL (approximately end-of-century under higher emissions scenarios) ( ''medium confidence'' ). Human activities such as reservoir operation and water abstraction have had a profound effect on low river flow characteristics and drought impacts in many Asian regions ( [[#Kazemzadeh--2016|Kazemzadeh and Malekian, 2016]] ; [[#Yang--2020b|Yang et al., 2020b]] ). There was no observed overall long-term change of both meteorological droughts and hydrological droughts over India during 1870–2018 ( [[#Mishra--2020|Mishra, 2020]] ), but there were strong trends towards drying of soil moisture in north-central India ( [[#Ganeshi--2020|Ganeshi et al., 2020]] ) and intensified droughts in north-west India, parts of Peninsular India, and Myanmar ( [[#Malik--2016|Malik et al., 2016]] ). The frequency of water scarcity connected with hydrological droughts has increased significantly in southern Russia since the beginning of the 21st century ( [[#Frolova--2017|Frolova et al., 2017]] ). Higher future temperatures are expected to alter the seasonal profile of hydrologic droughts given reduced summer snowmelt ( ''medium confidence'' ) downstream of mountains such as the Himalayas and the Tibetan Plateau ( [[#Sorg--2014|Sorg et al., 2014]] ). Several studies project more severe future hydrological drought in the Weihe River basin in northern China ( [[#Yuan--2016|Yuan et al., 2016]] ; [[#Sun--2020|Sun and Zhou, 2020]] ).

'''Agricultural and ecological drought:''' Section 11.9 assesses ''medium confidence'' in observed increases to agricultural and ecological droughts in West Central Asia, East Central Asia, and East Asia. Persistent droughts were the main factor for grassland degradation and desertification in Central Asia in the early 21st century (G. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#Emadodin--2019|Emadodin et al., 2019]] ). Compound meteorological drought and heat events, which lead to water stress conditions for agricultural and ecological systems, have become more frequent, widespread and persistent in China especially since the late 1990s ( [[#Yu--2020|Yu and Zhai, 2020]] ). There were more agricultural droughts in northern China than in southern China, and the intensity of agricultural drought increased during 1951–2018 ( [[#Zhao--2021|Zhao et al., 2021]] ).

Studies examining a 2°C GWL give ''low confidence'' for projected broad changes to agricultural and ecological drought across all Asia regions, although at 4°C GWL agricultural and ecological drought increases are projected for West Central Asia and East Asia along with a decrease in South Asia ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). Summer temperature increase will enhance evapotranspiration, facilitating ecological and agricultural drought over Central Asia towards the latter half of this century (Chapter 11; see also Figure 12.4 for soil moisture and DF indices; [[#Ozturk--2017|Ozturk et al., 2017]] ; [[#Reyer--2017b|Reyer et al., 2017b]] ; [[#Senatore--2019|Senatore et al., 2019]] ). However, broader changes in droughts could not be determined in Asia due to the mixture of total precipitation signals together with temperature increase patterns ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] and Atlas.5).

'''Fire weather:''' Under the global warming scenario of 2°C, the magnitude of length and frequency of fire seasons are projected to increase with strong effects in India, China and Russia ( ''medium confidence'' ) (Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ). [[#Abatzoglou--2019|Abatzoglou et al. (2019)]] found that higher fire weather conditions due to climate change emerge in the first part of the 21st century in South China, WCA as well as in boreal areas of Siberia and RFE. The potential burned areas in five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan) will increase by 2–8% in the 2030s and 3–13% in the 2080s compared with the baseline ( ''medium confidence'' ) (1971–2000; [[#Zong--2020|Zong et al., 2020]] ).

'''In conclusion, there is''' medium confidence '''that extreme precipitation, mean precipitation and river floods will increase across most Asian regions. There is''' low confidence '''for projected changes in aridity and drought given overall increases in precipitation and regional inconsistencies, with medium increases for West Central Asia and East Asia especially beyond the middle of the century and global warming levels beyond 2°C. Fire weather seasons are projected to lengthen and intensify particularly in the northern regions''' ( medium confidence ''').'''

<div id="12.4.2.3" class="h3-container"></div>

<span id="wind-2"></span>
==== 12.4.2.3 Wind ====

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'''Mean wind speed:''' There is ''high confidence'' of the slowdown in terrestrial near-surface wind speed (SWS) in Asia by approximately –0.1 m s <sup>–1</sup> per decade since the 1950s based on observations and reanalysis data, with the significant decreases in Central Asia among the highest in the world followed by EAS and SAS (J. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ; [[#Tian--2019|Tian et al., 2019]] ; R. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ). But a short-term strengthening in SWS was observed during the winter since 2000 in eastern China ( ''medium confidence'' ) ( [[#Zeng--2019|Zeng et al., 2019]] ; [[#Zha--2019|Zha et al., 2019]] ).

There is ''medium confidence'' of future declining mean SWS in Asia, except in SAS and SEA, as global projections indicate a decreasing trend in all climate scenarios for most of northern Asia, TIB and East Asia by the mid-century ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Fedotova--2019|Fedotova, 2019]] ; [[#Jung--2019|Jung and Schindler, 2019]] ; [[#Ohba--2019|Ohba, 2019]] ; J. [[#Wu--2020|]] [[#Wu--2020|Wu et al., 2020]] ; [[#Zha--2020|Zha et al., 2020]] ; Figure 12.4m–o), with negative effects on wind energy potential. Decreases in North Asia are generally modest, not exceeding 10% for the mid-century and 20% for the end of century for the RCP8.5 and RCP4.5 scenarios (Figure 12.4m–o).

'''Severe wind storms:''' Consistent with the general mean decreasing surface winds, there is ''medium confidence'' that strong winds declined faster than weak winds in the past few decades in Asia in general ( [[#Vautard--2010|Vautard et al., 2010]] ; [[#Tian--2019|Tian et al., 2019]] ), but evidence is lacking for spatial patterns. There is ''low confidence'' that extra-tropical cyclones will decline in number in future climate scenarios over WCA, TIB, WSB and ESB, and intensify over the Arctic regions as a result of the poleward shift of storm tracks ( [[#Basu--2018|Basu et al., 2018]] ; Chapter 11). There is ''limited evidence'' for projection of changes in severe winds occurring in convective storms in Asia.

'''Tropical cyclone:''' There was an increase in the number and intensification rate of intense tropical cyclones (TC), such as Category 4–5 (wind speeds >58 m s <sup>–1</sup> ), in the Western North Pacific (WNP) and Bay of Bengal since the mid-1980s ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.7|Section 11.7]] ; [[#Kim--2016|Kim et al., 2016]] ; [[#Mei--2016|Mei and Xie, 2016]] ; [[#Walsh--2016a|Walsh et al., 2016a]] ; [[#Knutson--2019|Knutson et al., 2019]] ). There is ''medium confidence'' that there has been a significant north-westward shift in TC tracks and a poleward shift in the average latitude where TCs reach their peak intensity in the WNP since the 1980s ( [[#Knutson--2019|Knutson et al., 2019]] ; J. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Lee--2020|Lee et al., 2020]] ), increasing exposure to TC passage and more destructive landfall over eastern China, Japan, and Korea in the last few decades ( [[#Kossin--2016|Kossin et al., 2016]] ; [[#Li--2017|Li et al., 2017]] ; [[#Altman--2018|Altman et al., 2018]] ; [[#Liu--2019|Liu and Chan, 2019]] ), and decreasing exposure in the region of SAS and southern China ( [[#Cinco--2016|Cinco et al., 2016]] ; [[#Kossin--2016|Kossin et al., 2016]] ; see Chapter 11). However, while the analysis shows fewer typhoons, more extreme TCs have affected the Philippines ( ''low confidence'' ) ( [[#Takagi--2016|Takagi and Esteban, 2016]] ). The frequency and duration of tropical cyclones has significantly increased over time over the Arabian Sea and insignificantly decreased over the Bay of Bengal during 1977–2018 ( ''low confidence'' ) ( [[#Fan--2020|Fan et al., 2020]] ).

There is ''medium confidence'' that future TC numbers will decrease but the maximum TC wind intensities will increase in the western Pacific as elsewhere (Chapter 11, see Figure 11.24; [[#Choi--2019|Choi et al., 2019]] ; [[#Cha--2020|Cha et al., 2020]] ; [[#Knutson--2020|Knutson et al., 2020]] ). The simulations for the late 21st century for the RCP8.5 scenario yield considerably more TCs in the WNP that exceed 49.4 m s <sup>−1</sup> (Category 3) intensity ( [[#Mclay--2019|Mclay et al., 2019]] ). There is ''medium confidence'' that the average location of the maximum wind will migrate poleward (see Chapter 11), and TC translation speeds at the higher latitudes would decrease ( [[#Yamaguchi--2020|Yamaguchi et al., 2020]] ). As a consequence, the intensity of TCs affecting the Japan Islands would increase in the future under the RCP8.5 scenario ( [[#Yoshida--2017|Yoshida et al., 2017]] ), whereas the frequency of TCs affecting the Philippine region and Vietnam is projected to decrease ( [[#Kieu-Thi--2016|Kieu-Thi et al., 2016]] ; [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|C. Wang et al., 2017]] ; [[#Gallo--2019|Gallo et al., 2019]] ) ( ''medium confidence'' ).

'''Sand and dust storm''' : The Asia-Pacific region contributes 26.8 per cent to global dust emissions as of 2012 ( [[#UNESCAP--2018|UNESCAP, 2018]] ). In West Asia, the frequency of dust events has increased markedly in some areas (east and north-east of Saudi Arabia, north-west of Iraq and east of Syria) from 1980 to the present ( [[#Nabavi--2016|Nabavi et al., 2016]] ; [[#Alobaidi--2017|Alobaidi et al., 2017]] ). This marked dust increase has been associated with drought conditions in the Fertile Crescent ( [[#Notaro--2015|Notaro et al., 2015]] ; [[#Yu--2015|Yu et al., 2015]] ), ''likely'' amplified by anthropogenic warming ( [[#Kelley--2015|Kelley et al., 2015]] ; Chapter 10). Dust storm frequency in most regions of northern China show a decreasing trend since the 1960s due to the decrease in surface wind speed ( ''medium confidence'' ) ( [[#Guan--2017|Guan et al., 2017]] ).

While dust activity has decreased greatly over EAS, current climate models are unable to reproduce the trends ( [[#Guan--2015|Guan et al., 2015]] , 2017; [[#Zha--2017|Zha et al., 2017]] ; [[#Wu--2018|]] [[#Wu--2018|C. Wu et al., 2018]] ). Thus, there is ''limited evidence'' for future trends of sand and dust storms in Asia.

'''In conclusion, surface wind speeds have been decreasing in Asia''' ( high confidence '''), but there is a large uncertainty in future trends. There is''' medium confidence '''that mean wind speeds will decrease in Central and northern Asia, and that tropical cyclones will have decreasing frequency and increasing intensity overall.'''

<div id="12.4.2.4" class="h3-container"></div>

<span id="snow-and-ice-2"></span>
==== 12.4.2.4 Snow and Ice ====

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'''Snow:''' There is no significant interannual trend of total snow cover from 2000 to 2016 over Eurasia (X. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] a; [[#Sun--2020|Sun et al., 2020]] ). Observations do show significant changes in the seasonal timing of Eurasian snow cover extent (especially for earlier spring snowmelt) since the 1970s, with seasonal changes expected to continue in the future ( ''high confidence'' ) ( [[#Yeo--2017|Yeo et al., 2017]] ; [[#Zhong--2021|Zhong et al., 2021]] ). By 2100, snowline elevations are projected to rise between 400 and 900 m (4.4 to 10.0 m yr <sup>–1</sup> ) in the Indus, Ganges and Brahmaputra basins under the RCP8.5 scenario ( [[#Viste--2015|Viste and Sorteberg, 2015]] ).

'''Glacier:''' Observation and future projection of glacier mass changes in Asia are assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.5.1|Section 9.5.1]] grouped in three main regions: northern Asia, High Mountains of Asia, and Caucuses and Middle East. All regions show continuing decline in glacier mass and area in the coming century ( ''high confidence'' ). Under RCP2.6 the pace of glacier loss slows, but glacier losses increase in RCP8.5 and peak in the mid to late 21st century. GlacierMIP projections indicate that glaciers in the High Mountains of Asia lose 42 ± 25%, 56 ± 24% and 71 ± 21% of their 2015 mass by the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios respectively. Under the same scenarios, glaciers in North Asia would lose 57 ± 40%, 72 ± 38% and 85 ± 30% of their mass, and glaciers in the Caucuses and the Middle East would lose 68 ± 32%, 83 ± 19% and 94 ± 13% of their mass (see also [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ; [[#Rounce--2020|Rounce et al., 2020]] ).

Although enhanced meltwater from snow and glaciers largely offsets hydrological drought-like conditions ( [[#Pritchard--2019|Pritchard, 2019]] ), this effect is unsustainable and may reverse as these cryospheric buffers disappear ( ''medium confidence'' ) ( [[#Gan--2015|Gan et al., 2015]] ; W. [[#Dong--2018|]] [[#Dong--2018|Dong et al., 2018]] ; [[#Huss--2018|Huss and Hock, 2018]] ). In the Himalayas and the TIB region higher temperatures will lead to higher glacier melt rates and significant glacier shrinkage and a summer runoff decrease ( ''medium confidence'' ) ( [[#Sorg--2014|Sorg et al., 2014]] ). Glacier runoff in the Asian high mountains will increase up to mid-century, and after that runoff might decrease due to the loss of glacier storage ( ''medium confidence'' ) ( [[#Lutz--2014|Lutz et al., 2014]] ; [[#Huss--2018|Huss and Hock, 2018]] ; [[#Rounce--2020|Rounce et al., 2020]] ).

Compared with the 1990s, the number of lakes in TIB in the 2010s decreased by 2%, whereas total lake area expanded by 25% (S. [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ) due to the joint effect of precipitation increase and glacier retreat. Many new lakes are predicted to form as a consequence of continued glacier retreat in the Himalaya-Karakoram region ( [[#Linsbauer--2016|Linsbauer et al., 2016]] ). As many of these lakes will develop at the immediate foot of steep icy peaks with degrading permafrost and decreasing slope stability, the risk of glacier lake outburst floods and floods from landslides into moraine-dammed lakes is increasing in Asian high mountains ( ''high confidence'' ) ( [[#Haeberli--2017|Haeberli et al., 2017]] ; [[#Kapitsa--2017|Kapitsa et al., 2017]] ; [[#Bajracharya--2018|Bajracharya et al., 2018]] ; [[#Narama--2018|Narama et al., 2018]] ; S. [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ).

'''Permafrost''' : Permafrost is thawing in Asia ( ''high confidence'' ). Temperatures in the cold continuous permafrost of north-eastern East Siberia rose from the 1980s up to 2017, and the active layer thicknesses in Siberia and Russian Far East generally increased from late 1990s to 2017 ( [[#Romanovsky--2018|Romanovsky et al., 2018]] ). The change in mean annual ground temperature for northern Siberia is about +0.1 to +0.3°C per decade since 2000 ( [[#Romanovsky--2018|Romanovsky et al., 2018]] ). Ground temperature in the permafrost regions of TIB (taking 40% of TIB currently) increased (0.02–0.26°C per decade for different boreholes) during 1980 to 2018, and the active layer thickened at a rate of 19.5 cm per decade (L. [[#Zhao--2020|]] [[#Zhao--2020|Zhao et al., 2020]] ). There is ''high confidence'' that permafrost in Asian high mountains will continue to thaw and the active layer thickness will increase ( [[#Bolch--2019|Bolch et al., 2019]] ). The permafrost area is projected to decline by 13.4–27.7% and 60–90% in TIB (L. [[#Zhao--2020|]] [[#Zhao--2020|Zhao et al., 2020]] ) and 32% ± 11% and 76% ± 12% in Russia ( [[#Guo--2016|Guo and Wang, 2016]] ) by the end of the 21st century under the RCP2.6 and RCP8.5 scenarios respectively ( ''high confidence'' ).

'''Lake and river ice:''' Lake ice cover duration got shorter in many lakes in TIB ( [[#Yao--2016|Yao et al., 2016]] ; [[#Cai--2019|Cai et al., 2019]] ; [[#Guo--2020|Guo et al., 2020]] ) and some other areas such as north-west China ( [[#Cai--2020|Cai et al., 2020]] ) and north-east China ( [[#Yang--2019|Yang et al., 2019]] ) in the last two decades ( ''high confidence'' ). River ice cover extent decreased in TIB as well (H. [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ; [[#Yang--2020a|Yang et al., 2020a]] ). Climate warming also leads to a significant reduction in the period with ice phenomena and the decrement of ice regime hazard in Russian lowland rivers ( [[#Agafonova--2017|Agafonova et al., 2017]] ), and the Inner Mongolia reach of the Yellow River in northern China ( [[#Wan--2020|Wan et al., 2020]] ) ( ''high confidence'' ). Lake ice and river ice in Asia are expected to decline with projected increases in surface air temperature towards the end of this century ( ''high confidence'' ) ( [[#Guo--2020|Guo et al., 2020]] ; [[#Yang--2020a|Yang et al., 2020a]] ).

'''Heavy snowfall and ice storm:''' Observed trends in heavy snowfall and ice storms are uncertain. Annual maximum snow depth decreased for the period between 1962 and 2016 on the western side of both eastern and western Japan, at rates of 12.3% and 14.6% per decade respectively ( [[#MOE--2018|MOE et al., 2018]] ). Observational results generally show a decrease in the frequency and an increase in the mean intensity of snowfalls in most Chinese regions ( ''medium confidence'' ) ( [[#Zhou--2018|]] [[#Zhou--2018|B. Zhou et al., 2018]] ). Because of the decrease in the snow frequency, the occurrence of large-scale snow disasters in TIB decreased ( ''low confidence'' ) ( [[#Qiu--2018|Qiu et al., 2018]] ; S. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ). Large parts of northern high-latitude continents (including Siberia and RFE) have experienced cold snaps and heavy snowfalls in the past few winters, and the reduction of Arctic sea ice would increase the chance of heavy snowfall events in those regions in the coming decades ( ''medium confidence'' ) ( [[#Song--2017|Song and Liu, 2017]] ). Heavy snowfall is projected to occur more frequently in Japan’s Northern Alps, the inland areas of Honshu Island and Hokkaido Island ( [[#Kawase--2016|Kawase et al., 2016]] , 2020; [[#MOE--2018|MOE et al., 2018]] ), and the heavy wet snowfall can be enhanced over the mountainous regions in central Japan and northern part of Japan ( [[#Ohba--2020|Ohba and Sugimoto, 2020]] ) ( ''medium confidence'' ).

'''Hail:''' The hailstorm in the Asian region shows a decreasing trend in several regions ( ''low confidence'' , ''limited evidence'' ). In China severe weather days including thunderstorms, hail and/or damaging wind have decreased by 50% from 1961 to 2010 (M. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Zhang--2017|Zhang et al., 2017]] ), and the hail size decreased since 1980 ( [[#Ni--2017|Ni et al., 2017]] ). A rate of decrease of 0.214 hail days per decade has also been reported for Mongolia between 1984–2013, where the annual number of hail days averaged is 0.74 ( [[#Lkhamjav--2017|Lkhamjav et al., 2017]] ).

'''Snow avalanche:''' There is as yet ''limited evidence'' for the evolution of avalanches in Asia. Tree-ring-based snow avalanche reconstructions in the Indian Himalayas show an increase in avalanche occurrence and runout distances in recent decades ( [[#Ballesteros-Cánovas--2018|Ballesteros-Cánovas et al., 2018]] ).

'''In summary, snowpack and glaciers are projected to continue decreasing and permafrost to continue thawing in Asia''' ( high confidence '''). There is''' medium confidence '''of increasing heavy snowfall in some regions, but''' limited evidence '''on future changes in hail and snow avalanches.'''

<div id="12.4.2.5" class="h3-container"></div>

<span id="coastal-and-oceanic-1"></span>
==== 12.4.2.5 Coastal and Oceanic ====

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'''Relative sea level:''' Around Asia, from 1900–2018, a new tide gauge-based reconstruction finds a regional mean RSL change of 1.33 [0.80 to 1.86] mm yr <sup>–1</sup> in the Indian Ocean–Southern Pacific and 1.68 [1.27 to 2.09] mm yr <sup>–1</sup> in the North-west Pacific ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). For the period 1993–2018, the RSLR rates around Asia, based on satellite altimetry, increased to 3.65 [3.23 to 4.08] mm yr <sup>–1</sup> and 3.53 [2.64 to 4.45] mm yr <sup>–1</sup> respectively ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). The rate of RSLR along the coastline of China ranges from –2.3 ± 1.9 to +5.7 ± 0.4 mm yr <sup>–1</sup> during 1980–2016; after removing the vertical land movement, the average rate of sea level rise is 2.9 ± 0.8 mm yr <sup>–1</sup> over 1980–2016 and 3.2 ± 1.1 mm yr <sup>–1</sup> since 1993 ( [[#Qu--2019|Qu et al., 2019]] ). However, the rates of land subsidence reported by [[#Minderhoud--2017|Minderhoud et al. (2017)]] are substantially higher than those reported by [[#Qu--2019|Qu et al. (2019)]] . RSL change in many coastal areas in Asia, especially in EAS, is affected by land subsidence due to sediment compaction under building mass and groundwater extraction ( ''high confidence'' ) ( [[#Erban--2014|Erban et al., 2014]] ; [[#Nicholls--2015|Nicholls, 2015]] ; [[#Minderhoud--2019|Minderhoud et al., 2019]] ; [[#Qu--2019|Qu et al., 2019]] ). During 1991–2016, the Mekong Delta in Vietnam sank on average about 18 cm as a consequence of groundwater withdrawal, and the subsidence related to groundwater extraction has gradually increased with highest sinking rates estimated to be 11 mm yr <sup>–1</sup> in 2015 ( [[#Minderhoud--2017|Minderhoud et al., 2017]] ).

Relative sea level rise is ''very likely'' to continue in the oceans around Asia. Regional mean RSLR projections for the oceans around Asia range from 0.3–0.5 m under SSP1-2.6 to 0.7–0.8 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), which is within the range of projected GMSL change ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.3|Section 9.6.3.3]] ). These RSLR projections may, however, be underestimated due to potential partial representation of land subsidence in their assessment ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ).

'''Coastal flood:''' The present-day 1-in-100-year ETWL is between 0.5–8 m around Asia, with values above 2.5 m or above common along the coasts of Central and north-eastern Asia ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Sea level rise and land subsidence will jointly lead to more flooding in delta areas in Asia ( ''high confidence'' ) ( [[#Takagi--2016|Takagi et al., 2016]] ; J. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ).

Extreme total water level magnitude and occurrence frequency are expected to increase throughout the region ( ''high confidence'' ) (Figure 12.4p–r and Figure 12.SM.6). Across the region, the 5–95th percentile range of the 1-in-100-year ETWL is projected to increase (relative to 1980–2014) by 7–44 cm and by 10–52 cm by 2050 under RCP4.5 and RCP8.5 respectively. By 2100, this range is projected to be 11–91 cm and 28–187 cm under RCP4.5 and RCP8.5 respectively ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Furthermore, the present-day 1-in-100-year ETWL is projected to have median return periods of around 1-in-50-years by 2050 and 1-in-10-years by 2100 under RCP4.5 in most of Asia, except SEA and ARP, in which the present-day 1-in-100-year ETWL is projected occur once per year or more, both by 2050 and 2100 ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ). The present-day 1-in-50-year ETWL is projected to occur around three times a year by 2100 with a SLR of 1 m across Asia ( [[#Vitousek--2017|Vitousek et al., 2017]] ). Compound impacts of precipitation change, land subsidence, sea level rise, upstream hydropower development, and local water infrastructure development may lead to larger flood extent and prolonged inundation in the Vietnamese Mekong Delta ( [[#Triet--2020|Triet et al., 2020]] ).

'''Coastal erosion:''' Over the past 30 years, South, South East and East Asia exhibit the most pronounced delta changes globally due to strong human-induced changes to the fluvial sediment flux ( [[#Nienhuis--2020|Nienhuis et al., 2020]] ). Satellite derived shoreline change estimates over 1984–2015 indicate shoreline retreat rates between 0.5 m yr <sup>–1</sup> and 1 m yr <sup>–1</sup> along the coasts of WCA and ARP, increasing to 3 m yr <sup>–1</sup> in SAS. Over the same period, shoreline progradation has been observed along the coasts of RFE (0.2 m yr <sup>–1</sup> ), SEA (0.1 m yr <sup>–1</sup> ) and EAS (0.5 m yr <sup>–1</sup> ) ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). Meanwhile, there has been a gross coastal area loss of 3,590 km <sup>2</sup> in South Asia, and a loss of 2,350 km <sup>2</sup> in Pacific Asia, over a 30-year period (1984–2015) ( [[#Mentaschi--2018|Mentaschi et al., 2018]] ).

Projections indicate that a majority of sandy coasts in the Asia region will experience shoreline retreat ( ''high confidence'' ) ( [[#Udo--2017|Udo and Takeda, 2017]] ; [[#Ritphring--2018|Ritphring et al., 2018]] ; [[#Vousdoukas--2020b|Vousdoukas et al., 2020b]] ), while parts of the RFE, EAS, SEA and WCA coastline are projected to prograde over the 21st century, if present ambient shoreline change trends continue. Median shoreline change projections (CMIP5), relative to 2010, presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] show that, by mid-century, sandy shorelines in Asia will retreat by between 10–50 m, except in SAS where shoreline retreat is projected to exceed 100 m, under both RCP4.5 and RCP8.5. By 2100, and under RCP4.5, shoreline retreats of around 85, 100 and 300 m are projected along the sandy coastlines of SEA and WCA, ARP and SAS respectively (50 m or less in other Asian regions), while under RCP8.5, over the same period, sandy shorelines along all regions with coastlines, except RFE and EAS, are projected to retreat by more than 100 m, with the retreat in SAS reaching 350 m (2100 RCP8.5 projections for RFE and EAS are about 60 m and about 85 m respectively; Figure 12.6).

'''Marine heatwave:''' There have been frequent marine heatwaves (MHW) in the coastal oceans of Asia, connected to the increase between 0.25°C and 1°C in mean SST of the coastal oceans since 1982–1998 ( [[#Oliver--2018|Oliver et al., 2018]] ). There is ''high confidence'' that MHWs will increase around most of Asia. Mean SST is projected to increase by 1°C (2°C) around Asia by 2100, with a hotspot of around 2°C (5°C) along the coastlines of the Sea of Japan and the RFE under RCP4.5 (RCP8.5; Interactive Atlas). Under global warming conditions, MHW intensity and duration are projected to increase in the coastal zones of all sub-regions of Asia, but most notably in SEA and SAS ( [[#Frölicher--2018|Frölicher et al., 2018]] ). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around Asia by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).

'''In general, there is''' high confidence '''that most coastal/ocean-related climatic impact-drivers in Asia will increase over the 21st century. Relative sea level rise is''' very likely '''to continue around Asia, contributing to increased coastal flooding in low-lying areas''' ( high confidence ''') and shoreline retreat along most sandy coasts''' ( high confidence '''). Marine heatwaves are also expected to increase around the region over the 21st century''' ( high confidence ''').'''

The assessed direction of change in climatic impact-drivers for Asia and associated confidence levels are illustrated in Table 12.4.

<div id="_idContainer044" class="Basic-Text-Frame"></div>

'''Table 12.4''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in Asia, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:70f9e96f64e1f7863919397ba6aa2df7 IPCC_AR6_WGI_Chapter12_Table_12_4.jpg]]

<div id="12.4.3" class="h2-container"></div>

<span id="australasia"></span>
=== 12.4.3 Australasia ===

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For the purpose of this assessment, Australasia is divided into five sub-regions as defined in [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] : Northern Australia (NAU), Central Australia (CAU), Eastern Australia (EAU), Southern Australia (SAU) and New Zealand (NZ).

The Fourth and Fifth IPCC Assessment Reports (AR4 and AR5) identify the most damaging historical hazards in this region to be inland flooding, drought, wildfire and episodic coastal erosion due to storms ( [[#Hennessy--2007|Hennessy et al., 2007]] ; [[#Reisinger--2014|Reisinger et al., 2014]] ). The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) projects ''very likely'' increases in the intensity and frequency of warm days and warm nights and decreases in the intensity and frequency of cold days and cold nights in Australasia. Furthermore, a ''likely'' increase in the frequency and duration of warm spells is also projected for Australia. The SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ) projects a ''likely'' global mean sea level rise (RCP8.5) that is up to 0.1 m higher than corresponding AR5 projections. The SROCC also projects an increase of mean significant wave height across the Southern Ocean ( ''high confidence'' ) and an increase in the occurrence of historically rare (1-in-100-year) extreme sea levels to 1-in-1-year or more frequent events all around the Australasian region by 2100 under RCP8.5.

A detailed national scale climate change assessment of observed and projected climate change, based on over 40 CMIP5 models and high resolution downscaling (CSIRO and BOM, 2015), and biannual short updates thereafter are available for Australia (CSIRO and BOM, 2016, 2018, 2020). Similar national assessments for New Zealand are also available (MfE and Stats NZ, 2017, 2020; [[#MfE--2018|MfE, 2018]] ). The severe extreme events such as heatwaves and river floods that have occurred in Australasia, especially over the last decade, have enabled a number of attribution studies, improving the understanding of regional climate change mechanisms that drive such extreme events (Chapter 11).

Figure 12.7 illustrates projected changes in two selected climatic impact-driver indices for Australasia.

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[[File:7d935506d0aa68d0dcf7f9e1596462d3 IPCC_AR6_WGI_Figure_12_7.png]]

'''Figure 12.7''' '''|''' '''Projected changes in selected climatic impact-driver indices for Australasia. (a)''' Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) from CORDEX-Australasia models for 2041–2060 relative to 1995–2014 for RCP8.5. '''(b)''' Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . '''(c)''' Bar plots for Q100 (m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. '''(d)''' Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

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<span id="heat-and-cold-3"></span>
==== 12.4.3.1 Heat and Cold ====

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'''Mean air temperature:''' Across Australia mean temperatures have increased by 1.44°C ± 0.24°C during the period 1910–2019, with most of the warming occurring since 1950 (Atlas.6.2; CSIRO and BOM, 2020; [[#Trewin--2020|Trewin et al., 2020]] ). In New Zealand, an increase of 1.1°C has been measured from 1909–2016 (Atlas.6.2; MfE and Stats NZ, 2020). In the period 1980–2014 a rate of increase of 0.1°C–0.3°C per decade has been observed (Atlas.11 and Atlas.20).

Mean temperature in Australasia is projected to continue to rise through the 21st century ( ''virtually certain'' ) (Atlas.6.4). Projections for Australia indicate that the average temperature will increase by +1.1°C (0.84–1.52°C 10–90th percentile range) by 2041–2060 (mid-century), and by +1.9°C (1.29–2.58°C) by 2081–2100 (end-century), relative to the baseline period of 1995–2014, under SSP2-4.5 (Interactive Atlas). For SSP5-8.5, the projected changes are up to +1.5°C (1.17–1.96°C) and +3.7°C (2.75–4.91°C) for mid- and end-century respectively. For SSP1-2.6, mean temperature is projected to rise by +0.9°C (0.55–1.26°C) and +1.0°C (0.55–1.54°C) relative to 1995–2014 by mid- and end-century respectively (Interactive Atlas). In New Zealand, an increase of mean temperature of +1.0°C (0.60–1.32°C) relative to 1995–2014 is projected by mid-century, and an increase of +1.6°C (1.03–2.26°C) by end-century under SSP2-4.5. For SSP5-8.5, the projected increase in mean temperature is +1.3°C (0.91–1.66°C 10–90th percentile range) and +3.1°C (2.20–4.05°C) relative to 1995–2014 by mid- and end-century respectively. For SSP1-2.6, the projected increase in mean temperature is +0.75°C (0.39–1.06°C) and +0.8°C (0.47–1.46°C) relative to 1995–2014 by mid- and end-century, respectively (Interactive Atlas).

'''Extreme heat:''' The region has a ''very likely'' trend of increasing frequency and severity of hot extremes since the 1950s (Table 11.10). Extreme minimum temperatures have increased in all seasons over most of Australia and exceeds the increase in extreme maximum temperatures (X.L. [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ; [[#Jakob--2016|Jakob and Walland, 2016]] ). Heatwave characteristics and hot extremes have increased across many Australian regions since the mid-20th century (Table 11.10; CSIRO and BOM, 2020). The number of days per year with maximum temperature greater than 35°C has increased over most parts of Australia from 1957–2015, with the largest increasing trends of 0.4–1 days/year occurring in north-western, Northern, north-eastern Australia and parts of Central Australia (CSIRO and BOM, 2016). Long-term changes of hot extremes in Australia have been attributed to anthropogenic influence (Table 11.10). In New Zealand, the number of annual heatwave days increased at 18 of 30 sites during the period 1972–2019 (MfE and Stats NZ, 2020).

More frequent hot extremes and heatwaves are expected over the 21st century in Australia ( ''virtually certain'' ) (Table 11.10). Heat thresholds potentially affecting agriculture and health, such as 35°C or 40°C, are projected to be exceeded more frequently over the 21st century in Australia under all RCPs ( ''high confidence'' ). By 2090 under RCP4.5, the average number of days per year with maximum temperatures above 35°C is highly spatially variable and is expected to increase by 50–100%, while the number of days per year with maximum temperatures above 40°C is expected to increase by 200%, relative to 1985–2005 (CSIRO and BOM, 2015). Under RCP8.5 the corresponding projected increases are even greater, with a greater than 100% increase in most of Australia, and far greater increases in Central and Northern Australia (up to a 20-fold increase in Darwin). Projections for New Zealand indicate more frequent hot extremes ( ''virtually certain'' ) (Table 11.10). Figure 12.4b, c shows CMIP6 projections of mean number of days per year with maximum temperature exceeding 35°C under SSP5-8.5, which are consistent with the above assessed literature and across the two CMIP generations, and indicate a strong difference depending on the mitigation scenario (e.g., over 100 days more per year under SSP5-8.5 in NAU, but, in general, less than 60 days more per year under SSP1-2.6 in NAU; Figure 12.SM.1).

The projected frequency of exceeding dangerous humid heat thresholds is increasing in Australia, with a strong increase in Northern Australia for RCP8.5 ( ''high confidence'' ) ( [[#Zhao--2015|Zhao et al., 2015]] ; [[#Mora--2017|Mora et al., 2017]] ; [[#Brouillet--2019|Brouillet and Joussaume, 2019]] ), consistently across CMIP5, CMIP6 and CORDEX simulations (Figure 12.4d–f and Figure 12.SM.2). Using the HI index, by end-century, the average number of days exceeding 41°C is projected to increase in NAU by about 100 days and by about 25 days under SSP5-8.5 and SSP1-2.6, respectively. The projections for New Zealand indicate no appreciable increase in the number of days with HI > 41°C across SSPs, time periods and CMIP generations (Figure 12.4d–f and Figure 12.SM.2).

'''Cold spell and frost:''' Excepting parts of Southern Australia, the Australasian region has a significant trend of decreasing frequency in cold extremes since the 1950s ( ''high confidence'' ) (Table 11.10) and there is ''high confidence'' that such trends are attributable to anthropogenic influence (Table 11.10). The number of frost days per year in Australia has on average declined at a rate of 0.15 days/decade in the past century ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ), except in some regions of Southern Australia, where an increase in both number and season length has been reported ( [[#Dittus--2014|Dittus et al., 2014]] ; [[#Crimp--2016b|Crimp et al., 2016b]] ). The number of frost days has decreased at 12 of 30 monitoring sites around New Zealand over the period 1972–2019 (MfE and Stats NZ, 2020).

Less frequent cold extremes are ''virtually certain'' in Australasia (Table 11.10) while a decrease of frost days is projected with ''high confidence'' for the region. Projections, relative to 1986–2005, for the number of frost days per year in Australia indicate declines of 0.9 days by mid-century and 1.1 days by end-century for RCP4.5, while for RCP8.5, the projected declines are 1.0 days and 1.3 days by mid- and end-century respectively ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Herold--2018|Herold et al., 2018]] ). Projections for New Zealand indicate that the number of frost days will decrease by 30% (RCP2.6) to 50% (RCP8.5) by 2040, relative to 1986–2005. By 2090, the decrease ranges from 30% (RCP2.6) to 90% (RCP8.5) (MfE and Stats NZ, 2017).

'''In general, there is''' high confidence '''that most heat hazards in Australasia will increase and that cold hazards will decrease over the 21st century. The mean temperature in Australasia is''' virtually certain '''to continue to rise through the 21st century, accompanied by less frequent cold extremes''' ( virtually certain ''') and frost days''' ( high confidence '''), and more frequent hot extremes''' ( virtually certain '''). Heat stress is projected to increase in Australia''' ( high confidence ''').'''

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<span id="wet-and-dry-3"></span>
==== 12.4.3.2 Wet and Dry ====

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'''Mean precipitation:''' Here, only increases in precipitation (under ‘Wet’) are addressed, with decreases (under ‘Dry’) addressed in ‘Aridity’ below.

In terms of wet climatic impact-drivers, detectable anthropogenic increases in precipitation in Australia have been reported particularly for north-central Australia for the period 1901–2010 ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). Figure Atlas.11 indicates no significant trend in precipitation over the region during the baseline period 1960–2015, except for the Global Precipitation Climatology Project (GPCP) dataset, which shows an increasing trend in north-central Australia. In New Zealand, increases in annual rainfall have been observed between 1960–2019 in the south and west of the South Island and east of the North Island. Note however, for the most part, the above reported trends in New Zealand have been classified as statistically not significant (Figure Atlas.20).

Annual mean precipitation is projected to increase in Central and north-east Australia ( ''low confidence'' ) and in the south and west of New Zealand ( ''medium confidence'' ) (Atlas.6.4). [[#Liu--2018a|Liu et al. (2018a)]] show that under 1.5°C warming, Central and north-east Australia will become wetter. Projected patterns in annual precipitation exhibit increases in the west and south of New Zealand (Atlas.6.4; [[#Liu--2018a|Liu et al., 2018a]] ) and project that the South Island will be wetter under both 1.5°C and 2°C warming. However, there is limited model agreement for projected rainfall changes in Australasia as shown in the Atlas.

'''River flood:''' Streamflow observations in Australia have shown that negative trends dominate in annual maximum flow and that stations with significant negative trends were mostly located in the south-east and south-west ( [[#Gu--2020|Gu et al., 2020]] ). The observed peak flow trend in Southern Australia is attributed to the decrease of soil moisture, although an increase of flood magnitude is possible for very rare events. For the more frequent flood events, the increase of extreme precipitation is balanced by the decrease of soil moisture. ( [[#Wasko--2019|Wasko and Nathan, 2019]] ).

While median annual runoff is projected to decrease in most of Australia ( [[#Chiew--2017|Chiew et al., 2017]] ), consistent with projected decreases in average rainfall (CSIRO and BOM, 2015; [[#Alexander--2017|Alexander and Arblaster, 2017]] ), river floods are projected to increase due to more intense extreme rainfall events and associated increase in runoff ( ''medium confidence'' ). [[#Asadieh--2017|Asadieh and Krakauer (2017)]] found a decrease in the value of the 95% percentile of mean streamflow with RCP8.5 by the end of the century in all of Australia, except in a small part in centre of the country. In terms of relative increases, flooding is expected to increase more in Northern Australia (driven by convective rainfall systems) than in Southern Australia (where more intense extreme rainfall may be compensated by drier antecedent moisture conditions; [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Dey--2019|Dey et al., 2019]] ) with flood frequency increasing in Northern Australia and along parts of the east coast and decreasing in south-western Western Australia ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ). [[#Gu--2020|Gu et al. (2020)]] project larger flood magnitude and volumes under both RCP2.6 and RCP8.5 in Northern Australia, and smaller flood magnitudes and volumes in Southern Australia under the same RCPs. These findings are in general agreement with the patterns in peak flow, corresponding to the 1-in-100-year return period streamflow, shown in Figure 12.7a,c for mid-21st century under RCP8.5.

There is ''medium confidence'' that river flooding will increase in New Zealand. Projections for New Zealand indicate that the 1-in-50-year and 1-in-100-year flood peaks for rivers in many parts of the country may increase by 5 to 10% by 2050 and more by 2100 (with large variation between models and emissions scenarios), with a corresponding decrease in return periods for specific flood levels ( [[#Gray--2005|Gray et al., 2005]] ; [[#Carey-Smith--2010|Carey-Smith et al., 2010]] ; [[#McMillan--2010|McMillan et al., 2010]] , 2012; [[#Ballinger--2011|Ballinger et al., 2011]] ).

'''Heavy precipitation and pluvial flood:''' Rainfall extremes have been detected to increase in Australasia, with ''low confidence'' (Table 11.10). There is ''high confidence'' that R × 1 day and R × 5 day precipitation extremes will increase for 2°C or lower warming for the region as a whole, but on a sub-regional basis there is only ''medium confidence'' of increases in NAU and CAU and ''low confidence'' of increases on EAU, SAU and NZ. For warming levels exceeding 2°C, these extremes are ''very likely'' to increase in NAU and CAU and they are ''likely'' to increase elsewhere in the region ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

'''Landslide:''' Based on local slope characteristics, lithology and seismic activity, the South Island and the eastern half of the North Island of New Zealand are vulnerable to landslide occurrence ( [[#Broeckx--2020|Broeckx et al., 2020]] ). The potential for land and rockslides increases with, amongst other factors, total precipitation rates, precipitation intensity, mountain permafrost thaw rates, glacier retreat and air temperature ( [[#Crozier--2010|Crozier, 2010]] ; [[#Allen--2013|Allen and Huggel, 2013]] ; [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#IPCC--2019a|IPCC, 2019a]] ). Given the increase of the magnitude of these physical variables in areas that are already highly susceptible to mass movements ( [[#MfE--2018|MfE, 2018]] ), there is ''low confidence'' that the occurrence of landslides will increase under future climate conditions.

'''Aridity:''' In terms of dry climatic impact-drivers, a substantial decrease in precipitation has been observed across Southern Australia during the cool season (April–October) ( ''medium confidence'' ). The drying trend has been particularly strong over south-west Western Australia between May and July, with rainfall since 1970 being around 20% less than the 1900–1969 average (CSIRO and BOM, 2020). Detectable decreases in mean precipitation, attributable at least in part to anthropogenic forcing, have been reported for parts of south-west Australia ( [[#Delworth--2014|Delworth and Zeng, 2014]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ), south-east Australia, and Tasmania ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). In New Zealand, the north-east of the South Island and western and the northern parts of the North Island show decreasing precipitation trends during 1960–2019 (MfE and Stats NZ, 2020).

Aridity is projected to increase, especially during winter and spring, with ''medium confidence'' in SAU but with ''high confidence'' in south-west Western Australia (Table 11.11 and Atlas.6.4). In EAU and in the north and east of NZ, aridity is projected to increase with ''medium confidence'' , while a decrease is projected with ''medium confidence'' in the south and west of NZ (Atlas.6.4). Although there is only ''low confidence'' in the projected decrease of mean annual precipitation in south-western and eastern Australia and the north and east of New Zealand, there is ''high confidence'' of reduced winter and spring precipitation in Australia in future, mostly in south-western and eastern Australia (Atlas.6.4). [[#Liu--2018b|Liu et al. (2018b)]] show that under 2°C warming, most of Australia is projected to become drier based on the Palmer Drought Severity Index (PDSI), with the exception of the tropical north-east. [[#Ferguson--2018|Ferguson et al. (2018)]] project that between 1976–2005 and 2070–2099, winters will become drier (mainly in Southern Australia) under RCP8.5. [[#Liu--2018b|Liu et al. (2018b)]] project that the North Island of New Zealand will be drier under both 1.5°C and 2°C warming.

'''Hydrological drought:''' There is ''low confidence'' of observed changes in hydrological droughts in Australasia, except in SAU where there is ''medium confidence'' of an observed increase in the south-east and south-west. Future projections indicate ''medium confidence'' in further hydrological drought increases for Southern Australia for warming levels of 2°C or higher ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). Mean annual runoff in far south-east and far south-west Australia are projected to decline by median values of 20 and 50% respectively, by mid-century under RCP8.5 ( [[#Chiew--2017|Chiew et al., 2017]] ). [[#Prudhomme--2014|Prudhomme et al. (2014)]] assess changes in the Drought Index (DI), defined as areal runoff less than the 10th percentile over the reference period 1976–2005, and project DI increases for both Australia and New Zealand by 10–20% by 2070–2099 under RCP8.5, with the greatest effects being in the southern parts of the Australian continent. These projections are consistent with the trends shown in Figure 12.4g–i (Figure 12.SM.3). The SPI drought frequency is projected to increase in SAU and particularly in south-west Western Australia by mid-century, while by the end of the century SPI drought frequency is projected to increase all over Australia, and particularly strongly in south-west Western Australia as well as southern Victoria (see Figure 12.4g–i). For the Murray–Darling basin, [[#Ferguson--2018|Ferguson et al. (2018)]] project effectively no change (–1%) in mean precipitation, a 27% decrease in P–E, and 30% increase in runoff in 2070–2099 relative to 1976–2005 with RCP8.5.

'''Agricultural and ecological drought:''' There is ''medium confidence'' in observations of agricultural and ecological droughts increasing in SAU and decreasing in NAU, while there is ''low confidence'' of changes elsewhere in the region ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). More regional studies have observed an increase in agricultural and ecological drought intensity in south-west Australia and an increase in drought intensity in parts of south-east Australia, while the length of droughts therein has increased ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). In New Zealand, since 1972–73, soils at 7 of 30 monitored sites became drier, while the 2012–13 drought was one of the most extreme in the previous 41 years (MfE and Stats NZ, 2017). Future evaporative demand is projected to lead to ''medium confidence'' increases in agricultural and ecological droughts for 2°C of global warming in SAU and EAU and ''low confidence'' for changes in CAU, NAU and NZ, although there is ''medium confidence'' of increases in CAU with 4°C of global warming ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). There is ''medium confidence'' for more time in agricultural and ecological drought in SAU by mid-21st century ( [[#Coppola--2021b|Coppola et al., 2021b]] ) as well as by the end of the 21st century ( [[#Herold--2018|Herold et al., 2018]] ). The Standardized Precipitation Evapotranspiration Index (SPEI) shows a springtime intensification in SAU with moderate and severe droughts in the south-west and moderate droughts in the south-east ( [[#Herold--2018|Herold et al., 2018]] ). There is consensus among the different model ensembles (CORDEX-CORE, CMIP5 and CMIP6) that the drought frequency (DF), one of several proxies for agricultural and ecological drought, will increase in all four Australian regions for both mid-century (NAU 0.2–2 DF increase, CAU 0.5–2 DF increase, SAU 1–3 DF increase and EAU 0.8–3 DF increase) and end-century (0.8–2.7 DF increase for NAU, 1.2–2 DF increase for CAU, 2.2–3.8 for SAU and 0.2–3 for EAU) for both RCP8.5 and SSP5-8.5, with CMIP6 showing the lowest increase (Figure 12.4g–l and Figure 12.SM.4; [[#Coppola--2021b|Coppola et al., 2021b]] ).

'''Fire weather:''' [[#Dowdy--2018|Dowdy and Pepler (2018)]] examined atmospheric conditions conducive to pyroconvection in the period 1979–2016, and found an increased risk in south-east Australia during spring and summer, due to changes in vertical atmospheric stability and humidity, in combination with adverse near-surface fire weather conditions. CSIRO and BOM (2018) and [[#Dowdy--2018|Dowdy (2018)]] found that the annual 90th percentile daily Forest Fire Danger Index (FFDI) has increased from 1950–2016 in parts of Australia, especially in Southern Australia (1–2.5 per decade) and in spring and summer. These studies indicate an increase in the frequency and magnitude of FFDI extreme quantiles, as well as a shift of the fire season start towards spring, lengthening the fire season. The unprecedented large fires of austral spring and summer of 2019 in south-east Australia were a result of extreme hot and dry weather in significantly drier than average conditions that had persisted since 2017, in combination with consistently stronger than average winds, resulting in above average to highest on record FFDI values in much of the country ( [[#Abram--2021|Abram et al., 2021]] ). These fires have been attributed to climate change through the temperature component of fire weather indices ( [[#van%20Oldenborgh--2021|van Oldenborgh et al., 2021]] ). In New Zealand, days with very high and extreme fire weather increased in 12 out of 28 monitored sites, and decreased in 8, in the period 1997–2019 (MfE and Stats NZ, 2020). Attribution studies indicate that there is ''medium confidence'' of an anthropogenically driven past increase in fire weather conditions, essentially due to increase in frequency of extreme heat waves. ( [[#Hope--2019|Hope et al., 2019]] ; [[#Lewis--2020|Lewis et al., 2020]] ; [[#van%20Oldenborgh--2021|van Oldenborgh et al., 2021]] ).

Fire weather indices are projected to increase in most of Australia ( ''high confidence'' ) and many parts of New Zealand ( ''medium confidence'' ), in particular with respect to extreme fire and induced pyroconvection ( [[#Dowdy--2019b|Dowdy et al., 2019b]] ). Increasing mean temperature, cool season rainfall decline, and changes in tropical climate variability all contribute to a future increase in extreme fire risk in Australia ( [[#Abram--2021|Abram et al., 2021]] ). Projections indicate that the annual cumulative FFDI will increase by 31–33% in Southern and Eastern Australia, and by 17–25% in Northern Australia and the Rangelands by 2090 (relative to 1995) under RCP8.5 (CSIRO and BOM, 2015). Using a CMIP5 ensemble of 17 models, [[#Abatzoglou--2019|Abatzoglou et al. (2019)]] found a statistically significant positive trend for fire weather intensity and fire season length for future mid-century conditions under RCP8.5, including a detectable anthropogenic influence on fire risk magnitude and fire season length by 2040 in Western Australia and along the Queensland coastline. Using the C-Haines and FFDI indices with A2 and RCP8.5 respectively, [[#Di%20Virgilio--2019|Di Virgilio et al. (2019)]] and [[#Clarke--2019|Clarke et al. (2019)]] have shown that extreme fire weather frequency will increase in south-eastern Australia by the end of the 21st century. Most of these projections indicate that the biggest increases in fire weather conditions will be in late spring, effectively resulting in longer (stronger) fire seasons in areas where spring is the shoulder (peak) season. In New Zealand, [[#Watt--2019|Watt et al. (2019)]] projected that the number of days with very high to extreme fire risk will increase by 71% by 2040, and by a further 12% by 2090, for the A1B scenario, with fire risk increase all along the east coast. The most marked relative changes by 2090 were projected for Wellington and Dunedin, where very high to extreme fire risk is projected to increase by, respectively, 89% to 32 days and 207% to 18 days, compared to the baseline period 1970–1999.

'''Annual mean precipitation is projected to increase in Central and north-east Australia''' ( low confidence ''') and in the south and west of New Zealand''' ( medium confidence '''), while it is projected to decrease in Southern Australia''' ( medium confidence '''), albeit with''' high confidence '''in south-west Western Australia, in Eastern Australia''' ( medium confidence '''), and in the north and east of New Zealand''' ( medium confidence '''). Heavy precipitation and pluvial flooding are projected to increase with''' medium confidence '''in Northern Australia and Central Australia. There is''' medium confidence '''that river flooding will increase in New Zealand and Australia, with higher increases in Northern Australia. Aridity is projected to increase with''' medium confidence '''in Southern Australia''' ( high confidence '''in south-west Western Australia), Eastern Australia''' ( medium confidence '''), and in the north and east of New Zealand''' ( medium confidence '''). Hydrological droughts are projected to increase in Southern Australia''' ( medium confidence '''), while agricultural and ecological droughts are projected to increase with''' medium confidence '''in Southern Australia and Eastern Australia. Fire weather is projected to increase throughout Australia''' ( high confidence ''') and New Zealand''' ( medium confidence ''').'''

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<span id="wind-3"></span>
==== 12.4.3.3 Wind ====

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'''Mean wind speed:''' There is ''low confidence'' of a mean wind speed trend in the last decades ( ''low agreement'' ) ( [[#McVicar--2012|McVicar et al., 2012]] ; [[#Troccoli--2012|Troccoli et al., 2012]] ; [[#Azorin-Molina--2018|Azorin-Molina et al., 2018]] ; J. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ), as long-term measurements are not homogeneous.

In future climate scenarios wind speed trends in Australia exhibit generally weak amplitudes with ''low agreement'' among models (Figure 12.4m–o and Figure 12.SM.5) with uncertain consequences on wind power potential (CSIRO and BOM, 2015; [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Jung--2019|Jung and Schindler, 2019]] ). However, there is ''medium confidence'' that, by the end of the century, annual mean wind power will significantly increase in north-eastern Australia under RCP8.5, but there is ''low confidence'' of an increase by end-century under RCP4.5, and for any scenario by mid-century ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ). In New Zealand, mean wind patterns are projected to become more north-easterly in summer, and westerlies to become more intense in winter ( ''low confidence'' ), in agreement with the strengthening of the Southern Hemisphere storm tracks ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ).

'''Severe wind storm:''' There is generally ''low confidence'' in observed changes in extreme winds and extratropical storms in Australasia ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.2|Section 11.7.2]] ). CMIP5 projections of severe winds indicate a general increase in north-eastern Australia, and decreases in some parts in Southern and Central Australia ( ''medium confidence'' ) by the end of the century under RCP8.5 (CSIRO and BOM, 2015; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Jung--2019|Jung and Schindler, 2019]] ). Elsewhere trends are diverse and vary across simulations with ''low agreement'' . Projections of changes in the 1-in-25-year return period winds (based on annual maxima) for 2074–2100 relative to 1979–2005 for RCP8.5 show an increase in tropical areas of Northern Australia ( [[#Kumar--2015|Kumar et al., 2015]] ).

In New Zealand, the frequency and magnitude of extreme winds have decreased (from 1980–2019) at 12 of 14 monitored sites and increased at two monitored sites (MfE and Stats NZ, 2020). Due to the intensification and the shift of the austral storm track by the end of the century ( [[#Yin--2005|Yin, 2005]] ), increases in extreme wind speed in New Zealand are projected over the South Island and the southern part of the North Island by mid- and end-century for all RCPs ( ''low confidence'' ) ( [[#MfE--2018|MfE, 2018]] ).

'''Tropical cyclone:''' In Australia, the number of TCs has generally declined since 1982, and the frequency of intense TCs that make landfall in north-eastern Australia has declined significantly since the 19th century ( ''medium confidence'' ) ( [[#Kuleshov--2010|Kuleshov et al., 2010]] ; [[#Callaghan--2011|Callaghan and Power, 2011]] ; [[#Holland--2014|Holland and Bruyère, 2014]] ; [[#Knutson--2019|Knutson et al., 2019]] ; CSIRO and BOM, 2020). There is ''high confidence'' that cyclones making landfall along north-eastern and northern Australian coastlines will decrease in number and ''low confidence'' of an increase in their intensities for 2°C of global warming as well as for the mid-century period with scenarios RCP4.5 and above ( [[#Roberts--2015|Roberts et al., 2015]] , 2020; [[#Bacmeister--2018|Bacmeister et al., 2018]] ; [[#Knutson--2020|Knutson et al., 2020]] ), with the amplitude of changes increasing from RCP4.5 to RCP8.5 ( [[#Bacmeister--2018|Bacmeister et al., 2018]] ). Decreases in frequency are projected for ‘east coast lows’ ( [[#Walsh--2016b|Walsh et al., 2016b]] ; [[#Dowdy--2019a|Dowdy et al., 2019a]] ).

'''Sand and dust storm:''' Australia is recognized to be the largest dust source in the Southern Hemisphere ( [[#Zheng--2016|Zheng et al., 2016]] ). Land-use and land-cover change have increased dust emissions in Australia in the past 200 years ( [[#Marx--2014|Marx et al., 2014]] ). While projections suggest a decrease in severe winds in Central and Southern Australia, changes in vegetation due to increased aridity and hydrological drought could be expected to result in increased wind erosion and dust emission across the country ( ''medium confidence'' ) ( [[#Webb--2020|Webb et al., 2020]] ).

'''In Australasia, there is''' low confidence '''in projected mean wind speeds and wind power potential, with a''' medium confidence '''increase projected only in north-eastern Australia under high emissions scenarios and by the end of the 21st century. Tropical cyclones in north-eastern and North Australia are projected to decrease in number''' ( high confidence ''') while their intensity is projected to increase''' ( low confidence ''').'''

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<span id="snow-and-ice-3"></span>
==== 12.4.3.4 Snow and Ice ====

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'''Snow:''' The snow season length in Australia has decreased by 5% during 2000–2013 relative to 1954–1999, especially in spring ( [[#Pepler--2015|Pepler et al., 2015]] ). A shift in the date of peak snowfall has also been observed with an 11-day advance over the same period ( [[#Pepler--2015|Pepler et al., 2015]] ). A decreasing trend in maximum snow depth has been observed for Australian alpine regions since the late 1950s, with the largest declines during spring and at lower altitudes. Maximum snow depth is highly variable and is strongly influenced by rare heavy snowfall days, which have no observed trends in frequency (CSIRO and BOM, 2020).

Projections for Southern Australia and New Zealand show a continuing reduction in snowfall during the 21st century ( ''high confidence'' ). The magnitude of decrease varies with the altitude of the region and the emissions scenario. At elevations lower than 1500 m, years without snowfall are projected from 2030 in some models. By 2090, and under RCP8.5, such years are projected to become common (CSIRO and BOM, 2015). The number of annual snow days in New Zealand is projected to decrease under all RCPs, by up to 30 days or more by 2090 under RCP8.5, relative to 1986–2005 ( [[#MfE--2018|MfE, 2018]] ).

'''Glacier:''' Glacier mass and areal extent in New Zealand is projected to continue to decease over the 21st century ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] ). Glacier ice volume from 1977–2018 in New Zealand has decreased from 26.6 to 17.9 km <sup>3</sup> (a loss of 33%; [[#Salinger--2019|Salinger et al., 2019]] ). Relative to 2015, glaciers in New Zealand are projected to lose 36 ± 44%, 53 ± 33% and 77 ± 27% of their mass by the end of the century under RCP2.6, RCP4.5 and RCP8.5 respectively, with the loss rates decreasing over time under RCP2.6 and increasing under RCP8.5 ( [[#Marzeion--2020|Marzeion et al., 2020]] ).

'''In summary, snowfall is expected to decrease throughout the region at high altitudes in both Australia''' ( high confidence ''') and New Zealand''' ( medium confidence '''). In New Zealand, glacier ice mass and extent are expected to decrease over the 21st century for all scenarios''' ( high confidence ''').'''

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<span id="coastal-and-oceanic-2"></span>
==== 12.4.3.5 Coastal and Oceanic ====

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'''Relative sea level:''' Around Australasia, from 1900–2018, a new tide gauge-based reconstruction finds a regional mean RSL change of 1.33 [0.80 to 1.86] mm yr <sup>–1</sup> in the Indian Ocean–South Pacific region ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). For the period 1993–2018, the RSLR rates, based on satellite altimetry, increased to 3.65 [3.23 to 4.08] mm yr <sup>–1</sup> ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5).

Relative sea level is ''virtually certain'' to increase throughout the region over the 21st century ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3|Section 9.6.3]] , Figure 9.28). Regional mean RSLR projections for the oceans around Australasia range from 0.4–0.5 m under SSP1-2.6 to 0.7–0.9 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), which means local RSL change falls within the range of mean projected GMSL change ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.1|Section 9.6.3.1]] ). However these RSLR projections may be underestimated due to potential partial representation of land subsidence ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ).

'''Coastal flood:''' The most commonly used index for episodic coastal inundation in Australia is the summation of a high end SLR and the 1-in-100-year storm tide level (the combined sea level due to storm surge and tide) (CSIRO and BOM, 2016; [[#McInnes--2016|McInnes et al., 2016]] ). However, episodic coastal flooding is caused by extreme total water levels (ETWL), which is the combination of SLR, tides, surge and wave setup ( [[#12.3.5.2|Section 12.3.5.2]] ). The present-day 1-in-100-year ETWL is between 0.5–2.5 m around most of Australia, except the north-western coast where 1-in-100-year ETWL can be as large as 6–7 m ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#O’Grady--2019|O’Grady et al., 2019]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ).

Extreme total water level magnitude and occurrence frequency are expected to increase throughout the region ( ''high confidence'' ) (Figure 12.4p–r and Figure 12.SM.6). Across the region, the 5–95th percentile range of the 1-in-100-year ETWL is projected increase (relative to 1980–2014) by 5–35 cm and by 10–40 cm by 2050 under RCP4.5 and RCP8.5 respectively (Figure 12.4q). By 2100 (Figure 12.4p,r), this range is projected to be 25–80 cm and 50–190 cm under RCP4.5 and RCP8.5 respectively ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Furthermore, the present-day 1-in-100-year ETWL is projected to have median return periods of around 1-in-20-years by 2050 and 1-in-1-year by 2100 in SAU and NZ and return periods of around 1-in-50-years by 2050 and 1-in-20-years by 2100 in NAU under RCP4.5 ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ), while the present-day 1-in-50-year ETWL is projected to occur around three times a year by 2100 with a SLR of 1 m around Australasia ( [[#Vitousek--2017|Vitousek et al., 2017]] ).

'''Coastal erosion:''' Satellite derived shoreline retreat rates for the period between 1984–2015 show retreat rates between 0.5 and 1 m yr <sup>–1</sup> around the region, except in SAU where a shoreline progradation rate of 0.1 m yr <sup>–1</sup> has been observed ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). [[#Mentaschi--2018|Mentaschi et al. (2018)]] report a coastal area loss of 350 km <sup>2</sup> over the same period in Western Australia from satellite observations.

Projections indicate that a majority of sandy coasts in the region will experience shoreline retreat, throughout the 21st century ( ''high confidence'' ) (Figure 12.7b,d). Median shoreline change projections (CMIP5) under both RCP4.5 and RCP8.5 presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] show that, by mid-century, sandy shorelines will retreat (relative to 2010) by between 50 and 80 m all around Australasia, except in SAU and NZ where the projected retreat (relative to 2010) is between 35 and 50 m. By 2100, median shoreline retreats exceeding 100 m (relative to 2010) are projected along the sandy coasts of NAU (about 150 m), CAU (about 160 m), and EAU (about 110 m) under RCP4.5m, while projections for SAU and NZ are around 80–90 m. Under RCP8.5, shoreline retreat exceeding 100 m is projected all around the region by 2100 (relative to 2010) with retreats as high as 220 m in NAU and CAU (about 170 m in EAU and about 130 m in SAU and NZ; Figure 12.7b,d). The total length of sandy coasts in Australasia that is projected to retreat by more than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,500 and 16,000 km respectively, an increase of approximately 30%.

Distinct from long-term coastline recession, storms and storm surges also result in episodic coastal erosion. In general, the historically measured maximum episodic coastal erosion (either eroded volume or coastline retreat distance) or that due to a 1-in-100-year return period storm wave height is used as a design criterion for coastal zone management and planning in Australia ( [[#Wainwright--2014|Wainwright et al., 2014]] ; [[#Mortlock--2017|Mortlock et al., 2017]] ).

While there is wide recognition in Australia that the combined effect of SLR, changing storm surge and wave climates will directly affect future episodic coastal erosion ( [[#McInnes--2016|McInnes et al., 2016]] ; [[#Ranasinghe--2016|Ranasinghe, 2016]] ; [[#Harley--2017|Harley et al., 2017]] ) only a few projections of how this hazard may evolve are available for Australia. In one such study, [[#Jongejan--2016|Jongejan et al. (2016)]] provide projections of how the full exceedance probability curve of the maximum erosion per year may evolve over the 21st century (due to the combined action of SLR, storm surge and storm waves). Their results show that, for example, the 0.01 exceedance probability maximum coastline retreat in 2025 will have an exceedance probability of 0.015 by 2050 and 0.07 by 2100.

'''Marine heatwave:''' The mean SST of the ocean around Australia and east of New Zealand has warmed at a rate of about 0.22°C per decade between 1992 and 2016 ( [[#Wijffels--2018|Wijffels et al., 2018]] ), which is higher than the global average SST increase of 0.16°C per decade ( [[#Oliver--2018|Oliver et al., 2018]] ). This mean ocean surface warming is connected to longer and more frequent marine heatwaves in the region ( [[#Oliver--2018|Oliver et al., 2018]] ). Over the period 1982–2016, the coastal ocean of Australia experienced on average more than 1.5 marine heatwaves (MHWs) per year, with the north coast of Western Australia and the Tasman Sea experiencing on average 2.5–3 MHWs per year. The average duration was between 10 and 15 days, with somewhat longer and hotter MHWs in the Tasman Sea. In New Zealand, the south-east coast of South Island experiences the most MHWs (2.5–3 per year). The duration of MHW in New Zealand is on average 10–15 days ( [[#Oliver--2018|Oliver et al., 2018]] ). Changes around Australasia over the 20th century, derived from MHW proxies, show an increase in frequency between 0.3 and 1.5 MHW per decade, except along the south-east coast of New Zealand (Box 9.2); an increase in duration per event; and the total number of MHW days per decade, with the change being stronger in the Tasman Sea than elsewhere ( [[#Oliver--2018|Oliver et al., 2018]] ).

There is ''high confidence'' that MHWs will increase around most of Australasia. Under RCP4.5 and RCP8.5 respectively, mean SST is projected to increase by 1°C and 2°C around Australia by 2100, with a hotspot of around 2°C for RCP4.5 and of 4°C for RCP8.5 along the south-east coast between Sydney and Tasmania (Interactive Atlas). Under all RCPs, the mean SST around Australia is expected to increase in the future, with median values of around 0.4°C–1.0°C by 2030 under RCP4.5, and 2°C–4°C by 2090 under RCP8.5 (CSIRO and BOM, 2015). Warming is expected to be largest along the north-west coast of Australia, southern Western Australia, and along the east coast of Tasmania (CSIRO and BOM, 2018). More frequent, extensive, intense and longer lasting MHWs are projected around Australia and New Zealand for GWLs of 1.5°C, 2°C and 3.5°C relative to the modelled reference value for 1861–1880 ( [[#Frölicher--2018|Frölicher et al., 2018]] ). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around Australasia by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).

'''In general, there is''' high confidence '''that most coastal/ocean-related hazards in Australasia will increase over the 21st century. Relative sea level rise is''' virtually certain '''to continue in the oceans around Australasia, contributing to increased coastal flooding in low-lying areas''' ( high confidence ''') and shoreline retreat along most sandy coasts''' ( high confidence '''). Marine heatwaves are also expected to increase around the region over the 21st century''' ( high confidence ''').'''

The assessed direction of change in climatic impact-drivers for Australasia and associated confidence levels are illustrated in Table 12.5, together with emergence time information ( [[#12.5.2|Section 12.5.2]] ). No assessable literature could be found for hail and snow avalanches, although these phenomena may be relevant in parts of the region.

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'''Table 12.5''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in Australasia, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:dbce92f0fefa1d58b3e5175c9a9c6586 IPCC_AR6_WGI_Chapter12_Table_12_5.jpg]]

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<span id="central-and-south-america"></span>
=== 12.4.4 Central and South America ===

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For the purpose of this assessment, Central and South America is divided into eight sub-regions, as defined in Chapter 1: Southern Central America (SCA), North-Western South America (NWS), Northern South America (NSA), South American Monsoon (SAM), North-Eastern South America (NES), South-Western South America (SWS), South-Eastern South America (SES) and Southern South America (SSA). The Caribbean is placed under the Small Islands section (12.4.7) of this chapter.

Previous assessments have documented ongoing and projected changes in several CIDs. IPCC AR5 projections ( [[#IPCC--2014b|IPCC, 2014b]] ) pointed to increases in mean temperature between 2°C and 6°C by the end of the century ( ''high confidence'' ) and increases in the occurrence of warm days and nights under various future climate scenarios ( ''medium confidence'' ). The AR5 also pointed to patterns of changes in precipitation ( ''medium confidence'' ), changes in the duration of dry spells ( ''medium confidence'' ) and decreases in water supply ( ''high confidence'' ). The SR1.5 projections indicated expected increases in river flooding and extreme runoff at 2°C warming in parts of South America, and decreases in runoff in Central America, central and Southern South America, and the Amazon basin. The SROCC reported an increased number of landslides, a decreased volume of lahars from ice and snow-clad volcanoes, and increased frequency of glacier lake outburst floods. The SROCC also indicated that regional and local-scale projections point to decreasing trends in glacier runoff.

New literature is now available for the regional climate as a result of observational research and coordinated modelling outputs of CORDEX South America ( [[#Solman--2013|Solman, 2013]] ; [[#Sánchez--2015|Sánchez et al., 2015]] ) and CORDEX-CORE ( [[#Giorgi--2018|Giorgi et al., 2018]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ). Of particular interest are the new projections of both mean climate and extremes. This new regional climate information is key to main sectors sensitive to climate change in Central and South America such as water resources, infrastructure, agriculture, livestock, forestry, silviculture and fisheries ( [[#Magrin--2015|Magrin, 2015]] ; [[#López%20Feldman--2016|López Feldman and Hernández Cortés, 2016]] ), human health (changes in morbidity and mortality, and emergence of diseases in previously non-endemic areas; [[#Núñez--2016|Núñez et al., 2016]] ) and biodiversity ( [[#Uribe%20Botero--2015|Uribe Botero, 2015]] ), urban planning, navigation and tourism.

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<span id="heat-and-cold-4"></span>
==== 12.4.4.1 Heat and Cold ====

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'''Mean air temperature:''' New literature confirms a continuous warming since the beginning of the 20th century in the majority of the eight sub-regions (Atlas.7). However, observational datasets in several areas are still short and trend estimation is hindered by year-to-year and interannual variability. [[IPCC:Wg1:Chapter:Atlas|Atlas]] projections point to a ''virtually certain'' warming across all sub-regions, with the largest increases taking place in the Amazon basin (NSA and SAM; Atlas.7.2.4). A consistent increase in temperature-related indices linked to several climate-sensitive sectors (e.g., growing degree days, cooling degree days) is found across CMIP5, CMIP6 and CORDEX-CORE projections, with smaller increases for cooling degree days in mid-latitude regions than in SCA and the Amazon ( [[#Coppola--2021b|Coppola et al., 2021b]] ). Daily mean temperature exceedances of a typical 21.5°C threshold for a successful incubation of disease pathogens inside many mosquito vectors ( [[#Lambrechts--2011|Lambrechts et al., 2011]] ; [[#Blanford--2013|Blanford et al., 2013]] ; [[#Mordecai--2013|Mordecai et al., 2013]] , 2017) will be crossed much more frequently, potentially driving increases in the incidence of vector-borne diseases ( [[#Laporta--2015|Laporta et al., 2015]] ; [[#Messina--2019|Messina et al., 2019]] ).

'''Extreme heat:''' [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] found ''high confidence'' of increased heatwaves in all regions except SSA over the past decades. There is evidence of increasing heat stress over summer in much of SES and SWS using the wet bulb globe temperature (WBGT) index for the period 1973–2012, and this has been attributed to human influence on the climate system ( [[#Knutson--2016|Knutson and Ploshay, 2016]] ). Climate change projections point to major increases in several heat indices across the region for all scenarios ( ''high confidence'' ). Largest increases in the frequency of hot days (maximum temperatures, Tx > 35°C) are projected for the Amazon basin under SSP5-8.5 with more than 200 days per year at the end of the century under SSP5-8.5 relative to 1995-2014, while such increases remain moderate (50–100 days) in SSP1-2.6. For the dangerous heat threshold of HI > 41°C, increases in frequency are similar to that in Tx > 35°C (Figure 12.4 and Figures 12.SM.1 and 12.SM.2; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ).

'''Cold spell and frost:''' A decreasing frequency of cold days and nights has been observed in many sub-regions (Table 11.13). There is ''medium confidence'' ( ''limited agreement'' ) of a decrease in frost days in SWS, SES and SSA. Projections consistently suggest a general decrease in the frequency of cold spells and frost days in the region as indicated by several indices based on minimum temperature ( [[#Chou--2014|Chou et al., 2014]] ; [[#López-Franca--2016|López-Franca et al., 2016]] ; [[#Li--2021|]] [[#Li--2021|C. Li et al., 2021]] ). Heating degree days are consistently projected to decrease by 5 degree days per year in the Amazon region, and up to 20–30 degree days per year in NWS, SWS and SES, under RCP8.5/SSP5-8.5 by mid century ( [[#Coppola--2021b|Coppola et al., 2021b]] ).

'''In conclusion, it is''' virtually certain '''that warming will continue everywhere in Central and South America and there is''' high confidence '''that by the end of the century most regions will undergo extreme heat stress conditions much more often than in recent past (e.g., increase of dangerous heat with HI > 41°C, or Tx > 35°C) with more than 200 additional days per year under SSP5-8.5, while such conditions will be met typically 5''' '''0–1''' '''00 more days per year under SSP1-2.6 over the same regions. Cold spells and frost days will have a decreasing trend''' ( high confidence ''').'''

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<span id="wet-and-dry-4"></span>
==== 12.4.4.2 Wet and Dry ====

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'''Mean precipitation:''' The ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] documents diverse historical precipitation trends in the region, including a small but not significant increasing trend in SCA, a decreasing trend in south-eastern and north-eastern Brazil, and an increasing trend in SSA. Projections indicate a drying signal for SCA ( ''medium confidence'' ) ( [[#Coppola--2014a|Coppola et al., 2014a]] ; [[#Nakaegawa--2014|Nakaegawa et al., 2014]] ), NES and SWS ( ''high confidence'' ) (Atlas.7.2.5) and the well-known dipole for South America, meaning increasing precipitation over subtropical regions like the Río de La Plata basin (SES) ( ''high confidence'' ) and decreasing precipitation in the Amazon (NSA) ( ''medium confidence'' ) ( [[#Chou--2014|Chou et al., 2014]] ; [[#Llopart--2014|Llopart et al., 2014]] ; [[#Reboita--2014|Reboita et al., 2014]] ; [[#Sánchez--2015|Sánchez et al., 2015]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ). These features are consistent with observations ( [[#Sena--2018|Sena et al., 2018]] ) and are evident in regional and global model projections by mid- and end-of-century for both RCP4.5 and RCP8.5 ( [[#Jones--2013|Jones and Carvalho, 2013]] ).

'''River flood:''' Emerging literature in the region documents ongoing changes in river floods. [[#Mernild--2018|Mernild et al. (2018)]] report decreases and increases in annual runoff west of the Andes Cordillera’s continental divide, with the greatest decreases in the number of low (<10th percentile) runoff conditions and the greatest increases in high (>90th percentile) runoff conditions. In coastal north-east Peru, extreme precipitation events recently caused devastating river floods and landslides ( [[#Son--2020|Son et al., 2020]] ). In Brazil, floods are becoming more frequent and intense in wet regions but less frequent and intense in drier regions ( [[#Bartiko--2019|Bartiko et al., 2019]] ; [[#Borges%20de%20Amorim--2019|Borges de Amorim and Chaffe, 2019]] ), with higher propagation of hydrological changes through anthropogenically modified agricultural basins ( [[#Chagas--2018|Chagas and Chaffe, 2018]] ). Record, catastrophic, unprecedented, and once-in-a-century flooding events have also been reported in recent decades in the tributaries of the Amazon River or along its mainstream ( [[#Sena--2012|Sena et al., 2012]] ; [[#Espinoza--2013|Espinoza et al., 2013]] ; [[#Marengo--2013|Marengo et al., 2013]] ; [[#Filizola--2014|Filizola et al., 2014]] ), in Argentinean rural and urban areas ( [[#Barros--2015|Barros et al., 2015]] ), in the lower reaches of the Atrato, Cauca and Magdalena rivers in Colombia ( [[#Hoyos--2013|Hoyos et al., 2013]] ; [[#Ávila--2019|Ávila et al., 2019]] ), in basins whose mainstreams flow through important metropolitan areas such as Concepción, Chile ( [[#Rojas--2017|Rojas et al., 2017]] ), and even in one of Earth’s driest regions, the Atacama Desert ( [[#Wilcox--2016|Wilcox et al., 2016]] ). In the Amazon basin, the significant increase in extreme flow is associated with the strengthening of the Walker circulation ( [[#Barichivich--2018|Barichivich et al., 2018]] ).

Available projections for the region show increases in river floods in SES and SAM ( ''medium confidence'' ). Projections indicate that SES and the coasts of Ecuador and Peru will experience a tendency towards wetter conditions that can be a proxy for longer periods of flooding and enhanced river discharges ( [[#Zaninelli--2019|Zaninelli et al., 2019]] ). CORDEX models project the strongest changes for the peak flow with a return period of 100 years in SES by mid-century and under RCP8.5 (Figure 12.8). At the continental scale, on the contrary, [[#Alfieri--2017|Alfieri et al. (2017)]] suggest that 100-year river floods will decrease under RCP8.5. Regional projections of river floods have high uncertainty, however, owing to differences in hydrological models ( ''low confidence'' ) ( [[#Reyer--2017a|Reyer et al., 2017a]] ). [[#Fábrega--2013|Fábrega et al. (2013)]] projected increases in surface runoff for Panama, while [[#Zulkafli--2016|Zulkafli et al. (2016)]] identified increases in 100-year floods of 7.5 and 12.0% in projections for the Peruvian Amazon wet season under RCPs 4.5 and 8.5 respectively. Wetter conditions and ±20% variations in annual mean streamflow are also projected for the Río de La Plata under the warming levels of 1.5°C, 2°C and 3°C above pre‐industrial conditions ( [[#Montroull--2018|Montroull et al., 2018]] ). In central Chile, 50-year peak flows are expected to be greater by mid-century than 100-year peak flows observed over the reference period ( [[#Bozkurt--2018|Bozkurt et al., 2018]] ).

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[[File:53a6b519979090f129d90509a8f91c08 IPCC_AR6_WGI_Figure_12_8.png]]

'''Figure 12.8''' '''|''' '''Projected changes in selected climatic impact-driver indices for Central and South America.'''

'''Heavy precipitation and pluvial flood:''' Table 11.14 indicated that there is ''low confidence'' due to ''limited evidence'' of extreme precipitation trends in almost all Central and South America, except in SES where increases in the magnitude and frequency of heavy precipitation have been observed ( ''high confidence'' ). In general, data scarcity persists for a representative continental assessment. [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] projections indicate ''low confidence'' of increase, compared to the modern period, in the intensity and frequency of heavy precipitation in SCA and SWS for all GWLs, and ''medium confidence'' of increase in NSA, NES, SSA, SAM and SES for GWL of 4°C. In NWS, a wide range of changes is projected ( ''low confidence'' ).

'''Landslide:''' Several regions in Central America, as well as Colombia and south-eastern Brazil, are considered areas of high incidence of observed fatal landslides. In these areas, El Niño–Southern Oscillation (ENSO)-driven fluctuations in rainfall amounts ( [[#Sepúlveda--2015|Sepúlveda and Petley, 2015]] ) and climate change ( [[#Nehren--2019|Nehren et al., 2019]] ) seem to be key factors. Rockfalls, ice- and rock-ice avalanches, lahars and landslides have been reported frequently in the southern, extratropical Andes in the last decades ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ). A large number of ice- and moraine-dammed lakes have consequently failed, causing floods that rank amongst the largest events ever recorded ( [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ). However, published literature is largely missing for a reliable assessment of past and future trends of such hazards.

'''Aridity:''' Several regional studies suggest increasing trends in the frequency and length of droughts in the region, such as: over NWS ( [[#Domínguez-Castro--2018|Domínguez-Castro et al., 2018]] ), NSA ( [[#Marengo--2016|Marengo and Espinoza, 2016]] ; [[#Cunha--2019|Cunha et al., 2019]] ) and NES ( [[#Marengo--2015|Marengo and Bernasconi, 2015]] ), over southern Amazonia ( [[#Fu--2013|Fu et al., 2013]] ; [[#Boisier--2015|Boisier et al., 2015]] ), in the São Francisco River basin and the capital city Distrito Federal in Brazil ( [[#Borges--2018|Borges et al., 2018]] ; [[#Bezerra--2019|Bezerra et al., 2019]] ), in the southern Andes ( [[#Vera--2015|Vera and Díaz, 2015]] ), in central southern Chile ( [[#Boisier--2018|Boisier et al., 2018]] ), in SES ( [[#Rivera--2014|Rivera and Penalba, 2014]] ) and, during recent years, in SSA ( [[#Rivera--2014|Rivera and Penalba, 2014]] ). [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] indicated ''medium confidence'' of anthropogenic forcing on observed drying trends in central Chile. Additional discussion on droughts and aridity trends in South America is presented in Sections 8.3.1.6, 8.4.1.6 and 8.6.2.1.

Sections 8.3.2.4 and 8.4.1.6 point to two important drying hotspots in South America with long-term soil moisture decline and precipitation declines: the Amazon basin (SAM and NSA) and SWS ( ''medium confidence'' ) (Figure 12.4). End-of-century RCP8.5 projections show a longer dry season in the central part of South America and decreased precipitation over the Amazon and central Brazil ( [[#Teichmann--2013|Teichmann et al., 2013]] ; [[#Coppola--2014a|Coppola et al., 2014a]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Llopart--2014|Llopart et al., 2014]] ; Atlas.7). Seasonal changes are also projected by end century under RCP 8.5, with decreases in June–July–August (JJA) rainfall projected for NSA, the coastal region of SES, SAM and the southern portion of SWS ( [[#Marengo--2016|Marengo et al., 2016]] ). Decreases in December –January–February (DJF) rainfall are projected for the central part of South America in the near term ( [[#Kitoh--2011|Kitoh et al., 2011]] ; [[#Chou--2014|Chou et al., 2014]] ; [[#Cabré--2016|Cabré et al., 2016]] ). Regional projections for Central and South America also indicate an increase in dryness in SCA and NES by mid- to end-century ( ''medium confidence'' ) ( [[#Chou--2014|Chou et al., 2014]] ; [[#Marengo--2015|Marengo and Bernasconi, 2015]] ).

'''Hydrological drought:''' [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assessed mostly ''low confidence'' in observed changes in hydrological droughts given a lack of studies and clear evidence, with ''medium confidence'' only for a streamflow decrease in sub-regions of SWS (Table 11.15). Some trends are becoming more clear, such as the ones reported for Colombia (NWS) by [[#Carmona--2014|Carmona and Poveda (2014)]] , who indicated that 62% of the 25- to 50-year-long monthly average streamflow time series exhibited significant decreasing trends. However, studies of discharge changes indicate that uncertainty is still large, as argued by [[#Pabón-Caicedo--2020|Pabón-Caicedo et al. (2020)]] for the full extent of the Andes.

A number of studies project decreases in runoff and river discharge for SCA, Colombia, Brazil and the southern part of South America by the end of this century ( [[#Nakaegawa--2010|Nakaegawa and Vergara, 2010]] ; [[#Arnell--2013|Arnell and Gosling, 2013]] ; [[#van%20Vliet--2013|van Vliet et al., 2013]] ). [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] assessed ''high confidence'' in projections of increases in hydrological droughts in NSA, SAM, SWS, and SSA under for 4°C GWL, ''medium confidence'' in SCA, and ''low confidence'' in the rest of the sub-regions given insufficient evidence, lack of signal or mixed signals among the available studies. Signals are much more uncertain for the middle of the century (or for a 2°C GWL).

'''Agricultural and ecological drought:''' Section 11.9 assessed ''low confidence'' in observed changes in agricultural and ecological drought across Central and South America due to regional heterogeneity and differences depending on the drought metrics used, except in NES, which has seen a dominant increase in drought severity ( ''medium confidence'' ).

NSA and SAM are the two regions where the strongest signal of increasing number of dry days (NDD) and drought frequency (DF) is projected compared to other regions of the world ( [[#Coppola--2021b|Coppola et al., 2021b]] ). By the end of this century and under RCP8.5, the NSA area average value for NDD reaches 43, 32 and 27, within the CORDEX-CORE, CMIP5 and CMIP6 ensembles respectively. For the frequency of droughts, the NSA area average value is of about 4.6, 3.4 and 3.8. For the same period and scenario, the SAM region shows NDD and DF values of 29, 20 and 21, and of 4, 3 and 3.5 respectively (Figure 12.4j–l). In Central America, a significantly drier northern region and a wetter southern region are projected for mid-century by ( [[#Hidalgo--2017|Hidalgo et al., 2017]] ), whilst [[#Fuentes-Franco--2015|Fuentes-Franco et al. (2015)]] pointed to more pronounced dry periods during the rainy season in SCA by the end of this century under RCP8.5. Increases in the frequency of meteorological droughts that may initiate other drought types are projected for the eastern part of the Amazon and the opposite for the west under RCP8.5 ( [[#Duffy--2015|Duffy et al., 2015]] ). In central Chile, the occurrence of extended droughts, such as the recently experienced 2010–2015 megadrought (which is still driving impacts), is projected to increase from one to up to five events per 100 years under RCP8.5 ( [[#Bozkurt--2018|Bozkurt et al., 2018]] ). [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] highlights change in confidence in increases in drought severity in SCA, NSA, NES, SAM, SWS, and SSA from ''low'' to ''high'' under the three GWLs of 1.5°C, 2°C and 4°C. NES and SES change from ''low'' ''confidence'' to ''medium'' ''confidence'' increases in agricultural and ecological drought severity by 4°C GWL with different metrics and ''high agreement'' between studies. Only SAM and SSA have projections of agricultural and ecological drought increasing with ''high confidence'' for the middle of the century, or for a 2°C GWL, and NSA, NES and SCA are projected to increase with ''medium confidence'' .

'''Fire weather:''' There is evidence of increases in forest fire activity (number of fires, burned area and fire duration) in central and south-central Chile, where more conducive fire weather conditions have been proposed as the main driver ( [[#González--2018|González et al., 2018]] ; [[#Urrutia-Jalabert--2018|Urrutia-Jalabert et al., 2018]] ). Projections indicate that the Amazon will be one of the regions in the world with the highest increase in fire weather indices over the 21st century and under all RCPs ( ''high confidence'' ) ( [[#Betts--2015|Betts et al., 2015]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ; Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ). This is consistent with the large increase in the frequency of joint occurrence of extreme hot and dry days projected for a 2°C warming level or more ( [[#Vogel--2020|Vogel et al., 2020]] ). Projections of fire weather indices also show an increased risk in SWS ( ''high confidence'' ), SSA and SCA ( ''medium confidence'' ). However, wildfires highly depend on land use and appropriate management may help mitigate future increases in fire risk ( [[#Fonseca--2019|Fonseca et al., 2019]] ).

'''Mean precipitation is projected to change in a dipole pattern with increases in NWS and SES and decreases in NES and SWS''' ( high confidence ''') with further decreases in NSA and SCA''' ( medium confidence '''). There is''' medium confidence '''of an increase in river floods in SAM and SES. There is''' high confidence '''of an increase in drought duration in NES, an in the number of dry days and drought frequency in NSA and SAM. Dry climatic impact-drivers are projected to increase at the regional level with higher global warming levels. The strongest signal of future increase in agricultural and ecological drought, aridity and fire weather is over the Amazon region''' ( '''high confidence''' ''').'''

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<span id="wind-4"></span>
==== 12.4.4.3 Wind ====

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'''Mean wind speed:''' Due to the lack of long-term homogeneous records or limited observations in the region, past wind speed trends are difficult to establish. Global climate models project an increase in wind speeds, under all future scenarios, augmenting wind power potential in most parts of Central and South America, especially in NES, where changes lie in the range 0–20% by 2050 under RCP8.5 and 0–40% under RCP8.5 ( ''medium confidence'' ) ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Reboita--2018|Reboita et al., 2018]] ; [[#Jung--2019|Jung and Schindler, 2019]] ). In Patagonia, wind speeds are projected to decrease. For RCP4.5 changes remain marginal and have ''low confidence'' ( ''low agreement'' ) (Figure 12.4m–o).

'''Severe wind storm:''' Similar observational limitations inhibit an assessment of long-term extreme wind trends. However, [[#Pes--2017|Pes et al. (2017)]] found extreme wind increases in most of Brazil over the past decades. Future projections indicate a slight decrease in the number of extratropical cyclones in mid-latitudes ( ''limited evidence'' , ''low confidence'' ) ( [[#Reboita--2018|Reboita et al., 2018]] ), and an increase of extreme winds in tropical areas ( ''limited evidence'' , ''low confidence'' ) ( [[#Kumar--2015|Kumar et al., 2015]] ). Climate models project a shift and an intensification of southern storm tracks, with most effects offshore over the Southern Ocean (Chapter 4), with ''low confidence'' ( ''low agreement'' ) of significant extreme wind changes over land and coastal areas across the 21st century ( [[#Chang--2017|Chang, 2017]] ; [[#Augusto%20Sanabria--2018|Augusto Sanabria and Carril, 2018]] ; [[#Reboita--2021|Reboita et al., 2021]] ).

'''Tropical cyclone:''' CMIP5 and CMIP6 simulations, including the new High Resolution Model Intercomparison Project (HighResMIP), project a decrease in the frequency of tropical cyclones in the Atlantic and Pacific coasts of Central America for the mid-century or under a 2°C GWL, accompanied with an increased frequency of intense cyclones ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] ; [[#Diro--2014|Diro et al., 2014]] ; [[#Knutson--2020|Knutson et al., 2020]] ; [[#Roberts--2020|Roberts et al., 2020]] ).

'''In summary, there is''' limited evidence '''of current trends in observed wind speed and wind storms in Central and South America. Climate projections indicate a decrease in frequency of tropical cyclones in Central America accompanied with an increased frequency of intense cyclones, and an increase in mean wind''' '''speed and wind power potential in most parts of Central and South America''' ( medium confidence ''').'''

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<span id="snow-and-ice-4"></span>
==== 12.4.4.4 Snow and Ice ====

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'''Snow:''' Historical studies of seasonal snow cover are limited and restricted to the Andes Cordillera. [[#Mernild--2017|Mernild et al. (2017)]] indicated that much of the area north of 23°S experienced a decrease in the number of snow cover days, while the southern half of the Andes Cordillera experienced the opposite. A reduction in snow cover of about 15% was simulated for areas with altitudes in the range of 3000–5000 m, whereas in regions with altitude below 1000 m (Patagonia) snow cover extent increased. The reduced snowfall over the Chilean and Argentinean Andes mountains, which has resulted in unprecedented reductions in river flow, reservoir volumes and groundwater levels, led to the most severe and long-lasting hydrological drought (2010–2015) reported in the adjacent semi-arid foothills of the central Andes ( [[#Garreaud--2017|Garreaud et al., 2017]] ; [[#Rivera--2017|Rivera et al., 2017]] ; [[#Masiokas--2020|Masiokas et al., 2020]] ). Projections (based on process understanding) in [[IPCC:Wg1:Chapter:Chapter-9#9.5.3.3|Section 9.5.3.3]] point to decreases in seasonal snow cover extent and duration across South America as global climate continues to warm ( ''high confidence'' ).

'''Glacier:''' Observation and future projection of Central and South America glacier mass changes are assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.5.1|Section 9.5.1]] , grouped in two main regions: Low Latitude region (98% of which is glaciers in the Andes) and the Southern Andes region. An increase in the number and areal extent of glacial lakes in the Southern Andes was reported for the period 1986–2016 ( [[#Wilson--2018|Wilson et al., 2018]] ). Similar changes are being observed in the central Andes ( [[#Colonia--2017|Colonia et al., 2017]] ). Since 1800 at least 15 ice‐dammed lakes and 16 moraine‐dammed lakes have failed in the extratropical Andes, causing high-magnitude glacial lake outburst floods ( [[#Rojas--2014|Rojas et al., 2014]] ; [[#Cook--2016|Cook et al., 2016]] ; [[#Wilson--2018|Wilson et al., 2018]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ). Partially due to glacier shrinkage and lake growth, the frequency of outburst floods has increased in the last 30–40 years ( [[#Carey--2012|Carey et al., 2012]] ; [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ).

Glaciers across South America are expected to continue to lose mass and glacier area in the coming century ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5|Section 9.5]] ). In terms of their mass, glaciers in the Low Latitude region are projected by GlacierMIP to lose 67 ± 42%, 86 ± 24% and 94 ± 13%, of their 2015 baseline mass by the end of the century under RCP2.6, RCP4.5 and RCP8.5 respectively ( [[#Marzeion--2020|Marzeion et al., 2020]] ). Glaciers in the Southern Andes show decreasing mass loss rates for RCP2.6, and increasing rates for RCP8.5, which peak in the mid to late 21st century. Glaciers in the Southern Andes are projected to lose 26 ± 27%, 33 ± 26% and 47 ± 26% of their 2015 mass by the end of the century under RCP2.6, RCP4.5 and RCP8.5 respectively ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] ).

The central Andes will experience the highest disturbance to the thermal regime of the 21st century. As a consequence, in the Argentinean Andes up to 95% of rock glaciers in the southern desert Andes and in the central Andes will rest in areas above 0°C under the worst case scenario of warming (the freezing level might move up more than twice as much as during the entire Holocene; [[#Drewes--2018|Drewes et al., 2018]] )

'''Permafrost:''' There is limited information on the ongoing changes and projections of permafrost conditions in the region. Based on model projections under the IPCC A1B scenario, permafrost areas in the Bolivian Andes will eventually be lost, but this could take years to decades or longer depending on permafrost thickness ( [[#Rangecroft--2016|Rangecroft et al., 2016]] ).

'''In conclusion, glacier volume loss and permafrost thawing will continue in the Andes Cordillera under all climate scenarios''' ( ''high confidence'' '''), causing important reductions in river flow and potentially high-magnitude glacial lake outburst floods.'''

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<span id="coastal-and-oceanic-3"></span>
==== 12.4.4.5 Coastal and Oceanic ====

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'''Relative sea level:''' Around Central and South America, over 1900–2018, a new tide gauge-based reconstruction finds a regional mean relative sea level (RSL) change of 2.07 [1.36 to 2.77] mm yr <sup>–1</sup> in the South Atlantic, 2.49 [1.89 to 3.06] mm yr <sup>–1</sup> in the subtropical North Atlantic and 1.20 [0.76 to 1.62] mm yr <sup>–1</sup> in the East Pacific ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a global mean sea level (GMSL) change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). For the period 1993–2018, these RSLR rates, based on satellite altimetry, increased to 3.45 [3.04 to 3.86)] mm yr <sup>–1</sup> , 4.04 [2.77 to 5.24] mm yr <sup>–1</sup> and 2.35 [0.70 to 4.06] mm yr <sup>–1</sup> respectively ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5).

Relative sea level rise is ''extremely likely'' to continue in the oceans around Central and South America. Regional mean RSLR projections for the oceans around Central and South America range from 0.3–0.5 m under SSP1-2.6 to 0.5–0.9 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), which is around the projected GMSL change ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.3|Section 9.6.3.3]] ). These RSLR projections may, however, be underestimated due to potential partial representation of land subsidence in their assessment ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ).

'''Coastal flood:''' The present-day 1-in-100-year ETWL ranges from 0.5 to 2.5 m around most of Central and South America, except in SSA and SWS, where 1-in-100-year ETWLs can be as large as 5 to 6 m ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ). ETWL magnitude and occurrence frequency are expected to increase throughout the region ( ''high confidence'' ) (Figure 12.4p–r and Figure 12.SM.6). Across the region, the 5–95th percentile range of the 1-in-100-year ETWL is projected to increase (relative to 1980–2014) by 8–34 cm and by 10–43 cm by 2050 under RCP4.5 and RCP8.5 respectively ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). By 2100, this range is projected to be 21–93 cm and 34–190 cm under RCP4.5 and RCP8.5 respectively ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Furthermore, under RCP4.5, the present-day 1-in-100-year ETWL is projected to have median return periods of 1-in-10-years to 1-in-50-years by 2050 and 1-in-1-year by 2100 in SES, SSA and SWS. In other regions of Central and South America, the present-day 1-in-100-year ETWL is projected to occur once per year or more by both 2050 and 2100 under RCP4.5. The present-day 1-in-50-year ETWL is projected to occur around three times a year by 2100 with a SLR of 1 m ( [[#Vitousek--2017|Vitousek et al., 2017]] ).

'''Coastal erosion:''' According to satellite data, shoreline retreat rates of around 1 m yr <sup>–1</sup> have been observed along the sandy coasts of SCA, SES and SSA over the period 1984–2015, while shoreline progradation rates of around 0.25 m yr <sup>–1</sup> has been observed in NWS and NSA. The sandy shorelines in NES and SWS have remained more or less stable over the same period ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). Using satellite observations, [[#Mentaschi--2018|Mentaschi et al. (2018)]] report a coastal area loss of 250 km <sup>2</sup> over a 30-year period (1984–2015) along the Pacific coast of South America, and of 780 km <sup>2</sup> along the Atlantic coastlines.

Projections indicate that a majority of sandy coasts in the region will experience shoreline retreat throughout the 21st century ( ''high confidence'' ). Median shoreline change projections (CMIP5) for the mid-century period show that, relative to 2010, sandy shorelines will retreat by between 30 and 75 m in SCA, NES, SES and SSA under both RCP4.5 and RCP8.5, while the projected mid-century retreats are less than 30 m in NSA, NWS and SWS for both RCPs ( [[#Vousdoukas--2020b|Vousdoukas et al., 2020b]] ). Parts of the coastline in these latter three regions are projected to prograde over the 21st century, if present ambient shoreline change trends continue ( [[#Vousdoukas--2020b|Vousdoukas et al., 2020b]] ). By 2100, median retreats of more than 100 m are projected in SCA, NES, SES and SSA under both RCPs, while retreats between 50–100 m are projected for NSA, NWS and SWS under both RCPs (Figure 12.8). Notably, the projected shoreline retreats in SCA and SES approach 150 m by 2100 under RCP4.5 and 200 m under RCP8.5. The total length of sandy coasts in Central and South America that is projected to retreat by more than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,000 and 15,000 km respectively, an increase of approximately 30%.

'''Marine heatwave:''' The mean sea surface temperature (SST) of the Atlantic Ocean and the Caribbean around Central and South America increased from 0.25°C to 1°C over the period 1982–1998 ( [[#Oliver--2018|Oliver et al., 2018]] ). This mean ocean surface warming is connected to longer and more frequent marine heatwaves (MHW) in the region ( [[#Oliver--2018|Oliver et al., 2018]] ). Over the period 1982–2016, the coastlines experienced on average more than 1.0 MHW per year, with the Pacific coast of Northern Central America and the coast of SES (Atlantic) experiencing on average 2.5–3 MHWs per year. The average duration was between 10 and 15 days, with the notable exception of the equatorial Pacific coastline, which experiences MHWs with >30 days average duration related to ENSO conditions. In the south-western Atlantic shelf (32–38°S), more than half of the days with MHWs have occurred since 2014, and the most intense event (1.7°C above previous maximum) took place in the austral summer of 2017 ( [[#Manta--2018|Manta et al., 2018]] ). Changes over the 20th century, derived from MHW proxies, show an increase in frequency between 0.5 and 2 MHW per decade in the South Atlantic, the Caribbean and the Pacific coast of Northern Central America, an increase in intensity per event in the South Atlantic, and a decrease along the equatorial Pacific coastline. The total number of MHW days per year increases around Central and South America, with the exception of the equatorial Pacific coastline ( [[#Oliver--2018|Oliver et al., 2018]] ).

There is ''high confidence'' that MHWs will increase around Central and South America. Mean SST is projected to increase by 1°C (2°C) by 2100, with a hotspot of about 2°C (4°C) along the coast of South-Eastern South America and North-Western South America under RCP4.5 (RCP8.5; Interactive Atlas). More frequent MHWs are projected around the region for GWLs of 1.5°C, 2°C and 3.5°C relative to the modelled reference value for 1861–1880 ( [[#Frölicher--2018|Frölicher et al., 2018]] ). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around Central and South America by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).

'''In summary, relative sea level rise is''' extremely likely '''to continue in the oceans around Central and South America, contributing to increased coastal flooding in low-lying areas''' ( high confidence ''') and shoreline retreat along most sandy coasts''' ( high confidence '''). Marine heatwaves are also expected to increase around the region over the 21st century''' ( high confidence ''').'''

The assessed direction of change in climatic impact-drivers for Central and South America and associated confidence levels are illustrated in Table 12.6. No assessable literature could be found for sand and dust storm, lake and sea ice, heavy snowfall and ice storms, hail and snow avalanches, although these phenomena may be relevant in parts of the region.

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'''Table 12.6''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in Central and South America, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2 and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:6cd63b98ba7233b45f39a352d335d491 IPCC_AR6_WGI_Chapter12_Table_12_6.jpg]]

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<span id="europe"></span>
=== 12.4.5 Europe ===

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The regional European climate and main hazards have been previously assessed in SREX, AR5 WGII, SR1.5, SROCC and SRCCL and a summary of key findings can be found in the Europe section of Atlas.8.1. For the purpose of this assessment Europe is divided into four climatic regions: Northern Europe (NEU), Western and Central Europe (WCE), Eastern Europe (EEU) and Mediterranean (MED) (Figure Atlas.24).

Since AR5 and SR1.5, a large body of literature that uses the EURO-CORDEX and MED-CORDEX ensembles of high-resolution simulations ( [[#Jacob--2014|Jacob et al., 2014]] ; [[#Ruti--2016|Ruti et al., 2016]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Vautard--2020|Vautard et al., 2020]] ; [[#Coppola--2021a|Coppola et al., 2021a]] ) to assess signals of climate change in Europe has emerged. These scenario-based simulations have been the basis of a number of impact studies (e.g., [[#Jacob--2014|Jacob et al., 2014]] , 2018; [[#Somot--2018|Somot et al., 2018]] ; [[#Faggian--2019|Faggian and Decimi, 2019]] ) highlighting the use of the climatic impact-drivers. The development of the science of attribution of weather events ( [[#Stott--2016|Stott et al., 2016]] ) has provided evidence of links between climate change and hazard changes such as the 2017 Mediterranean heatwave ( [[#Kew--2019|Kew et al., 2019]] ) and many others (Chapter 11).

The ability of global and regional models to reproduce the observed changes in mean and extreme temperature and precipitation in Europe is assessed in the literature (Atlas.8.3). In summary, both GCMs and RCMs have their limitations but, in general, the increased resolution of RCMs is shown to clearly add value in terms of resolving spatial patterns and seasonal cycles of precipitation and precipitation extremes in many European regions, especially in regions of complex topography such as the Alps and for quantities such as snowmelt-driven runoff, regional winds and Mediterranean hurricanes (Medicanes).

Examples of projected climatic impact-driver thresholds are illustrated in Figures 12.4 and 12.9 based on the most recently updated EURO-CORDEX RCM projections, CMIP5 and CMIP6 GCMs for comparison. For a more comprehensive representation of other climatic impact-driver index trends assessed in this section the reader is referred to the interactive Atlas.

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<span id="heat-and-cold-5"></span>
==== 12.4.5.1 Heat and Cold ====

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'''Mean air temperature:''' Since AR5, studies have confirmed that the mean warming trend in Europe is increasing (Atlas.8.2). The observed warming trend patterns are largely consistent with those simulated by global and regional climate models and it is ''very likely'' that such trends are, in large part, due to human influence on climate ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1|Section 3.3.1]] ).

All temperature trends are ''very likely'' to continue for a global warming of 1.5°C or 2°C and 3°C (Atlas.8.4). Future warming leads to the exceedance of different temperature thresholds relevant for vector-borne diseases ( ''medium confidence'' ) ( [[#Caminade--2012|Caminade et al., 2012]] ; [[#Medlock--2013|Medlock et al., 2013]] ), invasive allergens ( ''medium confidence'' ) ( [[#Storkey--2014|Storkey et al., 2014]] ; [[#Hamaoui-Laguel--2015|Hamaoui-Laguel et al., 2015]] ), SST thresholds in the Mediterranean ( ''likely'' to exceed 20°C), or relevant for the ''Vibrio'' bacteria development ( [[#Vezzulli--2015|Vezzulli et al., 2015]] ). Future warming is also projected to lead to the exceedance of cooling degree day index (>22°C) thresholds, characterizing a potential increase in energy demand for cooling in southern Europe with increases ''likely'' exceeding 40% in some areas ( [[#Spinoni--2015|Spinoni et al., 2015]] ) by 2050 under RCP8.5 ( ''high confidence'' ) ( [[#12.3|Section 12.3]] and Atlas.8; [[#Coppola--2021a|Coppola et al., 2021a]] ).

'''Extreme heat:''' The frequency of heatwaves observed in Europe has ''very likely'' increased in recent decades due to human-induced change in atmospheric composition ( [[IPCC:Wg1:Chapter:Chapter-11#11.3|Section 11.3]] ) and a detectable anthropogenic increase in a summer heat stress index over all regions of Europe has been identified based on WBGT index trends for 1973–2012 ( ''medium confidence'' , ''limited evidence'' ) ( [[#Knutson--2016|Knutson and Ploshay, 2016]] ).

It is ''very likely'' that the frequency of heatwaves will increase during the 21st century regardless of the emissions scenario in each European region, and for 1.5°C and 2°C GWLs ( [[IPCC:Wg1:Chapter:Chapter-11#11.3.5|Section 11.3.5]] ). Heat stress due to both high temperature and humidity, affecting morbidity, mortality and labour capacity ( [[#12.3|Section 12.3]] ) is projected to increase under all emissions scenarios and GWLs by the middle of the century (Figure 12.4a–f). Under RCP8.5, the expected number of days with WBGT higher than 31°C is about 25, 30 and 40 days per year, as projected by EURO-CORDEX, CMIP5 and CMIP6 respectively on average over the Mediterranean region, and around 30, 40 and 60 days per year in low coastal plain areas such as the Po Valley, the Italian, Greek and Spanish coasts, and the Mediterranean islands ( [[#Coppola--2021a|Coppola et al., 2021a]] ). An average increase of a few days per year of maximum daily temperature exceeding 35°C, a typical critical threshold for crop productivity, is expected by the mid-century in central Europe, and an increase of 10–20 days is expected for the Mediterranean areas (Figure 12.4b; [[#Coppola--2021a|Coppola et al., 2021a]] ). By contrast, under SSP1-2.6, the increase in this number of days remains limited to less than about 10 days, and confined to the Mediterranean regions. Mitigation is expected to have a strong effect, with the dangerous heat threshold of HI > 41°C projected to be crossed 5–10 days more per year in the Mediterranean regions and a few days per year more in WCE and EEU under SSP5-8.5, while such increases would be virtually absent under SSP1-2.6 (Figure 12.4d–f).

'''Cold spell and frost:''' Temperature observations for winter cold spells in Europe show a long-term decreasing frequency ( [[#Brunner--2018|Brunner et al., 2018]] ), with their probability of occurrence projected to decrease in the future ( ''high confidence'' ) and virtually disappear by the end of the century ( [[IPCC:Wg1:Chapter:Chapter-11#11.3|Section 11.3]] ). The frequency of frost days will ''very likely'' decrease for all scenarios and all time horizons ( [[#Lindner--2014|Lindner et al., 2014]] ; [[#Coppola--2021a|Coppola et al., 2021a]] ) with consequences for agriculture and forests. A simple heating degree day index, characterizing heating demand, shows a large observed decreasing trend for winter heating energy demand in Europe ( [[#Spinoni--2015|Spinoni et al., 2015]] ). This trend is ''very likely'' to continue through the 21st century, with decreases in the range of 20–30% for Northern Europe, about 20% for central Europe and 35% for southern Europe, by mid-century under RCP8.5 ( [[#Spinoni--2018b|Spinoni et al., 2018b]] ; [[#Coppola--2021a|Coppola et al., 2021a]] ; Interactive Atlas).

'''In summary, irrespective of the scenario, it is''' virtually certain '''that warming will continue in Europe, and there is''' high confidence '''that the observed increase in heat extremes is due to human activities. It is''' very likely '''that the frequency of heat extremes will increase over the 21st century with an increasing gradient toward the southern regions. Extreme heat will exceed critical thresholds for health, agriculture and other sectors more frequently''' ( high confidence '''), with strong differences between mitigation scenarios. It is''' very likely '''that the frequency of cold spells and frost days will keep decreasing over the course of this century and it is''' likely '''that cold spells will virtually disappear towards the end of the century.'''

<div id="12.4.5.2" class="h3-container"></div>

<span id="wet-and-dry-5"></span>
==== 12.4.5.2 Wet and Dry ====

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'''Mean precipitation:''' Precipitation has generally increased in northern Europe and decreased in southern Europe, especially in winter ( [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ) but in the latter, precipitation trends are strongly dependent on the examined period (Atlas.8). These trends in precipitation increases in the north and decreases in the south are also represented by global and regional climate simulations ( [[#Jacob--2014|Jacob et al., 2014]] ; [[#Rajczak--2017|Rajczak and Schär, 2017]] ; [[#Lionello--2018|Lionello and Scarascia, 2018]] ; [[#Coppola--2021a|Coppola et al., 2021a]] ; Atlas.8.2) and have been attributed to climate change (Sections 3.3.2, 8.3.1).

Studies since AR5, together with EURO-CORDEX and MED-CORDEX experiments and the latest CMIP6 ensemble, have increased confidence in regional projections of mean and extreme precipitation ( [[#Prein--2016|Prein et al., 2016]] ) despite their wet bias, and show that it is ''very likely'' that precipitation will increase in Northern Europe in DJF and decrease in the Mediterranean in JJA under all climate scenarios except RCP2.6/SSP1-2.6 and for both mid- and end-century periods ( [[#Coppola--2021a|Coppola et al., 2021a]] ; Atlas.8.5).

'''River flood:''' There is ''high confidence'' of an observed increasing trend of river floods in Western and Central Europe (WCE) and ''medium confidence'' of a decrease in Northern (NEU) and southern Europe (MED).

The SR1.5 shows evidence of an increase in reported floods in the UK over the period 1884–2013, and increasing trends in annual maximum daily streamflow data over 1966–2005 in parts of Europe. Although high flow does not show uniform trends for the entire region ( [[#Hall--2014|Hall et al., 2014]] ; [[#Mediero--2015|Mediero et al., 2015]] ) or specific regions ( [[#Mudersbach--2017|Mudersbach et al., 2017]] ; [[#Vicente-Serrano--2017|Vicente-Serrano et al., 2017]] ; [[#Tramblay--2019|Tramblay et al., 2019]] ), regional patterns of significant flood trends do exist. Based on the most extended river flow database spanning the period 1960–2010, an increase in floods frequency in north-western Europe, decreasing in medium and large catchments in southern Europe and decreasing floods in Eastern Europe has been detected ( [[#Blöschl--2019|Blöschl et al., 2019]] ) in line with [[#Mediero--2014|Mediero et al. (2014)]] , [[#Arheimer--2015|Arheimer and Lindström (2015)]] , [[#Gudmundsson--2017|Gudmundsson et al. (2017)]] , [[#Krysanova--2017|Krysanova et al. (2017)]] , [[#Kundzewicz--2018|Kundzewicz et al. (2018)]] and [[#Mangini--2018|Mangini et al. (2018)]] .

There is ''high confidence'' of river floods increasing in Western and Central Europe (WCE) and ''medium confidence'' of a decrease in Northern (NEU), Eastern (EEU) and southern Europe (MED) for mid- and end-century under RCP8.5 and ''low confidence'' under RCP2.6. The projected increase in WCE is roughly 10% (18% by end of century) and the projected decrease in NEU is 5% (11% by end of century) for the peak flow with a return period of 100 years for mid-century, under RCP8.5 ( [[#Di%20Sante--2021|Di Sante et al., 2021]] ; Figure 12.9a for mid-century (Q100) projections of flood discharges per unit catchment area ( [[#Blöschl--2019|Blöschl et al., 2019]] ) based on EURO-CORDEX models).

<div id="_idContainer077" class="Basic-Text-Frame"></div>

[[File:86f12b8af8261abcbf2a23eda4f3289c IPCC_AR6_WGI_Figure_12_9.png]]

'''Figure 12.9''' '''|''' '''Projected changes in selected climatic impact-driver indices for Europe. (a)''' Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ), and '''(b)''' median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from EURO-CORDEX models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction) of change. '''(c)''' Bar plots for Q100 (m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. '''(d)''' As for (c) but showing absolute values for number of days with SWE > 100mm, masked to grid cells with at least 14 such days in the recent past. See Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

Using frequency analysis of extreme peak flow events above a 100-year return period as a threshold, which is the average protection level of the European river network ( [[#Rojas--2013|Rojas et al., 2013]] ), [[#Alfieri--2017|Alfieri et al. (2017)]] and [[#Alfieri--2015|Alfieri et al. (2015)]] show that Europe is one of the regions where the largest increases in flood risk may occur, with only few countries in Eastern Europe showing a decrease (Poland, Lithuania, Belarus) ( [[#Osuch--2017|Osuch et al., 2017]] ). They find a significant increase of events with peak discharge above 100-year return period (Q100) in most of Europe in line with [[#Rojas--2012|Rojas et al. (2012)]] , [[#Hirabayashi--2013|Hirabayashi et al. (2013)]] , [[#Dankers--2014|Dankers et al. (2014)]] , [[#Forzieri--2016|Forzieri et al. (2016)]] , [[#Roudier--2016|Roudier et al. (2016)]] , [[#Thober--2018|Thober et al. (2018)]] , and an increase in the magnitude of floods in southern Europe, although [[#Giuntoli--2015|Giuntoli et al. (2015)]] projects no change. A modest but significant decrease in the 100-year return period river flood is projected for southern (due to reduction of precipitation) and north-eastern European regions, the latter because of the strong reduction in snowmelt induced river floods ( [[#Thober--2018|Thober et al., 2018]] ; [[#Di%20Sante--2021|Di Sante et al., 2021]] ).

'''Heavy precipitation and pluvial flood:''' Heavy precipitation frequency trends have been detected in Europe with ''high confidence'' for the NEU and Alpine regions and with ''medium confidence'' in WCE, and also attributed to climate change with ''high'' ''confidence'' in NEU ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). [[#Guerreiro--2017|Guerreiro et al. (2017)]] , based on observations, showed that 20% of city areas in WCE and MED regions are affected by pluvial flooding and less than 10% of city areas in the northern and western coastal cities.

Projections based on multiple lines of evidence from global to convective permitting model scales show ''high confidence'' in extreme precipitation increase in the northern, central and eastern European regions (NEU, WCE, EEU) and in the Alpine area. Increases with ''medium confidence'' are projected for the Mediterranean basin (with a negative gradient towards the south) for mid- and end-century under RCP4.5, RCP8.5 and SSP5-8.5 and for 2°C GWL and higher ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ; [[#MedECC--2020|MedECC, 2020]] ).

'''Landslide:''' Rainfall periods connected to landslides are projected to increase in central Europe by up to one more period per year in flat areas in low altitudes and by up to 14 more periods per year at higher altitudes by mid-century, becoming even more evident by the end of the century ( [[#Schlögl--2018|Schlögl and Matulla, 2018]] ). An increase of landslides by up to 45.7% and 21.2% is projected for southern Italy (Calabria region) by mid-century under both RCP4.5 and RCP8.5 ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ) and by up to 40% in central Italy (Umbria) during the winter season ( [[#Ciabatta--2016|Ciabatta et al., 2016]] ). A decrease of landslides is projected in the Peloritani mountains in southern Italy (RCP4.5 and 8.5) by mid-century ( [[#Peres--2018|Peres and Cancelliere, 2018]] ). A slight increase for a 10-year return period landslide is projected in the eastern Carpathians, the Moldavian Subcarpathians and the northern part of the Moldavian Tableland and a higher increase in the 100-year return period event is projected in the western hilly and plateau areas of Romania ( [[#Jurchescu--2017|Jurchescu et al., 2017]] ).

'''Aridity:''' The Mediterranean region shows evidence of large-scale decreasing precipitation trends over 1901–2010, which are at least partly attributable to anthropogenic forcing according to CMIP5 models ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). Nevertheless, there is ''low agreement'' among studies on observed precipitation trend in the Mediterranean region ( [[IPCC:Wg1:Chapter:Chapter-11#11.9.4|Section 11.9.4]] and Atlas.8.2).

Precipitation is projected to decrease by mid- and end-century for the RCP8.5 and SSP5-8.5 with ''strong agreement'' among CMIP5, CMIP6 and CORDEX regional climate ensemble models on the direction of change. With both temperature increase and precipitation decrease there is ''high confidence'' on increased aridity in the MED region (Sections 8.4.1.6 and 11.9.4 and Atlas.8.2; [[#Coppola--2021a|Coppola et al., 2021a]] ). In NEU there is '''high confidence''' of decrease in aridity linked to mean precipitation increase ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.1.6|Section 8.4.1.6]] , [[IPCC:Wg1:Chapter:Atlas|Atlas]] 8.2) and meteorological drought decrease based on indicators like Standardized Precipitation Index and consecutive dry days ( [[IPCC:Wg1:Chapter:Chapter-11#11.9.4|Section 11.9.4]] , Figure 12.4, Coppola et al,, 2021a).

'''Hydrological drought:''' There is ''high confidence'' that hydrological droughts have increased in the Mediterranean basin with ''medium confidence'' in anthropogenic attribution of the signal, and ''high confidence'' that they will continue to increase through the 21st century for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6. (Sections 8.3.1.6, 8,4.1.6, and 11.9.4). There is ''medium confidence'' in hydrological drought increase in WCE and ''low confidence'' in direction of change for EEU and NEU from mid-century onwards and for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6 ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] and Figure 12.4g–i).

Streamflow droughts are projected to become more severe and persistent in the Mediterranean and western Europe (current 100-year events could occur approximately every 2–5 years by 2080; [[#Forzieri--2016|Forzieri et al., 2016]] ).

The opposite tendency is projected in Northern, Eastern and central Europe where higher precipitation that outweighs the effects of increased evapotranspiration is expected to result in a decrease in streamflow drought frequency ( [[#Forzieri--2014|Forzieri et al., 2014]] ). For a 2°C GWL droughts will become more intense in the MED and in France and longer mainly due to less rainfall and higher evapotranspiration. A reduction of drought length and magnitude is projected for NEU and EEU ( [[#Roudier--2016|Roudier et al., 2016]] ). In the southern Alps, both winter and summer low flows are projected to be more severe, with a 25% decrease in the 2050s ( [[#Vidal--2016|Vidal et al., 2016]] ).

'''Agricultural and ecological drought:''' There is ''medium confidence'' that agricultural and ecological droughts have increased in Western and Central Europe and in the Mediterranean region, and ''medium confidence'' that anthropogenic drivers contributed to the Mediterranean increase (Sections 8.3.1.6 and 11.9).

( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that agricultural and ecological droughts will increase in the Mediterranean regions ( ''high confidence'' ) and Western and Central Europe ( ''medium confidence'' ) by mid-century and with ''high confidence'' by the end of the century for the MED for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6 ( [[IPCC:Wg1:Chapter:Chapter-11#11.9.4|Section 11.9.4]] ). ''Low confidence'' in direction of change is assessed for EEU and NEU under all scenarios and global warming levels (Figure 12.4k).

Recent local studies provide additional risk-relevant context to changes in European drought. Agricultural and ecological drought conditions are expected to intensify in southern Europe by end-of-century based on the 12-month rainfall Drought Severity Index (a soil moisture indicator), precipitation deficit SPI and SPEI indices. There will be regions in southern Europe where this type of drought could be up to 14 times worse than the worst drought in the historical period ( [[#Guerreiro--2018|Guerreiro et al., 2018]] ). One-in-10-year drought events are projected to happen every second year ( [[#Mora--2018|Mora et al., 2018]] ; [[#Ruosteenoja--2018|Ruosteenoja et al., 2018]] ). The Mediterranean region will have 100 additional stress years (years with three consecutive months of precipitation deficits greater than 25%; [[#Giorgi--2018|Giorgi et al., 2018]] ); an increase of both drought frequency (up to two events per decade) and severity ( [[#Spinoni--2014|Spinoni et al., 2014]] , 2020) and an increase of consecutive dry days in the southern part of the MED region ( [[#Lionello--2020|Lionello and Scarascia, 2020]] ). In contrast, droughts are expected to decrease in winter in Northern Europe ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ; [[#Spinoni--2018a|Spinoni et al., 2018a]] ). These findings are confirmed by the EURO-CORDEX, CMIP5 and CMIP6 ensemble that show a change of frequency of drought events in the MED between 2–3 events per decade by mid-century for scenario RCP8.5 (Figure 12.SM.3; [[#Coppola--2021a|Coppola et al., 2021a]] ).

'''Fire weather:''' Fire weather conditions have been increasing since about 1980 over a few regions in Europe including Mediterranean areas ( ''low confidence'' ) ( [[#Venäläinen--2014|Venäläinen et al., 2014]] ; [[#Urbieta--2019|Urbieta et al., 2019]] ; [[#Barbero--2020|Barbero et al., 2020]] ; [[#Giannaros--2021|Giannaros et al., 2021]] ). However, beyond a few studies, evidence is largely missing on attribution of these trends to anthropogenic climate change ( [[#Forzieri--2016|Forzieri et al., 2016]] ). An increase in fire weather is projected for most of Europe, especially western, eastern and central regions, by 2080 (current 100-year events will occur every 5–50 years), with a progressive increase in confidence and model agreement along the 21st century ( ''medium confidence'' ) ( [[#Forzieri--2016|Forzieri et al., 2016]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). With increased drying and heat combined, in Mediterranean areas, an increase in fire weather indices is projected under RCP4.5 and RCP8.5, or SRES A1B, as early as by mid-century ( ''high confidence'' ) ( [[#Bedia--2014|Bedia et al., 2014]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ; [[#Dupuy--2020|Dupuy et al., 2020]] ; [[#Fargeon--2020|Fargeon et al., 2020]] ; [[#Ruffault--2020|Ruffault et al., 2020]] ) and an increase in burned area of 40% and 100% for a 2°C and 3°C GWL, respectively ( [[#Turco--2018|Turco et al., 2018]] ).

'''In summary, there is''' high confidence '''that river floods will increase in central and Western Europe and''' medium confidence '''that they will decrease in Northern, Eastern and southern Europe, for mid- and end of century under RCP8.5 and with''' low confidence '''under RCP2.6. There is''' high confidence '''that aridity will increase by mid- and end-century under the RCP8.5 and SSP5-8.5, and''' high confidence '''that agricultural, ecological and hydrological droughts will increase in the Mediterranean region by mid- and far end of century under all RCPs except RCP2.6/SSP1-2.6 and''' '''also for 2°C and higher GWLs. There is''' high confidence '''in fire weather increase in the Mediterranean region.'''

<div id="12.4.5.3" class="h3-container"></div>

<span id="wind-5"></span>
==== 12.4.5.3 Wind ====

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'''Mean wind speed:''' Mean surface wind speeds have decreased in Europe as in many other areas of the Northern Hemisphere over the past four decades ( ''medium confidence'' ) (AR5 WGI), with a reversal to an increasing trend in the last decade ( ''low confidence'' ) that is, however, not fully consistent across studies ( [[#Tian--2019|Tian et al., 2019]] ; [[#Zeng--2019|Zeng et al., 2019]] ; Z. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Deng--2021|Deng et al., 2021]] ; see [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.4|Section 2.3.1.4.4]] ). Re-analyses also show declining winds in Europe ( [[#Deng--2021|Deng et al., 2021]] ) with large interdecadal variability ( [[#Laurila--2021|Laurila et al., 2021]] ). The declining trend has induced a corresponding decline in wind power potential indices across Europe ( ''low confidence'' ) ( [[#Tian--2019|Tian et al., 2019]] ). However, there is ''low agreement'' and ''limited evidence'' that climate model historical trends are consistent with observed trends ( [[#Tian--2019|Tian et al., 2019]] ; [[#Deng--2021|Deng et al., 2021]] ). Several factors have been attributed to these trends, including forest growth, urbanization, local changes in wind measurement exposure and aerosols ( [[#Bichet--2012|Bichet et al., 2012]] ), as well as natural variability ( [[#Zeng--2019|Zeng et al., 2019]] ).

Due to changes in mean surface wind speed patterns ( [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|C. Li et al., 2018]] ) and the poleward shift of the North Atlantic jet stream exit, mean surface wind speeds are projected to decrease in the Mediterranean areas under RCP4.5 and RCP8.5 by the middle of the century and beyond, or for GWLs of 2°C and higher ( ''high confidence'' ), with a subsequent decrease in wind power potential ( ''medium confidence'' ) ( [[#Hueging--2013|Hueging et al., 2013]] ; [[#Tobin--2015|Tobin et al., 2015]] , 2018; [[#Davy--2018|Davy et al., 2018]] ; [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Moemken--2018|Moemken et al., 2018]] ; Figure 12.4). However, sub-regional patterns of change are shown in regional climate models, such as an increase in wind speeds in the Aegean Sea and in the northern Adriatic Sea, where a reduction of Bora events and an increase of Scirocco events are projected for mid-century and beyond under RCP4.5 and RCP8.5 ( ''medium confidence'' ) ( [[#Tobin--2016|Tobin et al., 2016]] ; [[#Davy--2018|Davy et al., 2018]] ; [[#Belušić%20Vozila--2019|Belušić Vozila et al., 2019]] ). Projections (as cited above) also indicate a decrease in mean wind speed in Northern Europe ( ''medium confidence'' , ''medium agreement'' ) ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Tobin--2018|Tobin et al., 2018]] ; [[#Jung--2019|Jung and Schindler, 2019]] ). Daily and interannual wind variability is projected to increase under RCP8.5 only in Northern Europe ( ''low confidence'' ) ( [[#Moemken--2018|Moemken et al., 2018]] ), which can influence electrical grid management and wind energy production ( ''low confidence'' ). Wind speeds are projected to shift towards more frequent occurrences below thresholds inhibiting wind power production ( [[#Weber--2018|Weber et al., 2018]] ). Wind stagnation events may become more frequent in future climate scenarios in some areas of Europe in the second half of the 21st century ( [[#Horton--2014|Horton et al., 2014]] ; [[#Vautard--2018|Vautard et al., 2018]] ), with potential consequences on air quality ( ''low confidence'' ).

'''Severe wind storm:''' There are large uncertainties in past evolutions of windstorms and extreme winds in Europe. Extreme near-surface winds have been decreasing in the past decades ( [[#Smits--2005|Smits et al., 2005]] ; [[#Tian--2019|Tian et al., 2019]] ; [[#Vautard--2019|Vautard et al., 2019]] ) according to near-surface observations. Significant negative trends of cyclone frequency in spring and positive trends in summer have been found in the Mediterranean basin for the period 1979–2008 ( [[#Lionello--2016|Lionello et al., 2016]] ). By contrast increasing trends have been found in Arctic Ocean areas ( [[#Wickström--2020|Wickström et al., 2020]] ). These trends are not associated with significant trends in extratropical cyclones (Sections 8.3.2.8 and 11.7.2).

There is ''medium confidence'' that serial clustering of storms, inducing cumulated economic losses, in future climate will increase in many areas in Europe under climate projections over Europe ( [[#Karremann--2014|Karremann et al., 2014]] ; [[#Economou--2015|Economou et al., 2015]] ). Strong winds and extratropical storms are projected to have a slightly increasing frequency and amplitude in the future in northern, western and Central Europe ( [[#Outten--2013|Outten and Esau, 2013]] ; [[#Feser--2015|Feser et al., 2015]] ; [[#Forzieri--2016|Forzieri et al., 2016]] ; [[#Mölter--2016|Mölter et al., 2016]] ; [[#Ruosteenoja--2019a|Ruosteenoja et al., 2019a]] ; [[#Vautard--2019|Vautard et al., 2019]] ) under RCP8.5 and SRES A1B by the end of the century ( ''medium confidence'' ), as well as off the European coasts ( [[#Martínez-Alvarado--2018|Martínez-Alvarado et al., 2018]] ) due to the increase of intensity of extratropical storms at a 2°C GWL or above ( [[#Zappa--2013|Zappa et al., 2013]] ) in these areas. The frequency of storms, including Medicanes, is projected to decrease in Mediterranean regions, and their intensities are projected to increase, by the middle of the century and beyond for SRES A1B, A2 and RCP8.5 ( ''medium confidence'' ) ( [[#Nissen--2014|Nissen et al., 2014]] ; [[#Feser--2015|Feser et al., 2015]] ; [[#Forzieri--2016|Forzieri et al., 2016]] ; [[#Mölter--2016|Mölter et al., 2016]] ; [[#Tous--2016|Tous et al., 2016]] ; [[#Romera--2017|Romera et al., 2017]] ; [[#González-Alemán--2019|González-Alemán et al., 2019]] ; [[#MedECC--2020|MedECC, 2020]] ; Chapter 11).

Projections of smaller-scale hazard phenomena such as tornadoes, wind gusts, hail storms and lightning are currently not directly available partly due to the inability of climate models to simulate such phenomena. Observational networks for such phenomena usually lack homogeneity over long periods, hindering clear trends to be detected. For instance, while no robust trends have been identified ( [[#Hermida--2015|Hermida et al., 2015]] ; [[#Mohr--2015a|Mohr et al., 2015a]] ; [[#Burcea--2016|Burcea et al., 2016]] ; [[#Ćurić--2016|Ćurić and Janc, 2016]] ), hail storm environments (favourable atmospheric configurations) have increased in frequency ( ''low confidence'' , ''limited evidence'' ) ( [[#Sanchez--2017|Sanchez et al., 2017]] ). In future climate periods it is ''more likely than not'' that severe convection environments will become more frequent by the end of the century under RCP8.5 ( [[#Mohr--2015b|Mohr et al., 2015b]] ; [[#Púčik--2017|Púčik et al., 2017]] ), and there is ''medium confidence'' that such environments will become more frequent by the 2050s in RCP4.5. There is no evidence for changes in tornado frequencies in Europe in the observations ( [[#Groenemeijer--2014|Groenemeijer and Kühne, 2014]] ) as well as in future climate projections. Insufficient observational record length for lightning numbers does not allow an assessment of trends.

'''There is''' high confidence '''that mean wind speeds will decrease in Mediterranean areas and''' medium confidence '''of such decreases in Northern Europe for global warming levels of 2°C or more and beyond the middle of the century. A slightly increased frequency and amplitude of extratropical cyclones, strong winds and extratropical storms is projected for northern, central and western Europe by the middle of the century and beyond and for global warming levels of 2°C or higher''' ( medium confidence '''). The frequency of Medicanes is projected to decrease''' ( medium confidence '''), but their intensity is projected to increase by mid century and beyond and for global warming levels of 2°C or more. Proxies of intense convection indicate that the large-scale conditions conducive to severe convection will tend to increase in the future climate''' ( low confidence ''').'''

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<span id="snow-and-ice-5"></span>
==== 12.4.5.4 Snow and Ice ====

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'''Snow:''' Widespread and accelerated declines in snow depth ( [[#Fontrodona%20Bach--2018|Fontrodona Bach et al., 2018]] ) and snow water equivalent ( [[#Marty--2017a|Marty et al., 2017a]] ; see Figure 12.9b) have been observed in Europe. In the Pyrenees a slow snow cover decline has been observed starting from the industrial period with a sharp increase since 1955 ( [[#López-Moreno--2020|López-Moreno et al., 2020]] ). Under the RCP2.6, RCP4.5 and RCP8.5 scenarios the reliability elevation for snowmaking will rise by 200–300 m in the Alps and 400–600 m in the Pyrenees by mid-century. End of century projections of natural snow conditions are highly dependent on the scenario, being stationary for the RCP2.6 and continuously decreasing under RCP8.5 to not have any more natural snow conditions at any of the locations in the French Alps and Pyrenees ( [[#Spandre--2019|Spandre et al., 2019]] ). Similarly Norway and Austria will also see a rising of the natural snow elevation with consequences for the ski season ( [[#Scott--2020|Scott et al., 2020]] ; [[#Steiger--2020|Steiger and Scott, 2020]] ). In the Alps, recent simulations project a reduction in snow water equivalent (SWE) at 1500 m above sea level of 80–90% by 2100 under the A1B scenario and a snow season that would start 2-4 weeks later and end 5-10 weeks earlier than the 1992–2012 average ( [[#Schmucki--2015|Schmucki et al., 2015]] ), which is equivalent to a shift in elevation of about 700 m ( [[#Marty--2017b|Marty et al., 2017b]] ). For elevations above 3000 m above sea level, a decline in SWE of at least 10% is expected by the end of the century even when assuming the largest projected precipitation increase. Similar trends are observed for the Pyrenees and Scandinavia ( [[#López-Moreno--2009|López-Moreno et al., 2009]] ; [[#Räisänen--2012|Räisänen and Eklund, 2012]] ). For the northern French Alps above 1500 m and the Ötztal locations in the Austrian alps SWE has a similar decreasing trend altitudinally dependent for RCP2.6, RCP4.5 and RCP8.5 until mid-century and with significant differentiation among them in the second half of the century up to snow-free conditions under RCP8.5 ( [[#Hanzer--2018|Hanzer et al., 2018]] ; [[#Verfaillie--2018|Verfaillie et al., 2018]] ).

'''Glacier:''' Observations and future projections of European glacier mass changes are assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.5.1%20|Section 9.5.1]] grouped in two main regions: Scandinavia and central Europe regions. It is ''virtually certain'' that glaciers will shrink in the future and there is ''medium confidence'' in the timing and mass change rates ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1|Section 9.5.1]] ). Central Europe is one of the regions where glaciers are projected to lose substantial mass even under low-emissions scenarios ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.3|Section 9.5.1.3]] ; [[#MedECC--2020|MedECC, 2020]] ). GlacierMIP projections indicate that glaciers in the central Europe region will lose 63 ± 31%, 80 ± 22% and 93 ± 13% of their 2015 mass by the end of the century under RCP2.6, RCP4.5 and RCP8.5 respectively ( [[#Marzeion--2020|Marzeion et al., 2020]] ). For the same scenarios, glaciers in Scandinavia are projected to lose 55 ± 33%, 66 ± 34% and 82 ± 24% of their 2015 mass. The ''virtually certain'' shrink in glaciers is bolstered by RCM simulations from the EURO-CORDEX ensemble, with the Global Glacier Evolution Model (GloGEM) indicating a substantial reduction of glacier ice volumes in the European Alps by 2050 (47–52% with respect to 2017 for RCP2.6, RCP4.5 and RCP8.5). Under RCP2.6, about two-thirds (63 ± 11%) of the present-day (2017) ice volume is projected to be lost by 2100. In contrast, under the strong warming of RCP8.5, glaciers in the European Alps are projected to largely disappear by 2100 (94 ± 4% volume loss compared to 2017; [[#Zekollari--2019|Zekollari et al., 2019]] ).

'''Permafrost:''' In Europe, permafrost is found in high mountains and in Scandinavia, as well as in Arctic Islands (e.g., Iceland, Novaya Zemlia or Svalbard). In recent decades permafrost has been lost ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ) and accelerated warming at high altitudes and latitudes has favoured an increase of permafrost temperatures of the order of 0.2 ± 0.1°C between 2007 and 2016 ( [[#Romanovsky--2018|Romanovsky et al., 2018]] ; [[#Noetzli--2019|Noetzli et al., 2019]] ). Over the 21st century, permafrost is ''very'' ''likely'' to undergo increasing thaw and degradation under all scenarios ( [[#Hock--2019|Hock et al., 2019]] ) and it is ''virtually certain'' that permafrost extent and volume will decrease with increase of global warming ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ).

Permafrost thawing is projected to affect the frequency and magnitude of high-mountain mass wasting processes ( [[#Stoffel--2012|Stoffel and Huggel, 2012]] ). The temporal frequency of periglacial debris flows in the Alps is ''unlikely'' to change significantly by the mid-21st century but is ''likely'' to decrease during the second part of the century under the A1B scenario, especially in summer ( [[#Stoffel--2011|Stoffel et al., 2011]] , 2014). There is ''medium confidence'' that most of the Northern Europe periglacial processes will disappear by the end of the century, even in the RCP2.6 scenario ( [[#Aalto--2017|Aalto et al., 2017]] ). The magnitude of debris flow events might increase ( [[#Lugon--2010|Lugon and Stoffel, 2010]] ) and the debris-flow season may last longer under the A1B scenario ( [[#Stoffel--2018|Stoffel and Corona, 2018]] ) ''.'' Quantitative data for the European Alps is highly site dependent ( [[#Haeberli--2013|Haeberli, 2013]] ).

'''Heavy snowfall, ice storms and hail:''' There is ''low confidence'' that climate change will affect ice and snow-related episodic hazards ( ''limited evidence'' ). The change in snowpack in the Alps is expected to lead to a possible reduction in overall avalanche activity by end of the century ( ''low confidence'' ), except possibly in winter and at high altitudes ( [[#Castebrunet--2014|Castebrunet et al., 2014]] ).

For ice storms, or freezing rainstorms, there is also ''limited evidence'' due to a limited number of studies. Heavy snowfalls have decreased in frequency in the past decades and this is expected to continue in the future climate ( ''low confidence'' ) ( [[#Beniston--2018|Beniston et al., 2018]] ). Freezing rain is projected to increase in western, central and southern Europe by the end of the century under RCP4.5 and RCP8.5 ( ''low confidence'' ) ( [[#Kämäräinen--2018|Kämäräinen et al., 2018]] ). Rain-on-snow events, are decreasing in northern regions ( [[#Pall--2019|Pall et al., 2019]] ) and by 48% on average in southern Scandinavia ( [[#Poschlod--2020|Poschlod et al., 2020]] ) due to decreases in snowfall.

'''In summary, future snow cover extent and seasonal duration will reduce''' ( high confidence ''') and it is''' virtually certain '''that glaciers will continue to shrink.''' '''A reduction of glacier ice volume is projected in the European Alps and Scandinavia''' ( high confidence '''). There is''' high confidence '''that permafrost will undergo increasing thaw and degradation over the 21st century.''' '''Most of the Northern Europe periglacial will disappear by the end of the century even for a lower emissions scenario''' ( medium confidence ''') and the debris-flow season may last longer in a warming climate''' ( medium confidence ''').'''

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<span id="coastal-and-oceanic-4"></span>
==== 12.4.5.5 Coastal and Oceanic ====

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'''Relative sea level:''' Around Europe, over 1900–2018, a new tide gauge-based reconstruction finds a regional mean RSL change of 1.08 [0.79 to 1.38] mm yr <sup>–1</sup> in the subpolar North Atlantic ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). For the period 1993–2018, the RSLR rates around Europe, based on satellite altimetry, increased to 2.17 [1.66 to 2.66] mm yr <sup>–1</sup> ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5).

Relative sea level rise is ''extremely likely'' to continue in the oceans around Europe. Regional mean RSLR projections for the oceans around Europe range from 0.4–0.5 m under SSP1-2.6 to 0.7–0.8 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), which means that there are locally large deviations from the projected GMSL change ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.3|Section 9.6.3.3]] ). These RSLR projections may however be underestimated due to potential partial representation of land subsidence in their assessment ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ). The signal is strongest for the North Sea and Atlantic coasts, followed by the Black Sea. The Baltic Sea, on the contrary, shows the lowest increase due to land uplift ( [[#Vousdoukas--2017|Vousdoukas et al., 2017]] ). The model agreement is higher for the Mediterranean and in line with previous findings by [[#Gualdi--2013|Gualdi et al. (2013)]] .

'''Coastal flood:''' The present-day 1-in-100-year ETWL is between 0.5 and 1.5 m in the MED basin and 2.5 and 5.0 m in the western Atlantic European coasts, around the UK and along the North Sea coast, and lower at 1.5–2.5 m along the Baltic Sea coast ( [[#Kirezci--2020|Kirezci et al., 2020]] ). Similar values are reported by [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] .

There is ''high confidence'' that extreme total water level (ETWL) magnitude and occurrence frequency will increase throughout Europe (see Figure 12.4p–r), except in the northern Baltic Sea. Across the region, the 5–95th percentile range of the 1-in-100-year ETWL is projected to increase (relative to 1980–2014) by 4–40 cm and by 6–47 cm by 2050 under RCP4.5 and RCP8.5, respectively. By 2100, this range is projected to be 6–88 cm and 25–186 cm under RCP4.5 and RCP8.5, respectively (Figure 12.SM.6; [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Mass addition across the Gibraltar Strait may play a role, although the extent of this contribution is currently unclear ( [[#Lionello--2017|Lionello et al., 2017]] ). Furthermore, under RCP4.5, the present day 1-in-100-year ETWL is projected to have median return periods of between 1-in-5 and 1-in-20 years by 2050 and occur at least once per year by 2100 in the Mediterranean and Black Sea, while in the rest of Europe it is mostly projected to have median return periods of between 1-in-20-years and 1-in-50-years by 2050 and between 1-in-5-years and 1-in-20-years by 2100 ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ). Under RCP8.5, occurrence of the present day 1-in-100-year ETWL is projected to increase further to median return periods of 1-in-1-year to 1-in-5-years by 2050 and occur more than once per year by 2100 in the Mediterranean and Black Sea, while in the rest of Europe it is mostly projected to have median return periods between 1-in-5-years and more than once per year by 2100.

'''Coastal erosion:''' Satellite-derived shoreline change estimates over 1984–2015 indicate shoreline retreat rates of around 0.5 m yr <sup>–1</sup> along the sandy coasts of WCE and MED, around 4 m yr <sup>–1</sup> in EEU (Caspian Sea region) and more or less stable shorelines in NEU ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). [[#Mentaschi--2018|Mentaschi et al. (2018)]] report a coastal area loss of 270 km <sup>2</sup> over a 30-year period (1984–2015) along the Atlantic coastlines of Europe.

Projections indicate that sandy coasts throughout the continent (except those bordering the northern Baltic Sea) will experience shoreline retreat through the 21st century ( ''high confidence'' ). Median shoreline change projections (CMIP5) relative to 2010, show that, by mid-century, shorelines will retreat by between 25 m and 60 m along sandy coasts in WCE and MED under both RCP4.5 and RCP8.5 ( [[#Athanasiou--2020|Athanasiou et al., 2020]] ; [[#Vousdoukas--2020b|Vousdoukas et al., 2020b]] ). Mid-century median projections for NEU indicate virtually no shoreline retreat under RCP4.5, but a retreat of around 40 m under RCP8.5. By 2100, median shoreline retreats of around 50 m are projected in NEU and MED under RCP4.5, increasing to around 80 m under RCP8.5. End-century median projections for WCE are far higher at 100 m (RCP4.5) and 160 m (RCP8.5). The total length of sandy coasts in Europe that is projected to retreat by more than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,000 km and 18,000 km respectively, an increase of approximately 54% ( [[#Vousdoukas--2020b|Vousdoukas et al., 2020b]] ).

Local assessments of both long term shoreline retreat and episodic coastal erosion are given by [[#Li--2014b|Li et al. (2014b)]] , [[#Toimil--2017|Toimil et al. (2017)]] , [[#Bon%20de%20Sousa--2018|Bon de Sousa et al. (2018)]] and [[#Le%20Cozannet--2019|Le Cozannet et al. (2019)]] . In terms of episodic coastal erosion, 31–88% of all Aegean beaches are projected to experience complete erosion, with a RCP4.5 sea level rise of 0.5 m and a surge of 0.6 m, but with substantial uncertainty ( [[#Monioudi--2017|Monioudi et al., 2017]] ).

'''Marine heatwave:''' The mean SST of the Atlantic Ocean and the Mediterranean has increased between 0.25°C and 1°C since 1982–1998. This mean ocean surface warming is correlated to longer and more frequent marine heatwaves in the region ( [[#Oliver--2018|Oliver et al., 2018]] ). Over the period 1982–2016, the coastlines of Europe experienced on average more than 2.0 MHW yr <sup>–1</sup> , with the eastern Mediterranean and Scandinavia experiencing 2.5–3 MHWs yr <sup>–1</sup> . The average duration was between 10 and 15 days. Changes over the 20th century, derived from MHW proxies, show an increase in frequency of between 1.0 and 2.0 MHWs per decade in Europe, although the trend is not statistically significant; with an increase in intensity per event in the North Atlantic and the Mediterranean, and a decrease in the Atlantic off the British Isles. The total number of MHW days per decade has increased in the Mediterranean ( [[#Oliver--2018|Oliver et al., 2018]] ).

Mean SST is projected to increase by 1°C–3°C around Europe by 2100, with a hotspot of around 4°C–5°C along the Arctic coastline of Europe under RCP4.5 and RCP8.5 scenarios (see Interactive Atlas), leading to a continued increase in MHW frequency, magnitude and duration ( [[#Oliver--2018|Oliver et al., 2018]] ; [[#MedECC--2020|MedECC, 2020]] ). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around Europe by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1). [[#Darmaraki--2019|Darmaraki et al. (2019)]] project that, by the end of the 21st century and under RCP8.5, there will be one MHW occurring every year in the northern Mediterranean sea, and that these MHWs would be three months longer, four times more intense, and 42 times more severe than present day MHWs in the region. [[#Frölicher--2018|Frölicher et al. (2018)]] show that, in Europe, the change in the probability for the number of days of MHWs exceeding the 99th percentile of the pre-industrial level is 4%, 15% and 30% for global warming levels of 1°C, 2°C and 3.5°C, respectively. MHW increase in the Mediterranean will impact on many species that live in shallow waters and have reduced motility, with consequences for related economic activities ( [[#Galli--2017|Galli et al., 2017]] ).

'''In general, there is''' high confidence '''that most coastal/ocean-related climatic impact-drivers in Europe will increase over the 21st century for all scenarios and time horizons. Relative sea level rise is''' extremely likely '''to continue around Europe (except in the northern Baltic Sea), contributing to increased coastal flooding in low-lying areas and shoreline retreat along most sandy coasts''' ( high confidence '''). Marine heatwaves are also expected to increase around the region over the 21st century''' ( high confidence ''').'''

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<span id="other"></span>
==== 12.4.5.6 Other ====

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'''Compound events:''' One typical compound event that is observed in the European area is compound flooding due to the combination of extreme sea level events and extreme precipitation events associated with high levels of runoff. In the present climate, the Mediterranean coasts are exposed to a higher probability of this type of compound flooding event ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ). Under RCP8.5, the probability of these events is projected to increase along northern European coasts (west coast of UK, northern France, the east and south coast of the North Sea, and the eastern half of the Black Sea), with the percentage of coastline now experiencing such events at least once every 6 years increasing by between 3% and 11% by the end of the 21st century ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ).

Under RCP8.5, regions in Russia, France and Germany are projected to experience an increase in the frequency and the length of wet and cold compound events, while Spain and Bulgaria are projected to stay longer in the hot and dry state by mid-century ( [[#Sedlmeier--2016|Sedlmeier et al., 2016]] ).

Compound events of dry and hot summers have increased in Europe. [[#Manning--2019|Manning et al. (2019)]] found that the probability of such compound events has increased across much of Europe between 1950–1979 and 1984–2013, notably in southern, eastern and western Europe. Compound hot and dry extremes are projected to increase in Europe by mid-century for the SRES A1B and RCP8.5 with a particularly strong signal projected in southern and eastern Germany and the Czech Republic ( [[#Sedlmeier--2016|Sedlmeier et al., 2016]] ).

The assessed direction of change in climatic impact-drivers for Europe and associated confidence levels are illustrated in Table 12.7, together with emergence time information ( [[#12.5.2|Section 12.5.2]] ). No assessable literature could be found for sand and dust storms, although these phenomena may be relevant in parts of the region.

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'''Table 12.7''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in Europe, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:fbc83e7f7056db2332db339e39a0ab01 IPCC_AR6_WGI_Chapter12_Table_12_7.jpg]]

<div id="12.4.6" class="h2-container"></div>

<span id="north-america"></span>
=== 12.4.6 North America ===

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Major changes in North American CIDs were assessed in WGII AR5 Chapter 26 ( [[#Romero-Lankao--2014|Romero-Lankao et al., 2014]] ), with additional detail on connections to warming levels provided by SR1.5 ( [[#IPCC--2018|IPCC, 2018]] ), and climate information related to land degradation and land-use suitability in SRCCL ( [[#IPCC--2019c|IPCC, 2019c]] ), and ocean and coastal hazards in the SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ). Recent national assessments in the USA (USGCRP, 2017, 2018) and Canada (Bush and Lemmen, 2019) enhance the local perspective and assessments across a number of CIDs and their sectoral connections. For the purpose of this assessment, North America is sub-divided into six sub-regions, as defined in Chapter 1: North Central America (NCA), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), North-Eastern North America (NEN), and North-Western North America (NWN). Greenland and Arctic regions of North-Eastern and North-Western North America are further assessed in [[#12.4.9|Section 12.4.9]] , and the Caribbean and Hawaiian Islands are assessed in [[#12.4.7|Section 12.4.7]] .

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<span id="heat-and-cold-6"></span>
==== 12.4.6.1 Heat and Cold ====

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'''Mean air temperature:''' Atlas.9.2 assessed ''very likely'' mean warming in observations across North America, with highest increases at higher latitudes and in the winter season. Atlas.9.4 assessed ''very likely'' mean warming in future decades in all North American regions, with CMIP and CORDEX models showing median increases exceeding 2°C in much of the continental interior under RCP8.5 (2041–2060 compared to 1995–2014) and higher increases towards the north. Mean temperatures at the end of century show strong scenario dependence, rising between 1°C and 2.5°C in RCP2.6 and about 4°C to 8°C in RCP8.5 (Figures Atlas.12, Atlas.26 and Atlas.27). Warming also raises stream temperatures across the continent ( [[#DOE--2015|DOE, 2015]] ; [[#Trtanj--2016|Trtanj et al., 2016]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ; [[#Chapra--2017|Chapra et al., 2017]] ), and [[#Hill--2014|Hill et al. (2014)]] projected US stream warming by 0.6°C (±0.3°C) per 1°C increase in local air temperature. Mean warming drives shifts in the seasonal timing of temperature thresholds, including increasing growing degree days ( [[#Mu--2017|Mu et al., 2017]] ), longer growing seasons ( [[#Gowda--2018|Gowda et al., 2018]] ; G. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Vincent--2018|L.A. Vincent et al., 2018]] ), reduced chill hours ( [[#Luedeling--2012|Luedeling, 2012]] ; [[#Lee--2015|Lee and Sumner, 2015]] ; [[#Xie--2015|Xie et al., 2015]] ; [[#Parker--2019|Parker and Abatzoglou, 2019]] ), and longer pollen and allergy seasons ( [[#Fann--2016|Fann et al., 2016]] ; [[#Anenberg--2017|Anenberg et al., 2017]] ; [[#Sapkota--2019|Sapkota et al., 2019]] ). Warmer temperatures reduce heating degree days and increase cooling degree days ( '''high confidence''' ) ( [[#Bartos--2016|Bartos et al., 2016]] ; [[#US%20EPA--2016|US EPA, 2016]] ; [[#Craig--2018|Craig et al., 2018]] ; X. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] )

'''Extreme heat:''' Section 11.9 assessed that extreme temperatures in North America have increased in recent decades ( ''medium evidence'' , ''medium agreement'' ) other than in Central and Eastern North America ( ''low confidence'' ), and extreme heat in all regions is projected to increase with climate change ( ''high confidence'' ). Observed trends in extreme heat are more positive for heat extreme indices that include temperature and humidity given historical expansion of irrigation and intensification of agriculture ( [[#Mueller--2017|Mueller et al., 2017]] ; [[#Grotjahn--2018|Grotjahn and Huynh, 2018]] ; [[#Thiery--2020|Thiery et al., 2020]] ). Several studies noted statistically significant increases in intensity and particularly the frequency, duration, and seasonal length of the physiologically hazardous extreme heat conditions across North America ( [[#Grineski--2015|Grineski et al., 2015]] ; [[#Habeeb--2015|Habeeb et al., 2015]] ; [[#Martínez-Austria--2016|Martínez-Austria et al., 2016]] ; [[#Petitti--2016|Petitti et al., 2016]] ; [[#Vincent--2018|L.A. Vincent et al., 2018]] ; [[#García-Cueto--2019|García-Cueto et al., 2019]] ).

Figure 12.4b shows over a month of additional days at CMIP6 SSP5-8.5 mid-century where temperatures exceed 35°C across much of southern Mexico and regions near the US–Mexico border, and these extreme temperatures occur at least once per year up to southern Canada. [[#Coppola--2021b|Coppola et al. (2021b)]] found similar patterns in CMIP5 and CORDEX-Core. Using locally tailored heat thresholds, [[#Maxwell--2018|Maxwell et al. (2018)]] found that ‘very hot’ days in five US cities will occur a median of three to five times more often by 2036–2065 under RCP8.5 (2 to 3.5 times more often in RCP4.5), [[#Oleson--2018|Oleson et al. (2018)]] projected that annual heatwave duration will exceed one month in Houston in RCP8.5 2061–2080, and [[#Anderson--2018|Anderson et al. (2018)]] projected 7 to 12 times more exceedances of thresholds associated with high-mortality by 2061–2080 under RCP8.5 (6 to 7 times more exceedances in RCP4.5). [[#Schwingshackl--2021|Schwingshackl et al. (2021)]] found that Central and Eastern North America are among the regions with the strongest trend in heat stress indicators. Studies also project increasingly surpassed heat extreme thresholds for North American crops ( [[#Gourdji--2013|Gourdji et al., 2013]] ), airplane weight restrictions ( [[#Coffel--2017|Coffel et al., 2017]] ), and peak load energy systems ( [[#Auffhammer--2017|Auffhammer et al., 2017]] ).

The number of days crossing dangerous heat thresholds such as HI > 41°C will be very sensitive to the mitigation scenario at the end of the century ( [[#Wuebbles--2014|Wuebbles et al., 2014]] ; [[#Zhao--2015|Zhao et al., 2015]] ; [[#Dahl--2019|Dahl et al., 2019]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). At the end of the century under SSP5-8.5, a CMIP6 median increase of exceedances of 75–150 days per year is projected over much of North Central America, Central North America and the south-western USA while this increase is projected to remain limited below 60 days under SSP1-2.6 (Figure 12.4d,f and Figure 12.SM.2). [[#Steinberg--2018|Steinberg et al. (2018)]] also projected more frequent and longer ‘heat-health’ events in California extending into October.

'''Cold spell:''' [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assessed ''high confidence'' in decreasing frequency and intensity of cold spells over North America ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). The number of days with extreme wind chill hours (humidex <–30) decreased at 76% of examined Canadian stations from 1953 to 2012 ( [[#Mekis--2015|Mekis et al., 2015]] ) and cold days and coldest nights decreased in Mexico from 1980 to 2010 ( [[#García-Cueto--2019|García-Cueto et al., 2019]] ).

Cold spells are projected to decrease over North America under climate change, with the largest decreases most common in the winter season ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). Minimum winter temperatures are projected to rise faster than the mean winter temperature ( [[#Underwood--2017|Underwood et al., 2017]] ) and alter cold-hardiness zones used to determine agricultural suitability ( [[#Parker--2016|Parker and Abatzoglou, 2016]] ). [[#Wuebbles--2014|Wuebbles et al. (2014)]] projections for RCP8.5 end-of-century show that the four-day cold spell that happens on average once every five years is projected to warm by more than 10°C and CMIP5 models do not project current 1-in-20-year annual minimum temperature extremes to recur over much of the continent. Multiple studies have shown that Arctic warming can alter large-scale variability and change the frequency and duration of mid-latitude cold air outbreaks, potentially leading to increasing cold hazards in some regions ( ''low agreement'' ) ( [[#Barcikowska--2019|Barcikowska et al., 2019]] ; [[#Cohen--2020|Cohen et al., 2020]] ; [[#Zhou--2021|Zhou et al., 2021]] ).

'''Frost:''' An expansion of the frost-free season is underway and projections for North America indicate a continuation of this trend in the future ( ''high confidence'' ). Significant decreases in frost days, consecutive frost days, and ice days were identified in 1948–2016 station observations across Canada, along with a resulting lengthening of the frost-free season by more than a month in many regions ( [[#Vincent--2018|L.A. Vincent et al., 2018]] ). Frost days also declined in nearly all Mexican cities from 1980 to 2010 ( [[#García-Cueto--2019|García-Cueto et al., 2019]] ), and a 1917–2016 decline of about three weeks in frigid winter conditions challenges ecosystems in the north-east USA and south-east Canada ( [[#Contosta--2020|Contosta et al., 2020]] ). Studies connect projections of a longer frost-free season in North America to a longer outdoor construction season, shifts in frost variance to orchard damages, and lower weight tolerances for runways ( [[#Daniel--2018|Daniel et al., 2018]] ; [[#DeGaetano--2018|DeGaetano, 2018]] ; [[#Jacobs--2018|Jacobs et al., 2018]] ). Frosts are projected to persist as an episodic hazard in many regions given natural variability and cold air outbreaks even as mean temperature rises ( ''high confidence'' ).

'''Climate change is''' virtually certain '''to shift the balance of temperature towards warming trends and away from cold extremes, with increases in the magnitude, frequency, duration and seasonal and spatial extent of heat extremes driving impacts across North America. The frequency of dangerous heat threshold exceedance (such as HI > 41°C) is particularly sensitive to scenario pathway, with 7''' '''5–1''' '''50 days more under SSP5-8.5 but less than 60 days more under SSP1-2.6 by the end of the century in NCA, CNA and the south-western USA.'''

<div id="12.4.6.2" class="h3-container"></div>

<span id="wet-and-dry-6"></span>
==== 12.4.6.2 Wet and Dry ====

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'''Mean precipitation:''' Atlas.9.2 found that trends in annual precipitation over 1960–2015 are generally non-significant, though there are consistent positive trends over parts of ENA and CNA, together with significant decreases in precipitation in parts of the south-western USA and north-western Mexico.

Atlas.9.4 assessed ''very high confidence'' in increases in mean precipitation over most of northern and Eastern North America, with ''medium confidence'' of decrease over Northern Central America and ''low confidence'' elsewhere (see Figure Atlas.26, and Cross-Chapter Box Atlas.1, Figure 1). Changes are most dramatic in the spring and winter, when wet conditions are projected to extend from the northern portions of the continent as far south as the central Great Plains, while Mexico becomes drier; in contrast, summer changes are uncertain across most of the continent other than wetter conditions in northern Canada ( [[#Easterling--2017|Easterling et al., 2017]] ; [[#Bukovsky--2020|Bukovsky and Mearns, 2020]] ; [[#Almazroui--2021|Almazroui et al., 2021]] ; [[#Teichmann--2021|Teichmann et al., 2021]] ).

'''River flood:''' There is ''limited evidence'' and ''low agreement'' on observed climate change influences for river floods in North America ( [[IPCC:Wg1:Chapter:Chapter-11#11.5|Section 11.5]] ). Trends in streamflow indices are mixed and difficult to separate from river engineering influences, with large changes but little spatial coherence across the USA, making it difficult to identify trends with confidence ( [[#Peterson--2013|Peterson et al., 2013]] ; [[#Mallakpour--2015|Mallakpour and Villarini, 2015]] ; [[#Archfield--2016|Archfield et al., 2016]] ; [[#Wehner--2017|Wehner et al., 2017]] ; [[#Villarini--2018|Villarini and Slater, 2018]] ; [[#Hodgkins--2019|Hodgkins et al., 2019]] ; [[#Neri--2019|Neri et al., 2019]] ). There is ''high confidence'' in historical shifts in the timing of peak streamflow towards higher winter and earlier spring flows in snowmelt-driven basins in Canada ( [[#Burn--2016|Burn and Whitfield, 2016]] ; [[#Bonsal--2019|Bonsal et al., 2019]] ) and the USA ( [[#Dudley--2017|Dudley et al., 2017]] ; [[#Wehner--2017|Wehner et al., 2017]] , [[#Neri--2020|Neri et al., 2020]] ). Some rivers show ice-jam floods occurring a week earlier, but changes are mixed, given localized positive and negative changes across the continent ( [[#Rokaya--2018|Rokaya et al., 2018]] ). There is ''medium confidence'' that climate change will increase river floods over the USA and Canada but ''low confidence'' for changes in Mexico. [[#Wobus--2017a|Wobus et al. (2017a)]] used a regional hydrologic model for 57,000 streams to project more than a doubling in the frequency of current 1-in-100-year flow events in many portions of the USA for RCP8.5 2050 with additional contributions from earlier snowmelt. CMIP6 projections for SSP5-8.5 2065–2099 show strongest peak USA runoff increases in the east ( [[#Villarini--2020|Villarini and Zhang, 2020]] ); however, several studies applying global hydrological models disagree with regional streamflow projections, indicating a decrease in the magnitude or frequency of floods over a large portion of North America (e.g., [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; see Figure 12.10a,c).

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[[File:2789e0f641f8b8383f22e976ab34b8be IPCC_AR6_WGI_Figure_12_10.png]]

'''Figure 12.10''' '''|''' '''Projected changes in selected climatic impact-driver indices for North America. (a)''' Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ), and '''(b)''' median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from CORDEX-North America models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction) of change. '''(c)''' Bar plots for Q100 (m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. '''(d)''' As for (c) but showing absolute values for number of days with SWE > 100 mm, masked to grid cells with at least 14 such days in the recent past. See Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. A Caribbean (CAR) Q100 bar plot is included here but assessed in the Small Islands section ( [[#12.4.7|Section 12.4.7]] ). Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

'''Heavy precipitation and pluvial flood:''' Section 11.4 assessed ''high confidence'' in observed increases in extreme precipitation events (including hourly totals) in Central and Eastern North America with ''low confidence'' in broad trends elsewhere in the continent despite observational increases in some portions of each region ( [[#Vincent--2018|L.A. Vincent et al., 2018]] ; [[#García-Cueto--2019|García-Cueto et al., 2019]] ; X. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ).

( [[IPCC:Wg1:Chapter:Chapter-11#11.4|Section 11.4]] found that high precipitation is projected to increase across North America ( ''high confidence'' ) except for portions of Western North America where projections are mixed ( ''medium confidence'' of increase). [[#Maxwell--2018|Maxwell et al. (2018)]] identified regional ‘heavy precipitation day’ thresholds for five cities across the USA and projected that a tripling (or more) of these events is possible by RCP8.5 mid-century. Projections indicate changes to intensity-duration-frequency (IDF) curves typically used for construction design and automobile hazards, as well as increases in the 10-year recurrence level of 24-hour rainfall intensities that challenge storm water drainage systems ( [[#Hambly--2013|Hambly et al., 2013]] ; [[#Cheng--2015|Cheng and AghaKouchak, 2015]] ; J.E. [[#Neumann--2015|]] [[#Neumann--2015|Neumann et al., 2015]] ; [[#Prein--2017b|Prein et al., 2017b]] ; [[#Hettiarachchi--2018|Hettiarachchi et al., 2018]] ; [[#Ragno--2018|Ragno et al., 2018]] ). Precise levels of regional IDF characteristics may still depend substantially on the method and resolution of downscaling applied ( [[#DeGaetano--2017|DeGaetano and Castellano, 2017]] ; L.M. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ).

'''Landslide:''' There is growing yet ''limited evidence'' for unique climate-driven changes in landslide and rockfall hazards in North America, even as theory suggests decreases in slope and rockface stability due to more intense rainfall, rain-on-snow events, mean warming, permafrost thaw, glacier retreat, and coastal erosion ( [[#Cloutier--2017|Cloutier et al., 2017]] ; [[#Coe--2018|Coe et al., 2018]] ; [[#Handwerger--2019|Handwerger et al., 2019]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Patton--2019|Patton et al., 2019]] ) although dry trends can decelerate mass movements ( [[#Bennett--2016|Bennett et al., 2016]] ). Landslide frequency has increased in British Columbia (Canada; [[#Geertsema--2006|Geertsema et al., 2006]] ) and is expected to increase in North-Western North America given the combination of these factors ( ''medium confidence'' ) ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ). [[#Cloutier--2017|Cloutier et al. (2017)]] projected an increase in landslides in western Canada due to wetter overall conditions and reduced return period for extreme rainfall. [[#Robinson--2017|Robinson et al. (2017)]] used scenarios based upon projection of 50-year recurrence of 7-day precipitation periods to highlight the potential for increased landslide hazards near Seattle (USA). Broad projections for the USA are more uncertain given increases in evapotranspiration that will counteract precipitation changes over much of the country ( [[#Coe--2016|Coe, 2016]] ) ''.''

'''Aridity:''' [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] showed that aridity in North America generally moves opposite to mean precipitation change with an added evaporative demand from warmer temperatures ( ''high confidence'' in aridity increase for Northern Central America; ''medium confidence'' for an increase in Central North America; ''high confidence'' for a decrease in North-Eastern North America; ''medium confidence'' decreases in Eastern and North-Western North America; see also [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ). Projected soil moisture declines (Figure 12.4j–l) are most widespread across North America during the summer, with the largest declines in Mexico and the southern Great Plains but also extending into Canada ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.1.6|Section 8.4.1.6]] ; [[#Swain--2015|Swain and Hayhoe, 2015]] ; [[#Easterling--2017|Easterling et al., 2017]] ; [[#Bonsal--2019|Bonsal et al., 2019]] ; J. [[#Lu--2019|]] [[#Lu--2019|Lu et al., 2019]] ). [[#Yoon--2018|Yoon et al. (2018)]] found net reduction in southern Great Plains groundwater storage in RCP8.5 mid-century projections despite increases in mean precipitation and both wet and dry extremes. Soil moisture drying could reach unprecedented levels by the CMIP6 RCP8.5 end-of-century, even when evaluating deeper soil columns relevant for crop rooting depth (B.I. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ). Projected changes in the aridity index portray a shift the geographic range of temperate drylands northward and eastward in Central and Western North America ( [[#Schlaepfer--2017|Schlaepfer et al., 2017]] ; [[#Seager--2018|Seager et al., 2018]] ) which also diminishes aquifer recharge rates in the southern Great Plains and in some western regions where snowpack is reduced ( [[#Meixner--2016|Meixner et al., 2016]] ).

'''Hydrological drought:''' Section 11.9 asssessed ''low confidence'' of significant observational trends and projected future changes in the characteristics of episodic hydrological drought in North America given ''limited evidence'' and ''low agreement'' in modeled changes. [[#Zhao--2020|]] [[#Zhao--2020|C. Zhao et al. (2020)]] found that increases in hydrological drought frequency (particularly the 100 yr drought) were far more prevalent than for meteorological drought across 5797 watersheds in the USA and Canada, indicating a strong influence of evaporative demand. Reductions in the overall supply of meltwater from a declining snowpack also increase the potential for intermittent hydrological droughts in the western USA ( [[#Mote--2018|Mote et al., 2018]] ; [[#Livneh--2020|Livneh and Badger, 2020]] ).

'''Agricultural and ecological drought:''' Section 11.9 assessed ''medium confidence'' for an increase in agricultural and ecological drought in Western North America but otherwise found ''limited evidence'' for broadly observed changes in North American agricultural and ecological drought, even as increasing evaporative demand intensified vegetation stress and soil moisture deficits in recent events (Sections 11.6, 11.9). [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] asssessed ''medium confidence'' for more intense agricultural and ecological drought conditions over North Central America, Western North America and Central North America in a 2°C global warming level (about mid-century), with ''medium confidence'' extending to Eastern North America and ''high confidence'' for Northern Central America and Central North America under a 4°C global warming level associated with higher emissions scenarios past 2050. Figure 12.4g–i shows that the frequency of meteorological droughts (which often initiate hydrological, agricultural and ecological droughts) is largely projected to increase in North American areas where total precipitation decreases (and vice versa; see [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] and [[#Coppola--2021b|Coppola et al., 2021b]] ), and higher evaporative demand will extend the regions where more intense ecological and agricultural droughts develop when meteorological droughts occur ( [[#Wehner--2017|Wehner et al., 2017]] ; B.I. [[#Cook--2019|Cook et al., 2019]] , 2020). Studies utilizing a variety of drought indices and soil moisture projections consistently project increased drought extending from Mexico into the southern Canadian Plains during the summer ( [[#Swain--2015|Swain and Hayhoe, 2015]] ; [[#Ahmadalipour--2017|Ahmadalipour et al., 2017]] ; [[#Feng--2017|Feng et al., 2017]] ; [[#Bonsal--2019|Bonsal et al., 2019]] ; [[#Tam--2019|Tam et al., 2019]] ; B.I. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ).

'''Fire weather:''' Climatic conditions conducive to wildfire have increased in Mexico, Western and North-Western North America, primarily due to warming ( ''high confidence'' ). [[#Abatzoglou--2016|Abatzoglou and Williams (2016)]] found climate change led to higher values for eight fuel aridity indices over the western USA in recent decades, with 2000–2015 changes exposing 75% more forested area to high fuel aridity and adding nine more high fire-potential days each year, similar to 1979–2013 western USA and Mexico fire season expansion reported in [[#Jolly--2015|Jolly et al. (2015)]] . Increases in lightning-initiated fires have been distinguished from trends in man-made fire in western Canada and the USA ( [[#Balch--2017|Balch et al., 2017]] ; [[#Hanes--2019|Hanes et al., 2019]] ). [[#Jain--2017|Jain et al. (2017)]] identified a 1979–2015 expansion in fire weather season in eastern Canada and the south-western USA (with a smaller reduction in the northern Mountain West) along with regional shifts in the 99th percentile Canadian Fire Weather Index (FWI) and potential fire-spread days. [[#Girardin--2009|Girardin and Wotton (2009)]] noted that 1951–2002 trends in the Monthly Drought Code fire index in eastern Canada could hardly be distinguished from decadal variability.

Climate change drives future increases in North American fire weather, particularly in the south-west ( ''high confidence'' ), although further studies on shifts in exposure and vulnerability are needed to understand overall fire risks (see WGII Chapter 14). A significant increase of FWI is apparent before 2050 under RCP8.5 in much of North America, including the frequency of 95th-percentile FWI days, peak seasonal FWI average, fire weather season length, and maximum fire weather index ( [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ), and fire season across North America expands dramatically beyond 2°C global warming levels (Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Jain--2020|Jain et al., 2020]] ). X. [[#Wang--2017b|Wang et al. (2017b)]] simulated fire-spread days across Canada and found increases across most of the areas studied by 2071–2100, with median changes of –20 to +140% (RCP2.6), –20 to +250% (RCP4.5) and 40 to 360% (RCP8.5) compared to 1976–2015. [[#Prestemon--2016|Prestemon et al. (2016)]] found more conducive conditions for lightning-ignited fires in the south-eastern USA by mid-century, while warming conditions in Alaska increasingly push July temperatures above 13.4°C, a threshold for fire danger across Alaska’s tundra and boreal forest ( [[#Partain--2016|Partain et al., 2016]] ; [[#Young--2017|Young et al., 2017]] ). Longer and more intense fire seasons would also raise particulate matter and black carbon concentrations in the western USA, reducing visibility at many National Parks ( [[#Yue--2013|Yue et al., 2013]] ; [[#Val%20Martin--2015|Val Martin et al., 2015]] ).

'''Changes in North American wet and dry climatic impact-drivers are largely organized by the ‘north-east (more wet) to south-west (more dry)’ pattern of mean precipitation change, although heavy precipitation increases are widespread and increasing evaporative demand expands aridity, agricultural and ecological drought, and fire weather (particularly in summer)''' ( high confidence ''').'''

<div id="12.4.6.3" class="h3-container"></div>

<span id="wind-6"></span>
==== 12.4.6.3 Wind ====

<div id="h3-63-siblings" class="h3-siblings"></div>

'''Mean wind speed:''' Mean wind speeds have declined in North America – as in other Northern Hemisphere areas – over the past four decades ( ''medium confidence'' ) (AR5 WGI) with a reversal in the last decade ( ''low confidence'' ) not fully consistent across studies ( [[#Tian--2019|Tian et al., 2019]] ; [[#Zeng--2019|Zeng et al., 2019]] ; Z. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ). [[#Tian--2019|Tian et al. (2019)]] found a corresponding reduction in the wind power potential across the eastern parts of North America.

Mean wind speeds are expected to decline over much of North America (Figure 12.4m–o), but the only broad signal of consistent change across model types is a reduction in wind speed in Western North America ( ''high confidence'' ). These declines reduce wind power endowment by 2050 and as early as the 2020–2040 near-term period in the USA Mountain West, while there is disagreement between global- and regional-model change projections in the upper and lower Great Plains, Ohio River Valley, Mexico and eastern Canada ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Jung--2019|Jung and Schindler, 2019]] ; [[#Chen--2020|Chen, 2020]] ).

'''Severe wind storm:''' There is ''limited evidence'' and ''low agreement'' in observed changes in North American CID indices associated with extratropical cyclones ( [[IPCC:Wg1:Chapter:Chapter-11#11.7|Section 11.7]] ), severe thunderstorms, severe wind bursts ( ''derechos'' ), tornadoes, or lightning strikes ( [[#Vose--2014|Vose et al., 2014]] ; [[#Easterling--2017|Easterling et al., 2017]] ; [[#Kossin--2017|Kossin et al., 2017]] ). Observational studies have indicated a reduction in the number of tornado days in the USA, but increases in outbreaks with 30 or more tornadoes in one day ( [[#Brooks--2014|Brooks et al., 2014]] ), the density of tornado clusters ( [[#Elsner--2015|Elsner et al., 2015]] ), and overall tornado power ( [[#Elsner--2019|Elsner et al., 2019]] ).

There is ''medium confidence'' of a general decrease in the number of extratropical cyclones producing high wind speeds in North America, except over northernmost parts, for a global warming level of 2°C or by the end of the century under RCP4.5 and RCP8.5 ( [[#Kumar--2015|Kumar et al., 2015]] ; [[#Jeong--2018a|Jeong and Sushama, 2018a]] ; [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|C. Li et al., 2018]] ). GCMs cannot directly resolve tornadoes and severe thunderstorms, however projections of favourable environments for severe storms (based on convective available potential energy and wind shear) indicate ''medium confidence'' for more severe storms and a longer convective storm season in the USA, weaker increases extending north and east ( [[#Seeley--2015|Seeley and Romps, 2015]] ; [[#Glazer--2021|Glazer et al., 2021]] ), and a corresponding increase in autumn and winter tornadic storms (H.E. [[#Brooks--2013|]] [[#Brooks--2013|Brooks, 2013]] ; [[#Diffenbaugh--2013|Diffenbaugh et al., 2013]] ; [[#Brooks--2014|Brooks et al., 2014]] ; see also [[IPCC:Wg1:Chapter:Chapter-11#11.7.2|Section 11.7.2]] ). [[#Prein--2017a|Prein et al. (2017a)]] used a convection-permitting model to project a tripling of mesoscale convective systems over the USA for end-of-century RCP8.5.

'''Tropical cyclone''' : [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] identified recent reductions in tropical cyclone translation speed and higher tropical cyclone rainfall totals over the North Atlantic, as well as substantial natural variability. Projections indicate ''low confidence'' in change in North Atlantic tropical cyclone numbers, but ''medium confidence'' in Mexico and the US Gulf and Atlantic coasts for more intense storms with higher wind, precipitation, and storm surge totals when they do occur ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ; [[#Diro--2014|Diro et al., 2014]] ; [[#DOE--2015|DOE, 2015]] ; [[#Walsh--2016a|Walsh et al., 2016a]] ; [[#Kossin--2017|Kossin et al., 2017]] ; [[#Marsooli--2019|Marsooli et al., 2019]] ; [[#Ting--2019|Ting et al., 2019]] ; [[#Knutson--2020|Knutson et al., 2020]] ). A more rapid intensification of tropical cyclone winds and destructive power also heightens the tropical cyclone hazard ( [[#Bhatia--2019|Bhatia et al., 2019]] ). Greenhouse gas forcing is projected to shift tropical cyclones poleward ( [[#Kossin--2016|Kossin et al., 2016]] ), while also holding the potential for higher precipitation totals ( [[#Risser--2017|Risser and Wehner, 2017]] ; [[#Knutson--2020|Knutson et al., 2020]] ) particularly given evidence that storms increasingly stall near North American coastlines ( [[#Hall--2019|Hall and Kossin, 2019]] ).

'''Sand and dust storm:''' Land-use change has increased dust emissions in the western USA in the past 200 years ( [[#Neff--2008|Neff et al., 2008]] ). However, there is ''medium confidence'' for observed increases in Western North American sand and dust storm activity since 1980. In their study of Valley Fever spread, [[#Tong--2017|Tong et al. (2017)]] identified a rapid intensification of dust storm activity using PM <sub>10</sub> and PM <sub>2.5</sub> observations from 1980–2011 across 29 monitoring sites in the south-western USA, similar to contiguous USA observations by [[#Brahney--2013|Brahney et al. (2013)]] . [[#Hand--2016|Hand et al. (2016)]] attributed the earlier onset of spring dusts in the south-west in large part to the Pacific Decadal Oscillation, however. The increasing trend in dust since the 1990s in the south-western USA can be explained by precipitation deficit and surface bareness ( [[#Pu--2018|Pu and Ginoux, 2018]] ). Projections of future sand and dust storms over North America are based on aridity as a primary proxy for conducive conditions which lends ''medium confidence'' of an increase over Mexico and the south-western USA. [[#Pu--2017|Pu and Ginoux (2017)]] project about five more dusty days in spring and summer in the southern Great Plains under RCP8.5 at the end of the century, while dusty days decrease in northern regions where mean precipitation tends towards wetter conditions.

'''Tropical cyclones, severe wind and dust storms''' '''in North America are shifting towards more extreme characteristics, with a stronger signal towards heightened intensity than increased frequency, although specific regional patterns are more uncertain''' ( medium confidence '''). Mean wind speed and wind power potential are projected to decrease in Western North America''' ( medium confidence ''') with differences between global and regional models lending''' low confidence '''elsewhere.'''

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<span id="snow-and-ice-6"></span>
==== 12.4.6.4 Snow and Ice ====

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'''Snow:''' The seasonal extent of snow cover has reduced over North America in recent decades ( ''robust evidence'' , ''high agreement'' ) (see also Sections 2.3.2.2 and 9.5.3, and Figure Atlas.25). The average snow-cover extent in North America decreased at a rate of about 8500 km <sup>2</sup> yr <sup>–1</sup> over the 1972–2015 period, reducing the average snow cover season by two weeks, primarily due to earlier spring melt ( [[#US%20EPA--2016|US EPA, 2016]] ). Observations indicate earlier spring snowpack melting ( [[#Dudley--2017|Dudley et al., 2017]] ) and a reduction in end-of-season snowpack metrics important to water resources over the Rocky Mountains (particularly since 1980) and Pacific Northwest ( [[#Pederson--2013|Pederson et al., 2013]] ; [[#Kormos--2016|Kormos et al., 2016]] ; [[#Kunkel--2016|Kunkel et al., 2016]] ; [[#Fyfe--2017|Fyfe et al., 2017]] ; [[#Mote--2018|Mote et al., 2018]] ). In situ measurements in Canada show more heterogenous trends in snow amount and density ( [[#Brown--2019|Brown et al., 2019]] ).

Climate change is expected to reduce the total snow amount and the length of the snow cover season over most of North America, with a corresponding decrease in the proportion of total precipitation falling as snow and a reduction in end-of-season snowpack ( ''high confidence'' ) (see Atlas.9.5). Changes include a reduction in the number of days with snowfall in across all of North America, with the exception of northern Canada ( [[#Danco--2016|Danco et al., 2016]] ; [[#McCrary--2019|McCrary and Mearns, 2019]] ), a delay of about a week in first snowfall in the western USA by 2050 under RCP8.5 ( [[#Pierce--2013|Pierce and Cayan, 2013]] ), and more prominent reductions in Canadian snow cover in the October –December period ( [[#Mudryk--2018|Mudryk et al., 2018]] ). Reduced total snowpack and earlier snowmelt lower dry season streamflow ( [[#Kormos--2016|Kormos et al., 2016]] ; [[#Rhoades--2018|Rhoades et al., 2018]] ). Figure 12.10b shows a reduction in days suitable for skiing (SWE > 10 cm; [[#Wobus--2017b|Wobus et al., 2017b]] ) across the USA and southern Canada, although some portions of northern central Canada see an increase.

'''Glacier:''' Section 9.5.1 assessed that glaciers in Alaska, western Canada and the western USA are expected to continue to lose mass and areal extent ( ''high confidence'' ). Compared to their 2015 state, glaciers in the western Canada and the USA region will lose 62 ± 30%, 75 ± 29% and 85 ± 23%, of their mass by the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively ( [[#Marzeion--2020|Marzeion et al., 2020]] ). Meanwhile glaciers in Alaska will lose 26 ± 21%, 31 ± 24% and 44 ± 27%, of their 2015 mass under the same scenarios. The overall loss of glacial mass can act as a meltwater supply for freshwater resources, although this is expected to peak in the middle of the century and then fade as glaciers disappear ( [[#Fyfe--2017|Fyfe et al., 2017]] ; [[#Derksen--2018|Derksen et al., 2018]] ). Continued shrinkage of glaciers is projected to create further glacial lakes ( ''medium confidence'' ) similar to those that have led to outburst floods in Alaska and Canada ( [[#Carrivick--2016|Carrivick and Tweed, 2016]] ; [[#Harrison--2018|Harrison et al., 2018]] ).

'''Permafrost:''' Warmer ground temperatures are expected to extend the geographical extent and depth of permafrost thaw across northern North America ( ''very'' ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ). Observations across Canada show that permafrost temperature is increasing and the active layer is getting thicker ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] ; [[#Derksen--2018|Derksen et al., 2018]] ; [[#Biskaborn--2019|Biskaborn et al., 2019]] ; [[#Romanovsky--2020|Romanovsky et al., 2020]] ). [[#Slater--2013|Slater and Lawrence (2013)]] note that the RCP8.5 end-of-century period in North America only has shallow permafrost as the most probable condition in the Canadian Archipelago. [[#Melvin--2017|Melvin et al. (2017)]] noted the loss of shallow permafrost in five RCP8.5 CMIP5 models across a wide swathe of southern Alaska by 2050, along with increases of active layer thickness. There is ''high confidence'' in continued reductions in mountain near-surface permafrost area with high spatial variability given local snow and temperature changes ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ; [[#Peng--2018|Peng et al., 2018]] ; [[#Hock--2019|Hock et al., 2019]] ).

'''Lake, river and sea ice:''' Anthropogenic warming reduces the seasonal extent of lake and river ice over many North American freshwater systems, with ice-free winter conditions pushing further north with rising temperatures ( ''high confidence'' ). Observations in Central and Eastern North America show reduced average seasonal lake-ice cover duration ( [[#Benson--2012|Benson et al., 2012]] ; [[#Mason--2016|Mason et al., 2016]] ; [[#US%20EPA--2016|US EPA, 2016]] ). Satellite observations show declines in lake ice ( [[#Du--2017|Du et al., 2017]] ) and loss of more than 20% of winter river-ice length in much of Alaska (2008–2018 compared to 1984–1994; [[#Yang--2020a|Yang et al., 2020a]] ). Spring lake and river ice in Canada is projected to break up 10–25 days earlier while autumn freeze-up occurs 5–15 days later by mid-century, with larger declines in lake-ice season closer to the coasts ( [[#Dibike--2012|Dibike et al., 2012]] ) and for rivers in the Rocky Mountains and north-eastern USA ( [[#Yang--2020a|Yang et al., 2020a]] ), although global models have difficulty with frozen freshwater system dynamics ( [[#Derksen--2018|Derksen et al., 2018]] ). Substantial ice loss is projected over the Laurentian Great Lakes ( [[#Hewer--2019|Hewer and Gough, 2019]] ; [[#Matsumoto--2019|Matsumoto et al., 2019]] ). The southern extent of lakes experiencing intermittent winter ice cover moves northward with rising temperature, pushing nearly out of the continental USA at low elevations under a 4.5°C GWL ( [[#Sharma--2019|Sharma et al., 2019]] ). Higher spring flows and the potential for winter thaws are also projected to heighten the threat of ice jams ( [[#Rokaya--2018|Rokaya et al., 2018]] ; [[#Bonsal--2019|Bonsal et al., 2019]] ) while reducing the seasonal viability of ice roads and recreational use ( [[#Pendakur--2016|Pendakur, 2016]] ; [[#Mullan--2017|Mullan et al., 2017]] ; [[#Knoll--2019|Knoll et al., 2019]] ).

Seasonal sea ice coverage along the majority of Canadian and Alaskan coastlines is declining ( ''robust evidence'' , ''high agreement'' ) and there is ''high confidence'' that sea ice loss continues under climate change, as further assessed in [[#12.4.9|Section 12.4.9]] .

'''Heavy snowfall:''' There is ''low agreement'' ( ''limited evidence'' ) for observed changes in heavy snowfall in North America. [[#Kluver--2015|Kluver and Leathers (2015)]] noted a 1930–2008 frequency increase for all snow intensities in the northern Great Plains but declines in heavier snow events in the Pacific Northwest and declines in the south-eastern USA. [[#Changnon--2018|Changnon (2018)]] found that most extreme 30-day high-snowfall periods in the 1900–2016 record over the eastern USA occurred in the 1959–1987 period, which lies between the 1930s Dust Bowl and recent warming. There is ''low agreement'' and ''medium evidence'' for broad projected changes to heavy snowfall over North America given increased heavy precipitation and warmer winter temperatures. Several recent regional studies have projected that low-intensity events decrease more rapidly than heavy snowfall events, resulting in an increase in the snowfall proportion from heavy snowfall events even as the number of such events decreases ( [[#O’Gorman--2014|O’Gorman, 2014]] ; [[#Lute--2015|Lute et al., 2015]] ; [[#Zarzycki--2016|Zarzycki, 2016]] ; [[#Janoski--2018|Janoski et al., 2018]] ; [[#Ashley--2020|Ashley et al., 2020]] ).

'''Ice storm:''' There is ''limited evidence'' in the literature of unique changes in ice storms observed or projected over North America. [[#Groisman--2016|Groisman et al. (2016)]] examined 40 years of observations and found weak decreases in freezing rain events over the south-eastern USA in the most recent decade. [[#Ning--2015|Ning and Bradley (2015)]] project that the average snow–rain transition line, which is associated with mixed precipitation, moves 2° latitude northward over Eastern North America by the end of the 21st century under RCP4.5 (4° under RCP8.5; see also [[#Klima--2015|Klima and Morgan, 2015]] ).

'''Hail:''' There is ''limited evidence'' and ''low agreement'' for observed changes in the frequency or intensity of North American hail storms. J.T. [[#Allen--2015|]] [[#Allen--2015|Allen et al. (2015)]] and [[#Allen--2018|Allen (2018)]] found that temporal inconsistencies in the US and Canadian hail records made long-term climate analysis difficult, although B.H. [[#Tang--2019|]] [[#Tang--2019|Tang et al. (2019)]] identified an increasing frequency of environmental conditions conducive for large hail (diameter ≥ 5 cm) over the central and eastern USA. There is ''limited evidence'' and ''medium agreement'' in projections of increased hail damage potential over North America. Some regional and convective-permitting model projections indicate a longer hail season with fewer events and larger hail sizes that result in higher hail damage potential ( [[#Brimelow--2017|Brimelow et al., 2017]] ; [[#Trapp--2019|Trapp et al., 2019]] ).

'''Snow avalanche:''' There is ''limited evidence'' of directional changes in snow avalanches over North America. [[#Mock--2000|Mock and Birkeland (2000)]] identified a 1969–1995 decrease in snow avalanches over the western United States, although they note the heavy influence of natural variability. A similar decline was observed over western Canada ( [[#Bellaire--2016|Bellaire et al., 2016]] ; [[#Sinickas--2016|Sinickas et al., 2016]] ), but clear trends are difficult to discern given sparse observations and shifts in avalanche management. We concur with the SROCC assessment of ''medium confidence'' and ''high agreement'' that snow avalanche hazards generally decrease at low elevations given lower snowpack, even as high elevations are increasingly susceptible to wet-snow avalanches ( [[#Hock--2019|Hock et al., 2019]] ; see also [[#Lazar--2008|Lazar and Williams, 2008]] ).

'''Observations and projections agree that snow and ice CIDs over North America are characterized by reduction in glaciers and the seasonality of snow and ice formation, loss of shallow permafrost, and shifts in the rain/snow transition line that alters the seasonal and geographic range of snow and ice conditions in the coming decades''' ( very high confidence ''').'''

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<span id="coastal-and-oceanic-5"></span>
==== 12.4.6.5 Coastal and Oceanic ====

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'''Relative sea level:''' [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] found that observations indicate increasing sea levels along most North American coasts ( ''robust evidence'' , ''high agreement'' ), although there is substantial regional variation in relative sea level rise ( ''robust evidence'' , ''high agreement'' ). Around North America, over 1900–2018, a new tide gauge-based reconstruction finds a regional mean RSL change of 1.08 [0.79 to 1.38] mm yr <sup>–1</sup> in the subpolar North Atlantic, 2.49 [1.89 to 3.06] mm yr <sup>–1</sup> in the subtropical North Atlantic, and 1.20 [0.76 to 1.62] in the East Pacific ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). For the period 1993–2018, these RSLR rates, based on satellite altimetry, increased to 2.17 [1.66 to 2.66] mm yr <sup>–1</sup> , 4.04 [2.77 to 5.24] mm yr <sup>–1</sup> and 2.35 [0.70 to 4.06] mm yr <sup>–1</sup> , respectively ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). Relative sea level (RSL) is falling in portions of southern Alaska ( [[#Sweet--2018|Sweet et al., 2018]] ) and much of the northern part of north-eastern Canada and around Hudson Bay (where land is rising by >10 mm/year; [[#Greenan--2018|Greenan et al., 2018]] ).

Relative sea level rise is ''virtually certain'' to continue in the oceans around North America, except in the northern part of north-eastern Canada and portions of southern Alaska. Regional mean RSLR projections for the oceans around North America range from 0.4–1.0 m under SSP1-2.6 to 0.7–1.4 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), which means that there are locally large deviations from the projected GMSL change ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.3|Section 9.6.3.3]] ), including decreases in RSL in northern north-eastern Canada from land uplift (see also [[#Sweet--2017|Sweet et al., 2017]] ; [[#Greenan--2018|Greenan et al., 2018]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). The RSLR projections here may however be underestimated due to potential partial representation of land subsidence in their assessment ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ).

'''Coastal flood:''' Observations indicate that episodic coastal flooding is increasing along many coastlines in North America ( ''robust evidence'' , ''high agreement'' ), and this episodic coastal flooding will increase in many North American regions under future climate change ( ''high confidence'' ) although land uplift from glacial isostatic adjustment in northern and Hudson Bay portions of North-Eastern North America leads to only ''medium confidence'' of coastal flood increases in that region. [[#Sweet--2018|Sweet et al. (2018)]] found 2000–2015 observed increases of about 125% in high-tide flooding frequencies along the southern Atlantic USA coastline, with 75% increases along the USA Gulf Coast and USA northern Atlantic coastlines. That same study noted that a GMSL of 0.5 m in 2100 would increase high tide (‘nuisance’) flooding from current rates of about once a month for most coastal regions to about once every other day along the USA Atlantic and Gulf coasts and smaller increases in frequency along the Pacific coast, and [[#Dahl--2017a|Dahl et al. (2017a)]] found similar trends on the USA East Coast prior to mid-century. The present day 1-in-50-year ETWL is projected to occur around three times per year by 2100 with an SLR of 1 m all around North America, except in most of Eastern North America where it is expected to have return periods of 1-in-1-year to 1-in-2-years ( [[#Vitousek--2017|Vitousek et al., 2017]] ). [[#Ghanbari--2019|Ghanbari et al. (2019)]] projected corresponding shifts towards higher frequencies of major flooding events for 20 US cities. Figure 12.4r and Figure 12.SM.6 show increases of 70 cm or more in the 100-year return period extreme total water level (ETWL) over much of the USA East Coast, British Columbia, Alaska, and the Hudson Bay under RCP8.5 by 2100 (relative to 1980–2014), with lower increases in northern Mexico, northern Canada, Labrador, and the Pacific and Gulf coasts of the USA ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ). Projected increases in coastal flooding generally follow patterns of RSL change, although sea ice loss in the north also increases open water storm surge ( [[#Greenan--2018|Greenan et al., 2018]] ).

'''Coastal erosion:''' There is ''limited evidence'' of changes in North American episodic storm erosion caused by waves and storm surges. Observations show increased extreme wave energy on the Pacific coast, but no clear trend on other USA coasts given substantial natural variability ( [[#Bromirski--2013|Bromirski et al., 2013]] ; [[#Vose--2014|Vose et al., 2014]] ). In terms of long-term coastal erosion, shoreline retreat rates of around 1 m yr <sup>–1</sup> have been observed during 1984–2015 along the sandy coasts of NWN and NCA while portions of the US Gulf Coast have seen a retreat rate approaching 2.5 m yr <sup>–1</sup> ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). Sandy shorelines along ENA and WNA have remained more or less stable during 1984–2014, but a shoreline progradation rate of around 0.5 m yr <sup>–1</sup> has been observed in NEN. [[#Mentaschi--2018|Mentaschi et al. (2018)]] report 1984–2015 coastal area land losses of 630 km <sup>2</sup> and 1260 km <sup>2</sup> along the Pacific and Atlantic coasts of the USA, respectively.

Projections indicate that sandy coasts in most of the region will experience shoreline retreat through the 21st century ( ''high confidence'' ). Median shoreline change projections presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] show that sandy shorelines in NWN, ENA, and NCA will retreat by between 40 and 80 m by mid-century (relative to 2010) under both RCP4.5 and RCP8.5. Projections for NEN and WNA are lower at 20–30 m under the same RCPs. The highest median mid-century projection in the region is for CNA at around 125 m under both RCPs. RCP4.5 projections for 2100 show shoreline retreats of 100 m or more along the sandy coasts of NWN, CNA, and NCA, while retreats of between 40 and 80 m are projected in other regions. Under RCP8.5, retreats exceeding 100 m are projected in all regions except NEN and WNA (approximately 80 m) by 2100, with particularly high retreats in NWN (160 m) and CNA (330 m). The total length of sandy coasts in North America that are projected to retreat by more than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 15,000 km and 25,000 km respectively, an increase of approximately 70%.

'''Marine heatwave:''' There is ''high confidence'' in observed increases in marine heatwave (MHW) frequency and future increases in marine heatwaves are ''very likely'' around North America (Box 9.2). The total number of MHW days per decade increased in the North American coastal zone, albeit somewhat more in the Pacific ( [[#Oliver--2018|Oliver et al., 2018]] ; [[#Smale--2019|Smale et al., 2019]] ). Projected increases in degree heating weeks ( [[#Heron--2016|Heron et al., 2016]] ) and degree heating months ( [[#Frieler--2013|Frieler et al., 2013]] ) indicate increasing bleaching-level and mortality-level heating stress threshold events for reefs in Florida and Mexico.

Mean SST is projected to increase by 1°C (3°C) around North America by 2100, with a hotspot of around 4°C (5°C) off the North American Atlantic coastline under RCP4.5 (RCP8.5) conditions (see Interactive Atlas). [[#Frölicher--2018|Frölicher et al. (2018)]] projected increasing MHW frequency and spatial extent at a 2°C global warming level with the largest increases in the Gulf of Mexico and off the southern USA East Coast (>20×) as well as off the coast of the Pacific Northwest (>15×). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around North America by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).

'''There is''' high confidence '''that most coastal CIDs in North America will continue to increase in the future with climate change. An observed increase in relative sea level rise is''' virtually certain '''to continue in North America (other than around the Hudson Bay and southern Alaska) contributing to more frequent and severe coastal flooding in low-lying areas''' ( high confidence ''') and shoreline retreat along most sandy coasts''' ( high confidence '''). Marine heatwaves are also expected to increase all around the region over the 21st century''' ( high confidence ''').'''

The assessed direction of change in climatic impact-drivers for North America and associated confidence levels are illustrated in Table 12.8.

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'''Table 12.8''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in North America, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:04271e9d5dd8d10bb6f8bffafb98a88f IPCC_AR6_WGI_Chapter12_Table_12_8.jpg]]

<div id="12.4.7" class="h2-container"></div>

<span id="small-islands"></span>
=== 12.4.7 Small Islands ===

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This section covers the climatic impact-drivers affecting small islands around the world (see definition of SIDS in the Glossary; Cross-Chapter Box Atlas.2) with a particular focus on small islands in the Caribbean (CAR) Sea and the Pacific Ocean. Caribbean and Pacific small islands have mostly tropical climates and local conditions are also influenced by diverse topography ranging from low-lying islands and atolls to volcanic and mountainous terrain. Climate variability in these islands is influenced by the trade winds, easterly waves, tropical cyclones (TC), and the migrations of the Inter-tropical Convergence Zone (ITCZ), the North Atlantic Subtropical High, and the South Pacific Convergence Zone (SPCZ), and other modes of climate variability as discussed in Cross-Chapter Box Atlas.2. Furthermore, changes in the ocean temperature and chemistry, and relative sea level have strong impacts on these small islands given their geographical location and dependence on coastal and marine ecosystem services.

The AR5 recognized the heterogeneity in these small islands in terms of physical geography, socio-economic and cultural backgrounds, as well as their vulnerability to the impacts of climate change. Similar to previous reports, these regions have been assessed together in this section, given the similarities in the challenges they face in addressing climate change impacts and risk, which were thought – until AR4 – to be dominated by sea level rise ( [[#Nurse--2014|Nurse et al., 2014]] ; [[#Betzold--2015|Betzold, 2015]] ). Since then there has been a substantial increase in the number and complexity of the literature on the drivers and impacts of climate change on small islands (BOM and CSIRO, 2011, 2014; [[#Nurse--2014|Nurse et al., 2014]] ; [[#Gould--2018|Gould et al., 2018]] ; [[#Keener--2018|Keener et al., 2018]] ). There are also increasing efforts being made to produce higher resolution climate projections for small islands through downscaling methods ( [[#Elison%20Timm--2015|Elison Timm et al., 2015]] ; [[#McLean--2015|McLean et al., 2015]] ; [[#Khalyani--2016|Khalyani et al., 2016]] ; [[#Zhang--2016|]] [[#Zhang--2016|C. Zhang et al., 2016]] ; [[#Stennett-Brown--2017|Stennett-Brown et al., 2017]] ; [[#Bhardwaj--2018|Bhardwaj et al., 2018]] ; [[#Bowden--2021|Bowden et al., 2021]] ).

The AR5 identified the key climate and ocean-related hazards affecting small islands, which occur at different time scales and have diverse impacts on multiple sectors ( [[#Christensen--2013|Christensen et al., 2013]] ; [[#Nurse--2014|Nurse et al., 2014]] ). Recent findings from SR1.5 and SROCC emphasize that the multiple interrelated climate hazards currently faced by low-lying islands and coastal areas will be amplified in the future, especially at higher global warming levels ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#IPCC--2019b|IPCC, 2019b]] ).

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<span id="heat-and-cold-7"></span>
==== 12.4.7.1 Heat and Cold ====

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'''Mean air temperature:''' Significant warming trends are clearly evident in the small islands, such as those in the Pacific, CAR, and western Indian Ocean, particularly over the latter half of the 20th century (see Figure Atlas.11; Atlas.10.2; Cross-Chapter Box Atlas.2, Table 1). This observed warming signal in the tropical western Pacific has been attributed to anthropogenic forcing ( [[#Wang--2016|Wang et al., 2016]] ). There is ''high confidence'' of warming over small islands even at 1.5°C GWL (Atlas.10.4 and Figure Atlas.28; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Mean temperature is ''very likely'' to increase by 1°C–2°C (2°C–4°C) by 2041–2060 (2081–2100) under RCP8.5 (BOM and CSIRO, 2014) and SSP3-7.0 (Atlas.10.4, Figure 4.19 and Figure Atlas.12; [[#Almazroui--2021|Almazroui et al., 2021]] ).

'''Extreme heat:''' Observational records indicate warming trends in the temperature extremes since the 1950s in CAR and the Pacific small islands ( ''high confidence'' ) (Sections 11.3.2 and 11.9, and Table 11.13). A detectable anthropogenic increase in summer heat stress has been identified over a number of island regions in CAR, western tropical Pacific, and tropical Indian Ocean, based on wet bulb globe temperature (WBGT) index trends for 1973–2012 ( ''medium confidence'' ) ( [[#Knutson--2016|Knutson and Ploshay, 2016]] ). An increasing trend in the maximum daytime heat index is also noted in CAR during the 1980–2014 period, as well as more extreme heat events since 1991 ( [[#Ramirez-Beltran--2017|Ramirez-Beltran et al., 2017]] ).

Compared with the recent past, it is ''likely'' that the intensity and frequency of hot (cold) temperature extremes will increase (decrease) in the small islands ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] and Table 11.13; BOM and CSIRO, 2014). Warm spell conditions will occur up to half the year in CAR at 1.5°C GWL with an additional 70 days at 2°C ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Taylor--2018|Taylor et al., 2018]] ), with livestock temperature–humidity tolerance thresholds increasingly surpassed ( [[#Lallo--2018|Lallo et al., 2018]] ). In CAR, a median increase of more than a month per year where temperatures exceed 35°C is projected by end of the 21st century under SSP5-8.5 (Figure 12.4a–c and Figure 12.SM.1). Heatwaves are projected to increase in CAR by the mid- and end-century under RCP8.5 (Sections 11.3.5 and 11.9, and Table 11.13). Figure 12.4d–f and Figure 12.SM.2 also show an increase of about 30–60 days in which HI exceeds 41°C by 2041–2060 under SSP5-8.5 relative to 1995–2014 in CAR, with an additional increase of about 50–100 days by end of the 21st century for RCP8.5/SSP5-8.5, but this increase remains below 50 days for RCP2.6/SSP1-2.6. The Pacific Islands region is also among those projected to have an increase in WBGT by end-century under RCP8.5, increasing the risk of heat stress in the region ( [[#Newth--2018|Newth and Gunasekera, 2018]] ).

'''It is''' very likely '''that the significant recent warming trends observed in the small islands will continue in the 21st century, which will''' likely '''further increase heat stress in these regions.'''

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<span id="wet-and-dry-7"></span>
==== 12.4.7.2 Wet and Dry ====

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'''Mean precipitation:''' Observational datasets have generally revealed no significant long-term trends in rainfall in the Caribbean over the 20th century when analysed at seasonal and inter-decadal timescales, except for some areas where there is evidence for decreasing trends for the period 1901–2010 but not for the period 1951–2010 (Cross-Chapter Box Atlas.2, Table 1, and Atlas.10.2; [[#Knutson--2018|Knutson and Zeng, 2018]] ). Although there are spatial variations, annual rainfall trends in the western Indian Ocean are mostly decreasing, with generally non-significant trends in the western tropical Pacific since the 1950s ( ''low confidence'' ). Significant drying trends are noted in the southern Pacific subtropics and south-western French Polynesia during the 1951–2015 period ( [[#McGree--2019|McGree et al., 2019]] ), and in some areas of Hawaii during the 1920–2012 period ( ''medium confidence'' ) (Cross-Chapter Box Atlas.2, Table 1, and Atlas.10.2).

Atlas.10.4 projects precipitation reduction over the Caribbean ( ''high confidence'' ) ( [[#Almazroui--2021|Almazroui et al., 2021]] ) and parts of the Atlantic and Indian oceans, particularly in June to August, by end of 21st century under SSP5-8.5. Precipitation is generally projected to increase under SSP5-8.5 and for higher GWLs in the small islands in parts of the western and equatorial Pacific, but there is ''low confidence'' in broad changes given drier conditions projected for the southern subtropical and eastern Pacific Ocean ( ''limited agreement'' given spatial and seasonal variability) (Atlas.10.4 and Figure Atlas.28).

'''River flood:''' There is ''limited evidence'' on observed changes in river flooding in the small islands. Long-term records in Hawaii indicate no clear trends in peak flow, except for the significant decrease in peak streamflow in Hawaii Island over the period 1967–2016 ( [[#Bassiouni--2013|Bassiouni and Oki, 2013]] ; [[#Clilverd--2019|Clilverd et al., 2019]] ). Similarly, there is no significant trend in the frequency and height (after adjusting for average sea level rise) of river flood in Fiji over the period 1892–2013 ( [[#McAneney--2017|McAneney et al., 2017]] ). There is ''low confidence'' on the direction of future change of river flooding in the small islands due to the limited literature. In Oahu, Hawaii, extreme peak flow events with high return periods are projected to increase by end of the 21st century under RCP8.5, but there is also high uncertainty in these projections ( [[#Leta--2018|Leta et al., 2018]] ).

'''Heavy precipitation and pluvial flood:''' Heavy precipitation days in CAR have increased in magnitude, and have been more frequent in the northern part during the latter part of the 20th century ( ''low confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.2|Section 11.4.2]] and Table 11.14). The direction of change in extreme precipitation varies across the Pacific and depends on the season ( ''low confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.4.2|Section 11.4.2]] and Cross-Chapter Box Atlas.2, Table 1). Although pluvial flooding events have been observed in some islands, there is ''limited evidence'' for an assessment on past changes in pluvial flooding, unlike in other regions. There is ''low confidence'' in the projected change in magnitude of very heavy precipitation days in CAR across different GWLs (Table 11.14). On the other hand, there is ''high confidence'' in the increase in frequency and intensity of extreme rainfall events (i.e., 1-in-20-year rainfall events) in the western tropical Pacific in the 21st century, even for RCP2.6 scenario, based on model agreement and mechanistic understanding but ''low confidence'' in the magnitude of change in extreme rainfall due to model bias (BOM and CSIRO, 2014).

'''Landslide:''' Heavy rainfall, such as from tropical cyclones, can trigger landslides over steep terrain in the small islands ( [[#Bessette-Kirton--2019|Bessette-Kirton et al., 2019]] ). There is ''limited evidence'' to determine long-term trends in rainfall-induced landslides in the small islands ( [[#Kirschbaum--2015|Kirschbaum et al., 2015]] ; [[#Sepúlveda--2015|Sepúlveda and Petley, 2015]] ; [[#Froude--2018|Froude and Petley, 2018]] ; [[#Bessette-Kirton--2019|Bessette-Kirton et al., 2019]] ). There is ''low confidence'' in future changes in landslides in the small islands. The direction of change may depend on future changes in precipitation, tropical cyclones, climate modes (e.g., El Niño–Southern Oscillation, ENSO), as well as human disturbance, but more data and understanding of the complexity of these relationships are needed, especially in these vulnerable areas ( [[#Sepúlveda--2015|Sepúlveda and Petley, 2015]] ; [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Froude--2018|Froude and Petley, 2018]] ).

'''Aridity:''' Current estimates identify many small islands as being under water stress and thus particularly sensitive to variations in rainfall and groundwater, population growth and demand, and land-use change, among others (Cross-Chapter Box Atlas.2; [[#Holding--2016|Holding et al., 2016]] ). From 1950 to 2016, a heterogeneous but prevalent drying trend is found in CAR ( ''low confidence'' ), where drought variability is modulated by the tropical Pacific and North Atlantic oceans (Table 11.15 and Cross-Chapter Box Atlas.2, Table 1; [[#Herrera--2017|Herrera and Ault, 2017]] ). In the future, increased aridity and decreased freshwater availability are projected in many small islands due to higher evapotranspiration in a warmer climate that partially offsets increases or exacerbates reductions in precipitation ( [[#Karnauskas--2016|Karnauskas et al., 2016]] , 2018b; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Increased aridity is projected for the majority of the small islands, such as in CAR, southern Pacific and western Indian Ocean, by 2041–2059 relative to 1981–1999 under RCP8.5 or at 1.5°C and 2°C GWLs, which will further intensify by 2081–2099 ( ''medium confidence'' ) ( [[#Karnauskas--2016|Karnauskas et al., 2016]] , 2018b). Groundwater recharge is projected to increase in Maui, Hawaii except on the leeward side of the island, which underscores the importance of topography and elevation on freshwater availability in different island microclimates ( [[#Brewington--2019|Brewington et al., 2019]] ; [[#Mair--2019|Mair et al., 2019]] ).

'''Hydrological drought:''' There is ''low confidence'' of widespread changes to hydrological drought in CAR or Pacific small islands in recent decades, although an increasing number of studies document local changes. Records in Hawaii indicate downward trends in low streamflow and base flow from 1913 to 2008 ( [[#Bassiouni--2013|Bassiouni and Oki, 2013]] ). Decadal variability of Hawaiian streamflow coincides with rainfall fluctuations associated with the Pacific Decadal Variability although significant average declines in surface and baseflow runoff of about 8% and 11% per decade, respectively, have been noted during the 1987–2016 period ( [[#Clilverd--2019|Clilverd et al., 2019]] ).

There is ''low confidence'' in hydrological drought change projections, given low signal-to-noise ratios and the challenge in representing island scales in global analyses. [[#Prudhomme--2014|Prudhomme et al. (2014)]] recognized CAR as one of the regions with the highest increase in regional deficit index (RDI; a measure of the fraction of area in hydrological drought conditions) by the end of the 21st century under RCP8.5. Daily streamflow and extreme low flows in two watersheds in Oahu, Hawaii are projected to decline by mid- and end of the 21st century under RCP4.5 and RCP8.5, which would result in more frequent hydrological droughts in this area ( [[#Leta--2018|Leta et al., 2018]] ).

'''Agricultural and ecological drought:''' Recent trends toward more frequent and severe droughts have been noted in the small islands but only with ''low confidence'' in broad trend patterns, given high spatial variability including heightened drought on the leeward side of islands (e.g., [[#Frazier--2017|Frazier and Giambelluca, 2017]] ; [[#Herrera--2017|Herrera and Ault, 2017]] ; [[#McGree--2019|McGree et al., 2019]] ; see Table 11.15, Cross-Chapter Box Atlas.2, Table 1). Agricultural and ecological droughts are projected to increase in frequency, duration, magnitude, and extent in small islands, such as in CAR ( ''medium confidence'' ) and parts of the Pacific ( ''low confidence'' ), particularly where future declines in precipitation are compounded by higher evapotranspiration, under increasing levels of warming ( [[#Naumann--2018|Naumann et al., 2018]] ; [[#Taylor--2018|Taylor et al., 2018]] ; [[#Vichot-Llano--2021|Vichot-Llano et al., 2021]] ). Relative to the period 1985–2014, decreases in annual surface and total column soil moisture become more robust in more areas in CAR by 2071–2100 under SSP3-7.0 and SSP5-8.5 scenarios (B.I. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ), but reliably representing drought features in small island domains with global simulations is challenging (see also [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ).

'''Fire weather:''' There is ''limited evidence'' on trends in wildfire in CAR and the Pacific. Records of wildfire in Hawaii from 2005 to 2011 indicate a peak in area burned during the hot and dry summer months, but [[#Trauernicht--2015|Trauernicht et al. (2015)]] note the difficulty in establishing the link between past climate and wildfire trends due to human activities and vegetation changes. Availability of literature limits assessment on future fire weather in the small islands. Drying and warming trends tend to increase fire probability aside from the climate impact on fuel loading, for example, grassland fires in Hawaii ( [[#Trauernicht--2019|Trauernicht, 2019]] ), and wildfires in Puerto Rico ( [[#Van%20Beusekom--2018|Van Beusekom et al., 2018]] ).

'''Observed and projected rainfall trends vary spatially across the small islands. Higher evapotranspiration under a warming climate are projected to partially offset future increases or amplify future reductions in rainfall, resulting in drier conditions and increased water stress in the small islands''' ( medium confidence ''').'''

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<span id="wind-7"></span>
==== 12.4.7.3 Wind ====

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'''Mean wind speed:''' Scarcity of observations limits assessment of long-term changes in winds over the small islands in the Pacific and CAR. Records indicate that average daily wind speeds have slowly declined in Hawaii, but have remained constant across western and southern Pacific sites since the mid-20th century ( [[#Marra--2017|Marra and Kruk, 2017]] ). Recent studies of reanalyses and hindcast simulations indicate an intensification of the Pacific trade winds during the 1992–2011 period, which contributed to the ocean cooling in the tropical central and eastern Pacific ( [[#England--2014|England et al., 2014]] ; [[#Takahashi--2016|Takahashi and Watanabe, 2016]] ). Projections estimate up to 0.4 m s <sup>–1</sup> (8%) increase in annual winds in CAR under RCP8.5, which is associated with changes in the extension of the North Atlantic Subtropical High that enhances the Caribbean low-level jet during the wet season, and stronger local easterlies due to enhanced land–ocean temperature differences in the dry season ( [[#Costoya--2019|Costoya et al., 2019]] ) ( ''low confidence'' ).

'''Tropical cyclone:''' Tropical cyclones have devastating impacts on the small islands due to intense winds, storm surge and rainfall, although the associated rainfall can also be beneficial for freshwater resources. It is ''likely'' that tropical cyclone intensity and intensification rates at a global scale have increased in the past 40 years but it is not clear if regional-scale changes are basin-wide or due to shifts in tropical cyclone tracks ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ). Other, less data-sensitive tropical cyclone features, such as the poleward migration of where tropical cyclones reach peak intensity in the western North Pacific since the 1940s ( '''medium confidence''' ) and the slowdown in tropical cyclone translational speed over contiguous USA since 1900 ( '''medium confidence''' ), can affect rainfall and flooding over small islands in CAR and the Pacific ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ).

Projections of global changes in tropical cyclones indicate more frequent Category 4–5 storms ( ''high confidence'' ) and increased rain rates ( ''high confidence'' ) ( [[#Knutson--2020|Knutson et al., 2020]] ), with relative sea level rise exacerbating storm surge potential, but with large regional differences (see [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] ). By the late 21st century, tropical cyclones are projected to be less frequent in the basins of the western and eastern North Pacific, Bay of Bengal, Caribbean Sea and in the Southern Hemisphere, but will be more frequent in the subtropical central Pacific ( [[#Murakami--2014|Murakami et al., 2014]] ; [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Bell--2019|Bell et al., 2019]] ; [[#Knutson--2020|Knutson et al., 2020]] ). Over CAR, tropical cyclone intensity is expected to increase by the end of the century under RCP8.5 due to higher sea surface temperatures but can be inhibited by increases in vertical wind shear in the region ( ''medium confidence'' ) ( [[#Kossin--2017|Kossin, 2017]] ; [[#Ting--2019|Ting et al., 2019]] ). The poleward movement of the area in which tropical cyclones reach peak intensity in the western North Pacific is ''likely'' to continue, which affects the tropical cyclone frequency over the small islands in the area ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.5|Section 11.7.1.5]] ; [[#Kossin--2016|Kossin et al., 2016]] ). Projections also indicate an increase (decrease) in the tropical cyclone frequency during El Niño (La Niña) events in the Pacific at the end of the 21st century ( [[#Chand--2017|Chand et al., 2017]] ). RCP8.5 2080–2099 projections indicate a 2% increase in the number of tropical cyclones in the north-central Pacific relative to 1980–1999, with tracks shifting northward towards Hawaii (N. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). Given projected reductions to the overall number of tropical cyclones but increases in storm intensity, total rainfall and storm surge potential, we assess ''medium confidence'' of overall changes to tropical cyclones affecting the Caribbean and Pacific small islands.

'''Projections indicate that small islands will generally face fewer but more intense tropical cyclones''' ( medium confidence ''') although there is substantial variability across small island regions given projected regional shifts in storm tracks.'''

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<span id="coastal-and-oceanic-6"></span>
==== 12.4.7.4 Coastal and Oceanic ====

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'''Relative sea level:''' Relative sea level rise (RSLR) continues to be a major threat to small islands and atolls, since it can exacerbate the impacts of other climate hazards on low-lying coastal communities and infrastructures, ecosystems, and freshwater resources ( [[#Nurse--2014|Nurse et al., 2014]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). In the Indian Ocean–South Pacific region, a new tide gauge-based reconstruction finds a regional mean RSL change of 1.33 [0.80 to 1.86] mm yr <sup>–1</sup> over 1900–2018 ( [[#Frederikse--2020|Frederikse et al., 2020]] ) compared to a GMSL change of around 1.7 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5). RSLR rates based on satellite altimetry for the period 1993–2018 in the region increased to 3.65 [3.23 to 4.08] mm yr <sup>–1</sup> ( [[#Frederikse--2020|Frederikse et al., 2020]] ), compared to a GMSL change of 3.25 mm yr <sup>–1</sup> [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Table 9.5).

Relative sea-level rise is ''very likely'' to continue surrounding the oceans in the Small Island States. Around the small islands, regional mean RSLR projections vary widely, from 0.4–0.6 m under SSP1-2.6 to 0.7–1.6 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), but in general they are situated in areas with RSL changes ranging from the mean projected GMSL change to above-average values ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.3|Section 9.6.3.3]] ). These RSLR projections may however be underestimated due to potential partial representation of land subsidence in their assessment ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.2|Section 9.6.3.2]] ).

'''Coastal flood:''' Relative sea level rise, storm surges and swells contribute to coastal inundation in the small islands, where studies on historical trends in coastal flooding are currently limited. For example, a swell event due to distant extratropical cyclones in December 2008 raised extreme water levels leading to flooding affecting five Pacific island nations: Marshall Islands, Micronesia, Papua New Guinea, Kiribati and Solomon Islands ( [[#Hoeke--2013|Hoeke et al., 2013]] ; [[#Merrifield--2014|Merrifield et al., 2014]] ). Over low-lying atoll islands in the north-west tropical Pacific, potential increases in the frequency and areal extent of coastal flooding, especially at higher SLR scenarios, are expected to have negative consequences for freshwater resources and island habitability ( [[#Storlazzi--2015|Storlazzi et al., 2015]] , 2018). Select tide gauges across the Pacific also indicate increasing trends in the frequency of minor flooding since the 1960s ( [[#Marra--2017|Marra and Kruk, 2017]] ).

As relative sea levels increase, the potential for coastal flooding increases in the small islands ( ''high confidence'' ). Across the Pacific and CAR small islands, the 5–95th percentile range of the 1-in-100-year ETWL is projected to increase (relative to 1980–2014) by 10–35 cm and by 14–41 cm by 2050 under RCP4.5 and RCP8.5, respectively (Figure 12.4q). By 2100, this range is projected to be 27–81 cm and 44–188 cm under RCP4.5 and RCP8.5, respectively (Figure 12.4p,r; [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Furthermore, by 2050, the present-day 1-in-100-year ETWL is projected to have median return periods of between 1-in-1-year and 1-in-50-year in both the Pacific and CAR small islands, with some Pacific islands projected to experience the present-day 1-in-100-year ETWL more than once a year ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). By 2100, the present-day 1-in-50-year ETWL is projected to occur around three times a year by 2100 with an SLR of 1 m at Pacific and CAR small islands ( [[#Vitousek--2017|Vitousek et al., 2017]] ). In the western tropical Pacific, the magnitude and frequency of coastal flooding due to SLR can be modulated by changes in the wave climate ( [[#Shope--2016|Shope et al., 2016]] ).

'''Coastal erosion:''' Recent studies have indicated variable and dynamic changes in shorelines of reef islands ( ''medium confidence'' ), including both erosion and accretion, which suggest factors other than SLR affecting shoreline changes, such as in the central and western Pacific within the past 50-to-60-year timeframe ( [[#Webb--2010|Webb and Kench, 2010]] ; [[#Le%20Cozannet--2014|Le Cozannet et al., 2014]] ; [[#Ford--2015|Ford and Kench, 2015]] ; [[#Duvat--2017|Duvat and Pillet, 2017]] ). For example, islands on atolls in the central and western Pacific have not substantially eroded or reduced in size in the past decades while sea level has been rising, but their position and morphology have changed due to anthropogenic factors (e.g., seawalls, reclamation) and climate–ocean processes ( [[#Biribo--2013|Biribo and Woodroffe, 2013]] ; [[#McLean--2015|McLean and Kench, 2015]] ). Analysis of aerial and satellite imagery revealed severe shoreline retreat in six islands and the disappearance of five vegetated reef islands in Solomon Islands in the western Pacific between 1947 and 2014, which may be due to the interaction between SLR and waves ( [[#Albert--2016|Albert et al., 2016]] ). In French Polynesia, changes in shoreline and island area have been observed since the 1960s, partly due to the effect of TCs on sediment changes and human activities ( [[#Duvat--2017|Duvat and Pillet, 2017]] ; [[#Duvat--2017|Duvat et al., 2017]] ). Coastal erosion has also been noted over the small, low-lying, sandy islands, such as in French Polynesia and Solomon Islands, among others ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). Average shoreline retreat rates between 1 and 2 m yr <sup>–1</sup> are estimated for the islands in the equatorial Pacific and in CAR, while a retreat rate of 0.5 m yr <sup>–1</sup> is estimated for islands in the South Pacific, based on satellite observations from 1984–2016 ( [[#Luijendijk--2018|Luijendijk et al., 2018]] ; [[#Mentaschi--2018|Mentaschi et al., 2018]] ). There was also a loss of 610 km <sup>2</sup> compared with a gain of 520 km <sup>2</sup> in coastal area in Oceania during the 1984–2015 period ( [[#Mentaschi--2018|Mentaschi et al., 2018]] ).

Projections indicate that shoreline retreat will occur over most of the small islands in the Pacific and CAR throughout the 21st century with spatial variability ( ''high confidence'' ). Median shoreline change projections (CMIP5) relative to 2010, presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] , show that, by mid-century, shorelines in the islands in the equatorial Pacific and South Pacific will retreat by around 40 m, under both RCP4.5 and RCP8.5. In CAR islands, sandy shorelines are projected to retreat by about 80 m by mid-century under both RCPs. By 2100, more than 100 m of median shoreline retreat is projected for all small islands under both RCPs; notably in CAR where retreats approaching 200 m (relative to 2010) are projected under both RCPs. The total length of sandy coasts in CAR and Pacific small islands that is projected to retreat by more than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 1100 km and 1200 km respectively, an increase of approximately 14%.

'''Marine heatwave:''' Ocean temperatures from satellite observations noted a moderate increase of 1–4 annual marine heat wave (MHW) events between 1982–1988 and 2000–2016 over some areas in the Indian Ocean, subtropical parts of the North and South Atlantic, and central and western parts of the North and South Pacific, but a decrease in frequency (two annual events) over the eastern Pacific Ocean (Box 9.2; [[#Oliver--2018|Oliver et al., 2018]] ). The intensity of MHWs has also increased between 0.2°C and 0.5°C over the equatorial portions of the North Atlantic and the South Pacific. Over the eastern tropical Pacific, the decrease in intensity and duration of MHW is between 0.5°C and 1.0°C and between 30 and 75 days, respectively (Box 9.2; [[#Oliver--2018|Oliver et al., 2018]] ). There is ''high confidence'' that MHWs will increase around all small island nations. Marine heatwaves are projected to be more intense and prolonged where the largest changes are noted in the equatorial region with maximum annual intensities up to 1.2°C (1.8°C) and annual mean duration reaching 100 days (200 days) at 1.5°C (2.0°C) warming levels ( [[#Frölicher--2018|Frölicher et al., 2018]] ). Projections for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around all small island nations by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).

'''In summary, relative sea level rise is''' very likely '''in the oceans around small islands, and along with storm surges and waves will exacerbate coastal inundation in small islands. Shoreline retreat is projected along sandy coasts of most small islands''' ( high confidence '''). There is''' high confidence '''that MHWs will increase around all small island nations.'''

The assessed direction of change in climatic impact-drivers for CAR and Pacific small islands and associated confidence levels are illustrated in Table 12.9. Cold, snow, and ice-related climatic impact-drivers, and sand and dust storms are not broadly relevant in the small islands that were assessed.

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'''Table 12.9''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in the small islands, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] .

[[File:4b2e37635099f6556710bc00e9f658d1 IPCC_AR6_WGI_Chapter12_Table_12_9.jpg]]

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<span id="open-and-deep-ocean"></span>
=== 12.4.8 Open and Deep Ocean ===

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Oceans face challenges from anthropogenic perturbations to the global Earth system, which cause increasing ocean warming, carbon dioxide-induced acidification and oxygen loss ( [[#Bindoff--2019|Bindoff et al., 2019]] ). Climate change will affect the major oceanic CIDs described in [[#12.2|Section 12.2]] : mean ocean temperature, marine heatwave, ocean acidity, ocean salinity, and dissolved oxygen (O <sub>2</sub> ), as well as severe wind storm and sea ice. These changes result in a shifting profile of hazards relevant to impact and risk assessments ( [[#12.3|Section 12.3]] ). New evidence, the SROCC (IPCC 2019b) assessments and advances in the new CMIP6 climate simulations reinforce confidence in projected changes in climatic impact-drivers in the global oceans. As the ocean has taken up about 90% of the global warming for the period 1971–2018 ( [[IPCC:Wg1:Chapter:Chapter-7#7.2.2.2|Section 7.2.2.2]] ), the emergence of the sea surface temperature increase signal has already been observed in global oceans over the last century ( [[#Hawkins--2020|Hawkins et al., 2020]] ). The signal in sea ice extent decrease has already emerged in the Arctic Ocean ( [[#Landrum--2020|Landrum and Holland, 2020]] ), while ocean acidification and low oxygen have also already emerged in many oceanic regions and will emerge in all global oceans by 2050 under RCP8.5 ( [[#12.5.2|Section 12.5.2]] and Table 12.10). This section assesses key climatic impact-drivers that can be linked with sectoral and regional vulnerability and exposure in open and deep oceans, drawing from previous Chapters (Chapters 2, 3, 4, 5 and 9).

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'''Table 12.10''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in open and deep ocean regions, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] . Asterisks indicate regions that extend across both sides of the map.

[[File:29b4e958d29bffef109fcd88081100a0 IPCC_AR6_WGI_Chapter12_Table_12_10.jpg]]

'''Mean ocean temperature:''' It is ''very likely'' that global mean sea surface temperature (SST) has increased by 0.88 [0.68 to 1.01] °C from 1850–1900 to 2011–2020, and 0.60 [0.44 to 0.74] °C from 1980 to 2020 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.6|Section 2.3.1.1.6]] and Table 2.4). There is ''very high confidence'' that the Indian Ocean, the western equatorial Pacific Ocean, and western boundary currents have warmed faster than the global average, while the Southern Ocean, the eastern equatorial Pacific, and the North Atlantic Ocean have warmed more slowly or slightly cooled ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1.1|Section 9.2.1.1]] ). It is ''virtually certain'' that global mean SST will continue to increase in the 21st century at a rate depending on future emissions scenario, with CMIP6 projections indicating an increase of 0.86°C ( ''likely'' range 0.43°C–1.47°C) under SSP1-2.6 and 2.89°C (2.01°C–4.07°C) under SSP5-8.5, by 2081–2100, relative to 1995–2014 ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1.1|Section 9.2.1.1]] ). Global warming of 2°C above pre-industrial levels is projected to increase SST, resulting in the exceedance of numerous hazard thresholds for pathogens, seagrasses, mangroves, kelp forests, rocky shores, coral reefs and other marine ecosystems ( ''medium confidence'' ) ( [[#Poloczanska--2013a|Poloczanska et al., 2013a]] , b, 2016; [[#Liu--2014|Liu et al., 2014]] ; [[#Pörtner--2014|Pörtner et al., 2014]] ; [[#Graham--2015|Graham et al., 2015]] ; [[#Schoepf--2015|Schoepf et al., 2015]] ; [[#Gobler--2017|Gobler et al., 2017]] ; [[#Henson--2017|Henson et al., 2017]] ; [[#Hoegh-Guldberg--2017|Hoegh-Guldberg et al., 2017]] ; [[#Krueger--2017|Krueger et al., 2017]] ; [[#Hughes--2018a|Hughes et al., 2018a]] , b; [[#Perry--2018|Perry et al., 2018]] ). It is ''virtually certain'' that upper-ocean stratification has increased at a rate of 4.9 ± 1.5% during 1970–2018 and that this will continue to increase in the 21st century ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1.3|Section 9.2.1.3]] ), potentially leading to reduced nutrient supply and total productivity ( ''low confidence'' ) ( [[#Moore--2018|Moore et al., 2018]] ).

'''Marine heatwave:''' Marine heatwaves (MHWs) have increased in frequency over the 20th century, with an approximate doubling since the 1980s ( ''high confidence'' ), and their intensity and duration have also increased ( ''medium confidence'' ) (Box 9.2). Projections show that this increasing trend ''likely'' continues with 2–9 times more frequent MHWs (at global scale) projected by 2081–2100, relative to 1995–2014 under SSP1-2.6, and 4–18 times more frequent MWHs under SSP5-8.5. The largest changes in MHW frequency are ''likely'' to occur in the tropical ocean and the Arctic, while there is ''medium confidence'' of moderate increases in the mid-latitudes, and of small increases in the Southern Ocean (Box 9.2). Permanent MHWs (more than 360 days per year, relative to the historical climate conditions) are projected to occur in the 21st century in parts of the tropical ocean, in the Arctic Ocean, and around latitude 45°S, under SSP5-8.5 (Box 9.2). The occurrence of such permanent MHWs can be largely avoided under the SSP1-2.6 scenario (Box 9.2). MHWs can have devastating and long-term impacts on ecosystems ( [[#Oliver--2018|Oliver et al., 2018]] ), making them an emerging hazard for marine ecosystems ( [[#Frölicher--2018|Frölicher and Laufkötter, 2018]] ; [[#Smale--2019|Smale et al., 2019]] ). A series of MHWs that occurred in 2010–2011 had consequences for seagrass in western Australia ( [[#Wernberg--2013|Wernberg et al., 2013]] ; [[#Arias-Ortiz--2018|Arias-Ortiz et al., 2018]] ), and for the lobster fishery in the Gulf of Maine ( [[#Pershing--2018|Pershing et al., 2018]] ). The MHWs that occured western Australia in 2015/2016 led to the third-highest mass coral bleaching globally ( [[#Le%20Nohaïc--2017|Le Nohaïc et al., 2017]] ).

'''Ocean acidity:''' With the increasing CO <sub>2</sub> concentration, the global mean ocean surface pH is decreasing and is now the lowest it has been for at least a thousand years ( ''very high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.5|Section 2.3.3.5]] ). It is ''very'' ''likely'' that, since the 1980s, ocean surface pH has changed at a rate of –0.016 to –0.019 per decade in the subtropical open oceans, at –0.010 to –0.026 per decade in the tropical Pacific, and at –0.003 to –0.026 per decade in open subpolar and polar zones (Sections 2.3.3.5 and 5.3.3.2). Over the period 1870–1899 to 2080–2099, ocean surface pH is projected to decline by –0.16 ± 0.002 under SSP1–2.6, and by –0.44 ± 0.005 under SSP5-8.5 (Sections 4.3.2.5 and 5.3.4.1). Declining ocean pH will exacerbate negative impacts on marine species ( ''medium confidence'' ) ( [[#Albright--2016|Albright et al., 2016]] ; [[#Kwiatkowski--2016|Kwiatkowski et al., 2016]] ; [[#Watson--2017|Watson et al., 2017]] ).

'''Ocean salinity:''' Salinity contrasts have increased since the 1950s, near the ocean surface ( ''virtually certain'' ) and in the subsurface ( ''very likely'' ) , with high-salinity regions becoming more saline and low-salinity regions becoming fresher ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.2|Section 2.3.3.2]] ). At the basin scale, it is ''very likely'' that the Pacific and the Southern Oceans have freshened and that the Atlantic has become more saline ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.2|Section 2.3.3.2]] ). The SROCC (IPCC 2019b) assessment that a general pattern of fresh ocean regions getting fresher and salty ocean regions getting saltier will continue in the 21st century is confirmed in [[IPCC:Wg1:Chapter:Chapter-9#9.2.2.2|Section 9.2.2.2]] .

At the regional scale, by 2100 the average Arctic surface salinity is projected to decrease by 1.5 ± 1.1 psu (practical salinity units), and the liquid freshwater column in the Arctic Ocean is projected to increase by 5.4 ± 3.8 m under RCP8.5, ( [[#Shu--2018|Shu et al., 2018]] ). In the Indian Ocean, sea surface salinity is projected to decrease by between 0.49 and 0.75 psu by 2080, compared to 2015, under RCP2.6 and RCP2.6, respectively ( [[#Akhiljith--2019|Akhiljith et al., 2019]] ). Projections for the North and South Atlantic oceans indicate increasing salinity in the upper layer (0–500 m) under both RCP4.5 and RCP8.5, due to the decreasing freshwater input from the equator and increasing net evaporation ( [[#Skliris--2020|Skliris et al., 2020]] ). There is ''medium confidence'' that fresh ocean regions (Pacific, Southern and Indian oceans) will get fresher and salty ocean regions (Atlantic Ocean) will get saltier over the 21st century ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.2.2|Section 9.2.2.2]] ; [[#IPCC--2019b|IPCC, 2019b]] ). Ocean warming and high-latitude surface freshening is projected to continue to increase upper-ocean stratification in the 21st century ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.1.3|Section 9.2.1.3]] ).

'''Dissolved oxygen:''' Since the middle of the 20th century, oxygen concentrations of open and coastal waters have been declining, and such deoxygenation affects biological and biogeochemical processes in the ocean ( [[#Schmidtko--2017|Schmidtko et al., 2017]] ). In recent decades, low-oxygen zones in ocean ecosystems have expanded, and projections indicate an acceleration with global warming ( ''medium confidence'' ) ( [[#Diaz--2008|Diaz and Rosenberg, 2008]] ; [[#Gilly--2013|Gilly et al., 2013]] ; [[#Gobler--2014|Gobler et al., 2014]] ). A 2% loss (4.8 ± 2.1 pmoles O <sub>2</sub> ) in total dissolved oxygen in the upper ocean layer (100–600 m) has been observed during 1970–2010 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.4.2|Section 2.3.4.2]] ), with the highest oxygen loss of up to 30 mol m <sup>–2</sup> per decade in the equatorial and North Pacific, the Southern Ocean and the South Atlantic Ocean ( [[IPCC:Wg1:Chapter:Chapter-5#5.3.3.2|Section 5.3.3.2]] ). Global mean ocean oxygen concentration is projected to decrease by 6.36 ± 2.92 mmol m <sup>–3</sup> under SSP1-2.6 and by 13.27 ± 5.28 mmol m <sup>–3</sup> under SSP5-8.5 in the subsurface (100–600 m) by 2080–2099, compared to 1870–1899, which is respectively 71% and 40% greater than previous estimates based on CMIP5 models ( [[IPCC:Wg1:Chapter:Chapter-5#5.3.3.2|Section 5.3.3.2]] ). In the benthic ocean, projected future losses of dissolved oxygen concentration by 2080–2099, compared to 1870–1899, are −5.14 ± 2.04 mmol m <sup>−3</sup> under SSP1-2.6 and −6.04 ± 2.19 mmol m <sup>−3</sup> under SSP5-8.5 ( [[#Kwiatkowski--2020|Kwiatkowski et al., 2020]] ). [[IPCC:Wg1:Chapter:Chapter-5#5.3.3.2|Section 5.3.3.2]] assessed ''very likely'' global decreases in ocean oxygen concentrations although there is ''medium confidence'' in specific regional declines that are expected to expand both anoxic and hypoxic zones, with such reductions of oxygen expected to persist for thousands of years ( [[#Yamamoto--2015|Yamamoto et al., 2015]] ; [[#Frölicher--2020|Frölicher et al., 2020]] ).

'''Sea ice:''' The Arctic sea ice area for September has decreased from 6.23 to 3.76 million km <sup>2</sup> and for March from 14.52 to 13.42 million km <sup>2</sup> between 1979–1988 and 2010–19 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] ). There is ''high confidence'' that sea ice in the Arctic will further decrease in the future under all emissions scenarios ( [[IPCC:Wg1:Chapter:Chapter-9#9.3.1.1|Section 9.3.1.1]] ). In contrast, there is no clear observed trend in the Antarctic sea ice area over the past few decades and there is ''low confidence'' of future changes ( [[IPCC:Wg1:Chapter:Chapter-9#9.3.1.1|Section 9.3.1.1]] ). The duration of the summer ice season in the Arctic has increased by 5 to 20 weeks between 1979 and 2013, with a significant trend ranging from 5 to 17 days per decade for earlier spring retreat and from 5 to 25 days per decade for later autumn advance, with consequences for Arctic marine mammals (AMMs) due to sea ice habitat loss ( [[#Laidre--2015|Laidre et al., 2015]] ). The Arctic is projected to be ice-free more often during summer under 2°C global warming compared to 1.5°C global warming ( [[IPCC:Wg1:Chapter:Chapter-9#9.3.1.1|Section 9.3.1.1]] ; see also Sections 12.4.9 and 4.4.2.1), opening new shipping lanes for international commerce ( [[#Valsson--2011|Valsson and Ulfarsson, 2011]] ) and lengthening the season for offshore resource extraction ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ). Iceberg numbers are expected to increase as a result of global warming, forming an elevated hazard to shipping and offshore facilities ( [[#Bigg--2018|Bigg et al., 2018]] ).

'''It is''' virtually certain '''that global mean SST will continue to increase throughout the 21st century, resulting in the exceedance of numerous climatic impact-driver thresholds relevant to marine ecosystems''' ( medium confidence '''). Marine heatwave days are projected to increase in global oceans, with a larger increase in the tropical ocean and Arctic Ocean''' ( high confidence '''). It is''' virtually certain '''that upper-ocean stratification will continue to increase in the 21st century. Future ocean warming will''' very likely '''assist the development of both anoxic and hypoxic zones, with such reductions of oxygen expected to persist for thousands of years. Future projections also indicate freshening of the Pacific, Southern and Indian oceans and a saltier Atlantic Ocean''' ( medium confidence ''').'''

The assessed direction of change in climatic impact-drivers for open and deep ocean regions and associated confidence levels are illustrated in Table 12.10, following the AR6 WGI ocean reference regions (Figure Atlas.2b).

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=== 12.4.9 Polar Terrestrial Regions ===

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Several recent climate assessments of polar regions describe robust patterns of recent and future climatic changes driving impacts and risk for polar environmental, societal, and economic assets. These have included the IPCC SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ), the Report on Snow, Water, Ice and Permafrost in the Arctic ( [[#AMAP--2017|AMAP, 2017]] ), and national assessments for the USA ( [[#Markon--2018|Markon et al., 2018]] ) and Canada ( [[#Derksen--2018|Derksen et al., 2018]] ). This section examines Greenland and Iceland, the Russian Arctic, Antarctica, and the Arctic portions of Northern Europe and North America (Figure 1.18c).

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==== 12.4.9.1 Heat and Cold ====

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'''Mean air temperature:''' Atlas.11.2 shows ''high confidence'' in warming of the Arctic in observations and projections, measuring among the fastest-warming places at more than twice the global mean, with substantially higher temperature increases in the cold season (see also [[#AMAP--2017|AMAP, 2017]] ; [[#Meredith--2019|Meredith et al., 2019]] ). Atlas.11.1 assessed ''very likely'' warming in observations of West Antarctica from 1957 to 2016, but ''limited evidence'' of mean air temperature change across East Antarctica even as there is ''high confidence'' in future warming across the continent (Figure Atlas.29; [[#Meredith--2019|Meredith et al., 2019]] ).

'''Extreme heat, cold spell and frost:''' Ecosystem and societal temperature thresholds in polar regions often reflect lower tolerance to heat and higher tolerance to cold. Extreme heat events have increased around the Arctic and Iceland since 1979, including increases in cold season warm days and nights, melt days, and Arctic winter warm events (T > –10°C) as well as decreases in cold days and nights ( [[#Mernild--2014|Mernild et al., 2014]] ; [[#Matthes--2015|Matthes et al., 2015]] ; [[#Vikhamar-Schuler--2016|Vikhamar-Schuler et al., 2016]] ; [[#Graham--2017|Graham et al., 2017]] ; [[#Sui--2017|Sui et al., 2017]] ; [[#Dobricic--2020|Dobricic et al., 2020]] ; [[#Peña-Angulo--2020|Peña-Angulo et al., 2020]] ). Heatwaves causing high temperature records have been recently documented in West and East Antarctica ( [[#Wille--2019|Wille et al., 2019]] ; [[#Robinson--2020|Robinson et al., 2020]] ). There is ''high confidence'' that polar amplification will drive increases in Arctic heat extremes as well as continuing declines in the magnitude and frequency of cold extremes ( [[#Matthes--2015|Matthes et al., 2015]] ; [[#Kharin--2018|Kharin et al., 2018]] ), although dynamical effects will still bring substantial cold air anomalies over the Arctic ( [[#Wu--2019|Wu and Francis, 2019]] ). There is ''medium confidence'' for equivalent changes in extreme heat in Antarctica based primarily on higher mean temperatures, with J.R. [[#Lee--2017|]] [[#Lee--2017|Lee et al. (2017)]] projecting more than 50 additional degree days above freezing (2098 RCP8.5 compared with 2014) over parts of the Antarctic Peninsula but smaller changes over mainland Antarctica.

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==== 12.4.9.2 Wet and Dry ====

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'''Mean precipitation:''' Atlas.11.2 indicated ''medium confidence'' in observed increases in Arctic precipitation, with the largest rises in the cold season. Antarctic precipitation showed no significant overall trend since the 1970s, with a positive trend over the 20th century (Sections 9.4.2.1 and Atlas.11.1). Increases in Arctic and Antarctic precipitation during the 21st century are ''very'' ''likely'' , with projected percentage increases that are much higher than most subpolar regions of the world (Figure Atlas.29).

'''Floods and heavy precipitation:''' Observations and model projections indicate ''high confidence'' in increasing Arctic river runoff in response to increasing total precipitation ( [[#Box--2019|Box et al., 2019]] ; [[#Durocher--2019|Durocher et al., 2019]] ; [[#Meredith--2019|Meredith et al., 2019]] ) with a shift towards earlier meltwater flooding ( [[#AMAP--2017|AMAP, 2017]] ). Higher Arctic precipitable water totals are also connected with observed increases in heavy precipitation and convective activity ( ''high confidence'' ) ( [[#Ye--2015|Ye et al., 2015]] ; [[#Kharin--2018|Kharin et al., 2018]] ; [[#Chernokulsky--2019|Chernokulsky et al., 2019]] ). Higher flood magnitudes are also driven by future increases in rain-on-snow event days, amounts, and runoff, which are more significant in the Arctic than in mid-latitudes (where seasonal snow cover is often further reduced; [[#AMAP--2017|AMAP, 2017]] ; [[#Jeong--2018b|Jeong and Sushama, 2018b]] ).

'''Landslide and snow avalanche:''' There is a growing number of studies on mass movements in polar regions. Although there is ''low confidence'' in widespread observational trends for landslides or snow avalanches, a rise in the number of future landslides is supported by strong links to increases in heavy precipitation, glacier retreat, and thawing of ice-rich permafrost that can lead to retrogressive thaw slumps in Arctic regions ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] ; [[#Kokelj--2015|Kokelj et al., 2015]] ; [[#Derksen--2018|Derksen et al., 2018]] ; [[#Lewkowicz--2019|Lewkowicz and Way, 2019]] ; [[#Patton--2019|Patton et al., 2019]] ; [[#Ward%20Jones--2019|Ward Jones et al., 2019]] ).

'''Aridity and drought:''' Recent decades have seen a general decrease in Arctic aridity, with projections indicating a continuing trend towards reduced aridity ( ''high confidence'' ) as increased moisture transport leads to higher precipitation, humidity and streamflow ( [[#Meredith--2019|Meredith et al., 2019]] ) and a corresponding decrease in dry days ( [[#Khlebnikova--2019a|Khlebnikova et al., 2019a]] ). There is ''low confidence'' overall of recent or projected drought changes in polar regions ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ) even as increasing evidence shows that drainage from permafrost thaw, higher potential evapotranspiration, and changing seasonal patterns of melt have caused lake reduction and soil moisture deficits in several areas that match with projections of future drought increase despite overall precipitation increases ( [[#Andresen--2015|Andresen and Lougheed, 2015]] ; [[#Bring--2016|Bring et al., 2016]] ; [[#Spinoni--2018a|Spinoni et al., 2018a]] ; [[#Feng--2019|Feng et al., 2019]] ; [[#Finger%20Higgens--2019|Finger Higgens et al., 2019]] ).

'''Fire weather:''' Fire season lengthened from 1979 to 2015 over Arctic portions of North America ( [[#Jain--2017|Jain et al., 2017]] ), corresponding also to a 1975–2015 increase in lightning-ignited fires in Arctic North-Western North America ( [[#Girardin--2013|Girardin et al., 2013]] ; [[#Veraverbeke--2017|Veraverbeke et al., 2017]] ). [[#Abatzoglou--2019|Abatzoglou et al. (2019)]] climate model simulations project significant fire weather index increases in boreal forests of Arctic Europe, Arctic Russia and Arctic North-Eastern North America ( ''medium confidence'' ). Trends towards more frequent fires in tundra regions are expected to continue, driven in particular by increasing potential evapotranspiration and changes in vegetation ( ''high confidence'' ) ( [[#Hu--2015|Hu et al., 2015]] ; [[#AMAP--2017|AMAP, 2017]] ; [[#Young--2017|Young et al., 2017]] ).

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==== 12.4.9.3 Wind ====

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'''Mean wind speed and severe storm:''' There is ''medium confidence'' of mean wind decrease over the Russian Arctic, Greenland and Iceland, and Arctic North-Eastern North America ( [[#Karnauskas--2018a|Karnauskas et al., 2018a]] ; [[#Jung--2019|Jung and Schindler, 2019]] ), but ''low confidence'' of changes in the other Arctic regions and Antarctica. [[#Bintanja--2014|Bintanja et al. (2014)]] projected that a strengthening of the Southern Annular Mode would decrease easterlies along Antarctica’s coasts with only small changes in katabatic winds (although this effect may diminish with stratospheric ozone recovery). In contrast, [[#Gorter--2014|Gorter et al. (2014)]] regional climate model projections indicated a reduction in mean winds over the interior of Greenland by RCP4.5 2100 while coastal winds increase. Reanalysis data and climate models indicate few coherent regional trends of polar cyclone frequency or relationships with cyclone depth and size ( [[#Akperov--2018|Akperov et al., 2018]] , 2019; [[#Day--2018|Day and Hodges, 2018]] ; [[#Zahn--2018|Zahn et al., 2018]] ).

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==== 12.4.9.4 Snow and Ice ====

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'''Snow:''' Atlas.11.1 identified ''likely'' increases in surface mass balance (driven by snowfall) across Antarctica in the 20th century ( ''medium confidence'' ). In the Arctic, overall snow extent and seasonal duration are projected to continue recent declines ( ''high confidence'' ), although mid-winter snowpack increases in some of the coldest and high-elevation locations given higher precipitation totals ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] , Atlas.9 and Atlas.11.2; [[#Bring--2016|Bring et al., 2016]] ; [[#Danco--2016|Danco et al., 2016]] ; [[#AMAP--2017|AMAP, 2017]] ; [[#Meredith--2019|Meredith et al., 2019]] ). Higher temperatures result in a higher percentage of Arctic precipitation falling as rain (particularly in autumn and spring) ( ''high confidence'' ), with most land regions (outside of Greenland and Antarctica) becoming dominated by rainfall (more than 50% of total precipitation) by RCP8.5 2100 ( [[#Bintanja--2017|Bintanja and Andry, 2017]] ; [[#Irannezhad--2017|Irannezhad et al., 2017]] ).

'''Glacier and ice sheet:''' Section 9.5.1 and [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.3|Section 2.3.2.3]] found that glaciers have lost mass in all polar regions since 2000 ( ''high confidence'' ), and [[IPCC:Wg1:Chapter:Chapter-9#9.4|Section 9.4]] assessed ''high confidence'' in Greenland Ice Sheet mass losses since 1980 and Antarctic Ice Sheet losses since 1992 (dominated by West Antarctica, with losses in parts of East Antarctica in the past two decades). New simulations from GlacierMIP ( [[#Marzeion--2020|Marzeion et al., 2020]] ) indicate glaciers in Iceland will lose 31 ± 35%, 41 ± 46% and 53 ± 45% of their mass in 2015 by the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. [[#Marzeion--2020|Marzeion et al. (2020)]] projected mass losses ( ''high confidence'' ) for those same scenarios in the Greenland Periphery: 22 ± 23%, 29 ± 26%, and 42 ± 28%; Svalbard: 35 ± 34%, 50 ± 36%, and 66 ± 35%; Russian Arctic: 26 ± 26%, 38 ± 28%, and 52 ± 30%; Northern Arctic Canada: 12 ± 13%, 18 ± 12%, and 27 ± 18%; Southern Arctic Canada: 23 ± 27%, 33 ± 29%, and 48 ± 32%; and Antarctic Periphery: 7 ± 12%, 13 ± 10%, and 16 ± 19%. Areas with receding glaciers are also potentially vulnerable to glacial lake outburst floods ( [[#Harrison--2018|Harrison et al., 2018]] ).

'''Permafrost:''' Observations from recent decades (assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] and [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] ) show increases in permafrost temperature ( ''very high confidence'' ) and active layer thickness ( ''medium confidence'' ) across the Arctic ( [[#AMAP--2017|AMAP, 2017]] ; [[#Derksen--2018|Derksen et al., 2018]] ; [[#Markon--2018|Markon et al., 2018]] ; [[#Biskaborn--2019|Biskaborn et al., 2019]] ; [[#Farquharson--2019|Farquharson et al., 2019]] ; [[#Meredith--2019|Meredith et al., 2019]] ; [[#Romanovsky--2020|Romanovsky et al., 2020]] ). [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] noted that observations of active layer thickness in Antarctica are too limited to assess long-term trends (see also [[#Hrbáček--2018|Hrbáček et al., 2018]] ; [[#Biskaborn--2019|Biskaborn et al., 2019]] ). Future projections indicate continuing increases in permafrost temperature and active layer thickness with loss of permafrost across the Arctic ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|Section 9.5.2]] ). [[#Streletskiy--2019|Streletskiy et al. (2019)]] noted that changes to Russian permafrost temperature and active layer thickness are most pronounced in areas where permafrost is continuous (underlying >90% of landmass). CMIP5 analyses by [[#Slater--2013|Slater and Lawrence (2013)]] projected that, by RCP8.5 2100, shallow (<3 m) permafrost would be most probable only in portions of the Canadian Arctic Archipelago and the Russian Arctic coastal and eastern upland regions.

'''Sea ice:''' Consistent with SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ), [[IPCC:Wg1:Chapter:Chapter-9#9.3.1|Section 9.3.1]] and [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] assess ''very high confidence'' that Arctic sea ice thickness, extent, and average age have significantly decreased over the past four decades, with largest declines in September (when sea ice is at an annual minimum). Declines in landfast ice are most rapid in the Laptev Sea ( [[#Selyuzhenok--2015|Selyuzhenok et al., 2015]] ), with warming also breaking perennial landfast ice blocking ocean channels (‘ice plugs’) in the Canadian Archipelago ( [[#Pope--2017|Pope et al., 2017]] ), and landfast ice declining in the cold season by 7% per decade across the Arctic (1976–2007; [[#Yu--2014|Yu et al., 2014]] ). Observed trends and projections suggest that perennial sea ice is being replaced by thin, seasonal ice, although multi-year ice will persist above the Canadian Archipelago and drift into sea transportation lanes ( [[#Howell--2016|Howell et al., 2016]] ; [[#Derksen--2018|Derksen et al., 2018]] ). Trends from 1979 to 2013 show slightly earlier spring melt for Arctic sea ice, but substantially delayed autumn freeze-up and a melt season lengthened by more than 3 days per decade off northern Alaska and Canada with the exception of portions of the Bering Sea ( [[#Parkinson--2014|Parkinson, 2014]] ; [[#Stroeve--2014|Stroeve et al., 2014]] ). [[IPCC:Wg1:Chapter:Chapter-9#9.3.2|Section 9.3.2]] assessed ''low confidence'' in long-term trends in sea ice extent or thickness near Antarctica.

Future declines in Arctic sea ice are ''virtually certain'' , although there is ''low confidence'' in declines of Antarctic sea ice given dynamical processes in the Southern Ocean and the recovery of stratospheric ozone ( [[IPCC:Wg1:Chapter:Chapter-9#9.3|Section 9.3]] ; [[#Meredith--2019|Meredith et al., 2019]] ). Projections of an ‘ice-free’ Arctic vary, depending on definitions representing transportation needs, however [[#Laliberté--2016|Laliberté et al. (2016)]] noted that the median of 42 CMIP5 models projected <5% sea ice for the month of September by 2050, with equivalent conditions for the entirety of the August–October period by 2090. [[IPCC:Wg1:Chapter:Chapter-9#9.3.1|Section 9.3.1]] assessed ''high confidence'' that practically ice-free conditions (<1 million km <sup>2</sup> in the September mean) would ''likely'' first appear before 2050 even under strong mitigation scenarios ( [[#Sigmond--2018|Sigmond et al., 2018]] ; [[#Stroeve--2018|Stroeve and Notz, 2018]] ; Notz and SIMIP Community, 2020).

'''Lake and river ice:''' There is ''high confidence'' in observations of significant declines in seasonal ice cover thickness and duration over most Arctic lakes, with many lakes projected to lose more than one month of ice cover by mid-century ( ''medium confidence'' ) ( [[#Meredith--2019|Meredith et al., 2019]] ; [[#Sharma--2019|Sharma et al., 2019]] ). Some lakes that previously froze to the bottom (‘bedfast’) now maintain liquid bottom water year round, and others shift from perennial to seasonal ice cover ( [[#Surdu--2016|Surdu et al., 2016]] ; [[#Engram--2018|Engram et al., 2018]] ). [[#Yang--2020a|Yang et al. (2020a)]] identified a decline in Arctic cold-season river ice extent in satellite observations (particularly in Alaska) and projected reductions in average Northern Hemisphere seasonal river ice duration of 6.10 ± 0.08 days per degree global surface air temperature.

'''Heavy snowfall and ice storm:''' There is ''limited evidence'' of changes in heavy snowfall due to competing influences of shortened snowfall seasonality with more intense (and larger overall) precipitation in the Arctic. Episodic heavy snowfall trends in Antarctica are difficult to separate from large interannual variability ( ''limited evidence'' ) (Gorodetskaya et al., 2014, Turner et al., 2020). ''Limited evidence'' also hinders clear signals in ice storms, although warming shifts the freezing line (around which ice storms occur) poleward and upslope ( [[#Bintanja--2017|Bintanja and Andry, 2017]] ). [[#Groisman--2016|Groisman et al. (2016)]] used 40 years of observations to identify an increase of freezing rain events in Norway, North America, and eastern and western Russia. Increases in winter rainfall have led to more frequent development of difficult wildlife and livestock grazing conditions as basal ice conditions coat the ground below snowpack ( [[#Peeters--2019|Peeters et al., 2019]] ).

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'''Table 12.11''' '''|''' '''Summary of confidence in direction of projected change in climatic impact-drivers in the polar regions, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see [[#12.4|Section 12.4]] for more details of the assessment method).''' The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' in [[#12.5.2|Section 12.5.2]] . Note that the Arctic portions of the NEU, NEN and NWN differ from the full AR6 regions assessed in the Europe and North America sections above (see also Figure 1.18c).

[[File:35c186ec3522c779e133883cf3c3179a IPCC_AR6_WGI_Chapter12_Table_12_11_1.jpg]]

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<span id="coastal-and-oceanic-7"></span>
==== 12.4.9.5 Coastal and Oceanic ====

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'''Relative sea level:''' Satellite altimetry and tide data show that relative sea levels (with glacial isostatic adjustment) are rising in Arctic Europe and Arctic North-Western North America, declining in portions of southern Alaska and Arctic North-Eastern North America and no clear trend in Greenland and the Russian Arctic ( [[#Sweet--2018|Sweet et al., 2018]] ; [[#Rose--2019|Rose et al., 2019]] ), which is broadly consistent with findings in [[#Oppenheimer--2019|Oppenheimer et al. (2019)]] . Areas with low or negative change have substantial land uplift counteracting the global mean sea level trend ( [[#Greenan--2018|Greenan et al., 2018]] ; [[#Sweet--2018|Sweet et al., 2018]] ; [[#Madsen--2019|Madsen et al., 2019]] ). SROCC projections indicate ''high confidence'' in future rises in relative sea level for all Arctic regions other than areas of substantial land uplift in north-eastern Canada, the west coast of Greenland, and narrow portions of West Antarctica ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ).

'''Coastal flooding and erosion:''' Higher sea levels and reduced coastal sea ice protection will increase future extreme sea levels in the Arctic ( ''high confidence'' for Arctic Northern Europe, the Russian Arctic, and Arctic North-Western North America ( ''medium confidence'' '')'' for Greenland and Iceland and Arctic North-Eastern North America given glacial isostatic adjustment). [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] project that the current 1-in-100-year extreme total water level would have median return periods of 1-in-20-years to 1-in-50-years by 2050, increasing to 1-in-5-years to 1-in-20-years by 2100 under RCP4.5 along nearly the entire Arctic coastline by 2100 (excluding GIC for which projections are not available). Projections for RCP8.5 indicate that the present-day 1-in-100-year ETWL would have median return periods of 1-in-10-years to 1-in-50-years by 2050 and would occur once every five years (or more frequently) by 2100. Arctic coastal erosion is also expected to increase with climate change ( ''medium confidence'' ; ''high agreement'' but ''limited evidence'' of projections), accelerated in some regions by subsurface permafrost thaw and increased wave energy ( [[#Gibbs--2015|Gibbs and Richmond, 2015]] ; [[#Fritz--2017|Fritz et al., 2017]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ; [[#Casas-Prat--2020|Casas-Prat and Wang, 2020]] ). A longer ice-free season for the RCP8.5 2080s is projected to help drive more than 100 m of shoreline retreat in North-Western North America Arctic coastal communities ( [[#Melvin--2017|Melvin et al., 2017]] ; [[#Greenan--2018|Greenan et al., 2018]] ; [[#Magnan--2019|Magnan et al., 2019]] ). Assessment of coastal flooding and erosion changes in Antarctica are limited by a lack of studies.

'''Marine heatwave:''' Recent years have seen marine heatwaves (MHWs) and increasing extreme coastal SSTs in Arctic systems ( [[#Lima--2012|Lima and Wethey, 2012]] ; [[#Collins--2019|Collins et al., 2019]] ; [[#Frölicher--2019|Frölicher, 2019]] ). Projections show increases in MHW intensity, frequency and duration will be larger over the Arctic Ocean than mid-latitude oceans due in part to low interannual variability under current sea ice ( ''high confidence'' ). [[#Frölicher--2018|Frölicher et al. (2018)]] used 12 CMIP5 models to project median MHW days increasing about 25-fold and 50-fold at the 2°C and 3.5°C GWLs, respectively, in response to mean ocean warming and sea ice loss, and the smallest global changes still leading to increases in the Southern Ocean around Antarctica (see also Cross-chapter Box 9.1).

'''Climate change has caused and will continue to induce an enhanced warming trend, increasing heat-related extremes and decreasing cold spells and frosts in the Arctic''' ( high confidence '''), with similar changes in Antarctica but''' medium confidence '''for extreme heat increases and West Antarctic frost change decreases and''' low confidence '''for cold spell changes and East Antarctica frost. The water cycle is projected to intensify in polar regions, leading to more rainfall, higher river flood potential and more intense precipitation''' ( high confidence '''). Projections indicate reductions in glaciers at both poles, with sea ice loss, enhanced permafrost warming, decreasing permafrost extent, and decreasing seasonal duration and extent of snow cover in the Arctic''' ( high confidence ''') even as some of the coldest regions will see higher total snowfall given increased precipitation''' ( medium confidence '''). Projections indicate relative sea level rises in polar regions''' ( high confidence ''')''' , '''with the exception of regions with substantial land uplift including North-Eastern North America''' ( high confidence '''), western Greenland, the northern Baltic Sea, and portions of West Antarctica. Higher sea levels also contribute to''' high confidence '''for projected increases of Arctic coastal flooding and higher coastal erosion (aided by sea ice loss)''' ( medium confidence ''') with lower confidence for those CIDs in regions with substantial land uplift.'''

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<span id="specific-zones-and-hotspots"></span>
=== 12.4.10 Specific Zones and Hotspots ===

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This section focuses on CIDs affecting specific zones with heightened vulnerability and coherent characteristics that cut across traditional continental regions (see also [[#12.3|Section 12.3]] ). It is designed to match the structure of the Cross-Chapter Papers in the WGII Report, although polar regions were addressed in more extensive detail in Sections 12.4.8 and 12.4.9 of this Report and the Mediterranean Region will not be handled separately given that its climatic impact-drivers are discussed in Sections 12.4.1 and 12.4.5 as well as in Cross-Chapter Box 10.3.

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<span id="hotspots-of-biodiversity-land-coasts-and-oceans"></span>
==== 12.4.10.1 Hotspots of Biodiversity (Land, Coasts and Oceans) ====

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Hotspots of biodiversity are defined by the AR6 WGII as ‘geographic areas with exceptionally high richness of species, including rare (endemic) species’ (WGII Cross-Chapter Paper 1). The AR6 assessment is based on 238 distinctive regions often called the ‘Global 200 ecoregions’ ( [[#Olson--2002|Olson and Dinerstein, 2002]] ).

Mean temperature increase is a major climatic impact-driver for biodiversity hotspots, and it is ''very likely'' that it will affect all hotspot areas identified in the literature, at various rates in all climate scenarios, except those located in the North Atlantic where warming is uncertain (see Chapter 4). Terrestrial ecosystems will experience an enhanced warming compared to ocean ecosystems, because land temperatures are warming faster than ocean temperatures (Chapter 4). Marine ecoregions will experience ocean acidification and temperatures that increase faster in high latitudes ( ''high confidence'' ), but critical temperature and oxygen thresholds are projected to be crossed earlier (by mid-century RCP8.5) in tropical areas ( [[#Hughes--2017a|Hughes et al., 2017a]] ; [[#Bruno--2018|Bruno et al., 2018]] ). A warming trend is also expected for freshwater ecosystems, with different local magnitudes due to combined effects of groundwater system inertia as well as hydrology changes ( [[#Knouft--2017|Knouft and Ficklin, 2017]] ). In tropical land areas, because interannual temperature variability is weak compared to mean changes, the temperature distribution range is ''likely'' to be shifted to a very different range in all projection scenarios, with unprecedented values relative to pre-industrial conditions. High climate velocities are particularly noteworthy for biodiversity hotspots given complex ecosystem dynamics and niche climates not easily replicated under shifted geographies ( [[#Burrows--2014|Burrows et al., 2014]] ; [[#Halpern--2015|Halpern et al., 2015]] ; [[#Dobrowski--2016|Dobrowski and Parks, 2016]] ). In some regions (e.g., Central Africa, Amazon, South East Asia) the mean temperature change is already beyond the normal range of variations as it has reached levels higher than three (and up to six) times larger than the standard deviation of the interannual variations ( [[#Hawkins--2020|Hawkins et al., 2020]] ). Together with global warming, land and marine heatwaves are ''very likely'' to increase in the future climate in biodiversity hotspots (Sections 12.4.1–12.4.7).

There is ''low confidence'' in broad patterns of future drying or wet trends across the land and freshwater biodiversity hotspots in the humid tropics, although drying trends have been detected and predicted in parts of the Amazon ( [[#Fu--2013|Fu et al., 2013]] ; [[#Boisier--2015|Boisier et al., 2015]] ). There is ''medium confidence'' ( ''limited evidence'' , ''high agreement'' ) that in several regions the length of the dry season has already increased and is projected to further increase in some parts of the Mediterranean, Amazonia and sub-Saharan Africa ( [[#Debortoli--2015|Debortoli et al., 2015]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Hochman--2018|Hochman et al., 2018]] ; [[#Saeed--2018|Saeed et al., 2018]] ). Longer dry seasons also extend the seasonal length and geographical extent of fire weather in all future scenarios ( ''medium confidence'' ) ( [[#Jolly--2015|Jolly et al., 2015]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ).

'''In conclusion, biodiversity hotspots around the world will each face unique challenges as climatic impact-drivers change. However, heat, drought and length of dry season, fire weather, sea surface temperature and deoxygenation are relevant drivers to terrestrial, freshwater and marine ecosystems, and have marked increasing trends.'''

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<span id="cities-and-settlements-by-the-sea"></span>
==== 12.4.10.2 Cities and Settlements by the Sea ====

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Cities and settlements by the sea are exposed to specific climate and climate change patterns and to compound coastal hazard risks (AR6 WGII Cross-Chapter Paper 2). The AR5 WGII found that, in general, ‘urban climate change-related risks are increasing (including rising sea levels and storm surges, heat stress, extreme precipitation, inland and coastal flooding, landslides, drought, increased aridity, water scarcity, and air pollution)’ ( [[#Revi--2014|Revi et al., 2014]] ). Since AR5 a number of studies have been carried out to understand urban climate and its change. Box 10.3 identified a continuing strong role of the urban heat island in amplifying heat extremes in cities, although changes in the urban heat island are an order of magnitude smaller than projected localized warming trends ( ''very high confidence'' ).

Coastal cities’ proximity to the sea somewhat mitigates the effect of urban heat islands ( ''high confidence'' ) ( [[#Salvati--2017|Salvati et al., 2017]] ; [[#Santamouris--2017|Santamouris et al., 2017]] ; Y. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ; [[#Martinelli--2020|Martinelli et al., 2020]] ). Cities and settlements by the sea typically experience higher humidity levels than inland regions, combining with heat to enhance heat stress and induce exceedance of critical heat stress thresholds for outdoor activities, with potential enhanced exposure to heat for informal settlements (J. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ). Such threshold exceedances are projected to increase for many coastal areas ( ''high confidence'' ), including the Persian Gulf where heat stress is projected to be extreme ( [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Ahmadalipour--2018|Ahmadalipour and Moradkhani, 2018]] ), and some low-lying areas in Europe such as the Po Valley and coastal Mediterranean areas ( [[#Coppola--2021a|Coppola et al., 2021a]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ; see also the heat stress index shown in Figure 12.4d–f).

Climate change-related variations in oceanic drivers (e.g., relative sea level, storm surge, ocean waves), combined with tropical cyclones, extreme precipitation and river flooding, are expected to lead to more frequent and more intense coastal flooding and erosion ( ''very high confidence'' ) impacting cities and settlements located especially in low-elevation coastal zones and mega-deltas ( [[#Chan--2012|Chan et al., 2012]] , 2018; [[#Karymbalis--2012|Karymbalis et al., 2012]] ; [[#Hemer--2013|Hemer et al., 2013]] ; [[#Aerts--2014|Aerts et al., 2014]] ; [[#Neumann--2015|]] [[#Neumann--2015|B. Neumann et al., 2015]] ; [[#Hauer--2016|Hauer et al., 2016]] ; [[#Ranasinghe--2016|Ranasinghe, 2016]] ; [[#Hinkel--2018|Hinkel et al., 2018]] ; [[#Mavromatidi--2018|Mavromatidi et al., 2018]] ; [[#Marcos--2019|Marcos et al., 2019]] ; see also Sections 12.3, 12.4.1–12.4.7 and 12.4.9). Coastal erosion and flooding also pose challenges to critical infrastructure such as roads, subway tunnels, electricity and phone networks, wastewater management plants and buildings ( [[#Grahn--2017|Grahn and Nyberg, 2017]] ; [[#Pregnolato--2017|Pregnolato et al., 2017]] ). Compound flooding due to simultaneous storm surges and high river flows have been found to be increasingly frequent in several cities and/or low-lying areas in Europe and the USA ( ''high confidence'' ) ( [[#Wahl--2015|Wahl et al., 2015]] ; [[#Bevacqua--2019|Bevacqua et al., 2019]] ; [[#Ganguli--2019|Ganguli and Merz, 2019]] ). [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] found that the frequency of such compound flood events is projected to increase ( ''high confidence'' ). In addition to changes induced by sea level change, many cities and settlements by the sea are in regions where tropical cyclones are projected to become more intense and severe tropical cyclones more frequent ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11#11.7|Section 11.7]] ).

The SROCC highlighted coastal settlements in the Arctic as being particularly exposed to several CID changes ( [[#Magnan--2019|Magnan et al., 2019]] ). Enhanced waves, due to extended season of sea ice retreat, are projected to foster coastal flooding and erosion ( [[#12.4.9|Section 12.4.9]] ; [[#Gudmestad--2018|Gudmestad, 2018]] ; [[#Casas-Prat--2020|Casas-Prat and Wang, 2020]] ). Climate change is also affecting sea ice quality and season length along coasts of the Arctic Ocean where populations depend on sea ice for hunting or transportation ( [[#12.4.9|Section 12.4.9]] ; [[#Pearce--2015|Pearce et al., 2015]] ).

'''In summary, coastal cities and settlements are particularly affected by a number of climatic impact-drivers that have already changed and will continue to change whatever the emissions scenario. These include increases in extreme heat, pluvial floods, coastal erosion and coastal flood''' ( high confidence '''). Increasing relative sea level, compounding with increasing tropical cyclone storm surge and rainfall intensity, will increase the probability of coastal city flooding''' ( high confidence '''). Arctic coastal settlements are particularly exposed to climate change due to sea ice retreat''' ( high confidence ''').'''

<div id="12.4.10.3" class="h3-container"></div>

<span id="deserts-and-semi-arid-areas"></span>
==== 12.4.10.3 Deserts and Semi-arid Areas ====

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Drylands, which include hyper-arid, arid, semi-arid and dry sub-humid areas ( [[#IPCC--2019c|IPCC, 2019c]] ), lie on all continents and cover 46% of the global land area and host more than one-third of the current population ( [[#Olsson--2019|Olsson et al., 2019]] ). [[#Huang--2016b|Huang et al. (2016b)]] found that aridity changes have helped expand dryland area by about 4% from 1948 to 2004, with the largest expansion of drylands occurring in semi-arid regions since the early 1960s. [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] assessed ''high confidence'' of a future poleward expansion of the Hadley cell, leading to a poleward shift of dryland areas in all scenarios considered. There is no evidence of a future global trend in aridification of drylands ( [[#IPCC--2019a|IPCC, 2019a]] ), but ''high confidence'' of aridification in some areas (e.g., Mediterranean, Central America, Southern Africa; [[#IPCC--2019a|IPCC, 2019a]] ; see also Figure 12.4j–l). However, drivers of desertification largely include land-cover changes and land-use management, along with climate change ( [[#IPCC--2019a|IPCC, 2019a]] ).

Warming temperatures and extreme heat are major climatic impact-drivers with multiple potential impacts on societies, health, and habitability in semi-arid and arid regions that are already near physiological limits for outdoor activities. Semi-arid regions will ''very likely'' undergo a warming in all future scenarios ( [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] and Atlas) and ''likely'' undergo an increase in duration, magnitude and frequency of heatwaves (Chapter 11) (Figure 12.4a–c). It is ''likely'' that heat stress will be much more intense by the end of the century in many areas under all scenarios, such as deserts and semi-arid zones in Asia ( [[#Murari--2015|Murari et al., 2015]] ; [[#Mishra--2017|Mishra et al., 2017]] ), Australia and Africa ( [[#Zhao--2015|Zhao et al., 2015]] ; [[#Xia--2016|Xia et al., 2016]] ; [[#Guo--2017|Guo et al., 2017]] ; [[#Dosio--2018|Dosio et al., 2018]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ), with consequences for labour productivity with respect to high heat-humidity conditions (Figure 12.4d–f).

Drought is another major climatic impact-driver for semi-arid areas, imposing major challenges on agriculture given existing water availability constraints ( [[#Kusunose--2014|Kusunose and Lybbert, 2014]] ; [[#Barlow--2016|Barlow et al., 2016]] ; [[#Otto--2018|Otto et al., 2018]] ). Over the period 1961–2013, the annual area of drylands in drought has increased, on average by slightly more than 1% per year, with large interannual variability ( [[#Olsson--2019|Olsson et al., 2019]] ). In general, droughts have increased in several arid and semi-arid areas over the last decades ( ''medium confidence'' ), and are ''likely'' to increase in the future as indicated by a number of indices calculated from climate ( [[#Liu--2018b|Liu et al., 2018b]] ; [[#Zkhiri--2019|Zkhiri et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Driouech--2021|Driouech et al., 2021]] ; see also Figure 12.4j–l).

Deserts and semi-arid areas are prone to dust storms, which can drive impacts on health and several other sectors (X. [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ; [[#Tong--2017|Tong et al., 2017]] ). The SRCCL indicated that the evolution of dust under climate change is uncertain ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ), and there is a lack of evidence and agreement of a change in their frequency or intensity so far in most regions (Sections 12.4.1–12.4.9). Model projections of future changes in dust are hindered by the uncertainties in future regional wind and precipitation as the climate warms ( [[#Evan--2016|Evan et al., 2016]] ); in the effect of CO <sub>2</sub> fertilization on source extent ( [[#Huang--2017|Huang et al., 2017]] ); and in the impact of human activities upon the land surface ( [[#Ginoux--2012|Ginoux et al., 2012]] ; see Chapter 10). Projected trends in dust storms and dust loads in deserts and semi-arid areas vary from region to region. Dust loadings are expected to decrease over most of the Sahara and Sahel ( ''low confidence'' ) ( [[#12.4.1|Section 12.4.1]] ), increase over Mexico and the south-west USA ( ''medium confidence'' ) ( [[#12.4.6|Section 12.4.6]] ), and there is ''low confidence'' of a future trend due to climate change in other continents (Sections 12.4.2–12.4.5).

'''In conclusion, desert and semi-arid areas are strongly affected by climatic impact-drivers such as extreme heat, drought and dust storms. Heat hazards are''' very likely '''increasing in all future climate scenarios, but uncertainty remains regarding any broadly consistent future changes in other climatic impact-drivers for deserts and semi-arid regions.'''

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<span id="mountains"></span>
==== 12.4.10.4 Mountains ====

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Mountains cover about 30% of the land areas on Earth (not counting Antarctica) and deliver a number of vital services to humanity (WGII Cross-Chapter Paper 5; [[#IPCC--2019b|IPCC, 2019b]] ). Climate change in high mountains was addressed in SROCC, which emphasized changes in several climatic impact-drivers. These included an observed general decline in low-elevation snow cover, glaciers and permafrost ( ''high confidence'' ), which induced changes in natural hazards such as decrease in slope stability ( ''high confidence'' ), changes to the frequency of glacial lake outbursts ( ''limited evidence'' ), and climate effects on other climatic impact-drivers (avalanche, rain-on-snow floods) with various degrees of confidence ( [[#Hock--2019|Hock et al., 2019]] ).

There is a growing body of literature indicating elevation-dependent warming (EDW; different rates of warming by altitude although not necessarily increasing with altitude) in several mountain regions but not globally ( [[#Hock--2019|Hock et al., 2019]] ; [[#Pepin--2019|Pepin et al., 2019]] ; [[#Ahmed--2020|Ahmed et al., 2020]] ; [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|B. Li et al., 2020]] ; [[#Williamson--2020|Williamson et al., 2020]] ; [[#You--2020|You et al., 2020]] ; [[#Micu--2021|Micu et al., 2021]] ). Statistically significant elevational enhancement to long-term trends in maximum near-surface air temperatures and diurnal temperature range were observed in southern central Himalaya and in the Swiss Alps ( [[#Rottler--2019|Rottler et al., 2019]] ; [[#Thakuri--2019|Thakuri et al., 2019]] ). [[#Aguilar-Lome--2019|Aguilar-Lome et al. (2019)]] reported that winter daytime land surface temperatures in the Andean region between 7°S and 20°S show the strongest trends at higher elevations: +1.7°C per decade above 5000 m above sea level. [[#Palazzi--2019|Palazzi et al. (2019)]] identified changes in albedo and downward thermal radiation as key drivers of EDW according to the simulation outputs of a high-spatial-resolution model in three important mountainous areas: the Colorado Rocky Mountains, the Greater Alpine Region and the Himalayas–Tibetan Plateau, but mechanisms for EDW remain complex ( [[#Hock--2019|Hock et al., 2019]] ). Warming is also affecting mountain lake surface temperatures, increasing probabilities of ice-free winters and the frequency and duration of ‘lake heatwaves’ ( ''high confidence'' ) ( [[#O’Reilly--2015|O’Reilly et al., 2015]] ; [[#Woolway--2020|Woolway et al., 2020]] , 2021) with a high variability from lake to lake.

Elevation-dependent warming could speed up the observed, rapid upward shifts of the freezing level height (FLH) in several mountainous regions of the world and lead to faster changes in the snowline, the glacier equilibrium-line altitude and the snow/rain transition height ( ''high confidence'' ). In the Indus, Ganges and Brahmaputra basins in Asia, the FLH is projected to rise at a rate of 4.4 to 10.0 m yr <sup>–1</sup> under RCP8.5 ( [[#Viste--2015|Viste and Sorteberg, 2015]] ). In the Argentinian Andes, FLH is projected under RCP8.5 to move up more than twice as much by 2070 as during the entire Holocene under the worst case scenario ( [[#Drewes--2018|Drewes et al., 2018]] ). On the western slope of the subtropical Andes (30°S–38°S) in central Chile, the mean value of the free tropospheric height of the 0°C isotherm under wet conditions is projected to be close to or higher than the upper quartile of the distribution in the current climate, towards the end of the century and under RCP8.5 ( [[#Mardones--2020|Mardones and Garreaud, 2020]] ). In the Alps and the Pyrenees, [[#Spandre--2019|Spandre et al. (2019)]] projected a rise in the natural snow elevation of between 200–300 m and 400–600 m by mid-century under RCP2.6 and RCP8.5, respectively. In the same region, the environmental equilibrium-line altitude is projected to exceed the maximum elevation of 69%, 81% and 92% of the glaciers by the end of the century under RCPs 2.6, 4.5 and 8.5, respectively ( [[#Žebre--2021|Žebre et al., 2021]] ).

Orographic effects enhance convection and stratiform heavy precipitation (due to uplift) and make mountains prone to extreme precipitation events. These events are projected to increase in major mountainous regions (Alps, parts of the Andes, British Columbia, North-Western North America, Calabria, Carpathian, Hindu-Kush-Himalaya, Rocky Mountains, Umbria; ''medium'' to ''high confidence'' depending on location), with potential cascading consequences of floods, landslides and lake outbursts in mountainous areas in all scenarios ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] and Sections 12.4.1–12.4.9; [[#Geertsema--2006|Geertsema et al., 2006]] ; [[#Gaire--2015|Gaire et al., 2015]] ; [[#Kim--2015|Kim et al., 2015]] ; [[#Ciabatta--2016|Ciabatta et al., 2016]] ; [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Kharuk--2016|Kharuk et al., 2016]] ; [[#Syed--2016|Syed and Al Amin, 2016]] ; [[#Cloutier--2017|Cloutier et al., 2017]] ; [[#Gądek--2017|Gądek et al., 2017]] ; [[#Jurchescu--2017|Jurchescu et al., 2017]] ; [[#Rajczak--2017|Rajczak and Schär, 2017]] ; [[#Alvioli--2018|Alvioli et al., 2018]] ; [[#Coe--2018|Coe et al., 2018]] ; [[#Schlögl--2018|Schlögl and Matulla, 2018]] ; C.-W. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ; [[#Handwerger--2019|Handwerger et al., 2019]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Patton--2019|Patton et al., 2019]] ; [[#Vaidya--2019|Vaidya et al., 2019]] ; [[#Kirschbaum--2020|Kirschbaum et al., 2020]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ).

Declines in low-elevation snow depth and seasonal extent are projected for all SSP-RCPs (see Sections 12.4.1–12.4.6), along with reductions in mountain glacier surface area, increases in permafrost temperature, decreases in permafrost thickness, changes in lake and river ice, changes in the amount and seasonality of streamflows and hydrologic droughts in snow-dominated and glacier-fed river basins (e.g., in Central Asia; [[#Sorg--2014|Sorg et al., 2014]] ; [[#Reyer--2017b|Reyer et al., 2017b]] ) ( ''medium confidence'' ), and decreases in the stability of mountain slopes and snowfields. Glacier recession could lead to the creation of new glacial lakes in places like the Himalaya-Karakoram region ( [[#Linsbauer--2016|Linsbauer et al., 2016]] ) and in Alaska and Canada ( [[#Carrivick--2016|Carrivick and Tweed, 2016]] ; [[#Harrison--2018|Harrison et al., 2018]] ) ( ''medium confidence'' ). With increasing temperature and precipitation these can increase the occurrence of glacier lake outburst floods and landslides over moraine-dammed lakes ( ''high confidence'' ) ( [[#Carey--2012|Carey et al., 2012]] ; [[#Rojas--2014|Rojas et al., 2014]] ; [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ; [[#Cook--2016|Cook et al., 2016]] ; [[#Haeberli--2017|Haeberli et al., 2017]] ; [[#Kapitsa--2017|Kapitsa et al., 2017]] ; [[#Narama--2018|Narama et al., 2018]] ; [[#Wilson--2018|Wilson et al., 2018]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ; S. [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ).

'''In conclusion, mountains face complex challenges from specific climatic impact-drivers drastically influenced by climate change: regional elevation-dependent warming''' ( high confidence '''), low-to-mid-altitude snow cover and sno''' '''w-sea''' '''son decrease even as some high elevations see more snow''' ( high confidence '''), glacier mass reduction and permafrost thawing''' ( high confidence '''), and increases in extreme precipitation and floods in most parts of major mountain ranges''' ( medium confidence ''').'''

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<span id="tropical-forests"></span>
==== 12.4.10.5 Tropical Forests ====

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Tropical forests, which are among the world’s most biologically diverse ecosystems, are essentially located in Central and South America, Africa and South East Asia (AR6 WGII Cross-Chapter Paper 7). The AR5 and SR1.5 indicated several specific climatic impact-driver changes that are particularly important to tropical forests: mean temperature increase, long-term drying trends (including shifts in the length of the dry season), prolonged drought, wildfires and surface CO <sub>2</sub> increase for inland forests ( [[#IPCC--2013|IPCC, 2013]] , 2018). The SRCCL assessed an enhanced risk and severity of wildfires in tropical rainforests ( ''high confidence'' ), but fires are not only a natural process but are also affected by deforestation and other human influences ( [[#IPCC--2019a|IPCC, 2019a]] ).

Temperature is rising in all tropical regions covered with forests and will ''very likely'' continue to rise, reaching levels unprecendented in recent decades as the temperature trends rapidly emerge from weak historical interannual variability (Sections 12.4.1–12.4.4 and 12.5.2; see also [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] and Atlas).

Regional patterns of increasing drought or unusual wet and dry periods are predicted with agreement over many climate models such as over the Amazon basin ( [[#Boisier--2015|Boisier et al., 2015]] ; [[#Duffy--2015|Duffy et al., 2015]] ; [[#Zulkafli--2016|Zulkafli et al., 2016]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ). There is ''medium confidence'' ( ''limited evidence'' , ''high agreement'' ) that in several tropical-forest regions (e.g., Amazonia, West Africa) the dry season length has increased ( [[#Fu--2013|Fu et al., 2013]] ; [[#Debortoli--2015|Debortoli et al., 2015]] ; [[#Saeed--2017|Saeed et al., 2017]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Wadsworth--2019|Wadsworth et al., 2019]] ), and there is ''low confidence'' ( ''limited evidence'' ) that deforestation influences the shift in the onset of the wet season in south Amazonia ( [[#Leite-Filho--2019|Leite-Filho et al., 2019]] ). In contrast, the wet season is increasing in northern Australia tropical forests ( [[#Catto--2012|Catto et al., 2012]] ).

Tropical forests typically reach peak fire weather conditions in the dry season ( [[#Taufik--2017|Taufik et al., 2017]] ), in particular during long-lived droughts ( [[#Brando--2014|Brando et al., 2014]] ; [[#Marengo--2018|Marengo et al., 2018]] ), with consequences for tree mortality, forest and carbon sink loss ( [[#Brando--2019|Brando et al., 2019]] ), and on the hydrological cycle in South America ( [[#Martinez--2014|Martinez and Dominguez, 2014]] ; [[#Espinoza--2020|Espinoza et al., 2020]] ). Observations and reanalyses over the past three to four decades, combined into fire risk indices, show that the fire weather season length has been increasing by about 20% globally ( [[#Jolly--2015|Jolly et al., 2015]] ), and this index exhibits particularly high trend values over tropical forest areas of South and Central America and Africa. There is generally ''low confidence'' in future projections of general fire weather risk evolution in tropical forests and evolutions depend on the region ( [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). Over the Amazon basin the fire risk increase is projected to emerge well before 2050 while for other equatorial forests no significant evolution is found. In Savanna areas the risk increase is found to be more general.

'''In conclusion, most tropical forests are challenged by a mix of emerging warming trends that are particularly large in comparison to historical variability''' ( medium confidence '''). Water cycle changes bring prolonged drought, longer dry seasons, and increased fire weather to many tropical forests, with plants also responding to CO''' <sub>2</sub> '''increases''' ( medium confidence ''').'''

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<span id="global-perspective-on-climatic-impact-drivers"></span>
== 12.5 Global Perspective on Climatic Impact-drivers ==

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<span id="a-global-synthesis"></span>
=== 12.5.1 A Global Synthesis ===

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( [[#12.4|Section 12.4]] assessed changes in climatic impact-drivers by region, primarily based on a large number of local- and regional-scale studies (even though global studies are also used). This section presents an assessment of changes in CIDs at the global scale. It is based on both a bottom-up synthesis of the results in [[#12.4|Section 12.4]] , and a top-down assessment from global-scale studies undertaken here. Cross-Chapter Box 12.1 summarizes global-scale CIDs with levels of warming.

Global-scale studies use similar indices of climatic impact-drivers across space, although these indices may not always be those used at the local or regional scale. Most published global-scale studies concentrate on single sectors or climatic impact-drivers, but some take a multi-sectoral perspective (e.g., [[#Warszawski--2014|Warszawski et al., 2014]] ; [[#Arnell--2016|Arnell et al., 2016]] , 2019; [[#Schleussner--2016|Schleussner et al., 2016]] ; [[#Mitchell--2017|Mitchell et al., 2017]] ; [[#Betts--2018|Betts et al., 2018]] ; [[#Byers--2018|Byers et al., 2018]] ; [[#Mora--2018|Mora et al., 2018]] ; [[#O’Neill--2018|O’Neill et al., 2018]] ; [[#Zscheischler--2018|Zscheischler et al., 2018]] ). Only a few published global-scale studies (e.g., Coppola et al., 2021; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ) have used CMIP6 scenarios to date.

All regions will experience, before 2050, increased warming, an increase of extreme heat and a decrease in cold spells, regardless of the emissions trajectory ( ''high confidence'' ). Tropical regions, but also mid-latitude regions to a lesser extent, will experience an increasing number of days with heat indices crossing dangerous thresholds used to characterize heat stress, such as HI > 41°C (Figure 12.4). The increase, by the end of century, exceeds 100 days per year in most tropical areas under SSP5-8.5 but remains much more limited (almost half) under SSP1-2.6. Several global-scale studies have shown that high temperature extremes will increase everywhere ( ''high confidence'' ) ( [[#Gourdji--2013|Gourdji et al., 2013]] ; [[#Perkins-Kirkpatrick--2017|Perkins-Kirkpatrick and Gibson, 2017]] ; [[#Harrington--2018|Harrington et al., 2018]] ; [[#Jones--2018|Jones et al., 2018]] ; [[#Lehner--2018|Lehner et al., 2018]] ; [[#Shi--2018|Shi et al., 2018]] ; [[#Tebaldi--2018|Tebaldi and Wehner, 2018]] ; [[#Arnell--2019|Arnell et al., 2019]] ; [[#Russo--2019|Russo et al., 2019]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ), although the change depends on the indicator (see also Chapter 11). For example, by 2080, at least 80% of the land surface is expected to experience average summer temperatures greater than the historical (1920–2014) maximum with high (RCP8.5) emissions ( [[#Lehner--2018|Lehner et al., 2018]] ). The areas of rice and maize cropland with damaging extreme temperatures during the reproductive season will increase by a factor of three under RCP8.5 ( [[#Gourdji--2013|Gourdji et al., 2013]] ). Under a high emissions scenario, heatwaves that are currently considered rare are projected to become the norm almost everywhere by the end of the century ( [[#Russo--2014|Russo et al., 2014]] ). Heat stress as a combined function of temperature and humidity also increases at the global scale, especially with high emissions (e.g., [[#Matthews--2017|Matthews et al., 2017]] ). Growing degree-days and cooling degree-days also increase everywhere ( [[#Arnell--2019|Arnell et al., 2019]] ) with the absolute and proportional changes depending on temperature threshold. Increases in temperatures will result in reductions in heating degree days ( [[#Arnell--2019|Arnell et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ) and a widespread reduced frequency of cold extremes ( ''high confidence'' ).

Integrating the results of the regional assessments in [[#12.4|Section 12.4]] shows that changes in CIDs linked with the water cycle or atmospheric dynamics (e.g., storms) vary more among regions, largely due to the spatial pattern of changes in atmospheric circulation and changes in precipitation and evaporation (Chapters 8 and 11). There is ''high confidence'' that heavy precipitation and pluvial floods will be increasing in a majority of land regions, primarily due to the well-understood Clausius–Clapeyron relationship describing the increase in moisture content with air temperature (Chapters 8 and 11), but there is a large spatial variability in fluvial flood hazards. Top-down global-scale studies show that although fluvial flood hazards are projected to decrease in regions where there are large reductions in seasonal rainfall totals or where warmer temperatures mean less accumulated snow, at the global scale, fluvial flood hazard (characterized as the area affected, size of peak or likelihood of an event) is projected to increase substantially through the century ( [[#Giuntoli--2015|Giuntoli et al., 2015]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Winsemius--2016|Winsemius et al., 2016]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ; [[#Dottori--2018|Dottori et al., 2018]] ; [[#Arnell--2019|Arnell et al., 2019]] ). Projected changes in agricultural and hydrological drought characteristics are dependent on the indicator used to define drought (Sections 11.6 and [[#12.3.2|Section 12.3.2]] ), but there is at least ''medium confidence'' of an increase in the drought hazard in many parts of the world. This is also reflected in global-scale studies, with [[#Naumann--2018|Naumann et al. (2018)]] , for example, showing that the global mean average drought duration (based on the SPEI index which is calculated from the difference between precipitation and potential evaporation) increased from 7 months with the current climate to 18.5 months for a global warming level of 3°C. The apparent global increase in drought occurrence is greater when evaporation is captured in the drought indicator (e.g., SPEI) than when the indicator is based on precipitation alone (as in SPI; [[#Carrão--2018|Carrão et al., 2018]] ). There is evidence that the likelihood of simultaneous events in several locations will increase: [[#Trnka--2019|Trnka et al. (2019)]] found that the proportion of wheat-growing areas experiencing simultaneous severe water stress events (based on SPEI) in a year increased from 15% under current conditions to up to 60% at the end of the 21st century under high emissions.

The regional assessment in [[#12.4|Section 12.4]] shows that fire weather is projected to increase with ''medium'' or ''high confidence'' in every continent of the world, including Arctic polar regions. Globally, fire weather is projected to increase in future, primarily due to higher temperatures and exacerbated where precipitation reduces. By 2050, 60% of the global land area would see a significant increase in fire weather under RCP8.5 ( [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). There is less confidence in the projected distribution of change in fire weather across regions in global-scale studies. For example, Moritz et al., (2012) projected an increase in fire weather in mid- and high latitudes but a reduction in the tropics, whilst [[#Yu--2019|Yu et al. (2019)]] and [[#Bedia--2015|Bedia et al. (2015)]] projected an increase in the tropics. These differences reflect differences in methodologies and fire weather indices adopted in different studies.

Integration of the results of ( [[#12.4|Section 12.4]] shows that the total number of tropical cyclones is projected to decrease through the 21st century, particularly with high emissions, but the number of very intense tropical cyclones is projected to increase in most areas (at least ''medium confidence'' ) (e.g., [[#Bacmeister--2018|Bacmeister et al., 2018]] ; see [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ). Furthermore, regions with glaciers will lose glacier mass and regions concerned with snow cover will see a reduction in snow depth, the duration, or extent of cover ( ''medium confidence'' in polar regions '', high confidence'' elsewhere).

Relative sea level rise (RSLR) is projected in all regions (except for a few Arctic polar regions) with likelihoods varying from ''very likely'' to ''virtually certain'' depending on the region. This will increase the frequency of extreme sea levels and, depending on the level of coastal flood protection, coastal flooding ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). In terms of globally averaged extreme total water level (ETWL) frequency changes, the present-day 1-in-100-year event is projected to become 1-in-30-year and 1-in-20-year events by 2050 under RCP4.5 and RCP8.5, respectively. The present day 1-in-100-year ETWL is projected to become a 1-in-5-year event by 2100 under RCP4.5, while under RCP8.5, such events are projected to occur more than once a year ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ).

There is ''high confidence'' that most of the world’s sandy coasts will experience shoreline retreat, in the absence of terrestrial or offshore sediment sources. Median projections presented by ( [[#Vousdoukas--2020b|Vousdoukas et al., 2020b]] ) indicate that 13.6% (36,097 km) and 15.2% (40,511 km) of the world’s sandy beaches could retreat by more than 100 m by 2050 (relative to 2010) under RCP4.5 and RCP8.5 respectively, implying a 12% increase in severely threatened shoreline length under RCP8.5, relative to RCP4.5. These median projections increase to 35.7%–49.5% (RCP4.5 and RCP8.5, respectively; or 95,061 km–131,745 km) by the end of the century, implying a 38% increase in severely threatened shoreline length under RCP8.5, relative to RCP4.5.

Figure 12.11 highlights that each region will, with ''high confidence'' , experience changes in multiple CIDs, challenging the vulnerability of the region and its adaptation and mitigation capacity. All non-polar regions with a coastline will see an increase in relative sea level, extreme sea level and coastal erosion, and will also see an increase in hot extremes, a decrease in cold extremes, and many will experience an increase in heavy precipitation. One cluster of regions – East Southern and West Southern Africa regions, the Mediterranean, Northern Central America, Western North America, several regions in South America and Australia – will experience, in addition to the aforementioned globally changing CIDs, increases in either drought/aridity or fire weather ( ''high confidence'' ). This will impact upon agricultural resources, infrastructure and health and ecosystems. A second cluster of regions including mountainous areas or regions with seasonal snow cover will experience (in addition to increases in heat extremes, more intense short-duration rainfall, and increases in coastal hazards where coasts exist) reductions in snow and ice cover and/or increases in river flooding in many cases (Western, North-Western, Central and Eastern North America, Arctic regions, Andes regions, Europe, Siberia, Central and East Asia, Southern Australia and New Zealand) ( ''high confidence'' ). These are places where energy production, ski tourism, river transportation, and infrastructure could in particular face increased risks.

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[[File:cf4b28c478912fa18db35405a6ec8e7e IPCC_AR6_WGI_Figure_12_11.png]]

'''Figure 12.11''' '''|''' '''Synthesis of the climatic impact-driver (CID) changes projected by 2050 (204''' '''1–2''' '''060) with''' ''high confidence'' ''', relative to reference period (199''' '''5–2''' '''014), together with the sign (direction) of change.''' Information is taken from the CID tables in [[#12.4|Section 12.4]] . Some CIDs are grouped in order to streamline the information in order to fit in all information in the figure. Mean temperature, extreme heat, cold spells and frost are grouped under a single ‘heat’ icon, as they are projected to change simultaneously, albeit heat and cold are changing in opposite directions. Coastal CIDs (relative sea level, coastal flooding and coastal erosion at sandy beaches) are also grouped. In the figure, the ‘coastal’ icon indicates regions where at least two of the three individual coastal CIDs are projected to change with ''high confidence'' . Cases where only two of the three CIDs increase with high confidence are in Arctic Northern Europe, Russian Arctic and Arctic North-Western North America. A single icon is used for aridity, hydrological drought, and agricultural and ecological drought, and only the number of drought types that change is indicated. For the ‘Snow, ice’ icon, information is taken from the evolution of the ‘Snow, glacier and ice sheet’ CID; most regions also have similar changes for ‘permafrost’ and ‘lake, river and sea ice’. Exceptions are for North-Eastern North America, Russian Arctic and Arctic North-Western North America where snow is decreasing with ''medium confidence'' (thus not appearing in the figure), while permafrost and lake, river and sea ice is decreasing with ''high confidence'' . The location of the icons within the regions is arbitrary. Icon sources: https://www.flaticon.com/authors/freepik .

In a few other regions, only a small number of CIDs are projected to change with ''high confidence'' (e.g., Sahara, Central Africa, Western Africa, Madagascar, Arabian Peninsula, South-Eeastern South America, New Zealand, Small Islands). The lower confidence levels associated with changes in CIDs in these regions can be due either to weaker change signals compared to natural variability, or due to ''limited evidence'' and model uncertainties leading to ''low agreement'' , and does not mean that climate change will affect these regions any less than in other regions.

'''In summary, there is''' high confidence '''that all regions of the world will experience changes in several climatic impact-drivers by mid-century, albeit at region-specific rates of change and confidence levels for each CID. Consequently, changing CIDs have the potential to affect climate-related risks in all regions of the world.'''

<div id="12.5.2" class="h2-container"></div>

<span id="emergence-of-climatic-impact-drivers-across-time-and-scenarios"></span>
=== 12.5.2 Emergence of Climatic Impact-drivers Across Time and Scenarios ===

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The emergence of a climate change signal occurs when that signal exceeds some critical threshold (usually taken to be a measure of natural variability; see for example, [[#Hawkins--2012|Hawkins and Sutton, 2012]] ) or when the probability distribution of an indicator becomes significantly different to that over a reference period (e.g., [[#Chadwick--2019|Chadwick et al., 2019]] ; see also [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] and [[IPCC:Wg1:Chapter:Chapter-1#1.4.2|Section 1.4.2]] ), in which case external anthropogenic forcings can be detected as causal factors. The ‘time of emergence’ (ToE) or ‘temperature of emergence’ is the time or global warming level thresholds associated with this exceedance. Emergence is particularly relevant to impacts, risk assessment and adaptation because human and natural systems are largely adapted to natural variability but may be vulnerable if exposed to changes that go beyond this variability range; this is not to say that changes within natural variability have no impact, as occurrence of damaging extremes proves. Emergence also informs the timing of adaptation measures. The emergence of a change is always relative to a reference period (e.g., the pre-industrial period or a recent past), depending on the framing question. In the former case, the goal is to estimate the amplitude of an anthropogenically driven change while in the latter, it is to estimate the amplitude of change relative to a baseline that is familiar to stakeholders. Both questions are important for risk assessment, but the former may be more directly interpretable in a mitigation context. The variability also refers to a time scale, generally interannual to inter-decadal. See ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.2|Section 1.4.2]] and [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] for more details about how emergence is defined and used in the literature.

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'''Table 12.12 |''' '''Emergence of CIDs in different time periods, as assessed in this section.''' The colour corresponds to the confidence of the region with the highest confidence: white cells indicate where evidence is lacking or the signal is not present, leading to overall ''low confidence'' of an emerging signal.

[[File:fd050b1890340bd9cf8ad2435a50cca8 IPCC_AR6_WGI_Chapter12_Table_12_12_1.jpg]] [[File:3a65d20c43a262742bf56749e9ece726 IPCC_AR6_WGI_Chapter12_Table_12_12_2.jpg]]

Changes in climatic impact-drivers may remain within the range of natural variability or have a time of emergence that varies by region and scenario. This section assesses the evidence for the effects of anthropogenic climate change on the emergence of changes in CID index, past, present and future, as evidenced by the literature assessed in other chapters, as well as additional literature assessed here, at both global and regional scales. In many cases, however, sufficient literature for a robust region-by-region assessment of ToE is lacking. The assessment herein is made by CID. Regional emergence assessment is reported in Tables 12.3–12.11 but is undertaken in this section.

Estimations of ToE must be done with caution given the many sources of inherent uncertainties, such as observations representing only a single realization of climate history, internal variability (whose frequency – e.g., annual or decadal – needs to be precisely defined), model biases, and potential low-frequency changes in variability (Chapter 10; [[#Lehner--2017|Lehner et al., 2017]] ). In addition, a homogeneous interpretation of multiple studies is hampered by heterogeneous methodologies used to calculate emergence. In this section, we assess emergence and its confidence level based on such multiple methods as provided by the literature, and unless specified otherwise, emergence here refers to a signal to noise ration S/N > 1 relative to a pre-industrial baseline and interannual variability (the ‘noise’). Furthermore, observed trends and attribution are taken into account in combination with climate simulations (historical or projections) for assessing whether a trend has already emerged in the historical period.

'''Mean air temperature:''' Warming of mean annual temperatures has already emerged in all land regions, as obtained from past observations and confirmed by historical simulations ( ''high confidence'' ) (Figure 1.13; [[#King--2015|King et al., 2015]] ; [[#Hawkins--2020|Hawkins et al., 2020]] ), with S/N ratios larger than two. In the current climate, the highest S/N ratios exceed five over Central Africa, Amazonia, East and South East Asia. Seasonal warming emergence depends on the season. Because the temperature variability in the mid-latitudes is higher in winter than in summer, the emergence of seasonal warming occurs for summer but not for winter in most of this part of the world. In Europe, summer warming has emerged in all regions ( ''medium confidence'' , ''medium agreement'' ), and in North America, it has emerged only over Eastern and Western regions while in winter there is ''low confidence'' of an emergence in warming in all regions for both Europe and North America ( [[#Lehner--2017|Lehner et al., 2017]] ; [[#Hawkins--2020|Hawkins et al., 2020]] ). When considering the climate of the end of the 20th century (i.e., recent past) as a baseline, the emergence of mean temperature is projected at very different times depending on the scenario. For instance, emergence is reached by 2050 under RCP8.5 in most areas of Europe, Australia or East Asia, but it does not occur within the 21st century under RCP2.6 ( ''medium confidence'' ) ( [[#Sui--2014|Sui et al., 2014]] ; [[#Im--2021|Im et al., 2021]] ). This means that under RCP2.6, mean temperatures stay within the recent climate variability range observed in the mid-latitudes. However, even under RCP2.6, mean temperatures in tropical regions that have not already emerged are projected to emerge before 2050 ( ''medium confidence'' ).

'''Extreme heat and cold:''' An increase in heat extremes has emerged or will emerge in the coming three decades in most land regions ( ''high confidence'' ) (Chapter 11; [[#King--2015|King et al., 2015]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ), relative to the pre-industrial period, as found by testing significance of differences in distributions of yearly temperature maxima in simulated 20-year periods. In tropical regions, wherever observed changes can be established with statistical significance, and in most mid-latitude regions, there is ''high confidence'' that hot and cold extremes have emerged in the historical period, but only ''medium confidence'' elsewhere. In other regions emergence is projected at the latest in the first half of the 21st century under RCP8.5 ( ''high confidence'' ) ( [[#King--2015|King et al., 2015]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). Relative to the end of 20th-century conditions, changes in humid heat stress as characterized by wet bulb temperature, indicates a ToE as early as in the first two decades of the 21st century in RCP8.5 at least in many tropical regions (most of Africa in the band 20°S–20°N, South Asia and South East Asia) ( ''medium confidence'' ) ( [[#Im--2021|Im et al., 2021]] ). By 2050 and under RCP8.5, wet bulb temperature is projected to emerge in many other areas such as Southern Africa, North Africa, Europe, and most of Central, Southern and Eastern Asia and Northern and Eastern Australia, while under RCP2.6, emergence is either reached later in the century (Europe, Central Asia, Northern Australia), or never reached in the century ( [[#Im--2021|Im et al., 2021]] ). Decrease of cold spells has already emerged above the interannual variability in Australasia, Africa and most of Northern South America, and they are projected to emerge before 2050 in the northern mid-latitudes and in Southern South America ( [[#King--2015|King et al., 2015]] ) under RCP8.5 ( ''medium confidence, limited evidence'' and ''high agreement'' ).

'''Mean precipitation:''' Mean precipitation changes only emerged over a few regions in the historical period (increase in Northern and Eastern Europe and decrease in West Africa and Amazonia) from observations with an S/N ratio larger than one ( ''low confidence'' ) ( [[#Hawkins--2020|Hawkins et al., 2020]] ). The emergence of increasing precipitation before the middle of the 21st century is found across scenarios in Siberian regions, Russian Far East, Northern Europe, Arctic regions and the northernmost parts of North America ( ''high confidence'' ) and later in other northern mid-latitude areas, depending on the scenario, albeit with different methods and emergence definitions used in climate projections (Chapter 8; [[#Giorgi--2009|Giorgi and Bi, 2009]] ; [[#Maraun--2013|Maraun, 2013]] ; [[#King--2015|King et al., 2015]] ; [[#Akhter--2018|Akhter et al., 2018]] ; [[#Kumar--2018|Kumar and Ganguly, 2018]] ; [[#Nguyen--2018|Nguyen et al., 2018]] ; [[#Barrow--2019|Barrow and Sauchyn, 2019]] ; [[#Rojas--2019|Rojas et al., 2019]] ; [[#Kusunoki--2020|Kusunoki et al., 2020]] ; [[#Pohl--2020|Pohl et al., 2020]] ; W. [[#Li--2021|]] [[#Li--2021|Li et al., 2021]] ). Decreases in mean precipitation are projected to emerge in parts of Africa by the middle of the century, and later in the Mediterranean and Southern Australia, but the emergence depends on the scenario, and specific seasons for crop growth ( [[#Nguyen--2018|Nguyen et al., 2018]] ; [[#Rojas--2019|Rojas et al., 2019]] ). Mean precipitation does not emerge in any of these regions at any time in the 21st century under RCP2.6, but emerges in all under RCP8.5. ToE under RCP4.5 is projected to be around 25 years later relative to RCP8.5 in many of the early emergence regions, highlighting the importance of mitigation to gain more time for adaptation.

'''Heavy precipitation and floods:''' There is ''low confidence'' in the emergence of heavy precipitation and pluvial and river flood frequency in observations, despite trends that have been found in a few regions (Chapters 8 and Chapter 11, and across ( [[#12.4|Section 12.4]] ). In climate projections, the emergence of increase in heavy precipitation strongly depends on the scale of aggregation ( [[#Kirchmeier-Young--2019|Kirchmeier-Young et al., 2019]] ), with, in general, no emergence before a 1.5°C or 2°C warming level, and before the middle of the century ( ''medium confidence'' ), but results depend on the method used for the calculation of the ToE ( [[#Maraun--2013|Maraun, 2013]] ; [[#King--2015|King et al., 2015]] ; [[#Kusunoki--2020|Kusunoki et al., 2020]] ). Emergent increases in heavy precipitation are found in several regions when aggregated at a regional scale in Northern Europe, Northern Asia and East Asia, at latest by the end of the century in SRES A1B or RCP8.5 scenarios or when considering the decadal variability as a reference ( ''medium confidence'' ) ( [[#Maraun--2013|Maraun, 2013]] ; W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] , 2021; [[#Kusunoki--2020|Kusunoki et al., 2020]] ). There have been few emergence studies for streamflow and flooding, although one study showed emergence of different hydrological regimes at different times during the 21st century across the USA ( [[#Leng--2016|Leng et al., 2016]] ). Variability in extreme streamflows from year to year can be high relative to a trend ( [[#Zhuan--2018|Zhuan et al., 2018]] ). Given the heterogeneity of methods and results, there is only ''low confidence'' in the emergence of heavy precipitation and flood signals in any region when considering the S/N ratio.

'''Droughts, aridity and fire weather:''' There is ''low confidence'' in the emergence of drought frequency in observations, for any type of drought, in all regions. Even though significant drought trends are observed in several regions with at least ''medium confidence'' (Sections 11.6 and 12.4), agricultural and ecological drought indices have interannual variability that dominates trends, as can be seen from their time series ( ''medium confidence'' ) (H. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ; [[#Spinoni--2019|Spinoni et al., 2019]] ; [[#Haile--2020|Haile et al., 2020]] ; M. [[#Wu--2020|]] [[#Wu--2020|Wu et al., 2020]] ). Studies of the emergence of drought with systematic comparisons between trends and variability of indices are lacking, precluding a comprehensive assessment of future drought emergence. Historical climate simulations indicate that fire weather indices have already emerged in several regions (the Amazon basin, Mediterranean, Central America, West and Southern Africa) ( ''low confidence'' , ''limited evidence'' ) ( [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ), and emergence is projected with ''low confidence'' by the middle of the century in several other regions (Southern Australia, Siberia, most of North America and Europe) when considering several indices together.

'''Wind:''' Observed mean surface wind speed trends are present in many areas ( [[#12.4|Section 12.4]] ), but the emergence of these trends from the interannual natural variability and their attribution to human-induced climate change remains of ''low confidence'' due to various factors such as changes in the type and exposure of recording instruments, and their relation to climate change is not established. For future conditions, there is ''limited evidence'' of the emergence of trends in mean wind speeds due to the lack of studies quantifying wind speed changes and their interannual variability. The same limitation also holds for wind extremes (severe storms, tropical cyclones, sand and dust storms).

'''Snow and ice:''' The decrease in the Northern Hemisphere snow cover extent in spring has already emerged from natural variability ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.2|Section 3.4.2]] ). The snow cover duration period is projected to emerge over large parts of Eastern and Western North America and Europe by the mid-century both in spring and autumn, and emergence is expected in the second half of the 21st century in the Arctic regions in the high RCP8.5 scenario ( ''medium confidence'' ) (Chapter 9, SROCC). For snow depth or snow water equivalent, there is ''low confidence'' ( ''limited evidence'' ) of the emergence of a decrease before 2050 because climate change also increases the variability of the snow depth signal, for example in Europe ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.2|Section 3.4.2]] ; [[#Willibald--2020|Willibald et al., 2020]] ). Terrestrial permafrost is warming worldwide due to climate change (Sections [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|2.3.2.5]] and [[IPCC:Wg1:Chapter:Chapter-9#9.5.2|9.5.2]] ). Due to weak interannual variability of permafrost temperatures, terrestrial permafrost warming has emerged above natural variability in almost all observed time series of the Northern Hemisphere ( ''medium confidence'' , ''limited evidence'' , ''high agreement'' ) ( [[#Biskaborn--2019|Biskaborn et al., 2019]] ), but the active layer thickness exhibits considerable interannual variability inhibiting evidence for emergence (Chapter 9).

'''Sea ice:''' Sea ice area decrease in the Arctic in all seasons has already emerged from the interannual variability ( ''high confidence'' ) (Chapter 9). By contrast, the Antarctic sea ice area shows no significant trend, and therefore no emergence.

For other snow and ice CIDs (heavy snowfall and ice storm, hail, snow avalanche), there is ''limited evidence'' of emerging signals.

'''Relative sea level, coastal flood and coastal erosion:''' Near-coast RSLR will emerge before 2050 for RCP4.5 along the coasts of all AR6 regions (with coasts) except East Asia, the Russian Far East, Madagascar, the southern part of Eastern North America and the Antarctic regions ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.1.4|Section 9.6.1.4]] ; [[#Bilbao--2015|Bilbao et al., 2015]] ). Under RCP8.5, emergence of near-coast RSLR is projected by mid-century along the coasts of all AR6 regions (with coasts), except WAN where emergence is projected to occur before 2100 ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.1.4|Section 9.6.1.4]] ; [[#Lyu--2014|Lyu et al., 2014]] ) ( ''medium confidence'' ). Emergence studies for ETWL and coastal erosion are lacking and hence it is not currently possible to robustly assess emergence in these CIDs.

'''Mean ocean temperature and marine heatwave:''' The emergence of the sea surface temperature increase signal has been observed in global oceans over the last century, and the largest S/N values are found in the tropical Atlantic and tropical Indian oceans ( [[#Hawkins--2020|Hawkins et al., 2020]] ). There is ''high confidence'' in the widespread occurrence of marine heatwaves in all basins and marginal seas over the last decades (Chapter 9), but the emergence of this signal above the natural variability has not yet been addressed in detail.

'''Ocean acidity, ocean salinity and dissolved oxygen:''' The global ocean pH decline has ''very likely'' emerged from natural variability for more than 95% of the global open ocean (SROCC, Chapter 2). The regional signals are more variable, but in all ocean basins, the signal of ocean acidification in the surface ocean is projected to emerge in the early 21st century (Chapter 5). The mean ToE for acidity in the coastal subtropical to temperate north-east Pacific and north-west Atlantic is above two decades ( ''high agreement'' , ''medium evidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-5#5.3.5.2|Section 5.3.5.2]] ). Salinity change signals have already emerged with 20–45% of the zonally averaged basin in the Atlantic, 20–55% in the Pacific and 25–50% in the Indian oceans and will be reaching 35–55% in the Atlantic in 2050 to 55–65% in 2080; 45–65% to 60–75% in the Pacific; and 45–65% to 60–80% in the Indian oceans (Chapter 9; [[#Silvy--2020|Silvy et al., 2020]] ). Deoxygenization has already emerged in many open oceans. The signal is most evident in the Pacific and Southern oceans but not evident in the North Atlantic Ocean ( [[#Andrews--2013|Andrews et al., 2013]] ; [[#Levin--2018|Levin, 2018]] ). However, there is ''medium confidence'' in the emergence of the anthropogenic signal in many other oceanic regions by 2050 ( [[#Henson--2017|Henson et al., 2017]] ; [[#Levin--2018|Levin, 2018]] ).

'''There is''' high confidence '''that several CID changes have already emerged above historical period natural variability in many regions (e.g., mean temperature in most regions, heat extremes in tropical areas, sea ice, salinity). Heat and cold CIDs (excluding frost) that have not already emerged will emerge by 2050 whatever the scenario''' '''in almost all land regions''' ( medium confidence '''). The emergence of increasing precipitation before the middle of the century is also projected in Siberian regions, Russian Far East, Northern Europe and the northernmost parts of North America and Arctic regions across scenarios with the various methods and emergence definitions used''' ( high confidence '''). Studies are missing to properly assess S/N emergence for droughts and for wind CIDs. Arctic sea ice extent declines have mostly emerged above noise level''' ( medium '''to''' high confidence '''), and the emergence of declining snow cover is expected by the end of the century under RCP8.5. There is''' medium confidence '''that, under RCP8.5, the anthropogenic forced signal in near-coast relative sea level change will emerge by mid-century in all regions with coasts, except in the West Antarctic region where emergence is projected to occur before 2100. In all ocean basins, the signal of ocean acidification in the surface ocean is projected to emerge before 2050''' ( high confidence ''').'''

<div id="cross-chapter-box-12.1" class="h2-container box-container"></div>

'''Cross-Chapter Box 12.1 | Projections by Warming Levels of Hazards Relevant to the Assessment of Representative Key Risks and Reasons for Concern'''

<div id="h2-19-siblings" class="h2-siblings"></div>

'''Contributors:''' Claudia Tebaldi (United States of America), Guofinna Aoalgeirsdottir (Iceland), Sybren Drijfhout (United Kingdom), John Dunne (United States of America), Tamsin Edwards (United Kingdom), Erich Fischer (Switzerland), John Fyfe (Canada), Richard Jones (United Kingdom), Robert Kopp (United States of America), Charles Koven (United States of America), Gerhard Krinner (France), Friederike Otto (United Kingdom/Germany), Alex C. Ruane (United States of America), Sonia I. Seneviratne (Switzerland), Jana Sillmann (Norway/Germany), Sophie Szopa (France), Prodromos Zanis (Greece)

A consistent risk framework ( [[#Reisinger--2020|Reisinger et al., 2020]] ) has been adopted across the three Working Groups (WGs) in IPCC AR6 while recognizing the diversity of risk concepts across disciplines. WGI is assessing changes in climatic impact-drivers (CIDs), which are physical climate system conditions (e.g., means, events and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions (Sections 12.1–12.3). In the assessment of Representative Key Risk (RKR) categories and Reasons for Concern (RFCs) in WGII Chapter 16, the focus lies on the adverse consequences of climate change, for which many types of CIDs (i.e., ‘hazards’ in the context of identified risks) play a key role. This box synthesizes the assessment of such hazards according to global warming levels (GWLs) from various chapters of WGI to inform understanding of their potential changes and associated risks with temperature levels in general, and in particular to facilitate WGII integrated assessments of RKRs and RFCs. Cross-Chapter Box 11.1, connects the organization of regional information according to GWLs to the other common dimension along which future projections are organized: that is, scenarios. [[IPCC:Wg1:Chapter:Chapter-1#1.6|Section 1.6]] describes all dimensions of integration adopted in this Report, adding cumulative carbon emissions to GWLs and scenarios.

Eight RKRs are identified within WGII Chapter 16:

* RKR-A: risk to the low-lying coastal socio-ecological systems;
* RKR-B: risk to terrestrial and ocean ecosystems;
* RKR-C: risks associated with critical physical infrastructure, networks and services;
* RKR-D: risk to living standards;
* RKR-E: risk to human health;
* RKR-F: risk to food security;
* RKR-G: risk to water security; and
* RKR-H: risks to peace and to human mobility.

RFCs further synthesize the landscape of risks from climatic changes into five categories (from the IPCC Third Assessment Report onward; [[#Smith--2001|Smith et al., 2001]] ):

* RFC1: risks to unique and threatened systems;
* RFC2: risks associated with extreme weather events;
* RFC3: risks associated with the distribution of impacts;
* RFC4: risks associated with global aggregate impacts; and
* RFC5: risks associated with large-scale singular events.

Importantly, the assessment of risk in WGII considers hazards as only one component of an integrated assessment that involves their complex interaction with exposure and vulnerability of the systems at risk ( [[#Reisinger--2020|Reisinger et al., 2020]] ).

Hazards relevant to RKRs and RFCs are identified among aspects of the climate system that have an episodic, short-term nature, like extreme events (particularly relevant to RFC2 but contributing to many other risk categories). Increasing GWLs translate into changing characteristics of frequency, duration, intensity, seasonality and spatial extent for many of these hazards that are also apparent in scenario-based results (Chapters 11 and 12, and Sections 12.4 and 12.5.1). Other relevant hazards coincide with long-term trends embodying a gradual change that may result in unfavourable environmental conditions. Also, increasing GWLs increase the likelihood of compound temporal or spatial occurrence of similar or different hazards ( [[IPCC:Wg1:Chapter:Chapter-11#11.8|Section 11.8]] ). Furthermore, RFC5’s focus on singular events includes concern surrounding potential tipping points and irreversible behaviour in the physical climate system.

Cross-Chapter Box 12.1, Table 1 organizes information by hazard and presents current state and future change assessments with increasing GWLs (defined by increasing global surface air temperature, GSAT; see Cross-Chapter Box 2.3). We draw on individual chapters across the WGI report for the assessment of how these hazards vary with GWL. Hazards for which a relation to GWLs has not been assessed are not reported in the table.

Cross-Chapter Box 12.1

'''Cross-Chapter Box 12.1, Table 1'''

'''|'''

'''Summary of CIDs/hazards that are identified as driving RKRs and RFCs.''' The behaviour of each (in most cases considered at the global scale, but for some types in terms of spatially resolved patterns) as a function of GWLs is described and when possible quantified, together with the level of confidence of the assessment, to be found in more detail in the chapter/sections indicated in the corresponding column. For the relation with GSAT levels, two columns detail current state, which can be associated to about 1°C of global warming, and future behaviour. Tipping Points and Irreversibility are comprehensively assessed with CMIP6 models up to GWL = 3°C, with fewer studies and lower confidence at higher GWLs up to 5°C.
{| class="wikitable"
|-
! '''Hazard Category'''

'''Sub-category'''

! '''RKR/RFC Relevance'''

! '''Behaviour at About 1°C (Present)'''

! '''Behaviour as a Function of GWL (Future)'''

! '''WGI Chapter References'''

|-
| colspan="5"| '''Extreme Events'''

|-
| Hot and Cold Extremes

| All RKRs; RFC2, RFC3

| Frequency and intensity of hot extremes increased and cold extremes decreased at the global scale and in most regions since 1950 (GSAT change about 0.6°C) ( ''virtually certain'' ). Number of warm days and nights increased; intensity and duration of heatwaves increased; number of cold days and nights decreased ( ''virtually certain'' ). Regional-to-continental scale trends generally consistent with global-scale trends ( ''high confidence'' ). Limited data in a few regions (especially Africa) hampers trend assessment.

| Strong linear relation between magnitude and intensity of heat and cold extremes and GSAT, detectable from warming as low as 1.5°C; changes in the extreme metrics twice as large (in mid-latitude regions) or more (in high-latitude regions) than GSAT warming ( ''very likely'' ) ; metrics related to frequency of exceedance may show stronger than linear relationships (exponential) ( ''very likely'' ) . Compared to today, changes in extremes at +2°C at least two times larger than at +1.5°C, and four times larger at +3°C.

| Sections 11.3, 11.9, 12.4; Figures 11.3, 11.4, 11.6, 11.11, 11.12; Table 11.2.

|-
| Extreme Precipitation Events

| All RKRs; RFC2, RFC3

| Frequency and intensity of heavy precipitation events increased at the global scale over a majority of land regions with good observational coverage ( ''high confidence'' ) and at the continental scale in North America, Europe and Asia. Larger percentage increases in heavy precipitation observed in the northern high latitudes in all seasons, and in the mid-latitudes in the cold season ( ''high confidence'' ). Regional increases in the frequency and/or intensity of heavy rainfall also observed in most parts of Asia, north-west Australia, northern Europe, South-Eastern South America, and most of the USA ( ''high confidence'' ), and West and Southern Africa, Central Europe, the eastern Mediterranean region, Mexico, and North-Western South America ( ''medium confidence'' ). GHGs ''likely'' the main cause.

| Precipitation events – including those associated with tropical cyclones (TCs) – increase with GSAT. For GWLs >2°C very rare (e.g., 1-in-10 or more years) heavy precipitation events more frequent and more intense over all continents ( ''virtually certain'' ) and nearly all AR6 regions ( ''likely'' ) . Likelihood lower at lower GWLs and for less-rare events. At the global scale, intensification of heavy precipitation generally follows Clausius–Clapeyron (about 6–7% per °C of GSAT warming; ''high confidence'' ). Increase in frequency of heavy precipitation events accelerates with warming, higher for rarer events ( ''high confidence'' ), with approximately a doubling and tripling frequency of 10-year and 50-year events, respectively, at 4°C of global warming.

| Sections 11.4, 11.7, 12.4; Figures 11.4, 11.7, 11.15, 11.16; Table 11.2.

|-
| Drought

| All RKRs; RFC2, RFC3

| Increased atmospheric evaporative demand in dry seasons over a majority of land areas due to human-induced climate change ( ''medium confidence'' ). Especially observed in dry summer climates in Europe, North America and Africa ( ''high confidence'' ).

| Upward trend with GSAT ( ''high confidence'' ).

| Sections 11.6, 12.4; Figure 11.18; Table 11.2.

|-
| Inland Floods

| All RKRs; RFC2, RFC3

|
| Upward trend with GSAT for flooded area extent, starting from 2°C compared with 1.5°C and higher levels. Increase in the frequency and magnitude of pluvial floods ( ''high confidence'' ). Increasing flood potential in urban areas where heavy precipitation projected to increase, especially at high GWLs ( ''high confidence'' ).

| Sections 11.5, 12.4; Table 11.2.

|-
| Tropical Cyclones (TCs)

| All RKRs; RFC2, RFC3

| Human contribution to extreme rainfall amount from specific TC events ( ''high confidence'' ). Global proportion of major TC intensities ''likely'' increased over the past four decades.

| Increase in precipitation from TC with GSAT; average peak TC wind speeds, proportion of intense TCs, and peak wind speeds of most intense TCs increase globally with GSAT ( ''high confidence'' ). Decrease or lack of change in global frequency of TCs (all categories) with GSAT ( ''medium confidence'' ).

| Sections 11.7.1, 12.4; Table 11.2.

|-
| Marine Heatwaves (MHWs)

| RKR A,B,F; RFC1,2,3

| ''High confidence'' that MHWs have increased in frequency over the 20th century, with an approximate doubling from 1982 to 2016, and ''medium confidence'' that they have become more intense and longer since the 1980s.

| MHWs ''very likely'' become 2–9 times more frequent in 2081–2100 compared to 1985–2014 under SSP1-2.6 corresponding to a GWL of 2.0 [1.3 to 2.8] °C (95% CI), or 3–15 times more frequent under SSP5-8.5 corresponding to a GWL of 4.8 [3.6 to 6.5] °C. Spatial heterogeneity with larger changes in the tropical oceans and Arctic Ocean ( ''medium confidence'' ).

| [[#12.4|Section 12.4]] ; Box 9.2.

|-
| Concurrent Events in Time and Space

| All RKRs; RFC2, RFC3

| Higher frequency already detected: more frequent concurrent heatwaves and droughts. Increased compound flooding risk (storm surge, extreme rainfall and/or river flow) in some locations; the probability of concurrent events ''likely'' increased.

| Higher frequency with increasing GSAT. Increasing trend in more frequent concurrent heatwaves and droughts with GSAT ( ''high confidence'' ). More frequent concurrent (in time) extreme events at different locations with increasing GSAT, for GWLs > 2°C ( ''high confidence'' ). Compound flooding risk (storm surge, extreme rainfall and/or river flow) increasing with GSAT ( ''high confidence'' ).

| [[IPCC:Wg1:Chapter:Chapter-11#11.8|Section 11.8]] ; Table 11.2; Boxes 11.2, 11.4.

|-
| colspan="5"| '''Trends'''

|-
| Fire Weather Trends

| RKR-B, C; RFC1,2,3

| Weather conditions that promote wildfire (compound hot, dry and windy events) more probable in some regions over the last century ( ''medium confidence'' ).

| Weather conditions promoting wildfire (compound hot, dry and windy events) ''likely'' more frequent with GSAT.

| [[#12.4|Section 12.4]] ; Table 11.2.

|-
| Air Pollution Weather

| RKR-E; RFC3

| Not discernible.

| Behaviour to first order controlled by emissions and policies, not by meteorology. Ozone decreases with GSAT in low-polluted regions (−0.2 to –2 ppbv per °C). Ozone increases with GSAT in regions close to sources of precursors (0.2 to 2 ppbv per °C).

| Sections 6.5, 12.4.

|-
| Patterns of Mean Warming

| RKR-B, D, F, RFC1,3,4

| Spatial patterns of temperature changes associated with the 0.5°C difference in GMST warming between 1991–2010 and 1960–1970 consistent with projected changes under 1.5°C and 2°C of global warming.

| Temperatures scale approximately linearly with GSAT, largely independently of scenario ( ''high confidence'' ). High latitudes of Northern Hemisphere warm faster ( ''virtually certain'' ). Antarctic polar amplification smaller than Arctic ( ''high confidence'' ). Arctic annual mean temperatures warm between 2 and 2.4 times faster for GWLs between 1.5°C and 4°C. In the Southern Hemisphere relatively high rates of warming in subtropical continental areas of South America, Southern Africa and Australia ( ''high confidence'' ).

| Sections 4.6.1.1, 12.4; Atlas; Figures 4.31, Atlas.13 and all ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] Sections’ figures for mean temperature changes.

|-
| Arctic Warming Trends

| RKR-A,C,G,H; RFC1, RFC3

| Emerged already from internal variability.

| ''Very likely'' more pronounced (2–2.4 times faster) than the global average over the 21st century ( ''high confidence'' ).

| Sections 4.6.1, 7.4.4.1, 12.4.9 Atlas.11; Figures 4.19, 4.31, Atlas.29; Table 4.2.

|-
| Patterns of Precipitation Change

| RKR-B, D, F, RFC1, RFC3

| Regional patterns of recent trends, over at least the past three decades, consistent with documented increase in precipitation over tropical wet regions and decrease over dry areas.

| Changes in large-scale atmospheric circulation and precipitation with each 0.5°C of warming ( ''high confidence'' ). Stable pattern of change over time and scenarios. Some departures from linearity possible at regional scale ( ''medium confidence'' ). Precipitation increase on land higher at 3°C and 4°C compared to 1.5°C and 2°C. Precipitation increases in large parts of the monsoon regions, tropics and high latitudes, decreases in the Mediterranean and large parts of the subtropics ( ''high confidence'' ).

| Sections 2.3.1.3.4, 4.5.1, 4.6.1, 12.4;

Figures 2.15, 4.32, Atlas.13.

|-
| Sea Surface Temperature (SST) Warming

| RKR-A, B, D; RFC1, RFC4

| Increased 0.81 (0.65–0.94) per °C of GSAT (1850–1900 average compared with 2009–2018 average).

| Models and observations show globally averaged SSTs warming at a lower rate of about 80% that of GSAT. It is ''virtually certain'' that SST will continue to increase at a rate depending on future emissions scenario ranging from 0.4°C–1.5°C in 2081–2100 relative to 1995–2014 under SSP1-2.6, corresponding to a GWL of 2.0 [1.3 to 2.8] °C, to 2°C–4°C under SSP5-8.5, corresponding to a GWL of 4.8 [3.6 to 6.5] °C.

| Sections 2.3.1.1.3, 9.2.1, 12.4.

|-
| Ocean Acidification/pH

| RKR-A,B; RFC1, RFC4

| ''Virtually certain'' decline of surface pH globally over the last 40 years at a rate of 0.017–0.027 pH units per decade; decline also in the subsurface over the past 2–3 decades ( ''medium confidence'' ). Surface pH now the lowest of at least the last 26,000 years ( ''very high confidence'' ).

| Increase of net ocean carbon flux throughout the century irrespective of the emissions scenario considered ( ''high confidence'' ). Decrease of ocean surface pH through the 21st century, except for SSP1-1.9 and SSP1-2.6 where values increase slightly starting from 2070–2100 ( ''high confidence'' ).

| Sections 2.3.3.5, 4.3.2.4, 5.3.4, 5.4.2, 12.4; Figure 4.8.

|-
| SPEI Index Global

| RKR-B,F,G,H; RFC3

| 9.4% chance of at least three months of drought in a year at current levels (about 1°C).

| Increase at the global scale in the chance of at least three months of drought in a year to about 20 [15 to 30] % at 1.5°C, 35 [20 to 45] % at 2°C to 60 [45 to 75] % at 4°C.

| Sections 12.4, 12.5.1.

|-
| El Niño–Southern Oscillation (ENSO) Variability

| RKR-B,D,F,G; RFC2,3,5

| ''Medium confidence'' that both ENSO amplitude and frequency of high-magnitude events since 1950 higher than over the pre-industrial period (before 1850) but ''low confidence'' of this being outside the range of internal variability. No clear evidence shifts in ENSO or associated features or its teleconnections.

| No change in the amplitude of ENSO variability ( ''medium confidence'' ); enhanced ENSO-related variability of precipitation under SSP2-4.5 and higher ( ''high confidence'' ). ''Likely'' shift eastward of the pattern of teleconnection over North Pacific and North America.

| Sections 2.4.2, 4.3.3.2, 4.5.3.2; Figure 4.10.

|-
| Sea Ice Loss

| RKR-A, B, H; RFC1,3,5

| Arctic sea ice area decreased for all months since 1970s; strongest decrease in summer ( ''very high confidence'' ). Arctic sea ice younger, thinner and faster moving ( ''very high confidence'' ). Current pan-Arctic sea ice levels unprecedented since 1850 ( ''high confidence'' ). ''Low confidence'' in all aspects of Antarctic sea ice prior to the satellite era. Antarctic sea ice area experienced little net change since 1979 ( ''high confidence'' ).

| The Arctic Ocean will ''likely'' become sea ice-free in September before 2050 in all considered SSP scenarios; such disappearance consistently occurring in most years at 2°C–3°C ( ''medium confidence'' ) and including several months in most years at 3°C–5°C ( ''high confidence'' ).

| Sections 2.3.2, 4.3.2.1, 9.3.1, 9.3.2, 12.4.9; Figures 4.2c, 4.5.

|-
| Permafrost Thaw

| RKR-A,C; RFC3,5

| Increases in permafrost temperatures in the upper 30 m over the past three to four decades throughout the permafrost regions ( ''high confidence'' ).

| Global permafrost volume in the top 3 m decreasing by about 25 ± 5% per °C for GWLs <4°C. Relative to 1995–2014: at 1.5°C and 2°C decreasing by less than 40% ( ''medium confidence'' ), at 2°C and 3°C by less than 75% ( ''medium confidence'' ), at 3°C and 5°C by more than 60% loss ( ''medium confidence'' ).

| Sections 2.3.2.5, 9.5.2, 12.4.9.

|-
| Sea Level Change

| RKR-A,C,D,E,F,G,H; RFC1,3,4

| Gobal mean sea level (GMSL) is rising at an accelerated rate since the 19th century ( ''high confidence'' ) '','' almost doubled during past two decades (about 0.1 mm yr <sup>–2</sup> ). GMSL increase over the 20th century faster than over any preceding century in at least the last three millennia ( ''high confidence'' ).

| Up to 2050, limited scenario/GWL dependency ( ''likely'' sea level rise about 0.15–0.30 m). By 2100, ''likely'' GMSL rise with respect to 1995–2014 of 0.51 (0.40–0.69) m, 0.62 (0.50–0.81) m and 0.70 (0.58–0.91) m for, respectively, GWLs of 2.0°C, 3.0°C, and 4.0°C ( ''medium confidence'' ). Deep uncertainty in projections for GWLs >3°C because of ice-sheet behaviour. For example, incorporation of ''low confidence'' ice-sheet processes under SSP5-8.5 (approximately 5°C) leads to a rise of 0.6-1.6 m rather than 0.7–1.1 m.

| Sections 9.6.1.2, 9.6.3.3, 9.6.3.4, 12.4.

|-
| Sea-Level Change Commitment (2,000 years after peak GWL)

| RFC5

|
| GMSL commitment (over the 2000-year-long period following peak warming) of 2–6 m for 2°C peak warming, 4–10 m for 3°C peak warming, 12–16 m for 4°C peak warming, and 19–22 m for 5°C peak warming ( ''medium agreement, limited evidence'' ).

| [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.5|Section 9.6.3.5]] .

|-
| Northern Hemisphere (NH) Spring Snow Cover

| RKR-G, RFC1,3

| Substantial reductions in spring snow cover extent in the NH since 1978 ( ''very high confidence'' ) ''.'' Since 1981, general decline in NH spring snow water equivalent ( ''high confidence'' ).

| Linear change of NH snow cover in spring of about 8% (area) per °C ofglobal warming (for GWLs <4°C). Relative to 1995–2014: at 1.5°C–2°C NH spring snow cover extent ''likely'' decreases by less than 20% ( ''medium confidence'' ); at 2°C–3°C ''likely'' decreases by less than 30%; at 3°C–5°C, ''likely'' decreases by more than 25%.

| Sections 2.3.2.2,

9.5.3, 12.4.

|-
| Mass Loss of Glaciers

| RKR-B,G; RFC1, RFC3

| ''Very high confidence'' global glaciers continuing retreat since about 1850. Current global glacier mass loss highly unusual over at least the last 2000 years ( ''medium confidence'' ). Increased rate of glacier mass loss over the last 3 to 4 decades ( ''high confidence'' ). Glaciers not in balance with respect to current climate conditions and will continue to lose mass for at least several decades.

| For 1.5°C–2°C about 50–60% ( ''low confidence'' ) of glacier mass outside the two ice sheets and excluding peripheral glaciers in Antarctica remaining, predominantly in the polar regions. At 2°C–3°C about 40–50% ( ''low confidence'' ) of current glacier mass outside Antarctica remaining. At sustained 3°C–5°C 25–40% ( ''low confidence'' ) of current glacier mass outside Antarctica remaining. Likely nearly all glacier mass lost in low latitudes, Central Europe, Caucasus, western Canada and USA, North Asia, Scandinavia and New Zealand.

| Sections 2.3.2.3, 9.5.1, 12.4.

|-
| colspan="5"| '''Tipping Points/Irreversibility'''

|-
| Amazon Forest Dieback

| RFC1, RFC5

| Highly dependent on human disturbance.

| Amazon drying and deforestation expected to cause a rapid change in the regional water cycle, possibly linked to the crossing of a climate threshold. ''Low confidence'' change will occur by 2100.

| Sections 5.4.9, 8.6.2.1, 12.4.10; Table 4.10.

|-
| Boreal Forest Dieback

| RFC1, RFC5

| Highly dependent on human disturbance.

| Possible if climate threshold is exceeded, but counteracted by poleward expansion.

| [[IPCC:Wg1:Chapter:Chapter-5#5.4.9|Section 5.4.9]] .

|-
| Ice Sheets

| RFC5

| Greenland Ice Sheet mass-loss rate increased substantially since the turn of the 21st century ( ''high confidence'' ). The Antarctic Ice Sheet has lost mass between 1992 and 2017 ( ''very high confidence'' ), with an increasing mass-loss rate over this period ( ''medium confidence'' ).

| At sustained warming levels between 1.5°C and 2°C, the ice sheets will continue to lose mass ( ''high confidence'' ); on time scales of multiple centuries, the Greenland and West Antarctic ice sheets will partially be lost ( ''medium confidence'' ); there is ''limited evidence'' that the Greenland and West Antarctic ice sheets will be lost almost completely and irreversibly over multiple millennia; at sustained warming levels between 2°C and 3°C, there is ''limited evidence'' that the Greenland and West Antarctic ice sheets will be lost almost completely and irreversibly over multiple millennia, and ''high confidence'' in increasing risk of complete loss and increasing rate of mass loss for higher warming; At sustained warming levels between 3°C and 5°C, near-complete loss of the Greenland Ice Sheet and complete loss of the West Antarctic Ice Sheet will occur irreversibly over multiple millennia ( ''medium confidence'' ); substantial parts or all of Wilkes Subglacial Basin in East Antarctica will be lost over multiple millennia ( ''low confidence'' ).

| Sections 2.3.2.4,

9.4.1, 9.4.2; Table 4.10.

|-
| Glaciers

| RFC5

| See Trends section of this table.

| Continuing substantial global mass loss.

| Sections 9.5.1, 12.4.9.

|-
| Global Ocean Temperature

| RFC5

| See Trends section of this table.

| Centennial-scale irreversibility of ocean warming.

| Sections 4.7.2, 9.6.3; Table 4.10.

|-
| Sea Level Rise (SLR)

| RFC5

| See Trends section of this table.

| Centennial-scale irreversibility of sea level rise. Tipping point linked to ice-sheet behaviour. Deep uncertainty on SLR above 3°C warming.

| Sections 4.7.2, 9.6.3; Table 4.10.

|-
| Atlantic Meridional Overturning Circulation (AMOC)

| RFC5

| ''Low agreement'' on 20th century trend between models and most reconstructions. Observed decline since the mid-2000s cannot be distinguished from internal variability ( ''high confidence'' ).

| There is ''medium confidence'' an abrupt collapse will not occur before 2100; for 1.5–2°C, 2–3°C, 3–5°C warming in 2100, AMOC decline is 29, 32 and 39%, respectively, of its pre-industrial strength.

| Sections 2.3.3.4.1,

9.2.3.1; Table 4.10.

|-
| Permafrost Carbon

| RFC5

| See Trends section of this table.

| Will contribute as a feedback with warming, of approximately 18 ± 12 PgC per °C. Possibly non-linear but ''low confidence'' in the value of any threshold for such behaviour. Likely irreversible at centennial time scales.

| [[IPCC:Wg1:Chapter:Chapter-5#5.4.9|Section 5.4.9]] ; Table 4.10;

Box 5.1.

|-
| Arctic Sea Ice

| RFC5

| Abrupt change already observed.

| Reversible within years to decades; no tipping point or threshold beyond which loss of ice becomes irreversible ( ''high confidence'' ).

| Sections 4.3.2, 9.3.1; Table 4.10.

|-
| Snow Cover of Northern Hemisphere

| RFC5

| See Trends section of the table

| Not anticipated to present tipping point/irreversible behaviour.

| [[IPCC:Wg1:Chapter:Chapter-9#9.5.3|Section 9.5.3]] .

|-
| Global Monsoon

| RFC5

| Has ''likely'' increased over the last 40 years ( ''medium confidence'' ) and can be explained by a phase change in Atlantic Multi-decadal Variability.

| Not anticipated to present tipping point/irreversible behaviour, unless AMOC collapse occurs.

| Sections 4.4.1.4, 4.5.1.5, 8.6.1; Table 4.10.

|-
| ENSO

| RFC5

| See Trends section of the table.

| Not anticipated to present tipping point/irreversible behaviour.

| [[IPCC:Wg1:Chapter:Chapter-4#4.5.3.2|Section 4.5.3.2]] .

|-
| Methane Clathrates

| RFC5

| Methane release from shelf clathrates is <10 TgCH <sub>4</sub> yr <sup>–1</sup> .

| Not anticipated to present tipping point/irreversible behaviour.

| [[IPCC:Wg1:Chapter:Chapter-5#5.4.9|Section 5.4.9]] ; Table 4.10.

|}

<div id="12.6" class="h1-container"></div>

<span id="climate-change-information-in-climate-services"></span>
== 12.6 Climate Change Information in Climate Services ==

<div id="h1-7-siblings" class="h1-siblings"></div>

Climate services are a significantly evolving source of climate change information to support adaptation, mitigation and risk management decisions. As an evolving field, there are multiple definitions of climate services ( [[#Brasseur--2016|Brasseur and Gallardo, 2016]] ). The Global Framework for Climate Services defines a climate service as the provision of climate information to assist decision-making. The service includes appropriate engagement from users and providers, is based on scientifically credible information and expertise, has an effective access mechanism, and responds to user needs ( [[#Hewitt--2012|Hewitt et al., 2012]] ).

The AR5 WGII introduced climate services as bridging the generation and application of climate knowledge, also describing their history and concepts ( [[#Jones--2014|Jones et al., 2014]] ). Since then, this transdisciplinary field has been growing rapidly ( [[#Brasseur--2016|Brasseur and Gallardo, 2016]] ; [[#Hewitt--2020a|Hewitt et al., 2020a]] ), with the social sciences in particular pointing out knowledge requirements for co-design and co-development of climate services ( [[#Larosa--2019|Larosa and Mysiak, 2019]] ; [[#Daniels--2020|Daniels et al., 2020]] ; [[#Steynor--2020|Steynor et al., 2020]] ). Climate services differ from more research-driven vulnerability, impacts, and adaptation research in their orientation towards decision support ( [[#Stone--2005|Stone and Meinke, 2005]] ; [[#Ruane--2016|Ruane et al., 2016]] ; [[#Golding--2019|Golding et al., 2019]] ), but overlaps exist ( [[#Bruno%20Soares--2019|Bruno Soares and Buontempo, 2019]] ). Climate services are often targeted at building resilience to climate-related hazards from near real-time to seasonal and multi-decadal time horizons, to inform adaptation to climate variability and change ( [[#Hewitt--2012|Hewitt et al., 2012]] ), widely recognized as an important challenge for sustainable development and risk management ( [[#Moss--2010|Moss et al., 2010]] ; [[#Jones--2014|Jones et al., 2014]] ; [[#Vaughan--2018|Vaughan et al., 2018]] ). This section focuses largely on climate change time scales (past, present and future), which are the focus of AR6 WGI.

This section introduces the current climate services landscape, assesses climate service practices and products related to climate change information and associated challenges. Cross-Chapter Box 12.2 provides concrete examples of climate services. The section builds on the introduction to climate services in [[IPCC:Wg1:Chapter:Chapter-1#1.2.3.3|Section 1.2.3.3]] and the assessment of regional climate information construction – including storylines – discussed in Sections 10.3.4.2, 10.5.3 and Cross-Chapter Box 10.3. The ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] supports the provision of climate information across WGs by providing interactive maps and further details to the material made publicly accessible for use in climate services. WGII (Chapter 17) further elaborates on climate services as enablers for climate risk management.

<div id="12.6.1" class="h2-container"></div>

<span id="context-of-climate-services"></span>
=== 12.6.1 Context of Climate Services ===

<div id="h2-20-siblings" class="h2-siblings"></div>

The idea of climate services is not new and has its roots in meteorology and climatology ( [[#Larosa--2019|Larosa and Mysiak, 2019]] ). It can be traced back to the late 1970s and the US National Climate Program Act of 1978 ( [[#Henderson--2016|Henderson, 2016]] ). The development of the Global Framework for Climate Services (GFCS) after the World Climate Conference-3 in Geneva brought international attention and renewed impetus to the climate services field ( [[#Hewitt--2012|Hewitt et al., 2012]] ). As a result, large investments have been made globally and regionally in the development of user-driven climate services. WMO has created Regional Climate Centres (RCCs) to facilitate climate service development by regional and national providers ( [[#Hewitt--2020a|Hewitt et al., 2020a]] ). The European Union declared its ambition to stimulate ‘the creation of a community of climate services application developers and users that matches supply and demand for climate information and prediction’, giving primacy to climate services that are user-driven and science-informed ( [[#Lourenço--2016|Lourenço et al., 2016]] ), thus embracing concepts of co-design, co-development and co-evaluation of climate services ( [[#Street--2016|Street, 2016]] ). Diverse and action-driven international initiatives allowed climate services to progressively shift from mitigation towards adaptation ( [[#Larosa--2019|Larosa and Mysiak, 2019]] ). Opportunities for the development of climate services have emerged through the 2015 Agendas (Paris Agreement, Sustainable Development Goals and Sendai Framework), Nationally Determined Contributions, National Adaptation Plans, Multilateral Development Banks and Task Force on Climate-related Financial Disclosure (see [[IPCC:Wg1:Chapter:Chapter-1#1.2.2|Section 1.2.2]] ).

Scientific advancements in climate services related to meteorology and climatology are still closely linked to essential climate variables ( [[#Larosa--2019|Larosa and Mysiak, 2019]] ) and benefit from consistently growing computational power, infrastructure and storage capacity to meet the demands of higher spatially and temporally resolved climate information ( [[#Buontempo--2020|Buontempo et al., 2020]] ). Climate services also focus on impact chains, providing decision makers with information on climate change with cross-sectoral impact assessments for adaptation ( [[#Jacob--2017|Jacob and Solman, 2017]] ). Today there is a diversity of climate services that involve interpretation, analysis, and communication of different sources of climate data, ideally combining different types of knowledge (scientific/technical, experiential, indigenous, etc.), to a targeted group of decision makers ( [[#Parris--2016|Parris et al., 2016]] ; [[#Olazabal--2018|Olazabal et al., 2018]] ; [[#Pezij--2019|Pezij et al., 2019]] ). [[#Jacobs--2020|Jacobs and Street (2020)]] argue that climate services should be expanded to also address societal challenges, such as system transformation, that include climate in the context of other risks and development challenges.

Climate services are undertaken in public and private sectors at global, regional, national, and local scales ( [[#Hewitt--2012|Hewitt et al., 2012]] , 2020b; [[#Cortekar--2020|Cortekar et al., 2020]] ). Intermediaries such as private sector consulting companies, national climate service providers, research organizations, government agencies or academic institutions provide climate services that translate aspects of climate research to the specific context of decision makers (see also [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] ). The EU Roadmap for Climate Services ( [[#EC--2015|EC, 2015]] ; [[#Street--2016|Street, 2016]] ) focuses on developing a market for climate services comprising of both public and private domains. The GFCS, under the leadership of several United Nations Agencies, emphasizes the public domain by supporting national and regional capacity building and development of climate services mainly through National Meteorological and Hydrological Services ( [[#Hewitt--2012|Hewitt et al., 2012]] ; [[#Domingos--2016|Domingos et al., 2016]] ; [[#Sivakumar--2018|Sivakumar and Lucio, 2018]] ; [[#WMO--2018|WMO, 2018]] ). There are ongoing debates about the commercialization of climate services (M.S. [[#Brooks--2013|]] [[#Brooks--2013|Brooks, 2013]] ; [[#WMO--2015|WMO, 2015]] ; [[#Webber--2017|Webber and Donner, 2017]] ; [[#Hoa--2018|Hoa, 2018]] ; [[#Troccoli--2018|Troccoli et al., 2018]] ; [[#Bruno%20Soares--2019|Bruno Soares and Buontempo, 2019]] ; [[#Hewitt--2020a|Hewitt et al., 2020a]] ). Some argue that the commercialization of climate services is needed to meet the diverse needs of specific clients and to drive innovation in the field (M.S. [[#Brooks--2013|]] [[#Brooks--2013|Brooks, 2013]] ; [[#Troccoli--2018a|Troccoli, 2018a]] ). Others argue that if climate services shift incentives for climate science away from the public interest towards profit-seeking, this will result in less publicly accessible and transparent climate information and more private knowledge ( [[#Keele--2019|Keele, 2019]] ; [[#Tart--2020|Tart et al., 2020]] ).

Some climate adaptation planning already uses climate information as provided by the IPCC. However, depending on the decision context, this information may be too coarse, too broad or too disciplinary to directly inform decision-making at the scale where adaptation measures are taken ( [[#Howarth--2016|Howarth and Painter, 2016]] ; [[#Nissan--2019|Nissan et al., 2019]] ). Thus, while the IPCC’s role is clearly perceived as that of a reference – an authoritative starting point – there is a need for complementary information to translate the assessments at the national, local or sectoral level ( [[#Howarth--2016|Howarth and Painter, 2016]] ; [[#Kjellström--2016|Kjellström et al., 2016]] ; [[#van%20den%20Hurk--2018|van den Hurk et al., 2018]] ; [[#Vaughan--2018|Vaughan et al., 2018]] ). The AR6 Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] (see Atlas.2) does provide a collection of observational data and global and regional climate projections. It is designed as a climate service towards the needs of WGI and beyond, to assess the state of the climate by offering data, maps and a level of expert analysis by aggregation of results to regions, scenarios and warming levels.

<div id="12.6.2" class="h2-container"></div>

<span id="assessment-of-climate-services-practice-and-products-related-to-climate-change-information"></span>
=== 12.6.2 Assessment of Climate Services Practice and Products Related to Climate Change Information ===

<div id="h2-21-siblings" class="h2-siblings"></div>

The climate services landscape is fast growing and very broad, as reflected in the vast diversity of practices and products that can be found in the peer-reviewed literature ( ''very high confidence'' ). However, a large part of climate services practices and products is published in ‘grey’ literature (i.e., non-peer reviewed or non-academic) by private consultancy and non-scientific civil organizations, many of which are not in the public domain. In addition, the respective climate service context of a specific stakeholder in a sector dictates what climate information is required and on what scales and in what format it is most usefully provided. The extent and type of engagement between scientists and users is another critical aspect of climate services (see Cross-Chapter Box 12.2, Figure 1, and [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] ). The assessment here can thus only provide a partial and rather general representation of available practices and products in the evolving climate services field.

User needs and decision-making contexts are very diverse and there is no ‘one size fits all’ solution to climate services ( ''very high confidence'' ) ( [[#Hewitt--2017b|Hewitt et al., 2017b]] ; K. [[#Vincent--2018b|]] [[#Vincent--2018|Vincent et al., 2018]] b ). In many cases this requires recognizing that stakeholders make decisions through a combination of scientific information and additional values ( [[#Vanderlinden--2017|Vanderlinden et al., 2017]] ; [[#Parker--2019|Parker and Lusk, 2019]] ; see also Sections [[IPCC:Wg1:Chapter:Chapter-1#1.2.3|1.2.3]] and [[IPCC:Wg1:Chapter:Chapter-10#10.5|10.5.4]] ). The emerging climate service literature may clarify some features of climate information requested by users, for instance climatic impact-driver identification and prioritization through stakeholder engagement; the specification of thresholds for various regions/sectors; the types of metrics (magnitude/intensity, frequency, duration, timing, spatial extent) that are of primary interest; and decision support systems where informatics allow stakeholders to custom-make impact-relevant thresholds and then query databases to understand current and future characteristics ( [[#Bachmair--2016|Bachmair et al., 2016]] ; [[#Buontempo--2020|Buontempo et al., 2020]] ). However, users also ask for capacity building activities related to basic knowledge in climate change sciences and climate-related risks ( [[#De%20Bruin--2020|De Bruin et al., 2020]] ; [[#Sultan--2020|Sultan et al., 2020]] ).

Since AR5 and SROCC (Chapter 2) there has been considerable progress in understanding climate information user needs ( [[#Baztan--2017|Baztan et al., 2017]] ; [[#Golding--2017a|Golding et al., 2017a]] , b, 2019; [[#Bruno%20Soares--2018a|Bruno Soares et al., 2018a]] ; [[#Hewitt--2018|Hewitt and Golding, 2018]] ; [[#Singh--2018|Singh et al., 2018]] ; [[#Sivakumar--2018|Sivakumar and Lucio, 2018]] ; [[#Bessembinder--2019|Bessembinder et al., 2019]] ; [[#Hewitt--2020b|Hewitt et al., 2020b]] ; [[#Sultan--2020|Sultan et al., 2020]] ; Y. [[#Wang--2020|]] [[#Wang--2020|Wang et al., 2020]] ), better facilitation of user engagement ( [[#Buontempo--2014|Buontempo et al., 2014]] , 2018; [[#Buontempo--2018|Buontempo and Hewitt, 2018]] ) and an appreciation from climate scientists of the need to involve communication specialists and social scientists to support the co-design and co-development process that is fundamental to a successful climate service ( [[#Buontempo--2014|Buontempo et al., 2014]] ; [[#Gregow--2016|Gregow et al., 2016]] ; [[#Damm--2020|Damm et al., 2020]] ).

Climate services require user engagement and can take various forms in which climate information and data are delivered or communicated to the users ( ''very high confidence'' ). Different levels of user engagement exist, which can range from passive engagement to interactive group activities, to focused relationships between climate service provider and users. These result in different types of climate service products including websites, capacity building, and co-design of tailored climate indices (Cross-Chapter Box 12.2, Figure 1; [[#Hewitt--2017a|Hewitt et al., 2017a]] ). The fundamental basis for climate service development is the co-production process between climate service provider and user ( [[#Valiela--2006|Valiela, 2006]] ; [[#Briley--2015|Briley et al., 2015]] ; [[#Golding--2017a|Golding et al., 2017a]] ; K. [[#Vincent--2018a|]] [[#Vincent--2018|Vincent et al., 2018]] a ; [[#Bruno%20Soares--2019|Bruno Soares and Buontempo, 2019]] ; [[#Schipper--2019|Schipper et al., 2019]] ), which can be very resource intensive ( [[#Buontempo--2018|Buontempo et al., 2018]] ; [[#Falloon--2018|Falloon et al., 2018]] ; [[#Kolstad--2019|Kolstad et al., 2019]] ) and varies strongly from case to case ( [[#Reinecke--2015|Reinecke, 2015]] ; [[#Bremer--2019|Bremer et al., 2019]] ; [[#Goodess--2019|Goodess et al., 2019]] ; [[#Jung--2019|Jung and Schindler, 2019]] ). Climate services scholars and practitioners can better facilitate and embrace the knowledge co-production process if it is recognized as a multi-faceted phenomenon with several dimensions (e.g., constitutive, interactional, institutional, pedagogical, empowerment) ( [[#Kruk--2017|Kruk et al., 2017]] ; [[#Knaggård--2019|Knaggård et al., 2019]] ; [[#Weichselgartner--2019|Weichselgartner and Arheimer, 2019]] ).

Information moves from ‘useful’ to ‘usable’ only when users effectively incorporate this information into a decision process ( [[#Lemos--2012|Lemos et al., 2012]] ; [[#Bruno%20Soares--2016|Bruno Soares and Dessai, 2016]] ; [[#Prokopy--2017|Prokopy et al., 2017]] ; see also WGII, Chapter 17). Climate services include a range of knowledge brokerage activities such as: identifying knowledge needs; dissemination of knowledge; coordinating and networking; compiling and translating; building capacity through informed decision-making; analysing, evaluating and developing policy; and personal consultation (e.g., [[#De%20Bruin--2020|De Bruin et al., 2020]] ). When analysing four European climate services, [[#Reinecke--2015|Reinecke (2015)]] found that different climate services emphasized different knowledge brokerage activities.

There are various types of climate service providers and products related to key sectors and regions, such as those described in Sections 12.3 and 12.4 ( [[#Hewitt--2017b|Hewitt et al., 2017b]] ). For instance, studies have described sectoral climate services in support of agriculture ( [[#Falloon--2018|Falloon et al., 2018]] ; [[#Hansen--2019|Hansen et al., 2019]] ), health ( [[#Jancloes--2014|Jancloes et al., 2014]] ; [[#Lowe--2017|Lowe et al., 2017]] ), tourism ( [[#Morin--2018|Morin et al., 2018]] ; [[#Damm--2020|Damm et al., 2020]] ; [[#Matthews--2021|Matthews et al., 2021]] ), energy ( [[#Troccoli--2018b|Troccoli, 2018b]] ; [[#Goodess--2019|Goodess et al., 2019]] ; [[#Soret--2019|Soret et al., 2019]] ), disaster risk reduction ( [[#Golding--2019|Golding et al., 2019]] ; [[#Street--2019|Street et al., 2019]] ), water ( [[#van%20den%20Hurk--2016|van den Hurk et al., 2016]] ; [[#Vano--2018|Vano et al., 2018]] ), ocean and coastal ecosystems ( [[#Weisse--2015|Weisse et al., 2015]] ; [[#Le%20Cozannet--2017|Le Cozannet et al., 2017]] ), cities ( [[#Rosenzweig--2014|Rosenzweig and Solecki, 2014]] ; [[#Rosenzweig--2015|Rosenzweig et al., 2015]] ; [[#Gidhagen--2020|Gidhagen et al., 2020]] ), and cultural heritage ( [[#ICOMOS--2019|ICOMOS, 2019]] ). Many countries (including almost every country in Europe – see Atlas.8.2) have established a climate service centre, which follow different practices of user engagement and provide different products (e.g., [[#Kjellström--2016|Kjellström et al., 2016]] ; [[#Skelton--2017|Skelton et al., 2017]] ; [[#Kolstad--2019|Kolstad et al., 2019]] ). Climate services in other countries may be distributed across agencies and programmes, although these are often not centrally coordinated ( [[#Parris--2016|Parris et al., 2016]] ). One of the key pillars of the GFCS is the Climate Services Information System (CSIS), which is the principal mechanism through which information about past, present and future climate is archived, analysed, modelled, exchanged and processed for users ( [[#Hewitt--2020a|Hewitt et al., 2020a]] ). Some national governments also have organized national climate projections to be used for official planning (e.g., [[#EEA--2018|EEA, 2018]] ). A list of available national products (e.g., observational datasets) and projections can be found in the [[IPCC:Wg1:Chapter:Atlas|Atlas]] (e.g., Atlas.1.4).

Figure 12.12 maps a general categorization of practices and products that have emerged from reviewing climate service literature and user interviews ( [[#Visscher--2020|Visscher et al., 2020]] ). The categories range from very generic products or expert analysis focused particularly on climate information (climate-centric approaches) to more integrated products that include shared open-source products and capacity building as well as tailored products that treat climate information as part of a larger decision-making context (climate-inclusive approaches). Three specific examples that elaborate in more detail on specific practices and products related to those general categories are provided in Cross-Chapter Box 12.2.

<div id="_idContainer138" class="Basic-Text-Frame"></div>

[[File:c4d63952d748298ec4b3de32e7bb1a12 IPCC_AR6_WGI_Figure_12_12.png]]

'''Figure 12.12''' '''|''' '''Illustration of different types of climate services.''' Products, for instance, can focus only on climate-related information or can be designed to integrate climate information with other decision-relevant context (vertical axis) and they can be very generic in terms of relevance to a wide range of sectors or stakeholders or customized to fit the needs of a specific sector or stakeholder (horizontal axis). Figure adapted from [[#Visscher--2020|Visscher et al. (2020)]] .

<div id="12.6.3" class="h2-container"></div>

<span id="challenges"></span>
=== 12.6.3 Challenges ===

<div id="h2-22-siblings" class="h2-siblings"></div>

Climate services set new scientific challenges to physical climate research ( ''high confidence'' ). Over at least the last decade, for instance, many questions have appeared in terms of optimal estimation of changes and uncertainties from projections of model ensembles, ensemble optimization, or adjustment of model biases while preserving essential information on trends and cross-variable, time and space consistencies, downscaling information at the local scale ( [[#Benestad--2017|Benestad et al., 2017]] ; [[#Hewitt--2017b|Hewitt et al., 2017b]] ; [[#Marotzke--2017|Marotzke et al., 2017]] ; [[#Hewitt--2018|Hewitt and Lowe, 2018]] ; [[#Knutti--2019|Knutti, 2019]] ; see also [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] ). Other challenges related to climate services are the inter-operability of data ( [[#Giuliani--2017|Giuliani et al., 2017]] ), access to data (open/FAIR Guiding principles; [[#Wilkinson--2016|Wilkinson et al., 2016]] ; [[#Georgeson--2017|Georgeson et al., 2017]] ), format of data (including moving away from percentile-based probabilistic forecasts (e.g., [[#Haines--2019|Haines, 2019]] )) and funding mechanisms ( [[#Bruno%20Soares--2019|Bruno Soares and Buontempo, 2019]] ).

Understanding and modelling of weather and climate extremes is of great relevance for climate services and is continuing to set challenges for research, such as modelling changes in impact-relevant threshold exceedance and return periods for a variety of extremes ( [[#Maraun--2015|Maraun et al., 2015]] ; [[#Sillmann--2017|Sillmann et al., 2017]] ; [[#Hewitt--2021|Hewitt et al., 2021]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ; see also Chapter 11). Extreme event attribution has also been used in context of climate services ( [[#Philip--2020|Philip et al., 2020]] ) as it is of interest to some stakeholder groups ( [[#Sippel--2015|Sippel et al., 2015]] ; [[#Marjanac--2018|Marjanac and Patton, 2018]] ; [[#Jézéquel--2019|Jézéquel et al., 2019]] , 2020). The usefulness or applicability of available extreme event attribution methods ( [[IPCC:Wg1:Chapter:Chapter-11#11.2.4|Section 11.2.4]] and Cross-Working Group Box on Attribution in Chapter 1) for assessing climate-related risks remains subject to debate ( [[#Shepherd--2016|Shepherd, 2016]] ; [[#Mann--2017|Mann et al., 2017]] ; [[#Lloyd--2018|Lloyd and Oreskes, 2018]] ).

The design of climate services involves addressing certain key challenges, such as a domain challenge where users, tasks and data may be unknown; or an informational challenge related to the use and adoption of novel and complex scientific data ( [[#Christel--2018|Christel et al., 2018]] ). This includes challenges in the uptake of climate information in terms of coordinated delivery of data, information, expertise and training by public research institutes, the inclusion of climate change adaptation in public and private regulation, and uncertainties and confidence in climate projections ( [[#Cavelier--2017|Cavelier et al., 2017]] ). Quality control and quality assurance are still weak elements in the development of climate service products ( [[#Jacob--2020|Jacob, 2020]] ). Quality criteria or standards (that go beyond good practice) will have to be developed and agreed ( [[#Baldissera%20Pacchetti--2021|Baldissera Pacchetti et al., 2021]] ). These challenges reflect the dilemma that exists at the interface between the climate modelling community and climate services regarding: (i) the purposes of the models for climate research versus service development; (ii) the gap between the spatial and temporal scales of the models versus the scales needed in applications; and (iii) tailoring climate model results to real-world applications ( [[#Benestad--2017|Benestad et al., 2017]] ; [[#Hackenbruch--2017|Hackenbruch et al., 2017]] ; [[#van%20den%20Hurk--2018|van den Hurk et al., 2018]] ).

Climate services require a sustained engagement between scientists, service providers and users that is often hindered by limited resources for the co-design and co-production process ( ''high confidence'' ). There are recurring challenges related to successful climate service applications: (i) climate services are not visible and are poorly understood by ‘end users’ ( [[#Weichselgartner--2019|Weichselgartner and Arheimer, 2019]] ); (ii) data can be of unknown or poor quality, data formats can be hard to access or process, and it can be difficult to utilize data disseminated from large databases (e.g., [[IPCC:Wg1:Chapter:Chapter-1#1.5.4|Section 1.5.4]] ) without appropriate user guidance; (iii) users are unsure how to choose from available climate services to meet their needs ( [[#Rössler--2019|Rössler et al., 2019]] ); (iv) building trust between climate service users and providers ( [[#Baztan--2020|Baztan et al., 2020]] ); (v) the lack of understanding of users and their contexts by the climate science and service community ( [[#Porter--2017|Porter and Dessai, 2017]] ); (vi) the difficulty in scaling up services ( [[#Tall--2014|Tall et al., 2014]] ; [[#van%20Huysen--2018|van Huysen et al., 2018]] ); (vii) the lack of trained scientists skilled at conducting societally relevant research ( [[#Rozance--2020|Rozance et al., 2020]] ).

Challenges also arise in determining the effectiveness and added value of climate services, particularly in terms of providing quantitative estimates of economic benefits and making a business case for climate services ( [[#Bruno%20Soares--2017|Bruno Soares, 2017]] ). The market for climate services is still in its infancy ( [[#Cavelier--2017|Cavelier et al., 2017]] ; [[#Bruno%20Soares--2018b|Bruno Soares et al., 2018b]] ; [[#Tall--2018|Tall et al., 2018]] ; [[#Damm--2020|Damm et al., 2020]] ). One form of value may be determined by a particular user community’s willingness to pay ( [[#Acquah--2011|Acquah and Onumah, 2011]] ; [[#Ouédraogo--2018|Ouédraogo et al., 2018]] ; [[#Antwi-Agyei--2021|Antwi-Agyei et al., 2021]] ), which however cannot reflect the value of climate services as a public good and for society as a whole ( [[#Hewitt--2012|Hewitt et al., 2012]] ). Literature is only recently emerging on the socio-economic benefits of weather and climate services ( [[#Vaughan--2019|Vaughan et al., 2019]] ). Early studies and guidelines from the WMO focus on cost–benefit ratios ( [[#Perrels--2013|Perrels et al., 2013]] ; [[#WMO--2015|WMO, 2015]] ). Issues related to demand-driven versus supply-driven climate services ( [[#Lourenço--2016|Lourenço et al., 2016]] ; [[#Street--2016|Street, 2016]] ; [[#Daniels--2020|Daniels et al., 2020]] ), public versus private climate services ( [[#Hewitt--2020a|Hewitt et al., 2020a]] ) and business models for climate services ( [[#Hoa--2018|Hoa, 2018]] ) have been raised. A large share of climate services documented in peer-reviewed literature is currently provided in non-market frameworks (e.g., public service obligations and research and development grants) ( [[#Hoa--2018|Hoa, 2018]] ; [[#Kolstad--2019|Kolstad et al., 2019]] ; [[#Cortekar--2020|Cortekar et al., 2020]] ).

Other challenges related to governance and dealing with complex systems are sometimes acknowledged but less well described in the climate services domain ( [[#Hewitt--2020a|Hewitt et al., 2020a]] ). Importantly, decision contexts are strongly rooted in past practice (which often does not even make optimal use of past climate information), stakeholder experience, and history. Even important emerging concepts of co-production, entry points, and champions do not always fall naturally into these realities without significant effort. The social sciences have an important role in helping understand and tackle these challenges ( [[#Bruno%20Soares--2019|Bruno Soares and Buontempo, 2019]] ).

<div id="cross-chapter-box-12.2" class="h2-container box-container"></div>

Cross-Chapter Box 12.2 | Climate Services and Climate Change Information

<div id="h2-23-siblings" class="h2-siblings"></div>

'''Contributing Authors:''' Suraje Dessai (United Kingdom/Portugal), Jana Sillmann (Norway/Germany), Carlo Buontempo (United Kingdom/Italy), Cecilia Conde (Mexico), Aida Diongue-Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Christopher Jack (South Africa), Richard Jones (United Kingdom), Benjamin Lamptey (Niger/Ghana), Xianfu Lu (United Kingdom/China), Douglas Maraun (Austria/Germany), Ben Orlove (United States of America), Roshanka Ranasinghe (Netherlands/Sri Lanka/Australia), Alex C. Ruane (United States of America), Anna Steynor (South Africa), Bart van den Hurk (Netherlands), Robert Vautard (France)

Climate services involve the provision of climate information in such a way as to assist decision-making. The service needs to have appropriate engagement from users and providers; be based on scientifically credible information and expertise; have an effective access mechanism; and meet the users’ needs ( [[#Hewitt--2012|Hewitt et al., 2012]] ). Predominantly, climate services are targeted at informing and enabling risk management in adaptation to climate variability and change ( [[#Jones--2014|Jones et al., 2014]] ; [[#Vaughan--2018|Vaughan et al., 2018]] ). [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] introduces climate services in a broader context of interaction between science and society, including how climate information can be tailored and co-produced for greatest utility in specific contexts. [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] assesses the key foundations for the generation of climate information about regional climate change. Chapters 11, 12 and [[IPCC:Wg1:Chapter:Atlas|Atlas]] comprehensively assess regional climate change information. The Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] gives access to various repositories of quantitative climate information. In WGII, Chapter 17 assesses climate services in the context of climate risk management.

Cross-Chapter Box 12.2

Climate service contexts are diverse and complex. They can be characterized using different factors such as sectors, regions, purposes, time horizons, data sources, level of processing of climate data, background knowledge, type of climate service providers, as well as the nature of the interactions between providers, users and other stakeholders ( [[#Bessembinder--2019|Bessembinder et al., 2019]] ). To illustrate the wide diversity of climate change information in climate services, a useful categorization is by user –provider engagement of climate services (Cross-Chapter Box 12.2, Figure 1). One broad category includes ‘websites and web tools’ which generally focuses on data and information provision ( [[#Hewitson--2017|Hewitson et al., 2017]] ). Websites are generally able to reach many users, but engagement is passive through one-way transfer of information. The second broad category involves ‘interactive group activities’, such as workshops, meetings and interactive forums, which create a stronger dialogue between climate service providers and decision makers. Multi-way communication and regular interaction enable building of trust, co-learning and co-production of products and services. The third broad category involves ‘focused relationships’ which are tailored, targeted and address very specific needs of the user. Effective engagement arises from an iterative process between the provider and user to ensure the user’s needs are being addressed appropriately ( [[#Hewitt--2017a|Hewitt et al., 2017a]] ).

The diversity of climate services practices and products is illustrated here using three case studies each representing one of the broad categories of user –provider engagement (Cross-Chapter Box 12.2, Figure 1).

[[File:9b57b8c914bd9465a29be2fb3e0f3af2 IPCC_AR6_WGI_CCBox_12_2_Figure_1.png]]

'''Cross-Chapter Box 12.2, Figu'''

'''re 1 |'''

'''Schematic of three broad categories of engagement between users and providers of climate services.''' Figure adapted from [[#Hewitt--2017a|Hewitt et al. (2017a)]] .
'''Case study 1: Websites and web tools'''
The Copernicus Climate Change Service (C3S) provides free and open access to climate data, tools and information through a website. It also includes demonstration projects that show how C3S data can be used in practice through case studies, training sessions and workshops ( [[#Thepaut--2018|Thepaut et al., 2018]] ). A large audience of the service is composed of intermediate users, loosely defined as the community of operators in one of the intermediate steps between the primary producers of climate data and the ultimate beneficiaries.

To address this audience, the strategy of C3S is to provide free and open access climate data and tools such as historical observations (both satellite and ground-based), climate data records relevant for a number of Essential Climate Variables ( [[#Bojinski--2014|Bojinski et al., 2014]] ), global and regional reanalyses, climate monitoring bulletins, seasonal predictions, as well as both global (a selection of simulations from the Coupled Model Intercomparison Project, CMIP; [[#Taylor--2012|Taylor et al., 2012]] ; [[#Eyring--2016|Eyring et al., 2016]] ) and regional climate projections from the Coordinated Regional Downscaling Experiment (Euro- and Med-CORDEX; [[#Jacob--2014|Jacob et al., 2014]] ; [[#Ruti--2016|Ruti et al., 2016]] ). A number of indices for various sectors can be calculated through cloud-based tools. For instance, in order to address the specific needs of key sectoral users, climate impact indicators for common variables such as ‘heating degree days’ can be calculated by the users and made available to others ( [[#Buontempo--2020|Buontempo et al., 2020]] ). All this material is quality controlled following a standardized, transparent and traceable framework.

C3S also facilitates the tailoring process, by providing a series of working open-source, cloud-based demonstrators which show how climate data can be transformed into actionable information to meet specific user requirements. This tailoring process covers the chain between the definition of key indicators all the way to the user interface. The definition and production of C3S products involve scientists that produced and assessed the data. A variety of potential users are involved in the definition of indicators and other products.

Through its quality assurance process and demonstrators, C3S provides a basic evaluation of all climate data; it provides access, and it encourages the users to develop their own case-specific analysis within the C3S infrastructure. Trustworthiness and relevance of such an analysis are substantially strengthened through a distillation process, co-designed by the user and data provider, and drawing upon multiple lines of evidence and process-based evaluation of model fitness ( [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] ).

'''Case study 2: Interactive group activities'''
Science-application engagement is extremely challenging, especially in critically important but complex contexts such as rapidly growing cities in developing nations ( [[#Culwick--2017|Culwick and Patel, 2017]] ). The publicly funded Future Resilience for African CiTies And Lands (FRACTAL) project was conceived and designed in response to extensive and strong evidence and experience that useful and useable climate services require strong mutual relationships across the science-application interface that can be built using supportive processes and structures ( [[#Taylor--2017|]] [[#Taylor--2017|A. Taylor et al., 2017]] ).

Informed by this understanding, FRACTAL was grounded in a very reflexive and context-guided approach with city decision-making at its core ( [[#Taylor--2017|]] [[#Taylor--2017|A. Taylor et al., 2017]] ). Representatives from selected southern African cities were included in the proposal design and, throughout the project, a core principle was to allow the city partners to lead and guide the process.

Two important elements were deployed in FRACTAL: ‘embedded researchers’ and ‘learning labs’. Embedded researchers were seconded into the municipality and served as the essential connection for the learning process within each city ( [[#Steynor--2020|Steynor et al., 2020]] ). Learning labs ( [[#Arrighi--2016|Arrighi et al., 2016]] ) were interactive structures in which participants from academia, local city government and councils, state-owned enterprises, communities and community development institutions, and others could interact. Embedded researchers and learning labs were the backbone of ongoing learning processes within each city and resulted in more focused small-group dialogues, capacity development and training processes, and within-city research and engagement activities. Each learning lab focused initially on identifying ‘burning issues’ without a requirement that they involve strong climate linkages. However, with the overarching focus on resilience, discussions evolved in that direction and the burning issues identified often centred around water in peri-urban areas, for example in Windhoek, Namibia ( [[#Scott--2018|Scott et al., 2018]] ).

The learning labs also introduced and developed the concept of Climate Risk Narratives (CRNs) as a process and product to generate and integrate climate and socio-economic information relevant to adaptation and resilience (see Cross-Chapter Box 12.2, Figure 2, and Box 10.2 on storylines) ( [[#Jack--2020|Jack et al., 2020]] ). The first CRNs were informed primarily by climate evidence, but also included some tentative socio-economic impact elements gleaned from literature and other studies. Their content was intentionally provocative and designed to promote debate and discussion, and subsequent iteration. Many participants noted that this was the first time that various important conversations across governance structures and disciplinary areas had occurred around what climate change may actually mean. This demonstrates the engagement value of CRNs as a key element in an iterative co-production process to ensure important details are included correctly, such as the local context, terms and names as well as providing reality checks on the impacts and societal responses ( [[#Jack--2020|Jack et al., 2020]] ).

Cross-Chapter Box 12.2

This case study emphasizes the positive contributions of the fit, tailoring and contextualization of climate information with respect to the specific decision-making needs of particular users ( [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] ; AR6 WGII Section 17.4.4.2.2), the importance of participatory planning for risk management in urban areas (AR6 WGII Sections 6.3.3.3 and 6.4.2) and the importance of networks and organizations which link researchers, policymakers and end-users to promote adaptation in African cities (AR6 WGII Box 9.4). Upscaling this type of interactive activity to cater for the large number of user demands remains a challenge.

[[File:2635486ac58ac406ff5417aa417692de IPCC_AR6_WGI_CCBox_12_2_Figure_2.png]]

'''Cross-Chapter Box 12.2, Figure 2'''

'''|'''

'''Climate Risk Narrative infographic developed through the FRACTAL Windhoek Learning Lab process.''' Figure adapted from [[#Jack--2020|Jack et al. (2020)]] .
'''Case study 3: Focused relationships'''
This broad category involves one-to-one engagement between a provider and a user with very specific needs. One such user is the Asian Development Bank (ADB), which has committed to making all its investments climate resilient by implementing a climate risk management framework ( [[#ADB--2014|ADB, 2014]] , 2018; [[#Lu--2019|Lu, 2019]] ). The climate risk management framework mandates all climate-sensitive investment projects undertake a climate risk and adaptation assessment, to identify material risks of a changing climate to the proposed project and potential adaptive measures to be incorporated into project design, implementation, maintenance and/or monitoring. Typically, loan project processing teams procure consulting services for a bespoke climate risk and adaptation assessment (CRA) for a specific project. The user –provider engagement is highly targeted and goal-oriented. An example of such a focused user –provider engagement is the CRA carried out as part of an investment project in Vietnam, the Water Efficiency Improvement in Drought-Affected Provinces (WEIDAP) project.

In the wake of the El Niño-induced 2015–2016 severe drought, which caused major damage to agricultural land in the Central Highlands of Vietnam, the WEIDAP project was initiated to improve water productivity of irrigated agriculture. Proposed project interventions include a package of both ‘soft’ (e.g., policy, institutional and capacity building, on-farm water efficiency practices) and ‘hard’ (modernized irrigation schemes) activities. To ensure that the project delivers expected benefits under a changing climate, consultants were recruited to carry out a detailed CRA, working as part of the overall project processing team. Through extensive consultations with the rest of the project team and review of literature including relevant climate projections, the CRA consultants chose to construct three broad climate scenarios for the 2050s (a time frame appropriate for the lifetime of the irrigation schemes being proposed under the project): a warm-and-wet, a hot-and-wet, and a hotter future. Outputs from a selection of CMIP5 models were analysed under these three scenarios, to derive changes in temperature, rainfall and potential evapotranspiration, which in turn were used as inputs to hydrological, crop and agro-economics models to assess the impacts of climate change on the overall project performance. Table 1 presents the summary of the key parameters under the three scenarios. Recommendations from the CRA included (largely minor) refinements and additional activities for drought planning, detailed engineering design of the relevant project components (such as access roads, river crossings and foundations), and support for poorer farmers who may not be able to afford access to water and climate-resilient technologies.

This case study illustrates that climate information distillation including a sustained iterative engagement between climate information users, producers and translators can improve the quality of the information and the decision-making ( [[IPCC:Wg1:Chapter:Chapter-10#10.5|Section 10.5]] ; WGII Section 17.4.4.2.2).

'''Cross-Chapter Box 12.2, Table'''

'''1 |'''

'''Summary of annual province-level changes in temperature, precipitation and evapotranspiration under the three broad scenarios in southern Vietnam.''' Scenario 1: warm-and-wet; Scenario 2: hot-and-wet; Scenario 3: hotter. Source: Table 3 in [[#ADB--2020|ADB (2020)]] .
{| class="wikitable"
|-
| rowspan="3"| '''Item'''

| colspan="15"| '''Province and Scenario Number'''

|-
| colspan="3"| '''B''' '''ì''' '''nh Thu''' '''â''' '''n'''

| colspan="3"| '''Ð''' '''ă''' '''´''' '''k L''' '''ă''' '''´''' '''k'''

| colspan="3"| '''Ð''' '''ă''' '''´''' '''k N''' '''ô''' '''ng'''

| colspan="3"| '''Khánh H''' '''ò''' '''a'''

| colspan="3"| '''Ninh Thu''' â '''n'''

|-
| '''1'''

| '''2'''

| '''3'''

| '''1'''

| '''2'''

| '''3'''

| '''1'''

| '''2'''

| '''3'''

| '''1'''

| '''2'''

| '''3'''

| '''1'''

| '''2'''

| '''3'''

|-
| '''ΔT (°C)'''

| 1.1

| 1.8

| 2.6

| 1.1

| 1.5

| 2.0

| 1.2

| 2.1

| 2.7

| 1.1

| 1.8

| 2.6

| 1.1

| 1.5

| 2.6

|-
| '''ΔP (%)'''

| 28

| –12

| 4

| 8

| 17

| –8

| 8

| –8

| 7

| 3

| –10

| 7

| 27

| 1

| 5

|-
| '''ΔPET (%)'''

| 3

| 6

| 8

| 4

| 5

| 7

| 4

| 7

| 9

| 3

| 6

| 8

| 3

| 5

| 8

|}

ΔT = change in temperature; ΔP = change in precipitation; ΔPET = change in potential evapotranspiration.

Note: Colour scale indicates significance of changes for the water balance.

<div id="12.7" class="h1-container"></div>

<span id="final-remarks"></span>
== 12.7 Final Remarks ==

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The assessment in this chapter is based on a rapidly growing body of new evidence from the peer-reviewed literature, direct calculations of climate projections from several new model ensembles, and results from other AR6 WGI chapters. Although a large amount a new information on CID changes and their uptake in climate services has become available since AR5, some challenges still remain. This section summarizes some of these main challenges, with a view to facilitating improved assessments in future. The section is organized following the order of chapter sections and consolidated according to key assessment components.

* The adoption of the climatic impact-driver (CID) framework could benefit from stronger connections across disciplines, including between physical climate and impact scientists, and between the science community and practitioners/stakeholders on the ground. Co-development of CID index definitions with impact scientists or stakeholders helps ensure their salience and utility (Sections 12.1, 12.2, 12.3 and 12.6).
* The ability to project all aspects of shifting CID profiles and their effects at fine, local scales is often reliant on dynamical downscaling and additional impact modelling steps, making a robust and full quantification of the uncertainties involved more challenging. Availability of multiple models and ease of connecting physical climate models at different scales can facilitate assessment (Sections 12.2, 12.3, 12.4 and 12.5).
* Regional and sub-regional differences in coverage and access of homogeneous historical records, in the deployment of regional model ensembles and the exploration of scenarios, and ultimately in peer-reviewed studies addressing the full range of past and current behaviour, detection and attribution, and future projections challenge a uniformly robust assessment across all CIDs and regions of the world (Sections 12.4 and 12.5).
* Efforts to assess a consistent global, large-scale view of CID changes across regions and sectors would benefit from additional coordinated studies adopting common CID indices, model protocols, time horizons and scenarios or global warming levels (Sections 12.3 and 12.5).
* Even though the body of peer-reviewed literature regarding climate services practices and products is growing, a large part is still documented only in grey literature arising from commercial consultancy, and thus is not publicly and freely accessible ( [[#12.6|Section 12.6]] ).

<div id="frequently-asked-questions" class="h1-container"></div>

== Frequently Asked Questions ==

<span id="faq-12.1-what-is-a-climatic-impact-driver-cid"></span>
=== FAQ 12.1 | What Is a Climatic Impact-driver (CID)? ===

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A climatic impact-driver is a physical climate condition that directly affects society or ecosystems. Climatic impact-drivers may represent a long-term average condition (such as the average winter temperatures that affect indoor heating requirements), a common event (such as a frost that kills off warm-season plants), or an extreme event (such as a coastal flood that destroys homes). A single climatic impact-driver may lead to detrimental effects for one part of society while benefiting another, while others are not affected at all. A climatic impact-driver (or its change caused by climate change) is therefore not universally hazardous or beneficial, but we refer to it as a ‘hazard’ when experts determine it is detrimental to a specific system.

Climate change can alter many aspects of the climate system, but efforts to identify impacts and risks usually focus on a smaller set of changes known to affect, or potentially affect, things that society cares about. These climatic impact-drivers (CIDs) are formally defined in this Report as ‘physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions’. Because people, infrastructure and ecosystems interact directly with their immediate environment, climate experts assess CIDs locally and regionally. CIDs may relate to temperature, the water cycle, wind and storms, snow and ice, oceanic and coastal processes or the chemistry and energy balance of the climate system. Future impacts and risk may also be directly affected by factors unrelated to the climate (such as socio-economic development, population growth, or a viral outbreak) that may also alter the vulnerability or exposure of systems.

CIDs capture important characteristics of the average climate and both common and extreme events that shape society and nature (see FAQ 12.2). Some CIDs focus on aspects of the average climate (such as the seasonal progression of temperature and precipitation, average winds or the chemistry of the ocean) that determine, for example, species distribution, farming systems, the location of tourist resorts, the availability of water resources and the expected heating and cooling needs for buildings in an average year. CIDs also include common episodic events that are particularly important to systems, such as thaw events that can trigger the development of plants in spring, cold spells that are important for fruit crop chill requirements, or frost events that eliminate summer vegetation as winter sets in. Finally, CIDs include many extreme events connected to impacts such as hailstorms that damage vehicles, coastal floods that destroy shoreline property, tornadoes that damage infrastructure, droughts that increase competition for water resources, and heatwaves that can strain the health of outdoor labourers.

Many aspects of our daily lives, businesses and natural systems depend on weather and climate, and there is great interest in anticipating the impacts of climate change on the things we care about. To meet these needs, scientists engage with companies and authorities to provide climate services – meaningful and possibly actionable climate information designed to assist decision-making. Climate science and services can focus on CIDs that substantially disrupt systems to support broader risk management approaches. A single CID change can have dramatically different implications for different sectors or even elements of the same sector, so engagement between climate scientists and stakeholders is important to contextualize the climate changes that will come. Climate services responding to planning and optimization of an activity can focus on more gradual changes in operating climate conditions.

FAQ 12.1, Figure 1 tracks example outcomes of seasonal snow cover changes that connect climate science to the need for mitigation, adaptation and regional risk management. The length of the season with snow on the ground is just one of many regional climate conditions that may change in the future, and it becomes a CID because there are many elements of society and ecosystems that rely on an expected seasonality of snow cover. Climate scientists and climate service providers examining human-driven climate change may identify different regions where the length of the season with snow cover could increase, decrease, or stay relatively unaffected. In each region, change in seasonal snow cover may affect different systems in beneficial or detrimental ways (in the latter case, changing seasonal snow cover would be a ‘hazard’), although systems such as coastal aquaculture remain relatively unaffected. The changing profile of benefits and hazards connected to these changes in the seasonal snow cover CID affects the profile of impacts, risks and benefits that stakeholders in the region will grapple with in response to climate change.

[[File:e2fda007da2c24c01e0d8179514e0108 IPCC_AR6_WGI_FAQ_12_1_Figure_1.png]]
'''FAQ 12.1, Figure 1 |''' '''A single climatic impact-driver can affect ecosystems and society in different ways.''' A variety of impacts from the same climatic impact-driver change, illustrated with the example of regional seasonal snow cover.

<span id="faq-12.2-what-are-climatic-thresholds-and-why-are-they-important"></span>

=== FAQ 12.2 | What Are Climatic Thresholds and Why Are They Important? ===

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Climatic thresholds tell us about the tolerance of society and ecosystems so that we can better scrutinize the types of climate changes that are expected to impact things we care about. Many systems have natural or structural thresholds. If conditions exceed those thresholds, the result can be sudden changes or even collapses in health, productivity, utility or behaviour. Adaptation and risk management efforts can change these thresholds, altering the profile of climate conditions that would be problematic and increasing overall system resilience.

Decision makers have long observed that certain weather and climate conditions can be problematic, or hazardous, for things they care about (i.e., things with socio-economic, cultural or intrinsic value). Many elements of society and ecosystems operate in a suitable climate zone selected naturally or by stakeholders considering the expected climate conditions. However, as climate change moves conditions beyond expected ranges, they may cross a climatic ‘threshold’ – a level beyond which there are either gradual changes in system behaviour or abrupt, non-linear and potentially irreversible impacts.

Climatic thresholds can be associated with either natural or structural tolerance levels. Natural thresholds, for instance, include heat and humidity conditions above which humans cannot regulate their internal temperatures through sweat, drought durations that heighten competition between species, and winter temperatures that are lethal for pests or disease-carrying vector species. Structural thresholds include engineered limits of drainage systems, extreme wind speeds that limit wind turbine operation, the height of coastal protection infrastructure, and the locations of irrigation infrastructure or tropical cyclone sheltering facilities.

Thresholds may be defined according to raw values (such as maximum temperature exceeding 35°C) or percentiles (such as the local 99th percentile daily rainfall total). They also often have strong seasonal dependence (see FAQ 12.3). For example, the amount of snowfall that a deciduous tree can withstand depends on whether the snowfall occurs before or after the tree sheds its leaves. Most systems respond to changes in complex ways, and those responses are not determined solely or precisely by specific thresholds of a single climate variable. Nonetheless, thresholds can be useful indicators of system behaviours, and an understanding of these thresholds can help inform risk management decisions.

FAQ 12.2 Figure 1 illustrates how threshold conditions can help us understand climate conditions that are suitable for normal system operation and the thresholds beyond which impacts occur. Crops tend to grow most optimally within a suitable range of daily temperatures that is influenced by the varieties being cultivated and the way the farm is managed. As daily temperatures rise above a ‘critical’ temperature threshold, plants begin to experience heat stress that reduces growth and may lower resulting yields. If temperatures reach a higher ‘limiting’ temperature threshold, crops may suffer leaf loss, pollen sterility, or tissue damage that can lead to crop failure. Farmers typically select a cropping system with some consideration to the probability of extreme temperature events that may occur within a typical season, and so identifying hot temperature thresholds helps farmers select their seed and field management strategies as part of their overall risk management. Climate experts may therefore aim to assist farm planning by providing information about the climate change-induced shifts to the expected frequency of daily heat extremes that exceed crop tolerance thresholds.

Adaptation and other changes in societies and environment can shift climatic thresholds by modifying vulnerability and exposure. For example, adaptation efforts may include breeding new crops with higher heat tolerance levels so that corresponding dangerous thresholds occur less frequently. Likewise, increasing the height of a flood embankment protecting a given community can increase the level of river flow that may be tolerated without flooding, reducing the frequency of damaging floods. Stakeholders therefore benefit from climate services that are based on a co-development process, with scientists identifying system-relevant thresholds and developing tailored climatic impact-driver indices that represent these thresholds (FAQ 12.1). These thresholds help focus the provision of action-relevant climate information for adaptation and risk management.

[[File:27abe67361ec565a5ca1fd05366f77f4 IPCC_AR6_WGI_FAQ_12_2_Figure_1.png]]
'''FAQ 12.2, Figure''' '''1 |''' '''Crop response to maximum temperature thresholds.''' Crop growth rate responds to daily maximum temperature increases, leading to reduced growth and crop failure as temperatures exceed critical and limiting temperature thresholds, respectively. Note that changes in other environmental factors (such as carbon dioxide and water) may increase the tolerance of plants to increasing temperatures.

<span id="faq-12.3-how-will-climate-change-affect-the-regional-characteristics-of-a-climate-hazard"></span>

=== FAQ 12.3 | How Will Climate Change Affect the Regional Characteristics of a Climate Hazard? ===

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Human-driven climate change can alter the regional characteristics of a climate hazard by changing the magnitude or intensity of the climate hazard, the frequency with which it occurs, the duration that hazardous conditions persist, the timing when the hazard occurs, or the spatial extent threatened by the hazard. By examining each of these aspects of a hazard’s profile change, climate services may provide climate risk information that allows decision makers to better tailor adaptation, mitigation and risk management strategies.

A climate hazard is a climate condition with the potential to harm natural systems or society. Examples include heatwaves, droughts, heavy snowfall events and sea level rise. Climate scientists look for patterns in climatic impact-drivers to detect the signature of changing hazards that may influence stakeholder planning (FAQ 12.1). Climate service providers work with stakeholders and impacts experts to identify key system responses and tolerance thresholds (FAQ 12.2) and then examine historical observations and future climate projections to identify associated changes to the characteristics of a regional hazard’s profile. Climate change can alter at least five different characteristics of the hazard profile of a region (FAQ 12.3, Figure 1):

Magnitude or intensity is the raw value of a climate hazard, such as an increase in the maximum yearly temperature or in the height of flooding that results from a coastal storm with a 1% change of occurring each year.

Frequency is the number of times that a climate hazard reaches or surpasses a threshold over a given period. For example, increases to the number of heavy snowfall events, tornadoes, or floods experienced in a year or in a decade.

Duration is the length of time over which hazardous conditions persist beyond a threshold, such as an increase in the number of consecutive days where maximum air temperature exceeds 35°C, the number of consecutive months of drought conditions, or the number of days that a tropical cyclone affects a location.

Timing captures the occurrence of a hazardous event in relation to the course of a day, season, year, or other period in which sectoral elements are evolving or co-dependent (such as the time of year when migrating animals expect to find a seasonal food supply). Examples include a shift towards an earlier day of the year when the last spring frost occurs or a delay in the typical arrival date for the first seasonal rains, the length of the winter period when the ground is typically covered by snow, or a reduction in the typical time needed for soil moisture to move from normal to drought conditions.

Spatial extent is the region in which a hazardous condition is expected, such as the area currently threatened by tropical cyclones, geographical areas where the coldest day of the year restricts a particular pest or pathogen, terrain where permafrost is present, the area that would flood following a common storm, zones where climate conditions are conducive to outdoor labour, or the size of a marine heatwave.

Hazard profile changes are often intertwined or stem from related physical changes to the climate system. For example, changes in the frequency and magnitude of extreme events are often directly related to each other as a result of atmospheric dynamics and chemical processes. In many cases, one aspect of hazard change is more apparent than others, which may provide a first emergent signal indicating a larger set of changes to come (FAQ 1.2).

Information about how a hazard has changed or will change helps stakeholders prioritize more robust adaptation, mitigation and risk management strategies. For example, allocation of limited disaster relief resources may be designed to recognize that tropical cyclones are projected to become more intense even as the frequency of those storms may not change. Planning may also factor in the fact that even heatwaves that are not record-breaking in their intensity can still be problematic for vulnerable populations when they persist over a long period. Likewise, firefighters recognize new logistical challenges in the lengthening of the fire weather season and an expansion of fire conditions into parts of the world where fires were not previously a great concern. Strong engagement between climate scientists and stakeholders therefore helps climate services tailor and communicate clear information about the types of changing climate hazards to be addressed in resilience efforts.

[[File:56c81d37c7de891090e4e60ab7a6351d IPCC_AR6_WGI_FAQ_12_3_Figure_1.png]]
'''FAQ 12.3, Figure 1 |''' '''Types of changes to a region’s hazard profile.''' The first five panels illustrate how climate changes can alter a hazard’s intensity (or magnitude), frequency, duration, and timing (by seasonality and speed of onset) in relation to a hazard threshold (horizontal grey line, marked ‘H’). The difference between the historical climate (blue) and future climate (red) shows the changing aspects of climate change that stakeholders will have to manage. The bottom right-hand panel shows how a given climate hazard (such as a current once-in-100-year river flood, geographic extent in blue) may reach new geographical areas under a future climate change (extended area in red).

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Zhou, C., K. Wang, D. Qi, and J. Tan, 2019: Attribution of a Record-Breaking Heatwave Event in Summer 2017 over the Yangtze River Delta [in “Explaining Extremes of 2017 from a Climate Perspective”]. ''Bulletin of the American Meteorological Society'' , '''100(1)''' , S97–S103, doi: [https://dx.doi.org/10.1175/bams-d-18-0134.1 10.1175/bams-d-18-0134.1] .

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Zhou, C., A. Dai, J. Wang, and D. [[#Chen--2021|Chen, 2021]] : Quantifying Human-Induced Dynamic and Thermodynamic Contributions to Severe Cold Outbreaks Like November 2019 in the Eastern United States [in “Explaining Extremes of 2019 from a Climate Perspective”]. ''Bulletin of the American Meteorological Society'' , '''102(1)''' , S17–S23, doi: [https://dx.doi.org/10.1175/bams-d-20-0171.1 10.1175/bams-d-20-0171.1] .

<div id="Zhou--2018"></div>

Zhou, T., L. Ren, H. Liu, and J. Lu, 2018: Impact of 1.5°C and 2.0°C global warming on aircraft takeoff performance in China. ''Science Bulletin'' , '''63(11)''' , 700–707, doi: [https://dx.doi.org/10.1016/j.scib.2018.03.018 10.1016/j.scib.2018.03.018] .

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Zhu, C. et al., 2018: Carbon dioxide (CO <sub>2</sub> ) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. ''Science Advances'' , '''4(5)''' , eaaq1012, doi: [https://dx.doi.org/10.1126/sciadv.aaq1012 10.1126/sciadv.aaq1012] .

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Zhu, J., S. Wang, and G. Huang, 2019: Assessing Climate Change Impacts on Human-Perceived Temperature Extremes and Underlying Uncertainties. ''Journal of Geophysical Research: Atmospheres'' , '''124(7)''' , 3800–3821, doi: [https://dx.doi.org/10.1029/2018jd029444 10.1029/2018jd029444] .

<div id="Zhu--2019"></div>

Zhu, X. et al., 2019: Projected temperature and precipitation changes on the Tibetan Plateau: results from dynamical downscaling and CCSM4. ''Theoretical and Applied Climatology'' , '''138(1)''' , 861–875, doi: [https://dx.doi.org/10.1007/s00704-019-02841-9 10.1007/s00704-019-02841-9] .

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{| class="wikitable"
|-
| rowspan="3" colspan="2"|
| colspan="33"| '''Climatic Impact-driver'''

|-
| colspan="4"| '''Heat and Cold'''

| colspan="8"| '''Wet and Dry'''

| colspan="4"| '''Wind'''

| colspan="6"| '''Snow and Ice'''

| colspan="3"| '''Coastal'''

| colspan="5"| '''Open Ocean'''

| colspan="3"| '''Other'''

|-
| rowspan="2"| '''Mean air temperature'''

| rowspan="2"| '''Extreme heat'''

| rowspan="2"| '''Cold spell'''

| rowspan="2"| '''Frost'''

| rowspan="2"| '''Mean precipitation'''

| rowspan="2"| '''River flood'''

| rowspan="2"| '''Heavy precipitation and pluvial flood'''

| rowspan="2"| '''Landslide'''

| rowspan="2"| '''Aridity'''

| rowspan="2"| '''Hydrological drought'''

| rowspan="2"| '''Agricultural and ecological drought'''

| rowspan="2"| '''Fire weather'''

| rowspan="2"| '''Mean wind speed'''

| rowspan="2"| '''Severe wind storm'''

| rowspan="2"| '''Tropical cyclone'''

| rowspan="2"| '''Sand and dust storm'''

| rowspan="2"| '''Snow, glacier and ice sheet'''

| rowspan="2"| '''Permafrost'''

| rowspan="2"| '''Lake, river and sea ice'''

| rowspan="2"| '''Heavy snowfall and ice storm'''

| rowspan="2"| '''Hail'''

| rowspan="2"| '''Snow avalanche'''

| rowspan="2"| '''Relative sea level'''

| rowspan="2"| '''Coastal flood'''

| rowspan="2"| '''Coastal erosion'''

| rowspan="2"| '''Mean ocean temperature'''

| rowspan="2"| '''Marine heatwave'''

| rowspan="2"| '''Ocean acidity'''

| rowspan="2"| '''Ocean salinity'''

| rowspan="2"| '''Dissolved oxygen'''

| rowspan="2"| '''Air pollution weather'''

| rowspan="2"| '''Atmospheric CO''' <sub>2</sub> '''at surface'''

| rowspan="2"| '''Radiation at surface'''

|-
| '''Sector'''

| '''Asset'''

|-
| rowspan="4"| '''Food, Fibre and Other Ecosystem Products'''

'''(WGII Chapter 5)'''

| '''Crop systems'''

|
|-
| '''Livestock and pasture systems'''

|
|-
| '''Forestry systems'''

|
|-
| '''Fisheries and aquaculture systems'''

|
|-
| rowspan="4"| '''Cities, Settlements and Key Infrastructure'''

'''(WGII Chapter 6)'''

| '''Cities'''

|
|-
| '''Land and water transportation'''

|
|-
| '''Energy infrastructure'''

|
|-
| '''Built environment'''

|
|-
| rowspan="4"| '''Health, Well-being and Communities'''

'''(WGII Chapter 7)'''

| '''Labour productivity'''

|
|-
| '''Morbidity'''

|
|-
| '''Mortality'''

|
|-
| '''Recreation and tourism''' <sup>a</sup>

|
|-
| rowspan="4"| '''Poverty, Livelihoods and Sustainable Development'''

'''(WGII Chapter 8)'''

| '''Housing stock''' <sup>b</sup>

|
|-
| '''Farmland''' <sup>b</sup>

|
|-
| '''Livestock mortality''' <sup>b</sup>

|
|-
| '''Indigenous traditions'''

|
|}

<sup>a</sup> The Recreation and tourism asset category includes outdoor exercise and the tourism industry (including ecosystem services) assessed in many WGII chapters.

<sup>b</sup> This asset category is distinguished by the threat of a full loss of key investments and living environments rather than a recoverable damage or loss of productivity or profit.

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'''Figure 12.8: (a)''' Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) from CORDEX-South and Central America models for 2041–2060 relative to 1995–2014 for RCP8.5. '''(b)''' Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . '''(c)''' Bar plots for Q100 (m <sup>3</sup> s <sup>–1</sup> km <sup>–2</sup> ) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. '''(d)''' Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from [[#Vousdoukas--2020b|Vousdoukas et al. (2020b)]] . Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical ( [[IPCC:Wg1:Chapter:Annex-vi|Annex VI]] for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

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IPCC:AR6/WGI/Chapter-11

2026-05-13T13:59:53Z

172.18.0.1: /* Chapter 11: Weather and Climate Extreme Events in a Changing Climate */

<div id="___gatsby"></div>

<div id="gatsby-focus-wrapper"></div>

<span id="chapter-11-weather-and-climate-extreme-events-in-a-changing-climate"></span>
= Chapter 11: Weather and Climate Extreme Events in a Changing Climate =

<div id="chapter-authors"></div>

'''Coordinating''' '''Lead Authors:'''

Sonia I. Seneviratne (Switzerland), Xuebin Zhang (Canada)

'''Lead Authors:'''

Muhammad Adnan (Pakistan), Wafae Badi (Morocco), Claudine Dereczynski (Brazil), Alejandro Di Luca (Australia/Canada/Argentina), Subimal Ghosh (India), Iskhaq Iskandar (Indonesia), James Kossin (United States of America), Sophie Lewis (Australia), Friederike Otto (United Kingdom/Germany), Izidine Pinto (South Africa/Mozambique), Masaki Satoh (Japan), Sergio M. Vicente-Serrano (Spain), Michael Wehner (United States of America), Botao Zhou (China)

'''Contributing Authors:'''

Mathias Hauser (Switzerland), Megan Kirchmeier-Young (Canada/United States of America), Lisa V. Alexander (Australia), Richard P. Allan (United Kingdom), Mansour Almazroui (Saudi Arabia), Lincoln M. Alvez (Brazil), Margot Bador (France, Australia/France), Rondrotiana Barimalala (South Africa/Madagascar), Richard A. Betts (United Kingdom), Suzana J. Camargo (United States of America/Brazil, United States of America), Pep G. Canadell (Australia), Erika Coppola (Italy), Markus G. Donat (Spain/Germany, Australia), Hervé Douville (France), Robert J. H. Dunn (United Kingdom/Germany,United Kingdom), Erich Fischer (Switzerland), Hayley J. Fowler (United Kingdom), Nathan P. Gillett (Canada), Peter Greve (Austria/Germany), Michael Grose (Australia), Lukas Gudmundsson (Switzerland/Germany, Iceland), José Manuel Guttiérez (Spain), Lofti Halimi (Algeria), Zhenyu Han (China), Kevin Hennessy (Australia), Richard G. Jones (United Kingdom), Yeon-Hee Kim (Republic of Korea), Thomas Knutson (United States of America), June-Yi Lee (Republic of Korea), Chao Li (China), Georges-Noel T. Longandjo (South Africa/Democratic Republic of the Congo), Kathleen L. McInnes (Australia), Tim R. McVicar (Australia), Malte Meinshausen (Australia/Germany), Seung-Ki Min (Republic of Korea), Ryan S. Padron Flasher (Switzerland/Ecuador, United States of America), Christina M. Patricola (United States of America), Roshanka Ranasinghe (The Netherlands/Sri Lanka, Australia), Johan Reyns (The Netherlands/Belgium), Joeri Rogelj (United Kingdom/Belgium), Alex C. Ruane (United States of America), Daniel Ruiz Carrascal (United States of America/Colombia), Bjørn H. Samset (Norway), Jonathan Spinoni (Italy), Qiaohong Sun (Canada/China), Ying Sun (China), Mouhamadou Bamba Sylla (Rwanda/Senegal), Claudia Tebaldi (United States of America), Laurent Terray (France), Wim Thiery (Belgium), Jessica Tierney (United States of America), Maarten K. van Aalst (The Netherlands), Bart van den Hurk (The Netherlands), Robert Vautard (France), Wen Wang (China), Seth Westra (Australia), Jakob Zscheischler (Germany)

'''R''' '''eview Editors:'''

Johnny Chan (China), Asgeir Sorteberg (Norway), Carolina Vera (Argentina)

'''Chapter Scientists:'''

Mathias Hauser (Switzerland), Megan Kirchmeier-Young (Canada/United States of America), Hui Wan (Canada)

<div id="chapter-citation"></div>

'''This Chapter should be cited as:'''

Seneviratne, S.I., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I. Iskandar, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S.M. Vicente-Serrano, M. Wehner, and B. Zhou, 2021: Weather and Climate Extreme Events in a Changing Climate . In ''Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change'' [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766, doi: [https://doi.org/10.1017/9781009157896.013 10.1017/9781009157896.013] .

<div id="Executive" class="h1-container"></div>

<span id="executive-summary"></span>

== Executive Summary ==

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'''This chapter assesses changes in weather and climate extremes on regional and global scales, including observed changes and their attribution, as well as projected changes.''' The extremes considered include temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, storms (including tropical cyclones), as well as compound events (multivariate and concurrent extremes). The assessment focuses on land regions excluding Antarctica. Changes in marine extremes are addressed in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and Cross-Chapter Box 9.1. Assessments of past changes and their drivers are from 1950 onward, unless indicated otherwise. Projections for changes in extremes are presented for different levels of global warming, supplemented with information for the conversion to emissions scenario-based projections (Cross-Chapter Box 11.1 and Table 4.2). Since the IPCC Fifth Assessment Report (AR5), there have been important new developments and knowledge advances on changes in weather and climate extremes, in particular regarding human influence on individual extreme events, on changes in droughts, tropical cyclones, and compound events, and on projections at different global warming levels (1.5°C–4°C). These, together with new evidence at regional scales, provide a stronger basis and more regional information for the AR6 assessment on weather and climate extremes.

'''It is an established fact that human-induced greenhouse gas emissions have led to an increased frequency and/or intensity of some weather and climate extremes since pre-industrial time, in particular for temperature extremes.''' Evidence of observed changes in extremes and their attribution to human influence (including greenhouse gas and aerosol emissions and land-use changes) has strengthened since AR5, in particular for extreme precipitation, droughts, tropical cyclones and compound extremes (including dry/hot events and fire weather). Some recent hot extreme events would have been ''extremely unlikely'' to occur without human influence on the climate system. {11.2, 11.3, 11.4, 11.6, 11.7, 11.8}

'''Regional changes in the intensity and frequency of climate extremes generally scale with global warming. New evidence strengthens the conclusion from the IPCC Special Report on Global Warming of 1.5°C (SR1.5) that even relatively small incremental increases in global warming (+0.5°C) cause statistically significant changes in extremes on the global scale and for large regions''' ( ''high confidence'' '''). In particular, this is the case for temperature extremes''' ( ''very'' ''likely'' '''), the intensification of heavy precipitation''' ( ''high confidence'' ''') including that associated with tropical cyclones''' ( ''medium confidence'' '''), and the worsening of droughts in some regions''' ( ''high confidence'' ''').''' The occurrence of extreme events unprecedented in the observed record will rise with increasing global warming, even at 1.5°C of global warming. Projected percentage changes in frequency are higher for the rarer extreme events ( ''high confidence'' ). {11.1, 11.2, 11.3, 11.4, 11.6, 11.9, Cross-Chapter Box 11.1}

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=== Methods and Data for Extremes ===

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'''Since AR5, the confidence about past and future changes in weather and climate extremes has increased due to better physical understanding of processes, an increasing proportion of the scientific literature combining different lines of evidence, and improved accessibility to different types of climate models''' ( ''high confidence'' '''). There have been improvements in some observation-based datasets, including reanalysis data''' ( ''high confidence'' '''). Climate models can reproduce the sign (direction) of changes in temperature extremes observed globally and in most regions, although the magnitude of the trends may differ''' ( ''high confidence'' ''').''' Models are able to capture the large-scale spatial distribution of precipitation extremes over land ( ''high confidence'' ). The intensity and frequency of extreme precipitation simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models are similar to those simulated by CMIP5 models ( ''high confidence'' ). Higher horizontal model resolution improves the spatial representation of some extreme events (e.g., heavy precipitation events), in particular in regions with highly varying topography ( ''high confidence'' ). {11.2, 11.3, 11.4}

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=== Temperature Extremes ===

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'''The frequency and intensity of hot extremes (including heatwaves) have increased, and those of cold extremes have decreased on the global scale since 1950''' ( ''virtually certain'' '''). This also applies at regional scale, with more than 80% of AR6 regions''' [[#footnote-011|1]] '''showing similar changes assessed to be at least''' ''likely'' '''.''' In a few regions, ''limited evidence'' (data or literature) prevents the reliable estimation of trends. {11.3, 11.9}

'''Human-induced greenhouse gas forcing is the main driver of the observed changes in hot and cold extremes on the global scale''' ( ''virtually certain'' ''') and on most continents''' ( ''very likely'' ''').''' The effect of enhanced greenhouse gas concentrations on extreme temperatures is moderated or amplified at the regional scale by regional processes such as soil moisture or snow/ice-albedo feedbacks, by regional forcing from land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. Changes in anthropogenic aerosol concentrations have ''likely'' affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA ( ''medium confidence'' ). Urbanization has ''likely'' exacerbated changes in temperature extremes in cities, in particular for nighttime extremes. {11.1, 11.2, 11.3}

'''The frequency and intensity of hot extremes will continue to increase and those of cold extremes will continue to decrease, at global and continental scales and in nearly all inhabited regions''' <sup>1</sup> '''with increasing global warming levels.''' This will be the case even if global warming is stabilized at 1.5°C. Relative to present-day conditions, changes in the intensity of extremes would be at least double at 2°C, and quadruple at 3°C of global warming, compared to changes at 1.5°C of global warming. The number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves will increase over most land areas ( ''virtually certain'' ). In most regions, future changes in the intensity of temperature extremes will ''very likely'' be proportional to changes in global warming, and up to two to three times larger ( ''high confidence'' ). The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions and in the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ). The frequency of hot temperature extreme events will ''very likely'' increase nonlinearly with increasing global warming, with larger percentage increases for rarer events. {11.2, 11.3, 11.9; Table 11.1; Figure 11.3}

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=== Heavy Precipitation and Pluvial Floods ===

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'''The frequency and intensity of heavy precipitation events have''' ''likely'' '''increased at the global scale over a majority of land regions with good observational coverage.''' '''Heavy precipitation has''' ''likely'' '''increased on the continental scale over three continents: North America, Europe, and Asia.''' Regional increases in the frequency and/or intensity of heavy precipitation have been observed with at least ''medium confidence'' for nearly half of AR6 regions, including WSAF, ESAF, WSB, SAS, ESB, RFE, WCA, ECA, TIB, EAS, SEA, NAU, NEU, EEU, GIC, WCE, SES, CNA, and ENA. {11.4, 11.9}

'''Human influence, in particular greenhouse gas emissions, is''' ''likely'' '''the main driver of the observed global-scale intensification of heavy precipitation over land regions.''' It is ''likely'' that human-induced climate change has contributed to the observed intensification of heavy precipitation at the continental scale in North America, Europe and Asia. Evidence of a human influence on heavy precipitation has emerged in some regions ( ''high confidence'' ). {11.4, 11.9, Table 11.1}

'''Heavy precipitation will generally become more frequent and more intense with additional global warming. At a global warming level of 4°C relative to the pre-industrial level, very rare (e.g., one in 10 or more years) heavy precipitation events would become more frequent and more intense than in the recent past, on the global scale''' ( ''virtually certain'' ''') and in all continents and AR6 regions. The increase in frequency and intensity is''' ''extremely likely'' '''for most continents and''' ''very likely'' '''for most AR6 regions.''' At the global scale, the intensification of heavy precipitation will follow the rate of increase in the maximum amount of moisture that the atmosphere can hold as it warms ( ''high confidence'' ), of about 7% per 1°C of global warming. The increase in the frequency of heavy precipitation events will be non-linear with more warming and will be higher for rarer events ( ''high confidence'' ), with a ''likely'' doubling and tripling in the frequency of 10-year and 50-year events, respectively, compared to the recent past at 4°C of global warming. Increases in the intensity of extreme precipitation at regional scales will vary, depending on the amount of regional warming, changes in atmospheric circulation and storm dynamics ( ''high confidence'' ). {11.4, Box 11.1}

'''The projected increase in the intensity of extreme precipitation translates to an increase in the frequency and magnitude of pluvial floods – surface water and flash floods –''' ( ''high confidence'' '''), as pluvial flooding results from precipitation intensity exceeding the capacity of natural and artificial drainage''' '''systems.''' {11.4}

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=== River Floods ===

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'''Significant trends in peak streamflow have been observed in some regions over the past decades''' ( ''high confidence'' ). The seasonality of river floods has changed in cold regions where snow-melt is involved, with an earlier occurrence of peak streamflow ( ''high conf'' ''idence'' ). {11.5}

'''Global hydrological models project a larger fraction of land areas to be affected by an increase in river floods than by a decrease in river floods''' ( ''medium confidence'' ''').''' Regional changes in river floods are more uncertain than changes in pluvial floods because complex hydrological processes and forcings, including land cover change and human water management, are involved. {11.5}

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=== Droughts ===

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'''Different drought types exist, and they are associated with different impacts and respond differently to increasing greenhouse gas concentrations.''' Precipitation deficits and changes in evapotranspiration govern net water availability. A lack of sufficient soil moisture, sometimes amplified by increased atmospheric evaporative demand, results in agricultural and ecological drought. Lack of runoff and surface water result in hydrological drought. {11.6}

'''Human-induced climate change has contributed to increases in a''' '''gricultural and''' '''ecological droughts in some regions due to evapotranspiration increases''' ( ''medium confidence'' ''').''' Increases in evapotranspiration have been driven by increases in atmospheric evaporative demand induced by increased temperature, decreased relative humidity and increased net radiation ( ''high confidence'' ). Trends in precipitation are not a main driver in affecting global-scale trends in drought ( ''medium confidence'' ), but have induced increases in meteorological droughts in a few AR6 regions (NES: ''high confidence'' ; WAF, CAF, ESAF, SAM, SWS, SSA, SAS: ''medium confidence'' ). Increasing trends in agricultural and ecological droughts have been observed on all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, WNA, NES: ''medium confidence'' ), but decreases only in one AR6 region (NAU: ''medium confidence'' ). Increasing trends in hydrological droughts have been observed in a few AR6 regions (MED: ''high confidence'' ; WAF, EAS, SAU: ''medium confidence'' ). Regional-scale attribution shows that human-induced climate change has contributed to increased agricultural and ecological droughts (MED, WNA), and increased hydrological drought (MED) in some regions ( ''medium confidence'' ). {11.6, 11.9}

'''More regions are affected by increases in agricultural and ecological droughts with increasing global warming''' ( ''high confidence'' ''').''' Several regions will be affected by more severe agricultural and ecological droughts even if global warming is stabilised at 2°C, including MED, WSAF, SAM and SSA ( ''high confidence'' ), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, WCE, NCA, WNA and CNA ( ''medium confidence'' ). Some regions are also projected to be affected by more severe agricultural and ecological droughts at 1.5°C (MED, WSAF, ESAF, SAU, NSA, SAM, SSA, CNA, ''medium confidence'' ). At 4°C of global warming, about 50% of all inhabited AR6 regions would be affected by increases in agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF, MDG: ''medium confidence'' or higher), and only two regions (NEAF, SAS) would experience decreases in agricultural and ecological drought ( ''medium confidence'' ). There is ''high confidence'' that the projected increases in agricultural and ecological droughts are strongly affected by evapotranspiration increases associated with enhanced atmospheric evaporative demand. Several regions are projected to be more strongly affected by hydrological droughts with increasing global warming (at 4°C of global warming: NEU, WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, MDG: ''medium confidence'' or higher). There is ''low confidence'' that effects of enhanced atmospheric carbon dioxide (CO <sub>2</sub> ) concentrations on plant water-use efficiency alleviate extreme agricultural and ecological droughts in conditions characterized by limited soil moisture and enhanced atmospheric evaporative demand. There is also ''low confidence'' that these effects will substantially reduce global plant transpiration and the severity of hydrological droughts. There is ''high confidence'' that the land carbon sink will become less efficient due to soil moisture limitations and associated drought conditions in some regions in higher-emissions scenarios, in particular under global warming levels above 4°C. {11.6, 11.9, Cross-Chapter Box 5.1}

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=== Extreme Storms, Including Tropical Cyclones ===

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'''The average and maximum rain rates associated with tropical cyclones (TCs), extratropical cyclones and atmospheric rivers across the globe, and severe convective storms in some regions,''' '''increase in a warming world''' ( ''high confidence'' ''')''' ''.'' Available event attribution studies of observed strong TCs provide ''medium confidence'' for a human contribution to extreme TC rainfall. Peak TC rain rates increase with local warming at least at the rate of mean water vapour increase over oceans (about 7% per 1°C of warming) and in some cases exceeding this rate due to increased low-level moisture convergence caused by increases in TC wind intensity ( ''medium confidence'' ). {11.7, 11.4, Box 11.1}

'''It is''' ''likely'' '''that the global proportion of Category 3–5 tropical cyclone instances''' [[#footnote-010|2]] '''has increased over the past four decades.''' The average location where TCs reach their peak wind intensity has ''very likely'' migrated poleward in the western North Pacific Ocean since the 1940s, and TC translation speed has ''likely'' slowed over the conterminous USA since 1900. Evidence of similar trends in other regions is not robust. The global frequency of TC rapid intensification events has ''likely'' increased over the past four decades. None of these changes can be explained by natural variability alone ( ''medium'' ''confidence'' ).

'''The proportion of intense TCs, average peak TC wind speeds, and peak wind speeds of the most intense TCs will increase on the global scale with increasing global warming''' ( ''high confidence'' ''').''' The total global frequency of TC formation will decrease or remain unchanged with increasing global warming ( ''medium confide'' ''nce'' ). {11.7.1}

'''There is''' ''low confidence'' '''in past changes of maximum wind speeds and other measures of dynamical intensity of extratropical cyclones. Future wind speed changes are expected to be small, although poleward shifts in the storm tracks could lead to substantial changes in extreme wind speeds in some regions''' ( ''medium confidence'' ''').''' There is ''low confidence'' in past trends in characteristics of severe convective storms, such as hail and severe winds, beyond an increase in precipitation rates. The frequency of spring severe convective storms is projected to increase in the USA, leading to a lengthening of the severe convective storm season ( ''medium confidence'' ); evidence in other regions is limited. {11.7.2, 11.7.3} .

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=== Compound Events, Including Dry/Hot Events, Fire Weather, Compound Flooding, and Concurrent Extremes ===

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'''The probability of compound events has''' ''likely'' '''increased in the past due to human-induced climate change and will''' ''likely'' '''continue to increase with further global warming.''' Concurrent heatwaves and droughts have become more frequent, and this trend will continue with higher global warming ( ''high confidence'' ). Fire weather conditions (compound hot, dry and windy events) have become more probable in some regions ( ''medium confidence'' ) and there is ''high confidence'' that they will become more frequent in some regions at higher levels of global warming. The probability of compound flooding (storm surge, extreme rainfall and/or river flow) has increased in some locations ( ''medium confidence'' ), and will continue to increase due to sea level rise and increases in heavy precipitation, including changes in precipitation intensity associated with tropical cyclones ( ''high confidence'' ). The land area affected by concurrent extremes has increased ( ''high confidence'' ). Concurrent extreme events at different locations, but possibly affecting similar sectors (e.g., critical crop-producing areas for global food supply) in different regions, will become more frequent with increasing global warming, in particular above 2°C of global warming ( ''high confidence'' ). {11.8, Box 11.2, Box 11.4} .

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=== Low-likelihood, High-impact Events Associated With Climate Extremes ===

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'''The future occurrence of low-likelihood, high-impact events linked to climate extremes is generally associated with''' ''low confidence'' ''', but cannot be excluded, especially at global warming levels above 4°C.''' Compound events, including concurrent extremes, are a factor increasing the probability of low-likelihood, high-impact events ( ''high confidence'' ). With increasing global warming, some compound events with low likelihood in past and current climates will become more frequent, and there is a higher chance of occurrence of historically unprecedented events and surprises ( ''high confidence'' ). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1°C of global warming) can be perceived as surprises because of the pace of global warming ( ''high confidence'' ). {Box 11.2}

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== 11.1 Introduction ==

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=== 11.1.1 Scope of the Chapter ===

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This chapter provides assessments of changes in weather and climate extremes (collectively referred to as extremes) framed in terms of the relevance to the Working Group II (WGII) assessment. It assesses observed changes in extremes, their attribution to causes, and future projections, at three global warming levels: 1.5°C, 2°C, and 4°C. This chapter is also one of the four ‘regional chapters’ of the WGI Report (along with Chapters 10 and 12 and the Atlas). Consequently, while it encompasses assessments of changes in extremes at global and continental scales to provide a large-scale context, it also addresses changes in extremes at regional scales.

Extremes are climatic impact-drivers (Annex VII: Glossary, see [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] for a comprehensive assessment). The IPCC risk framework (Chapter 1) articulates clearly that the exposure and vulnerability to climatic impact-drivers, such as extremes, modulate the risk of adverse impacts of these drivers, and that adaptation which reduces exposure and vulnerability will increase resilience, resulting in a reduction in impacts. Nonetheless, changes in extremes lead to changes in impacts as a direct consequence of changes in their magnitude and frequency, and also through their influence on exposure and resilience.

The Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (referred as the SREX report, IPCC, 2012) provided a comprehensive assessment on changes in extremes and how exposure and vulnerability to extremes determine the impacts and likelihood of disasters. [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] of that report ( [[#Seneviratne--2012|Seneviratne et al., 2012]] , hereafter also referred to as SREX Chapter 3) assessed physical aspects of extremes, and laid a foundation for the follow-up IPCC assessments. Several chapters of the IPCC Fifth Assessment Report (AR5) (IPCC, 2013) addressed climate extremes with respect to observed changes ( [[#Hartmann--2013|Hartmann et al., 2013]] ), model evaluation ( [[#Flato--2013|Flato et al., 2013]] ), attribution ( [[#Bindoff--2013|Bindoff et al., 2013]] ), and projected long-term changes ( [[#Collins--2013|Collins et al., 2013]] ). Assessments were also provided in the IPCC Special Report on Global Warming of 1.5°C (SR1.5) ( [[#IPCC--2018|IPCC, 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), on climate change and land (SRCCL; ( [[#IPCC--2019a|IPCC, 2019a]] ), and on oceans and the cryosphere (SROCC; [[#IPCC--2019b|IPCC, 2019b]] ). These assessments are the starting point for the present assessment.

This chapter is structured as follows (Figure 11.1): This section (11.1) provides the general framing and introduction to the chapter, highlighting key aspects that underlie the confidence and uncertainty in the assessment of changes in extremes, and introducing some main elements of the chapter. To provide readers with a quick overview of past and future changes in extremes, a synthesis of global-scale assessments for different types of extremes is included at the end of this section (Tables 11.1 and 11.2). [[#11.2|Section 11.2]] introduces methodological aspects of research on climate extremes. Sections 11.3 to 11.7 assess past changes and their attribution to causes, and projected future changes in extremes, for different types of extremes, including temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, and storms, in separate sections. [[#11.8|Section 11.8]] addresses compound events. [[#11.9|Section 11.9]] summarizes regional assessments of changes in temperature extremes, in precipitation extremes and in droughts by continents in tables. The chapter also includes several boxes and FAQs on more specific topics.

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[[File:559628200b7daeef7fb327d7ea06b439 IPCC_AR6_WGI_Figure_11_1.png]]

'''Figure 11.1 |''' '''Visual guide to Chapter 11.'''

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=== 11.1.2 What Are Extreme Events and How are Their Changes Studied? ===

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Building on the SREX report and AR5, this Report defines an extreme weather event as ‘an event that is rare at a particular place and time of year’, and an extreme climate event as ‘a pattern of extreme weather that persists for some time, such as a season’ (see Glossary). The definitions of ‘rare’ are wide ranging, depending on applications. Some studies consider an event as an extreme if it is unprecedented; other studies consider events that occur several times a year as moderate extreme events. Rarity of an event with a fixed magnitude also changes under human-induced climate change, making events that are unprecedented so far rather probable under present conditions, but unique in the observational record – and thus often considered as ‘surprises’ (see Box 11.2).

Various approaches are used to define extremes. These are generally based on the determination of relative (e.g., 90 <sup>th</sup> percentile) or absolute (e.g., 35°C for a hot day) thresholds above which conditions are considered extremes. Changes in extremes can be examined from two perspectives, either focusing on changes in frequency of given extremes, or on changes in their intensity. These considerations in the definition of extremes are further addressed in [[#11.2.1|Section 11.2.1]] .

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=== 11.1.3 Types of Extremes Assessed in this Chapter ===

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The types of extremes assessed in this chapter include temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, and storms. The drought assessment addresses meteorological droughts, agricultural and ecological droughts, and hydrological droughts (see Glossary). The storms assessment addresses tropical cyclones, extratropical cyclones, and severe convective storms. This chapter also assesses changes in compound events – that is, multivariate or concurrent extreme events – because of their relevance to impacts as well as the emergence of new literature on the subject. Most of the considered extremes were also assessed in SREX and AR5. Compound events were not assessed in depth in past IPCC reports (SREX Chapter 3; [[#11.8|Section 11.8]] of this Report). Marine-related extremes such as marine heatwaves and extreme sea level, are assessed in [[IPCC:Wg1:Chapter:Chapter-9#9.6.4|Section 9.6.4]] and Box 9.2 of this Report.

Extremes and related phenomena are of various spatial and temporal scales. Tornadoes have a spatial scale as small as less than 100 metres and a temporal scale as short as a few minutes. In contrast, a drought can last for multiple years, affecting vast regions. The level of complexity of the involved processes differs from one type of extreme to another, affecting our capability to detect, attribute and project changes in weather and climate extremes. Temperature and precipitation extremes studied in the literature are often based on extremes derived from daily values. Studies of events on longer time scales for temperature or precipitation, or on sub-daily extremes, are scarcer, which generally limits the assessment for such events. Nevertheless, extremes on time scales different from daily are assessed for temperature extremes and heavy precipitation, when possible (Sections 11.3 and 11.4). Droughts and tropical cyclones are treated as phenomena in general in the assessment, not limited by their extreme forms, because these phenomena are relevant to impacts (Sections 11.6 and 11.7). Both precipitation and wind extremes associated with storms are considered.

Multiple concomitant extremes can lead to stronger impacts than those resulting from the same extremes had they happened in isolation. For this reason, the occurrence of multiple extremes that are multivariate and/or concurrent and/or happening in succession, also called ‘compound events’ (SREX Chapter 3), are assessed in this chapter based on emerging literature on this topic ( [[#11.8|Section 11.8]] ). Box 11.2 also provides an assessment on low-likelihood, high-impact scenarios associated with extremes.

The assessment of projected future changes in extremes is presented as function of different global warming levels ( [[#11.2.4|Section 11.2.4]] and Cross-Chapter Box 11.1). This provides traceability and comparison to the SR1.5 assessment ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] , hereafter referred to as SR1.5 Chapter 3). Also, this is useful for decision makers as actionable information, as much of the mitigation policy discussion and adaptation planning can be tied to the level of global warming. For example, regional changes in extremes, and thus their impacts, can be linked to global mitigation efforts. There is also the advantage of separating uncertainty in future projections due to regional responses as a function of global warming levels from other factors such as differences in global climate sensitivity and emissions scenarios (Cross-Chapter Box 11.1). Information is also provided on the translation between information provided at global warming levels and for single emissions scenarios (Cross-Chapter Box 11.1). This facilitates easier comparison with the AR5 assessment and with some analyses provided in other chapters as function of emissions scenarios.

A global-scale synthesis of this chapter’s assessments is provided in [[#11.1.7|Section 11.1.7]] . In particular, Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes, and projected changes in extremes, respectively, at different global warming levels (1.5°C, 2°C, and 4°C). Tables on regional-scale assessments for changes in temperature extremes, heavy precipitation and droughts, are provided in [[#11.9|Section 11.9]] .

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=== 11.1.4 Effects of Greenhouse Gas and Other External Forcings on Extremes ===

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The SREX, AR5, and SR1.5 assessed that there is evidence from observations that some extremes have changed since the mid-20th century, that some of the changes are a result of anthropogenic influences, and that some observed changes are projected to continue into the future. Additionally, other changes are projected to emerge from natural climate variability under enhanced global warming (SREX Chapter 3; AR5 Chapter 10).

At the global scale, and also at the regional scale to some extent, many of the changes in extremes are a direct consequence of enhanced radiative forcing, and the associated global warming and/or resultant increase in the water-holding capacity of the atmosphere, as well as changes in vertical stability and meridional temperature gradients that affect climate dynamics (see Box 11.1). Widespread observed and projected increases in the intensity and frequency of hot extremes, together with decreases in the intensity and frequency of cold extremes, are consistent with global and regional warming ( [[#11.3|Section 11.3]] and Figure 11.2). Extreme temperatures on land tend to increase more than the global mean temperature (Figure 11.2), due in large part to the land–sea warming contrast, and additionally to regional feedbacks in some regions ( [[#11.1.6|Section 11.1.6]] ). Increases in the intensity of temperature extremes scale robustly, and in general linearly, with global warming across different geographical regions in projections up to 2100, with minimal dependence on emissions scenarios ( [[#11.2.4|Section 11.2.4]] , Figure 11.3,and Cross-Chapter Box 11.1; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Kharin--2018|Kharin et al., 2018]] ). The frequency of hot temperature extremes (see Figure 11.6), the number of heatwave days and the length of heatwave seasons in various regions also scale well, but nonlinearly (because of threshold effects, [[#11.2.1|Section 11.2.1]] ), with global mean temperatures ( [[#Wartenburger--2017|Wartenburger et al., 2017]] ; Y. [[#Sun--2018a|]] [[#Sun--2018|Sun et al., 2018]] a ).

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[[File:0bcabfcb85203ec3ac5629fcc018677d IPCC_AR6_WGI_Figure_11_2.png]]

'''Figure 11.2 |''' '''Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature (green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue).''' Global and land mean temperature anomalies are relative to their 1850–1900 means and are based on the multi-product mean annual time series assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] (see text for references). TXx and TNn anomalies are relative to their respective 1961–1990 means and are based on the HadEX3 dataset ( [[#Dunn--2020|Dunn et al., 2020]] ) using values for grid boxes with at least 90% temporal completeness over 1961–2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Changes in annual maximum one-day precipitation (Rx1day) are proportional to mean global surface temperature changes, at about 7% increase per 1°C of warming, that is, following the Clausius–Clapeyron relation (Box 11.1), both in observations ( [[#Westra--2013|Westra et al., 2013]] ) and in future projections ( [[#Kharin--2013|Kharin et al., 2013]] ) at the global scale. Extreme short-duration precipitation in North America also scales with global surface temperature ( [[#Prein--2016b|Prein et al., 2016b]] ; [[#Li--2019a|]] [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|C. Li et al., 2019]] a ). At the local and regional scales, changes in extremes are also strongly modulated and controlled by regional forcings and feedback mechanisms ( [[#11.1.6|Section 11.1.6]] ), whereby some regional forcings, for example, associated with changes in land cover and land use or aerosol emissions, can have non-local or some (non-homogeneous) global-scale effects. In general, there is ''high confidence'' in changes in extremes due to global-scale thermodynamic processes (i.e., global warming, mean moistening of the air) as the processes are well understood, while the confidence in those related to dynamic processes or regional and local forcing, including regional and local thermodynamic processes, is much lower due to multiple factors (see the following subsection and Box 11.1).

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[[File:8e0a33b4b21ac32fe7915a86e48b0251 IPCC_AR6_WGI_Figure_11_3.png]]

'''Figure 11.3 |''' '''Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socio-economic Pathway (SSP) forci''' '''ng scena''' '''ri''' '''os, SSP1''' '''-1.9''' ''', SSP1''' '''-2.6''' ''', SSP2''' '''-4.5,''' '''SSP3-7.''' ''0, and SSP5-8.5.'' Changes in TXx and GSAT are relative to the 1850–1900 baseline, and changes in GSAT are expressed as global warming level. ''(a)'' Individual models from the CMIP6 ensemble (grey), the multi-model median under three selected SSPs (colours), and the multi-model median (black); ''(b) to (l)'' Multi-model median for the pooled data for individual AR6 regions. Numbers in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2.

Since AR5, the attribution of extreme weather events, or the investigation of changes in the frequency and/or magnitude of individual and local- and regional-scale extreme weather events due to various drivers ( [[#11.2.3|Section 11.2.3]] and Cross-Working Group Box 1.1) has provided evidence that greenhouse gases and other external forcings have affected individual extreme weather events. The events that have been studied are geographically uneven. For example, extreme rainfall events in the UK ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Vautard--2016|Vautard et al., 2016]] ; [[#Otto--2018b|Otto et al., 2018b]] ) or heatwaves in Australia ( [[#King--2014|King et al., 2014]] ; [[#Perkins-Kirkpatrick--2016|Perkins-Kirkpatrick et al., 2016]] ; [[#Lewis--2017b|Lewis et al., 2017b]] ) have spurred more studies than other events. Many highly impactful extreme weather events have not been studied in the event attribution framework. Studies in the developing world are also generally lacking. This is due to various reasons ( [[#11.2|Section 11.2]] ) including lack of observational data, lack of reliable climate models and other problems ( [[#Otto--2020|Otto et al., 2020]] ). While the events that have been studied are not representative of all extreme events that occurred, and results from these studies may also be subject to selection bias, the large number of event attribution studies provide evidence that changes in the properties of these local and individual events are in line with expected consequences of human influence on the climate and can be attributed to external drivers ( [[#11.9|Section 11.9]] ). Figure 11.4 summarizes assessments of observed changes in temperature extremes, in heavy precipitation and in droughts, and their attribution in a map form.

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[[File:04c90b94b4a0466285f1e5a28d9446a8 IPCC_AR6_WGI_Figure_11_4.png]]

'''Figure 11.4 |''' '''Overview of observed changes for cold, hot, and wet extremes and their potential human contribution.''' Shown are the direction of change and the confidence in: 1) the observed changes in cold and hot as well as wet extremes across the world; and 2) whether human-induced climate change contributed to causing these changes (attribution). In each region changes in extremes are indicated by colour (orange – increase in the type of extreme; blue – decrease; both colours – changes of opposing direction within the region, with the signal depending on the exact event definition; grey – there are no changes observed; and no fill – the data/evidence is too sparse to make an assessment). The squares and dots next to the symbol indicate the level of confidence for observing the trend and the human contribution, respectively. The more black dots/squares, the higher the level of confidence. The information on this figure is based on regional assessment of the literature on observed trends, detection and attribution and event attribution in [[#11.9|Section 11.9]] .

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Box 11.1 | Thermodynamic and Dynamic Changes in Extremes Across Scales

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Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities (thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and dynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing to changes in climate extremes (e.g., [[#Shepherd--2014|Shepherd, 2014]] ).

''''''Temperature extremes''''''
An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth’s surface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity of warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other thermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour content of the atmosphere (water vapour feedback, see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ) and a change in the vertical profile of temperature (lapse rate feedback, see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.2|Section 7.4.2.2]] ). While the water vapour feedback always amplifies the initial temperature increases (positive feedback), the lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces temperature increases (negative feedback) in tropical regions ( [[#Pithan--2014|Pithan and Mauritsen, 2014]] ).

Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations show that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due to decreases in surface albedo (see [[#11.3.1|Section 11.3.1]] ). In some mid-latitude areas, temperature increases are amplified by the higher atmospheric evaporative demand ( [[#Fu--2014|Fu and Feng, 2014]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ) that results in a drying of soils in some regions ( [[#11.6|Section 11.6]] ), leading to increased sensible heat fluxes (soil-moisture – temperature feedback, see Sections 11.1.6 and 11.3.1 for more background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced atmospheric carbon dioxide (CO <sub>2</sub> ) concentrations that can reduce the overall transpiration, and thus also enhance temperature in projections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6).

Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather patterns (e.g., [[#Suarez-Gutierrez--2020a|Suarez-Gutierrez et al., 2020a]] ). For example, a robust thermodynamic effect of polar amplification is a weakened north-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes because of the reduction of cold air advection ( [[#Holmes--2015|Holmes et al., 2015]] ; [[#Schneider--2015|Schneider et al., 2015]] ; [[#Gross--2020|Gross et al., 2020]] ). Much less robust is the dynamic effect of polar amplification ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.1|Section 7.4.4.1]] ) and the reduced low-altitude meridional temperature gradient that has been linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes (Cross-Chapter Box 10.1; [[#Francis--2012|Francis and Vavrus, 2012]] ; Coumou et al. , 2015, 2018; Mann et al., 2017).

''''''Precipitation extremes''''''
Changes in temperature also control changes in water vapour through increases in evaporation and in the water-holding capacity of the atmosphere ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). At the global scale, column-integrated water vapour content increases roughly following the Clausius–Clapeyron (C-C) relation, with an increase of approximately 7% per 1°C of global-mean surface warming ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ). Nonetheless, at regional scales, water vapour increases differ from this C-C rate due to several reasons ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|Section 8.2.2]] ), including a change in weather regimes and limitations in moisture transport from the ocean, which warms more slowly than land ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). Observational studies ( [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Sun--2021|Sun et al., 2021]] ) have shown that the observed rate of increased precipitation extremes is similar to the C-C rate at the global scale. Climate model projections show that the increase in water vapour leads to robust increases in precipitation extremes everywhere, with a magnitude that varies between 4% and 8% per 1°C of surface warming (thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models show that the dynamic contribution (Box 11.1, Figure 1c) can be substantial and strongly modify the projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large regions in the subtropics showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1, Figure 1c). However, the dynamic contributions show large differences across models and are more uncertain than thermodynamic contributions (Box 11.1, Figure 1c; [[#Shepherd--2014|Shepherd, 2014]] ; [[#Trenberth--2015|Trenberth et al., 2015]] ; [[#Pfahl--2017|Pfahl et al., 2017]] ).

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[[File:335c666394fc1f6cf3fa86f0e95dd4d4 IPCC_AR6_WGI_Box_11_1_Figure_1.png]]

'''Box 11.1, Figure 1:'''

'''Multi-model Coupled Model Intercomparison Project Phase 5 (CMIP5) mean fractional changes (in % per degree of warming). (a)''' changes in annual maximum precipitation (Rx1day); (b) changes in Rx1day due to the thermodynamic contribution; and (c) changes in Rx1day due to the dynamic contribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period 1950–2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models (n=22) agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl et al. (2017). Figure adapted from Pfahl et al. (2017), originally published in ''Nature Climate Change/Springer Nature.'' Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of temperature (thermodynamics) and can affect the frequency and intensity of synoptic and subsynoptic phenomena, including tropical cyclones, extratropical cyclones, fronts, mesoscale-convective systems and thunderstorms. For example, the poleward shift and strengthening of the Southern Hemisphere mid-latitude storm tracks ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1|Section 4.5.1]] ) can modify the frequency or intensity of extreme precipitation. However, the precise way in which dynamic changes will affect precipitation extremes is unclear due to several competing effects ( [[#Shaw--2016|Shaw et al., 2016]] ; [[#Allan--2020|Allan et al., 2020]] ).

Box 11.1

Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra latent heat released from the thermodynamic increases in moisture ( [[#Lackmann--2013|Lackmann, 2013]] ; Willison et al. , 2013; Marciano et al. , 2015; Nie et al. , 2018; [[#Mizuta--2020|Mizuta and Endo, 2020]] ). The extra latent heat released within storms has been shown to increase precipitation extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., [[#Molnar--2015|Molnar et al., 2015]] ; [[#Nie--2018|Nie et al., 2018]] ), although weakening effects have also been found in mid-latitude cyclones (e.g., [[#Kirshbaum--2017|Kirshbaum et al., 2017]] ). Additionally, the increase in latent heat can also suppress convection at larger scales due to atmospheric stabilization ( [[#Nie--2018|Nie et al., 2018]] ; [[#Tandon--2018|Tandon et al., 2018]] ; [[#Kendon--2019|Kendon et al., 2019]] ). As these dynamic effects result from feedback processes within storms where convective processes are crucial, their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, in current climate models (i.e., Kendon et al. , 2014; Westra et al. , 2014; Ban et al. , 2015; Meredith et al. , 2015; Prein et al. , 2015; Nie et al., 2018).

''''''Droughts''''''
Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect droughts by increasing atmospheric evaporative demand ( [[#Martin--2018|Martin, 2018]] ; [[#Gebremeskel%20Haile--2020|Gebremeskel Haile et al., 2020]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through changes in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight ( [[#11.6|Section 11.6]] ). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing land–ocean warming contrast, vegetation feedbacks and responses to rising CO <sub>2</sub> concentrations, and dynamic shifts in the location of the wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability ( [[#Byrne--2015|Byrne and O’Gorman, 2015]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Allan--2020|Allan et al., 2020]] ).

In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur ( ''high confidence'' ). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is ''low confidence'' in how dynamic changes affect the location and magnitude of extreme events in a warming climate.

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<span id="effects-of-large-scale-circulation-on-changes-in-extremes"></span>
=== 11.1.5 Effects of Large-scale Circulation on Changes in Extremes ===

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Atmospheric large-scale circulation patterns and associated atmospheric dynamics are important determinants of the regional climate (Chapter 10). As a result, they are also important to the magnitude, frequency, and duration of extremes (Box 11.4). Aspects of changes in large-scale circulation patterns are assessed in Chapters 2, 3, 4 and 8, and representative atmospheric and oceanic modes are described in Annex IV. This subsection provides some general concepts, through a couple of examples, on why the uncertainty in the response of large-scale circulation patterns to external forcing can cascade to uncertainty in the response of extremes to external forcings. Details for specific types of extremes are covered in the relevant subsections. For example, the occurrence of the El Niño–Southern Oscillation (ENSO) influences precipitation regimes in many areas, favouring droughts in some regions and heavy rains in others (Box 11.4). The extent and strength of the Hadley circulation influences regions where tropical and extratropical cyclones occur, with important consequences for the characteristics of extreme precipitation, drought, and winds ( [[#11.7|Section 11.7]] ). Changes in circulation patterns associated with land–ocean heat contrast, which affect the monsoon circulations ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.4|Section 8.4.2.4]] ), lead to heavy precipitation along the coastal regions in East Asia ( [[#Freychet--2015|Freychet et al., 2015]] ). As a result, changes in the spatial and/or temporal variability of the atmospheric circulation in response to warming affect characteristics of weather systems such as tropical cyclones ( [[#Sharmila--2018|Sharmila and Walsh, 2018]] ), storm tracks ( [[#Shaw--2016|Shaw et al., 2016]] ), and atmospheric rivers ( [[#11.7|Section 11.7]] ; [[#Waliser--2017|Waliser and Guan, 2017]] ). Changes in weather systems come with changes in the frequency and intensity of extreme winds, extreme temperatures, and extreme precipitation, on the backdrop of thermodynamic responses of extremes to warming (Box 11.1). Floods are also affected by large-scale circulation modes, including ENSO, the North Atlantic Oscillation (NAO), the Atlantic Multi-decadal Variability (AMV), and the Pacific Decadal Variability (PDV) ( [[#Kundzewicz--2018|Kundzewicz et al., 2018]] ; Annex IV). Aerosol forcing, through changes in patterns of sea surface temperatures (SSTs), also affects circulation patterns and tropical cyclone activities ( [[#Takahashi--2017|Takahashi et al., 2017]] ).

In general, changes in atmospheric large-scale circulation due to external forcing are uncertain, but there are some robust changes (Sections 2.3.1.4 and 8.2.2.2). Among them, there has been a ''very likely'' widening of the Hadley circulation since the 1980s and the extratropical jets and cyclone tracks have ''likely'' been shifting poleward since the 1980s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4|Section 2.3.1.4]] ). The poleward expansion affects drought occurrence in some regions ( [[#11.6|Section 11.6]] ), and results in poleward shifts of tropical cyclones and storm tracks (Sections 11.7.1 and 11.7.2). Although it is ''very likely'' that the amplitude of ENSO variability will not robustly change over the 21st century ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.3.2|Section 4.3.3.2]] ), the frequency of extreme ENSO events (Box 11.4), defined by precipitation threshold, is projected to increase with global warming (Section 6.5 of SROCC). This would have implications for projected changes in extreme events affected by ENSO, including droughts over wide areas ( [[#11.6|Section 11.6]] ; Box 11.4) and tropical cyclones ( [[#11.7.1|Section 11.7.1]] ). A case study is provided for extreme ENSO events in 2015–2016 in Box 11.4 to highlight the influence of ENSO on extremes.

In summary, large-scale atmospheric circulation patterns are important drivers for local and regional extremes. There is overall ''low confidence'' about future changes in the magnitude, frequency, and spatial distribution of these patterns, which contributes to uncertainty in projected responses of extremes, especially in the near term.

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<span id="effects-of-regional-scale-processes-and-forcings-and-feedbacks-on-changes-in-extremes"></span>
=== 11.1.6 Effects of Regional-scale Processes and Forcings and Feedbacks on Changes in Extremes ===

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At the local and regional scales, changes in extremes are strongly modulated by local and regional feedbacks (SRCCL, [[#Jia--2019|Jia et al., 2019]] ; [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Lorenz--2016|Lorenz et al., 2016]] ; [[#Vogel--2017|Vogel et al., 2017]] ), changes in large-scale circulation patterns ( [[#11.1.5|Section 11.1.5]] ), and regional forcings such as changes in land use or aerosol concentrations (Chapters 3 and 7; [[#Findell--2017|Findell et al., 2017]] ; [[#Hirsch--2017|Hirsch et al., 2017]] , 2018; [[#Thiery--2017|Thiery et al., 2017]] ; Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] b). In some cases, such responses may also include non-local effects (e.g., [[#de%20Vrese--2016|de Vrese et al., 2016]] ; [[#Persad--2018|Persad and Caldeira, 2018]] ; [[#Miralles--2019|Miralles et al., 2019]] ; [[#Schumacher--2019|Schumacher et al., 2019]] ). Regional-scale forcing and feedbacks often affect temperature distributions asymmetrically, with generally higher effects for the hottest percentiles ( [[#11.3|Section 11.3]] ).

Land use can affect regional extremes, in particular hot extremes, in several ways ( ''high confidence'' ). This includes effects of land management (e.g., cropland intensification, irrigation, double cropping) as well as of land cover changes (deforestation; Sections 11.3.2 and 11.6). Some of these processes are not well represented (e.g., effects of forest cover on diurnal temperature cycle) or not integrated (e.g., irrigation) in climate models (Sections 11.3.2 and 11.3.3). Overall, the effects of land-use forcing may be particularly relevant in the context of low-emissions scenarios, which include large land-use modifications, for instance those associated with the expansion of biofuels, bioenergy with carbon capture and storage, or re-/afforestation to ensure negative emissions, as well as with the expansion of food production (e.g., SR1.5, Chapter 3; Cross-Chapter Box 5.1 in this Report; [[#van%20Vuuren--2011|van Vuuren et al., 2011]] ; [[#Hirsch--2018|Hirsch et al., 2018]] ). There are also effects on the water cycle through freshwater use ( [[#11.6|Section 11.6]] and Cross-Chapter Box 5.1).

Aerosol forcing also has a strong regional footprint associated with regional emissions, which affects temperature and precipitation extremes ( ''high confidence'' ) (Sections 11.3 and 11.4). From around the 1950s to 1980s, enhanced aerosol loadings led to regional cooling due to decreased global solar radiation (‘global dimming’) which was followed by a phase of ‘global brightening’ due to a reduction in aerosol loadings (Chapters 3 and 7; [[#Wild--2005|Wild et al., 2005]] ). [[#King--2016b|King et al. (2016b)]] show that aerosol-induced cooling delayed the timing of a significant human contribution to record-breaking heat extremes in some regions. However, the decreased aerosol loading since the 1990s has led to an accelerated warming of hot extremes in some regions. Based on Earth system model (ESM) simulations, [[#Dong--2017|Dong et al. (2017)]] suggest that a substantial fraction of the warming of the annual hottest days in Western Europe since the mid-1990s has been due to decreases in aerosol concentrations in the region. [[#Dong--2016b|Dong et al. (2016b)]] also identify non-local effects of decreases in aerosol concentrations in Western Europe, which they estimate played a dominant role in the warming of the hottest daytime temperatures in north-east Asia since the mid-1990s, via induced coupled atmosphere–land surface and cloud feedbacks, rather than a direct impact of anthropogenic aerosol changes on cloud condensation nuclei.

In addition to regional forcings, regional feedback mechanisms can also substantially affect extremes ( ''high confidence'' ) (Sections 11.3, 11.4 and 11.6). In particular, soil moisture feedbacks play an important role for extremes in several mid-latitude regions, leading to a marked additional warming of hot extremes compared to mean global warming ( [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Bathiany--2018|Bathiany et al., 2018]] ; [[#Miralles--2019|Miralles et al., 2019]] ), which is superimposed on the known land–sea contrast in mean warming ( [[#Vogel--2017|Vogel et al., 2017]] ). Soil moisture–atmosphere feedbacks also affect drought development ( [[#11.6|Section 11.6]] ). Additionally, effects of land surface conditions on circulation patterns have also been reported ( [[#Koster--2016|Koster et al., 2016]] ; [[#Sato--2019|Sato and Nakamura, 2019]] ). These regional feedbacks are also associated with substantial spread in models ( [[#11.3|Section 11.3]] ), and contribute to the identified higher spread of regional projections of temperature extremes as a function of global warming, compared with the spread resulting from the differences in projected global warming (global transient climate responses) in climate models ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). In addition, there are also feedbacks between soil moisture content and precipitation occurrence, generally characterized by negative spatial feedbacks and positive local feedbacks (Taylor et al., 2012; [[#Guillod--2015|Guillod et al., 2015]] ). Climate model projections suggest that these feedbacks are relevant for projected changes in heavy precipitation ( [[#Seneviratne--2013|Seneviratne et al., 2013]] ). However, there is evidence that climate models do not capture the correct sign of the soil moisture–precipitation feedbacks in several regions, in particular spatially, and/or in some cases also temporally (Taylor et al., 2012; [[#Moon--2019|Moon et al., 2019]] ). In the Northern Hemisphere high latitudes, the snow- and ice-albedo feedback, along with other factors, is projected to largely amplify temperature increases (e.g., [[#Pithan--2014|Pithan and Mauritsen, 2014]] ), although the effect on temperature extremes is still unclear. It also remains unclear whether snow-albedo feedbacks in mountainous regions might have an effect on temperature and precipitation extremes (e.g., [[#Gobiet--2014|Gobiet et al., 2014]] ). However, these feedbacks play an important role in projected changes in high-latitude warming ( [[#Hall--2006|Hall and Qu, 2006]] ), and, in particular, in changes in cold extremes in these regions ( [[#11.3|Section 11.3]] ).

Finally, extreme events may also regionally amplify one another. For example, this is the case for heatwaves and droughts, with high temperatures and stronger radiative forcing leading to drying tendencies on land due to increased evapotranspiration ( [[#11.6|Section 11.6]] ), and drier soils then inducing decreased evapotranspiration and higher sensible heat flux and hot temperatures (Box 11.1, [[#11.8|Section 11.8]] ; [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Vogel--2017|Vogel et al., 2017]] ; [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ; S. [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ; [[#Kong--2020|Kong et al., 2020]] ).

In summary, regional forcings and feedbacks – in particular those associated with land use and aerosol forcings – and soil-moisture–temperature, soil moisture–precipitation, and snow/ice–albedo–temperature feedbacks, play an important role in modulating regional changes in extremes. These can also lead to a higher warming of extreme temperatures compared to mean temperature ( ''high confidence'' ), and possibly cooling in some regions ( ''medium confidence'' ). However, there is only ''medium confidence'' in the representation of the associated processes in state-of-the-art ESMs.

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<span id="global-scale-synthesis"></span>
=== 11.1.7 Global-scale Synthesis ===

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Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes in extremes, and projected changes in extremes, respectively, at different levels of global warming. This synthesis assessment focuses on the assessed range of observed and projected changes. In this chapter, the assessed ''likely'' range in a projection typically corresponds to the 90% range of the multi-model ensemble spread to take into account other sources of uncertainty, unless stated otherwise. Some low-likelihood, high-impact scenarios that can be of high relevance are addressed in Box 11.2.

Building on the assessments from Tables 11.1 and 11.2, Figure 11.5 provides a synthesis on the level of confidence in the attribution and projection of changes in extremes. In the case where the signal in the observations is still relatively weak but the physical processes underlying the changes in extremes in response to human forcing are well understood, confidence in the projections would be higher than in the attribution because of strengthening in the signal with warming. But, when the observed signal is already strong and when observational evidence is consistent with model simulated responses, confidence in the projection may be lower than that in attribution if certain physical processes could be expected to behave differently in a much warmer world and under much higher greenhouse gas forcing, and in particular if such a behaviour is poorly understood.

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[[File:a5ba7c3af5ed593cda01d747d3b2d896 IPCC_AR6_WGI_Figure_11_5.png]]

'''Figure 11.5 |''' '''confidence and likelihood of past changes and projected future changes at 2°C of global warming on the global scale.''' The information in this figure is based on Tables 11.1 and 11.2.

Further synthesis for regional assessments are provided in Figure 11.4 (event attribution), Figure 11.6 (projected change in hot temperature extremes) and Figure 11.7 (projected changes in precipitation extremes). A synthesis on regional assessments for observed, attributed and projected changes in extremes is provided in [[#11.9|Section 11.9]] for all AR6 reference regions (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] and Figures 1.18 and Atlas.2 for definitions of AR6 regions).

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[[File:86a62770ec72d8bc8bed568c837492f8 IPCC_AR6_WGI_Figure_11_6.png]]

'''Figure 11.6 |''' '''Projected changes in the frequency of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 18''' ''50–1900 baseline.'' Extreme temperatures are defined as the maximum daily temperatures that were exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from [[#Li--2021|Li et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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[[File:1726f3bc034390a6043692705fb27ecd IPCC_AR6_WGI_Figure_11_7.png]]

'''Figure 11.7 |''' '''Projected changes in the frequency of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 1850''' ''–19'' ''0'' ''0 baseline.'' Extreme precipitation is defined as the annual maximum daily precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from [[#Li--2021|Li et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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'''Table 11.1 |''' '''Synthesis table on observed changes in extremes and contribution by human influence.''' Note that observed changes in marine extremes are assessed in Cross-Chapter Box 9.1.

{| class="wikitable"
|-
! Phenomenon and Direction of Trend

! Observed/Detected Trends Since 1950 (for +0.5°C global warming or higher)

! Human Contribution to the Observed Trends Since 1950 (for +0.5°C global warming or higher)

|-
| Warmer and/or more frequent hot days and nights over most land areas

Warmer and/or fewer cold days and nights over most land areas

Warm spells/heatwaves: increases in frequency or intensity over most land areas

Cold spells/cold waves: decreases in frequency or intensity over most land areas

| ''Virtually certain'' on global scale {11.3}

'''Continental-scale evidence:'''

Asia, Australasia, Europe, North America: ''Very likely''

Central and South America: ''High confidence''

Africa: ''Medium confidence''

{11.3, 11.9}

| ''Extremely likely'' main contributor on global scale {11.3}

'''Continental-scale evidence:'''

North America, Europe, Australasia, Asia: ''Very likely''

Central and South America: ''High confidence''

Africa: ''Medium confidence''

{11.3, 11.9}

|-
| Heavy precipitation events: increase in the frequency, intensity, and/or amount of heavy precipitation

| ''Likely'' on global scale, over majority of land regions with good observational coverage {11.3}

'''Continental-scale evidence:'''

Asia, Europe, North America: ''Likely''

Africa, Australasia, Central and South America: ''Low confidence''

{11.3, 11.9}

| ''Likely'' main contributor to the observed intensification of heavy precipitation in land regions on global scale.

{11.3}

'''Continental-scale evidence:'''

Asia, Europe, North America: ''Likely''

Africa, Australasia, Central and South America: ''Low confidence''

{11.3, 11.9}

|-
| Increases in agricultural and ecological drought events

| ''Medium confidence'' some regions {11.6, 11.9}

Increasing trends in agricultural and ecological droughts have been observed in AR6 regions on all continents ( ''medium confidence'' ) {11.6, 11.9}

| ''Medium confidence'' some regions

{11.6, 11.9}

|-
| Increase in precipitation associated with tropical cyclones (TCs)

| ''Medium confidence''

{11.7}

| ''High confidence''

{11.7}

|-
| Increase in likelihood that a TC will be at major TC intensity (Cat. 3–5)

| ''Likely''

{11.7}

| ''Medium confidence''

{11.7}

|-
| Changes in frequency of rapidly intensifying tropical cyclones

| ''Likely''

{11.7}

| ''Medium confidence''

{11.7}

|-
| Poleward migration of tropical cyclones in the western Pacific

| ''Medium confidence''

{11.7}

| ''Medium confidence''

{11.7}

|-
| Decrease in TC forward motion over the USA

| It is ''likely'' that TC translation speed has slowed over the USA since 1900.

{11.7}

| It is ''more likely than not'' that the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing.

{11.7}

|-
| Severe convective storms (tornadoes, hail, rainfall, wind, lightning)

| ''Low confidence'' in past trends in hail and winds and tornado activity due to short length of high-quality data records. {11.7}

| ''Low confidence''

{11.7}

|-
| Increase in compound events

| ''Likely'' increase in the probability of compound events.

''High confidence'' that concurrent heatwaves and droughts are becoming more frequent under enhanced greenhouse gas forcing at global scale.

''Medium confidence'' that fire weather, i.e. compound hot, dry and windy events, have become more frequent in some regions.

''Medium confidence'' that compound flooding risk has increased in some locations.

{11.8}

| ''Likely'' that human-induced climate change has increased the probability of compound events.

''High confidence'' that human influence has increased the frequency of concurrent heatwaves and droughts.

''Medium confidence'' that human influence has increased fire weather occurrence in some regions.

''Low confidence'' that human influence has contributed to changes in compound events leading to flooding.

{11.8}

|}

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'''Table 11.2 |''' '''Synthesis table on projected changes in extremes.''' Note that projected changes in marine extremes are assessed in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and Cross-Chapter Box 9.1 (marine heatwaves). Assessments are provided compared to pre-industrial conditions.

{| class="wikitable"
|-
! Phenomenon and Direction of Trend

! Projected Changes at +1.5ºC Global Warming

! Projected Changes at +2°C Global Warming

! Projected Changes at +4°C Global Warming

|-
| Warmer and/or more frequent hot days and nights over most land areas

Warmer and/or fewer cold days and nights over most land areas

Warm spells/heatwaves; increases in frequency or intensity over most land areas

Cold spells/cold waves: decreases in frequency or intensity over most land areas

| ''Virtually certain'' on global scale

''Extremely likely'' on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ) {11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ) {11.3}

'''Continental-scale projections:'''

''Extremely likely'' : Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

| ''Virtually certain'' on global scale

''Virtually certain'' on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ) {11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ) {11.3}

'''Continental-scale projections:'''

''Virtually certain:'' Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

| ''Virtually certain'' on global scale

''Virtually certain'' on all continents

Highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, and the South American Monsoon region, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ) {11.3, Figure 11.3}

Highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ) {11.3}

'''Continental-scale projections:'''

''Virtually certain:'' Africa, Asia, Australasia, Central and South America, Europe, North America

{11.3, 11.9}

|-
| Heavy precipitation events: increase in the frequency, intensity, and/or amount of heavy precipitation

| ''High confidence'' that increases take place in most land regions {11.4}

''Very likely :'' Asia, North America

''Likely:'' Africa, Europe

''High confidence:'' Central and South America

''Medium confidence:'' Australasia

{11.4, 11.9}

| ''Likely'' that increases take place in most land regions {11.4}

''Extremely likely :'' Asia, North America

''Very likely :'' Africa, Europe

''Likely:'' Australasia, Central and South America

{11.4, 11.9}

| ''Very likely'' that increases take place in most land regions {11.4}

''Virtually certain:'' Africa, Asia, North America

''Extremely likely :'' Central and South America, Europe

''Very likely'' Australasia

{11.4, 11.9}

|-
| Agricultural and ecological droughts: increases in intensity and/or duration of drought events

| More regions affected by increases in agricultural and ecological droughts compared to observed changes ( ''high confidence'' ). {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in some regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in some regions. ( ''high confidence'' )

{11.6, 11.9}

| More regions affected by increases in agricultural and ecological droughts than at 1.5°C of global warming ( ''high confidence'' ). {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in some regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in some regions. ( ''high confidence'' )

{11.6, 11.9}

| More regions affected by increases in agricultural and ecological droughts than at 2°C of global warming ( ''very likely'' ) . {11.6, 11.9}

Decreased precipitation is going to increase the severity of drought in several regions; atmospheric evaporative demand will continue to increase compared to pre-industrial conditions and lead to further increases in agricultural and ecological droughts due to increased evapotranspiration in several regions. ( ''high confidence'' )

{11.6, 11.9}

|-
| Increase in precipitation associated with tropical cyclones (TCs)

| ''High confidence'' in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 11%. {11.7}

''Medium confidence'' that rain rates will increase in every basin. {11.7}

| ''High confidence'' in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 14%. {11.7}

''Medium confidence'' that rain rates will increase in every basin. {11.7}

| ''High confidence'' in a projected increase of TC rain rates at the global scale with a median projected increase due to human emissions of about 28%. {11.7}

''Medium confidence'' that rain rates will increase in every basin. {11.7}

|-
| Increase in mean TC lifetime-maximum wind speed (intensity)

| ''Medium confidence''

{11.7}

| ''High confidence''

{11.7}

| ''High confidence''

{11.7}

|-
| Increase in likelihood that a TC will reach major TC intensity (Category 4–5)

| ''High confidence'' for an increase in the proportion of TCs that reach the strongest (Category 4–5) levels. The median projected increase in this proportion is about 10%.

{11.7}

| ''High confidence'' for an increase in the proportion of TCs that reach the strongest (Category 4–5) levels. The median projected increase in this proportion is about 13%.

{11.7}

| ''High confidence'' for an increase in the proportion of TCs that reach the strongest (Category 4–5) levels. The median projected increase in this proportion is about 20%.

{11.7}

|-
| Severe convective storms

| colspan="3"| ''High confidence'' that the average and maximum rain rates associated with severe convective storms increase in some regions, including the USA. ''High confidence'' that convective available potential energy (CAPE) increases in response to global warming in the tropics and subtropics, suggesting more favourable environments for severe convective storms. ''Medium confidence'' that the frequency of spring severe convective storms is projected to increase in the USA, leading to a lengthening of the severe convective storm season. {11.7}

|-
| Increase in compound events (frequency, intensity)

| colspan="3"| ''Likely'' that probability of compound events will continue to increase with global warming.

''High confidence'' that concurrent heatwaves and droughts will continue to increase under higher levels of global warming, with higher frequency/intensity with every additional 0.5°C of global warming.

''High confidence'' that fire weather, (i.e. compound hot, dry and windy events), will become more frequent in some regions at higher levels of global warming.

''High confidence'' that compound flooding at the coastal zone will increase under higher levels of global warming. {11.8}

|}

<div id="box-11.2" class="h2-container box-container"></div>

Box 11.2 | Changes in Low-likelihood, High-impact Extremes

<div id="h2-17-siblings" class="h2-siblings"></div>

The SREX (Chapter 3) assigned ''low confidence'' to changes in low-likelihood, high-impact (LLHI) events (termed ‘low-probability high-impact scenarios‘). Such events are often not anticipated and thus sometimes referred to as ‘surprises’. There are several types of LLHI events. Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result from tipping points in the climate system ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.3|Section 1.4.4.3]] ), such as the shutdown of the Atlantic thermohaline circulation (SROCC Chapter 6; [[#Collins--2019|Collins et al., 2019]] ) or the drydown of the Amazonian rainforest (SR1.5 Chapter 3, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Drijfhout--2015|Drijfhout et al., 2015]] ), or from uncertainties in climate processes, including climate feedbacks, that may enhance or damp extremes either related to global or regional climate responses ( [[#Seneviratne--2018a|Seneviratne et al., 2018a]] ; [[#Sutton--2018|Sutton, 2018]] ). The ''low confidence'' does not by itself exclude the possibility of such events occuring, rather it indicates a poor state of knowledge. Such outcomes, while improbable, could be associated with very high impacts, and are thus highly relevant from a risk perspective (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.3|Section 1.4.3]] and Box 11.4; [[#Sutton--2018|Sutton, 2018]] , 2019). Alternatively, high impacts can occur when different extremes occur at the same time, or in short succession at the same location, or in several regions with shared vulnerability (e.g., food-basket regions [[#Gaupp--2019|Gaupp et al., 2019]] ). These ‘compound events’ are assessed in [[#11.8|Section 11.8]] , and Box 11.4 provides a case study example.

Difficulties persist in determining the likelihood of occurrence and time frame of potential tipping points and LLHI events. However, new literature has emerged on unanticipated and LLHI events. There are some events that are sufficiently rare that they have not been observed in meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate system – see examples below and in [[#McCollum--2020|McCollum et al. (2020)]] . The rare nature of such events and the limited availability of relevant data makes it difficult to estimate their occurrence probability and thus gives little evidence on whether to include such hypothetical events in planning decisions and risk assessments. The estimation of such potential surprises is often limited to events that have historical analogues (including before the instrumental records began, [[#Wetter--2014|Wetter et al., 2014]] ), albeit the magnitude of the event may differ. Additionally, there is also a limitation of available resources to exhaust all plausible trajectories of the climate system. As a result, there will still be events that cannot be anticipated. These events can be surprises to many in that the events have not been experienced, although their occurrence could be inferred by statistical means or physical modelling approaches ( [[#Chen--2017|Chen et al., 2017]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; [[#Harrington--2018a|Harrington and Otto, 2018a]] ). Another approach focusing on the estimation of low-probability events and of events whose likelihood of occurrence is unknown consists in using physical climate models to create a physically self-consistent storyline of plausible extreme events and assessing their impacts and driving factors in past ( [[#11.2.3|Section 11.2.3]] ) or future conditions ( [[#11.2.4|Section 11.2.4]] ) (Hazeleger et al. , 2015; [[#Shepherd--2016|Shepherd, 2016]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; Cheng et al. , 2018; Shepherd et al. , 2018; [[#Sutton--2018|Sutton, 2018]] ; Schaller et al. , 2020; Wehrli et al., 2020) .

In many parts of the world, observational data are limited to 50–60 years. This means that the chance to observe an extreme event at a particular location that occurs once in several hundred or more years is small. Thus, when a very extreme event occurs, it becomes a surprise to many ( [[#Bao--2017|Bao et al., 2017]] ; [[#McCollum--2020|McCollum et al., 2020]] ), and very rare events are often associated with high impacts (van Oldenborgh et al. , 2017; Philip et al. , 2018b; Tozer et al. , 2020). Attributing and projecting very rare events in a particular location by assessing their likelihood of occurrence within the same larger region and climate thus provides another way to make quantitative assessments regarding events that are extremely rare locally. Some examples of such events include:

* Hurricane Harvey, that made landfall in Houston, TX in August 2017 ( [[#11.7.1.4|Section 11.7.1.4]] .)
* The 2010–2011 extreme floods in Queensland, Australia ( [[#Christidis--2013a|Christidis et al., 2013a]] )
* The 2018 concurrent heatwaves across the Northern Hemisphere (Box 11.4)
* Tropical Cyclone Idai in Mozambique (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)
* The California fires in 2018 and 2019
* The 2019–2020 Australia fires (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)

One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and that the framework of reference for adaptation is continuously moving. As an example, the concurrent heatwaves that occurred across the Northern Hemisphere in the summer of 2018 were considered very unusual and were unprecedented given the total area that was concurrently affected (Drouard et al. , 2019; Kornhuber et al. , 2019; Toreti et al. , 2019; Vogel et al. , 2019) ; however, the probability of this event under 1°C global warming was found to be about 16% ( [[#Vogel--2019|Vogel et al., 2019]] ), which is not particularly low. Similarly, the 2013 summer temperature over eastern China was the hottest on record at the time, but it had an estimated recurrence interval of about four years in the climate of 2013 ( [[#Sun--2014|Sun et al., 2014]] ). Furthermore, when other aspects of the risk, vulnerability, and exposure are historically high or have recently increased (see WGII, Chapter 16, Section 16.4), relatively moderate extremes can have very high impacts ( [[#Otto--2015b|Otto et al., 2015b]] ; [[#Philip--2018b|Philip et al., 2018b]] ). As warming continues, the climate moves further away from its historical state we are familiar with, resulting in an increased likelihood of unprecedented events and surprises. This is particularly the case under high global warming levels – for example, the climate of the late 21st century under high-emissions scenarios, above 4°C of global warming (Cross-Chapter Box 11.1).

Another factor highlighted in [[#11.8|Section 11.8]] and Box 11.4 making events high-impact and difficult to anticipate is that several locations under moderate warming levels could be affected simultaneously, or very repeatedly by different types of extremes (Mora et al., 2018; [[#Gaupp--2019|Gaupp et al., 2019]] ; [[#Vogel--2019|Vogel et al., 2019]] ). Box 11.4 shows that concurrent events at different locations, which can lead to major impacts across the world, can also result from the combination of anomalous circulation or natural variability (e.g., El Niño–Southern Oscillation) patterns with amplification of resulting responses to human-induced global warming. Also multivariate extremes at single locations pose specific challenges to anticipation ( [[#11.8|Section 11.8]] ), with low likelihoods in the current climate but the probability of occurrence of such compound events strongly increasing with increasing global warming levels ( [[#Vogel--2020a|Vogel et al., 2020a]] ). Therefore, in order to estimate whether, and at what level of global warming, very high impacts arising from extremes would occur, the spatial extent of extremes and the potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight increasing evidence that temperature extremes, higher intensity precipitation accompanying tropical cyclones, and compound events such as dry/hot conditions conducive to wildfire or storm surges resulting from sea level rise and heavy precipitation events, pose widespread threats to societies already at relatively low warming levels. Studies have already shown that the probability for some recent extreme events is so small in the undisturbed world that these events were ''extremely unlikely'' to occur without human influence ( [[#11.2.4|Section 11.2.4]] ). Box 11.2, Table 1, provides examples of projected changes in LLHI extremes (single extremes, compound events) of potential relevance for impact and adaptation assessments showing that today’s very rare events can become commonplace in a warmer future.

<div id="_idContainer031" class="Basic-Text-Frame"></div>

'''Box 11.2, Table 1 |'''

'''Examples of changes in low-likelihood, high-impact extreme conditions (single extremes, compound events) at different global''' '''warming levels.'''
{| class="wikitable"
|-
!
! +1°C (Present-day)

! +1.5°C

! +2°C

! +3°C and Higher

|-
| Risk ratio for annual hottest daytime temperature (TXx) with 1% of probability under present-day warming (+1°C) ( [[#Kharin--2018|Kharin et al., 2018]] ): Global land

| 1

| 3.3 (i.e., 230% higher probability)

| 8.2 (i.e., 720% higher probability)

| Not assessed

|-
| Risk ratio for heavy precipitation events (Rx1day) with 1% of probability under present-day warming (+1°C) ( [[#Kharin--2018|Kharin et al., 2018]] ): Global land

| 1

| 1.2 (i.e., 20% higher probability)

| 1.5 (i.e., 50% higher probability)

| Not assessed

|-
| Number of 1–5 day duration extreme floods with 1% of probability under present-day warming (+1°C) (H. [[#Ali--2019|]] [[#Ali--2019|Ali et al., 2019]] ) Indian subcontinent

| Up to 3 in individual locations

| Up to 5 in individual locations

| 2–6 in most locations

| Up to 12 in individual locations (4°C)

|-
| Probability of ‘extreme extremes’ hot days with 1/1000 probability at the end of the 20th century ( [[#Vogel--2020a|Vogel et al., 2020a]] ): Global land

| About 20 days over 20 years in most locations

| About 50 days in 20 years in most locations

| About 150 days in 20 years in most locations

| About 500 days in 20 years in most locations (3°C)

|-
| Probability of co-occurrence in the same week of hot days with 1/1000 probability and dry days with 1/1000 probability at the end of the 20th century ( [[#Vogel--2020a|Vogel et al., 2020a]] ): Amazon

| 0% probability

| About one week in 20 years

| About 4 to 5 weeks in 20 years

| More than 9 weeks in 20 years (3°C)

|-
| Projected soil moisture drought duration per year ( [[#Samaniego--2018|Samaniego et al., 2018]] ): Mediterranean region

| 41 days (+46% compared to the late 20th century)

| 58 days (+107% compared to the late 20th century)

| 71 days (+154% compared to the late 20th century)

| 125 days (+346% compared to the late 20th century) (3°C)

|-
| Increase in days exposed to dangerous extreme heat – measured in Health Heat Index (HHI) (Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ) global land

| Not assessed, baseline is 1981–2000

| 1.6 times higher risk of experiencing heat >40.6

| 2.3 times higher risk of experiencing heat >40.6

| Around 80% of land area exposed to dangerous heat, tropical regions 1/3 of the year (4°C)

|-
| Increase in regional mean fire season length (Q. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Xu--2020|Xu et al., 2020]] ) global land

| Not assessed, baseline is 1981–2000

| 6.2 days

| 9.5 days

| About 50 days (4°C)

|}

In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with ''low confidence'' , but cannot be excluded, especially at global warming levels above 4°C. Compound events, including concurrent extremes, are a factor increasing the probability of LLHI events ( ''high confidence'' ). With increasing global warming, some compound events with low likelihood in past and current climate will become more frequent, and there is a higher chance of historically unprecedented events and surprises ( ''high confidence'' ). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1°C of global warming) can be perceived as surprises because of the pace of global warming ( ''high confidence'' ).

<div id="11.2" class="h1-container"></div>

<span id="data-and-methods"></span>
== 11.2 Data and Methods ==

<div id="h1-3-siblings" class="h1-siblings"></div>

This section provides an assessment of observational data and methods used in the analysis and attribution of climate change specific to weather and climate extremes. It also introduces some concepts used in presenting future projections of extremes. Later sections (Sections 11.3–11.8) also provide additional assessments on relevant observational datasets and model validation specific to the type of extremes to be assessed. General background on climate modelling is provided in Chapters 4 and 10.

<div id="11.2.1" class="h2-container"></div>

<span id="definition-of-extremes"></span>
=== 11.2.1 Definition of Extremes ===

<div id="h2-18-siblings" class="h2-siblings"></div>

In the literature, an event is generally considered extreme if the value of a variable exceeds (or lies below) a threshold. The thresholds have been defined in different ways, leading to differences in the meaning of extremes that may share the same name. For example, two sets of metrics for the frequency of hot/warm days have been used in the literature. One set counts the number of days when maximum daily temperature is above a relative threshold defined as the 90th or higher percentile of maximum daily temperature for the calendar day over a base period. An event based on such a definition can occur at any time of the year, and the impact of such an event would differ depending on the season. The other set counts the number of days in which maximum daily temperature is above an absolute threshold such as 35°C, because exceeding this temperature can sometimes cause health impacts (however, these impacts may depend on location and whether ecosystems and the population are adapted to such temperatures). While both types of hot extreme indices have been used to analyse changes in the frequency of hot/warm events, they represent different events that occur at different times of the year, possibly affected by different types of processes and mechanisms, and possibly also associated with different impacts.

Changes in extremes have also been examined from two perspectives: changes in the frequency for a given magnitude of extremes; or changes in the magnitude for a particular return period (frequency). Changes in the probability of extremes (e.g., temperature extremes) depend on the rarity of the extreme event that is assessed, with a larger change in probability associated with a rarer event (e.g., [[#Kharin--2018|Kharin et al., 2018]] ). However, changes in the magnitude represented by the return levels of the extreme events may not be as sensitive to the rarity of the event. While the answers to the two different questions are related, their relevance may differ for distinct audiences. Conclusions regarding the respective contribution of greenhouse gas forcing to changes in magnitude versus frequency of extremes may also differ ( [[#Otto--2012|Otto et al., 2012]] ). Correspondingly, the sensitivity of changes in extremes to increasing global warming is also dependent on the definition of the considered extremes. In the case of temperature extremes, changes in magnitude have been shown to often depend linearly on global surface temperature ( [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ), while changes in frequency tend to be nonlinear and can, for example, be exponential for increasing global warming levels ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Kharin--2018|Kharin et al., 2018]] ). When similar damage occurs once a fixed threshold is exceeded, it is more important to ask a question regarding changes in the frequency. But when the exceedance of this fixed threshold becomes a normal occurrence in the future, this can lead to a saturation in the change of probability ( [[#Harrington--2018a|Harrington and Otto, 2018a]] ). Also, if the impact of an event increases with the intensity of the event, it would be more relevant to examine changes in the magnitude. Finally, adaptation to climate change might change the relevant thresholds over time, although such aspects are still rarely integrated in the assessment of projected changes in extremes. Framing is considered when forming the assessments of this Chapter, including how extremes are defined and how the questions are asked in the literature ''.''

<div id="11.2.2" class="h2-container"></div>

<span id="data"></span>
=== 11.2.2 Data ===

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Studies of past and future changes in weather and climate extremes, and in the mean state of the climate, use the same original sources of weather and climate observations, including in situ observations, remotely sensed data, and derived data products such as reanalyses. Sections 2.3 and 10.2 assess various aspects of these data sources and data products from the perspective of their general use, and in the analysis of changes in the mean state of the climate in particular. Building on these previous chapters, this subsection highlights particular aspects that are related to extremes and are most relevant to the assessment of this Chapter. The SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) and AR5 (Chapter 2, [[#Hartmann--2013|Hartmann et al., 2013]] ) addressed critical issues regarding the quality and availability of observed data and their relevance for the assessment of changes in extremes.

Extreme weather and climate events occur on time scales of hours (e.g., convective storms that produce heavy precipitation) to days (e.g., tropical cyclones, heatwaves), to seasons and years (e.g., droughts). A robust determination of long-term changes in these events can have different requirements for the spatial and temporal scales and sample size of the data. In general, it is more difficult to determine long-term changes for events of fairly large temporal duration, such as ‘megadroughts’ that last several years or longer (e.g., [[#Ault--2014|Ault et al., 2014]] ), because of the limitations of the observational sample size. Literature that studies changes in extreme precipitation and temperature often uses indices representing specifics of extremes that are derived from daily precipitation and temperature values. Station-based indices would have the same issues as those for the mean climate regarding the quality, availability, and homogeneity of the data. For the purpose of constructing regional information and/or for comparison with model outputs, such as model evaluation, and detection and attribution, these station-based indices are often interpolated onto regular grids. Two different approaches, involving two different orders of operation, have been used in producing such gridded datasets.

In some cases, such as for the HadEX3 dataset ( [[#Dunn--2020|Dunn et al., 2020]] ), indices of extremes are computed using time series directly derived from stations first, and are then gridded over the space. As the indices are computed at the station level, the gridded data products represent point estimates of the indices averaged over the spatial scale of the grid box. In other instances, daily values of station observations are first gridded (e.g., [[#Contractor--2020a|Contractor et al., 2020a]] ), and the interpolated values can then be used to compute various indices. Depending on the station density, values for extremes computed from data gridded this way represent extremes of spatial scales anywhere from the size of the grid box to a point. In regions with high station density (e.g., North America, Europe), the gridded values are closer to extremes of area means and are thus more appropriate for comparisons with extremes estimated from climate model output, which is often considered to represent areal means ( [[#Chen--2008|Chen and Knutson, 2008]] ; [[#Gervais--2014|Gervais et al., 2014]] ; [[#Avila--2015|Avila et al., 2015]] ; [[#Di%20Luca--2020b|Di Luca et al., 2020b]] ). In regions with very limited station density (e.g., Africa), the gridded values are closer to point estimates of extremes. The difference in spatial scales among observational data products and model simulations needs to be carefully accounted for when interpreting the comparison among different data products. For example, the average annual maximum daily maximum temperature (TXx) over land computed from the original ERA-Interim reanalysis (at 0.75° resolution) is about 0.4°C warmer than that computed when the ERA-Interim dataset is upscaled to the resolution of 2.5° × 3.75° ( [[#Di%20Luca--2020|Di Luca et al., 2020]] ).

Extreme indices computed from various reanalysis data products have been used in some studies, but reanalysis extreme statistics have not been rigorously compared to observations ( [[#Donat--2016a|Donat et al., 2016a]] ).

In general, changes in temperature extremes from various reanalyses were most consistent with gridded observations after about 1980, but larger differences were found during the pre-satellite era ( [[#Donat--2014b|Donat et al., 2014b]] ). Overall, lower agreement across reanalysis datasets was found for extreme precipitation changes, although temporal and spatial correlations against observations were found to be still significant. In regions with sparse observations (e.g., Africa and parts of South America), there is generally less agreement for extreme precipitation between different reanalysis products, indicating a consequence of the lack of an observational constraint in these regions ( [[#Donat--2014b|Donat et al., 2014b]] , 2016a). More recent reanalyses, such as ERA5 ( [[#Hersbach--2020|Hersbach et al., 2020]] ), seem to have improved over previous products, at least over some regions (e.g., [[#Mahto--2019|Mahto and Mishra, 2019]] ; [[#Gleixner--2020|Gleixner et al., 2020]] ; [[#Sheridan--2020|Sheridan et al., 2020]] ). Caution is needed when reanalysis data products are used to provide additional information about past changes in these extremes in regions where observations are generally lacking.

Satellite remote sensing data have been used to provide information about precipitation extremes because several products provide data at sub-daily resolution for precipitation, for example, Tropical Rainfall Measuring Mission (TRMM; [[#Maggioni--2016|Maggioni et al., 2016]] ) and clouds, for example, Himawari (Bessho et al., 2016; [[#Chen--2019|Chen et al., 2019]] ). However, satellites do not observe the primary atmospheric state variables directly and polar orbiting satellites do not observe any given place at all times. Hence, their utility as a substitute for high-frequency (i.e., daily) ground-based observations is limited. For instance, [[#Timmermans--2019|Timmermans et al. (2019)]] found little relationship between the timing of extreme daily and five-day precipitation in satellite and gridded station data products over the USA.

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Box 11.3 | Extremes in Paleoclimate Archives Compared to Instrumental Records

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Examining extremes in pre-instrumental information can help to put events occurring in the instrumental record (referred to as ‘observed’) in a longer-term context. This box focuses on extremes in the Common Era (CE, the last 2000 years), because there is generally higher confidence in pre-instrumental information gathered from the more recent archives from the Common Era than from earlier evidence. It addresses evidence of extreme events in paleoreconstructions, documentary evidence (such as grape harvest data, religious documents, newspapers, and logbooks) and model-based analyses, and whether observed extremes have or have not been exceeded in the Common Era. This box provides overviews of: (i) AR5 assessments; (ii) types of evidence assessed here; evidence of: (iii) droughts; (iv) temperature extremes; (v) paleofloods; and (vi) paleotempests; and (vii) a summary.

( [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] of AR5 ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ) concluded with ''high confidence'' that droughts of greater magnitude and of longer duration than those observed in the instrumental period occurred in many regions during the preceding millennium. There was ''high confidence'' in evidence that floods during the past five centuries in northern and Central Europe, the western Mediterranean region, and eastern Asia were of a greater magnitude than those observed instrumentally, and ''medium confidence'' in evidence that floods in the Near East, India and Central North America were comparable to modern observed floods. While AR5 assessed 20th century summer temperatures compared to those reconstructed in the Common Era, it did not assess shorter duration temperature extremes.

Many factors affect confidence in information on pre-instrumental extremes. First, the geographical coverage of paleoclimate reconstructions of extremes is not spatially uniform ( [[#Smerdon--2016|Smerdon and Pollack, 2016]] ) and depends on both the availability of archives and records, which are environmentally dependent, and also the differing attention and focus from the scientific community. In Australia, for example, the paleoclimate network is sparser than for other regions, such as Asia, Europe and North America, and synthesized products rely on remote proxies and assumptions about the spatial coherence of precipitation between remote climates ( [[#Cook--2016c|Cook et al., 2016c]] ; [[#Freund--2017|Freund et al., 2017]] ). Second, pre-instrumental evidence of extremes may be focused on understanding archetypal extreme events, such as the climatic consequences of the 1815 eruption of Mount Tambora, Indonesia (Veale and Endfield, 2016). These studies provide narrow evidence of extremes in response to specific forcings (M. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al., 2017]] ) for specific epochs. Third, natural archives may provide information about extremes in one season only and may not represent all extremes of the same types.

Evidence of shorter duration extreme event types, such as floods and tropical storms, is further restricted by the comparatively low chronological controls and temporal resolution (e.g., monthly, seasonal, yearly, multiple years) of most archives compared to the events (e.g., minutes to days). Natural archives may be sensitive only to intense environmental disturbances, and so only sporadically record short-duration or small spatial-scale extremes. Interpreting sedimentary records as evidence of past short-duration extremes is also complex and requires a clear understanding of natural processes (Wilhelm et al. , 2019) . For example, paleoflood reconstructions of flood recurrence and intensity produced from geological evidence (e.g., river and lake sediments), speleothems ( [[#Denniston--2017|Denniston and Luetscher, 2017]] ), botanical evidence (e.g., flood damage to trees, or tree ring reconstructions), and floral and faunal evidence (e.g., diatom fossil assemblages) require understanding of sediment sources and flood mechanisms. Pre-instrumental records of tropical storm intensity and frequency (also called paleotempest records) derived from overwash deposits of coastal lake and marsh sediments are difficult to interpret. Many factors have an impact on whether disturbances are deposited in archives ( [[#Muller--2017|Muller et al., 2017]] ) and deposits may provide sporadic and incomplete preservation histories (e.g., [[#Tamura--2018|Tamura et al., 2018]] ).

Overall, the most complete pre-instrumental evidence of extremes occurs for long-duration, large spatial-scale extremes, such as for multi-year meteorological droughts or seasonal- and regional-scale temperature extremes. Additionally, more precise insights into recent extremes emerge where multiple studies have been undertaken, compared to the confidence in extremes reported at single sites or in single studies, which may not necessarily be representative of large-scale changes, or for reconstructions that synthesize multiple proxies over large areas (e.g., drought atlases). Multiproxy synthesis products combine paleoclimate temperature reconstructions and cover sub-continental- to hemispheric-scale regions to provide continuous records of the Common Era (e.g., Ahmed et al. , 2013; Neukom et al. , 2014 fo r temperature).

There is ''high confidence'' in the occurrence of long-duration and severe drought events during the Common Era for many locations, although their severity compared to recent drought events differs between locations and the lengths of reconstruction provided. Recent observed drought extremes in some regions – such as the eastern Mediterranean Levant ( [[#Cook--2016a|Cook et al., 2016a]] ), California in the USA( [[#Cook--2014b|Cook et al., 2014b]] ; [[#Griffin--2014|Griffin and Anchukaitis, 2014]] ), and in the Andes (Garreaud et al. , 2017; Domínguez-Castro et al. , 2018) – do not have precedents within the multi-century periods reconstructed in these studies, in terms of duration and/or severity. In some regions (in south-western North America ( [[#Asmerom--2013|Asmerom et al., 2013]] ; [[#Cook--2015|Cook et al., 2015]] ), the Great Plains region ( [[#Cook--2004|Cook et al., 2004]] ), the Middle East ( [[#Kaniewski--2012|Kaniewski et al., 2012]] ), and China ( [[#Gou--2015|Gou et al., 2015]] )), recent drought extremes may have been exceeded in the Common Era. In further locations, there is conflicting evidence for the severity of pre-instrumental droughts compared to observed extremes, depending on the length of the reconstruction and the seasonal perspective provided (see Cook et al. , 2016c; Freund et al. , 2017 for Australia). There can also be differing conclusions for the severity, or even the occurrence, of specific individual pre-instrumental droughts when different evidence is compared (e.g., [[#Wetter--2014|Wetter et al., 2014]] ; [[#Büntgen--2015|Büntgen et al., 2015]] ).

There is ''medium confidence'' that the magnitudes of large-scale, seasonal-scale extreme high temperatures in observed records exceed those reconstructed over the Common Era in some locations, such as Central Europe. In one example, multiple studies have examined the unusualness of present-day European summer temperature records in a long-term context, particularly in comparison to the exceptionally warm year of 1540 CE in Central Europe. Several studies indicate that recent extreme summers (2003 and 2010) in Europe have been unusually warm in the context of the last 500 years ( [[#Barriopedro--2011|Barriopedro et al., 2011]] ; [[#Wetter--2013|Wetter and Pfister, 2013]] ; [[#Wetter--2014|Wetter et al., 2014]] ; [[#Orth--2016b|Orth et al., 2016b]] ), or longer ( [[#Luterbacher--2016|Luterbacher et al., 2016]] ). Others studies show that summer temperatures in Central Europe in 1540 were warmer than the present-day (1966–2015) mean, but note that it is difficult to assess whether or not the 1540 summer was warmer than observed record extreme temperatures ( [[#Orth--2016b|Orth et al., 2016b]] ).

There is ''high confidence'' that the magnitude of floods over the Common Era exceeded observed records in some locations, including Central Europe and eastern Asia. Recent literature supports the AR5 assessments of floods ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ). For example, high temporally resolved records provide evidence of Common Era floods exceeding the probable maximum flood levels in the Upper Colorado River, USA ( [[#Greenbaum--2014|Greenbaum et al., 2014]] ) and peak discharges that are double gauge levels along the middle Yellow River, China ( [[#Liu--2014|Liu et al., 2014]] ). Further studies demonstrate pre-instrumental or early instrumental differences in flood frequency compared to the instrumental period, including reconstructions of high and low flood frequency in the European Alps (e.g., [[#Swierczynski--2013|Swierczynski et al., 2013]] ; [[#Amann--2015|Amann et al., 2015]] ) and Himalayas ( [[#Ballesteros%20Cánovas--2017|Ballesteros Cánovas et al., 2017]] ). The combination of extreme historical flood episodes determined from documentary evidence also increases confidence in the determination of flood frequency and magnitude, compared to using geomorphological archives alone ( [[#Kjeldsen--2014|Kjeldsen et al., 2014]] ). In regions, such as Europe and China, that have rich historical flood documents, there is strong evidence of high-magnitude flood events over pre-instrumental periods (Kjeldsen et al., 2014; [[#Benito--2015|Benito et al., 2015]] ; [[#Macdonald--2017|Macdonald and Sangster, 2017]] ). A key feature of paleoflood records is variability in flood recurrence at centennial timescales ( [[#Wilhelm--2019|Wilhelm et al., 2019]] ), although constraining climate-flood relationships remains challenging. Pre-instrumental floods often occurred in considerably different contexts in terms of land use, irrigation, and infrastructure, and may not provide direct insight into modern river systems, which further prevents long-term assessments of flood changes being made based on these sources.

There is ''medium confidence'' that periods of both more and less tropical cyclone activity (frequency or intensity) than observed occurred over the Common Era in many regions. Paleotempest studies cover a limited number of locations that are predominantly coastal, and hence provide information on specific locations that cannot be extrapolated basin-wide (see [[#Muller--2017|Muller et al., 2017]] ). In some locations, such as the Gulf of Mexico and the New England, USA, coast, similarly intense storms to those observed recently have occurred multiple times over centennial timescales ( [[#Donnelly--2001|Donnelly et al., 2001]] ; [[#Bregy--2018|Bregy et al., 2018]] ). Further research focused on the frequency of tropical storm activity. Extreme storms occurred considerably more frequently in particular periods of the Common Era, compared to the instrumental period in north-east Queensland, Australia ( [[#Nott--2009|Nott et al., 2009]] ; [[#Haig--2014|Haig et al., 2014]] ), and the Gulf Coast (e.g., [[#Brandon--2013|Brandon et al., 2013]] ; [[#Lin--2014|Lin et al., 2014]] ).

The probability of finding an unprecedented extreme event increases with a longer length of past record-keeping, in the absence of longer-term trends. Thus, as a record is extended to the past based on paleoreconstruction, there is a higher chance of very rare extreme events having occurred at some time prior to instrumental records. Such an occurrence is not, in itself, evidence of a change, or lack of a change, in the magnitude or the likelihood of extremes in the past or in the instrumental period at regional and local scales. Yet, the systematic collection of paleoclimate records over wide areas may provide evidence of changes in extremes. In one study, extended evidence of the last millennium from observational data and paleoclimate reconstructions using tree rings indicates that human activities affected the worldwide occurrence of droughts as early as the beginning of the 20th century ( [[#Marvel--2019|Marvel et al., 2019]] ).

In summary, there is ''low confidence'' in overall changes in extremes derived from paleo-archives. There is ''high confidence'' that long-duration and severe drought events occurred at many locations during the last 2000 years. There is also ''high confidence'' that high-magnitude flood events occurred at some locations during the last 2000 years, but overall changes in infrastructure and human water management make the comparison with present-day records difficult. But these isolated paleo-drought and paleo-flood events are not evidence of a change, or lack of a change, in the magnitude or the likelihood of relevant extremes.

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=== 11.2.3 Attribution of Extremes ===

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Attribution science concerns the identification of causes for changes in characteristics of the climate system (e.g., trends, single extreme events). A general overview and summary of methods of attribution science is provided in the Cross-Working Group Box 1.1. Trend detection using optimal fingerprinting methods is a well-established field, and has been assessed in AR5 (Chapter 10, [[#Bindoff--2013|Bindoff et al., 2013]] ), and [[IPCC:Wg1:Chapter:Chapter-3#3.2.1|Section 3.2.1]] of this Report. There are specific challenges when applying optimal fingerprinting to the detection and attribution of trends in extremes and on regional scales where the lower signal-to-noise ratio is a challenge. In particular, the method generally requires the data to follow a Normal (Gaussian) distribution, which is often not the case for extremes. However, recent studies showed that extremes can be transformed to a Gaussian distribution, for example, by averaging over space, so that optimal fingerprinting techniques can still be used ( [[#Wen--2013|Wen et al., 2013]] ; [[#Zhang--2013|Zhang et al., 2013]] ; [[#Wan--2019|Wan et al., 2019]] ). Non-stationary extreme value distributions, which allow for the detailed detection and attribution of regional trends in temperature extremes, have also been used (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] a).

Apart from the detection and attribution of trends in extremes, new approaches have been developed to answer the question of whether, and to what extent, external drivers have altered the probability and intensity of an individual extreme event ( [[#NASEM--2016|NASEM, 2016]] ). In AR5, there was an emerging consensus that the role of external drivers of climate change in specific extreme weather events could be estimated and quantified in principle, but related assessments were still confined to particular case studies, often using a single model, and typically focusing on high-impact events with a clear attributable signal.

However, since AR5, the attribution of extreme weather events has emerged as a growing field of climate research with an increasing body of literature (see series of supplements to the annual State of the Climate report ( [[#Peterson--2012|Peterson et al., 2012]] , 2013a; [[#Herring--2014|Herring et al., 2014]] , 2015, 2016, 2018), including the number of approaches to examining extreme events(described in [[#Easterling--2016|Easterling et al., 2016]] ; [[#Otto--2017|Otto, 2017]] ; [[#Stott--2016|Stott et al., 2016]] )). A commonly used approach – often called the risk-based approach in the literature, and referred to here as the ‘probability-based approach’ – produces statements such as ‘anthropogenic climate change made this event type twice as likely ’ or ‘anthropogenic climate change made this event 15% more intense’. This is done by estimating probability distributions of the index characterizing the event in today’s climate, as well as in a counterfactual climate, and either comparing intensities for a given occurrence probability (e.g., 1-in-100-year event) or probabilities for a given magnitude (see FAQ 11.3). There are a number of different analytical methods encompassed in the probability-based approach, building on observations and statistical analyses (e.g., van Oldenborgh et al., 2012), optimal fingerprint methods ( [[#Sun--2014|Sun et al., 2014]] ), regional climate and weather forecast models (e.g., [[#Schaller--2016|Schaller et al., 2016]] ), global climate models (GCMs) (e.g., [[#Lewis--2013|Lewis and Karoly, 2013]] ), and large ensembles of atmosphere-only GCMs (e.g., [[#Lott--2013|Lott et al., 2013]] ). A key component in any event attribution analysis is the level of conditioning on the state of the climate system. In the least conditional approach, the combined effect of the overall warming and changes in the large-scale atmospheric circulation are considered and often utilize fully coupled climate models ( [[#Sun--2014|Sun et al., 2014]] ). Other more conditional approaches involve prescribing certain aspects of the climate system. These range from prescribing the pattern of the surface ocean change at the time of the event (e.g., [[#Hoerling--2013|Hoerling et al., 2013]] , 2014), often using Atmospheric Model Intercomparison Project (AMIP) style global models, where the choice of sea surface temperature and ice patterns influences the attribution results ( [[#Sparrow--2018|Sparrow et al., 2018]] ), to prescribing the large-scale circulation of the atmosphere and using weather forecasting models or methods (e.g., [[#Pall--2017|Pall et al., 2017]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ; [[#Wehner--2018a|Wehner et al., 2018a]] ). These highly conditional approaches have also been called ‘storylines’ (Cross-Working Group Box 1.1; [[#Shepherd--2016|Shepherd, 2016]] ) and can be useful when applied to extreme events that are too rare to otherwise analyse, or where the specific atmospheric conditions were central to the impact. These methods are also used to enable the use of very high-resolution simulations in cases were lower-resolution models do not simulate the regional atmospheric dynamics well ( [[#Shepherd--2016|Shepherd, 2016]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ). However, the imposed conditions limit an overall assessment of the anthropogenic influence on an event, as the fixed aspects of the analysis may also have been affected by climate change. For instance, the specified initial conditions in the highly conditional hindcast attribution approach often applied to tropical cyclones (e.g., [[#Takayabu--2015|Takayabu et al., 2015]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ) permit only a conditional statement about the magnitude of the storm if similar large-scale meteorological patterns could have occurred in a world without climate change, thus precluding any attribution statement about the change in frequency if used in isolation. Combining conditional assessments of changes in the intensity with a multi-model approach does allow for the latter as well ( [[#Shepherd--2016|Shepherd, 2016]] ).

The outcome of event attribution is dependent on the definition of the event ( [[#Leach--2020|Leach et al., 2020]] ), as well as the framing ( [[#Otto--2016|Otto et al., 2016]] ; [[#Christidis--2018|Christidis et al., 2018]] ; [[#Jézéquel--2018|Jézéquel et al., 2018]] ) and uncertainties in observations and modelling. Observational uncertainties arise in estimating the magnitude of an event as well as its rarity ( [[#Angélil--2017|Angélil et al., 2017]] ). Results of attribution studies can also be very sensitive to the choice of climate variables ( [[#Sippel--2014|Sippel and Otto, 2014]] ; [[#Wehner--2016|Wehner et al., 2016]] ). Attribution statements are also dependent on the spatial (Uhe et al., 2016; [[#Cattiaux--2018|Cattiaux and Ribes, 2018]] ; Kirchmeier‐Young et al., 2019) and temporal ( [[#Harrington--2017|Harrington, 2017]] ; [[#Leach--2020|Leach et al., 2020]] ) extent of event definitions, as events of different scales involve different processes (W. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ) and large-scale averages generally yield higher attributable changes in magnitude or probability due to the smoothing out of noise. In general, confidence in attribution statements for large-scale heat and lengthy extreme precipitation events have higher confidence than shorter and more localized events, such as extreme storms, an aspect also relevant for determining the emergence of signals in extremes or the confidence in projections (see also Cross-Chapter Box Atlas.1).

The reliability of the representation of the event in question in the climate models used in a study is essential ( [[#Angélil--2016|Angélil et al., 2016]] ; [[#Herger--2018|Herger et al., 2018]] ). Extreme events characterized by atmospheric dynamics that stretch the capabilities of current-generation models ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.4|Section 10.3.3.4]] ; [[#Shepherd--2014|Shepherd, 2014]] ; [[#Woollings--2018|Woollings et al., 2018]] ) limit the applicability of the probability-based approach of event attribution. The lack of model evaluation, in particular in early event attribution studies, has led to criticism of the emerging field of attribution science as a whole ( [[#Trenberth--2015|Trenberth et al., 2015]] ) and of individual studies ( [[#Angélil--2017|Angélil et al., 2017]] ). In this regard, the storyline approach ( [[#Shepherd--2016|Shepherd, 2016]] ) provides an alternative option that does not depend on the model’s ability to represent the circulation reliably. In addition, several ways of quantifying statistical uncertainty ( [[#Paciorek--2018|Paciorek et al., 2018]] ) and model evaluation ( [[#Lott--2016|Lott and Stott, 2016]] ; [[#Philip--2018b|Philip et al., 2018b]] , 2020) have been employed to evaluate the robustness of event attribution results. For the unconditional probability-based approach, multi-model and multi-approach (e.g., combining observational analyses and model experiments) methods have been used to improve the robustness of event attribution ( [[#Hauser--2017|Hauser et al., 2017]] ; [[#Otto--2018a|Otto et al., 2018a]] ; [[#Philip--2018b|Philip et al., 2018b]] , 2019, 2020; [[#van%20Oldenborgh--2018|van Oldenborgh et al., 2018]] ; [[#Kew--2019|Kew et al., 2019]] ).

In the regional tables provided in [[#11.9|Section 11.9]] , the different lines of evidence from event attribution studies and trend attributions are assessed alongside one another to provide an assessment of the human contribution to observed changes in extremes in all AR6 regions.

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<span id="projecting-changes-in-extremes-as-a-function-of-global-warming-levels"></span>
=== 11.2.4 Projecting Changes in Extremes as a Function of Global Warming Levels ===

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The most important quantity used to characterize past and future climate change is global warming relative to its pre-industrial level. Changes in global warming are linked quasi-linearly to global cumulative carbon dioxide (CO <sub>2</sub> ) emissions (IPCC, 2013), and for their part, changes in regional climate, including many types of extremes, scale quasi-linearly with changes in global warming, often independently of the underlying emissions scenarios (SR1.5 Chapter 3; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Matthews--2017|Matthews et al., 2017]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Kharin--2018|Kharin et al., 2018]] ; Y. [[#Sun--2018a|]] [[#Sun--2018|Sun et al., 2018]] a ; [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ; [[#Beusch--2020|Beusch et al., 2020]] ; [[#Li--2021|Li et al., 2021]] ). In addition, the use of global warming levels in the context of global policy documents – in particular the 2015 Paris Agreement ( [[#UNFCCC--2016|UNFCCC, 2016]] ) implies that information on changes in the climate system, and specifically extremes, as a function of global warming are of particular policy relevance. Cross-Chapter Box 11.1 provides an overview on the translation between information at global warming levels (GWLs) and scenarios.

The assessment of projections of future changes in extremes as function of GWL has an advantage in separating uncertainty associated with the global warming response (see Chapter 4) from the uncertainty resulting from the regional climate response as a function of GWLs ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). If the interest is in the projection of regional changes at certain GWLs, such as those defined by the Paris Agreement, projections based on time periods and emissions scenarios have unnecessarily larger uncertainty due to differences in model global transient climate responses. To take advantage of this feature and to provide easy comparison with SR1.5, assessments of projected changes in this chapter are largely provided in relation to future GWLs, with a focus on changes at +1.5°C, +2°C, and +4°C of global warming above pre-industrial levels (e.g., Tables 11.1 and 11.2 and regional tables in [[#11.9|Section 11.9]] ). These encompass a scenario compatible with the lowest limit of the Paris Agreement (+1.5°C), a scenario slightly overshooting the aims of the Paris Agreement (+2°C), and a ‘worst-case’ scenario with no mitigation (+4°C). Cross-Chapter Box 11.1 provides a background on the GWL sampling approach used in AR6, for the computation of GWL projections from climate models contributing to Phase 6 of the Coupled Model Intercomparison Project (CMIP6) as well as for the mapping of existing scenario-based literature for CMIP6 and the CMIP Phase 5 (CMIP5) to assessments as function of GWLs (see also [[#11.9|Section 11.9]] . and Table 11.3 for an example).

While regional changes in many types of extremes do scale robustly with global surface temperature, generally irrespective of emissions scenarios ( [[#11.1.4|Section 11.1.4]] , Figures 11.3, 11.6 and 11.7 and Cross-Chapter Box 11.1), effects of local forcing can distort this relation. For example, emissions scenarios with the same radiative forcing can have different regional extreme precipitation responses resulting from different aerosol forcing (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] b). Another example is related to forcing from land-use and land cover changes ( [[#11.1.6|Section 11.1.6]] ). Climate models often either overestimate or underestimate observed changes in annual maximum daily maximum temperature, depending on the region and considered models ( [[#Donat--2017|Donat et al., 2017]] ; [[#Vautard--2020|Vautard et al., 2020]] ). Part of the discrepancies may be due to the lack of representation of some land forcings, in particular crop intensification and irrigation (N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ; [[#Findell--2017|Findell et al., 2017]] ; [[#Thiery--2017|Thiery et al., 2017]] , 2020). Since these local forcings are not represented, and their future changes are difficult to project, these can be important caveats when using GWL scaling to project future changes for these regions. However, these caveats also apply to the use of scenario-based projections.

The SR1.5 (Chapter 3) assessed different climate responses at +1.5°C of global warming, including transient climate responses, short-term stabilization responses, and long-term equilibrium stabilization responses, and their implications for future projections of different extremes. Indeed, the temporal dimension – that is, when the given GWL occurs – also matters for projections, in particular beyond the 21st century, and for some climate variables related to components of the climate system associated with large inertia (e.g., sea level rise and associated extremes). Nonetheless, for assessments focused on conditions within the next decades, and for the main extremes considered in this chapter, derived projections are relatively insensitive to details of climate scenarios and can be well-estimated based on transient simulations (Cross-Chapter Box 11.1; see also SR1.5).

An important question is the identification of the GWL at which a given change in a climate extreme can begin to emerge from climate noise. Figure 11.8 displays analyses of the GWLs at which emergence in hot extremes – annual maximum daily temperature represented by TXx and heavy precipitation represented by Rx1day is identified in AR6 regions for the whole CMIP5 and CMIP6 ensembles. Overall, signals for extremes emerge very early for TXx, already below 0.2°C in many regions (Figure 11.8a,b), and at around 0.5°C in most regions. This is consistent with conclusions from the SR1.5 ( [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] for less-rare temperature extremes (TXx on the yearly time scale), which shows that a difference as small as 0.5°C of global warming – for example, between +1.5°C and +2°C of global warming – leads to detectable differences in temperature extremes in TXx in most WGI AR6 regions in CMIP5 projections (e.g., [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Seneviratne--2018b|Seneviratne et al., 2018b]] ). The GWL emergence for Rx1day is also largely consistent with analyses for less-extreme heavy precipitation events (Rx5day on the yearly time scale) in SR1.5 (see Chapter 3).

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[[File:cbeef96f6cb682637c8719e08f470d31 IPCC_AR6_WGI_Figure_11_8.png]]

'''Figure 11.8 |''' '''Global and regional-scale emergence of changes in temperature (a) and precipitation (b) extremes for the globe (glob.), global oceans (oc.), global lands (land), and the AR6 regions.''' Colours indicate the multi-model mean global warming level at which the difference in 20-year means of the annual maximum daily maximum temperature (TXx) and the annual maximum daily precipitation (Rx1day) become significantly different from their respective mean values during the 1850–1900 base period. Results are based on simulations from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) multi-model ensembles. See Atlas.1.3.2 for the definition of regions. Adapted from [[#Seneviratne--2020|Seneviratne and Hauser (2020)]] under the terms of the Creative Commons Attribution licence.

To some extent, analyses as functions of GWLs replace the time axis with a global surface temperature axis. Nonetheless, information on the timing of given changes in extremes is obviously also relevant. (For information on the time frame at which given GWLs are reached, see Cross-Chapter Box 11.1 and [[IPCC:Wg1:Chapter:Chapter-4#4.6|Section 4.6]] ). Figure 11.5 provides a synthesis of attributed and projected changes in extremes as function of GWLs (see also Figures. 11.3, 11.6 and 11.7 for regional analyses).

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'''Cross-Chapter Box 11.1 | Translating Between Regional Information at Global Warming Levels''' '''Versus Scenario''' '''s for End Users'''

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'''Contributors:''' Erich Fischer (Switzerland), Mathias Hauser (Switzerland), Sonia I. Seneviratne (Switzerland), Richard Betts (United Kingdom), José M. Gutiérrez (Spain), Richard G. Jones (United Kingdom), June-Yi Lee (Republic of Korea), Malte Meinshausen (Australia/Germany), Friederike Otto (United Kingdom/Germany), Izidine Pinto (Mozambique), Roshanka Ranasinghe (The Netherlands/Sri Lanka/Australia), Joeri Rogelj (Germany/Belgium), Bjørn Samset (Norway), Claudia Tebaldi (United States of America), Laurent Terray (France)

''''''Background''''''
Traditionally, projections of climate variables are summarized and communicated as function of time and emissions scenarios. Recently, quantifying global and regional climate at specific global warming levels (GWLs) has become widespread, motivated by the inclusion of explicit GWLs in the long-term temperature goal of the Paris Agreement ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.2|Section 1.6.2]] ). GWLs, expressed as changes in global surface temperature relative to the 1850–1900 period (see Cross-Chapter Box 2.3), are used in SR1.5 and in the assessment of Reasons for Concerns in the WGII reports (see also Cross-Chapter Box 12.1). Cross-Chapter Box 11.1, Figure 1 illustrates how the assessment of the climate response at GWLs relates to the uncertainty in scenarios regarding the timing of the respective GWLs, as well as to the uncertainty in the associated regional climate responses, including extremes and other climatic impact-drivers (CIDs). For many (but not all) climate variables and CIDs, the response pattern for a given GWL is consistent across different scenarios (Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-term means (e.g., 20-year averages) compared to the pre-industrial period, are commonly used in the literature, and were also underlying main assessments of SR1.5 (Chapter 3).

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[[File:b7a56ba76272d0ff7a301f5255e3b28c IPCC_AR6_WGI_CCBox_11_1_Figure_1.png]]

'''Cross-Chapter Box 11.1, Figure 1 |'''

'''Schematic representation of relationship between emissions scenarios, global warming levels (GWLs), regional climate responses, and impacts.''' The illustration shows the implied uncertainty problem associated with differentiating between 1.5°C, 2°C, and other GWLs. Focusing on GWLs raises questions associated with emissions pathways to get to these temperatures (scenarios), as well as regional climate responses and the associated impacts at the corresponding GWL (the impacts question). Adapted from [[#James--2017|James et al. (2017)]] and [[#Rogelj--2013|Rogelj (2013)]] under the terms of the Creative Commons Attribution licence.

Numerous studies have compared the regional response to anthropogenic forcing at GWLs in annual and seasonal mean values and extremes of different climate and impact variables across different multi-model ensembles and/or different scenarios (e.g., Frieler et al. , 2012; Schewe et al. , 2014; Herger et al. , 2015; Schleussner et al. , 2016; Seneviratne et al. , 2016; Wartenburger et al. , 2017; Betts et al. , 2018; [[#Dosio--2018|Dosio and Fischer, 2018]] ; Samset et al. , 2019; Tebaldi et al. , 2020 ; see Sections 4.6.1, 8.5.3, 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrass et al. , 2015; Seneviratne et al. , 2016; Wartenburger et al. , 2017; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ) . The consistency tends to be higher for temperature-related variables than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions scenarios than for low-emissions scenarios (e.g., for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 scenario: [[#Pendergrass--2015|Pendergrass et al., 2015]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at a given GWL are smaller than model uncertainty and internal variability in many regions ( [[#Herger--2015|Herger et al., 2015]] ). The response pattern is further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h).

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[[File:ae46a5e11d09f48a6a3483bfefac89b4 IPCC_AR6_WGI_CCBox_11_1_Figure_2.png]]

'''Cross-Chapter Box 11.1, Figure 2 |'''

'''(a–c) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean precipitation change at 2°C global warming level (GWL) (20-year mean) in three different Shared Socio-economic Pathway (SSP) scenarios relative to 1850–1900.''' All models reaching the corresponding GWL in the corresponding scenario are averaged. The number of models averaged across is shown at the top right of the panel. The maps for the other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown) are consistent. '''(d–f)''' Same as (a–c) but for annual mean temperature. '''(g)''' Annual mean temperature change at 2°C in CMIP6 models with high warming rate reaching the GWL in the corresponding scenario before the earliest year of the assessed very likely range ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ). '''(h)''' Climate response at 2°C GWL across all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not shown in (g). The close agreement of (g) and (h) demonstrates that the mean temperature response at 2°C is not sensitive to the rate of warming, and thereby the global mean surface air temperature (GSAT) warming of the respective models in 2081–2100. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on the sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1.

In contrast to linear pattern scaling ( [[#Mitchell--2003|Mitchell, 2003]] ; [[#Collins--2013|Collins et al., 2013]] ), the use of GWLs as a dimension of integration does not require linearity in the response of a climate variable. It is therefore useful even for metrics that do not show a linear response, such as the frequency of heat extremes over land and oceans ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Frölicher--2018|Frölicher et al., 2018]] ; [[#Kharin--2018|Kharin et al., 2018]] ; [[#Perkins-Kirkpatrick--2017|Perkins-Kirkpatrick and Gibson, 2017]] ) if the relationship of the variable of interest to the GWL is scenario independent. The latter means that the response is independent of the pathway and relative contribution of various radiative forcings. For some more complex indices like warm-spell duration, or for regions with strong aerosol changes, discrepancies can be larger (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] b; [[#King--2018|King et al., 2018]] ; [[#Tebaldi--2020|Tebaldi et al., 2020]] ). (See also the subsection below on GWLs vs scenarios for further caveats.)

The limited scenario dependence of the GWL-based response for many variables implies that the regional response to emissions scenarios can be split in almost independent contributions of: (i) the transient global warming response to scenarios (see Chapter 4); and (ii) the regional response as function of a given GWL, which has also been referred to as ‘regional climate sensitivity’ ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). This property has also been used to develop regionally resolved emulators for global climate models, using global surface temperature as input ( [[#Beusch--2020|Beusch et al., 2020]] ; [[#Tebaldi--2020|Tebaldi et al., 2020]] ). Analyses of the CMIP6 and CMIP5 multi-model ensembles shows that the GWL-based responses are very similar for temperature and precipitation extremes across the ensembles ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ; [[#Wehner--2020|Wehner, 2020]] ; Li et al. , 2021 ). This is despite their difference in global warming response (Chapter 4), confirming a substantial decoupling between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the GWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty induced by scenario, global mean model response and internal variability (Cross-Chapter Box, Figure 1).

''''''Mapping between GWL- and scenario-based responses in''' '''model analyses''''''
To map scenario-based climate projections into changes at specific GWLs, first, all individual Earth system model (ESM) simulations that reach a certain GWL are identified. Second, the climate response patterns at the respective GWL are calculated using an approach termed here ‘GWL-sampling’ – sometimes also referred to as epoch analysis, time shift, or time sampling approach – taking into account all models and scenarios (Cross-Chapter Box, Figure 3). Note that the range of years when a given GWL is reached in the CMIP6 ensemble is different from the AR6 assessed range of projected global surface temperature ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ; Table 4.5). The latter further takes into account different lines of evidence, including the assessed observed warming between pre-industrial and present day, information from observational constraints on CMIP6, and emulators using the assessed transient climate response (TCR) and equilibrium climate sensitivity (ECS) ranges ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ). Hence the [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] assessed range (Table 4.5) is the reference to determine when a given GWL is ''likely'' reached under given scenarios, while the mapping between scenarios/time frames and GWLs is used to assess the respective regional responses happening at these time frames (which also allows accounting for the global surface temperature assessment, rather than using scenarios analyses directly from CMIP6 output).

In the model-based asssessment of Chapters 4, 8, 10, 11, 12 and the Atlas, the estimation of changes at GWLs are generally defined as the 20-year time period in which the mean global surface air temperature (GSAT; Cross-Chapter Box 2.3) first exceeds a certain anomaly relative to 1850–1900 – for simulations that start after 1850, relative to all years up to 1900 (Cross-Chapter Box Figure 3). The years when each individual model reaches a given GWL for CMIP6 and CMIP5 can be found in [[#Hauser--2021|Hauser et al. (2021)]] . The changes at given GWLs are identified for each ensemble member (for all scenarios) individually. Thereby, a given GWL is potentially reached a few years earlier or later in different realizations of the same model due to internal variability, but the temperature averaged across the 20-year period analysed in any simulation is consistent with the GWL. Instead of blending the information from the different scenarios, the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] can be used to compare the GWL spatial patterns and timings across the different scenarios (see Section ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1.3.1).

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[[File:bb5854aa2ebde4943d2aca103b5e727e IPCC_AR6_WGI_CCBox_11_1_Figure_3.png]]

'''Cross-Chapter Box11.1, Figure 3'''

|

'''Illustration of the AR6 global warming level (GWL) sampling approach to derive the timing and the response at a given GWL for the case of Coupled Model Intercomparison Project Phase 6 (CMIP6) data.''' For the mapping of scenarios/time slices into GWLs for CMIP6, please refer to Table 4.2. Respective numbers for the CMIP5 multi-model experiment are provided in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM.1). Note that the time frames used to derive the GWL time slices can also include a different number of years (e.g., 30 years for some analyses).

''''''Mapping between GWL- and scenario-based responses''' '''for literature''''''
A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based information is required instead, an approximated mapping of the multi-model mean can be derived based on the known GWL in the given experiments for a particular time period. As a rough approximation, CMIP6 multi-model mean projections for the near-term (2021–2040) correspond to changes at about 1.5°C, and projections for the high-end scenario (SSP5-8.5) for the long-term (2081–2100) correspond to about 4°C–5°C of global warming (see Table 4.2 for changes in the CMIP6 ensemble and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM.1) and [[#Hauser--2021|Hauser (2021)]] for details on other time periods and CMIP5). These approximated changes are used for some of the GWL-based assessments provided in the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] regional tables ( [[#11.9|Section 11.9]] and Table 11.3) when literature based on scenario projections is used to assess estimated changes at given GWLs.

''''''GWLs ver''' '''sus scenarios''''''
The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways are consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ), and to assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for variables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land management changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium states. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need to account for the uncertainty in GSAT changes or climate-carbon feedbacks.

Forcing dependence of the GWL response is found for global mean precipitation ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.3|Section 8.4.3]] ), but less for regional patterns of mean precipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the cryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence (Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with a substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent for sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach a given GWL ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.4|Section 9.6.3.4]] ).

In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example in the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in the response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state when the land–sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context of projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables are less dependent on the pathway than for projections beyond 2100 ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.4|Section 9.6.3.4]] ).

''''''Key conclusions on assessment''' '''s based on GWLs''''''
GWL-based projections can inform society and policymakers on how climate would change under GWLs consistent with the aims of the Paris Agreement (stabilization at 1.5°C/well below 2°C), as well as on the consequences of missing these aims and reaching GWLs of 3°C or 4°C by the end of the century. The AR6 assessment shows that every bit of global warming matters and that changes in global warming of 0.5°C lead to statistically significant changes in mean climate and climate extremes on global scale and for large regions (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6 and 11.9, Figures 11.8 and 11.9, [[IPCC:Wg1:Chapter:Atlas|Atlas]] and Interactive Atlas), as also assessed in IPCC SR1.5.

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== 11.3 Temperature Extremes ==

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This section assesses changes in temperature extremes at global, continental and regional scales. The main focus is on the changes in the magnitude and frequency of moderate extreme temperatures (those that occur several times a year) to very extreme temperatures (those that occur once in 10 or more years) of time scales from a day to a season, though there is a strong emphasis on the daily scale where literature is most concentrated.

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=== 11.3.1 Mechanisms and Drivers ===

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The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that greenhouse gas forcing is the dominant factor for the increases in the intensity, frequency, and duration of warm extremes and the decrease in those of cold extremes. This general global-scale warming is modulated by large-scale atmospheric circulation patterns, as well as by feedbacks such as soil moisture-evapotranspiration–temperature and snow/ice-albedo–temperature feedbacks, and local forcings such as land-use change or changes in aerosol concentrations at the regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). Therefore, changes in temperature extremes at regional and local scales can have heterogeneous spatial distributions. Changes in the magnitudes (or intensities) of extreme temperatures are often larger than changes in global surface temperature, because of larger warming on land than on the ocean surface ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ), and because of feedbacks, though they are of similar magnitude to changes in the local mean temperature (Figure 11.2).

Extreme temperature events are associated with large-scale meteorological patterns ( [[#Grotjahn--2016|Grotjahn et al., 2016]] ). Quasi-stationary anticyclonic circulation anomalies or atmospheric blocking events are linked to temperature extremes in many regions, such as in Australia ( [[#Parker--2014|Parker et al., 2014]] ; [[#Perkins-Kirkpatrick--2016|Perkins-Kirkpatrick et al., 2016]] ), Europe ( [[#Brunner--2017|Brunner et al., 2017]] , 2018; [[#Schaller--2018|Schaller et al., 2018]] ), Eurasia ( [[#Yao--2017|Yao et al., 2017]] ), Asia ( [[#Chen--2016|Chen et al., 2016]] ; [[#Ratnam--2016|Ratnam et al., 2016]] ; [[#Rohini--2016|Rohini et al., 2016]] ), and North America ( [[#Yu--2018|Yu et al., 2018]] , 2019; [[#Zhang--2019|Zhang and Luo, 2019]] ). Mid-latitude planetary wave modulations affect short-duration temperature extremes such as heatwaves ( [[#Perkins--2015|Perkins, 2015]] ; [[#Kornhuber--2020|Kornhuber et al., 2020]] ). The large-scale modes of variability (Annex IV) affect the strength, frequency and persistence of these meteorological patterns and, hence, temperature extremes. For example, cold and warm extremes in the mid-latitudes are associated with atmospheric circulation patterns such as the Pacific-North American (PNA) pattern, as well as atmosphere–ocean coupled modes such as Pacific Decadal Variability (PDV), the North Atlantic Oscillation (NAO), and Atlantic Multi-decadal Variability (AMV) ( [[#11.1.5|Section 11.1.5]] ; [[#Kamae--2014|Kamae et al., 2014]] ; [[#Johnson--2018|Johnson et al., 2018]] ; [[#Ruprich-Robert--2018|Ruprich-Robert et al., 2018]] ; [[#Yu--2018|Yu et al., 2018]] , 2020; [[#Müller--2020|Müller et al., 2020]] ; [[#Qasmi--2021|Qasmi et al., 2021]] ). Changes in the modes of variability in response to warming would therefore affect temperature extremes ( [[#Clark--2013|Clark and Brown, 2013]] ; [[#Horton--2015|Horton et al., 2015]] ). The level of confidence in those changes varies, both in the observations and in future projections, affecting the level of confidence in changes in temperature extremes in different regions. As highlighted in Chapters 2 to 4 of this Report, it is ''likely'' that there have been observational changes in the extratropical jets and mid-latitude jet meandering ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] and Cross-Chapter Box 10.1). There is ''low confidence'' in possible effects of Arctic warming on mid-latitude temperature extremes (Cross-Chapter Box 10.1). A large portion of the multi-decadal changes in extreme temperature remains after the removal of the effect of these modes of variability, and can be attributed to human influence ( [[#Kamae--2017b|Kamae et al., 2017b]] ; [[#Wan--2019|Wan et al., 2019]] ). Thus, global warming dominates changes in temperature extremes at the regional scale and it is ''very unlikely'' that dynamic responses to greenhouse-gas induced warming would alter the direction of these changes.

Land–atmosphere feedbacks strongly modulate regional- and local-scale changes in temperature extremes ( ''high confidence'' ) ( [[#11.1.6|Section 11.1.6]] ; [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Lemordant--2016|Lemordant et al., 2016]] ; [[#Donat--2017|Donat et al., 2017]] ; [[#Sillmann--2017b|Sillmann et al., 2017b]] ; [[#Hirsch--2019|Hirsch et al., 2019]] ). This effect is particularly notable in mid-latitude regions where the drying of soil moisture amplifies high temperatures, especially through increases in sensible heat flux ( [[#Whan--2015|Whan et al., 2015]] ; [[#Douville--2016|Douville et al., 2016]] ; [[#Vogel--2017|Vogel et al., 2017]] ). Land–atmosphere feedbacks amplifying temperature extremes also include boundary-layer feedbacks and effects on atmospheric circulation ( [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Schumacher--2019|Schumacher et al., 2019]] ). Soil-moisture–temperature feedbacks affect past and present-day heatwaves in observations and model simulations, both locally ( [[#Miralles--2014a|Miralles et al., 2014a]] ; [[#Cowan--2016|Cowan et al., 2016]] , 2020; [[#Hauser--2016|Hauser et al., 2016]] ; [[#Meehl--2016|Meehl et al., 2016]] ; [[#Wehrli--2019|Wehrli et al., 2019]] ) and beyond the regions of feedback occurrence through changes in regional circulation patterns ( [[#Stéfanon--2014|Stéfanon et al., 2014]] ; [[#Koster--2016|Koster et al., 2016]] ; [[#Sato--2019|Sato and Nakamura, 2019]] ). The uncertainty due to the representation of land–atmosphere feedbacks in ESMs is a cause of discrepancy between observations and simulations ( [[#Clark--2006|Clark et al., 2006]] ; [[#Mueller--2014|Mueller and Seneviratne, 2014]] ; [[#Meehl--2016|Meehl et al., 2016]] ). The decrease of plant transpiration or the increase of stomata resistance under enhanced CO <sub>2</sub> concentrations is a direct CO <sub>2</sub> forcing of land temperatures (warming due to reduced evaporative cooling), which contributes to higher warming on land ( [[#Lemordant--2016|Lemordant et al., 2016]] ; [[#Vicente-Serrano--2020b|Vicente-Serrano et al., 2020b]] ). The snow/ice-albedo feedback plays an important role in amplifying temperature variability in the high latitudes ( [[#Diro--2018|Diro et al., 2018]] ) and can be the largest contributor to the rapid warming of cold extremes in the mid- and high latitudes of the Northern Hemisphere ( [[#Gross--2020|Gross et al., 2020]] ).

Regional external forcings, including land-use changes and emissions of anthropogenic aerosols, play an important role in the changes of temperature extremes in some regions ( ''high confidence'' ) ( [[#11.1.6|Section 11.1.6]] ). Deforestation may have contributed to about one third of the warming of hot extremes in some mid-latitude regions since the pre-industrial time ( [[#Lejeune--2018|Lejeune et al., 2018]] ). Aspects of agricultural practice, including no-till farming, irrigation, and overall cropland intensification, may cool hot temperature extremes ( [[#Davin--2014|Davin et al., 2014]] ; N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ). For instance, cropland intensification has been suggested to be responsible for a cooling of the highest temperature percentiles in Midwest USA (N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ). Irrigation has been shown to be responsible for a cooling of hot temperature extremes of up to 1°C–2°C in many mid-latitude regions in the present climate (Thieryet al., 2017, 2020), a process not represented in most of state-of-the-art ESMs (CMIP5, CMIP6). Double cropping may have led to increased hot extremes in the inter-cropping season in part of China ( [[#Jeong--2014|Jeong et al., 2014]] ). Rapid increases in summer warming in western Europe and north-east Asia since the 1980s are linked to a reduction in anthropogenic aerosol precursor emissions over Europe ( [[#Nabat--2014|Nabat et al., 2014]] ; [[#Dong--2016b|Dong et al., 2016b]] , 2017), in addition to the effect of increased greenhouse gas forcing (see also [[IPCC:Wg1:Chapter:Chapter-10#10.1.3.1|Section 10.1.3.1]] ). This effect of aerosols on temperature-related extremes is also noted for declines in short-lived anthropogenic aerosol emissions over North America ( [[#Mascioli--2016|Mascioli et al., 2016]] ). On the local scale, the urban heat island (UHI) effect results in higher temperatures in urban areas than in their surrounding regions, and contributes to warming in regions of rapid urbanization, in particular for nighttime temperature extremes (Box 10.3; [[#Phelan--2015|Phelan et al., 2015]] ; [[#Chapman--2017|Chapman et al., 2017]] ; Y. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ). But these local and regional forcings are generally not or not well represented in the CMIP5 and CMIP6 simulations (see also [[#11.3.3|Section 11.3.3]] ), contributing to uncertainty in model simulated changes.

In summary, greenhouse gas forcing is the dominant driver leading to the warming of temperature extremes. At regional scales, changes in temperature extremes are modulated by changes in large-scale patterns and modes of variability, feedbacks including soil-moisture–evapotranspiration–temperature or snow/ice–albedo–temperature feedbacks, and local and regional forcings such as land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. This leads to heterogeneity in regional changes and their associated uncertainties ( ''high confidence'' ). Changes in anthropogenic aerosol concentrations have ''likely'' affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA ( ''medium confidence'' ). Urbanization has ''likely'' exacerbated the effects of global warming in cities, in particular for nighttime temperature extremes.

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<span id="observed-trends"></span>
=== 11.3.2 Observed Trends ===

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The SREX (IPCC, 2012) reported a ''very likely'' decrease in the number of cold days and nights and increase in the number of warm days and nights at the global scale. Confidence in trends was assessed as regionally variable ( ''low to medium confidence'' ) due to either a lack of observations or varying signals in sub-regions.

Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional-scale studies have examined trends in temperature extremes using different metrics that are based on daily temperatures, such as the Commission for Climatology/World Climate Research Program/Commission for Oceanography and Marine Meteorology joint Expert Team on Climate Change Detection and Indices (ETCCDI) indices ( [[#Dunn--2020|Dunn et al., 2020]] ). The additional observational records, along with a stronger warming signal, show very clearly that changes observed at the time of AR5 (IPCC, 2014) continued, providing strengthened evidence of an increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes. While the magnitude of the observed trends in temperature-related extremes varies depending on the region, spatial and temporal scales, and metric assessed, evidence of a warming effect is overwhelming, robust, and consistent. In particular, an increase in the intensity and frequency of hot extremes is almost always associated with an increase in the hottest temperatures and in the number of heatwave days. It is also the case for changes (decreases) in cold extremes. For this reason, and to simplify the presentation, the phrase ‘increase in the intensity and frequency of hot extremes’ is used to represent, collectively, an increase in the magnitude of extreme day and/or night temperatures, in the number of warm days and/or nights, and in the number of heatwave days. Changes in cold extremes are assessed similarly.

On the global scale, evidence of an increase in the number of warm days and nights and a decrease in the number of cold days and nights, and an increase in the coldest and hottest extreme temperatures is very robust and consistent among all variables. Figure 11.2 displays time series of globally averaged TXx and TNn on land. Warming of land mean TXx is similar to the mean temperature warming on land, which is about 45% higher than global warming ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1|Section 2.3.1]] ). Warming of land mean TNn is even higher, with about 3°C of warming since 1960 (Figure 11.2). Figure 11.9 shows maps of linear trends over 1960–2018 in TXx, TNn, and frequency of warm days (TX90p). The maps for TXx and TNn show trends consistent with overall warming in most regions, with a particularly high warming of TXx in Europe and north-western South America, and a particularly high warming of TNn in the Arctic. Consistent with the observed warming in global surface temperature ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.2|Section 2.3.1.2]] ) and the observed trends in TXx and TNn, the frequency of TX90p has increased, while that of cold nights (TN10p) has decreased since the 1950s: Nearly all land regions showed statistically significant decreases in TN10p ( [[#Alexander--2016|Alexander, 2016]] ; [[#Dunn--2020|Dunn et al., 2020]] ), though trends in TX90p are variable with some decreases in Southern South America, mainly during austral summer ( [[#Rusticucci--2017|Rusticucci et al., 2017]] ). A decrease in the number of cold spell days is also observed over nearly all land surface areas ( [[#Easterling--2016|Easterling et al., 2016]] ) and in the northern mid-latitudes in particular ( [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ). These observed changes are also consistent when a new global land surface daily air temperature dataset is analysed (P. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ). Warming trends in temperature extremes globally, and in most land areas, over the path century are also found to be consistent in a range of observation-based datasets ( [[#Fischer--2014|Fischer and Knutti, 2014]] ; [[#Donat--2016a|Donat et al., 2016a]] ; [[#Dunn--2020|Dunn et al., 2020]] ), with the extremes related to daily minimum temperatures changing faster than those related to daily maximum temperatures ( [[#Dunn--2020|Dunn et al., 2020]] ; see Figure 11.2). Seasonal variations in trends in temperature-related extremes have been demonstrated. A warming in warm-season temperature extremes is detected, even during the ‘slower surface global warming’ period from the late 1990s to early 2010s (Cross-Chapter Box 3.1; [[#Kamae--2014|Kamae et al., 2014]] ; [[#Seneviratne--2014|Seneviratne et al., 2014]] ; [[#Imada--2017|Imada et al., 2017]] ). Many studies of past changes in temperature extremes for particular regions or countries show trends consistent with this global picture, as summarized below and in Tables 11.4, 11.7, 11.10, 11.13, 11.16 and 11.19.

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[[File:9f3091f4b853f00de2588be1834e43f8 IPCC_AR6_WGI_Figure_11_9.png]]

'''Figure 11.9 |''' '''Linear trends over 1960–2018 for three temperature extreme indices: (a)''' the annual maximum daily maximum temperature (TXx), '''(b)''' the annual minimum daily minimum temperature (TNn), and '''(c)''' the annual number of days when daily maximum temperature exceeds its 90th percentile from a base period of 1961–1990 (TX90p); based on the HadEX3 dataset (Dunn et al. , 20 20). Linear trends are calculated only for grid points with at least 66% of the annual values over the period and which extend to at least 2009. Areas without sufficient data are shown in grey. No overlay indicates regions where the trends are significant at the p = 0.1 level. Crosses indicate regions where trends are not significant. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

In Africa (Table 11.4), while it is difficult to assess changes in temperature extremes in parts of the continent because of a lack of data, evidence of an increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes is clear and robust in regions where data are available. These include an increase in the frequency of warm days and nights and a decrease in the frequency of cold days and nights with ''high confidence'' ( [[#Donat--2013a|Donat et al., 2013a]] , 2014b; [[#Kruger--2013|Kruger and Sekele, 2013]] ; [[#Chaney--2014|Chaney et al., 2014]] ; [[#Filahi--2016|Filahi et al., 2016]] ; [[#Moron--2016|Moron et al., 2016]] ; [[#Ringard--2016|Ringard et al., 2016]] ; [[#Barry--2018|Barry et al., 2018]] ; [[#Gebrechorkos--2018|Gebrechorkos et al., 2018]] ) and an increase in heatwaves ( [[#Russo--2016|Russo et al., 2016]] ; [[#Ceccherini--2017|Ceccherini et al., 2017]] ). The increase in TNn is more notable than in TXx (Figure 11.9). Cold spells occasionally strike subtropical areas, but are ''likely'' to have decreased in frequency ( [[#Barry--2018|Barry et al., 2018]] ). The frequency of cold events has ''likely'' decreased in South Africa ( [[#Song--2014|Song et al., 2014]] ; [[#Kruger--2017|Kruger and Nxumalo, 2017]] ), North Africa ( [[#Filahi--2016|Filahi et al., 2016]] ; [[#Driouech--2021|Driouech et al., 2021]] ), and the Sahara ( [[#Donat--2016a|Donat et al., 2016a]] ). Over the whole continent, there is ''medium confidence'' in an increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes; it is ''likely'' that similar changes have also occurred in areas with poor data coverage, as warming is widespread and as projected future changes are similar over all regions ( [[#11.3.5|Section 11.3.5]] ).

In Asia (Table 11.7), there is very ''robust evidence'' for a ''very likely'' increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes in recent decades. This is clear in global studies (e.g., [[#Alexander--2016|Alexander, 2016]] ; [[#Dunn--2020|Dunn et al., 2020]] ), as well as in numerous regional studies (Table 11.7). The area fraction with extreme warmth in Asia increased during 1951–2016 ( [[#Imada--2018|Imada et al., 2018]] ). The frequency of warm extremes increased and the frequency of cold extremes decreased in East Asia ( [[#Zhou--2016|]] [[#Zhou--2016|B. Zhou et al., 2016]] ; [[#Chen--2017|Chen and Zhai, 2017]] ; [[#Yin--2017|Yin et al., 2017]] ; W. [[#Lee--2018|]] [[#Lee--2018|]] [[#Lee--2018|Lee et al., 2018]] ; [[#Qian--2019|Qian et al., 2019]] ) and west Asia (Acar Deniz and Gönençgil, 2015; [[#Erlat--2016|Erlat and Türkeş, 2016]] ; [[#Rahimi--2018|Rahimi and Hejabi, 2018]] ; [[#Rahimi--2018|Rahimi et al., 2018]] ) with ''high confidence'' . The duration of heat extremes has also lengthened in some regions, for example, in southern China ( [[#Luo--2016|Luo and Lau, 2016]] ), but there is ''medium confidence'' of heat extremes increasing in frequency in South Asia ( [[#AlSarmi--2014|AlSarmi and Washington, 2014]] ; [[#Sheikh--2015|Sheikh et al., 2015]] ; [[#Mazdiyasni--2017|Mazdiyasni et al., 2017]] ; [[#Zahid--2017|Zahid et al., 2017]] ; [[#Nasim--2018|Nasim et al., 2018]] ; [[#Khan--2019|Khan et al., 2019]] ; [[#Sen%20Roy--2019|Sen Roy, 2019]] ). Warming trends in daily temperature extremes indices have also been observed in central Asia ( [[#Hu--2016|Hu et al., 2016]] ; [[#Feng--2018|Feng et al., 2018]] ), the Hindu Kush Himalaya ( [[#Sun--2017|Sun et al., 2017]] ), and South East Asia ( [[#Supari--2017|Supari et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ). The intensity and frequency of cold spells in all Asian regions have been decreasing since the beginning of the 20th century ( ''high confidence'' ) ( [[#Sheikh--2015|Sheikh et al., 2015]] ; [[#Donat--2016a|Donat et al., 2016a]] ; [[#Dong--2018|Dong et al., 2018]] ; [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ).

In Australasia (Table 11.10), there is very ''robust evidence'' for ''very likely'' increases in the number of warm days and warm nights and decreases in the number of cold days and cold nights since 1950 ( [[#Lewis--2015|Lewis and King, 2015]] ; [[#Jakob--2016|Jakob and Walland, 2016]] ; [[#Alexander--2017|Alexander and Arblaster, 2017]] ). The increase in extreme minimum temperatures occurs in all seasons over most of Australia and typically exceeds the increase in extreme maximum temperatures (X.L. [[#Wang--2013|Wang et al., 2013]] b; [[#Jakob--2016|Jakob and Walland, 2016]] ). However, some parts of Southern Australia have shown stable or increased numbers of frost days since the 1980s ( [[#Dittus--2014|Dittus et al., 2014]] ) (see also [[#11.3.4|Section 11.3.4]] ). Similar positive trends in extreme minimum and maximum temperatures have been observed in New Zealand, in particular in the autumn and winter seasons, although they generally show higher spatial variability ( [[#Caloiero--2017|Caloiero, 2017]] ). In the tropical Western Pacific region, spatially coherent warming trends in maximum and minimum temperature extremes have been reported for the period 1951–2011 ( [[#Whan--2014|Whan et al., 2014]] ; [[#McGree--2019|McGree et al., 2019]] ).

In Central and South America (Table 11.13), there is ''high confidence'' that observed hot extremes (TN90p, TX90p) have increased, and cold extremes (TN10p, TX10p) have decreased over recent decades, though trends vary among different extremes types, datasets, and regions ( [[#Skansi--2013|Skansi et al., 2013]] ; [[#Dittus--2016|Dittus et al., 2016]] ; [[#Rusticucci--2017|Rusticucci et al., 2017]] ; [[#Meseguer-Ruiz--2018|Meseguer-Ruiz et al., 2018]] ; [[#Salvador--2018|Salvador and de Brito, 2018]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Olmo--2020|Olmo et al., 2020]] ). An increase in the intensity and frequency of heatwave events was also observed between 1961 and 2014 in an area covering most of South America ( [[#Ceccherini--2016|Ceccherini et al., 2016]] ; [[#Geirinhas--2018|Geirinhas et al., 2018]] ). However, there is ''medium confidence'' that warm extremes (TXx and TX90p) have decreased in the last decades over the central region of South-Eastern South America (SES) during austral summer ( [[#Tencer--2012|Tencer and Rusticucci, 2012]] ; [[#Skansi--2013|Skansi et al., 2013]] ; [[#Rusticucci--2017|Rusticucci et al., 2017]] ; [[#Wu--2017|Wu and Polvani, 2017]] ). There is ''medium confidence'' that TNn extremes are warming faster than TXx extremes, with the largest warming rates observed over North-East Brazil (NEB) and Northern South America (NSA) for cold nights ( [[#Skansi--2013|Skansi et al., 2013]] ).

In Europe (Table 11.16), there is very ''robust evidence'' for a ''very likely'' increase in maximum temperatures and the frequency of heatwaves. The increase in the magnitude and frequency of high maximum temperatures has been observed consistently across regions, including in central Europe ( [[#Twardosz--2013|Twardosz and Kossowska-Cezak, 2013]] ; [[#Christidis--2015|Christidis et al., 2015]] ; [[#Lorenz--2019|Lorenz et al., 2019]] ) and southern Europe ( [[#Croitoru--2013|Croitoru and Piticar, 2013]] ; [[#El%20Kenawy--2013|El Kenawy et al., 2013]] ; [[#Christidis--2015|Christidis et al., 2015]] ; [[#Nastos--2015|Nastos and Kapsomenakis, 2015]] ; [[#Fioravanti--2016|Fioravanti et al., 2016]] ; [[#Ruml--2017|Ruml et al., 2017]] ). In Northern Europe, a strong increase in extreme winter warming events has been observed ( [[#Matthes--2015|Matthes et al., 2015]] ; [[#Vikhamar-Schuler--2016|Vikhamar-Schuler et al., 2016]] ). Temperature observations for winter cold spells show a long-term decreasing frequency in Europe (Brunner et al., 2018; [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ), and typical cold spells, such as that observed during the 2009–2010 winter, had an occurrence probability two times smaller currently than if climate change had not occurred ( [[#Christiansen--2018|Christiansen et al., 2018]] ).

In North America (Table 11.19), there is very ''robust evidence'' for a ''very likely'' increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes for the whole continent, though there are substantial spatial and seasonal variations in the trends. Minimum temperatures display warming consistently across the continent, while there are more contrasting trends in the annual maximum daily temperatures in parts of the USA (Figure 11.9; [[#Lee--2014|Lee et al., 2014]] ; [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ; [[#Dunn--2020|Dunn et al., 2020]] ). In Canada, there is a clear increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes ( [[#Vincent--2018|Vincent et al., 2018]] ). In Mexico, a clear warming trend in TNn was found, particularly in the northern arid region ( [[#Montero-Martínez--2018|Montero-Martínez et al., 2018]] ). The number of warm days has increased and the number of cold days has decreased ( [[#García-Cueto--2019|García-Cueto et al., 2019]] ). Cold spells have undergone a reduction in magnitude and intensity in all regions of North America ( [[#Bennett--2015|Bennett and Walsh, 2015]] ; [[#Donat--2016a|Donat et al., 2016a]] ; [[#Grotjahn--2016|Grotjahn et al., 2016]] ; [[#Vose--2017|Vose et al., 2017]] ; [[#García-Cueto--2019|García-Cueto et al., 2019]] ; [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ).

Extreme heat events have increased around the Arctic since 1979, particularly over Arctic North America and Greenland ( [[#Matthes--2015|Matthes et al., 2015]] ; [[#Dobricic--2020|Dobricic et al., 2020]] ), which is consistent with summer melt ( [[IPCC:Wg1:Chapter:Chapter-9#9.4.1|Section 9.4.1]] ). Observations north of 60˚N show increases in winter warm days and nights over 1979–2015, while cold days and nights declined ( [[#Sui--2017|Sui et al., 2017]] ). Extreme heat days are particularly strong in winter, with observations showing the warmest mid-winter temperatures at the North Pole rising at twice the rate of mean temperature ( [[#Moore--2016|Moore, 2016]] ), as well as increases in Arctic winter warm days ( [[#Vikhamar-Schuler--2016|Vikhamar-Schuler et al., 2016]] ; [[#Graham--2017|Graham et al., 2017]] ). Arctic annual minimum temperatures have increased at about three times the rate of global surface temperature since the 1960s (Figures 11.2 and 11.9), consistent with the observed mean cold season (October–May) warming of 3.1°C in the region ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 11.2).

Trends in some measures of heatwaves are also observed at the global scale. Globally averaged heatwave intensity, heatwave duration, and the number of heatwave days have significantly increased from 1950–2011 ( [[#Perkins--2015|Perkins, 2015]] ). There are some regional differences in trends in characteristics of heatwaves, with significant increases reported in Europe ( [[#Russo--2015|Russo et al., 2015]] ; [[#Forzieri--2016|Forzieri et al., 2016]] ; [[#Sánchez-Benítez--2020|Sánchez-Benítez et al., 2020]] ) and Australia (CSIRO and BOM, 2016; [[#Alexander--2017|Alexander and Arblaster, 2017]] ). In Africa, there is ''medium confidence'' that heatwaves, regardless of the definition, have been becoming more frequent, longer-lasting, and hotter over more than three decades ( [[#Fontaine--2013|Fontaine et al., 2013]] ; [[#Mouhamed--2013|Mouhamed et al., 2013]] ; [[#Ceccherini--2016|Ceccherini et al., 2016]] , 2017; [[#Forzieri--2016|Forzieri et al., 2016]] ; [[#Moron--2016|Moron et al., 2016]] ; [[#Russo--2016|Russo et al., 2016]] ). The majority of heatwave characteristics examined in China between 1961 and 2014 show increases in heatwave days, consistent with warming ( [[#You--2017|You et al., 2017]] ; [[#Xie--2020|Xie et al., 2020]] ). Increases in the frequency and duration of heatwaves are also observed in Mongolia ( [[#Erdenebat--2016|Erdenebat and Sato, 2016]] ) and India ( [[#Ratnam--2016|Ratnam et al., 2016]] ; [[#Rohini--2016|Rohini et al., 2016]] ). In the UK, the lengths of short heatwaves have increased since the 1970s, while the lengths of long heatwaves (more than 10 days) have decreased over some stations in the south-east of England (M. [[#Sanderson--2017|]] [[#Sanderson--2017|Sanderson et al., 2017]] ). In Central and South America, there are increases in the frequency of heatwaves ( [[#Barros--2015|Barros et al., 2015]] ; [[#Bitencourt--2016|Bitencourt et al., 2016]] ; [[#Ceccherini--2016|Ceccherini et al., 2016]] ; [[#Piticar--2018|Piticar, 2018]] ), although decreases in Excess Heat Factor (EHF), which is a metric for heatwave intensity, are observed in South America in data derived from HadGHCND ( [[#Cavanaugh--2015|Cavanaugh and Shen, 2015]] ).

In summary, it is ''virtually certain'' that there has been an increase in the number of warm days and nights and a decrease in the number of cold days and nights on the global scale since 1950. Both the coldest extremes and hottest extremes display increasing temperatures. It is ''very likely'' that these changes have also occurred at the regional scale in Europe, Australasia, Asia, and North America. It is ''virtually certain'' that there has been increases in the intensity and duration of heatwaves and in the number of heatwave days at the global scale. These trends ''likely'' occur in Europe, Asia, and Australia. There is ''medium confidence'' in similar changes in temperature extremes in Africa and ''high confidence'' in South America; the lower confidence is due to reduced data availability and fewer studies. Annual minimum temperatures on land have increased about three times more than global surface temperature since the 1960s, with particularly strong warming in the Arctic ( ''hi'' ''gh confidence'' ).

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<span id="model-evaluation"></span>
=== 11.3.3 Model Evaluation ===

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The AR5 assessed that CMIP3 and CMIP5 models generally captured the observed spatial distributions of the mean state and that the inter-model range of simulated temperature extremes was similar to the spread estimated from different observational datasets; the models generally captured trends in the second half of the 20th century for indices of extreme temperature, although they tended to overestimate trends in hot extremes and underestimate trends in cold extremes ( [[#Flato--2013|Flato et al., 2013]] ). Post-AR5 studies on the CMIP5 models’ performance in simulating mean and changes in temperature extremes continue to support the AR5 assessment ( [[#Fischer--2014|Fischer and Knutti, 2014]] ; [[#Sillmann--2014|Sillmann et al., 2014]] ; [[#Ringard--2016|Ringard et al., 2016]] ; [[#Borodina--2017b|Borodina et al., 2017b]] ; [[#Donat--2017|Donat et al., 2017]] ; [[#Di%20Luca--2020b|Di Luca et al., 2020b]] ). Over Africa, the observed warming in temperature extremes is captured by CMIP5 models, although it is underestimated in Western and Central Africa ( [[#Sherwood--2014|Sherwood et al., 2014]] ; [[#Diedhiou--2018|Diedhiou et al., 2018]] ). Over East Asia, the CMIP5 ensemble performs well in reproducing the observed trend in temperature extremes averaged over China ( [[#Dong--2015|Dong et al., 2015]] ). Over Australia, the multi-model mean performs better than individual models in capturing observed trends in gridded station-based ETCCDI temperature indices ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ).

Initial analyses of CMIP6 simulations (H. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ; [[#Di%20Luca--2020a|Di Luca et al., 2020a]] ; [[#Kim--2020|Kim et al., 2020]] ; [[#Thorarinsdottir--2020|Thorarinsdottir et al., 2020]] ; [[#Wehner--2020|Wehner et al., 2020]] ; [[#Li--2021|Li et al., 2021]] ) indicate that the CMIP6 models perform similarly to the CMIP5 models regarding biases in hot and cold extremes. In general, CMIP5 and CMIP6 historical simulations are similar in their performance in simulating the observed climatology of extreme temperatures ( ''high confidence'' ) ''.'' The general warm bias in hot extremes and cold bias in cold extremes reported for CMIP5 models ( [[#Kharin--2013|Kharin et al., 2013]] ; [[#Sillmann--2013a|Sillmann et al., 2013a]] ) remain in CMIP6 models ( [[#Di%20Luca--2020a|Di Luca et al., 2020a]] ). However, there is some evidence that CMIP6 models better represent some of the underlying processes leading to extreme temperatures, such as seasonal and diurnal variability and synoptic-scale variability ( [[#Di%20Luca--2020a|Di Luca et al., 2020a]] ). Whether these improvements are sufficient to enhance our understanding of past changes, or to reduce uncertainties in future projections, remains unclear. The relative error estimates in the simulation of various indices of temperature extremes in the available CMIP6 models show that no single model performs the best on all indices, and the multi-model ensemble seems to outperform any individual model due to its reduction in systematic bias ( [[#Kim--2020|Kim et al., 2020]] ). Figure 11.10 show errors in the 1979–2014 average annual TXx and annual TNn simulated by available CMIP6 models in comparison with HadEX3 and ERA5 ( [[#Kim--2020|Kim et al., 2020]] ; [[#Wehner--2020|Wehner et al., 2020]] ; [[#Li--2021|Li et al., 2021]] ). While the magnitude of the model error depends on the reference dataset, the model evaluations drawn from different reference datasets are quite similar. In general, models reproduce the spatial patterns and magnitudes of both cold and hot temperature extremes quite well. There are also systematic biases. Hot extremes tend to be too cool in mountainous and high-latitude regions, but too warm in the eastern USA and South America. For cold extremes, CMIP6 models are too cool, except in north-eastern Eurasia and the southern mid-latitudes. Errors in seasonal mean temperatures are uncorrelated with errors in extreme temperatures and are often of opposite sign ( [[#Wehner--2020|Wehner et al., 2020]] ).

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[[File:77e630c252e7fc746ad65fc1d186f932 IPCC_AR6_WGI_Figure_11_10.png]]

'''Figure 11.10 |''' '''Multi-model mean bias in temperature extremes (°C) for the period 1979–2014, calculated as the difference between the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean and the average of observations from the values avail''' ab '''le''' in HadEX3. ''(a)'' The annual hottest temperatu '''re''' (TXx); and ''(b)'' the annual coldest temperature (TNn). Areas without sufficient data are shown in grey. Adapted from [[#Wehner--2020|Wehner et al. (2020)]] under the terms of the Creative Commons Attribution licence. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Atmospheric Model Intercomparison Project (AMIP) simulations are often used in event attribution studies to assess the influence of global warming on observed temperature-related extremes. These simulations typically capture the observed trends in temperature extremes, though some regional features, such as the lack of warming in daytime warm temperature extremes over South America and parts of North America, are not reproduced in the model simulations ( [[#Dittus--2018|Dittus et al., 2018]] ), possibly due to internal variability, deficiencies in local surface processes, or forcings that are not represented in the sea surface temperatures (SSTs). Additionally, the AMIP models assessed tend to produce overly persistent heatwave events. This bias in the duration of the events does not impact on the reliability of the models’ positive trends ( [[#Freychet--2018|Freychet et al., 2018]] ).

Several regional climate models (RCMs) have also been evaluated in terms of their performance in simulating the climatology of extremes in various regions of the Coordinated Regional Downscaling Experiment (CORDEX) ( [[#Giorgi--2009|Giorgi et al., 2009]] ), especially in East Asia ( [[#Ji--2015|Ji and Kang, 2015]] ; [[#Yu--2015|Yu et al., 2015]] ; [[#Park--2016|Park et al., 2016]] ; [[#Bucchignani--2017|Bucchignani et al., 2017]] ; [[#Gao--2017a|Gao et al., 2017a]] ; [[#Niu--2018|Niu et al., 2018]] ; Y. [[#Sun--2018b|]] [[#Sun--2018|Sun et al., 2018]] b ; [[#Wang--2019|Wang et al., 2019]] ), Europe ( [[#Vautard--2013|Vautard et al., 2013]] , 2021; [[#Smiatek--2016|Smiatek et al., 2016]] ; [[#Gaertner--2018|Gaertner et al., 2018]] ; [[#Cardoso--2019|Cardoso et al., 2019]] ; [[#Lorenz--2019|Lorenz et al., 2019]] ; [[#Jacob--2020|Jacob et al., 2020]] ; [[#Kim--2020|Kim et al., 2020]] ), and Africa (J. [[#Kim--2014|]] [[#Kim--2014|Kim et al., 2014]] ; [[#Diallo--2015|Diallo et al., 2015]] ; [[#Dosio--2017|Dosio, 2017]] ; [[#Samouly--2018|Samouly et al., 2018]] ; [[#Mostafa--2019|Mostafa et al., 2019]] ). Compared to GCMs, RCM simulations show an added value in simulating temperature-related extremes, though this depends on topographical complexity and the parameters employed (see [[IPCC:Wg1:Chapter:Chapter-10#10.3.3|Section 10.3.3]] ). The improvement with resolution is noted in East Asia ( [[#Park--2016|Park et al., 2016]] ; W. [[#Zhou--2016|]] [[#Zhou--2016|Zhou et al., 2016]] ; [[#Shi--2017|Shi et al., 2017]] ; [[#Hui--2018|Hui et al., 2018]] ). However, in the European CORDEX ensemble, different aerosol climatologies with various degrees of complexity were used in projections ( [[#Bartók--2017|Bartók et al., 2017]] ; [[#Lorenz--2019|Lorenz et al., 2019]] ) and the land surface models used in the RCMs do not account for physiological CO <sub>2</sub> effects on photosynthesis leading to enhanced water-use efficiency and decreased evapotranspiration ( [[#Schwingshackl--2019|Schwingshackl et al., 2019]] ), which could lead to biases in the representation of temperature extremes in these projections ( [[#Boé--2020|Boé et al., 2020]] ). In addition, there are key cold biases in temperature extremes over areas with complex topography ( [[#Niu--2018|Niu et al., 2018]] ). Over North America, 12 RCMs were evaluated over the ARCTIC-CORDEX region ( [[#Diaconescu--2018|Diaconescu et al., 2018]] ). Models performed well at simulating climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Two RCMs were evaluated against observed extremes indices over North America over the period 1989–2009, with a cool bias in minimum temperature extremes shown in both RCMs ( [[#Whan--2016|Whan and Zwiers, 2016]] ). The most significant biases are found in TXx and TNn, with fewer differences in the simulation of annual minimum daily maximum temperature (TXn) and annual maximum daily minimum temperature (TNx) in Central and Western North America. Over Central and South America, maximum temperatures from the Eta RCM are generally underestimated, although hot days, warm nights, and heatwaves are increasing in the period 1961–1990, in agreement with observations ( [[#Chou--2014b|Chou et al., 2014b]] ; [[#Tencer--2016|Tencer et al., 2016]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ).

Some land forcings are not well represented in climate models. As highlighted in the Special Report on Climate Change and Land (SRCCL) Chapter 2, there is ''high agreement'' that temperate deforestation leads to summer warming and winter cooling ( [[#Anderson--2011|Anderson et al., 2011]] ; [[#Gálos--2011|Gálos et al., 2011]] , 2013; [[#Anderson-Teixeira--2012|Anderson-Teixeira et al., 2012]] ; [[#Chen--2012|Chen et al., 2012]] ; [[#Wickham--2013|Wickham et al., 2013]] ; [[#Zhao--2014|Zhao and Jackson, 2014]] ; [[#Ahlswede--2017|Ahlswede and Thomas, 2017]] ; [[#Bright--2017|Bright et al., 2017]] ; [[#Strandberg--2019|Strandberg and Kjellström, 2019]] ), which has substantially contributed to the warming of hot extremes in the northern mid-latitudes over the course of the 20th century ( [[#Lejeune--2018|Lejeune et al., 2018]] ) and in recent years ( [[#Strandberg--2019|Strandberg and Kjellström, 2019]] ). However, observed forest effects on the seasonal and diurnal cycle of temperature are not well-captured in several ESMs: while observations show a cooling effect of forest cover compared to non-forest vegetation during daytime ( [[#Li--2015|Li et al., 2015]] ), in particular in arid, temperate, and tropical regions ( [[#Alkama--2016|Alkama and Cescatti, 2016]] ), several ESMs simulate a warming of daytime temperatures for regions with forest versus non-forest cover ( [[#Lejeune--2017|Lejeune et al., 2017]] ). Also irrigation effects, which can lead to regional cooling of temperature extremes, are generally not integrated in current generations of ESMs ( [[#11.3.1|Section 11.3.1]] ).

In summary, there is ''high confidence'' that climate models can reproduce the mean state and overall warming of temperature extremes observed globally and in most regions, although the magnitude of the trends may differ. The ability of models to capture observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time periods and spatial scales considered. Regional climate models add value in simulating temperature-related extremes over GCMs in some regions. Some land forcings on temperature extremes are not well-captured (effects of deforestation) or generally not representated (irrigation) in ESMs.

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<span id="detection-and-attribution-event-attribution"></span>
=== 11.3.4 Detection and Attribution, Event Attribution ===

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The SREX (IPCC, 2012) assessed that it is ''likely'' anthropogenic influences have led to the warming of extreme daily minimum and maximum temperatures at the global scale. The AR5 concluded that human influence has ''very likely'' contributed to the observed changes in the intensity and frequency of daily temperature extremes on the global scale in the second half of the 20th century (IPCC, 2014). With regard to individual, or regionally or locally specific events, AR5 concluded that it is ''likely'' human influence has substantially increased the probability of occurrence of heatwaves in some locations.

Studies since AR5 continue to attribute the observed increase in the frequency or intensity of hot extremes and the observed decrease in the frequency or intensity of cold extremes to human influence, dominated by anthropogenic greenhouse gas emissions, on global and continental scales, and for many AR6 regions. These include attribution of changes in the magnitude of annual TXx, TNx, TXn, and TNn, based on different observational datasets including, HadEX2 and HadEX3, CMIP5 and CMIP6 simulations, and different statistical methods ( [[#Kim--2016|Kim et al., 2016]] ; Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] a; [[#Seong--2021|Seong et al., 2021]] ). As is the case for an increase in mean temperature ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1|Section 3.3.1]] ), an increase in extreme temperature is mostly due to greenhouse gas forcing, offset by aerosol forcing. The aerosols’ cooling effect is clearly detectable over Europe and Asia ( [[#Seong--2021|Seong et al., 2021]] ). As much as 75% of the moderate daily hot extremes (above 99.9th percentile) over land are due to anthropogenic warming ( [[#Fischer--2015|Fischer and Knutti, 2015]] ). New results are found to be more robust due to the extended period that improves the signal-to-noise ratio. The effect of anthropogenic forcing is clearly detectable and attributable in the observed changes in these indicators of temperature extremes, even at country and sub-country scales, such as in Canada ( [[#Wan--2019|Wan et al., 2019]] ). Changes in the number of warm nights, warm days, cold nights, and cold days, and other indicators such as the Warm Spell Duration Index (WSDI), are also attributed to anthropogenic influence ( [[#Christidis--2016|Christidis and Stott, 2016]] ; [[#Hu--2020|Hu et al., 2020]] ).

Regional studies, including for Asia ( [[#Dong--2018|Dong et al., 2018]] ; [[#Lu--2018|Lu et al., 2018]] ), Australia ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ), and Europe ( [[#Christidis--2016|Christidis and Stott, 2016]] ), found similar results. A clear anthropogenic signal is also found in the trends in the Combined Extreme Index (CEI) for North America, Asia, Australia, and Europe ( [[#Dittus--2016|Dittus et al., 2016]] ). While various studies have described increasing trends in several heatwave metrics (heatwave duration, the number of heatwave days, etc.) in different regions (e.g., [[#Cowan--2014|Cowan et al., 2014]] ; [[#Bandyopadhyay--2016|Bandyopadhyay et al., 2016]] ; M. [[#Sanderson--2017|]] [[#Sanderson--2017|Sanderson et al., 2017]] ), few recent studies have explicitly attributed these changes to causes; most of them stated that observed trends are consistent with anthropogenic warming. The detected anthropogenic signals are clearly separable from the response to natural forcing, and the results are generally insensitive to the use of different model samples, as well as different data availability, indicating robust attribution. Studies of monthly, seasonal, and annual records in various regions ( [[#Kendon--2014|Kendon, 2014]] ; [[#Lewis--2015|Lewis and King, 2015]] ; [[#Bador--2016|Bador et al., 2016]] ; [[#Meehl--2016|Meehl et al., 2016]] ; [[#Zhou--2019|]] [[#Zhou--2019|C. Zhou et al., 2019]] ) and globally ( [[#King--2017|King, 2017]] ) show an increase in the breaking of hot records and a decrease in the breaking of cold records ( [[#King--2017|King, 2017]] ). Changes in anthropogenically attributablerecord-breaking rates are noted to be largest over the Northern Hemisphere land areas ( [[#Shiogama--2016|Shiogama et al., 2016]] ). Yin and Sun (2018) found clear evidence of an anthropogenic signal in the changes in the number of frost and ice days, when multiple model simulations were used. In some key wheat-producing regions of Southern Australia, increases in frost days or frost season length have been reported ( [[#Dittus--2014|Dittus et al., 2014]] ; [[#Crimp--2016|Crimp et al., 2016]] ); these changes are linked to decreases in rainfall, cloud-cover, and subtropical ridge strength, despite an overall increase in regional mean temperatures ( [[#Dittus--2014|Dittus et al., 2014]] ; [[#Pepler--2018|Pepler et al., 2018]] ).

A significant advance since AR5 has been a large number of studies focusing on extreme temperature events at monthly and seasonal scales, using various extreme event attribution methods. [[#Diffenbaugh--2017|Diffenbaugh et al. (2017)]] found that anthropogenic warming has increased the severity and probability of the hottest month by more than 80% of the available observational area on the global scale. [[#Christidis--2014|Christidis and Stott (2014)]] provide clear evidence that warm events have become more probable because of anthropogenic forcings. [[#Sun--2014|Sun et al. (2014)]] found that human influence has caused a more than 60-fold increase in the probability of the extreme warm 2013 summer in eastern China since the 1950s. Human influence is found to have increased the probability of the historically hottest summers in many regions of the world, both in terms of mean temperature ( [[#Mueller--2016|]] [[#Mueller--2016|B. Mueller et al., 2016]] ) and wet bulb globe temperature (WBGT; [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ). In most regions of the Northern Hemisphere, changes in the probability of extreme summer average WBGT were found to be about an order of magnitude larger than changes in the probability of extreme hot summers estimated by surface air temperature ( [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ). In addition to these generalized, global-scale approaches, extreme event studies have found an attributable increase in the probability of hot annual and seasonal temperatures in many locations, including Australia ( [[#Knutson--2014b|Knutson et al., 2014b]] ; [[#Lewis--2014|Lewis and Karoly, 2014]] ), China ( [[#Sun--2014|Sun et al., 2014]] ; [[#Sparrow--2018|Sparrow et al., 2018]] ; [[#Zhou--2020|Zhou et al., 2020]] ), Korea (Y.-H. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ) and Europe ( [[#King--2015b|King et al., 2015b]] ).

There have also been many extreme event attribution studies that examined short-duration temperature extremes, including daily temperatures, temperature indices, and heatwave metrics. Examples of these events from different regions are summarized in various annual Explaining Extreme Events supplements of the ''Bulletin of the American Meteorological Society'' ( [[#Peterson--2012|Peterson et al., 2012]] , 2013a; [[#Herring--2014|Herring et al., 2014]] , 2015, 2016, 2018, 2019, 2020), including a number of approaches to examine extreme events (described in [[#Easterling--2016|Easterling et al., 2016]] ; [[#Stott--2016|Stott et al., 2016]] ; [[#Otto--2017|Otto, 2017]] ). Several studies of recent events from 2016 onwards have determined an infinite risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that the occurrence probability for such events is close to zero in model simulations without anthropogenic influences (see [[#Herring--2018|Herring et al., 2018]] , 2019, 2020; [[#Imada--2019|Imada et al., 2019]] ; [[#Vogel--2019|Vogel et al., 2019]] ). Though it is difficult to accurately estimate the lower bound of the uncertainty range of the FAR in these cases ( [[#Paciorek--2018|Paciorek et al., 2018]] ), the fact that those events are so far outside the envelop of the models with only natural forcing indicates that it is ''extremely unlikely'' for those events to occur without human influence.

Studies that focused on the attributable signal in observed cold extreme events show human influence reducing the probability of those events. Individual attribution studies on the extremely cold winter of 2011 in Europe ( [[#Peterson--2012|Peterson et al., 2012]] ), in the eastern USA during 2014 and 2015 ( [[#Trenary--2015|Trenary et al., 2015]] , 2016; [[#Wolter--2015|Wolter et al., 2015]] ; [[#Bellprat--2016|Bellprat et al., 2016]] ), in the cold spring of 2013 in the United Kingdom ( [[#Christidis--2014|Christidis et al., 2014]] ), and of 2016 in eastern China ( [[#Qian--2018|Qian et al., 2018]] ; Y. [[#Sun--2018b|]] [[#Sun--2018|Sun et al., 2018]] b ) all showed a reduced probability due to human influence on the climate. An exception is the study of [[#Grose--2018|Grose et al. (2018)]] , which found an increase in the probability of the severe western Australian frost of 2016 due to anthropogenically-driven changes in circulation patterns that drive cold outbreaks and frost probability.

Different event attribution studies can produce a wide range of changes in the probability of event occurrence because of different framing. The temperature event definition itself plays a crucial role in the attributable signal ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; Kirchmeier‐Young et al., 2019). Large-scale, longer-duration events tend to have notably larger attributable risk ratios ( [[#Angélil--2014|Angélil et al., 2014]] , 2018; [[#Uhe--2016|Uhe et al., 2016]] ; [[#Harrington--2017|Harrington, 2017]] ; Kirchmeier‐Young et al., 2019), as natural variability is smaller. While uncertainty in the best estimates of the risk ratios may be large, their lower bounds can be quite insensitive to uncertainties in observations or model descriptions, thus increasing confidence in conservative attribution statements ( [[#Jeon--2016|Jeon et al., 2016]] ).

The relative strength of anthropogenic influences on temperature extremes is regionally variable, in part due to differences in changes in atmospheric circulation, land–surface feedbacks, and other external drivers such as aerosols. For example, in the Mediterranean and over western Europe, risk ratios on the order of 100 have been found ( [[#Kew--2019|Kew et al., 2019]] ; [[#Vautard--2020|Vautard et al., 2020]] ), whereas in the USA, changes are much less pronounced. This is probably a reflection of the land–surface feedback enhanced extreme 1930s temperatures that reduce the rarity of recent extremes, in addition to the definition of the events and framing of attribution analyses (e.g., spatial and temporal scales considered). Local forcing may mask or enhance the warming effect of greenhouse gases. In India, short-lived aerosols or an increase in irrigation may be masking the warming effect of greenhouse gases ( [[#Wehner--2018c|Wehner et al., 2018c]] ). Irrigation and crop intensification have been shown to lead to a cooling in some regions, in particular in North America, Europe, and India ( ''high confidence'' ) (N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ; [[#Thiery--2017|Thiery et al., 2017]] , 2020; [[#Chen--2019|Chen and Dirmeyer, 2019]] ). Deforestation has contributed about one third of the total warming of hot extremes in some mid-latitude regions since pre-industrial times ( [[#Lejeune--2018|Lejeune et al., 2018]] ). Despite all of these differences, and larger uncertainties at the regional scale, nearly all studies demonstrated that human influence has contributed to an increase in the frequency or intensity of hot extremes and to a decrease in the frequency or intensity of cold extremes.

In summary, long-term changes in various aspects of long- and short-duration extreme temperatures, including intensity, frequency, and duration have been detected in observations and attributed to human influence at global and continental scales. It is ''extremely likely'' that human influence is the main contributor to the observed increase in the intensity and frequency of hot extremes and the observed decrease in the intensity and frequency of cold extremes on the global scale. It is ''very likely'' that this applies on continental scales as well. Some specific recent hot extreme events would have been ''extremely unlikely'' to occur without human influence on the climate system. Changes in aerosol concentrations have affected trends in hot extremes in some regions, with the presence of aerosols leading to attenuated warming, in particular from 1950 to 1980. Crop intensification, irrigation and no-till farming have attenuated increases in summer hot extremes in some regions, such as Central North America ( ''medi'' ''um confidence'' ).

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<span id="projections"></span>
=== 11.3.5 Projections ===

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The AR5 (Chapter12, [[#Collins--2013|Collins et al., 2013]] ) concluded that it is ''virtually certain'' there will be more frequent hot extremes and fewer cold extremes at the global scale and over most land areas in a future warmer climate, and it is ''very likely'' that heatwaves will occur with a higher frequency and longer duration.The SR1.5 (Chapter 3, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessment on projected changes in hot extremes at 1.5°C and 2°C global warming is consistent with the AR5 assessment, concluding that it is ''very likely'' a global warming of 2°C, when compared with a 1.5°C warming, would lead to more frequent and more intense hot extremes on land, as well as to longer warm spells, affecting many densely inhabited regions. The SR1.5 also assessed it is ''very likely'' that the strongest increases in the frequency of hot extremes are projected for the rarest events, while cold extremes will become less intense and less frequent, and cold spells will be shorter.

New studies since AR5 and SR1.5 confirm these assessments. New literature since AR5 includes projections of temperature-related extremes in relation to changes in mean temperatures, projections based on CMIP6 simulations, projections based on stabilized global warming levels, and the use of new metrics. Constraints for the projected changes in hot extremes were also provided ( [[#Borodina--2017b|Borodina et al., 2017b]] ; [[#Sippel--2017b|Sippel et al., 2017b]] ; [[#Vogel--2017|Vogel et al., 2017]] ). Overall, projected changes in the magnitude of extreme temperatures over land are larger than changes in global mean temperature, over mid-latitude land regions in particular (Figures 11.3, 11.11; [[#Fischer--2014|Fischer et al., 2014]] ; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; B.M. [[#Sanderson--2017|]] [[#Sanderson--2017|Sanderson et al., 2017]] ; [[#Wehner--2018b|Wehner et al., 2018b]] ; [[#Di%20Luca--2020b|Di Luca et al., 2020b]] ). Large warming in hot and cold extremes will occur, even at the 1.5°C GWL (Figure 11.11). At this level, widespread significant changes at the grid-box level occur for different temperature indices ( [[#Aerenson--2018|Aerenson et al., 2018]] ). In agreement with CMIP5 projections, CMIP6 simulations show that a 0.5°C increment in global warming will significantly increase the intensity and frequency of hot extremes, and decrease the intensity and frequency of cold extremes on the global scale (Figures 11.6, 11.8 and 11.12). It takes less than half of a degree for the changes in TXx to emerge above the level of natural variability (Figure 11.8) and the 66% ranges of the land medians of the 10-year or 50-year TXx events do not overlap between 1.0°C and 1.5°C in the CMIP6 multi-model ensemble simulations(Figure 11.6, [[#Li--2021|Li et al., 2021]] ).

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[[File:f76a530dedf85907da4303bd377c6445 IPCC_AR6_WGI_Figure_11_11.png]]

'''Figure 11.11 |''' '''Projected changes in (a–c) annual maximum temperature (TXx) and (d–f) annual minimum temperature (TNn) at 1.5°C, 2°C, and 4°C of global warming compared to the 1850–1900 baseline.''' Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathways (SSPs) SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1. For details on the methods see Supplementary Material 11.SM.2. Changes in TXx and TNn are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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[[File:edd08452914b028e39967547a032c0ea IPCC_AR6_WGI_Figure_11_12.png]]

'''Figure 11.12 |''' '''Projected changes in the intensity of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 185''' ''0–1900 baseline.'' Extreme temperature events are defined as the daily maximum temperatures (TXx) that were exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the global land. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the intensity changes across the multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from [[#Li--2021|Li et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Projected warming is larger for TNn and exhibits strong equator-to-pole amplification, similar to the warming of boreal winter mean temperatures. The warming of TXx is more uniform over land and does not exhibit this behaviour (Figure 11.11). The warming of temperature extremes on global and regional scales tends to scale linearly with global warming ( [[#11.1.4|Section 11.1.4]] ; Fischer et al., 2014; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Li--2021|Li et al., 2021]] ; see also SR1.5, Chapter 3). In the mid-latitudes, the rate of warming of hot extremes can be as large as twice the rate of global warming (Figure 11.11). In the Arctic winter, the rate of warming of the temperature of the coldest nights is about three times the rate of global warming (Appendix, Figure 11.A.1). Projected changes in temperature extremes can deviate from projected changes in annual mean warming in the same regions (Figures 11.3, 11.A.1 and 11.A.2; [[#Di%20Luca--2020b|Di Luca et al., 2020b]] ; [[#Wehner--2020|Wehner, 2020]] ) due to the additional processes that control the response of regional extremes, including, in particular, soil moisture–evapotranspiration–temperature feedbacks for hot extremes in the mid-latitudes and subtropical regions, and snow/ice–albedo–temperature feedbacks in high-latitude regions.

The probability of exceeding a certain hot extreme threshold will increase, while those for cold extreme will decrease with global warming ( [[#Mueller--2016|]] [[#Mueller--2016|B. Mueller et al., 2016]] ; [[#Lewis--2017b|Lewis et al., 2017b]] ; [[#Suarez-Gutierrez--2020b|Suarez-Gutierrez et al., 2020b]] ). The changes tend to scale nonlinearly with the level of global warming, with larger changes for more rare events ( [[#11.2.4|Section 11.2.4]] ; Cross-Chapter Box 11.11; Figures 11.6 and 11.12; [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Kharin--2018|Kharin et al., 2018]] ; [[#Li--2021|Li et al., 2021]] ). For example, the CMIP5 ensemble projects the frequency of the present-day climate 20-year hottest daily temperature to increase by 80% at the 1.5°C GWL and by 180% at the 2.0°C GWL, and the frequency of the present-day climate 100-year hottest daily temperature to increase by 200% and more than 700% at the 1.5°C and 2.0°C warming levels, respectively ( [[#Kharin--2018|Kharin et al., 2018]] ). CMIP6 simulations project similar changes ( [[#Li--2021|Li et al., 2021]] ).

[[#Tebaldi--2018|Tebaldi and Wehner (2018)]] showed that, at the middle of the 21st century, 66% of the land surface area would experience the present-day 20-year return values of TXx and the running three-day average of the daily maximum temperature every other year, on average, under the Representative Concentration Pathway 8.5 (RCP8.5) scenario, as opposed to only 34% under RCP4.5. By the end of the century, these area fractions increase to 92% and 62%, respectively. Such nonlinearities in the characteristics of future regional extremes are shown, for instance, for Europe ( [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Spinoni--2018b|Spinoni et al., 2018b]] ; [[#Lionello--2020|Lionello and Scarascia, 2020]] ), Asia ( [[#Guo--2017|Guo et al., 2017]] ; [[#Harrington--2018b|Harrington and Otto, 2018b]] ; [[#King--2018|King et al., 2018]] ), and Australia ( [[#Lewis--2017a|Lewis et al., 2017a]] ) under various global warming thresholds. The nonlinear increase in fixed-threshold indices (e.g., based on a percentile for a given reference period, or on an absolute threshold) as a function of global warming is consistent with a linear warming of the absolute temperature of the temperature extremes (e.g., [[#Whan--2015|Whan et al., 2015]] ). Compared to the historical climate, warming will result in strong increases in heatwave area, duration and magnitude ( [[#Vogel--2020b|Vogel et al., 2020b]] ). These changes are mostly due to the increase in mean seasonal temperature, rather than changes in temperature variability, though the latter can have an effect in some regions ( [[#Brown--2020|Brown, 2020]] ; [[#Di%20Luca--2020b|Di Luca et al., 2020b]] ; [[#Suarez-Gutierrez--2020a|Suarez-Gutierrez et al., 2020a]] ).

Projections of temperature-related extremes in RCMs in the CORDEX regions demonstrate robust increases under future scenarios and can provide information on finer spatial scales than GCMs (e.g., [[#Coppola--2021b|Coppola et al., 2021b]] ). Five RCMs in the CORDEX–East Asia region project increases in the 20-year return values of temperature extremes (summer maxima), with models that exhibit warm biases projecting stronger warming ( [[#Park--2019|Park and Min, 2019]] ). Similarly, in the African domain, future increases in TX90p and TN90p are projected ( [[#Dosio--2017|Dosio, 2017]] ; [[#Mostafa--2019|Mostafa et al., 2019]] ). This regional-scale analysis provides fine-scale information, such as distinguishing the increase in TX90p over sub-equatorial Africa (Democratic Republic of the Congo, Angola, and Zambia) with values over the Gulf of Guinea, Central African Republic, South Sudan, and Ethiopia. Empirical statistical downscaling has also been used to produce more robust estimates for future heatwaves compared to RCMs based on large multi-model ensembles ( [[#Furrer--2010|Furrer et al., 2010]] ; [[#Keellings--2014|Keellings and Waylen, 2014]] ; [[#Wang--2015|Wang et al., 2015]] ; [[#Benestad--2018|Benestad et al., 2018]] ).

In all continental regions, including Africa (Table 11.4), Asia (Table 11.7), Australasia (Table 11.10), Central and South America (Table 11.13), Europe (Table 11.16), North America (Table 11.19) and at the continental scale, it is ''very likely'' that the intensity and frequency of hot extremes will increase and the intensity and frequency of cold extremes will decrease compared with the 1995–2014 baseline, even under 1.5°C global warming. Those changes are ''virtually certain'' to occur under 4°C global warming. At the regional scale, and for almost all AR6 regions, it is ''likely'' that the intensity and frequency of hot extremes will increase and the intensity and frequency of cold extremes will decrease compared with the 1995–2014 baseline, even under 1.5°C global warming. Those changes are ''virtually certain'' to occur under 4°C global warming. Exceptions include lower confidence in the projected decrease in the intensity and frequency of cold extremes compared with the 1995–2014 baseline under 1.5°C of global warming ( ''medium confidence'' ) and 4°C of global warming ( ''very likely'' ) in Northern Central America, Central North America, and Western North America.

In Africa (Table 11.4), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations (Giorgi et al., 2014; [[#Engelbrecht--2015|Engelbrecht et al., 2015]] ; [[#Lelieveld--2016|Lelieveld et al., 2016]] ; [[#Russo--2016|Russo et al., 2016]] ; [[#Dosio--2017|Dosio, 2017]] ; [[#Bathiany--2018|Bathiany et al., 2018]] ; [[#Mba--2018|Mba et al., 2018]] ; [[#Nangombe--2018|Nangombe et al., 2018]] ; [[#Weber--2018|Weber et al., 2018]] ; [[#Kruger--2019|Kruger et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Li--2021|Li et al., 2021]] ). Cold spells are projected to decrease under all RCPs, and even at low warming levels in Western and Central Africa ( [[#Diedhiou--2018|Diedhiou et al., 2018]] ). The number of cold days is projected to decrease in East Africa ( [[#Ongoma--2018b|Ongoma et al., 2018b]] ).

In Asia (Table 11.7), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations ( [[#Sillmann--2013b|Sillmann et al., 2013b]] ; [[#Zhou--2014|Zhou et al., 2014]] ; R. [[#Zhang--2015|Zhang et al., 2015]] ; [[#Zhao--2015|Zhao et al., 2015]] ; [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Singh--2016|Singh and Goyal, 2016]] ; [[#Xu--2017|Xu et al., 2017]] ; [[#Gao--2018|Gao et al., 2018]] ; [[#Han--2018|Han et al., 2018]] ; [[#Shin--2018|Shin et al., 2018]] ; [[#Sui--2018|Sui et al., 2018]] ; L. [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Zhu--2020|Zhu et al., 2020]] ). More intense heatwaves of longer durations and occurring at a higher frequency are projected over India ( [[#Murari--2015|Murari et al., 2015]] ; [[#Mishra--2017|Mishra et al., 2017]] ) and Pakistan ( [[#Nasim--2018|Nasim et al., 2018]] ). Future mid-latitude warm extremes, similar to those experienced during the 2010 event, are projected to become more extreme, with temperature extremes increasing potentially by 8.4°C (RCP8.5) over north-west Asia ( [[#van%20der%20Schrier--2018|van der Schrier et al., 2018]] ). Over West and East Siberia, and Russian Far East, an increase in extreme heat durations is expected in all scenarios ( [[#Sillmann--2013b|Sillmann et al., 2013b]] ; [[#Kattsov--2017|Kattsov et al., 2017]] ; [[#Reyer--2017|Reyer et al., 2017]] ). In the MENA regions (Arabian Peninsula and Western Central Asia), extreme temperatures could increase by almost 7°C by 2100 under RCP8.5 ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ).

In Australasia (Table 11.10), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations (CSIROand BOM, 2015; [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Lewis--2017a|Lewis et al., 2017a]] ; [[#Herold--2018|Herold et al., 2018]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Evans--2021|Evans et al., 2021]] ). Over most of Australia, increases in the intensity and frequency of hot extremes are projected to be predominantly driven by the long-term increase in mean temperatures ( [[#Di%20Luca--2020b|Di Luca et al., 2020b]] ). Future projections indicate a decrease in the number of frost days regardless of the region and season considered ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Herold--2018|Herold et al., 2018]] ).

In Central and South America (Table 11.13), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations ( [[#Chou--2014a|Chou et al., 2014a]] ; [[#Cabré--2016|Cabré et al., 2016]] ; [[#López-Franca--2016|López-Franca et al., 2016]] ; [[#Stennett-Brown--2017|Stennett-Brown et al., 2017]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Li--2021|Li et al., 2021]] ; [[#Vichot-Llano--2021|Vichot-Llano et al., 2021]] ). Over South-Eastern South America during the austral summer, the increase in the frequency of TN90p is larger than that projected for TX90p, consistent with observed past changes ( [[#López-Franca--2016|López-Franca et al., 2016]] ). Under RCP8.5, the number of heatwave days are projected to increase for the intra-Americas region for the end of the 21st century ( [[#Angeles-Malaspina--2018|Angeles-Malaspina et al., 2018]] ). A general decrease in the frequency of cold spells and frost days is projected, as indicated by several indices based on minimum temperature ( [[#López-Franca--2016|López-Franca et al., 2016]] ).

In Europe (Table 11.16), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations ( [[#Lau--2014|Lau and Nath, 2014]] ; [[#Ozturk--2015|Ozturk et al., 2015]] ; [[#Russo--2015|Russo et al., 2015]] ; [[#Schoetter--2015|Schoetter et al., 2015]] ; [[#Vogel--2017|Vogel et al., 2017]] ; [[#Winter--2017|Winter et al., 2017]] ; [[#Jacob--2018|Jacob et al., 2018]] ; [[#Lhotka--2018|Lhotka et al., 2018]] ; [[#Rasmijn--2018|Rasmijn et al., 2018]] ; [[#Suarez-Gutierrez--2018|Suarez-Gutierrez et al., 2018]] ; [[#Cardoso--2019|Cardoso et al., 2019]] ; [[#Lionello--2020|Lionello and Scarascia, 2020]] ; [[#Molina--2020|Molina et al., 2020]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ; [[#Li--2021|Li et al., 2021]] ). Increases in heatwaves are greater over the southern Mediterranean and Scandinavia ( [[#Forzieri--2016|Forzieri et al., 2016]] ; [[#Abaurrea--2018|Abaurrea et al., 2018]] ; [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Rohat--2019|Rohat et al., 2019]] ). Thebiggest increases in the number of heatwave days are expected for southern European cities ( [[#Guerreiro--2018a|Guerreiro et al., 2018a]] ; [[#Junk--2019|Junk et al., 2019]] ), and Central European cities will see the biggest increases in maximum heatwave temperatures ( [[#Guerreiro--2018a|Guerreiro et al., 2018a]] ).

In North America (Table 11.19), evidence includes increases in the intensity and frequency of hot extremes, such as warm days, warm nights, and heatwaves, and decreases in the intensity and frequency of cold extremes, such as cold days and cold nights over the continent, as projected by CMIP5, CMIP6, and CORDEX simulations ( [[#Grotjahn--2016|Grotjahn et al., 2016]] ; [[#Vose--2017|Vose et al., 2017]] ; [[#Alexandru--2018|Alexandru, 2018]] ; [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|C. Li et al., 2018]] , 2021; [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|C. Yang et al., 2018]] ; X. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ). Projections of temperature extremes for the end of the 21st century show that warm days and nights are ''very likely'' to increase, and cold days and nights are ''very likely'' to decrease in all regions. There is ''medium confidence'' in large increases in warm days and warm nights in summer, particularly over the USA, and in large decreases in cold days in Canada in autumn and winter ( [[#Grotjahn--2016|Grotjahn et al., 2016]] ; [[#Vose--2017|Vose et al., 2017]] ; [[#Alexandru--2018|Alexandru, 2018]] ; [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|C. Li et al., 2018]] , 2021; [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|C. Yang et al., 2018]] ; X. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ). Minimum winter temperatures are projected to rise faster than mean winter temperatures ( [[#Underwood--2017|Underwood et al., 2017]] ). Projections for the end of the century under RCP8.5 showed the four-day cold spell that happens on average once every five years is projected to warm by more than 10°C. CMIP5 models do not project current 1-in-20-year annual minimum temperature extremes to recur over much of the continent ( [[#Wuebbles--2014|Wuebbles et al., 2014]] ).

In summary, it is ''virtually certain'' that further increases in the intensity and frequency of hot extremes, and decreases in the intensity and frequency of cold extremes, will occur throughout the 21st century and around the world. It is ''virtually certain'' that the number of hot days and hot nights and the length, frequency, and/or intensity of warm spells or heatwaves compared to 1995–2014 will increase over most land areas. In most regions, changes in the magnitude of temperature extremes are proportional to global warming levels ( ''high confidence'' ). The highest increase of temperature of hottest days is projected in some mid-latitude and semi-arid regions, at about 1.5 times to twice the rate of global warming ( ''high confidence'' ). The highest increase of temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming ( ''high confidence'' ). The probability of temperature extremes generally increases nonlinearly with increasing global warming levels ( ''high confidence'' ). Confidence in assessments depends on the spatial and temporal scales of the extreme in question, with ''high confidence'' in projections of temperature-related extremes at global and continental scales for daily to seasonal scales. There is ''high confidence'' that, on land, the magnitude of temperature extremes increases more strongly than global mean temperature.

<div id="11.4" class="h1-container"></div>

<span id="heavy-precipitation"></span>
== 11.4 Heavy Precipitation ==

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This section assesses changes in heavy precipitation at global and regional scales. The main focus is on extreme precipitation at a daily scale where literature is most concentrated, though extremes of shorter (sub-daily) and longer (five-day or more) durations are also assessed to the extent the literature allows.

<div id="11.4.1" class="h2-container"></div>

<span id="mechanisms-and-drivers-1"></span>
=== 11.4.1 Mechanisms and Drivers ===

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The SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) assessed changes in heavy precipitation in the context of the effects of thermodynamic and dynamic changes. Box 11.1 assesses thermodynamic and dynamic changes in a warming world to aid the understanding of changes in observations and projections in some extremes and the sources of uncertainties (see also [[IPCC:Wg1:Chapter:Chapter-8#8.2.3.2|Section 8.2.3.2]] ). In general, warming increases the atmospheric water-holding capacity following the Clausius–Clapeyron (C-C) relation. This thermodynamic effect results in an increase in extreme precipitation at a similar rate at the global scale. On a regional scale, changes in extreme precipitation are further modulated by dynamic changes (Box 11.1).

Large-scale modes of variability, such as the North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Atlantic Multi-decadal Variability (AMV), and Pacific Decadal Variability (PDV) (Annex IV), modulate precipitation extremes through changes in environmental conditions or embedded storms ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2|Section 8.3.2]] ). Latent heating can invigorate these storms ( [[#Nie--2018|Nie et al., 2018]] ; Z. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] a); changes in dynamics can increase precipitation intensity above that expected from the C-C scaling rate (Sections 8.2.3.2 and 11.7; Box 11.1). Additionally, the efficiency of converting atmospheric moisture into precipitation can change as a result of cloud microphysical adjustment to warming,resulting in changes in the characteristics of extreme precipitation; but changes in precipitation efficiency in a warming world are highly uncertain ( [[#Sui--2020|Sui et al., 2020]] ).

It is difficult to separate the effect of global warming from internal variability inthe observed changes in the modes of variability ( [[IPCC:Wg1:Chapter:Chapter-2#2.4|Section 2.4]] ). Future projections of modes of variability are highly uncertain [[IPCC:Wg1:Chapter:Chapter-4#4.3.3|Section 4.3.3]] ),resulting in uncertainty in regional projections of extreme precipitation. Future warming may amplify monsoonal extreme precipitation. Changes in extreme storms, including tropical/extratropical cyclones and severe convective storms, result in changes in extreme precipitation ( [[#11.7|Section 11.7]] ). Also, changes in sea surface temperatures (SSTs) alter land–sea contrast, leading to changes in precipitation extremes near coastal regions. For example, the projected larger SST increase near the coasts of East Asia and India can result in heavier rainfall near these coastal areas from tropical cyclones ( [[#Mei--2016|Mei and Xie, 2016]] ) or torrential rains ( [[#Manda--2014|Manda et al., 2014]] ). The warming in the western Indian Ocean is associated with increases in moisture surges on the low-level monsoon westerlies towards the Indian subcontinent, which may lead to an increase in the occurrence of precipitation extremes over central India ( [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Roxy--2017|Roxy et al., 2017]] ).

Decreases in atmospheric aerosols results in warming and thus an increase in extreme precipitation ( [[#Samset--2018|Samset et al., 2018]] ; [[#Sillmann--2019|Sillmann et al., 2019]] ). Changes in atmospheric aerosols also result in dynamic changes such as in tropical cyclones ( [[#Takahashi--2017|Takahashi et al., 2017]] ; [[#Strong--2018|Strong et al., 2018]] ). Uncertainty in the projections of future aerosol emissions results in additional uncertainty in the heavy precipitation projections of the 21st century ( [[#Lin--2016|Lin et al., 2016]] ).

There has been new evidence of the effect of local land-use and land-cover change on heavy precipitation. There is a growing set of literature linking increases in heavy precipitation in urban centres to urbanization ( [[#Argüeso--2016|Argüeso et al., 2016]] ; Y. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] b). Urbanization intensifies extreme precipitation, especially in the afternoon and early evening, over the urban area and its downwind region ( ''medium confidence'' ) (Box 10.3). There are four possible mechanisms: (i) increases in atmospheric moisture due to horizontal convergence of air associated with the urban heat island effect ( [[#Shastri--2015|Shastri et al., 2015]] ; [[#Argüeso--2016|Argüeso et al., 2016]] ); (ii) increases in condensation due to urban aerosol emissions ( [[#Han--2011|Han et al., 2011]] ; [[#Sarangi--2017|Sarangi et al., 2017]] ); (iii) aerosol pollution that impacts cloud microphysics (Box 8.1; [[#Schmid--2017|Schmid and Niyogi, 2017]] ); and (iv) urban structures that impede atmospheric motion (Shepherd, 2013; [[#Ganeshan--2015|Ganeshan and Murtugudde, 2015]] ; [[#Paul--2018|Paul et al., 2018]] ). Other local forcing, including reservoirs ( [[#Woldemichael--2012|Woldemichael et al., 2012]] ), irrigation ( [[#Devanand--2019|Devanand et al., 2019]] ), or large-scale land-use and land-cover change ( [[#Odoulami--2019|Odoulami et al., 2019]] ), can also affect local extreme precipitation.

In summary, precipitation extremes are controlled by both thermodynamic and dynamic processes. Warming-induced thermodynamic change results in an increase in extreme precipitation, at a rate that closely follows the C-C relationship at the global scale ( ''high confidence'' ). The effects of warming-induced changes in dynamic drivers on extreme precipitation are more complicated, difficult to quantify, and are an uncertain aspect of projections. Precipitation extremes are also affected by forcings other than changes in greenhouse gases, including changes in aerosols, land-use and land-cover change, and urbanization ( ''mediu'' ''m confidence'' ).

<div id="11.4.2" class="h2-container"></div>

<span id="observed-trends-1"></span>
=== 11.4.2 Observed Trends ===

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Both SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) and AR5 (IPCC, 2014 Chapter 2) concluded it was ''likely'' that the number of heavy precipitation events over land had increased in more regions than it had decreased, though there were wide regional and seasonal variations, and trends in many locations were not statistically significant. This assessment has been strengthened with multiple studies finding ''robust evidence'' of the intensification of extreme precipitation at global and continental scales, regardless of spatial and temporal coverage of observations and the methods of data processing and analysis.

The average annual maximum precipitation amount in a day (Rx1day) has significantly increased since the mid-20th century over land ( [[#Du--2019|Du et al., 2019]] ; [[#Dunn--2020|Dunn et al., 2020]] ) and in the humid and dry regions of the globe ( [[#Dunn--2020|Dunn et al., 2020]] ). The percentage of observing stations with statistically significant increases in Rx1day is larger than expected by chance, while the percentage of stations with statistically significant decreases is smaller than expected by chance, over the global land as a whole and over North America, Europe, and Asia (Figure 11.13; [[#Sun--2021|Sun et al., 2021]] ) and over global monsoon regions ( [[#Zhang--2019|Zhang and Zhou, 2019]] ) where data coverage is relatively good. The addition of the past decade of observational data shows a more robust increase in Rx1day over the global land region ( [[#Sun--2021|Sun et al., 2021]] ). Light, moderate, and heavy daily precipitation has all intensified in a gridded daily precipitation dataset ( [[#Contractor--2020a|Contractor et al., 2020a]] ). Daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.3|Section 8.3.1.3]] ). The probability of precipitation exceeding 50 mm/day increased during 1961–2018 ( [[#Benestad--2019|Benestad et al., 2019]] ). The globally averaged annual fraction of precipitation from days in the top 5% (R95pTOT) has also significantly increased ( [[#Dunn--2020|Dunn et al., 2020]] ). The increase in the magnitude of Rx1day in the 20th century is estimated to be at a rate consistent with C-C scaling with respect to global mean temperature ( [[#Fischer--2016|Fischer and Knutti, 2016]] ; [[#Sun--2021|Sun et al., 2021]] ). Studies on past changes in extreme precipitation of durations longer than a day are more limited, though there are some studies examining long-term trends in annual maximum five-day precipitation (Rx5day). On global and continental scales, long-term changes in Rx5day are similar to those of Rx1day in many aspects (Zhang and Zhou 2019; [[#Sun--2021|Sun et al., 2021]] ). As discussed below, at the regional scale, changes in Rx5day are also similar to those of Rx1day where there are analyses of changes in both Rx1day and Rx5day.

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[[File:c58ed0d3631d679741c575dba07df416 IPCC_AR6_WGI_Figure_11_13.png]]

'''Figure 11.13 |''' '''Signs and significance of the observed trends in annual maximum daily precipitation (Rx1day) during 1950–2018 at 8345 stations with suficient data.''' ''(a)'' Percentage of stations with statistically significant trends in Rx1day; green dots show positive trends and brown dots negative trends. Box and ‘whisker’ plots indicate the expected percentage of stations with significant trends due to chance estimated from 1000 bootstrap realizations under a no-trend null hypothesis. The boxes mark the median, 25th percentile, and 75th percentile. The upper and lower whiskers show the 97.5th and the 2.5th percentiles, respectively. Maps of stations with positive ''(b)'' and negative ''(c)'' trends. The light colour indicates stations with non-significant trends, and the dark colour stations with significant trends. Significance is determined by a two-tailed test conducted at the 5% level. Adapted from [[#Sun--2021|Sun et al. (2021)]] . Figure copyright © American Meteorological Society (used with permission). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Overall, there is a lack of systematic analysis of long-term trends in sub-daily extreme precipitation at the global scale. Often, sub-daily precipitation data have only sporadic spatial coverage and are of limited length. Additionally, the available data records are far shorter than needed for a robust quantification of past changes in sub-daily extreme precipitation ( [[#Li--2019a|]] [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|C. Li et al., 2019]] a ). Despite these limitations, there are studies in regions of almost all continents that generally indicate intensification of sub-daily extreme precipitation, although there remains ''low'' ''confidence'' in an overall increase at the global scale. Studies include an increase in extreme sub-daily rainfall in summer over South Africa ( [[#Sen%20Roy--2013|Sen Roy and Rouault, 2013]] ), annually in Australia ( [[#Guerreiro--2018b|Guerreiro et al., 2018b]] ), over 23 urban locations in India ( [[#Ali--2018|Ali and Mishra, 2018]] ), in Peninsular Malaysia ( [[#Syafrina--2015|Syafrina et al., 2015]] ), and in eastern China in the summer season during 1971–2013 ( [[#Xiao--2016|Xiao et al., 2016]] ). In some regions in Italy ( [[#Arnone--2013|Arnone et al., 2013]] ; [[#Libertino--2019|Libertino et al., 2019]] ) and in the USA during 1950–2011 ( [[#Barbero--2017|Barbero et al., 2017]] ), there is also an increase. In general, an increase in sub-daily heavy precipitation results in an increase in pluvial floods over smaller watersheds ( [[#Ghausi--2020|Ghausi and Ghosh, 2020]] ).

There is a considerable body of literature examining scaling of sub-daily precipitation extremes, conditional on day-to-day air or dew-point temperatures ( [[#Westra--2014|Westra et al., 2014]] ; [[#Fowler--2021|Fowler et al., 2021]] ). This scaling, also termed ‘apparent scaling’ (Fowler et al., 2021), is robust when different methodologies are used in different regions, ranging between the C-C and two-times the C-C rate (e.g., [[#Formayer--2017|Formayer and Fritz, 2017]] ; [[#Lenderink--2017|Lenderink et al., 2017]] ; [[#Burdanowitz--2019|Burdanowitz et al., 2019]] ). This is confirmed when sub-daily precipitation data collected from multiple continents ( [[#Lewis--2019|Lewis et al., 2019]] ) are analysed in a consistent manner using different methods ( [[#Ali--2021|Ali et al., 2021]] ). It has been hoped that apparent scaling might be used to help understand past and future changes in extreme sub-daily precipitation. However, apparent scaling samples multiple synoptic weather states, mixing thermodynamic and dynamic factors that are not directly relevant for climate change responses ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.3.2|Section 8.2.3.2]] ; [[#Prein--2016b|Prein et al., 2016b]] ; [[#Bao--2017|Bao et al., 2017]] ; X. [[#Zhang--2017|]] [[#Zhang--2017|]] [[#Zhang--2017|Zhang et al., 2017]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ; [[#Sun--2020|Sun et al., 2020]] ). The spatial pattern of apparent scaling is different from those of projected changes over Australia ( [[#Bao--2017|Bao et al., 2017]] ) and North America (Sun et al., 2020) in regional climate model simulations. It thus remains difficult to use the knowledge about apparent scaling to infer past and future changes in extreme sub-daily precipitation according to observed and projected changes in local temperature.

In Africa (Table 11.5), evidence shows an increase in extreme daily precipitation for the late half of the 20th century over the continent where data are available; there is a larger percentage of stations showing significant increases in extreme daily precipitation than decreases ( [[#Sun--2021|Sun et al., 2021]] ). There are increases in different metrics relevant to extreme precipitation in various regions of the continent ( [[#Chaney--2014|Chaney et al., 2014]] ; [[#Harrison--2019|Harrison et al., 2019]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Sun--2021|Sun et al., 2021]] ). There is an increase in extreme precipitation events in Southern Africa (Weldon and Reason, 2014; [[#Kruger--2019|Kruger et al., 2019]] ) and a general increase in heavy precipitation over East Africa, the Greater Horn of Africa ( [[#Omondi--2014|Omondi et al., 2014]] ). Over sub-Saharan Africa, increases in the frequency and intensity of extreme precipitation have been observed over the well-gauged areas during 1950–2013; however, this covers only 15% of the total area of sub-Saharan Africa ( [[#Harrison--2019|Harrison et al., 2019]] ). There is ''medium'' ''confidence'' about the increase in extreme precipitation for some regions where observations are more abundant '','' but for Africa as whole, there is ''low confidence'' because of a general lack of continent-wide systematic analysis, the sporadic nature of available precipitation data over the continent, and spatially non-homogenous trends in places where dataare available (Donat et al., 2014a; [[#Mathbout--2018b|Mathbout et al., 2018b]] ; [[#Alexander--2019|Alexander et al., 2019]] ; [[#Funk--2020|Funk et al., 2020]] ).

In Asia (Table 11.8), there is ''robust evidence'' that extreme precipitation has increased since the 1950s ( ''high confidence'' ), however, this is dominated by high spatial variability. Increases in Rx1day and Rx5day during 1950–2018 are found over two-thirds of stations. The percentage of stations with statistically significant trends is larger than can be expected by chance (Figure 11.13; [[#Sun--2021|Sun et al., 2021]] ). An increase in extreme precipitation has also been observed in various regional studies based on different metrics of extreme precipitation and spatial and temporal coverage of the data. These include an increase in daily precipitation extremes over central Asia ( [[#Hu--2016|Hu et al., 2016]] ), most of South Asia ( [[#Zahid--2012|Zahid and Rasul, 2012]] ; [[#Pai--2015|Pai et al., 2015]] ; [[#Sheikh--2015|Sheikh et al., 2015]] ; [[#Adnan--2016|Adnan et al., 2016]] ; [[#Malik--2016|Malik et al., 2016]] ; [[#Dimri--2017|Dimri et al., 2017]] ; [[#Priya--2017|Priya et al., 2017]] ; [[#Roxy--2017|Roxy et al., 2017]] ; [[#Hunt--2018|Hunt et al., 2018]] ; [[#Kim--2019|Kim et al., 2019]] ; [[#Wester--2019|Wester et al., 2019]] ), the Arabian Peninsula ( [[#Rahimi--2019|Rahimi and Fatemi, 2019]] ; [[#Almazroui--2020|Almazroui and Saeed, 2020]] ; [[#Atif--2020|Atif et al., 2020]] ), South East Asia ( [[#Siswanto--2015|Siswanto et al., 2015]] ; [[#Supari--2017|Supari et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ); the north-west Himalaya ( [[#Malik--2016|Malik et al., 2016]] ), parts of East Asia (Baeket al., 2017; [[#Nayak--2017|Nayak et al., 2017]] ; [[#Ye--2017|Ye and Li, 2017]] ), the western Himalayas since the 1950s ( [[#Ridley--2013|Ridley et al., 2013]] ; [[#Dimri--2015|Dimri et al., 2015]] ; [[#Madhura--2015|Madhura et al., 2015]] ), West and East Siberia, and Russian Far East ( [[#Donat--2016a|Donat et al., 2016a]] ). A decrease was found over the eastern Himalayas ( [[#Sheikh--2015|Sheikh et al., 2015]] ; [[#Talchabhadel--2018|Talchabhadel et al., 2018]] ). Increases have been observed over Jakarta ( [[#Siswanto--2015|Siswanto et al., 2015]] ), but Rx1day over most parts of the Maritime Continent has decreased ( [[#Villafuerte--2015|Villafuerte and Matsumoto, 2015]] ). Trends in extreme precipitation over China are mixed with increases and decreases (G. [[#Fu--2013|]] [[#Fu--2013|Fu et al., 2013]] ; [[#Jiang--2013|Jiang et al., 2013]] ; [[#Ma--2015|Ma et al., 2015]] ; [[#Yin--2015|Yin et al., 2015]] ; [[#Xiao--2016|Xiao et al., 2016]] ) and are not significant over China as whole ( [[#Jiang--2013|Jiang et al., 2013]] ; [[#Hu--2016|Hu et al., 2016]] ; [[#Ge--2017|Ge et al., 2017]] ; [[#Deng--2018|Deng et al., 2018]] ; [[#He--2018|He and Zhai, 2018]] ; W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] a; [[#Tao--2018|Tao et al., 2018]] ; M. [[#Liu--2019|Liu et al., 2019]] b; [[#Chen--2021|Chen et al., 2021]] ). With few exceptions, most South East Asian countries have experienced an increase in rainfall intensity, but with a reduced number of wet days ( [[#Donat--2016a|Donat et al., 2016a]] ; [[#Cheong--2018|Cheong et al., 2018]] ; [[#Naveendrakumar--2019|Naveendrakumar et al., 2019]] ), though large differences in trends exists if the trends are estimated from different datasets, including gauge-based, remotely sensed, and reanalysis data, over a relatively short period ( [[#Kim--2019|Kim et al., 2019]] ). There is a significant increase in heavy rainfall (>100 mm day <sup>–1</sup> ) and a significant decrease in moderate rainfall (5–100 mm day <sup>–1</sup> ) in central India during the South Asian monsoon season ( [[#Deshpande--2016|Deshpande et al., 2016]] ; [[#Roxy--2017|Roxy et al., 2017]] ).

In Australasia (Table 11.11), available evidence has not shown an increase or a decrease in heavy precipitation over Australasia as a whole ( ''medium confidence'' ), but heavy precipitation tends to increase over Northern Australia (particularly the north-west) and decrease over the eastern and southernregions (e.g., Jakob and Walland, 2016; [[#Guerreiro--2018b|Guerreiro et al., 2018b]] ; [[#Dey--2019b|Dey et al., 2019b]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Sun--2021|Sun et al., 2021]] ). Available studies that used long-term observations since the mid-20th century showed nearly as many stations with an increase as those with a decrease in heavy precipitation ( [[#Jakob--2016|Jakob and Walland, 2016]] ) or slightly more stations with a decrease than with an increase in Rx1day and Rx5day ( [[#Sun--2021|Sun et al., 2021]] ), or strong differences in Rx1day trends with increases over Northern Australia and Central Australia in general, but mostly decreases over Southern Australia and Eastern Australia ( [[#Dunn--2020|Dunn et al., 2020]] ). Over New Zealand, decreases are observed for moderate–heavy precipitation events, but there are no significant trends for very heavy events (more than 64 mm in a day) for the period 1951–2012. The number of stations with an increase in very wet days is similar to that with a decrease during 1960–2019 (MfE and Stats NZ, 2020). Overall, there is ''low confidence'' in trends in the frequency of heavy rain days, with mostly decreases over New Zealand (Harringtonand Renwick, 2014; [[#Caloiero--2015|Caloiero, 2015]] ).

In Central and South America (Table 11.14), evidence shows an increase in extreme precipitation, but in general there is ''low'' ''confidence;'' while continent-wide analyses produced wetting trends are not robust. Rx1day increased at more stations than it decreased in South America between 1950 and 2018 ( [[#Sun--2021|Sun et al., 2021]] ). Over the period 1950–2010, both Rx5day and R99p increased over large regions of South America, including North-Western South America, Northern South America, and South-Eastern South America ( [[#Skansi--2013|Skansi et al., 2013]] ). There are large regional differences. A decrease in daily extreme precipitation is observed in north-eastern Brazil (Skansi et al., 2013; [[#Bezerra--2018|Bezerra et al., 2018]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ). Trends in extreme precipitation indices were not statistically significant over the period 1947–2012 within the São Francisco River basin in the Brazilian semi-arid region ( [[#Bezerra--2018|Bezerra et al., 2018]] ). An increase in extreme rainfall is observed in the Amazon with ''medium confidence'' ( [[#Skansi--2013|Skansi et al., 2013]] ) and in South-Eastern South America with ''high confidence'' ( [[#Skansi--2013|Skansi et al., 2013]] ; [[#Valverde--2014|Valverde and Marengo, 2014]] ; [[#Barros--2015|Barros et al., 2015]] ; [[#Ávila--2016|Ávila et al., 2016]] ; [[#Wu--2017|Wu and Polvani, 2017]] ; [[#Lovino--2018|Lovino et al., 2018]] ; [[#Dereczynski--2020|Dereczynski et al., 2020]] ). Among all sub-regions, South-Eastern South America shows the highest rate of increase for rainfall extremes, followed by the Amazon ( [[#Skansi--2013|Skansi et al., 2013]] ). Increases in the intensity of heavy daily rainfall events have been observed in the southern Pacific and in the Titicaca basin ( [[#Skansi--2013|Skansi et al., 2013]] ; Huerta and Lavado‐Casimiro, 2021). In Southern Central America, trends in annual precipitation are generally not significant, although small (but significant) increases are found in Guatemala, El Salvador, and Panama ( [[#Hidalgo--2017|Hidalgo et al., 2017]] ). Small positive trends were found in multiple extreme precipitation indices over the Caribbean region over a short time period (1986–2010) ( [[#Stephenson--2014|Stephenson et al., 2014]] ; [[#McLean--2015|McLean et al., 2015]] ).

In Europe (Table 11.17), there is ''robust evidence'' that the magnitude and intensity of extreme precipitation has ''very likely'' increased since the 1950s. There is a significant increase in Rx1day and Rx5day during 1950–2018 in Europe as a whole ( [[#Sun--2021|Sun et al., 2021]] , also Figure 11.13). The number of stations with increases far exceeds those with decreases in the frequency of daily rainfall exceeding its 90th or 95th percentile in century-long series ( [[#Cioffi--2015|Cioffi et al., 2015]] ). The five-, 10-, and 20-year events of one-day and five-day precipitation during 1951–1960 became more common since the 1950s ( [[#van%20den%20Besselaar--2013|van den Besselaar et al., 2013]] ). There can be large discrepancies among studies and regions and seasons ( [[#Croitoru--2013|Croitoru et al., 2013]] ; [[#Willems--2013|Willems, 2013]] ; [[#Casanueva--2014|Casanueva et al., 2014]] ; [[#Roth--2014|Roth et al., 2014]] ; [[#Fischer--2015|Fischer et al., 2015]] ); evidence for increasing extreme precipitation is more frequently observed for summer and winter, but not in other seasons ( [[#Madsen--2014|Madsen et al., 2014]] ; [[#Helama--2018|Helama et al., 2018]] ) ''.'' An increase is observed in central Europe ( [[#Volosciuk--2016|Volosciuk et al., 2016]] ; [[#Zeder--2020|Zeder and Fischer, 2020]] ), and in Romania ( [[#Croitoru--2016|Croitoru et al., 2016]] ). Trends in the Mediterranean region are in general not spatially consistent ( [[#Reale--2013|Reale and Lionello, 2013]] ), with decreases in the western Mediterranean and some increases in the eastern Mediterranean ( [[#Rajczak--2013|Rajczak et al., 2013]] ; [[#Casanueva--2014|Casanueva et al., 2014]] ; [[#de%20Lima--2015|de Lima et al., 2015]] ; [[#Gajić-Čapka--2015|Gajić-Čapka et al., 2015]] ; [[#Sunyer--2015|Sunyer et al., 2015]] ; [[#Pedron--2017|Pedron et al., 2017]] ; [[#Serrano-Notivoli--2018|Serrano-Notivoli et al., 2018]] ; [[#Ribes--2019|Ribes et al., 2019]] ). In the Netherlands, the total precipitation contributed from extremes higher than the 99th percentile doubles per 1°C increase in warming ( [[#Myhre--2019|Myhre et al., 2019]] ), though extreme rainfall trends in Northern Europe may differ in different seasons ( [[#Irannezhad--2017|Irannezhad et al., 2017]] ).

In North America (Table 11.20), there is ''robust evidence'' that the magnitude and intensity of extreme precipitation has ''very likely'' increased since the 1950s. Both Rx1day and Rx5day have significantly increased in North America during 1950–2018 ( [[#Sun--2021|Sun et al., 2021]] , also Figure 11.13). There is, however, regional diversity. In Canada, there is a lack of detectable trends in observed annual maximum daily (or shorter duration) precipitation ( [[#Shephard--2014|Shephard et al., 2014]] ; [[#Mekis--2015|Mekis et al., 2015]] ; [[#Vincent--2018|Vincent et al., 2018]] ). In the USA, there is an overall increase in one-day heavy precipitation, both in terms of intensity and frequency (Villarini et al.,2012; [[#Donat--2013b|Donat et al., 2013b]] ; [[#Wu--2015|Wu, 2015]] ; Easterling et al., 2017; H. [[#Huang--2017|Huang et al., 2017]] ; [[#Howarth--2019|Howarth et al., 2019]] ; [[#Sun--2021|Sun et al., 2021]] ), except for the southern USA ( [[#Hoerling--2016|Hoerling et al., 2016]] ) where internal variability may have played a substantial role in the lack of observed increases. In Mexico, increases are observed in R10mm and R95p ( [[#Donat--2016a|Donat et al., 2016a]] ), very wet days over the cities ( [[#García-Cueto--2019|García-Cueto et al., 2019]] ) and in total precipitation (PRCPTOT) and Rx1day ( [[#Donat--2016b|Donat et al., 2016b]] ).

In Small Islands, there is a lack of evidence showing changes in heavy precipitation overall. There were increases in extreme precipitation in Tobago from 1985–2015 ( [[#Stephenson--2014|Stephenson et al., 2014]] ; [[#Dookie--2019|Dookie et al., 2019]] ) and decreases in south-western French Polynesia and the southern subtropics ( ''low confidence'' ) (Table 11.5; Atlas.10). Extreme precipitation leading to flooding in the Small Islands has been attributed in part to tropical cyclones, as well as being influenced by ENSO (Box 11.5; [[#Khouakhi--2016|Khouakhi et al., 2016]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ).

In summary, the frequency and intensity of heavy precipitation have ''likely'' increased at the global scale over a majority of land regions with good observational coverage. Since 1950, the annual maximum amount of precipitation falling in a day, or over five consecutive days, has ''likely'' increased over land regions with sufficient observational coverage for assessment, with increases in more regions than there are decreases. Heavy precipitation has ''likely'' increased on the continental scale over three continents (North America, Europe, and Asia) where observational data are more abundant. There is ''very low confidence'' about changes in sub-daily extreme precipitation due to the limited number of studies and available data.

<div id="11.4.3" class="h2-container"></div>

<span id="model-evaluation-1"></span>
=== 11.4.3 Model Evaluation ===

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Evaluating climate model competence in simulating heavy precipitation extremes is challenging due to a number of factors, including the lack of reliable observations and the spatial scale mismatch between simulated andobserved data ( [[#Avila--2015|Avila et al., 2015]] ; [[#Alexander--2019|Alexander et al., 2019]] ). Simulated precipitation represents areal means, but station-based observations are conducted at point locations and are often sparse. The areal-reduction factor, the ratio between pointwise station estimates of extreme precipitation and extremes of the areal mean, can be as large as 130% at CMIP6 resolutions (about 100 km) ( [[#Gervais--2014|Gervais et al., 2014]] ). Hence, the order in which gridded station based extreme values are constructed (i.e., if the extreme values are extracted at the station first and then gridded, or if the daily station values are gridded and then the extreme values are extracted) represents different spatial scales of extreme precipitation and needs to be taken into account in model evaluation (Wehner et al. 2020). This aspect has been considered in some studies. Reanalysis products are used in place of station observations for their spatial completeness as well as spatial-scale comparability( [[#Sillmann--2013a|Sillmann et al., 2013a]] ; [[#Kim--2020|Kim et al., 2020]] ; [[#Li--2021|Li et al., 2021]] ). However, reanalyses share similar parametrizations to the models themselves, reducing the objectivity of the comparison.

Different generations of CMIP models have improved over time, though quite modestly ( [[#Flato--2013|Flato et al., 2013]] ; [[#Watterson--2014|Watterson et al., 2014]] ). Improvements in the representation of the magnitude of the Expert Team on Climate Change Detection and Indices (ETCCDI) in CMIP5 over CMIP3( [[#Sillmann--2013a|Sillmann et al., 2013a]] ; [[#Chen--2015a|Chen and Sun, 2015a]] ) have been attributed to higher resolution, as higher-resolution models represent smaller areas at individual grid boxes. Additionally, the spatial distribution of extreme rainfall simulated by high-resolution models is generally more comparable to observations ( [[#Sillmann--2013b|Sillmann et al., 2013b]] ; [[#Kusunoki--2017|Kusunoki, 2017]] , 2018b; [[#Scher--2017|Scher et al., 2017]] ) as these models tend to produce more realistic storms compared to coarser models ( [[#11.7.2|Section 11.7.2]] ). Higher horizontal resolution alone improves simulation of extreme precipitation in some models ( [[#Wehner--2014|Wehner et al., 2014]] ; [[#Kusunoki--2017|Kusunoki, 2017]] , 2018b), but this is insufficient in other models ( [[#Bador--2020|Bador et al., 2020]] ) as parametrization also plays a significant role (M. [[#Wu--2020|]] [[#Wu--2020|Wu et al., 2020]] ). A simple comparison of climatology may not fully reflect the improvements of the new models that have more comprehensive process formulations ( [[#Di%20Luca--2015|Di Luca et al., 2015]] ). [[#Dittus--2016|Dittus et al. (2016)]] found that many of the eight CMIP5 models they evaluated reproduced the observed increase in the difference between areas experiencing an extreme high (90%) and an extreme low (10%) proportion of the annual total precipitation from heavy precipitation (R95p/PRCPTOT) for Northern Hemisphere regions. Additionally, CMIP5 models reproduced the relation between changes in extreme and non-extreme precipitation: an increase in extreme precipitation is at the cost of a decrease in non-extreme precipitation ( [[#Thackeray--2018|Thackeray et al., 2018]] ), a characteristic found in the observational record ( [[#Gu--2018|Gu and Adler, 2018]] ).

The CMIP6 models perform reasonably well in capturing large-scale features of precipitation extremes, including intense precipitation extremes in the intertropical convergence zone (ITCZ), and weak precipitation extremes in dry areas in the tropical regions ( [[#Li--2021|Li et al., 2021]] ) but a double-ITCZ bias over the equatorial central and eastern Pacific that appeared in CMIP5 models remains ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ). There are also regional biases in the magnitude of precipitation extremes ( [[#Kim--2020|Kim et al., 2020]] ). The models also have difficulties in reproducing detailed regional patterns of extreme precipitation, such as over the north-east USA ( [[#Agel--2020|Agel and Barlow, 2020]] ), though they performed better for summer extremes over the USA ( [[#Akinsanola--2020|Akinsanola et al., 2020]] ). The comparison between climatologies in the observations and in model simulations shows that the CMIP6 and CMIP5 models that have similar horizontal resolutions also have similar model evaluation scores, and their error patterns are highly correlated ( [[#Wehner--2020|Wehner et al., 2020]] ). In general, extreme precipitation in CMIP6 models tends to be somewhat larger than in CMIP5 models ( [[#Li--2021|Li et al., 2021]] ), reflecting smaller spatial scales of extreme precipitation represented by slightly higher-resolution models ( [[#Gervais--2014|Gervais et al., 2014]] ). This is confirmed by [[#Kim--2020|Kim et al. (2020)]] , who showed that Rx1day and Rx5day simulated by CMIP6 models tend to be closer to point estimates of HadEX3 data ( [[#Dunn--2020|Dunn et al., 2020]] ) than those simulated by CMIP5. Figure 11.14 shows the multi-model ensemble bias in mean Rx1day over the period 1979–2014 from 21 available CMIP6 models when compared with observations and reanalyses. Measured by global land root-mean-square error, the model performance is generally consistent across different observed/reanalysis data products for the extreme precipitation metric (Figure 11.14). The magnitude of extreme area mean precipitation simulated by the CMIP6 models is consistently smaller than the point estimates of HadEX3, but the model values are more comparable to those of areal-mean values (Figure 11.14) of the ERA5 reanalysis or REGEN ( [[#Contractor--2020b|Contractor et al., 2020b]] ). Taylor-plot-based performance metrics reveal strong similarities in the patterns of extreme precipitation errors over land regions between CMIP5 and CMIP6 ( [[#Srivastava--2020|Srivastava et al., 2020]] ; [[#Wehner--2020|Wehner et al., 2020]] ) and between annual mean precipitation errors and Rx1day errors for both generations of models ( [[#Wehner--2020|Wehner et al., 2020]] ).

<div id="_idContainer057" class="Basic-Text-Frame"></div>

[[File:aee292997bbe519c407ddaba59181c17 IPCC_AR6_WGI_Figure_11_14.png]]

'''Figure 11.14 |''' '''Multi-model mean bias in annual maximum daily precipitation (Rx1day, %) for the period 1979–2014.''' Calculated as the difference between the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean and the average of available observational or reanalysis products including ''(a)'' ERA5, ''(b)'' HadEX3, and ''(c)'' REGEN. Bias is expressed as the percent error relative to the long-term mean of the respective observational data products. Brown indicates that models are too dry, while green indicates that they are too wet. Areas without sufficient observational data are shown in grey. Adapted from [[#Wehner--2020|Wehner et al. (2020)]] under the terms of the Creative Commons Attribution licence. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

In general, there is ''high confidence'' that historical simulations by CMIP5 and CMIP6 models of similar horizontal resolutions are interchangeable in their performance in simulating the observed climatology of extreme precipitation ''.''

Studies using regional climate models (RCMs), for example, CORDEX ( [[#Giorgi--2009|Giorgi et al., 2009]] ) over Africa ( [[#Dosio--2015|Dosio et al., 2015]] ; [[#Klutse--2016|Klutse et al., 2016]] ; [[#Pinto--2016|Pinto et al., 2016]] ; [[#Gibba--2019|Gibba et al., 2019]] ), Australia, East Asia ( [[#Park--2016|Park et al., 2016]] ), Europe ( [[#Prein--2016a|Prein et al., 2016a]] ; [[#Fantini--2018|Fantini et al., 2018]] ), and parts of North America ( [[#Diaconescu--2018|Diaconescu et al., 2018]] ) suggest that extreme rainfall events are better captured in RCMs compared to their host GCMs due to their ability to address regional characteristics, for example, topography and coastlines. However, CORDEX simulations do not show good skill over South Asia for heavy precipitation, and do not add value with respect to their GCM source of boundary conditions ( [[#Mishra--2014b|Mishra et al., 2014b]] ; S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ). The evaluation of models in simulating regional processes is discussed in detail in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.4|Section 10.3.3.4]] . The high-resolution simulation of mid-latitude winter extreme precipitation over land is of similar magnitude to point observations. Simulation of summer extreme precipitation has a large bias when compared with observations at the same spatial scale. Simulated extreme precipitation in the tropics also appears to be too large, indicating possible deficiencies in the parametrization of cumulus convection at this resolution. Indeed, precipitation distributions at both daily and sub-daily time scales are much improved with a convection-permitting model ( [[#Belušić--2020|Belušić et al., 2020]] ) over Western Africa ( [[#Berthou--2019b|Berthou et al., 2019b]] ), East Africa ( [[#Finney--2019|Finney et al., 2019]] ), North America and Canada ( [[#Cannon--2019|Cannon and Innocenti, 2019]] ; [[#Innocenti--2019|Innocenti et al., 2019]] ) and over Belgium in Europe ( [[#Vanden%20Broucke--2019|Vanden Broucke et al., 2019]] ).

In summary, there is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land. The magnitude and frequency of extreme precipitation simulated by CMIP6 models are similar to those simulated by CMIP5 models ( ''hig'' ''h confidence'' ).

<div id="11.4.4" class="h2-container"></div>

<span id="detection-and-attribution-event-attribution-1"></span>
=== 11.4.4 Detection and Attribution, Event Attribution ===

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Both SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) and AR5 (Chapter 10, IPCC, 2014) concluded with ''medium confidence'' that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. These assessments were based on the evidence of anthropogenic influence on aspects of the global hydrological cycle, in particular, the human contribution to the warming-induced observed increase in atmospheric moisture that leads to an increase in heavy precipitation, and ''limited evidence'' of anthropogenic influence on extreme precipitation of durations of one and five days.

Since AR5 there has been new and ''robust evidence'' and improved understanding of human influence on extreme precipitation. In particular, detection and attribution analyses have provided consistent and ''robust evidence'' of human influence on extreme precipitation of one- and five-day durations at global to continental scales. The observed increases in Rx1day and Rx5day over the Northern Hemisphere land area during 1951–2005 can be attributed to the effect of combined anthropogenic forcing, including greenhouse gases and anthropogenic aerosols, as simulated by CMIP5 models and the rate of intensification with regard to warming is consistent with C-C scaling ( [[#Zhang--2013|Zhang et al., 2013]] ). This is confirmed to be robust when an additional nine years of observational data and the CMIP6 model simulations were used (Cross-Chapter Box 3.2, Figure 1; [[#Paik--2020|Paik et al., 2020]] ). The influence of greenhouse gases is attributed as the dominant contributor to the observed intensification. The global average of Rx1day in the observations is consistent with simulations by both CMIP5 and CMIP6 models under anthropogenic forcing, but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The observed increase in the fraction of annual total precipitation falling into the top fifth or top first percentiles of daily precipitation can also be attributed to human influence at the global scale ( [[#Dong--2021|Dong et al., 2021]] ). The CMIP5 models were able to capture the fraction of land experiencing a strong intensification of heavy precipitation during 1960–2010 under anthropogenic forcing, but not in unforced simulations ( [[#Fischer--2014|Fischer et al., 2014]] ). But the models underestimated the observed trends ( [[#Borodina--2017a|Borodina et al., 2017a]] ). Human influence also significantly contributed to the historical changes in record-breaking one-day precipitation ( [[#Shiogama--2016|Shiogama et al., 2016]] ). There is also ''limited evidence'' of the influences of natural forcing. Substantial reductions in Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation intensity over the global summer monsoon regions occurred during 1957–2000 after explosive volcanic eruptions ( [[#Paik--2018|Paik and Min, 2018]] ). The reduction in post-volcanic eruption extreme precipitation in the simulations is closely linked to the decrease in mean precipitation, for which both thermodynamic effects (moisture reduction due to surface cooling) and dynamic effects (monsoon circulation weakening) play important roles.

There has been new evidence of human influence on extreme precipitation at continental scales, including the detection of the combined effect of greenhouse gases and aerosol forcing on Rx1day and Rx5day over North America, Eurasia, and mid-latitude land regions ( [[#Zhang--2013|Zhang et al., 2013]] ) and of greenhouse gas forcing in Rx1day and Rx5day in the mid-to-high latitudes, western and eastern Eurasia, and the global dry regions ( [[#Paik--2020|Paik et al., 2020]] ). These findings are corroborated by the detection of human influence in the fraction of extreme precipitation in the total precipitation over Asia, Europe, and North America ( [[#Dong--2021|Dong et al., 2021]] ). Human influence was found to have contributed to the increase in frequency and intensity of regional precipitation extremes in North America during 1961–2010, based on optimal fingerprinting and event attribution approaches ( [[#Kirchmeier-Young--2020|Kirchmeier-Young and Zhang, 2020]] ). [[#Tabari--2020|Tabari et al. (2020)]] found the observed latitudinal increase in extreme precipitation over Europe to be consistent with model-simulated responses to anthropogenic forcing.

Evidence of human influence on extreme precipitation at regional scales is more limited and less robust. In north-west Australia, the increase in extreme rainfall since 1950 can be related to increased monsoonal flow due to increased aerosol emissions, but cannot be attributed to an increase in greenhouse gases ( [[#Dey--2019a|Dey et al., 2019a]] ). Anthropogenic influence on extreme precipitation in China was detected in one study (H. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al., 2017]] ), but not in another using different detection and data-processing procedures (W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] a), indicating the lack of robustness in the detection results. A still weak signal-to-noise ratio seems to be the main cause for the lack of robustness, as detection would become robust 20 years in the future (W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] a). [[#Krishnan--2016|Krishnan et al. (2016)]] attributed the observed increase in heavy rain events (intensity >100 mm day <sup>–1</sup> ) in the post-1950s over central India to the combined effects of greenhouse gases, aerosols, land-use and land-cover changes, and rapid warming of the equatorial Indian Ocean SSTs. Roxyet al. (2017) and [[#Devanand--2019|Devanand et al. (2019)]] showed that the increase in widespread extremes over the South Asian Monsoon during 1950–2015 is due to the combined impacts of the warming of the Western Indian Ocean (Arabian Sea) and the intensification of irrigation water management over India.

Anthropogenic influence may have affected the large-scale meteorological processes necessary for extreme precipitation and the localized thermodynamic and dynamic processes, both contributing to changes in extreme precipitation events. Several new methods have been proposed to disentangle these effects by either conditioning on the circulation state or attributing analogues. In particular, the extremely wet winter of 2013–2014 in the UK can be attributed, approximately to the same degree, to both temperature-induced increases in saturation vapour pressure and changes in the large-scale circulation ( [[#Vautard--2016|Vautard et al., 2016]] ; [[#Yiou--2017|Yiou et al., 2017]] ). There are multiple cases indicating that very extreme precipitation may increase at a rate more than the C-C rate (7% per 1°C of warming) ( [[#Pall--2017|Pall et al., 2017]] ; [[#Risser--2017|Risser and Wehner, 2017]] ; [[#van%20der%20Wiel--2017|van der Wiel et al., 2017]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; S.-Y.S. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ).

Event attribution studies found an influence of anthropogenic activities on the probability or magnitude of observed extreme precipitation events, including European winters ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Otto--2018b|Otto et al., 2018b]] ), extreme 2014 precipitation over the northern Mediterranean ( [[#Vautard--2015|Vautard et al., 2015]] ), parts of the USA for individual events ( [[#Knutson--2014a|Knutson et al., 2014a]] ; [[#Szeto--2015|Szeto et al., 2015]] ; [[#Eden--2016|Eden et al., 2016]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ), extreme rainfall in 2014 over Northland, New Zealand ( [[#Rosier--2015|Rosier et al., 2015]] ) or China ( [[#Burke--2016|Burke et al., 2016]] ; [[#Sun--2018|Sun and Miao, 2018]] ; [[#Yuan--2018b|Yuan et al., 2018b]] ; [[#Zhou--2018|Zhou et al., 2018]] ). However, for other heavy rainfall events, studies identified a lack of evidence about anthropogenic influences ( [[#Imada--2013|Imada et al., 2013]] ; [[#Schaller--2014|Schaller et al., 2014]] ; [[#Otto--2015c|Otto et al., 2015c]] ; [[#Siswanto--2015|Siswanto et al., 2015]] ). There are also studies where results are inconclusive because of limited reliable simulations ( [[#Christidis--2013b|Christidis et al., 2013b]] ; [[#Angélil--2016|Angélil et al., 2016]] ). Overall, both the spatial and temporal scales on which extreme precipitation events are defined are important for attribution; events defined on larger scales have larger signal-to-noise ratios and thus the signal is more readily detectable. At the current level of global warming, there is a strong enough signal to be detectable for large-scale extreme precipitation events, but the chance of detecting such signals for smaller-scale events decreases ( [[#Kirchmeier-Young--2019|Kirchmeier-Young et al., 2019]] ).

In summary, most of the observed intensification of heavy precipitation over land regions is ''likely'' due to anthropogenic influence, for which greenhouse gases emissions are the main contributor. New and ''robust evidence'' since AR5 includes attribution to human influence of the observed increases in annual maximum one-day and five-day precipitation and in the fraction of annual precipitation falling in heavy events. The evidence since AR5 also includes a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, both of which can only be explained when anthropogenic greenhouse gas forcing is considered. Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are more abundant ( ''high confidence'' ) (North America, Europe and Asia). On the spatial scale of AR6 regions, there is ''limited evidence'' of human influence on extreme precipitation, but new evidence is emerging; in particular, studies attributing individual heavy precipitation events found that human influence was a significant driver of the events, particularly in the winter season.

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<span id="projections-1"></span>
=== 11.4.5 Projections ===

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The AR5 concluded it is ''very likely'' that extreme precipitation events will be more frequent and more intense over most of the mid-latitude land masses and wet tropics in a warmer world ( [[#Collins--2013|Collins et al., 2013]] ). Post-AR5 studies provide more and ''robust evidence'' to support the previous assessments. These include an observed increase in extreme precipitation ( [[#11.4.3|Section 11.4.3]] ) and human causes of past changes ( [[#11.4.4|Section 11.4.4]] ), as well as projections based on either GCM and/or RCM simulations. The CMIP5 models project that the rate of increase in Rx1day with warming is independent of the forcing scenario ( [[IPCC:Wg1:Chapter:Chapter-8#8.5.3.1|Section 8.5.3.1]] ; [[#Pendergrass--2015|Pendergrass et al., 2015]] ) or forcing mechanism ( [[#Sillmann--2017a|Sillmann et al., 2017a]] ). This is confirmed in CMIP6 simulations ( [[#Sillmann--2019|Sillmann et al., 2019]] ; [[#Li--2021|Li et al., 2021]] ). In particular, for extreme precipitation that occurs once a year or less frequently, the magnitudes of the rates of change per 1°C change in global mean temperature are similar, regardless of whether the temperature change is caused by increases in carbon dioxide (CO <sub>2</sub> ), methane (CH <sub>4</sub> ), solar forcing, or sulphate (SO <sub>4</sub> ) ( [[#Sillmann--2019|Sillmann et al., 2019]] ). In some models – CESM1 in particular – the extreme precipitation response to warming may follow a quadratic relation ( [[#Pendergrass--2019|Pendergrass et al., 2019]] ). Figure 11.15 shows changes in the 10- and 50-year return values of Rx1day at different warming levels as simulated by the CMIP6 models. The median value of the scaling over land, across all Shared Socio-economic Pathway (SSP) scenarios and all models, is close to 7% per 1°C of warming for the 50-year return value of Rx1day. It is just slightly smaller for the 10- and 50-year return values of Rx5day ( [[#Li--2021|Li et al., 2021]] ). The 90% ranges of the multimodel ensemble changes across all land grid boxes in the 50-year return values for Rx1day and Rx5day do not overlap between 1.5°C and 2°C warming levels ( [[#Li--2021|Li et al., 2021]] ), indicating that a small increment such as 0.5°C in global warming can result in a significant increase in extreme precipitation. Projected long-period Rx1day return value changes are larger than changes in mean Rx1day and with larger relative changes for more rare events ( [[#Pendergrass--2018|Pendergrass, 2018]] ; [[#Mizuta--2020|Mizuta and Endo, 2020]] ; [[#Wehner--2020|Wehner, 2020]] ). The rate of change of moderate extreme precipitation may depend more on the forcing agent, similar to the mean precipitation response to warming ( [[#Lin--2016|Lin et al., 2016]] , 2018). Thus, there is ''high confidence'' that extreme precipitation that occurs once a year or less frequently increases proportionally to the amount of surface warming, and the rate of change in precipitation is not dependent on the underlying forcing agents of warming.

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[[File:16ec961ba91dca7123ead5a0783a5a3d IPCC_AR6_WGI_Figure_11_15.png]]

'''Figure 11.15 |''' '''Projected changes in the intensity of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 1850–1900 baseline.''' Extreme precipitation events are defined as the annual maximum daily maximum precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the global land. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the intensity changes across the multi-model median, and the ‘whiskers’ extend to the 90% uncertainty range. The results are based on the multi-model ensemble estimated from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Based on [[#Li--2021|Li et al. (2021)]] . Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

The spatial patterns of the projected changes across different warming levels are quite similar, as shown in Figure 11.16, and confirmed by near-linear scaling between extreme precipitation and global warming levels at regional scales ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). Internal variability modulates changes in heavy rainfall ( [[#Wood--2020|Wood and Ludwig, 2020]] ), resulting in different changes in different regions ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). Extreme precipitation nearly always increases across land areas with larger increases at higher global warming levels, except in very few regions, such as Southern Europe around the Mediterranean Basin at low warming levels (Table 11.17). The ''very likely'' ranges of the multi-model ensemble changes across all land grid boxes in the 50-year return values for Rx1day and Rx5day between 1.5°C and 1°C warming levels are above zero for all continents except Europe, with the lower bound of the ''likely'' range above zero over Europe ( [[#Li--2021|Li et al., 2021]] ). Decreases in extreme precipitation are confined mostly to subtropical ocean areas and are highly correlated to decreases in mean precipitation due to storm track shifts. These subtropical decreases can extend to nearby land areas in individual realizations.

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[[File:94210254c8d018b47fc349341792c580 IPCC_AR6_WGI_Figure_11_16.png]]

'''Figure 11.16 |''' '''Projected changes in annual maximum daily precipitation at (a) 1.5°C, (b) 2°C, and (c) 4°C of global warming compared to the 1850–1900 baseline.''' Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers on the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1. For details on the methods see Supplementary Material 11.SM.2. Changes in Rx1day are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Projected increases in the probability of extreme precipitation of fixed magnitudes are nonlinear and show larger increases for more rare events (Figures 11.7 and 11.15; [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Kharin--2018|Kharin et al., 2018]] ; [[#Li--2021|Li et al., 2021]] ).The CMIP5 model projected increases in the probability of high (99th and 99.9th) percentile precipitation between 1.5°C and 2°C warming scenarios are consistent with what can be expected based on observed changes ( [[#Fischer--2015|Fischer and Knutti, 2015]] ), providing confidence in the projections. The CMIP5 model simulations show that the frequency for present-day climate 20-year extreme precipitation is projected to increase by 10% at the 1.5°C global warming level, and by 22% at the 2.0°C global warming level, while the increase in the frequency for present-day climate 100-year extreme precipitation is projected to increase by 20% and more than 45% at the 1.5°C and 2.0°C warming levels, respectively ( [[#Kharin--2018|Kharin et al., 2018]] ). CMIP6 simulations with SSP scenarios show that the frequency of 10-year and 50-year events will be approximately doubled and tripled, respectively, at a very high warming level of 4°C (Figure 11.7; [[#Li--2021|Li et al., 2021]] ).

There is a limited number of studies on the projections of extreme hourly precipitation. The ability of GCMs to simulate hourly precipitation extremes is limited ( [[#Morrison--2019|Morrison et al., 2019]] ) and very few modelling centres archive sub-daily and hourly precipitation prior to CMIP6 experiments. RCM simulations project an increase in extreme sub-daily precipitation in North America ( [[#Li--2019b|]] [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|C. Li et al., 2019]] b ) and Sweden ( [[#Olsson--2013|Olsson and Foster, 2013]] ), but these models still do not explicitly resolve convective processes that are important for properly simulating extreme sub-daily precipitation. Simulations by RCMs that explicitly resolve convective processes (convection-permitting models) are limited in length and only available in a few regions because of high computing costs. Yet, a majority of the available convection-permitting simulations project increases in the intensities of extreme sub-daily precipitation events, with the amount similar to or higher than the C-C scaling rate ( [[#Kendon--2014|Kendon et al., 2014]] , 2019; [[#Ban--2015|Ban et al., 2015]] ; [[#Prein--2016b|Prein et al., 2016b]] ; [[#Helsen--2020|Helsen et al., 2020]] ; [[#Fowler--2021|Fowler et al., 2021]] ). An increase is projected in extreme sub-daily precipitation over Africa ( [[#Kendon--2019|Kendon et al., 2019]] ); East Africa ( [[#Finney--2020|Finney et al., 2020]] ) and Western Africa ( [[#Berthou--2019a|Berthou et al., 2019a]] ; [[#Fitzpatrick--2020|Fitzpatrick et al., 2020]] ), even for areas where parametrized RCMs project a decrease; in Europe (Hodnebrog et al., 2019; [[#Chan--2020|Chan et al., 2020]] ); as well as in the continental USA ( [[#Prein--2016b|Prein et al., 2016b]] ). Overall, while limited, the available evidence points to an increase in extreme sub-daily precipitation in the future. Studies on future changes in extreme precipitation for a month or longer are limited. One study projects an increase in extreme monthly precipitation in Japan under 4°C global warming for around 80% of stations in the summer ( [[#Hatsuzuka--2019|Hatsuzuka and Sato, 2019]] ).

In Africa (Table 11.5), extreme precipitation will ''likely'' increase under warming levels of 2°C or below (compared to pre-industrial values) and ''very likely'' increase at higher warming levels. Simulations by CMIP5, CMIP6 and CORDEX regional models project an increase in daily extreme precipitation between 1.5°C and 2.0°C warming levels. The pattern of change in heavy precipitation under different scenarios or warming levels is similar with larger increases for higher warming levels (e.g., [[#Nikulin--2018|Nikulin et al., 2018]] ; [[#Li--2021|Li et al., 2021]] ). With increases in warming, extreme precipitation is projected to increase in the majority of land regions in Africa ( [[#Mtongori--2016|Mtongori et al., 2016]] ; [[#Pfahl--2017|Pfahl et al., 2017]] ; [[#Diedhiou--2018|Diedhiou et al., 2018]] ; [[#Dunning--2018|Dunning et al., 2018]] ; [[#Akinyemi--2019|Akinyemi and Abiodun, 2019]] ; [[#Giorgi--2019|Giorgi et al., 2019]] ). Over Southern Africa, heavy precipitation will ''likely'' increase by the end of the 21st century under RCP 8.5 ( [[#Dosio--2016|Dosio, 2016]] ; [[#Pinto--2016|Pinto et al., 2016]] ; [[#Abiodun--2017|Abiodun et al., 2017]] ; [[#Dosio--2019|Dosio et al., 2019]] ). However, heavy rainfall amounts are projected to decrease over western South Africa ( [[#Pinto--2018|Pinto et al., 2018]] ) as a result of a projected decrease in the frequency of the prevailing westerly winds south of the continent that translates into fewer cold fronts and closed mid-latitudes cyclones ( [[#Engelbrecht--2009|Engelbrecht et al., 2009]] ; [[#Pinto--2018|Pinto et al., 2018]] ). Heavy precipitation will ''likely'' increase by the end of the century under RCP8.5 in West Africa ( [[#Diallo--2016|Diallo et al., 2016]] ; [[#Dosio--2016|Dosio, 2016]] ; [[#Sylla--2016|Sylla et al., 2016]] ; [[#Abiodun--2017|Abiodun et al., 2017]] ; [[#Akinsanola--2019|Akinsanola and Zhou, 2019]] ; [[#Dosio--2019|Dosio et al., 2019]] ) and is projected to increase ( ''high confidence'' ) in Central Africa ( [[#Fotso-Nguemo--2018|Fotso-Nguemo et al., 2018]] , 2019; [[#Sonkoué--2019|Sonkoué et al., 2019]] ) and eastern Africa ( [[#Thiery--2016|Thiery et al., 2016]] ; [[#Ongoma--2018a|Ongoma et al., 2018a]] ). In north-east and central east Africa, extreme precipitation intensity is projected to increase across CMIP5, CMIP6 and CORDEX-CORE ( ''high confidence'' ) in most areas annually ( [[#Coppola--2021a|Coppola et al., 2021a]] ), but the trends differ from season to season in all future scenarios ( [[#Dosio--2019|Dosio et al., 2019]] ). In northern Africa, there is ''low confidence'' in the projected changes in heavy precipitation, either due to a lack of agreement among studies on the sign of changes ( [[#Sillmann--2013a|Sillmann et al., 2013a]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ) or due to insufficient evidence.

In Asia (Table 11.8), extreme precipitation will ''likely'' increase at global warming levels of 2°C and below, but ''very likely'' increase at higher warming levels for the region as whole. The CMIP6 multi-model median projects an increase in the 10- and 50-year return values of Rx1day and Rx5day over more than 95% of regions, even at the 2°C warming level, with larger increases at higher warming levels, independent of emissions scenarios ( [[#Li--2021|Li et al., 2021]] , also Figure 11.7). The CMIP5 models produced similar projections. Both heavy rainfall and rainfall intensity are projected to increase ( [[#Zhou--2014|Zhou et al., 2014]] ; [[#Guo--2016|Guo et al., 2016]] , 2018; Y. [[#Xu--2016|]] [[#Xu--2016|Xu et al., 2016]] ; [[#Endo--2017|Endo et al., 2017]] ; [[#Han--2018|Han et al., 2018]] ; G. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ). A half-degree difference in warming between the 1.5°C and 2.0°C warming levels can result in a detectable increase in extreme precipitation over the region ( [[#Li--2021|Li et al., 2021]] ), in the Asian–Australian monsoon region ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ), and over South Asia and China (D. [[#Lee--2018|]] [[#Lee--2018|]] [[#Lee--2018|Lee et al., 2018]] ; W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] b). While there are regional differences, extreme precipitation is projected to increase in almost all sub-regions, though there can be spatial heterogeneity within sub-regions, such as in India ( [[#Shashikanth--2018|Shashikanth et al., 2018]] ) and South East Asia ( [[#Ohba--2019|Ohba and Sugimoto, 2019]] ). In East and South East Asia, there is ''high confidence'' that extreme precipitation is projected to intensify (Seo et al., 2014; [[#Zhou--2014|Zhou et al., 2014]] ; Y. [[#Xu--2016|]] [[#Xu--2016|Xu et al., 2016]] ; [[#Nayak--2017|Nayak et al., 2017]] ; X. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; Y. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Guo--2018|Guo et al., 2018]] ; D. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Sui--2018|Sui et al., 2018]] ). Extreme daily precipitation is also projected to increase in South Asia (Xu et al., 2017; [[#Han--2018|Han et al., 2018]] ; [[#Shashikanth--2018|Shashikanth et al., 2018]] ). The extreme precipitation indices, including Rx5day, R95p, and days of heavy precipitation (i.e., R10mm), are all projected to increase under the RCP4.5 and RCP8.5 scenarios in central and northern Asia ( [[#Xu--2017|Xu et al., 2017]] ; [[#Han--2018|Han et al., 2018]] ). A general wetting across the whole Tibetan Plateau and the Himalayas is projected, with increases in heavy precipitation in the 21st century (Palazzi et al., 2013; [[#Zhou--2014|Zhou et al., 2014]] ; [[#Rajbhandari--2015|Rajbhandari et al., 2015]] ; R. [[#Zhang--2015|Zhang et al., 2015]] ; [[#Wu--2017|Wu et al., 2017]] ; [[#Gao--2018|Gao et al., 2018]] ; [[#Paltan--2018|Paltan et al., 2018]] ). Agreement in projected changes by different models is low in regions of complex topography such as Hindu-Kush Himalayas ( [[#Roy--2019|Roy et al., 2019]] ), but CMIP5, CMIP6 and CORDEX-CORE simulations consistently project an increase in heavy precipitation in higher latitude areas, such as West and East Siberia, and Russian Far East ( ''high confidence'' ) ( [[#Coppola--2021a|Coppola et al., 2021a]] ).

In Australasia (Table 11.11), most CMIP5 models project an increase in Rx1day under RCP4.5 and RCP8.5 scenarios for the late 21st century (CSIRO and BOM, 2015; [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Grose--2020|Grose et al., 2020]] ) and the CMIP6 multi-model median projects an increase in the 10- and 50-year return values of Rx1day and Rx5day at a rate between 5% and 6% per 1°C of near-surface global mean warming (Figure 11.7; [[#Li--2021|Li et al., 2021]] ). Yet, there is large uncertainty in the increase because projected changes in dynamic processes lead to a decrease in Rx1day that can offset the thermodynamic increase over a large portion of the region (Box 11.1, Figure 1; [[#Pfahl--2017|Pfahl et al., 2017]] ). Projected changes in moderate extreme precipitation (the 99th percentile of daily precipitation) by RCMs under RCP8.5 for 2070–2099 are mixed, with more regions showing decreases than increases ( [[#Evans--2021|Evans et al., 2021]] ). It is ''likely'' that daily rainfall extremes such as Rx1day will increase at the continental scale for global warming levels at or above 3°C. Daily rainfall extremes are projected to increase at the 2.0°C global warming level ( ''medium confidence'' ), and there is ''low confidence'' in changes at the 1.5°C ''.'' Projected changes show important regional differences with ''very likely'' increases over Northern Australia ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Herold--2018|Herold et al., 2018]] ; [[#Grose--2020|Grose et al., 2020]] ) and New Zealand ( [[#MfE--2018|MfE, 2018]] ) where projected dynamic contributions are small (Box 11.1 Figure 1; [[#Pfahl--2017|Pfahl et al., 2017]] ) and ''medium confidence'' on increases over central, eastern, and Southern Australia where dynamic contributions are substantial and can affect local phenomena (CSIRO and BOM, 2015; [[#Pepler--2016|Pepler et al., 2016]] ; [[#Bell--2019|Bell et al., 2019]] ; [[#Dowdy--2019|Dowdy et al., 2019]] ).

In Central and South America (Table 11.14), extreme precipitation will ''likely'' increase at global warming levels of 2°C and below, but ''very likely'' increase at higher warming levels for the region as whole. A larger increase in global surface temperature leads to a larger increase in extreme precipitation, independent of emissions scenarios ( [[#Li--2021|Li et al., 2021]] ). But there are regional differences in the projection, and projected changes for more moderate extreme precipitation are also more uncertain. Extreme precipitation, represented by the number of days with daily precipitation exceeding 50 mm and the annual fraction of precipitation falling during days with the top 10% daily precipitation amount, is projected to increase on the eastern coast of Southern Central America, but to decrease along the Pacific coasts of El Salvador and Guatemala ( [[#Imbach--2018|Imbach et al., 2018]] ). Chouet al. (2014b) and [[#Giorgi--2014|Giorgi et al. (2014)]] projected an increase in extreme precipitation over South-Eastern South America and the Amazon. Projected changes in moderate extreme precipitation represented by the 99th percentile of daily precipitation by different models under different emissions scenarios, even at high warming levels, are mixed: increases are projected for all regions by the CORDEX-CORE and CMIP5 simulations, while increases for some regions and decreases for other regions are projected by CMIP6 simulations ( [[#Coppola--2021a|Coppola et al., 2021a]] ). Extreme precipitation is projected to increase in the La Plata basin ( [[#Cavalcanti--2015|Cavalcanti et al., 2015]] ; [[#Carril--2016|Carril et al., 2016]] ). [[#Taylor--2018|Taylor et al. (2018)]] projected a decrease in days with intense rainfall in the Caribbean under 2°C global warming by the 2050s under RCP4.5 relative to 1971–2000.

In Europe (Table 11.17), extreme precipitation will ''likely'' increase at global warming levels of 2°C and below, but ''very likely'' increase for higher warming levels for the region as whole. The CMIP6 multi-model median projects an increase in the 10- and 50-year return values of Rx1day and Rx5day over a majority of the region at the 2°C global warming level, with more than 95% of the region showing an increase at higher warming levels (Figure 11.7; [[#Li--2021|C. Li et al., 2021]] ). The most intense precipitation events observed today in Europe are projected to almost double in occurrence for each 1°C of further global warming ( [[#Myhre--2019|Myhre et al., 2019]] ). Extreme precipitation is projected to increase in both boreal winter and summer over Europe ( [[#Madsen--2014|Madsen et al., 2014]] ; [[#Christensen--2015|Christensen et al., 2015]] ; [[#Nissen--2017|Nissen and Ulbrich, 2017]] ). There are regional differences, with decreases or no change for the southern part of Europe, such as the southern Mediterranean (Tramblay and Somot, 2018; [[#Lionello--2020|Lionello and Scarascia, 2020]] ; [[#Coppola--2021a|Coppola et al., 2021a]] ), uncertain changes over central Europe ( [[#Argüeso--2012|Argüeso et al., 2012]] ; [[#Croitoru--2013|Croitoru et al., 2013]] ; [[#Rajczak--2013|Rajczak et al., 2013]] ; [[#Casanueva--2014|Casanueva et al., 2014]] ; [[#Patarčić--2014|Patarčić et al., 2014]] ; [[#Paxian--2014|Paxian et al., 2014]] ; [[#Roth--2014|Roth et al., 2014]] ; [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Monjo--2016|Monjo et al., 2016]] ) and a strong increase in the remaining parts, including the Alps region ( [[#Gobiet--2014|Gobiet et al., 2014]] ; [[#Donnelly--2017|Donnelly et al., 2017]] ), particularly in winter ( [[#Fischer--2015|Fischer et al., 2015]] ), and in northern Europe. In a 3°C warmer world, there will be a robust increase in extreme rainfall over 80% of land areas in northern Europe ( [[#Madsen--2014|Madsen et al., 2014]] ; [[#Donnelly--2017|Donnelly et al., 2017]] ; [[#Cardell--2020|Cardell et al., 2020]] ).

In North America (Table 11.20), the intensity and frequency of extreme precipitation will ''likely'' increase at the global warming levels of 2°C and below, and ''very likely'' increase at higher warming levels. An increase is projected by CMIP6 model simulations ( [[#Li--2021|Li et al., 2021]] ) and by previous model generations (Wu,2015; Easterling et al., 2017; [[#Innocenti--2019|Innocenti et al., 2019]] ), as well as by RCMs (Coppola et al., 2021a). Projections of extreme precipitation over the southern portion of the continent and over Mexico are more uncertain, with decreases possible ( [[#Sillmann--2013b|Sillmann et al., 2013b]] ; [[#Alexandru--2018|Alexandru, 2018]] ; [[#Coppola--2021a|Coppola et al., 2021a]] ).

In summary, heavy precipitation will generally become more frequent and more intense with additional global warming. At global warming levels of 4°C relative to the pre-industrial, very rare (e.g., one in 10 or more years) heavy precipitation events would become more frequent and more intense than in the recent past, on the global scale ( ''virtually certain'' ), and in all continents and AR6 regions: The increase in frequency and intensity is ''extremely likely'' for most continents and ''very likely'' for most AR6 regions. The likelihood is lower at lower global warming levels and for less-rare heavy precipitation events. At the global scale, the intensification of heavy precipitation will follow the rate of increase in the maximum amount of moisture that the atmosphere can hold as it warms ( ''high confidence'' ), of about 7% per 1°C of global warming. The increase in the frequency of heavy precipitation events will be non-linear with more warming and will be higher for rarer events ( ''high confidence'' ), with 10- and 50-year events to be approximately double and triple, respectively, at the 4°C warming level. Increases in the intensity of extreme precipitation events at regional scales will depend on the amount of regional warming as well as changes in atmospheric circulation and storm dynamics leading to regional differences in the rate of heavy precipitation changes ( ''hi'' ''gh confidence'' ).

<div id="11.5" class="h1-container"></div>

<span id="floods"></span>
== 11.5 Floods ==

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Floods are the inundation of normally dry land, and are classified into types (e.g., pluvial floods, flash floods, river floods, groundwater floods, surge floods, coastal floods) depending on the space and time scales and the major factors and processes involved ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.3.2|Section 8.2.3.2]] ; [[#Nied--2014|Nied et al., 2014]] ; [[#Aerts--2018|Aerts et al., 2018]] ). Flooded area is difficult to measure or quantify and, for this reason, many of the existing studies on changes in floods focus on streamflow. Thus, this section assesses changes in flow as a proxy for river floods, in addition to some types of flash floods. Pluvial and urban floods – types of flash floods resulting from the precipitation intensity exceeding the capacity of natural and artificial drainage systems – are directly linked to extreme precipitation. Because of this link, changes in extreme precipitation are the main proxy for inferring changes in pluvial and urban floods (see also [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ), assuming there is no additional change in the surface condition. Changes in these types of floods are not assessed in this section, but can be inferred from the assessment of changes in heavy precipitation in [[#11.4|Section 11.4]] . Coastal floods due to extreme sea levels and flood changes at regional scales are assessed in [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] .

<div id="11.5.1" class="h2-container"></div>

<span id="mechanisms-and-drivers-2"></span>
=== 11.5.1 Mechanisms and Drivers ===

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Since AR5, the number of studies on understanding how floods may have changed, and will change in the future, has substantially increased. Floods are a complex interplay of hydrology, climate, and human management, and the relative importance of these factors varies for different flood types and regions.

In addition to the amount and intensity of precipitation, the main factors for river floods include antecedent soil moisture ( [[#Paschalis--2014|Paschalis et al., 2014]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ; [[#Grillakis--2016|Grillakis et al., 2016]] ; [[#Woldemeskel--2016|Woldemeskel and Sharma, 2016]] ) and snow water-equivalent in cold regions ( [[#Sikorska--2015|Sikorska et al., 2015]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ). Other factors are also important, including stream morphology ( [[#Borga--2014|Borga et al., 2014]] ; [[#Slater--2015|Slater et al., 2015]] ), river and catchment engineering ( [[#Pisaniello--2012|Pisaniello et al., 2012]] ; [[#Nakayama--2013|Nakayama and Shankman, 2013]] ; [[#Kim--2016|Kim and Sanders, 2016]] ), land-use and land-cover characteristics ( [[#Aich--2016|Aich et al., 2016]] ; [[#Rogger--2017|Rogger et al., 2017]] ) and changes ( [[#Knighton--2019|Knighton et al., 2019]] ), and feedbacks between climate, soil, snow, vegetation, etc. ( [[#Hall--2014|Hall et al., 2014]] ; [[#Ortega--2014|Ortega et al., 2014]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ; [[#Buttle--2016|Buttle et al., 2016]] ; [[#Teufel--2019|Teufel et al., 2019]] ). Water regulation and management have, in general, increased resilience to flooding ( [[#Formetta--2019|Formetta and Feyen, 2019]] ), masking effects of an increase in extreme precipitation on flood probability in some regions, even though they do not eliminate very extreme floods ( [[#Vicente-Serrano--2017|Vicente-Serrano et al., 2017]] ). This means that an increase in precipitation extremes may not always result in an increase in river floods ( [[#Sharma--2018|Sharma et al., 2018]] ; [[#Do--2020|Do et al., 2020]] ). Yet, as very extreme precipitation can become a dominant factor for river floods, there can be some correspondence in the changes in very extreme precipitation and river floods ( [[#Ivancic--2015|Ivancic and Shaw, 2015]] ; [[#Wasko--2017|Wasko and Sharma, 2017]] ; [[#Wasko--2019|Wasko and Nathan, 2019]] ). This has been observed in the western Mediterranean ( [[#Llasat--2016|Llasat et al., 2016]] ), in China (Q. [[#Zhang--2015a|]] [[#Zhang--2015|Zhang et al., 2015]] a ) and in the USA ( [[#Peterson--2013b|Peterson et al., 2013b]] ; [[#Berghuijs--2016|Berghuijs et al., 2016]] ; [[#Slater--2016|Slater and Villarini, 2016]] ).

In regions with a seasonal snow cover, snowmelt is the main cause of extreme river flooding over large areas ( [[#Pall--2019|Pall et al., 2019]] ). Extensive snowmelt combined with heavy and/or long-duration precipitation can cause significant floods (D. [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Krug--2020|Krug et al., 2020]] ). Changes in floods in these regions can be uncertain because of the compounding and competing effects of the responses of snow and rain to warming that affect snowpack size: warming results in an increase in precipitation, but also a reduction in the time period of snowfall accumulation ( [[#Teufel--2019|Teufel et al., 2019]] ). An increase in atmospheric CO <sub>2</sub> enhances water-use efficiency by plants ( [[#Roderick--2015|Roderick et al., 2015]] ; [[#Milly--2016|Milly and Dunne, 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Swann--2018|Swann, 2018]] ); this could reduce evapotranspiration and contribute to the maintenance of soil moisture and streamflow levels under enhanced atmospheric CO <sub>2</sub> concentrations ( [[#Yang--2019|Yang et al., 2019]] ). This mechanism would suggest an increase in the magnitude of some floods in the future ( [[#Kooperman--2018|Kooperman et al., 2018]] ). But this effect is uncertain as an increase in leaf area index, and vegetation coverage could also result in overall larger water consumption ( [[#Mátyás--2014|Mátyás and Sun, 2014]] ; [[#Mankin--2019|Mankin et al., 2019]] ; [[#Teuling--2019|Teuling et al., 2019]] ), and there are also other CO <sub>2</sub> -related mechanisms that come into play (Cross-Chapter Box 5.1).

Various factors, such as extreme precipitation ( [[#Cho--2016|Cho et al., 2016]] ; [[#Archer--2018|Archer and Fowler, 2018]] ), glacier lake outbursts ( [[#Schneider--2014|Schneider et al., 2014]] ; [[#Schwanghart--2016|Schwanghart et al., 2016]] ), or dam breaks ( [[#Biscarini--2016|Biscarini et al., 2016]] ) can cause flash floods. Very intense rainfall, along with a high fraction of impervious surfaces can result in flash floods in urban areas ( [[#Hettiarachchi--2018|Hettiarachchi et al., 2018]] ). Because of this direct connection, changes in very intense precipitation can translate to changes in urban flood potential ( [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ), though there can be a spectrum of urban flood responses to this flood potential ( [[#Smith--2013|Smith et al., 2013]] ), as many factors, such as the overland flow rate and the design of urban ( [[#Falconer--2009|Falconer et al., 2009]] ) and storm water drainage systems ( [[#Maksimović--2009|Maksimović et al., 2009]] ), can play an important role. Nevertheless, changes in extreme precipitation are the main proxy for inferring changes in some types of flash floods, (which are addressed in [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ), given the relation between extreme precipitation and pluvial floods, the very limited literature on urban and pluvial floods (e.g., [[#Skougaard%20Kaspersen--2017|Skougaard Kaspersen et al., 2017]] ), and limitations of existing methodologies for assessing changes in floods ( [[#Archer--2016|Archer et al., 2016]] ).

In summary, there is not always a one-to-one correspondence between an extreme precipitation event and a flood event, or between changes in extreme precipitation and changes in floods, because floods are affected by many factors in addition to heavy precipitation ( ''high confidence'' ). Changes in extreme precipitation may be used as a proxy to infer changes in some types of flash floods that are more directly related to extreme precipitation ( ''high'' ''confidence'' ).

<div id="11.5.2" class="h2-container"></div>

<span id="observed-trends-2"></span>
=== 11.5.2 Observed Trends ===

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The SREX ( [[#Seneviratne--2012|Seneviratne et al., 2012]] ) assessed ''low confidence'' for observed changes in the magnitude or frequency of floods at the global scale. This assessment was confirmed by AR5 ( [[#Hartmann--2013|Hartmann et al., 2013]] ). The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) found increases in flood frequency and extreme streamflow in some regions, but decreases in other regions. While the number of studies on flood trends has increased since AR5, and there were also new analyses after the release of SR1.5 ( [[#Berghuijs--2017|Berghuijs et al., 2017]] ; [[#Blöschl--2019|Blöschl et al., 2019]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ), hydrological literature on observed flood changes is heterogeneous, focusing at regional and sub-regional basin scales, making it difficult to synthesize at the global and sometimes regional scales. The vast majority of studies focus on river floods using streamflow as a proxy, with limited attention to urban floods. Streamflow measurements are not evenly distributed over space, with gaps in spatial coverage, and their coverage in many regions of Africa, South America, and parts of Asia is poor (e.g., [[#Do--2017|Do et al., 2017]] ), leading to difficulties in detecting long-term changes in floods ( [[#Slater--2017|Slater and Villarini, 2017]] ). See also [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.5|Section 8.3.1.5]] .

Peak flow trends are characterized by high regional variability and lack overall statistical significance of a decrease or an increase over the globe as a whole. Of more than 3500 streamflow stations in the USA, central and Northern Europe, Africa, Brazil, and Australia, 7.1% stations showed a significant increase, and 11.9% stations showed a significant decrease in annual maximum peak flow during 1961–2005 ( [[#Do--2017|Do et al., 2017]] ). This is in direct contrast to the global and continental scale intensification of short-duration extreme precipitation ( [[#11.4.2|Section 11.4.2]] ). There may be some consistency over large regions (see [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ), in high streamflows (>90th percentile), including a decrease in some regions (e.g., in the Mediterranean) and an increase in others (e.g., northern Asia), but gauge coverage is often limited. On a continental scale, a decrease seems to dominate in Africa ( [[#Tramblay--2020|Tramblay et al., 2020]] ) and Australia ( [[#Ishak--2013|Ishak et al., 2013]] ; [[#Wasko--2019|Wasko and Nathan, 2019]] ), an increase in the Amazon ( [[#Barichivich--2018|Barichivich et al., 2018]] ), and trends are spatially variable in other continents (Q. [[#Zhang--2015b|]] [[#Zhang--2015|Zhang et al., 2015]] b ; [[#Bai--2016|Bai et al., 2016]] ; [[#Do--2017|Do et al., 2017]] ; [[#Hodgkins--2017|Hodgkins et al., 2017]] ). In Europe, flow trends have large spatial differences ( [[#Hall--2014|Hall et al., 2014]] ; [[#Mediero--2015|Mediero et al., 2015]] ; [[#Kundzewicz--2018|Kundzewicz et al., 2018]] ; [[#Mangini--2018|Mangini et al., 2018]] ), but there appears to be a pattern of increase in north-western Europe, and a decrease in southern and eastern Europe in annual peak flow during 1960–2000 ( [[#Blöschl--2019|Blöschl et al., 2019]] ). In North America, peak flow has increased in north-east USA and decreased in south-west USA ( [[#Peterson--2013b|Peterson et al., 2013b]] ; [[#Armstrong--2014|Armstrong et al., 2014]] ; [[#Mallakpour--2015|Mallakpour and Villarini, 2015]] ; [[#Archfield--2016|Archfield et al., 2016]] ; [[#Burn--2016|Burn and Whitfield, 2016]] ; [[#Wehner--2017|Wehner et al., 2017]] ; [[#Neri--2019|Neri et al., 2019]] ). There are important changes in the seasonality of peak flows in regions where snowmelt dominates, such as northern North America ( [[#Burn--2016|Burn and Whitfield, 2016]] ; [[#Dudley--2017|Dudley et al., 2017]] ) and Northern Europe ( [[#Blöschl--2017|Blöschl et al., 2017]] ), corresponding to strong winter and spring warming.

In summary, the seasonality of floods has changed in cold regions where snowmelt dominates the flow regime in response to warming ( ''high confidence'' ). There is ''low confidence'' about peak flow trends over past decades on the global scale '','' but there are regions experiencing increases, including parts of Asia, Southern South America, north-east USA, north-western Europe, and the Amazon, and regions experiencing decreases, including parts of the Mediterranean, Australia, Africa, and south-western USA.

<div id="11.5.3" class="h2-container"></div>

<span id="model-evaluation-2"></span>
=== 11.5.3 Model Evaluation ===

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Hydrological models used to simulate floods are structurally diverse ( [[#Dankers--2014|Dankers et al., 2014]] ; [[#Mateo--2017|Mateo et al., 2017]] ; [[#Şen--2018|Şen, 2018]] ), often requiring extensive calibration since sub-grid processes and land-surface properties need to be parametrized, irrespective of the spatial resolutions ( [[#Döll--2016|Döll et al., 2016]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ). The data used to drive and calibrate the models are usually of coarse resolution, necessitating the use of a wide variety of downscaling techniques ( [[#Muerth--2013|Muerth et al., 2013]] ). This adds uncertainty not only to the models but also to the reliability of the calibrations. The quality of the flood simulations also depends on the spatial scale, as flood processes are different for catchments of different sizes. It is more difficult to replicate flood processes for large basins, as water management and water use are often more complex for these basins.

Studies that use different regional hydrological models show a large spread in flood simulations ( [[#Dankers--2014|Dankers et al., 2014]] ; [[#Roudier--2016|Roudier et al., 2016]] ; [[#Trigg--2016|Trigg et al., 2016]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ). Regional models reproduce moderate and high flows reasonably well (0.02–0.1 flow annual exceedance probabilities), but there are large biases for the most extreme flows (0–0.02 annual flow exceedance probability), independent of the climatic and physiographic characteristics of the basins (S. [[#Huang--2017|Huang et al., 2017]] a). Global-scale hydrological models have even more challenges, as they struggle to reproduce the magnitude of the flood hazard ( [[#Trigg--2016|Trigg et al., 2016]] ). Also, the ensemble mean of multiple models does not perform better than individual models ( [[#Zaherpour--2018|Zaherpour et al., 2018]] ).

The use of hydrological models for assessing changes in floods, especially for future projections, adds another dimension of uncertainty on top of uncertainty in the driving climate projections, including emissions scenarios, and in the driving climate models (both RCMs and GCMs) ( [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Hundecha--2016|Hundecha et al., 2016]] ; [[#Krysanova--2017|Krysanova et al., 2017]] ). The differences in hydrological models ( [[#Roudier--2016|Roudier et al., 2016]] ; [[#Thober--2018|Thober et al., 2018]] ), as well as post-processing of climate model output for the hydrological models ( [[#Muerth--2013|Muerth et al., 2013]] ; [[#Maier--2018|Maier et al., 2018]] ), add to uncertainty for flood projections.

In summary, there is ''medium confidence'' that simulations for the most extreme flows by regional hydrological models can have large biases. Global-scale hydrological models still struggle with reproducing the magnitude of floods. Projections of future floods are hampered by these difficulties and cascading uncertainties, including uncertainties in emissions scenarios and the climate models that generate inputs.

<div id="11.5.4" class="h2-container"></div>

<span id="detection-and-attribution-event-attribution-2"></span>
=== 11.5.4 Detection and Attribution, Event Attribution ===

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There are very few studies focused on the attribution of long-term changes in floods, but there are studies on changes in flood events. Most of the studies focus on flash floods and urban floods, which are closely related to intense precipitation events ( [[#Hannaford--2015|Hannaford, 2015]] ). In other cases, event attribution focused on runoff using hydrological models, and examples include river basins in the UK ( [[#11.4.4|Section 11.4.4]] ; [[#Schaller--2016|Schaller et al., 2016]] ; [[#Kay--2018|Kay et al., 2018]] ), the Okavango River in Africa ( [[#Wolski--2014|Wolski et al., 2014]] ), and the Brahmaputra River in Bangladesh ( [[#Philip--2019|Philip et al., 2019]] ). Findings about anthropogenic influences vary between different regions and basins. For some flood events, the probability of high floods in the current climate is lower than in a climate without an anthropogenic influence ( [[#Wolski--2014|Wolski et al., 2014]] ), while in other cases anthropogenic influence leads to more intense floods ( [[#Cho--2016|Cho et al., 2016]] ; [[#Pall--2017|Pall et al., 2017]] ; [[#van%20der%20Wiel--2017|van der Wiel et al., 2017]] ; [[#Philip--2018a|Philip et al., 2018a]] ; [[#Teufel--2019|Teufel et al., 2019]] ). Factors such as land-cover change and river management can also increase the probability of high floods ( [[#Ji--2020|Ji et al., 2020]] ). These, along with model uncertainties and the lack of studies overall, suggest a ''low confidence'' in general statements to attribute changes in flood events to anthropogenic climate change. A few individual regions have been well studied, which allows for ''high confidence'' in the attribution of increased flooding in these cases. For example, flooding in the UK following increased winter precipitation ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Kay--2018|Kay et al., 2018]] ) can be attributed to anthropogenic climate change ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Vautard--2016|Vautard et al., 2016]] ; [[#Yiou--2017|Yiou et al., 2017]] ; [[#Otto--2018b|Otto et al., 2018b]] ).

Attributing changes in heavy precipitation to anthropogenic activities ( [[#11.4.4|Section 11.4.4]] ) cannot be readily translated to attributing changes in floods to human activities, because precipitation is only one of the multiple factors, albeit an important one, that affect floods. For example, [[#Teufel--2017|Teufel et al. (2017)]] showed that, while human influence increased the odds of the flood-producing rainfall for the 2013 Alberta flood in Canada, it was not detected to have influenced the probability of the flood itself. [[#Schaller--2016|Schaller et al. (2016)]] showed that human influence on the increase in the probability of heavy precipitation translated linearly into an increase in the resulting river flow of the Thames in the UK in winter 2014, but its contribution to the inundation was inconclusive.

[[#Gudmundsson--2021|Gudmundsson et al. (2021)]] compared the spatial pattern of the observed regional trends in high river flows (>90th percentile) over 1971–2010 with that simulated by global hydrological models. The hydrological models were driven by outputs of climate model simulations under all historical forcing and pre-industrial forcing conditions. They found complex spatial patterns of extreme river flow trends. They also found the observed spatial patterns of trends can be reproduced only if anthropogenic climate change is considered, and that simulated effects of water and land management cannot reproduce the observed spatial pattern of trends. As there is only one study and multiple caveats associated with the study, including relatively poor observational data coverage, there is ''low confidence'' about human influence on the changes in high river flows on the global scale.

In summary there is ''low confidence'' in the human influence on the changes in high river flows on the global scale. In general, there is ''low confidence'' in attributing changes in the probability or magnitude of flood events to human influence because of a limited number of studies, differences in the results of these studies and large modelling uncertainties.

<div id="11.5.5" class="h2-container"></div>

<span id="future-projections"></span>
=== 11.5.5 Future Projections ===

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The SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) stressed the low availability of studies on flood projections under different emissions scenarios, and concluded that there was ''low confidence'' in projections of flood events given the complexity of the mechanisms driving floods at the regional scale. The AR5 WGII report (Chapter 3, [[#Jimenez%20Cisneros--2014|Jimenez Cisneros et al., 2014]] ) assessed with ''medium confidence'' the pattern of future flood changes, including flood hazards increasing over about half of the globe (parts of southern and South East Asia, tropical Africa, north-east Eurasia, and South America) and flood hazards decreasing in other parts of the world, despite uncertainties in GCMs and their coupling to hydrological models. The SR1.5 (Chapter 3, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessed with ''medium confidence'' that global warming of 2°C would lead to an expansion of the fraction of global area affected by flood hazards, compared to conditions at 1.5°C of global warming, as a consequence of changes in heavy precipitation.

The majority of new studies that produce future flood projections based on hydrological models do not typically consider aspects that are also important to actual flood severity or damages, such as flood prevention measures ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Şen--2018|Şen, 2018]] ), flood control policies ( [[#Barraqué--2017|Barraqué, 2017]] ), and future changes in land cover (see also [[IPCC:Wg1:Chapter:Chapter-8#8.4.1.5|Section 8.4.1.5]] ). At the global scale, [[#Alfieri--2017|Alfieri et al. (2017)]] used downscaled projections from seven GCMs as input to drive a hydrodynamic model. They found successive increases in the frequency of high floods in all continents except Europe, associated with increasing levels of global warming (1.5°C, 2°C, 4°C). These results are supported by [[#Paltan--2018|Paltan et al. (2018)]] , who applied a simplified runoff aggregation model forced by outputs from four GCMs. S. [[#Huang--2018|]] [[#Huang--2018|Huang et al. (2018)]] used three hydrological models forced with bias-adjusted outputs from four GCMs to produce projections for four river basins including the Rhine, Upper Mississippi, Upper Yellow, and Upper Niger under 1.5°C, 2°C, and 3°C global warming. This study found diverse projections for different basins, including a shift towards earlier flooding for the Rhine and the Upper Mississippi, a substantial increase in flood frequency in the Rhine only under the 1.5°C and 2°C scenarios, and a decrease in flood frequency in the Upper Mississippi under all scenarios.

At the continental and regional scales, the projected changes in floods are uneven in different parts of the world, but there is a larger fraction of regions with an increase than with a decrease over the 21st century ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Dankers--2014|Dankers et al., 2014]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Döll--2018|Döll et al., 2018]] ). These results suggest ''medium confidence'' in flood trends at the global scale, but ''low confidence'' in projected regional changes. Increases in flood frequency or magnitude are identified for south-eastern and northern Asia and India ( ''high agreement'' across studies), eastern and tropical Africa, and the high latitudes of North America ( ''medium agreement'' ), while decreasing frequency or magnitude is found for central and eastern Europe and the Mediterranean ( ''high confidence'' ), and parts of South America, southern and central North America, and south-west Africa ( ''low confidence'' ) ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Dankers--2014|Dankers et al., 2014]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Döll--2018|Döll et al., 2018]] ). Over South America, most studies based on global and regional hydrological models show an increase in the magnitude and frequency of high flows in the western Amazon ( [[#Guimberteau--2013|Guimberteau et al., 2013]] ; [[#Langerwisch--2013|Langerwisch et al., 2013]] ; [[#Sorribas--2016|Sorribas et al., 2016]] ; [[#Zulkafli--2016|Zulkafli et al., 2016]] ) and the Andes ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Bozkurt--2018|Bozkurt et al., 2018]] ). [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] provides a detailed assessment of regional flood projections.

In summary, global hydrological models project a larger fraction of land areas to be affected by an increase in river floods than by a decrease in river floods ( ''medium confidence'' ). There is ''medium confidence'' that river floods will increase in the western Amazon, the Andes, and south-eastern and northern Asia. Regional changes in river floods are more uncertain than changes in pluvial floods because complex hydrological processes and forcings are involved, including land cover change and human water management.

<div id="11.6" class="h1-container"></div>

<span id="droughts-1"></span>
== 11.6 Droughts ==

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Droughts refer to periods of time with substantially below-average moisture conditions, usually covering large areas, during which limitations in water availability result in negative impacts for various components of natural systems and economic sectors ( [[#Wilhite--2017|Wilhite and Pulwarty, 2017]] ; [[#Ault--2020|Ault, 2020]] ). Depending on the variables used to characterize it and the systems or sectors being impacted, drought may be classified in different types (Figure 8.6 and Appendix Table 11.A.1) such as meteorological (precipitation deficits), agricultural (e.g., crop yield reductions or failure, often related to soil moisture deficits), ecological (related to plant water stress that causes e.g., tree mortality), or hydrological droughts (e.g., water shortage in streams or storages such as reservoirs, lakes, lagoons, and groundwater; see Glossary). The distinction of drought types is not absolute, as drought can affect different sub-domains of the Earth system concomitantly, but sometimes also asynchronously, including propagation from one drought type to another ( [[#Brunner--2019|Brunner and Tallaksen, 2019]] ). Because of this, drought cannot be characterized using a single universal definition ( [[#Lloyd-Hughes--2014|Lloyd-Hughes, 2014]] ) or directly measured based on a single variable (SREX Chapter 3; [[#Wilhite--2017|Wilhite and Pulwarty, 2017]] ). Drought can happen on a wide range of timescales – from ‘flash droughts’ on a scale of weeks, and characterized by a sudden onset and rapid intensification of drought conditions ( [[#Hunt--2014|Hunt et al., 2014]] ; [[#Otkin--2018|Otkin et al., 2018]] ; [[#Pendergrass--2020|Pendergrass et al., 2020]] ) to multi-year or decadal rainfall deficits – sometimes termed ‘megadroughts’ (see Glossary; [[#Ault--2014|Ault et al., 2014]] ; [[#Cook--2016b|Cook et al., 2016b]] ; [[#Garreaud--2017|Garreaud et al., 2017]] ). Droughts are often analysed using indices that are measures of drought severity, duration and frequency (Sections 8.3.1.6, 8.4.1.6, 12.3.2.6 and 12.3.2.7, and Table 11.A.1). There are many drought indices published in the scientific literature, as also highlighted in SREX (SREX Chapter 3). These can range from anomalies in single variables (e.g., precipitation, soil moisture, runoff, evapotranspiration) to indices combining different atmospheric variables.

This assessment is focused on changes in physical conditions and metrics of direct relevance to droughts: (i) precipitation deficits; (ii) excess of atmospheric evaporative demand (AED); (iii) soil moisture deficits; (iv) hydrological deficits; and e) atmospheric-based indices combining precipitation and AED (Table 11.A.1). In the regional tables ( [[#11.9|Section 11.9]] ), the assessment is structured by drought types, addressing: (i) meteorological, (ii) agricultural and ecological, and (iii) hydrological droughts. Note that the latter two assessments directly inform the [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] assessment on projected regional changes in these climatic impact-drivers ( [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ). The text refers to AR6 region acronyms ( [[#11.9|Section 11.9]] , and see [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] ).

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<span id="mechanisms-and-drivers-3"></span>
=== 11.6.1 Mechanisms and Drivers ===

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Similar to many other extreme events, droughts occur as a combination of thermodynamic and dynamic processes (Box 11.1). Thermodynamic processes contributing to drought, which are modified by greenhouse gas forcing both at global and regional scales, are mostly related to heat and moisture exchanges, and are also partly modulated by plant coverage and physiology. They affect, for instance, atmospheric humidity, temperature, and radiation, which in turn affect precipitation and/or evapotranspiration in some regions and time frames. However, dynamic processes are particularly important to explain drought variability on different time scales, from a few weeks (flash droughts) to multiannual (megadroughts). There is ''low confidence'' in the effects of greenhouse gas forcing on changes in atmospheric dynamic ( [[IPCC:Wg1:Chapter:Chapter-2#2.4|Section 2.4]] ; [[IPCC:Wg1:Chapter:Chapter-4#4.3.3|Section 4.3.3]] ), and on associated changes in drought occurrence. Thermodynamic processes are thus the main driver of drought changes in a warming climate ( ''hig'' ''h confidence'' ).

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<span id="precipitation-deficits"></span>
==== 11.6.1.1 Precipitation Deficits ====

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Lack of precipitation is generally the main factor controlling drought onset. There is ''high confidence'' that atmospheric dynamics, which vary on interannual, decadal and longer time scales, is the dominant contributor to variations in precipitation deficits in the majority of world regions ( [[#Dai--2013|Dai, 2013]] ; [[#Miralles--2014b|Miralles et al., 2014b]] ; [[#Seager--2014|Seager and Hoerling, 2014]] ; [[#Burgman--2015|Burgman and Jang, 2015]] ; [[#Dong--2015|Dong and Dai, 2015]] ; [[#Schubert--2016|Schubert et al., 2016]] ; [[#Raymond--2018|Raymond et al., 2018]] ; [[#Baek--2019|Baek et al., 2019]] ; [[#Drumond--2019|Drumond et al., 2019]] ; [[#Herrera-Estrada--2019|Herrera-Estrada et al., 2019]] ; [[#Gimeno--2020|Gimeno et al., 2020]] ; [[#Mishra--2020|Mishra, 2020]] ). Precipitation deficits are driven by dynamic mechanisms taking place on different spatial scales, including synoptic processes – atmospheric rivers and extratropical cyclones, blocking and ridges ( [[#11.7|Section 11.7]] ; [[#Sousa--2017|Sousa et al., 2017]] ), dominant large-scale circulation patterns ( [[#Kingston--2015|Kingston et al., 2015]] ), and global ocean–atmosphere coupled patterns such as inter-decadal Pacific Oscillation (IPO), Atlantic Multi-decadal Oscillation (AMO) and El Niño–Southern Oscillation (ENSO; [[#Dai--2017|Dai and Zhao, 2017]] ). These various mechanisms occur on different scales, are not independent, and substantially interact with one another. Also regional moisture recycling and land–atmosphere feedbacks play an important role for some precipitation anomalies (see below).

There is ''high confidence'' that land–atmosphere feedbacks play a substantial or dominant role in affecting precipitation deficits in someregions (SREX, Chapter 3; [[#Koster--2011|Koster et al., 2011]] ; [[#Gimeno--2012|Gimeno et al., 2012]] ; [[#Taylor--2012|Taylor et al., 2012]] ; [[#Guillod--2015|Guillod et al., 2015]] ; [[#Tuttle--2016|Tuttle and Salvucci, 2016]] ; [[#Santanello%20Jr.--2018|Santanello Jr. et al., 2018]] ; [[#Haslinger--2019|Haslinger et al., 2019]] ; [[#Herrera-Estrada--2019|Herrera-Estrada et al., 2019]] ). The sign of the feedbacks can be either positive or negative, as well as local or non-local ( [[#Taylor--2012|Taylor et al., 2012]] ; [[#Guillod--2015|Guillod et al., 2015]] ; [[#Tuttle--2016|Tuttle and Salvucci, 2016]] ). Earth system models (ESMs) tend to underestimate non-local negative soil-moisture–precipitation feedbacks ( [[#Taylor--2012|Taylor et al., 2012]] ) and also show high variations in their representation in some regions ( [[#Berg--2017b|Berg et al., 2017b]] ). Soil-moisture–precipitation feedbacks contribute to changes in precipitation in climate model projections in some regions, but ESMs display substantial uncertainties in their representation, and there is thus only ''low confidence'' in these contributions ( [[#Berg--2017b|Berg et al., 2017b]] ; [[#Vogel--2017|Vogel et al., 2017]] , 2018).

<div id="11.6.1.2" class="h3-container"></div>

<span id="atmospheric-evaporative-demand"></span>
==== 11.6.1.2 Atmospheric Evaporative Demand ====

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Atmospheric evaporative demand (AED) quantifies the maximum amount of actual evapotranspiration (ET) that can happen from land surfaces if they are not limited by water availability (Table 11.A.1). AED is affected by radiative and aerodynamic components. For this reason, the atmospheric dryness, often quantified with the relative humidity or the vapour pressure deficit (VPD), is not equivalent to the AED, as other variables are also highly relevant, including solar radiation and wind speed ( [[#Hobbins--2012|Hobbins et al., 2012]] ; [[#McVicar--2012a|McVicar et al., 2012a]] ; [[#Sheffield--2012|Sheffield et al., 2012]] ). AED can be estimated using different methods ( [[#McMahon--2013|McMahon et al., 2013]] ), and those solely based on air temperature (e.g., Hargreaves, Thornthwaite) usually overestimate it in terms of magnitude and temporal trends ( [[#Sheffield--2012|Sheffield et al., 2012]] ), in particular, in the context of substantial background warming. Physically-based combination methods such as the Penman-Monteith equation are more adequate and recommended since 1998 by the United Nations Food and Agriculture Oganization ( [[#Pereira--2015|Pereira et al., 2015]] ). For this reason, the assessment of this Chapter, when considering atmospheric-based drought indices, only includes AED estimates using the latter (see also [[#11.9|Section 11.9]] ). AED is generally higher than ET, since AED represents an upper bound for ET. Hence, an AED increase does not necessarily lead to increased ET ( [[#Milly--2016|Milly and Dunne, 2016]] ), in particular under drought conditions given soil moisture limitation ( [[#Bonan--2014|Bonan et al., 2014]] ; [[#Berg--2016|Berg et al., 2016]] ; [[#Konings--2017|Konings et al., 2017]] ; [[#Stocker--2018|Stocker et al., 2018]] ). In general, AED is highest in regions where ET is lowest (e.g., desert areas), further illustrating the decoupling between the two variables under limited soil moisture.

The influence of AED on drought depends on the drought type, background climate, the environmental conditions and the moisture availability ( [[#Hobbins--2016|Hobbins et al., 2016]] , 2017; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ). This influence also includes effects not related to increased ET. Under low soil moisture conditions, increased AED increases plant stress, enhancing the severity of agricultural and ecological droughts ( [[#Williams--2013|Williams et al., 2013]] ; [[#Allen--2015|Allen et al., 2015]] ; [[#McDowell--2016|McDowell et al., 2016]] ; [[#Grossiord--2020|Grossiord et al., 2020]] ). Moreover, high VPD impacts overall plant physiology; it affects the leaf and xylem safety margins, and decreases the sap velocity and plant hydraulic conductance ( [[#Fontes--2018|Fontes et al., 2018]] ). VPD also affects the plant metabolism of carbon and, if prolonged, it may cause plant mortality via carbon starvation ( [[#Breshears--2013|Breshears et al., 2013]] ; [[#Hartmann--2015|Hartmann, 2015]] ). Drought projections based exclusively on AED metrics overestimate changes in soil moisture and runoff deficits. Nevertheless, AED also directly impacts hydrological drought, as ET from surface waters is not limited ( [[#Wurbs--2014|Wurbs and Ayala, 2014]] ; [[#Friedrich--2018|Friedrich et al., 2018]] ; [[#Hogeboom--2018|Hogeboom et al., 2018]] ; K. [[#Xiao--2018|]] [[#Xiao--2018|Xiao et al., 2018]] ), and this effect increases under climate change projections (W. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ; [[#Althoff--2020|Althoff et al., 2020]] ). In addition, high AED increases crop water consumptions in irrigated lands ( [[#García-Garizábal--2014|García-Garizábal et al., 2014]] ), contributing to intensifying hydrological droughts downstream ( [[#Fazel--2017|Fazel et al., 2017]] ; [[#Vicente-Serrano--2017|Vicente-Serrano et al., 2017]] ).

On subseasonal to decadal scales, temporal variations in AED are strongly controlled by circulation variability ( [[#Williams--2014|Williams et al., 2014]] ; [[#Chai--2018|Chai et al., 2018]] ; [[#Martens--2018|Martens et al., 2018]] ), but thermodynamic processes also play a fundamental role and, under human-induced climate change, dominate the changes in AED. Atmospheric warming due to increased atmospheric CO <sub>2</sub> concentrations increases AED by means of enhanced VPD in the absence of other influences ( [[#Scheff--2015|Scheff and Frierson, 2015]] ). Because of the greater warming over land than over oceans (Sections 2.3.1.1 and 11.3), the saturation pressure of water vapour increases more over land than over oceans; oceanic air masses advected over land thus contain insufficient water vapour to keep pace with the greater increase in saturation vapour pressure over land ( [[#Sherwood--2014|Sherwood and Fu, 2014]] ; [[#Byrne--2018|Byrne and O’Gorman, 2018]] ; [[#Findell--2019|Findell et al., 2019]] ). Land–atmosphere feedbacks are also important in affecting atmospheric moisture content and temperature, with resulting effects on relative humidity and VPD (Box 11.1; [[#Berg--2016|Berg et al., 2016]] ; [[#Haslinger--2019|Haslinger et al., 2019]] ; S. [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ).

<div id="11.6.1.3" class="h3-container"></div>

<span id="soil-moisture-deficits"></span>
==== 11.6.1.3 Soil Moisture Deficits ====

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Soil moisture shows an important correlation with precipitation variability ( [[#Khong--2015|Khong et al., 2015]] ; [[#Seager--2019|Seager et al., 2019]] ), but ET also plays a substantial role in further depleting moisture from soils, in particular in humid regions during periods of precipitation deficits ( [[#Teuling--2013|Teuling et al., 2013]] ; [[#Padrón--2020|Padrón et al., 2020]] ). In addition, soil moisture plays a role in drought self-intensification under dry conditions in which ET is decreased and leads to higher AED ( [[#Miralles--2019|Miralles et al., 2019]] ), an effect that can also contribute to triggering flash droughts ( [[#Otkin--2016|Otkin et al., 2016]] , 2018; [[#DeAngelis--2020|DeAngelis et al., 2020]] ; [[#Pendergrass--2020|Pendergrass et al., 2020]] ). If soil moisture becomes limited, ET is reduced, which may decrease the rate of soil drying, but can also lead to further atmospheric dryness through various feedback loops ( [[#Seneviratne--2010|Seneviratne et al., 2010]] ; [[#Miralles--2014a|Miralles et al., 2014a]] , 2019; [[#Teuling--2018|Teuling, 2018]] ; [[#Vogel--2018|Vogel et al., 2018]] ; S. [[#Zhou--2019|]] [[#Zhou--2019|Zhou et al., 2019]] ; [[#Liu--2020|Liu et al., 2020]] ). The process is complex since vegetation cover plays a role in modulating albedo and in providing access to deeper stores of water (both in the soil and groundwater). Also, changes in land cover and in plant phenology may alter ET ( [[#Sterling--2013|Sterling et al., 2013]] ; [[#Woodward--2014|Woodward et al., 2014]] ; [[#Frank--2015|Frank et al., 2015]] ; [[#Döll--2016|Döll et al., 2016]] ; [[#Ukkola--2016|Ukkola et al., 2016]] ; [[#Trancoso--2017|Trancoso et al., 2017]] ; [[#Hao--2019|Hao et al., 2019]] ; [[#Lian--2020|Lian et al., 2020]] ). Snow depth has strong and direct impacts on soil moisture in many systems ( [[#Gergel--2017|Gergel et al., 2017]] ; [[#Williams--2020|Williams et al., 2020]] ).

Soil moisture directly affects plant water stress and ET. Soil moisture is the primary factor that controls xylem hydraulic conductance – that is, water uptake in plants ( [[#Sperry--2016|Sperry et al., 2016]] ; [[#Hayat--2019|Hayat et al., 2019]] ; X. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ). For this reason, soil moisture deficits are the main driver of xylem embolism, the primary cause of plant mortality ( [[#Anderegg--2012|Anderegg et al., 2012]] , 2016; [[#Rowland--2015|Rowland et al., 2015]] ). Also carbon assimilation by plants strongly depends on soil moisture ( [[#Hartzell--2017|Hartzell et al., 2017]] ), with implications for carbon starvation and plant dying if soil moisture deficits are prolonged ( [[#Sevanto--2014|Sevanto et al., 2014]] ). These mechanisms explain that soil moisture deficits are usually more relevant than AED excess to explain gross primary production anomalies and vegetation stress, mostly in sub-humid and semi-arid regions ( [[#Stocker--2018|Stocker et al., 2018]] ; [[#Liu--2020|Liu et al., 2020]] ). High CO <sub>2</sub> concentrations are shown to potentially decrease plant ET and increase plant water-use efficiency, affecting soil moisture levels, but this effect interacts with other CO <sub>2</sub> physiological and radiative effects ( [[#11.6.5.2|Section 11.6.5.2]] and Cross-Chapter Box 5.1), and has less relevance under low soil moisture ( [[#Morgan--2011|Morgan et al., 2011]] ; Z. [[#Xu--2016|]] [[#Xu--2016|Xu et al., 2016]] ; [[#Nackley--2018|Nackley et al., 2018]] ; [[#Dikšaitytė--2019|Dikšaitytė et al., 2019]] ). ESMs represent both surface (around 10cm) and total column soil moisture, whereby total soil moisture is of more direct relevance for root water uptake, in particular by trees. There is evidence that surface soil moisture projections are substantially drier than total soil moisture projections, and may overestimate drying of relevance for most vegetation ( [[#Berg--2017a|Berg et al., 2017a]] ).

<div id="11.6.1.4" class="h3-container"></div>

<span id="hydrological-deficits"></span>
==== 11.6.1.4 Hydrological Deficits ====

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Drivers of streamflow and surface water deficits are complex and strongly depend on the hydrological system analysed (e.g., streamflows in the headwaters, medium course of the rivers, groundwater, highly regulated hydrological basins). Soil hydrological processes, which control the propagation of meteorological droughts throughout different parts of the hydrological cycle ( [[#Van%20Loon--2012|Van Loon and Van Lanen, 2012]] ), are spatially and temporally complex ( [[#Herrera-Estrada--2017|Herrera-Estrada et al., 2017]] ; S. [[#Huang--2017|Huang et al., 2017]] b) and difficult to quantify ( [[#Van%20Lanen--2016|Van Lanen et al., 2016]] ; [[#Apurv--2017|Apurv et al., 2017]] ; [[#Caillouet--2017|Caillouet et al., 2017]] ; [[#Konapala--2017|Konapala and Mishra, 2017]] ; [[#Hasan--2019|Hasan et al., 2019]] ). The physiographic characteristics of the basins also affect how droughts propagate throughout the hydrological cycle ( [[#Van%20Loon--2012|Van Loon and Van Lanen, 2012]] ; [[#Van%20Lanen--2013|Van Lanen et al., 2013]] ; [[#Van%20Loon--2015|Van Loon, 2015]] ; [[#Konapala--2020|Konapala and]] [[#Mishra--2020|Mishra, 2020]] ; Veettil and [[#Mishra--2020|Mishra, 2020]] ). In addition, the assessment of groundwater deficits is very difficult given the complexity of processes that involve natural and human-driven feedbacks and interactions with the climate system ( [[#Taylor--2013|Taylor et al., 2013]] ). Streamflow and surface water deficits are affected by land cover, groundwater and soil characteristics ( [[#Van%20Lanen--2013|Van Lanen et al., 2013]] ; [[#Van%20Loon--2015|Van Loon and Laaha, 2015]] ; [[#Barker--2016|Barker et al., 2016]] ; [[#Tijdeman--2018|Tijdeman et al., 2018]] ), as well as human activities (water management and demand, damming) and land-use changes ( [[#11.6.4.3|Section 11.6.4.3]] ; [[#Van%20Loon--2016|Van Loon et al., 2016]] ; [[#He--2017|He et al., 2017]] ; [[#Veldkamp--2017|Veldkamp et al., 2017]] ; J. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ; Y. [[#Xu--2019|]] [[#Xu--2019|Xu et al., 2019]] ; [[#Jehanzaib--2020|Jehanzaib et al., 2020]] ). Finally, snow and glaciers are relevant for water resources in some regions. For instance, warming affects snowpack levels ( [[#Dierauer--2019|Dierauer et al., 2019]] ; [[#Huning--2020|Huning and AghaKouchak, 2020]] ), as well as the timing of snow melt, thus potentially affecting the seasonality and magnitude of low flows ( [[#Barnhart--2016|Barnhart et al., 2016]] ).

<div id="11.6.1.5" class="h3-container"></div>

<span id="atmospheric-based-drought-indices"></span>
==== 11.6.1.5 Atmospheric-based Drought Indices ====

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Given the difficulties of drought quantification and data constraints, atmospheric-based drought indices combining both precipitation and AED have been developed, as they can be derived from meteorological data that is available in most regions (with few exceptions). These demand/supply indices are not intended to be metrics of soil moisture, streamflow or vegetation water stress. Because of their reliance on precipitation and AED, they are mostly related to the actual water balance in humid regions, in which ET is not limited by soil moisture and tends towards AED. In water-limited regions and in dry periods everywhere, they constitute an upper bound for overall water-balance deficits (e.g., of surface waters) but are also related to conditions conducive to vegetation stress, particularly under soil moisture limitation ( [[#11.6.1.2|Section 11.6.1.2]] ).

Although there are many atmospheric-based drought indices, two are assessed in this chapter: the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The PDSI has been widely used to monitor and quantify drought severity ( [[#Dai--2018|Dai et al., 2018]] ), but is affected by some constraints (SREX Chapter 3; [[#Mukherjee--2018a|Mukherjee et al., 2018a]] ). Although the calculation of the PDSI is based on a soil water budget, the PDSI is essentially a climate drought index that mostly responds to the precipitation and the AED ( [[#van%20der%20Schrier--2013|van der Schrier et al., 2013]] ; [[#Vicente-Serrano--2015|Vicente-Serrano et al., 2015]] ; [[#Dai--2018|Dai et al., 2018]] ). The SPEI also combines precipitation and AED, being equally sensitive to these two variables ( [[#Vicente-Serrano--2015|Vicente-Serrano et al., 2015]] ). The SPEI is more sensitive to AED than the PDSI ( [[#Cook--2014a|Cook et al., 2014a]] ; [[#Vicente-Serrano--2015|Vicente-Serrano et al., 2015]] ), although under humid and normal precipitation conditions, the effects of AED on the SPEI are small ( [[#Tomas-Burguera--2020|Tomas-Burguera et al., 2020]] ). Given the limitations associated with temperature-based AED estimates ( [[#11.6.1.2|Section 11.6.1.2]] ), only studies using the Penman-Monteith-based SPEI and PDSI (hereafter SPEI-PM and PDSI-PM) are considered in this assessment and in the regional tables in [[#11.9|Section 11.9]] .

<div id="11.6.1.6" class="h3-container"></div>

<span id="relation-of-assessed-variables-and-metrics-for-changes-in-different-drought-types"></span>
==== 11.6.1.6 Relation of Assessed Variables and Metrics for Changes in Different Drought Types ====

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This Chapter assesses changes in meteorological drought, agricultural and ecological droughts, and hydrological droughts. Precipitation-based indices are used for the estimation of changes in meteorological droughts, such as the Standardized Precipitation Index (SPI) and the number of consecutive dry days (CDD). Changes in total soil moisture and soil moisture-based drought events are used for the estimation of changes in agricultural and ecological droughts, complemented by changes in surface soil moisture, water-balance estimates (precipitation minus ET), and SPEI-PM and PDSI-PM. For hydrological droughts, changes in low flows are assessed, sometimes complemented by changes in mean streamflow.

In summary, different drought types exist and they are associated with different impacts and respond differently to increasing greenhouse gas concentrations. Precipitation deficits and changes in evapotranspiration govern net water availability. A lack of sufficient soil moisture, sometimes amplified by increased atmospheric evaporative demand, result in agricultural and ecological drought. Lack of runoff and surface water result in hydrological drought. Drought events are the result of dynamic and/or thermodynamic processes, with thermodynamic processes being the main driver of drought changes under human-induced climate change ( ''hig'' ''h confidence'' ).

<div id="11.6.2" class="h2-container"></div>

<span id="observed-trends-3"></span>
=== 11.6.2 Observed Trends ===

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Evidence on observed drought trends was limited at the time of SREX (Chapter 3) and AR5 (Chapter 2). The SREX concluded: ‘There is ''medium confidence'' that since the 1950s some regions of the world have experienced a trend to more intense and longer droughts, in particular in southern Europe and west Africa, but in some regions droughts have become less frequent, less intense, or shorter, for example, in Central North America and north-western Australia.’ The assessment at the time did not distinguish between different drought types. This Chapter includes numerous updates on observed drought trends, associated with extensive new literature and longer datasets since AR5.

<div id="11.6.2.1" class="h3-container"></div>

<span id="precipitation-deficits-1"></span>
==== 11.6.2.1 Precipitation Deficits ====

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Strong precipitation deficits have been recorded in recent decades in the Amazon (2005, 2010), south-western China (2009–2010), south-western North America (2011–2014), Australia (1997–2009), California (2014), the middle East (2012–2016), Chile (2010–2015), the Great Horn of Africa (2011), among others ( [[#van%20Dijk--2013|van Dijk et al., 2013]] ; [[#Mann--2015|Mann and Gleick, 2015]] ; [[#Rowell--2015|Rowell et al., 2015]] ; [[#Marengo--2016|Marengo and Espinoza, 2016]] ; [[#Dai--2017|Dai and Zhao, 2017]] ; [[#Garreaud--2017|Garreaud et al., 2017]] , 2020; [[#Marengo--2017|Marengo et al., 2017]] ; [[#Brito--2018|Brito et al., 2018]] ; [[#Cook--2018|Cook et al., 2018]] ). Global studies generally show no significant trends in SPI time series ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ; [[#Spinoni--2014|Spinoni et al., 2014]] ), and in derived drought frequency and severity data ( [[#Spinoni--2019|Spinoni et al., 2019]] ), with very few regional exceptions ( [[#11.9|Section 11.9]] and Figure 11.17). Long-term decreases in precipitation are found in some AR6 regions in Africa (Central Africa and East Southern Africa), and several regions in South America (North-Eastern South America, South American Monsoon, South-Western South America, and Southern South America) ( [[#11.9|Section 11.9]] ). Evidence of precipitation-based drying trends is also found in Western Africa, consistent with studies based on CDD trends (Figure 11.17; [[#Chaney--2014|Chaney et al., 2014]] ; [[#Donat--2014b|Donat et al., 2014b]] ; [[#Barry--2018|Barry et al., 2018]] ; [[#Dunn--2020|Dunn et al., 2020]] ), however, there is a partial recovery of the rainfall trends since the 1980s in this region ( [[IPCC:Wg1:Chapter:Chapter-10#10.4.2.1|Section 10.4.2.1]] ). Some AR6 regions show a decrease in meteorological drought, including Northern Australia, Central Australia, Northern Europe and Central North America ( [[#11.9|Section 11.9]] ). Other regions either do not show substantial trends in long-term meteorological drought, or they display mixed signals depending on the considered time frame and sub-regions, such as in Southern Australia ( [[#Gallant--2013|Gallant et al., 2013]] ; [[#Delworth--2014|Delworth and Zeng, 2014]] ; [[#Alexander--2017|Alexander and Arblaster, 2017]] ; [[#Spinoni--2019|Spinoni et al., 2019]] ; [[#Dunn--2020|Dunn et al., 2020]] ; [[#Rauniyar--2020|Rauniyar and Power, 2020]] ) and the Mediterranean ( [[#Camuffo--2013|Camuffo et al., 2013]] ; [[#Gudmundsson--2016|Gudmundsson and Seneviratne, 2016]] ; [[#Spinoni--2017|Spinoni et al., 2017]] ; [[#Stagge--2017|Stagge et al., 2017]] ; [[#Caloiero--2018|Caloiero et al., 2018]] ; [[#Peña-Angulo--2020b|Peña-Angulo et al., 2020b]] ; see also [[#11.9|Section 11.9]] and Atlas.8.2).

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[[File:a97af5c3786b1cd60461310b16197a9a IPCC_AR6_WGI_Figure_11_17.png]]

'''Figure 11.17 |''' '''Observed linear trend for (a) consecutive dry days (CDD) during 1960–2018, (b) standardized precipitation index (SPI) and (c) standardized precipitation-evapotranspiration index (SPEI) dur''' ing 1951–2016. CDD data are from the HadEx3 dataset ( [[#Dunn--2020|Dunn et al., 2020]] ), trend calculation of CDD as in Figure 11.9. Drought severity is estimated using 12-month SPI (SPI-12) and 12-month SPEI (SPEI-12). SPI and SPEI datasets are from [[#Spinoni--2019|Spinoni et al. (2019)]] . The threshold to identify drought episodes was set at -1 SPI/SPEI units. Areas without sufficient data are shown in grey. No overlay indicates regions where the trends are significant at the p = 0.1 level. Crosses indicate regions where trends are not significant. For details on the methods see Supplementary Material 11.SM.2. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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<span id="atmospheric-evaporative-demand-1"></span>
==== 11.6.2.2 Atmospheric Evaporative Demand ====

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In several regions, AED increases have intensified recent drought events ( [[#Williams--2014|Williams et al., 2014]] , 2020; [[#Seager--2015b|Seager et al., 2015b]] ; [[#Basara--2019|Basara et al., 2019]] ; [[#García-Herrera--2019|García-Herrera et al., 2019]] ), enhanced vegetation stress ( [[#Allen--2015|Allen et al., 2015]] ; [[#Sanginés%20de%20Cárcer--2018|Sanginés de Cárcer et al., 2018]] ; [[#Yuan--2019|Yuan et al., 2019]] ), or contributed to the depletion of soil moisture or runoff through enhanced ET ( ''high confidence'' ) ( [[#Teuling--2013|Teuling et al., 2013]] ; [[#Padrón--2020|Padrón et al., 2020]] ). Trends in pan evaporation measurements and Penman-Monteith AED estimates provide an indication of possible trends in the influence of AED on drought. Given the observed global temperature increases (Sections 2.3.1.1 and 11.3) and dominant decrease in relative humidity over land areas ( [[#Simmons--2010|Simmons et al., 2010]] ; [[#Willett--2014|Willett et al., 2014]] ), VPD has increased globally ( [[#Barkhordarian--2019|Barkhordarian et al., 2019]] ; [[#Yuan--2019|Yuan et al., 2019]] ). Pan evaporation has increased as a consequence of VPD changes in several AR6 regions, such as East Asia ( [[#Li--2013|Li et al., 2013]] ; Z. [[#Sun--2018|Sun et al., 2018]] ; M.-Z. [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ), Western and Central Europe ( [[#Mozny--2020|Mozny et al., 2020]] ), the Mediterranean, ( [[#Azorin-Molina--2015|Azorin-Molina et al., 2015]] ) and Central and Southern Australia ( [[#Stephens--2018|Stephens et al., 2018]] ). Nevertheless, there is an important regional variability in observed trends, and in other AR6 regions pan evaporation has decreased – for example, in North Central America ( [[#Breña-Naranjo--2017|Breña-Naranjo et al., 2017]] ) and in the Tibetan Plateau ( [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|C. Zhang et al., 2018]] )). Physical models also show an important regional diversity, with an increase in New Zealand ( [[#Salinger--2014|Salinger and Porteous, 2014]] ) and the Mediterranean ( [[#Gocic--2014|Gocic and Trajkovic, 2014]] ; [[#Azorin-Molina--2015|Azorin-Molina et al., 2015]] ; [[#Piticar--2016|Piticar et al., 2016]] ), a decrease in South Asia ( [[#Jhajharia--2015|Jhajharia et al., 2015]] ), and strong spatial variability in North America ( [[#Seager--2015b|Seager et al., 2015b]] ). This variability is driven by the role of other meteorological variables affecting AED. Changes in solar radiation as a consequence of solar dimming and brightening may affect trends ( [[IPCC:Wg1:Chapter:Chapter-7#7.2.2.2|Section 7.2.2.2]] ; [[#Kambezidis--2012|Kambezidis et al., 2012]] ; [[#Wang--2014|Wang and Yang, 2014]] ; [[#Sanchez-Lorenzo--2015|Sanchez-Lorenzo et al., 2015]] ). Wind speed is also relevant ( [[#McVicar--2012b|McVicar et al., 2012b]] ), and studies suggest a reduction of the wind speed in some regions (Z. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] b) that could compensate the role of the VPD increase. Nevertheless, the VPD trend seems to dominate the overall AED trends, compared to the effects of trends in wind speed and solar radiation ( [[#Wang--2012|Wang et al., 2012]] ; [[#Park%20Williams--2017|Park Williams et al., 2017]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ).

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<span id="soil-moisture-deficits-1"></span>
==== 11.6.2.3 Soil Moisture Deficits ====

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There are limited long-term measurements of soil moisture from ground observations ( [[#Dorigo--2011|Dorigo et al., 2011]] ; [[#Qiu--2016|Qiu et al., 2016]] ; [[#Quiring--2016|Quiring et al., 2016]] ), which impedes their use in the analysis of trends. Among the few existing observational studies covering at least two decades, several studies have investigated trends in ground soil moisture in East Asia ( [[#11.9|Section 11.9]] ; [[#Chen--2015b|Chen and Sun, 2015b]] ; [[#Liu--2015|Liu et al., 2015]] ; [[#Qiu--2016|Qiu et al., 2016]] ). Alternatively, microwave-based satellite measurements of surface soil moisture have also been used to analyse trends ( [[#Dorigo--2012|Dorigo et al., 2012]] ; [[#Jia--2018|Jia et al., 2018]] ). Although there is regional evidence that microwave-based soil moisture estimates can capture well drying trends in comparison with ground soil moisture observations ( [[#Jia--2018|Jia et al., 2018]] ), there is only ''medium confidence'' in the derived trends, since satellite soil moisture data are affected by inhomogeneities ( [[#Dorigo--2015|Dorigo et al., 2015]] ; [[#Rodell--2018|Rodell et al., 2018]] ; [[#Preimesberger--2021|Preimesberger et al., 2021]] ). Furthermore, microwave-based satellites only sense surface soil moisture, which differs from root-zone soil moisture ( [[#Berg--2017a|Berg et al., 2017a]] ), although relationships can be derived between the two ( [[#Brocca--2011|Brocca et al., 2011]] ). Several studies have also analysed long-term soil moisture time series from observation-driven land-surface or hydrological models, including land-based reanalysis products ( [[#Albergel--2013|Albergel et al., 2013]] ; [[#Jia--2018|Jia et al., 2018]] ; [[#Gu--2019b|Gu et al., 2019b]] ; [[#Markonis--2021|Markonis et al., 2021]] ). Such models have also been used to assess changes in land water availability, estimated as precipitation minus ET, which is equal to the sum of soil moisture and runoff ( [[#Greve--2014|Greve et al., 2014]] ; [[#Padrón--2020|Padrón et al., 2020]] ).

Overall, evidence from global studies suggests that several land regions have been affected by increased soil moisture drying or water balance drying in past decades, despite some spread among products ( [[#Albergel--2013|Albergel et al., 2013]] ; [[#Greve--2014|Greve et al., 2014]] ; [[#Gu--2019b|Gu et al., 2019b]] ; [[#Padrón--2020|Padrón et al., 2020]] ). Drying has not only occurred in dry regions but also in humid regions ( [[#Greve--2014|Greve et al., 2014]] ). Some studies have specifically addressed changes in soil moisture at regional scale ( [[#11.9|Section 11.9]] ). For AR6 regions, several studies suggest an increase in the frequency and areal extent of soil moisture deficits, with examples in East Asia ( [[#Cheng--2015|Cheng et al., 2015]] ; Y. [[#Qin--2015|]] [[#Qin--2015|Qin et al., 2015]] ; [[#Jia--2018|Jia et al., 2018]] ), Western and Central Europe ( [[#Trnka--2015b|Trnka et al., 2015b]] ), and the Mediterranean ( [[#Hanel--2018|Hanel et al., 2018]] ; [[#Moravec--2019|Moravec et al., 2019]] ; [[#Markonis--2021|Markonis et al., 2021]] ). Nonetheless, some analyses also show no long-term trends in soil drying in some AR6 regions – for example, in Eastern North America ( [[#Park%20Williams--2017|Park Williams et al., 2017]] ) and Central North America ( [[#Seager--2019|Seager et al., 2019]] ), as well as in North Eastern Africa ( [[#Kew--2021|Kew et al., 2021]] ). The soil moisture drying trends identified in both global and regional studies are generally related to increases in ET (associated with higher AED) rather than decreases in precipitation, as identified on global land for trends in water balance in the dry season ( [[#Padrón--2020|Padrón et al., 2020]] ), as well as for some regions ( [[#Teuling--2013|Teuling et al., 2013]] ; [[#Cheng--2015|Cheng et al., 2015]] ; [[#Trnka--2015a|Trnka et al., 2015a]] ; [[#van%20Der%20Linden--2019|van Der Linden et al., 2019]] ; X. [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ).

Evidence from observed or observations-derived trends in soil moisture and precipitation minus ET, are combined with evidence from SPEI and PDSI-PM studies to derive regional assessments of changes in agricultural and ecological droughts ( [[#11.9|Section 11.9]] ). This assessment is summarized in [[#11.6.2.6|Section 11.6.2.6]] .

<div id="11.6.2.4" class="h3-container"></div>

<span id="hydrological-deficits-1"></span>
==== 11.6.2.4 Hydrological Deficits ====

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There is evidence based on streamflow records of increased hydrological droughts in East Asia (D. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ) and southern Africa ( [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ). In areas of Western and Central Europe and Northern Europe, there is no evidence of changes in the severity of hydrological droughts since 1950 based on flow reconstructions ( [[#Caillouet--2017|Caillouet et al., 2017]] ; [[#Barker--2019|Barker et al., 2019]] ) and observations ( [[#Vicente-Serrano--2019|Vicente-Serrano et al., 2019]] ). In the Mediterranean region, there is ''high confidence'' in hydrological drought intensification ( [[#11.9|Section 11.9]] ; [[#Giuntoli--2013|Giuntoli et al., 2013]] ; [[#Lorenzo-Lacruz--2013|Lorenzo-Lacruz et al., 2013]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ). In south-eastern South America there is a decrease in the severity of hydrological droughts ( [[#Rivera--2018|Rivera and Penalba, 2018]] ). In North America, depending on the methods, datasets and study periods, there are differences between studies that suggest an increase ( [[#Shukla--2015|Shukla et al., 2015]] ; [[#Udall--2017|Udall and Overpeck, 2017]] ) versus a decrease in hydrological drought frequency ( [[#Mo--2018|Mo and Lettenmaier, 2018]] ), but in general there is strong spatial variability ( [[#Poshtiri--2016|Poshtiri and Pal, 2016]] ). Streamflow observation reference networks of near-natural catchments have also been used to isolate the effect of climate trends on hydrological drought trends in a few regions, but these show limited trends in Northern Europe and Western and Central Europe ( [[#Stahl--2010|Stahl et al., 2010]] ; [[#Bard--2015|Bard et al., 2015]] ; [[#Harrigan--2018|Harrigan et al., 2018]] ), North America ( [[#Dudley--2020|Dudley et al., 2020]] ) and most of Australia, with the exception of Eastern and Southern Australia (X.S. [[#Zhang--2016|Zhang et al., 2016]] ). Given the low availability of observations, there are few studies analysing trends of drought severity in the groundwater. Nevertheless, some studies suggest a noticeable response of groundwater droughts to climate variability ( [[#Lorenzo-Lacruz--2017|Lorenzo-Lacruz et al., 2017]] ) and increased drought frequency and severity associated with warming, probably as a consequence of enhanced ET induced by higher AED ( [[#Maxwell--2016|Maxwell and Condon, 2016]] ). This is supported by studies in Northern Europe ( [[#Bloomfield--2019|Bloomfield et al., 2019]] ) and North America ( [[#Condon--2020|Condon et al., 2020]] ).

<div id="11.6.2.5" class="h3-container"></div>

<span id="atmospheric-based-drought-indices-1"></span>
==== 11.6.2.5 Atmospheric-based Drought Indices ====

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Globally, trends in SPEI-PM and PDSI-PM suggest slightly higher increases of drought frequency and severity in regions affected by drying over the last decades in comparison to the SPI ( [[#Dai--2017|Dai and Zhao, 2017]] ; [[#Spinoni--2019|Spinoni et al., 2019]] ; [[#Song--2020|Song et al., 2020]] ), mainly in regions of Western and Southern Africa, the Mediterranean and East Asia (Figure 11.17), which is consistent with observed soil moisture trends ( [[#11.6.2.3|Section 11.6.2.3]] ). These indices suggest that AED has contributed to increase the severity of agricultural and ecological droughts compared to meteorological droughts ( [[#García-Herrera--2019|García-Herrera et al., 2019]] ; [[#Williams--2020|Williams et al., 2020]] ), reduce soil moisture during the dry season ( [[#Padrón--2020|Padrón et al., 2020]] ), increase plant water stress ( [[#Allen--2015|Allen et al., 2015]] ; [[#Grossiord--2020|Grossiord et al., 2020]] ; [[#Solander--2020|Solander et al., 2020]] ) and trigger more severe forest fires ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ; [[#Turco--2019|Turco et al., 2019]] ; [[#Nolan--2020|Nolan et al., 2020]] ). A number of regional studies based on these drought indices have also shown stronger drying trends in comparison to trends in precipitation-based indices in the following AR6 regions (see also [[#11.9|Section 11.9]] ): NSA (R. [[#Fu--2013|]] [[#Fu--2013|Fu et al., 2013]] ; [[#Marengo--2016|Marengo and Espinoza, 2016]] ), SCA ( [[#Hidalgo--2017|Hidalgo et al., 2017]] ), WCA ( [[#Tabari--2013|Tabari and Aghajanloo, 2013]] ; [[#Sharafati--2020|Sharafati et al., 2020]] ), SAS ( [[#Niranjan%20Kumar--2013|Niranjan Kumar et al., 2013]] ), NEAF ( [[#Zeleke--2017|Zeleke et al., 2017]] ), WSAF ( [[#Edossa--2016|Edossa et al., 2016]] ), NWN and NEN ( [[#Bonsal--2013|Bonsal et al., 2013]] ), EAS ( [[#Yu--2014|Yu et al., 2014]] ; [[#Chen--2015b|Chen and Sun, 2015b]] ; L. [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ; [[#Liang--2020|Liang et al., 2020]] ; Z. [[#Wu--2020|]] [[#Wu--2020|Wu et al., 2020]] ) and MED ( [[#Kelley--2015|Kelley et al., 2015]] ; [[#Stagge--2017|Stagge et al., 2017]] ; [[#González-Hidalgo--2018|González-Hidalgo et al., 2018]] ; [[#Mathbout--2018a|Mathbout et al., 2018a]] ).

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<span id="synthesis-for-different-drought-types"></span>
==== 11.6.2.6 Synthesis for Different Drought Types ====

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Few AR6 regions show observed increases in meteorological drought ( [[#11.9|Section 11.9]] ), mostly in Africa and South America (NES: ''high confidence'' ; WAF, CAF, ESAF, SAM, SWS, SSA, SAS: ''medium confidence'' ); a few others show a decrease (WSB, ESB, NAU, CAU, NEU, CNA: ''medium confidence'' ). There are stronger signals indicating observed increases in agricultural and ecological drought ( [[#11.9|Section 11.9]] ), which highlights the role of increased ET, driven by increased AED, for these trends (Sections 11.6.2.3 and11.6.2.5). Past increases in agricultural and ecological droughts are found on all continents and several regions (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, NES: ''medium confidence'' ), while decreases are found only in one AR6 region (NAU: ''medium confidence'' ). The more limited availability of datasets makes it more difficult to assess historical trends in hydrological drought at regional scale ( [[#11.9|Section 11.9]] ). Increasing (MED: ''high confidence'' ; WAF, EAS, SAU: ''medium confidence'' ) and decreasing (NEU, SES: ''medium confidence'' ) trends in hydrological droughts have only been observed in a few regions.

In summary, there is ''high confidence'' that AED has increased on average on continents, contributing to increased ET and resulting water stress during periods with precipitation deficits, in particular during dry seasons. There is ''medium confidence'' in increases in precipitation deficits in a few regions of Africa and South America. Based on multiple evidence, there is ''medium confidence'' that agricultural and ecological droughts have increased in several regions on all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, NES: ''medium confidence'' ), while there is only ''medium confidence'' in decreases in one AR6 region (NAU). More severe hydrological droughts are found in fewer regions (MED: ''high confidence'' ; WAF, EAS, SAU: ''mediu'' ''m confidence'' ).

<div id="11.6.3" class="h2-container"></div>

<span id="model-evaluation-3"></span>
=== 11.6.3 Model Evaluation ===

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<div id="11.6.3.1" class="h3-container"></div>

<span id="precipitation-deficits-2"></span>
==== 11.6.3.1 Precipitation Deficits ====

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ESMs generally show limited performance and large spread in identifying precipitation deficits and associated long-term trends in comparison with observations ( [[#Nasrollahi--2015|Nasrollahi et al., 2015]] ). Meteorological drought trends in the CMIP5 ensemble showed substantial disagreements compared with observations ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ) including a tendency to overestimate drying, in particular in mid- to high latitudes ( [[#Knutson--2018|Knutson and Zeng, 2018]] ). The CMIP6 models display a better performance in reproducing long-term precipitation trends or seasonal dynamics in some studies in Southern South America ( [[#Rivera--2020|Rivera and Arnould, 2020]] ), East Asia ( [[#Xin--2020|Xin et al., 2020]] ), southern Asia ( [[#Gusain--2020|Gusain et al., 2020]] ), and south-western Europe ( [[#Peña-Angulo--2020b|Peña-Angulo et al., 2020b]] ), but there is still too ''limited evidence'' to allow for an assessment of possible differences in performance between CMIP5 and CMIP6. Furthermore, ESMs are generally found to underestimate the severity of precipitation deficits and the dry day frequencies in comparison to observations ( [[#Fantini--2018|Fantini et al., 2018]] ; [[#Ukkola--2018|Ukkola et al., 2018]] ). This is probably related to shortcomings in the simulation of persistent weather events in the mid-latitudes ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.3|Section 10.3.3.3]] ). ESMs also show a tendency to underestimate precipitation-based drought persistence at monthly to decadal time scales ( [[#Ault--2014|Ault et al., 2014]] ; [[#Moon--2018|Moon et al., 2018]] ). The overall inter-model spread in the projected frequency of precipitation deficits is also substantial ( [[#Touma--2015|Touma et al., 2015]] ; [[#Zhao--2016|Zhao et al., 2016]] ; [[#Engström--2018|Engström and Keellings, 2018]] ). Moreover, there are spatial differences in the spread, which is higher in the regions where enhanced drought conditions are projected and under high-emissions scenarios ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ). Nonetheless, some event attribution studies have concluded that droughts at regional scales can be adequately simulated by some climate models ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Otto--2018c|Otto et al., 2018c]] ).

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<span id="atmospheric-evaporative-demand-2"></span>
==== 11.6.3.2 Atmospheric Evaporative Demand ====

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There is only ''limited evidence'' on the evaluation of AED in state-of-the-art ESMs, which is performed on externally computed AED, based on model output ( [[#Scheff--2015|Scheff and Frierson, 2015]] ; [[#Liu--2016|Liu and Sun, 2016]] , 2017). An evaluation of average AED in 17 CMIP5 ESMs for 1981–1999 based on potential evaporation show that the models’ spatial patterns resemble the observations, but the magnitude of potential evaporation displays strong divergence among models globally and regionally ( [[#Scheff--2015|Scheff and Frierson, 2015]] ). The evaluation of AED in 12 CMIP5 ESMs with pan evaporation observations in East Asia for 1961–2000 ( [[#Liu--2016|Liu and Sun, 2016]] , 2017) show that the ESMs capture seasonal cycles well, but that regional AED averages are underestimated due to biases in the meteorological variables controlling the aerodynamic and radiative components of AED. The CMIP5 ESMs also show a strong underestimation of atmospheric drying trends compared to reanalysis data ( [[#Douville--2017|Douville and Plazzotta, 2017]] ).

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<span id="soil-moisture-deficits-2"></span>
==== 11.6.3.3 Soil Moisture Deficits ====

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The performance of climate models for representing soil moisture deficits shows more uncertainty than for precipitation deficits since, in addition to the uncertainties related to cloud and precipitation processes, there is uncertainty related to the representation of complex soil hydrological and boundary-layer processes ( [[#van%20den%20Hurk--2011|van den Hurk et al., 2011]] ; [[#Lu--2019|Lu et al., 2019]] ; [[#Quintana-Seguí--2020|Quintana-Seguí et al., 2020]] ). Another limitation is the lack of observations, particularly for soil moisture, in most regions ( [[#11.6.2.3|Section 11.6.2.3]] ) and the paucity of land surface property data to parametrize land surface models, in particular soil types, soil properties and depth ( [[#Xia--2015|Xia et al., 2015]] ). The spatial resolution of models is an additional limitation since the representation of some land–atmosphere feedbacks and topographic effects requires detailed resolution ( [[#Nicolai-Shaw--2015|Nicolai-Shaw et al., 2015]] ; Van Der Linden et al., 2019). In addition to climate models, land surface and hydrological models are also used to derive historical and projected trends in soil moisture and related land water variables ( [[#Albergel--2013|Albergel et al., 2013]] ; [[#Cheng--2015|Cheng et al., 2015]] ; [[#Gu--2019b|Gu et al., 2019b]] ; [[#Padrón--2020|Padrón et al., 2020]] ; [[#Markonis--2021|Markonis et al., 2021]] ; [[#Pokhrel--2021|Pokhrel et al., 2021]] ).

Overall, there are contrasting results on the performance of land surface models and climate models in representing soil moisture. Some studies suggest that soil moisture anomalies are well captured by land surface models driven with observation-based forcing ( [[#Dirmeyer--2006|Dirmeyer et al., 2006]] ; [[#Albergel--2013|Albergel et al., 2013]] ; [[#Xia--2014|Xia et al., 2014]] ; [[#Balsamo--2015|Balsamo et al., 2015]] ; [[#Reichle--2017|Reichle et al., 2017]] ; [[#Spennemann--2020|Spennemann et al., 2020]] ), but other studies report limited agreement in the representation of interannual soil moisture variability ( [[#Stillman--2016|Stillman et al., 2016]] ; [[#Yuan--2017|Yuan and Quiring, 2017]] ; [[#Ford--2019|Ford and Quiring, 2019]] ) and noticeable seasonal differences in model skill in some regions ( [[#Xia--2014|Xia et al., 2014]] , 2015). Models with good skill can nonetheless display biases in absolute soil moisture ( [[#Xia--2014|Xia et al., 2014]] ; [[#Gu--2019a|Gu et al., 2019a]] ), but these are not necessarily of relevance for the simulation of surface water fluxes and drought anomalies ( [[#Koster--2009|Koster et al., 2009]] ). There is also substantial inter-model spread ( [[#Albergel--2013|Albergel et al., 2013]] ), particularly for the root-zone soil moisture ( [[#Berg--2017a|Berg et al., 2017a]] ).

Regarding the performance of regional and global climate models, an evaluation of an ensemble of RCM simulations for Europe ( [[#Stegehuis--2013|Stegehuis et al., 2013]] ) shows that these models display overly strong drying in early summer, resulting in an excessive decrease of latent heat fluxes, with potential implications for more severe droughts in dry environments ( [[#Teuling--2018|Teuling, 2018]] ; [[#van%20Der%20Linden--2019|van Der Linden et al., 2019]] ). Compared with a range of observational ET estimates, CMIP5 models show an overestimation of ET on annual scale, but an ET underestimation in boreal summer in many Northern Hemisphere mid-latitude regions, also suggesting a tendency towards excessive soil drying ( [[#Mueller--2014|Mueller and Seneviratne, 2014]] ), consistent with identified biases in soil-moisture–temperature coupling ( [[#Donat--2018|Donat et al., 2018]] ; [[#Vogel--2018|Vogel et al., 2018]] ; [[#Selten--2020|Selten et al., 2020]] ). Land surface models used in ESMs display a bias in their representation of the sensitivity of interannual land carbon uptake to soil moisture conditions, which appears related to a limited range of soil moisture variations compared to observations ( [[#Humphrey--2018|Humphrey et al., 2018]] ).

For future projections, the spread of soil moisture outputs among different ESMs is more important than internal variability and scenario uncertainty, and the bias is strongly related to the sign of the projected change ( [[#Ukkola--2018|Ukkola et al., 2018]] ; [[#Lu--2019|Lu et al., 2019]] ; [[#Selten--2020|Selten et al., 2020]] ). The CMIP5 ESMs that project more drying and warming in mid-latitude regions show a substantial bias in soil-moisture–temperature coupling ( [[#Donat--2018|Donat et al., 2018]] ; [[#Vogel--2018|Vogel et al., 2018]] ). Although CMIP6 and CMIP5 simulations for soil moisture changes are similar overall, some differences are found in projections in a few regions ( [[#11.9|Section 11.9]] ; [[#Cook--2020|Cook et al., 2020]] ). There is still ''limited evidence'' to assess whether there are substantial differences in model performance in the two ensembles, but improvements in modelling aspects relevant for soil moisture have been reported for precipitation ( [[#11.6.3.2|Section 11.6.3.2]] ), and a better performance has been found in CMIP6 for the representation of long-term trends in soil moisture in continental USA ( [[#Yuan--2021|Yuan et al., 2021]] ). Despite the mentioned model limitations, the representation of soil moisture processes in ESMs uses physical and biological understanding of the underlying processes, which can well represent the temporal anomalies associated with temporal variability and trends in climate. In summary, there is ''medium confidence'' in the representation of soil moisture deficits in ESMs and related land surface and hydrological models.

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<span id="hydrological-deficits-2"></span>
==== 11.6.3.4 Hydrological Deficits ====

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Streamflow and groundwater are not directly simulated by ESMs, which only simulate runoff, but they are generally represented in hydrological models ( [[#Prudhomme--2014|Prudhomme et al., 2014]] ; [[#Giuntoli--2015|Giuntoli et al., 2015]] ), which are typically driven in a stand-alone manner by observed or simulated climate forcing. The simulation of hydrological deficits is much more problematic than the simulation of mean streamflow or peak flows ( [[#Fundel--2013|Fundel et al., 2013]] ; [[#Stoelzle--2013|Stoelzle et al., 2013]] ; [[#Velázquez--2013|Velázquez et al., 2013]] ; [[#Staudinger--2015|Staudinger et al., 2015]] ), since models tend to be too responsive to the climate forcing and do not satisfactorily capture low flows ( [[#Tallaksen--2014|Tallaksen and Stahl, 2014]] ). Simulations of hydrological drought metrics show uncertainties related to the contribution of both GCMs and hydrological models ( [[#Bosshard--2013|Bosshard et al., 2013]] ; [[#Giuntoli--2015|Giuntoli et al., 2015]] ; [[#Samaniego--2017|Samaniego et al., 2017]] ; [[#Vetter--2017|Vetter et al., 2017]] ), but hydrological models forced by the same climate input data also show a large spread ( [[#van%20Huijgevoort--2013|van Huijgevoort et al., 2013]] ; [[#Ukkola--2018|Ukkola et al., 2018]] ). At the catchment scale, the hydrological model uncertainty is higher than both GCM and downscaling uncertainty ( [[#Vidal--2016|Vidal et al., 2016]] ), and the hydrological models show issues in representing drought propagation throughout the hydrological cycle ( [[#Barella-Ortiz--2019|Barella-Ortiz and Quintana Seguí, 2019]] ). A study on the evaluation of streamflow droughts in seven global (hydrological and land surface) models compared with observations in near-natural catchments of Europe showed a substantial spread among models, an overestimation of the number of drought events, and an underestimation of drought duration and drought-affected area ( [[#Tallaksen--2014|Tallaksen and Stahl, 2014]] ).

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<span id="atmospheric-based-drought-indices-2"></span>
==== 11.6.3.5 Atmospheric-based Drought Indices ====

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A number of studies have analysed the ability of models to capture drought severity and trends based on climatic drought indices. Given the limitations of ESMs in reproducing the dynamic of precipitation deficits and AED (11.6.3.1, 11.6.3.2), atmospheric-based drought indices derived from ESM data for these two variables are also affected by uncertainties and biases. A comparison of historical trends in PDSI-PM for 1950–2014 derived from CMIP3 and CMIP5, with respective estimates derived from observations ( [[#Dai--2017|Dai and Zhao, 2017]] ) show a similar behaviour at global scale (long-term decrease), but low spatial agreement in the trends except in a few regions (Mediterranean, South Asia, north-western USA). In future projections, there is an important spread in PDSI-PM and SPEI-PM among different models ( [[#Cook--2014a|Cook et al., 2014a]] ).

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<span id="synthesis-for-different-drought-types-1"></span>
==== 11.6.3.6 Synthesis for Different Drought Types ====

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The performance of ESMs used to assessed changes in variables related to meteorological droughts, agricultural and ecological droughts, and hydrological droughts, shows the presence of biases and uncertainties compared to observations, but there is ''medium confidence'' in their overall performance for assessing drought projections given process understanding. Given the substantial inter-model spread documented for all related variables, the consideration of multi-model projections increases the confidence of model-based assessments, with only ''low confidence'' in assessments based on single models.

In summary, the evaluation of ESMs, land surface and hydrological models for the simulation of droughts is complex, due to the regional scale of drought trends, their overall low signal-to-noise ratio, and the lack of observations in several regions, in particular for soil moisture and streamflow. There is ''medium confidence'' in the ability of ESMs to simulate trends and anomalies in precipitation deficits and AED, and also ''medium confidence'' in the ability of ESMs and hydrological models to simulate trends and anomalies in soil moisture and streamflow deficits, on global and regional scales.

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<span id="detection-and-attribution-event-attribution-3"></span>
=== 11.6.4 Detection and Attribution, Event Attribution ===

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<span id="precipitation-deficits-3"></span>
==== 11.6.4.1 Precipitation Deficits ====

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There are only two AR6 regions where there is at least ''medium confidence'' that human-induced climate change has contributed to changes in meteorological droughts ( [[#11.9|Section 11.9]] ). In South-Western South America, there is ''medium confidence'' that human-induced climate change has contributed to an increase in meteorological droughts ( [[#Boisier--2016|Boisier et al., 2016]] ; [[#Garreaud--2020|Garreaud et al., 2020]] ), while in Northern Europe, there is ''medium confidence'' that it has contributed to a decrease in meteorological droughts ( [[#11.9|Section 11.9]] ; [[#Gudmundsson--2016|Gudmundsson and Seneviratne, 2016]] ). In other AR6 regions, there is inconclusive evidence in the attribution of long-term trends, but a human contribution to single meteorological events or sub-regional trends has been identified in some instances ( [[#11.9|Section 11.9]] ; see also below). In the Mediterranean region, some studies have identified a precipitation decline or increase in meteorological drought probability for time frames since the early or mid 20th century, and a possible human contribution to these trends ( [[#Hoerling--2012|Hoerling et al., 2012]] ; [[#Gudmundsson--2016|Gudmundsson and Seneviratne, 2016]] ; [[#Knutson--2018|Knutson and Zeng, 2018]] ), also on sub-regional scale in Syria from 1930 to 2010 ( [[#Kelley--2015|Kelley et al., 2015]] ). On the contrary, other studies have not identified precipitation and meteorological drought trends in the region for the long term ( [[#Camuffo--2013|Camuffo et al., 2013]] ; [[#Paulo--2016|Paulo et al., 2016]] ; [[#Vicente-Serrano--2021|Vicente-Serrano et al., 2021]] ) and also from the mid 20th century ( [[#Norrant--2006|Norrant and Douguédroit, 2006]] ; [[#Stagge--2017|Stagge et al., 2017]] ). There is evidence of substantial internal variability in long-term precipitation trends in the region ( [[#11.6.2.1|Section 11.6.2.1]] ), which limits the attribution of human influence on variability and trends of meteorological droughts from observational records ( [[#Kelley--2012|Kelley et al., 2012]] ; [[#Peña-Angulo--2020b|Peña-Angulo et al., 2020b]] ). In addition, there are important sub-regional trends showing mixed signals ( [[#11.9|Section 11.9]] ; [[#MedECC--2020|MedECC, 2020]] ). The evidence thus leads to an assessment of ''low confidence'' in the attribution of observed short-term changes in meteorological droughts in the region ( [[#11.9|Section 11.9]] ). In North America, the human influence on precipitation deficits is complex ( [[#Wehner--2017|Wehner et al., 2017]] ), with ''low confidence'' in the attribution of long-term changes in meteorological drought in AR6 regions ( [[#11.9|Section 11.9]] ; [[#Lehner--2018|Lehner et al., 2018]] ). In Africa there is ''low confidence'' that human influence has contributed to the observed long-term meteorological drought increase in Western Africa (Sections 11.9 and 10.6.2). There is ''low confidence'' in the attribution of the observed increasing trends in meteorological drought in East Southern Africa, but evidence that human-induced climate change has affected recent meteorological drought events in the region ( [[#11.9|Section 11.9]] ).

Attribution studies for recent meteorological drought events are available for various regions. In Western and Central Europe, a multi-method and multi-model attribution study on the 2015 Central European drought did not find conclusive evidence for whether human-induced climate change was a driver of the rainfall deficit, as the results depended on model and method used ( [[#Hauser--2017|Hauser et al., 2017]] ). In the Mediterranean region, a human contribution was found in the case of the 2014 meteorological drought in the southern Levant based on a single-model study ( [[#Bergaoui--2015|Bergaoui et al., 2015]] ). In Africa, there is some evidence of a contribution of human emissions to single meteorological drought events, such as the 2015–2017 southern African drought ( [[#Funk--2018a|Funk et al., 2018a]] ; [[#Yuan--2018a|Yuan et al., 2018a]] ; [[#Pascale--2020|Pascale et al., 2020]] ), and the three-year (2015–2017) drought in the western Cape Town region of South Africa ( [[#Otto--2018c|Otto et al., 2018c]] ). An attributable signal was not found in droughts that occurred in different years with different spatial extents in the last decade in North and South Eastern Africa ( [[#Marthews--2015|Marthews et al., 2015]] ; [[#Uhe--2017|Uhe et al., 2017]] ; [[#Otto--2018a|Otto et al., 2018a]] ; [[#Philip--2018b|Philip et al., 2018b]] ; [[#Kew--2021|Kew et al., 2021]] ). However, an attributable increase in 2011 long rain failure was identified ( [[#Lott--2013|Lott et al., 2013]] ). Further studies have attributed some African meteorological drought events to large-scale modes of variability, such as the strong 2015 El Niño (Box 11.4; [[#Philip--2018b|Philip et al., 2018b]] ) and increased SSTs overall ( [[#Funk--2015a|Funk et al., 2015a]] , 2018b). Natural variability was dominant in the California droughts of 2011–2012 to 2013–2014 ( [[#Seager--2015a|Seager et al., 2015a]] ). In Asia, no climate change signal was found in the record dry spell over Singapore and Malaysia in 2014 ( [[#Mcbride--2015|Mcbride et al., 2015]] ) or the drought in central south-west Asia in 2013–2014 ( [[#Barlow--2015|Barlow and Hoell, 2015]] ). Nevertheless, the South East Asia drought of 2015 has been attributed to anthropogenic warming effects ( [[#Shiogama--2020|Shiogama et al., 2020]] ). Recent droughts occurring in South America, specifically in the southern Amazon region in 2010 ( [[#Shiogama--2013|Shiogama et al., 2013]] ) and in north-east South America in 2014 ( [[#Otto--2015b|Otto et al., 2015b]] ) and 2016 ( [[#Martins--2018|Martins et al., 2018]] ) were not attributed to anthropogenic climate change. Nevertheless, the central Chile drought between 2010 and 2018 has been suggested to be partly associated to global warming ( [[#Boisier--2016|Boisier et al., 2016]] ; [[#Garreaud--2020|Garreaud et al., 2020]] ). The 2013 New Zealand meteorological drought was attributed to human influence by Harrington et al. (2014, 2016) based on fully coupled CMIP5 models, but no corresponding change in the dry end of simulated precipitation from a stand-alone atmospheric model was found by [[#Angélil--2017|Angélil et al. (2017)]] .

Event attribution studies also highlight a complex interplay of anthropogenic and non-anthropogenic climatological factors for some events. For example, anthropogenic warming contributed to the 2014 drought in North Eastern Africa by increasing east African and west Pacific temperatures, and increasing the gradient between standardized western and central Pacific SSTs, causing reduced rainfall ( [[#Funk--2015a|Funk et al., 2015a]] ). As different methodologies, models and data sources have been used for the attribution of precipitation deficits, [[#Angélil--2017|Angélil et al. (2017)]] re-examined several events using a single analytical approach and climate model and observational datasets. Their results showed a disagreement in the original anthropogenic attribution in a number of precipitation deficit events, which increased uncertainty in the attribution of meteorological droughts events.

<div id="11.6.4.2" class="h3-container"></div>

<span id="soil-moisture-deficits-3"></span>
==== 11.6.4.2 Soil Moisture Deficits ====

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There is a growing number of studies on the detection and attribution of long-term changes in soil moisture deficits. [[#Mueller--2016|Mueller and Zhang (2016)]] concluded that anthropogenic forcing contributed significantly to soil moisture drying in the warm season in the Northern Hemisphere from 1951 to 2005 and also led to an increase in the land surface area affected by soil moisture deficits, which can be reproduced by CMIP5 models only if anthropogenic forcings are involved. [[#Gu--2019b|Gu et al. (2019b)]] similarly identified a global-scale soil moisture drying tendency in land surface model data from the Global Land Data Assimilation System 2 over the time frame 1948–2005, which was attributed to anthropogenic forcing based on evaluation with CMIP5 models using optimal fingerprinting. [[#Padrón--2019|Padrón et al. (2019)]] analysed long-term reconstructed and CMIP5 simulated dry season water availability, defined as precipitation minus ET (i.e., equivalent to soil moisture and runoff availability), also related to agricultural and ecological droughts. They found an intensification of dry-season precipitation minus evapotranspiration deficits over a predominant fraction of the land area in the last three decades, which can only be explained by anthropogenic forcing and is mostly related to increases in ET. Similarly, [[#Williams--2020|Williams et al. (2020)]] concluded that human-induced climate change contributed to the strong soil moisture deficits recorded in the last two decades in Western North America through VPD increases associated with higher air temperatures and lower air humidity. There are few studies analysing the attribution of particular episodes of soil moisture deficits to anthropogenic influence. Nevertheless, the available modelling studies coincide in supporting an anthropogenic attribution associated with more extreme temperatures, exacerbating AED and increasing ET, and thus depleting soil moisture, as observed in southern Europe in 2017 ( [[#García-Herrera--2019|García-Herrera et al., 2019]] ) and in Australia in 2018 ( [[#Lewis--2020|Lewis et al., 2020]] ) and 2019 ( [[#van%20Oldenborgh--2021|van Oldenborgh et al., 2021]] ), the latter event having strong implications in the propagation of widespread megafires ( [[#Nolan--2020|Nolan et al., 2020]] ).

<div id="11.6.4.3" class="h3-container"></div>

<span id="hydrological-deficits-3"></span>
==== 11.6.4.3 Hydrological Deficits ====

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It is often difficult to separate the role of climate trends from changes in land use, water management and demand for changes in hydrological deficits, especially on a regional scale. However, a global study based on a recent multi-model experiment with global hydrological models and covering several AR6 regions suggests a dominant role of anthropogenic radiative forcing for trends in low, mean and high flows, while simulated effects of water and land management do not suffice to reproduce the observed spatial pattern of trends ( [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ). Regional studies also suggest that climate trends have been dominant compared to land use and human water management for explaining trends in hydrological droughts in some regions, for instance in Ethiopia ( [[#Fenta--2017|Fenta et al., 2017]] ), China ( [[#Xie--2015|Xie et al., 2015]] ), and North America for the Missouri and Colorado basins, as well as in California ( [[#Shukla--2015|Shukla et al., 2015]] ; [[#Udall--2017|Udall and Overpeck, 2017]] ; [[#Ficklin--2018|Ficklin et al., 2018]] ; K. [[#Xiao--2018|]] [[#Xiao--2018|Xiao et al., 2018]] ; [[#Glas--2019|Glas et al., 2019]] ; [[#Martin--2020|Martin et al., 2020]] ; [[#Milly--2020|Milly and Dunne, 2020]] ).

In other regions, the influence of human water uses can be more important to explain hydrological drought trends (Y. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Mohammed--2016|Mohammed and Scholz, 2016]] ). There is ''medium confidence'' that human-induced climate change has contributed to an increase of hydrological droughts in the Mediterranean ( [[#Giuntoli--2013|Giuntoli et al., 2013]] ; [[#Vicente-Serrano--2014|Vicente-Serrano et al., 2014]] ; [[#Gudmundsson--2017|Gudmundsson et al., 2017]] ), but also ''medium confidence'' that changes in land use and terrestrial water management contributed to these trends ( [[#11.9|Section 11.9]] ; [[#Teuling--2019|Teuling et al., 2019]] ; [[#Vicente-Serrano--2019|Vicente-Serrano et al., 2019]] ). A global study with a single hydrological model estimated that human water consumption has intensified the magnitude of hydrological droughts by 20–40% over the last 50 years, and that the human water use contribution to hydrological droughts was more important than climatic factors in the Mediterranean, and central USA, as well as in parts of Brazil ( [[#Wada--2013|Wada et al., 2013]] ). However, [[#Gudmundsson--2021|Gudmundsson et al. (2021)]] concluded that the contribution of human water use is smaller than that of anthropogenic climate change to explain spatial differences in the trends of low flows based on a multi-model analysis. There is still ''limited evidence'' and thus ''low confidence'' in assessing these trends at the scale of single regions, with few exceptions ( [[#11.9|Section 11.9]] ).

<div id="11.6.4.4" class="h3-container"></div>

<span id="atmospheric-based-drought-indices-3"></span>
==== 11.6.4.4 Atmospheric-based Drought Indices ====

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Different studies using atmospheric-based drought indices suggest an attributable anthropogenic signal, characterized by the increased frequency and severity of droughts ( [[#Cook--2018|Cook et al., 2018]] ), associated to increased AED ( [[#11.6.4.2|Section 11.6.4.2]] ). The majority of studies are based on the PDSI-PM. [[#Williams--2015|Williams et al. (2015)]] and [[#Griffin--2014|Griffin and Anchukaitis (2014)]] concluded that increased AED has had an increased contribution to drought severity over the last decades, and played a dominant role in the intensification of the 2012–2014 drought in California. The same temporal pattern and physical mechanism was stressed by Z. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al. (2017)]] in central Asia. [[#Marvel--2019|Marvel et al. (2019)]] compared tree ring-based reconstructions of the PDSI-PM over the past millennium with PDSI-PM estimates based on output from CMIP5 models. The comparisons suggested a contribution of greenhouse gas forcing to the changes since the beginning of the 20th century, although characterized with temporal differences that could be driven by temporal variations in the aerosol forcing. This was in agreement with the dominant external forcings of aridification at global scale between 1950 and 2014 ( [[#Bonfils--2020|Bonfils et al., 2020]] ). In the Mediterranean region, there is ''medium confidence'' of drying attributable to antropogenic forcing as a consequence of the strong AED increase ( [[#Gocic--2014|Gocic and Trajkovic, 2014]] ; [[#Azorin-Molina--2015|Azorin-Molina et al., 2015]] ; [[#Liuzzo--2016|Liuzzo et al., 2016]] ; [[#Maček--2018|Maček et al., 2018]] ), which has enhanced the severity of drought events ( [[#Vicente-Serrano--2014|Vicente-Serrano et al., 2014]] ; [[#Stagge--2017|Stagge et al., 2017]] ; [[#González-Hidalgo--2018|González-Hidalgo et al., 2018]] ). In particular, this effect was identified to be the main driver of the intensification of the 2017 drought that affected south-western Europe, and was attributed to the human forcing ( [[#García-Herrera--2019|García-Herrera et al., 2019]] ). [[#Nangombe--2020|Nangombe et al. (2020)]] and L. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al. (2020)]] concluded from differences between precipitation and AED that anthropogenic forcing contributed to the 2018 droughts that affected southern Africa and south-eastern China, respectively, principally as consequence of the high AED that characterized these two events.

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<span id="synthesis-for-different-drought-types-2"></span>
==== 11.6.4.5 Synthesis for Different Drought Types ====

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The regional evidence on attribution for single AR6 regions generally shows ''low confidence'' for a human contribution to observed trends in meteorological droughts at regional scale, with few exceptions ( [[#11.9|Section 11.9]] ). There is ''medium confidence'' that human influence has contributed to increases in agricultural and ecological droughts in the dry season in some regions and has led to an overall increase in the affected land area. At regional scales, there is ''medium confidence'' in a contribution of human-induced climate change to increases in agricultural and ecological droughts in the Mediterranean and Western North America ( [[#11.9|Section 11.9]] ). There ''is medium confidence'' that human-induced climate change has contributed to an increase in hydrological droughts in the Mediterranean region, but also ''medium confidence'' in contributions from other human influences, including water management and land use ( [[#11.9|Section 11.9]] ). Several meteorological and agricultural and ecological drought events have been attributed to human-induced climate change, even in regions where no long-term changes are detected ( ''medium confidence'' ). However, a lack of attribution to human-induced climate change has also been shown for some events ( ''medi'' ''um confidence'' ).

In summary, human influence has contributed to increases in agricultural and ecological droughts in the dry season in some regions due to increases in evapotranspiration ( ''medium confidence'' ). The increases in evapotranspiration have been driven by increases in atmospheric evaporative demand induced by increased temperature, decreased relative humidity and increased net radiation over affected land areas ( ''high confidence'' ). There is ''low confidence'' that human influence has affected trends in meteorological droughts in most regions, but ''medium confidence'' that they have contributed to the severity of some single events. There is ''medium confidence'' that human-induced climate change has contributed to increasing trends in the probability or intensity of recent agricultural and ecological droughts, leading to an increase of the affected land area. Human-induced climate change has contributed to global-scale change in low flow, but human water management and land-use changes are also important drivers ( ''medi'' ''um confidence'' ).

<div id="11.6.5" class="h2-container"></div>

<span id="projections-2"></span>
=== 11.6.5 Projections ===

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The SREX (Chapter 3) asssessed with ''medium confidence'' projections of increased drought severity in some regions, including southern Europe and the Mediterranean, central Europe, central America and Mexico, north-east Brazil, and southern Africa, and ''low confidence'' elsewhere given large inter-model spread. The AR5 (Chapters 11 and 12) also assessed large uncertainties in drought projections at the regional and global scales. The assessment of drought mechanisms under future climate change scenarios depends on the model used ( [[#11.6.3|Section 11.6.3]] ). Moreover, uncertainties in drought projections are affected by the consideration of plant physiological responses to increasing atmospheric CO <sub>2</sub> (Cross-Chapter Box 5.1; [[#Milly--2016|Milly and Dunne, 2016]] ; [[#Greve--2019|Greve et al., 2019]] ; [[#Mankin--2019|Mankin et al., 2019]] ; [[#Yang--2020|Yang et al., 2020]] ), the role of soil-moisture–atmosphere feedbacks for changes in water balance and aridity ( [[#Berg--2016|Berg et al., 2016]] ; [[#Zhou--2021|Zhou et al., 2021]] ), and statistical issues related to considered drought time scales ( [[#Vicente-Serrano--2020c|Vicente-Serrano et al., 2020c]] ). Nonetheless, the extensive literature available since AR5 allows a substantially more robust assessment of projected changes in droughts, also subdivided in different drought types (meteorological drought, agricultural and ecological drought, and hydrological drought). This includes assessments of projected changes in droughts, including changes at 1.5°C, 2°C and 4°C of global warming, for all AR6 regions ( [[#11.9|Section 11.9]] ). Projected changes show increases in drought frequency and intensity in several regions as function of global warming ( ''high confidence'' ). There are also substantial increases in drought hazard probability from 1.5°C to 2°C global warming and for further additional increments of global warming ( ''high confidence'' ) (Figures 11.18 and 11.19). These findings are based on both CMIP5 and CMIP6 analyses ( [[#11.9|Section 11.9]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Greve--2018|Greve et al., 2018]] ; L. [[#Xu--2019|]] [[#Xu--2019|Xu et al., 2019]] ), and strengthen the conclusions of SR1.5 Chapter 3.

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<span id="precipitation-deficits-4"></span>
==== 11.6.5.1 Precipitation Deficits ====

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Studies based on CMIP5, CMIP6 and Coordinated Regional Climate Downscaling Experiment (CORDEX) projections show a consistent signal in the sign and spatial pattern of projections of precipitation deficits. Global studies based on these multi-model ensemble projections ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ; [[#Martin--2018|Martin, 2018]] ; [[#Spinoni--2020|Spinoni et al., 2020]] ; [[#Ukkola--2020|Ukkola et al., 2020]] ; [[#Coppola--2021b|Coppola et al., 2021b]] ) show particularly strong signal-to-noise ratios for increasing meteorological droughts in the following AR6 regions: MED, ESAF, WSAF, SAU, CAU, NCA, SCA, NSA and NES ( [[#11.9|Section 11.9]] ). There is also substantial evidence of changes in meteorological droughts at 1.5°C versus 2°C of global warming from global studies ( [[#Wartenburger--2017|Wartenburger et al., 2017]] ; L. [[#Xu--2019|]] [[#Xu--2019|Xu et al., 2019]] ). The patterns of projected changes in mean precipitation are consistent with the changes in the drought duration, but they are not consistent with the changes in drought intensity ( [[#Ukkola--2020|Ukkola et al., 2020]] ). In general, CMIP6 projections suggest a stronger increase of the probability of precipitation deficits than CMIP5 projections ( [[#Cook--2020|Cook et al., 2020]] ; [[#Ukkola--2020|Ukkola et al., 2020]] ). Projections for the number of CDDs in CMIP6 (Figure 11.19) for different levels of global warming relative to 1850–1900 show similar spatial patterns as projected precipitation deficits. The robustness of the patterns in projected precipitation deficits identified in the global studies is also consistent with results from regional studies ( [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Marengo--2016|Marengo and Espinoza, 2016]] ; [[#Pinto--2016|Pinto et al., 2016]] ; J. [[#Huang--2018|]] [[#Huang--2018|Huang et al., 2018]] ; [[#Maúre--2018|Maúre et al., 2018]] ; [[#Nangombe--2018|Nangombe et al., 2018]] ; [[#Tabari--2018|Tabari and Willems, 2018]] ; [[#Abiodun--2019|Abiodun et al., 2019]] ; [[#Dosio--2019|Dosio et al., 2019]] ).

In Africa, a strong increase in the length of dry spells (CDD) is projected for 4°C of global warming over most of the continent, with the exception of central and eastern Africa ( [[#11.9|Section 11.9]] ; [[#Sillmann--2013a|Sillmann et al., 2013a]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Han--2019|Han et al., 2019]] ). In West Africa, a strong reduction of precipitation is projected ( [[#Sillmann--2013a|Sillmann et al., 2013a]] ; [[#Diallo--2016|Diallo et al., 2016]] ; [[#Akinsanola--2019|Akinsanola and Zhou, 2019]] ; [[#Han--2019|Han et al., 2019]] ; [[#Todzo--2020|Todzo et al., 2020]] ) at 4°C of global warming, and CDD would increase with stronger global warming levels ( [[#Klutse--2018|Klutse et al., 2018]] ). The regions most strongly affected are southern Africa (ESAF, WSAF) ( [[#Nangombe--2018|Nangombe et al., 2018]] ; [[#Abiodun--2019|Abiodun et al., 2019]] ) and northern Africa (part of the MED region), with increases in meteorological droughts already at 1.5°C of global warming, and further increases with increasing global warming ( [[#11.9|Section 11.9]] ). CDD is projected to increase more in the southern Mediterranean (northern Africa) than in the northern part of the Mediterranean region ( [[#Lionello--2020|Lionello and Scarascia, 2020]] ).

In Asia, most AR6 regions show ''low confidence'' in projected changes in meteorological droughts at 1.5°C and 2°C of global warming, with a few regions displaying a decrease in meteorological droughts at 4°C of global warming (RAR, ESB, RFE, ECA; ''medium confidence'' ), although there is a projected increase in meteorological droughts in South East Asia at 4°C ( ''medium confidence'' ) ( [[#11.9|Section 11.9]] ). In South East Asia, an increasing frequency of precipitation deficits is projected as a consequence of an increasing frequency of extreme El Niño ( [[#Cai--2014b|Cai et al., 2014b]] , 2015, 2018).

In Central America, projections suggest an increase in mid-summer meteorological drought ( [[#Imbach--2018|Imbach et al., 2018]] ) and increased CDD ( [[#Chou--2014a|Chou et al., 2014a]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ; [[#Nakaegawa--2014|Nakaegawa et al., 2014]] ). In the Amazon, there is also a projected increase in dryness ( [[#Marengo--2016|Marengo and Espinoza, 2016]] ), which is the combination of a projected increase in the frequency and geographic extent of meteorological drought in the eastern Amazon, and an opposite trend in the west ( [[#Duffy--2015|Duffy et al., 2015]] ). In South-Western South America, there is a projected increase of CDD ( [[#Chou--2014a|Chou et al., 2014a]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ) and in Chile, drying is projected to prevail ( [[#Boisier--2018|Boisier et al., 2018]] ). In the South America monsoon region, an increase in CDD is projected ( [[#Chou--2014a|Chou et al., 2014a]] ; [[#Giorgi--2014|Giorgi et al., 2014]] ), but a decrease is projected in South-Eastern and Southern South America ( [[#Giorgi--2014|Giorgi et al., 2014]] ). In Central America, mid-summer meteorological drought is projected to intensify during 2071–2095 for the RCP8.5 scenario ( [[#Corrales-Suastegui--2020|Corrales‐Suastegui et al., 2020]] ).

An increase in the frequency, duration and intensity of meteorological droughts is projected in south-west, south and east Australia ( [[#Kirono--2020|Kirono et al., 2020]] ; [[#Shi--2020|Shi et al., 2020]] ). In Canada and most of the USA, based on the SPI, [[#Swain--2015|Swain and Hayhoe (2015)]] identified drier summer conditions in projections over most of the region, and there is a consistent signal toward an increase in duration and intensity of droughts in southern North America ( [[#Pascale--2016|Pascale et al., 2016]] ; [[#Escalante-Sandoval--2017|Escalante-Sandoval and Nuñez-Garcia, 2017]] ). In California, more precipitation variability is projected, characterized by increased frequency of consecutive drought and humid periods ( [[#Swain--2018|Swain et al., 2018]] ).

Substantial increases in meteorological drought are projected in Europe, in particular in the Mediterranean region, already at 1.5°C of global warming ( [[#11.9|Section 11.9]] ). In southern Europe, model projections display a consistent drying among models ( [[#Russo--2013|Russo et al., 2013]] ; [[#Hertig--2017|Hertig and Tramblay, 2017]] ; [[#Guerreiro--2018a|Guerreiro et al., 2018a]] ; [[#Raymond--2019|Raymond et al., 2019]] ). In Western and Central Europe there is some spread in CMIP5 projections, with some models projecting very strong drying, and others close to no trend ( [[#Vogel--2018|Vogel et al., 2018]] ), although CDD is projected to increase in CMIP5 projections under the RCP 8.5 scenario ( [[#Hari--2020|Hari et al., 2020]] ). The overall evidence suggests an increase in meteorological drought at 4°C in the WCE region ( ''medium confidence'' ) ( [[#11.9|Section 11.9]] ).

Overall, based on global and regional studies, several hot spot regions are identified, displaying more frequent and severe meteorological droughts with increasing global warming, including several AR6 regions at 1.5°C (WSAF, ESAF, SAU, MED, NES) and 2°C of global warming (WSAF, ESAF, EAU, SAU, MED, NCA, SCA, NSA, NES) ( [[#11.9|Section 11.9]] ). At 4°C of global warming, there is also ''confidence'' in increases in meteorological droughts in further regions (WAF, WCE, ENA, CAR, NWS, SAM, SWS, SSA; [[#11.9|Section 11.9]] ), showing a geographical expansion of meteorological drought with increasing global warming. Only few regions are projected to have less intense or frequent meteorological droughts ( [[#11.9|Section 11.9]] ).

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==== 11.6.5.2 Atmospheric Evaporative Demand ====

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Effects of AED on droughts in future projections is under debate. The CMIP5 models project an increase in AED over the majority of the world with increasing global warming, mostly as a consequence of strong VPD increases ( [[#Scheff--2015|Scheff and Frierson, 2015]] ; [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ). However, ET is projected to increase less than AED in many regions due to plant physiological responses related to: i) CO <sub>2</sub> effects on plant photosynthesis; and ii) soil moisture control on ET.

Several studies suggest that increasing atmospheric CO <sub>2</sub> could lead to reduced leaf stomatal conductance, which would increase water-use efficiency and reduce plant water needs, thus limiting ET (Cross-Chapter Box 5.1; [[#Roderick--2015|Roderick et al., 2015]] ; [[#Milly--2016|Milly and Dunne, 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Greve--2017|Greve et al., 2017]] ; [[#Scheff--2017|Scheff et al., 2017]] ; [[#Lemordant--2018|Lemordant et al., 2018]] ; [[#Swann--2018|Swann, 2018]] ). The implemention of a CO <sub>2</sub> -dependent land resistance parameter has been suggested for the estimation of AED ( [[#Yang--2019|Yang et al., 2019]] ). Nevertheless, there are other relevant mechanisms, as soil moisture deficits and VPD also play an important role in the control of the leaf stomatal conductance (Z. [[#Xu--2016|]] [[#Xu--2016|Xu et al., 2016]] ; [[#Menezes-Silva--2019|Menezes-Silva et al., 2019]] ; [[#Grossiord--2020|Grossiord et al., 2020]] ), and a number of ecophysiological and anatomical processes affect the response of plant physiology under higher atmospheric CO <sub>2</sub> concentrations (Cross-Chapter Box 5.1; [[#Mankin--2019|Mankin et al., 2019]] ; [[#Menezes-Silva--2019|Menezes-Silva et al., 2019]] ). The benefits of the atmospheric CO <sub>2</sub> for plant stress and agricultural and ecological droughts would be minimal precisely during dry periods given stomatal closure in response to limited soil moisture ( [[#Allen--2015|Allen et al., 2015]] ; Z. [[#Xu--2016|]] [[#Xu--2016|Xu et al., 2016]] ). In addition, CO <sub>2</sub> effects on plant stomatal conductance could not entirely compensate for the increased demand associated with warming ( [[#Liu--2017|Liu and Sun, 2017]] ); in large tropical and subtropical regions (e.g., southern Africa, the Amazon, the Mediterranean and southern North America), AED is projected to increase, even considering the possible CO <sub>2</sub> effects on land resistance ( [[#Vicente-Serrano--2020a|Vicente-Serrano et al., 2020a]] ). Moreover, these CO <sub>2</sub> effects would not affect the direct evaporation from soil and water bodies, which is very relevant in the reservoirs of warm areas ( [[#Friedrich--2018|Friedrich et al., 2018]] ). Because of these uncertainties, there is ''low confidence'' whether increased CO <sub>2</sub> -induced water-use efficiency in vegetation will substantially reduce global plant transpiration and will diminish the frequency and severity of soil moisture and streamflow deficits associated with the radiative effect of higher CO <sub>2</sub> concentrations (Cross-Chapter Box 5.1).

Another mechanism reducing the ET response to increased AED in projections is the control of soil moisture limitations on ET, which leads to reduced stomatal conductance under water stress ( [[#Berg--2018|Berg and Sheffield, 2018]] ; [[#Stocker--2018|Stocker et al., 2018]] ; [[#Zhou--2021|Zhou et al., 2021]] ). This response may be further amplified through VPD-induced decreases in stomatal conductance ( [[#Anderegg--2020|Anderegg et al., 2020]] ). However, the decreased stomatal conductance in response to soil moisture limitation and enhanced CO <sub>2</sub> would further enhance AED ( [[#Sherwood--2014|Sherwood and Fu, 2014]] ; [[#Berg--2016|Berg et al., 2016]] ; [[#Teuling--2018|Teuling, 2018]] ; [[#Miralles--2019|Miralles et al., 2019]] ), whereby the overall effects on AED in ESMs are found to be of similar magnitude for soil moisture limitation and CO <sub>2</sub> physiological effects on stomatal conductance ( [[#Berg--2016|Berg et al., 2016]] ). Increased AED is thus both a driver and a feedback with respect to changes in ET, complicating the interpretation of its role on drought changes with increasing CO <sub>2</sub> concentrations and global warming.

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==== 11.6.5.3 Soil Moisture Deficits ====

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Areas with projected soil moisture decreases do not fully coincide with areas that have projected precipitation decreases, although there is substantial consistency in the respective patterns ( [[#Dirmeyer--2013|Dirmeyer et al., 2013]] ; [[#Berg--2018|Berg and Sheffield, 2018]] ). However, there are more regions affected by increased soil moisture deficits (Figure 11.19) than precipitation deficits (Figures 2a,b,c and Cross-Chapter Box 11.1) as a consequence of enhanced AED and the associated increased ET, as highlighted by some studies ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ; [[#Dai--2018|Dai et al., 2018]] ; [[IPCC:Wg1:Chapter:Chapter-8#8.2.2.1|Section 8.2.2.1]] ). Moisture in the top soil layer is projected to decrease more than precipitation at all warming levels ( [[#Lu--2019|Lu et al., 2019]] ), extending the regions affected by severe soil moisture deficits over most of south and central Europe ( [[#Lehner--2017|Lehner et al., 2017]] ; [[#Ruosteenoja--2018|Ruosteenoja et al., 2018]] ; [[#Samaniego--2018|Samaniego et al., 2018]] ; [[#van%20Der%20Linden--2019|van Der Linden et al., 2019]] ), southern North America ( [[#Cook--2019|Cook et al., 2019]] ), South America ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ), southern Africa ( [[#Lu--2019|Lu et al., 2019]] ), East Africa ( [[#Rowell--2015|Rowell et al., 2015]] ), Southern Australia ( [[#Kirono--2020|Kirono et al., 2020]] ), India ( [[#Mishra--2014a|Mishra et al., 2014a]] ) and East Asia (Figure 11.19; [[#Cheng--2015|Cheng et al., 2015]] ). Projected changes in total soil moisture display less widespread drying than those for surface soil moisture ( [[#Berg--2017a|Berg et al., 2017a]] ), but still more than for precipitation (Cross-Chapter Box 11.1, Figures 2a,b,c). The severity of droughts based on surface soil moisture in future projections is stronger than projections based on precipitation and runoff ( [[#Dai--2018|Dai et al., 2018]] ; [[#Vicente-Serrano--2020c|Vicente-Serrano et al., 2020c]] ). Nevertheless, in many parts of the world where soil moisture is projected to decrease, the signal-to-noise ratio among models is low; only the projections in the Mediterranean, Europe, the south-western USA, and southern Africa show a high signal-to-noise ratio in soil moisture projections (Figure 11.19; [[#Lu--2019|Lu et al., 2019]] ). Increases in soil moisture deficits are found to be statistically signicant at regional scale in the Mediterranean region, southern Africa and western South America for changes as small as 0.5°C in global warming, based on differences between +1.5°C and +2°C of global warming ( [[#Wartenburger--2017|Wartenburger et al., 2017]] ). Several other regions are affected when considering changes in droughts for higher changes in global warming ( [[#11.9|Section 11.9]] and Figure 11.19). Seasonal projections of drought frequency for boreal winter (December–January–February) and summer (June–July–August), from CMIP6 multi-model ensemble for 1.5°C, 2°C and 4°C global warming levels, show contrasting trends (Figure 11.19). In the boreal winter in the Northern Hemisphere, the areas affected by drying show ''high agreement'' with those characterized by an increase in meteorological drought projections (Figures 8.14 and 12.4). On the contrary, in the boreal summer, the drought frequency increases worldwide in comparison to meteorological drought projections, with large areas of the Northern Hemisphere displaying a high signal-to-noise ratio (low spead between models). This stresses the dominant influence of ET (as a result of increased AED) in intensifying agricultural and ecological droughts in the warm season in many locations, including mid- to high latitudes.

Increased soil moisture limitation and associated changes in droughts are projected to lead to increased vegetation stress affecting the global land carbon sink in ESM projections ( [[#Green--2019|Green et al., 2019]] ), with implications for projected global warming (Cross-Chapter Box 5). There is ''high confidence'' that the global land sink will become less efficient due to soil moisture limitations and associated agricultural and ecological drought conditions in some regions in higher-emissions scenarios, specially under global warming levels above 4°C; however, there is ''low confidence'' in how these water cycle feedbacks will play out in lower-emissions scenarios (at 2°C global warming or lower; Cross-Chapter Box 5.1).

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[[File:a35dd7dfa52068b8566ee043aeb0b54e IPCC_AR6_WGI_Figure_11_18.png]]

'''Figure 11.18 |''' '''Projected changes in (a) the intensity and (b) the frequency of drought under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 1850–1900 baseline. (c)''' Summaries are computed for the AR6 regions in which there is at least medium confidence in an increase in agriculture/ecological drought at the 2°C global warming level (‘drying regions’), including Western North America, Central North America, North Central America, Southern Central America, Northern South America, North-Eastern South America, South American Monsoon, South-Western South America, Southern South America, West and Central Europe, Mediterranean, West Southern Africa, East Southern Africa, Madagascar, Eastern Australia, Southern Australia. Caribbean is not included in the calculation because the number of land grid points was too small. A drought event is defined as a 10-year drought event whose annual mean soil moisture was below its 10th percentile from the 1850–1900 base period. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency or the intensity changes across the multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The line of zero in (a) indicates no change in intensity, while the line of one in (b) indicates no change in frequency. The results are based on the multi-model ensemble estimated from simulations of global climate models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway (SSP) forcing scenarios. Intensity changes in (a) are expressed as standard deviations of the interannual variability in the period 1850–1900 of the corresponding model. For details on the methods see Supplementary Material 11.SM.2. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

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==== 11.6.5.4 Hydrological Deficits ====

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Some studies support wetting tendencies as a response to a warmer climate when considering globally averaged changes in runoff over land ( [[#Roderick--2015|Roderick et al., 2015]] ; [[#Greve--2017|Greve et al., 2017]] ; Y. [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ), and streamflow projections respond to enhanced CO <sub>2</sub> concentrations in CMIP5 models ( [[#Yang--2019|Yang et al., 2019]] ). Nevertheless, when focusing regionally on low-runoff periods, model projections also show an increase of hydrological droughts in large world regions ( [[#Wanders--2015|Wanders and Van Lanen, 2015]] ; [[#Dai--2018|Dai et al., 2018]] ; [[#Vicente-Serrano--2020c|Vicente-Serrano et al., 2020c]] ). In general, the frequency of hydrological deficits is projected to increase over most of the continents, although with regionally and seasonally differentiated effects ( [[#11.9|Section 11.9]] ), with ''medium confidence'' of increase in the following AR6 regions: WCE, MED, SAU, WCA, WNA, SCA, NSA, SAM, SWS, SSA, WSAF, ESAF and MDG ( [[#11.9|Section 11.9]] ; [[#Forzieri--2014|Forzieri et al., 2014]] ; [[#Prudhomme--2014|Prudhomme et al., 2014]] ; [[#Giuntoli--2015|Giuntoli et al., 2015]] ; [[#Wanders--2015|Wanders and Van Lanen, 2015]] ; [[#Roudier--2016|Roudier et al., 2016]] ; [[#Marx--2018|Marx et al., 2018]] ; [[#Cook--2019|Cook et al., 2019]] ; [[#Zhao--2020|Zhao et al., 2020]] ). However, there are large uncertainties related to the hydrological/impact model used ( [[#Prudhomme--2014|Prudhomme et al., 2014]] ; [[#Schewe--2014|Schewe et al., 2014]] ; [[#Gosling--2017|Gosling et al., 2017]] ), limited signal-to-noise ratio (due to model spread) in several regions ( [[#Giuntoli--2015|Giuntoli et al., 2015]] ), and also uncertainties in the projection of future human activities, including water demand and land cover changes, which may represent more than 50% of the projected changes in hydrological droughts in some regions ( [[#Wanders--2015|Wanders and Wada, 2015]] ).

Regions dependent on mountainous snowpack as a temporary reservoir may be affected by severe hydrological droughts in a warmer world. In the southern European Alps, both winter and summer low flows are projected to be more severe, with a 25% decrease in the 2050s ( [[#Vidal--2016|Vidal et al., 2016]] ). In western USA, a 22% reduction in winter snow water equivalent is projected at around 2°C of global warming, with a further decrease of a 70% reduction at 4°C global warming ( [[#Rhoades--2018|Rhoades et al., 2018]] ). This decline would cause less predictable hydrological droughts in snowmelt-dominated areas of North America ( [[#Livneh--2020|Livneh and Badger, 2020]] ). The exact magnitude of the influence of higher temperatures on snow-related droughts is, however, difficult to estimate ( [[#Mote--2016|Mote et al., 2016]] ), since the streamflow changes could affect the timing of peak streamflows but not necessarily their magnitude. In addition, projected changes in hydrological droughts downstream of declining glaciers can be very complex to assess (Chapter 9, see also SROCC).

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==== 11.6.5.5 Atmospheric-based Drought Indices ====

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Studies show a stronger drying in projections based on atmospheric-based drought indices compared to ESM projections of changes in soil moisture ( [[#Berg--2018|Berg and Sheffield, 2018]] ) and runoff ( [[#Yang--2019|Yang et al., 2019]] ). It has been suggested that this difference is due to physiological CO <sub>2</sub> effects ( [[#11.6.5.2|Section 11.6.5.2]] ; [[#Roderick--2015|Roderick et al., 2015]] ; [[#Milly--2016|Milly and Dunne, 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Lemordant--2018|Lemordant et al., 2018]] ; [[#Scheff--2018|Scheff, 2018]] ; [[#Swann--2018|Swann, 2018]] ; [[#Greve--2019|Greve et al., 2019]] ; [[#Yang--2020|Yang et al., 2020]] ). Nonetheless, there is evidence that differences in projections between atmospheric-based drought indices and water-balance metrics from ESMs are not alone due to CO <sub>2</sub> -plant effects ( [[#Berg--2016|Berg et al., 2016]] ; [[#Scheff--2021|Scheff et al., 2021]] ). Differences can also be related to the fact that AED is an upper bound for ET in dry regions and conditions ( [[#11.6.1.2|Section 11.6.1.2]] ) and that soil moisture stress limits increases in ET in projections ( [[#11.6.5.2|Section 11.6.5.2]] ; [[#Berg--2016|Berg et al., 2016]] ; [[#Zhou--2021|Zhou et al., 2021]] ). In general, atmospheric-based indices show more drying than total column soil moisture ( [[#Berg--2018|Berg and Sheffield, 2018]] ; [[#Cook--2020|Cook et al., 2020]] ; [[#Scheff--2021|Scheff et al., 2021]] ), but are more consistent with projected increases in surface soil moisture deficits ( [[#Dirmeyer--2013|Dirmeyer et al., 2013]] ; [[#Dai--2018|Dai et al., 2018]] ; [[#Lu--2019|Lu et al., 2019]] ; [[#Cook--2020|Cook et al., 2020]] ; [[#Vicente-Serrano--2020c|Vicente-Serrano et al., 2020c]] ).

Atmospheric-based drought indices are not metrics of soil moisture or runoff ( [[#11.6.1.5|Section 11.6.1.5]] ) so their projections may not necessarily reflect the same trend of online simulated soil moisture and runoff. Independently of effects on the land water balance, atmospheric-based drought indices will reflect the potential vegetation stress resulting from deficits between available water and enhanced AED, even in conditions with no or low ET. Under dry conditions, the enhanced AED associated with human forcing would increase plant water stress ( [[#Brodribb--2020|Brodribb et al., 2020]] ), with effects on widespread forest dieback and mortality ( [[#Anderegg--2013|Anderegg et al., 2013]] ; [[#Williams--2013|Williams et al., 2013]] ; [[#Allen--2015|Allen et al., 2015]] ; [[#McDowell--2015|McDowell and Allen, 2015]] ; [[#McDowell--2016|McDowell et al., 2016]] , 2020), and stronger risk of megafires ( [[#Flannigan--2016|Flannigan et al., 2016]] ; [[#Podschwit--2018|Podschwit et al., 2018]] ; [[#Clarke--2019|Clarke and Evans, 2019]] ; [[#Varela--2019|Varela et al., 2019]] ). For these reasons, there is ''high confidence'' that the future projections of enhanced drought severity showed by the PDSI-PM and the SPEI-PM are representative of more frequent and severe plant stress episodes and more severe agricultural and ecological drought impacts in some regions.

Global tendencies towards more severe and frequent agricultural and ecological drought conditions are identified in future projections when focusing on atmospheric-based drought indices such as the PDSI-PM or the SPEI-PM. They expand the spatial extent of drought conditions compared to meteorological drought to most of North America, Europe, Africa, Central and East Asia and Southern Australia ( [[#Cook--2014a|Cook et al., 2014a]] ; [[#Chen--2017a|Chen and Sun, 2017a]] , b; [[#Gao--2017b|Gao et al., 2017b]] ; [[#Lehner--2017|Lehner et al., 2017]] ; [[#Zhao--2017|Zhao and Dai, 2017]] ; [[#Dai--2018|Dai et al., 2018]] ; [[#Naumann--2018|Naumann et al., 2018]] ; [[#Potopová--2018|Potopová et al., 2018]] ; [[#Gu--2020|Gu et al., 2020]] ; Vicente-Serrano et al., 2020c; [[#Dai--2021|Dai, 2021]] ). Projections in PDSI-PM and SPEI-PM are used to complement total soil moisture projections in assessing projected changes in agricultural and ecological drought ( [[#11.9|Section 11.9]] ).

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==== 11.6.5.6 Synthesis for Different Drought Types ====

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The tables in [[#11.9|Section 11.9]] provide assessed projected changes in metorological drought, agricultural and ecological drought, and hydrological droughts. The assessment shows that several regions will be affected by more severe agricultural and ecological droughts even if global warming is stabilized at 2°C, including MED, WSAF, SAM and SSA ( ''high confidence'' ), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, WCE, NCA, WNA and CNA ( ''medium confidence'' ). Some regions are also projected to be affected by more severe agricultural and ecological droughts at 1.5°C (MED, WSAF, ESAF, SAU, NSA, SAM, SSA, can; ''medium confidence'' ) At 4°C of global warming, even more regions would be affected by agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF and MDG). NEAF, SAS are also projected to experience less agricultural and ecological drought with global warming ( ''medium confidence'' ). Projected changes in meteorological droughts are, overall, less extended but also affect several AR6 regions, at 1.5°C and 2°C (MED, EAU, SAU, SCA, NSA, NCA, WSAF, ESAF, MDG) and 4°C of global warming (WCE, MED, EAU, SAU, SEA, SCA, CAR, NWS, NSA, NES, SAM, SWS, SSA, NCA, ENA, WAF, WSAF, ESAF, MDG). Several regions are also projected to be affected by more hydrological droughts at 1.5°C and 2°C (WCE, MED, WNA, WSAF, ESAF) and 4°C of global warming (NEU, WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, MDG). To illustrate the changes in both intensity and frequency of drought in the regions where strongest changes are projected, Figure 11.18 displays changes in the intensity and frequency of soil moisture drought under different global warming levels (1.5°C, 2°C, 4°C) relative to the 1851-1900 baseline based on CMIP6 simulations under different SSP forcing scenarios averaged over “drying regions”, i.e. AR6 regions for which there is at least ''medium confidence'' in increase in agricultural and ecological drought at 2°C of global warming. The 90% uncertainty ranges for the projected changes in both intensity and frequency are above zero, indicating significant increase in both intensity and frequency of drought in these regions as whole.

In summary, more regions are affected by increases in agricultural and ecological droughts with increasing global warming ( ''high confidence'' ). New evidence strengthens the SR1.5 conclusion that even relatively small incremental increases in global warming (+0.5°C) cause a worsening of droughts in some regions ( ''high confidence'' ) ''.'' Some regions are projected to be affected by more severe agricultural and ecological droughts at 1.5°C of global warming (MED, WSAF, ESAF, SAU, NSA, SAM, SSA, can; ''medium confidence'' ). A larger number of regions are projected to be affected by more severe agricultural and ecological droughts at 2°C of global warming, including MED, WSAF, SAM and SSA ( ''high confidence'' ), and ESAF, MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, WCE, NCA, WNA and CNA ( ''medium confidence'' ). At 4°C of global warming, even more regions would be affected by agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF, ESAF and MDG). Some regions are also projected to experience less agricultural and ecological drought with global warming ( ''medium confidence;'' NEAF, SAS). There is ''high confidence'' that the projected increases in agricultural and ecological droughts are strongly affected by AED increases in a warming climate, although ET increases are projected to be smaller than those in AED due to soil moisture limitations and CO <sub>2</sub> effects on leaf stomatal conductance. Enhanced atmospheric CO <sub>2</sub> concentrations lead to enhanced water-use efficiency in plants ( ''medium confidence'' ), but there is ''low confidence'' that it can alleviate agricultural and ecological droughts, or hydrological droughts, at higher global warming levels characterized by limited soil moisture and enhanced AED.

Projected changes in meteorological droughts are overall less extended than for agricultural and ecological droughts, but also affect several AR6 regions, even at 1.5°C and 2°C of global warming. Several regions are also projected to be more strongly affected by hydrological droughts with increasing global warming (NEU, WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, MDG). Increased soil moisture limitation and associated changes in droughts are projected to lead to increased vegetation stress in many regions, with implications for the global land carbon sink (Cross-Chapter Box 5). There is ''high confidence'' that the global land carbon sink will become less efficient due to soil moisture limitations and associated drought conditions in some regions in higher-emissions scenarios, especially under global warming levels above 4°C; however, there is ''low confidence'' on how these water cycle feedbacks will play out in lower-emissions scenarios (at 2°C global warming or lower; Cross-Chapter Box5.1).

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<span id="extreme-storms"></span>
== 11.7 Extreme Storms ==

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Extreme storms, such as tropical cyclones (TCs), extratropical cyclones (ETCs), and severe convective storms often have substantial societal impacts. Quantifying the effect of climate change on extreme storms is challenging, partly because extreme storms are rare, short-lived, and local, and individual events are largely influenced by stochastic variability. The high degree of random variability makes detection and attribution of extreme storm trends more uncertain than detection and attribution of trends in other aspects of the environment in which the storms evolve (e.g., larger-scale temperature trends). Projecting changes in extreme storms is also challenging because of constraints in the models’ ability to accurately represent the small-scale physical processes that can drive these changes. Despite the challenges, progress has been made since AR5.

The SREX (Chapter 3) concluded that there is ''low confidence'' in observed long-term (40 years or more) trends in TC intensity, frequency, and duration, and any observed trends in phenomena such as tornadoes and hail; it is ''likely'' that extratropical storm tracks have shifted poleward in both the Northern and Southern Hemispheres, and that heavy rainfalls and mean maximum wind speeds associated with TCs will increase with continued greenhouse gas warming; it is ''likely'' that the global frequency of TCs will either decrease or remain essentially unchanged, while it is ''more likely than not'' that the frequency of the most intense storms will increase substantially in some ocean basins; there is ''low confidence'' in projections of small-scale phenomena such as tornadoes and hail storms; and there is ''medium confidence'' that there will be a reduced frequency and a poleward shift of mid-latitude cyclones due to future anthropogenic climate change.

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[[File:a6e6b4f9c35dc9ff1daaa3873f346b6c IPCC_AR6_WGI_Figure_11_19.png]]

'''Figure 11.19 |''' '''Projected changes in (a–c) the number of consecutive dry days (CDD), (d–f) annual mean soil moisture over the total column, and (g–l) the frequency and intensity of 1-i''' n '''-10-year soil moisture drought for the June-to-August and December-to-February seasons at 1.5°C, 2°C, and 4°C of global warming compared to the 18''' ''50–1900 baseline.'' The unit for soil moisture change is the standard deviation of interannual variability in soil moisture during 1850–1900. Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of about 1-in-6-year droughts during 1850–1900 becoming the norm in the future. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1. For details on the methods see Supplementary Material 11.SM.2. Changes in CDDs are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

Since SREX, several IPCC Reports also assessed storms. The AR5 (Chapter 2, [[#Hartmann--2013|Hartmann et al., 2013]] ) assessment observed with ''low confidence'' long-term trends in TC metrics, but revised the statement from SREX to state that it is ''virtually certain'' that there are increasing trends in North Atlantic TC activity since the 1970s, with ''medium confidence'' that anthropogenic aerosol forcing has contributed to these trends. The AR5 concluded that it is ''likely'' that TC precipitation and mean intensity will increase and ''more likely than not'' that the frequency of the strongest storms will increase with continued greenhouse gas warming. ''confidence'' in projected trends in overall TC frequency remained ''low'' . ''confidence'' in observed and projected trends in hail storm and tornado events also remained ''low'' . The SROCC (Chapter 6, [[#Collins--2019|Collins et al., 2019]] ) assessed past and projected TCs and ETCs, supporting the AR5 conclusions with some additional detail. Literature subsequent to AR5 adds support to the likelihood of increasing trends in TC intensity, precipitation, and frequency of the most intense storms, while some newer studies have added uncertainty to projected trends in overall frequency. A growing body of literature since AR5 on the poleward migration of TCs led to a new assessment in SROCC of ''low confidence'' that the migration in the western North Pacific represents a detectable climate change contribution from anthropogenic forcing. The SR1.5 (Chapter 3, [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) essentially confirmed the AR5 assessment of TCs and ETCs, adding that heavy precipitation associated with TCs is projected to be higher at 2°C compared to 1.5°C global warming ( ''medi'' ''um confidence'' ).

The SREX, AR5, SROCC, and SR1.5, do not provide assessments of the atmospheric rivers, and SROCC and SR1.5 do not assess severe convective storms and extreme winds. This section assesses the state of knowledge on the four phenomena of TCs, ETCs, severe convective storms, and extreme winds. Atmospheric rivers are addressed in Chapter 8. In this respect, this assessment closely mirrors the SROCC assessment of TCs and ETCs, while updating SREX and AR5 assessments of severe convective storms and extreme winds.

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<span id="tropical-cyclones"></span>
=== 11.7.1 Tropical Cyclones ===

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==== 11.7.1.1 Mechanisms and Drivers ====

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The genesis, development, and tracks of TCs depend on conditions of the larger-scale circulations of the atmosphere and ocean ( [[#Christensen--2013|Christensen et al., 2013]] ). Large-scale atmospheric circulations, such as the Hadley and Walker circulations and the monsoon circulations can significantly affect TCs, as can internal variability acting on various time scales (Annex IV), from intra-seasonal (e.g., the Madden–Julian and Boreal Summer Intraseasonal oscillations and equatorial waves) and interannual (e.g., the El Niño–Southern Oscillation and Pacific and Atlantic Meridional Modes), to inter-decadal (e.g., Atlantic Multidecadal Variability and Pacific Decadal Variability). This broad range of natural variability makes detection of anthropogenic effects difficult, and uncertainties in the projected changes of these modes of variability increase uncertainty in the projected changes in TC activity. Aerosol forcing also affects sea surface temperature (SST) patterns and cloud microphysics, and it is ''likely'' that observed changes in TC activity are partly caused by changes in aerosol forcing ( [[#Evan--2011|Evan et al., 2011]] ; [[#Ting--2015|Ting et al., 2015]] ; [[#Sobel--2016|Sobel et al., 2016]] , 2019; [[#Takahashi--2017|Takahashi et al., 2017]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Reed--2019|Reed et al., 2019]] ). Among possible changes from these drivers, there is ''medium confidence'' that the Hadley cell has widened and will continue to widen in the future (Sections 2.3, 3.3 and 4.5). This ''likely'' causes latitudinal shifts of TC tracks ( [[#Sharmila--2018|Sharmila and Walsh, 2018]] ). Regional TC activity changes are also strongly affected by projected changes in SST warming patterns ( [[#Yoshida--2017|Yoshida et al., 2017]] ), which are highly uncertain (Chapters 4 and 9).

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<span id="observed-trends-4"></span>
==== 11.7.1.2 Observed Trends ====

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Identifying past trends in TC metrics remains a challenge due to the heterogeneous character of the historical instrumental data, which are known as ‘best-track’ data ( [[#Schreck--2014|Schreck et al., 2014]] ). There is ''low confidence'' in most reported long-term (multi-decadal to centennial) trends in TC frequency- or intensity-based metrics due to changes in the technology used to collect the best-track data. This should not be interpreted as implying that no physical (real) trends exist, but rather as indicating that either the quality or the temporal length of the data is not adequate to provide robust trend detection statements, particularly in the presence of multi-decadal variability.

There are previous and ongoing efforts to homogenize the best-track data ( [[#Elsner--2008|Elsner et al., 2008]] ; [[#Kossin--2013|Kossin et al., 2013]] , 2020; [[#Choy--2015|Choy et al., 2015]] ; [[#Landsea--2015|Landsea, 2015]] ; [[#Emanuel--2018|Emanuel et al., 2018]] ) and there is substantial literature that finds positive trends in intensity-related metrics in the best-track during the ‘satellite period’, which is generally limited to around the past 40 years ( [[#Kang--2012|Kang and Elsner, 2012]] ; [[#Kishtawal--2012|Kishtawal et al., 2012]] ; [[#Kossin--2013|Kossin et al., 2013]] , 2020; [[#Mei--2016|Mei and Xie, 2016]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Tauvale--2019|Tauvale and Tsuboki, 2019]] ). When best-track trends are tested using homogenized data, the intensity trends generally remain positive, but are smaller in amplitude ( [[#Kossin--2013|Kossin et al., 2013]] ; [[#Holland--2014|Holland and Bruyère, 2014]] ). [[#Kossin--2020|Kossin et al. (2020)]] extended the homogenized TC intensity record to the period 1979–2017 and identified significant global increases in major TC exceedance probability of about 6% per decade. In addition to trends in TC intensity, there is evidence that TC intensification rates and the frequency of rapid intensification events have increased within the satellite era ( [[#Kishtawal--2012|Kishtawal et al., 2012]] ; [[#Balaguru--2018|Balaguru et al., 2018]] ; [[#Bhatia--2018|Bhatia et al., 2018]] ). The increase in intensification rates is found in the best-track and the homogenized intensity data.

A subset of the best-track data corresponding to hurricanes that have directly impacted the USA since 1900 is considered to be reliable, and shows no trend in the frequency of USA landfall events ( [[#Knutson--2019|Knutson et al., 2019]] ). However, an increasing trend in normalized USA hurricane damage, which accounts for temporal changes in exposed wealth ( [[#Grinsted--2019|Grinsted et al., 2019]] ), and a decreasing trend in TC translation speed over the USA (Kossin, 2019) have also been identified in this period. A similarly reliable subset of the data representing TC landfall frequency over Australia shows a decreasing trend in Eastern Australia since the 1800s ( [[#Callaghan--2011|Callaghan and Power, 2011]] ), as well as in other parts of Australia since 1982 ( [[#Chand--2019|Chand et al., 2019]] ; [[#Knutson--2019|Knutson et al., 2019]] ). A paleoclimate proxy reconstruction shows that recent levels of TC interactions along parts of the Australian coastline are the lowest in the past 550–1500 years ( [[#Haig--2014|Haig et al., 2014]] ). Existing TC datasets show substantial inter-decadal variations in basin-wide TC frequency and intensity in the western North Pacific, but a statistically significant north-westward shift in the western North Pacific TC tracks since the 1980s ( [[#Lee--2020|]] [[#Lee--2020|T.-C. Lee et al., 2020]] ). Inthe case of the North Indian Ocean, analyses of trends are highly dependent on the details of each analysis (e.g., pre- and/or post-monsoon season period, or Bay of Bengal and/or Arabian Sea region). The most consistent trends are an increase in the occurrence of the most intense TCs, and a decrease in the overall TC frequency, in particular in the Bay of Bengal ( [[#Sahoo--2016|Sahoo and Bhaskaran, 2016]] ; [[#Balaji--2018|Balaji et al., 2018]] ; [[#Singh--2019|Singh et al., 2019]] ; [[#Baburaj--2020|Baburaj et al., 2020]] ). In the South Indian Ocean (SIO), an increase in the occurrence of the most intense TCs has been noted; however, there are well-known data quality issues there ( [[#Kuleshov--2010|Kuleshov et al., 2010]] ; [[#Fitchett--2018|Fitchett, 2018]] ). When the SIO data are homogenized, a significant increase is found in the fractional proportion of global Category 3–5 TC instances (6-hourly intensity estimates during the lifetime of each TC) to all Category 1–5 instances ( [[#Kossin--2020|Kossin et al., 2020]] ).

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[[File:0adffd6e83a0f7f63ea9c6dad9ad8006 IPCC_AR6_WGI_Figure_11_20.png]]

'''Figure 11.20 |''' '''Summary schematic of past and projected changes in tropical cyclone (TC), extratropical cyclone (ETC), atmospheric river (AR), and severe convective storm (SCS) behaviour.''' Global changes (blue shading) from top to bottom: ''(i)'' Increased mean and maximum rain rates in TCs, ETCs, and ARs [past ( ''low confidence'' due to lack of reliable data) and projected ( ''high confidence'' )]; ''(ii)'' Increased proportion of stronger TCs [past ( ''medium confidence'' ) and projected ( ''high confidence'' )]; ''(iii)'' Decrease or no change in global frequency of TC genesis [past ( ''low confidence'' due to lack of reliable data) and projected ( ''medium confidence'' )]; and (iv) Increased and decreased ETC wind speed, depending on the region, as storm tracks change [past ( ''low confidence'' due to lack of reliable data) and projected ( ''medium confidence'' )]. Regional changes, from left to right: ''(i)'' Poleward TC migration in the western North Pacific and subsequent changes in TC exposure [past ( ''medium confidence'' ) and projected ( ''medium'' ''confidence'' )]; ''(ii)'' Slowdown of TC forward translation speed over the contiguous USA and subsequent increase in TC rainfall [past ( ''medium confidence'' ) and projected ( ''low'' ''confidence'' due to lack of directed studies)]; and ''(iii)'' Increase in mean and maximum SCS rain rate and increase in spring SCS frequency and season length over the contiguous USA [past ( ''low confidence'' due to lack of reliable data) and projected ( ''medium confidence'' )].

As with all confined regional analyses of TC frequency, it is generally unclear whether any identified changes are due to a basin-wide change in TC frequency, or to systematic track shifts (or both). From an impacts perspective, however, these changes over land are highly relevant and emphasize that large-scale modifications in TC behaviour can have a broad spectrum of impacts on a regional scale.

Subsequent to AR5, two metrics have been analysed that are argued to be comparatively less sensitive to data issues than frequency- and intensity-based metrics. Trends in these metrics have been identified over the past 70 years or more ( [[#Knutson--2019|Knutson et al., 2019]] ). The first metric – the mean latitude where TCs reach their peak intensity – exhibits a global and regional poleward migration during the satellite period ( [[#Kossin--2014|Kossin et al., 2014]] ). The poleward migration can influence TC hazard exposure and risk ( [[#Kossin--2016a|Kossin et al., 2016a]] ) and is consistent with the independently observed expansion of the tropics ( [[#Lucas--2014|Lucas et al., 2014]] ). The migration has been linked to changes in the Hadley circulation ( [[#Altman--2018|Altman et al., 2018]] ; [[#Sharmila--2018|Sharmila and Walsh, 2018]] ; [[#Studholme--2018|Studholme and Gulev, 2018]] ). The migration is also apparent in the mean locations where TCs exhibit eyes ( [[#Knapp--2018|Knapp et al., 2018]] ), which is when TCs are most intense. Part of the Northern Hemisphere poleward migration is due to basin-wide changes in TC frequency ( [[#Kossin--2014|Kossin et al., 2014]] , 2016b; [[#Moon--2015|Moon et al., 2015]] , 2016) and the trends, as expected, can be sensitive to the time period chosen ( [[#Tennille--2017|Tennille and Ellis, 2017]] ; [[#Kossin--2018|Kossin, 2018]] ; [[#Song--2018|Song and Klotzbach, 2018]] ) and to subsetting of the data by intensity ( [[#Zhan--2017|Zhan and Wang, 2017]] ). The poleward migration is particularly pronounced and well-documented in the western North Pacific basin ( [[#Kossin--2016a|Kossin et al., 2016a]] ; [[#Oey--2016|Oey and Chou, 2016]] ; [[#Liang--2017|Liang et al., 2017]] ; [[#Nakamura--2017|Nakamura et al., 2017]] ; [[#Altman--2018|Altman et al., 2018]] ; [[#Daloz--2018|Daloz and Camargo, 2018]] ; J. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Lee--2020|]] [[#Lee--2020|T.-C. Lee et al., 2020]] ; [[#Yamaguchi--2020a|Yamaguchi and Maeda, 2020a]] ; [[#Kubota--2021|Kubota et al., 2021]] ).

A second metric that is argued to be comparatively less sensitive to data issues than frequency- and intensity-based metrics is TC translation speed ( [[#Kossin--2018|Kossin, 2018]] ), which exhibits a global slowdown in the best-track data over the period 1949–2016. TC translation speed is a measure of the speed at which TCs move across the Earth’s surface, and is very closely related to local rainfall amounts (i.e., a slower translation speed causes greater local rainfall). TC translation speed also affects structural wind damage and coastal storm surge by changing the hazard event duration. The slowdown is observed in the best-track data from all basins except the Northern Indian Ocean, and is also found in a number of regions where TCs interact directly with land. The slowing trends identified in the best-track data by [[#Kossin--2018|Kossin (2018)]] have been argued to be largely due to data heterogeneity. [[#Moon--2019|Moon et al. (2019)]] and [[#Lanzante--2019|Lanzante (2019)]] provide evidence that meridional TC track shifts project onto the slowing trends, and argue that these shifts are due to the introduction of satellite data. Kossin (2019) provides evidence that the slowing trend is real by focusing on Atlantic TC track data over the contiguous USA in the 118-year period 1900–2017, which are generally considered reliable. In this period, mean TC translation speed has decreased by 17%. The slowing TC translation speed is expected to increase local rainfall amounts, which would increase coastal and inland flooding. In combination with slowing translation speed, abrupt TC track direction changes – that can be associated with track ‘meanders’ or ‘stalls’ – have become increasingly common along the North American coast since the mid-20th century, leading to more rainfall in the region ( [[#Hall--2019|Hall and Kossin, 2019]] ).

In summary, there is mounting evidence that a variety of TC characteristics have changed over various time periods. It is ''likely'' that the global proportion of Category 3–5 tropical cyclone instances and the frequency of rapid intensification events have increased globally over the past 40 years. It is ''very likely'' that the average location where TCs reach their peak wind intensity has migrated poleward in the western North Pacific Ocean since the 1940s. It is ''likely'' that TC translation speed has slowed over the USA since 1900.

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<span id="model-evaluation-4"></span>
==== 11.7.1.3 Model Evaluation ====

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Accurate projections of future TC activity have two principal requirements: accurate representation of changes in the relevant environmental factors (e.g., SSTs) that can affect TC activity, and accurate representation of actual TC activity in given environmental conditions.In particular, models’ capacity to reproduce historical trends or interannual variabilities of TC activity is relevant to the confidence in future projections. One test of the models is to evaluate their ability to reproduce the dependency of the TC statistics in the different basins in the real world, in addition to their capability of reproducing atmospheric and ocean environmental conditions. For the evaluation of projections of TC-relevant environmental variables, AR5 confidence statements were based on global surface temperature and moisture, but not on the detailed regional structure of SST and atmospheric circulation changes such as steering flows and vertical shear, which affect characteristics of TCs (genesis, intensity, tracks, etc.). Various aspects of TC metrics are used to evaluate how capable models are of simulating present-day TC climatologies and variability (e.g., TC frequency, wind intensity, precipitation, size, tracks, and their seasonal and interannual changes) ( [[#Walsh--2015|Walsh et al., 2015]] ; [[#Camargo--2016|Camargo and Wing, 2016]] ; [[#Knutson--2019|Knutson et al., 2019]] , 2020). Other examples of TC climatology/variability metrics are spatial distributions of TC occurrence and genesis ( [[#Walsh--2015|Walsh et al., 2015]] ), seasonal cycles and interannual variability of basin-wide activity ( [[#Zhao--2009|Zhao et al., 2009]] ; [[#Shaevitz--2014|Shaevitz et al., 2014]] ; [[#Kodama--2015|Kodama et al., 2015]] ; [[#Murakami--2015|Murakami et al., 2015]] ; [[#Yamada--2017|Yamada et al., 2017]] ) or landfalling activity ( [[#Lok--2018|Lok and Chan, 2018]] ), as well as newly developed process-diagnostics designed specifically for TCs in climate models (D. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ; [[#Wing--2019|Wing et al., 2019]] ; [[#Moon--2020|Moon et al., 2020]] ).

Confidence in the projection of intense TCs, such as those of Category 4–5, generally becomes higher as the resolution of the models becomes higher. The Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) class climate models (around 100–200 km grid spacing) cannot simulate TCs of Category 4–5 intensity. They do simulate storms of relatively high vorticity that are at best described as ‘TC-like’, but metrics such as storm counts are highly dependent on tracking algorithms ( [[#Camargo--2013|Camargo, 2013]] ; [[#Wehner--2015|Wehner et al., 2015]] ; [[#Zarzycki--2017|Zarzycki and Ullrich, 2017]] ; [[#Roberts--2020a|Roberts et al., 2020a]] ). High-resolution GCMs (around 10–60 km grid spacing), as used in HighResMIP ( [[#Haarsma--2016|Haarsma et al., 2016]] ; [[#Roberts--2020a|Roberts et al., 2020a]] ), begin to capture some structures of TCs more realistically, as well as produce intense TCs of Category 4–5 despite the effects of parametrized deep cumulus convection processes ( [[#Murakami--2015|Murakami et al., 2015]] ; [[#Wehner--2015|Wehner et al., 2015]] ; [[#Yamada--2017|Yamada et al., 2017]] ; [[#Roberts--2018|Roberts et al., 2018]] ; [[#Moon--2020|Moon et al., 2020]] ). Convection-permitting models (around 1–10 km grid-spacing), such as used in some dynamical downscaling studies, provide further realism with capturing TC eye-wall structures ( [[#Tsuboki--2015|Tsuboki et al., 2015]] ). Model characteristics besides resolution, especially details of convective parametrization, can influence a model’s ability to simulate intense TCs ( [[#Reed--2011|Reed and Jablonowski, 2011]] ; [[#Zhao--2012|Zhao et al., 2012]] ; [[#He--2015|He and Posselt, 2015]] ; D. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ; [[#Zhang--2018|Zhang and Wang, 2018]] ; [[#Camargo--2020|Camargo et al., 2020]] ). However, models’ dynamical cores and other physics also affect simulated TC properties ( [[#Reed--2015|Reed et al., 2015]] ; [[#Vidale--2021|Vidale et al., 2021]] ). Both wide-area regional and global convection-permitting models without the need for parameterized convection are becoming more useful for TC regional model projection studies ( [[#Tsuboki--2015|Tsuboki et al., 2015]] ; [[#Kanada--2017a|Kanada et al., 2017a]] ; [[#Gutmann--2018|Gutmann et al., 2018]] ) and global model projection studies ( [[#Satoh--2015|Satoh et al., 2015]] , 2017; [[#Yamada--2017|Yamada et al., 2017]] ), as they capture more realistic TC eye wall structures ( [[#Kinter%20III--2013|Kinter III et al., 2013]] ) and are becoming more useful for investigating changes in TC structures ( [[#Kanada--2013|Kanada et al., 2013]] ; [[#Yamada--2017|Yamada et al., 2017]] ). Large ensemble simulations of GCMs with 60 km grid spacing provide TC statistics that allow more reliable detection of changes in the projections, which are not well captured in any single experiment ( [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Yamaguchi--2020|Yamaguchi et al., 2020]] ). Variable resolution global models offer an alternative to regional models for individual TC or basin-wide simulations ( [[#Yanase--2012|Yanase et al., 2012]] ; [[#Zarzycki--2014|Zarzycki et al., 2014]] ; [[#Harris--2016|Harris et al., 2016]] ; [[#Reed--2020|Reed et al., 2020]] ; [[#Stansfield--2020|Stansfield et al., 2020]] ). Computationally less intense than equivalent uniform resolution global models, they also do not require lateral boundary conditions, thus reducing this source of error ( [[#Hashimoto--2016|Hashimoto et al., 2016]] ). Confidence in the projection of TC statistics and properties is increased by the use of higher-resolution models with more realistic simulations.

Operational forecasting models also reproduce TCs, and their use for climate projection studies shows promise. However, there is limited application for future projections as they are specifically developed for operational purposes, and TC climatology is not necessarily well evaluated. Intercomparison of operational models indicates that enhancement of horizontal resolution can provide more credible projections of TCs ( [[#Nakano--2017|Nakano et al., 2017]] ). Likewise, high-resolution climate models show promise as TC forecast tools ( [[#Zarzycki--2015|Zarzycki and Jablonowski, 2015]] ; [[#Reed--2020|Reed et al., 2020]] ), further narrowing the continuum of weather and climate models, and increasing confidence in projections of future TC behaviour. However, higher horizontal resolution does not necessarily lead to an improved TC climatology ( [[#Camargo--2020|Camargo et al., 2020]] ).

Atmosphere–ocean interaction is an important process in TC evolution. Atmosphere–ocean coupled models are generally better than atmosphere-only models at capturing realistic processes related to TCs ( [[#Murakami--2015|Murakami et al., 2015]] ; [[#Ogata--2015|Ogata et al., 2015]] , 2016; [[#Zarzycki--2016|Zarzycki, 2016]] ; [[#Kanada--2017b|Kanada et al., 2017b]] ; [[#Scoccimarro--2017|Scoccimarro et al., 2017]] ). However, the basin-scale SST biases commonly found in atmosphere–ocean models can introduce substantial errors in the simulated TC number ( [[#Hsu--2019|Hsu et al., 2019]] ). Higher-resolution ocean models improve the simulation of TCs by reducing the SST climatology bias ( [[#Li--2018|Li and Sriver, 2018]] ; [[#Roberts--2020a|Roberts et al., 2020a]] ). Coarse resolution atmospheric models may degrade coupled model performance as well. For example, in a case study of Hurricane Harvey, [[#Trenberth--2018|Trenberth et al. (2018)]] suggested that the lack of realistic hurricane frequency and intensity within coupled climate models hampers the models’ ability to simulate SST and ocean heat content and their changes.

Even with higher-resolution atmosphere–ocean coupled models, TC projection studies still rely on assumptions in experimental design that introduce uncertainties. Computational constraints often limit the number of simulations, resulting in relatively small ensemble sizes and incomplete analyses of possible future SST magnitude and pattern changes ( [[#Zhao--2011|Zhao and Held, 2011]] ; [[#Knutson--2013|Knutson et al., 2013]] ). Uncertainties in aerosol forcing also are reflected in TC projection uncertainty ( [[#Wang--2014|Wang et al., 2014]] ).

Regional climate models (RCM) with grid spacing around 15–50 km can be used to study the projection of TCs. RCMs are run with lateral and surface boundary conditions, which are specified by the atmospheric state and SSTs simulated by GCMs. Various combinations of the lateral and surface boundary conditions can be chosen for RCM studies, and uncertainties in the projection can be further examined in general. They are used for studying changes in TC characteristics in a specific area, such as Vietnam ( [[#Redmond--2015|Redmond et al., 2015]] ) and the Philippines ( [[#Gallo--2019|Gallo et al., 2019]] ).

Less computationally expensive downscaling approaches that allow larger ensembles and long-term studies are also used in the projection of TCs ( [[#Emanuel--2006|Emanuel et al., 2006]] ; C.Y. [[#Lee--2018|]] [[#Lee--2018|]] [[#Lee--2018|Lee et al., 2018]] ). A statistical–dynamical TC downscaling method requires assumptions of the rate of seeding of random initial disturbances, which are generally assumed to not change with climate change ( [[#Emanuel--2008|Emanuel et al., 2008]] ; [[#Emanuel--2013|Emanuel, 2013]] ). The results with the downscaling approach might depend on the assumptions, which are required for the simplification of the methods.

In summary, various types of models are useful to study how TCs change in response to climate changes, and there is no unique solution for choosing a model type. However, higher-resolution models generally capture TC properties more realistically ( ''high confidence'' ). In particular, models with horizontal resolutions of 10–60 km are capable of reproducing strong TCs with Category 4–5 and those of 1–10 km are capable of the eye wall structure of TCs. Uncertainties in TC simulations come from details of the model configuration of both dynamical and physical processes. Models with realistic atmosphere–ocean interactions are generally better than atmosphere-only models at reproducing realistic TC evolutions ( ''hi'' ''gh confidence'' ).

<div id="11.7.1.4" class="h3-container"></div>

<span id="detection-and-attribution-event-attribution-4"></span>
==== 11.7.1.4 Detection and Attribution, Event Attribution ====

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There is general agreement in the literature that anthropogenic greenhouse gases and aerosols have measurably affected observed oceanic and atmospheric variability in TC-prone regions (see Chapter 3). This underpinned the SROCC assessment of ''medium confidence'' that humans have contributed to the observed increase in Atlantic hurricane activity since the 1970s (Chapter 5, [[#Bindoff--2013|Bindoff et al., 2013]] ). Literature subsequent to AR5 lends further support to this statement ( [[#Knutson--2019|Knutson et al., 2019]] ). However, there is still no consensus on the relative magnitude of human and natural influences on past changes in Atlantic hurricane activity, and particularly on which factor has dominated the observed increase ( [[#Ting--2015|Ting et al., 2015]] ) and it remains uncertain whether past changes in Atlantic TC activity are outside the range of natural variability. A recent result using high-resolution dynamical model experiments suggested that the observed spatial contrast in TC trends cannot be explained only by multi-decadal natural variability, and that external forcing plays an important role ( [[#Murakami--2020|Murakami et al., 2020]] ).Observational evidence for significant global increases in the proportion of major TC intensities ( [[#Kossin--2020|Kossin et al., 2020]] ) is consistent with both theory and numerical modelling simulations, which generally indicate an increase in mean TC peak intensity and the proportion of very intense TCs in a warming world ( [[#Knutson--2015|Knutson et al., 2015]] , 2020; [[#Walsh--2015|Walsh et al., 2015]] , 2016). In addition, high-resolution coupled model simulations provide support that natural variability alone is ''unlikely'' to explain the magnitude of the observed increase in TC intensification rates and upward TC intensity trend in the Atlantic basin since the early 1980s ( [[#Bhatia--2019|Bhatia et al., 2019]] ; [[#Murakami--2020|Murakami et al., 2020]] ).

The cause of the observed slowdown in TC translation speed is not yet clear. [[#Yamaguchi--2020|Yamaguchi et al. (2020)]] used large ensemble simulations to argue that part of the slowdown is due to actual latitudinal shifts of TC tracks, rather than data artefacts, in addition to atmospheric circulation changes. G. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al. (2020)]] used large ensemble simulations to show that anthropogenic forcing can lead to a robust slowdown, particularly outside of the tropics at higher latitudes. [[#Yamaguchi--2020b|Yamaguchi and Maeda (2020b)]] found a significant slowdown in the western North Pacific over the past 40 years and attributed the slowdown to a combination of natural variability and global warming. The slowing trend since 1900 over the USA is robust and significant after removing multi-decadal variability from the time series (Kossin, 2019). Among the hypotheses discussed is the physical linkage between warming and slowing circulation ( [[#Held--2006|Held and Soden, 2006]] ; see also [[IPCC:Wg1:Chapter:Chapter-8#8.2.2.2|Section 8.2.2.2]] ), with expectations of Arctic amplification and weakening circulation patterns through weakening meridional temperature gradients ( [[#Coumou--2018|Coumou et al., 2018]] ; see also Cross-Chapter Box 10.1), or through changes in planetary wave dynamics ( [[#Mann--2017|Mann et al., 2017]] ). The tropics expansion and the poleward shift of the mid-latitude westerlies associated with warming is also suggested as the reason of the slowdown (G. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ). However, the connection of these mechanisms to the slowdown has not been robustly shown. Furthermore, slowing trends have not been unambiguously observed in circulation patterns that steer TCs, such as the Walker and Hadley circulations ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4|Section 2.3.1.4]] ), although these circulations generally slow down in numerical simulations under global warming (Sections 4.5.1.6 and 8.4.2.2).

The observed poleward trend in western North Pacific TCs remains significant after accounting for the known modes of dominant interannual to decadal variability in the region ( [[#Kossin--2016a|Kossin et al., 2016a]] ), and is also found in CMIP5 model-simulated TCs (in the recent historical period 1980–2005), although it is weaker than observed and is not statistically significant ( [[#Kossin--2016a|Kossin et al., 2016a]] ). However, the trend is significant in 21st-century CMIP5 projections under the RCP8.5 scenario, with a similar spatial pattern and magnitude to the past observed changes in that basin over the period 1945–2016, supporting a possible anthropogenic greenhouse gas contribution to the observed trends ( [[#Kossin--2016a|Kossin et al., 2016a]] ; [[#Knutson--2019|Knutson et al., 2019]] ).

The recent active TC seasons in some basins have been studied to determine whether there is anthropogenic influence. For 2015, [[#Murakami--2017b|Murakami et al. (2017b)]] explored the unusually high TC frequency near Hawaii and in the eastern Pacific basin. W. [[#Zhang--2016b|Zhang et al. (2016b)]] considered unusually high Accumulated Cyclone Energy (ACE) in the western North Pacific; and S.-H. [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|Yang et al. (2018)]] and [[#Yamada--2019|Yamada et al. (2019)]] looked at TC intensification in the western North Pacific. These studies suggest that the anomalous TC activity in 2015 was not solely explained by the effect of an extreme El Niño (see Box 11.4) and that there was also an anthropogenic contribution, mainly through the effects of SSTs in subtropical regions. In the post-monsoon seasons of 2014 and 2015, tropical storms with lifetime maximum winds greater than 46 m s <sup>−1</sup> were first observed over the Arabian Sea, and [[#Murakami--2017a|Murakami et al. (2017a)]] showed that the probability of late-season severe tropical storms is increased by anthropogenic forcing compared to the preindustrial era. [[#Murakami--2018|Murakami et al. (2018)]] concluded that the active 2017 Atlantic hurricane season was mainly caused by pronounced SSTs in the tropical North Atlantic and that these types of seasonal events will intensify with projected anthropogenic forcing. The trans-basin SST change, which might be driven by anthropogenic aerosol forcing, also affects TC activity. [[#Takahashi--2017|Takahashi et al. (2017)]] suggested that a decrease in sulphate aerosol emissions caused about half of the observed decreasing trends in TC genesis frequency in the south-eastern region of the western North Pacific during 1992–2011.

Event attribution is used in TC case studies to test whether the severities of recent intense TCs are explained without anthropogenic effects. In a case study of Hurricane Sandy (2012), [[#Lackmann--2015|Lackmann (2015)]] found no statistically significant impact of anthropogenic climate change on storm intensity, while projections in a warmer world showed significant strengthening. However, [[#Magnusson--2014|Magnusson et al. (2014)]] found that, in European Centre for Medium-Range Weather Forecast (ECMWF) simulations, the simulated cyclone depth and intensity, as well as precipitation, were larger when the model was driven by the warmer actual SSTs than the climatological average SSTs. In Super Typhoon Haiyan, which struck the Philippines on 8 November 2013, [[#Takayabu--2015|Takayabu et al. (2015)]] took an event attribution approach with cloud system-resolving (around 1 km) downscaling ensemble experiments to evaluate the anthropogenic effect on typhoons, and showed that the intensity of the simulated worst-case storm in the actual conditions was stronger than that in a hypothetical condition without historical anthropogenic forcing in the model. However, in a similar approach with two coarser parametrized convection models, Wehner et al. (2019) found conflicting human influences on Haiyan’s intensity. [[#Patricola--2018|Patricola and Wehner (2018)]] found little evidence of an attributable change in intensity of hurricanes Katrina (2005), Irma (2017), and Maria (2017) using a regional climate model configured between 3 km and 4.5 km resolution. They did, however, find attributable increases in heavy precipitation totals. These results imply that higher resolution, such as in a convective permitting 5 km or less mesh model, is required to obtain a robust anthropogenic intensification of a strong TC by simulating realistic rapid intensification ( [[#Kanada--2016|Kanada and Wada, 2016]] ; [[#Kanada--2017a|Kanada et al., 2017a]] ), and that whether the TC intensification can be attributed to the recent warming depends on the case.

The dominant factor in the extreme rainfall amounts during Hurricane Harvey’s passage onto the USA in 2017 was its slow translation speed. But studies published after the event have argued that anthropogenic climate change contributed to an increase in rain rate, which compounded the extreme local rainfall caused by the slow translation. [[#Emanuel--2017|Emanuel (2017)]] used a large set of synthetically-generated storms and concluded that the occurrence of extreme rainfall as observed in Harvey was substantially enhanced by anthropogenic changes to the larger-scale ocean and atmosphere characteristics; [[#Trenberth--2018|Trenberth et al. (2018)]] linked Harvey’s rainfall totals to the anomalously large ocean heat content from the Gulf of Mexico; and [[#van%20Oldenborgh--2017|van Oldenborgh et al. (2017)]] and [[#Risser--2017|Risser and Wehner (2017)]] applied extreme value analysis to extreme rainfall records in the Houston, Texas region, both attributing large increases to climate change. Large precipitation increases during Harvey due to global warming were also found using climate models ( [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; S.-Y.S. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ). Harvey precipitation totals were estimated in these papers to be three to 10 times more probable due to climate change. A best estimate from a regional climate and flood model is that urbanization increased the risk of the Harvey flooding by a factor of 21 (W. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ), using a regional climate and flood model, found that surface roughness from urbanization increased the risk of the Harvey flooding by a factor of 21. Anthropogenic effects on precipitation increases were also predicted in advance from a forecast model for Hurricane Florence in 2018 ( [[#Reed--2020|Reed et al., 2020]] ).

In summary, it is ''very likely'' that the recent active TC seasons in the North Atlantic, the North Pacific, and Arabian basins cannot be explained without an anthropogenic influence. The anthropogenic influence on these changes is principally associated to aerosol forcing, with stronger contributions to the response in the North Atlantic. It is ''more likely than not'' that the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing. It is ''likely'' that the poleward migration of TCs in the western North Pacific and the global increase in TC intensity rates cannot be explained entirely by natural variability. Event attribution studies of specific strong TCs provide ''limited evidence'' for anthropogenic effects on TC intensifications so far, but ''high confidence'' for increases in TC heavy precipitation. There is ''high confidence'' that anthropogenic climate change contributed to extreme rainfall amounts during Hurricane Harvey (2017) and other intense TCs.

<div id="11.7.1.5" class="h3-container"></div>

<span id="projections-3"></span>
==== 11.7.1.5 Projections ====

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A summary of studies on TC projections for the late 21st century, particularly studies since AR5, is given by [[#Knutson--2020|Knutson et al. (2020)]] , which is an assessment report mandated by the World Meteorological Organization (WMO). Studies subsequent to [[#Knutson--2020|Knutson et al. (2020)]] are generally consistent, and the confidence assessments here closely follow theirs ( [[#Cha--2020|Cha et al., 2020]] ), although there are some differences due to the varying confidence calibrations between the IPCC and WMO reports.

There is not an established theory for the drivers of future changes in the frequency of TCs. Most, but not all, high-resolution global simulations project significant reductions in the total number of TCs, with the bulk of the reduction at the weaker end of the intensity spectrum as the climate warms ( [[#Knutson--2020|Knutson et al., 2020]] ). Recent exceptions based on high-resolution coupled model results arenoted in [[#Bhatia--2018|Bhatia et al. (2018)]] and [[#Vecchi--2019|Vecchi et al. (2019)]] . [[#Vecchi--2019|Vecchi et al. (2019)]] showed that the representation of synoptic-scale seeds for TC genesis in their high-resolution model causes different projections of global TC frequency, and there is evidence for a decrease in cyclone seeds in some projected TCsimulations ( [[#Sugi--2020|Sugi et al., 2020]] ; Yamada et al., 2011). However, other research indicates that TC seeds are not an independent control on climatological TC frequency, rather the seeds covary with the large-scale controls on TCs ( [[#Patricola--2018|Patricola et al., 2018]] ). While empirical genesis indices derived from observations and reanalysis describe well the observed subseasonal and interannual variability of current TC frequency ( [[#Camargo--2007|Camargo et al., 2007]] , 2009; [[#Tippett--2011|Tippett et al., 2011]] ; [[#Menkes--2012|Menkes et al., 2012]] ), they fail to predict the decreased TC frequency found in most high-resolution model simulations ( [[#Zhang--2010|Zhang et al., 2010]] ; [[#Camargo--2013|Camargo, 2013]] ; [[#Wehner--2015|Wehner et al., 2015]] ), as they generally project an increase as the climate warms. This suggests a limitation of the use of the empirical genesis indices for projections of TC genesis, in particular due to their sensitivity to the humidity variable considered in the genesis index for these projections ( [[#Camargo--2014|Camargo et al., 2014]] ). In a different approach, a statistical–dynamical downscaling framework assuming a constant seeding rate with warming ( [[#Emanuel--2013|Emanuel, 2013]] , 2021) exhibits increases in TC frequency consistent with genesis indices-based projections, while downscaling with a different model leads to two different scenarios depending on the humidity variable considered (C.-Y. [[#Lee--2020|]] [[#Lee--2020|Lee et al., 2020]] ). This disparity in the sign of the projected change in global TC frequency, and the difficulty in explaining the mechanisms behind the different signed responses, further emphasize the lack of process understanding of future changes in tropical cyclogenesis ( [[#Walsh--2015|Walsh et al., 2015]] ; [[#Hoogewind--2020|Hoogewind et al., 2020]] ). Even within a single model, uncertainty in the pattern of future SST changes leads to large uncertainties (including the sign) in the projected change in TC frequency in individual ocean basins, although global TCs would appear to be less sensitive ( [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Bacmeister--2018|Bacmeister et al., 2018]] ).

Changes in SST and atmospheric temperature and moisture play a role in tropical cyclogenesis ( [[#Walsh--2015|Walsh et al., 2015]] ). Reductions in vertical convective mass flux due to increased tropical stability have been associated with a reduction in cyclogenesis ( [[#Held--2011|Held and Zhao, 2011]] ; [[#Sugi--2012|Sugi et al., 2012]] ). [[#Satoh--2015|Satoh et al. (2015)]] further posit that the robust simulated increase in the number of intense TCs, and hence increased vertical mass flux associated with intense TCs, must lead to a decrease in overall TC frequency because of this association. The Genesis Potential Index can be modified to mimic the TC frequency decreases of a model by altering the treatment of humidity ( [[#Camargo--2014|Camargo et al., 2014]] ). This supports the idea that increased mid-tropospheric saturation deficit ( [[#Emanuel--2008|Emanuel et al., 2008]] ) controls TC frequency, but the approach remains empirical. Other possible controlling factors, such as a decline in the number of seeds (held constant in Emanuel’s downscaling approach, or dependent on the genesis index formulation in the approach proposed by C.-Y. [[#Lee--2020|]] [[#Lee--2020|Lee et al., 2020]] ) caused by increased atmospheric stability have been proposed, but questioned as an important factor ( [[#Patricola--2018|Patricola et al., 2018]] ). The resolution of atmospheric models affects the number of seeds, hence TC genesis frequency ( [[#Vecchi--2019|Vecchi et al., 2019]] ; [[#Sugi--2020|Sugi et al., 2020]] ; [[#Yamada--2021|Yamada et al., 2021]] ). The diverse and sometimes inconsistent projected changes in global TC frequency by high-resolution models indicate that better process understanding and improvement of the models are needed to raise confidence in these changes.

Most TC-permitting model simulations (10–60 km or finer grid spacing) are consistent in their projection of increases in the proportion of intense TCs (Category 4–5), as well as an increase in the intensity of the strongest TCs defined by maximum wind speed or central pressure fall ( [[#Murakami--2012|Murakami et al., 2012]] ; [[#Tsuboki--2015|Tsuboki et al., 2015]] ; [[#Wehner--2018a|Wehner et al., 2018a]] ; [[#Knutson--2020|Knutson et al., 2020]] ). The general reduction in the total number of TCs, which is concentrated in storms weaker than or equal to Category 1, contributes to this increase. The models are somewhat less consistent in projecting an increase in the frequency of Category 4–5TCs (Wehner et al., 2018a; [[#Knutson--2020|Knutson et al., 2020]] ). The projected increase in the intensity of the strongest TCs is consistent with theoretical understanding (e.g., [[#Emanuel--1987|Emanuel, 1987]] ) and observations (e.g., [[#Kossin--2020|Kossin et al., 2020]] ). For a 2°C global warming, the median proportion of Category 4–5 TCs increases by 13%, while the median global TC frequency decreases by 14%, which implies that the median of the global Category 4–5 TC frequency is slightly reduced by 1% or almost unchanged ( [[#Knutson--2020|Knutson et al., 2020]] ). [[#Murakami--2020|Murakami et al. (2020)]] projected a decrease in TC frequency over the coming century in the North Atlantic due to greenhouse warming, as consistent with [[#Dunstone--2013|Dunstone et al. (2013)]] , and a reduction in TC frequency almost everywhere in the tropics in response to +1% CO <sub>2</sub> forcing. Exceptions include the central North Pacific (Hawaii region), east of the Philippines in the North Pacific, and two relatively small regions in the northern Arabian Sea and Bay of Bengal. These projections can vary substantially between ocean basins, possibly due to differences in regional SST warming and warming patterns ( [[#Sugi--2017|Sugi et al., 2017]] ; [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Bacmeister--2018|Bacmeister et al., 2018]] ). A summary of projections of TC characteristics is schematically shown by Figure 11.20.

The increase in global TC maximum surface wind speeds is about 5% for a 2°C global warming across a number of high-resolution multi-decadal studies ( [[#Knutson--2020|Knutson et al., 2020]] ). This indicates the deepening in global TC minimum surface pressure under the global warming conditions. A regional cloud-permitting model study shows that the strongest TC in the western North Pacific can be as strong as 857 hPa in minimum surface pressure with a wind speed of 88 m s <sup>–1</sup> under warming conditions in 2074–2087 ( [[#Tsuboki--2015|Tsuboki et al., 2015]] ). TCs are also measured by quantities such as ACE and the power dissipation index (PDI), which conflate TC intensity, frequency, and duration ( [[#Murakami--2014|Murakami et al., 2014]] ). Several TC modelling studies ( [[#Yamada--2010|Yamada et al., 2010]] ; H.S. [[#Kim--2014|]] [[#Kim--2014|Kim et al., 2014]] ; [[#Knutson--2015|Knutson et al., 2015]] ) project little change or decreases in the globally accumulated value of PDI or ACE, which is due to the decrease in the total number of TCs.

A projected increase in global average TC rain rates of about 12% for a 2°C global warming is consistent with the Clausius–Clapeyron scaling of saturation-specific humidity ( [[#Knutson--2020|Knutson et al., 2020]] ). Increases substantially greater than Clausius–Clapeyron scaling are projected in some regions, which is caused by increased low-level moisture convergence due to projected TC intensity increases in those regions ( [[#Knutson--2015|Knutson et al., 2015]] ; [[#Phibbs--2016|Phibbs and Toumi, 2016]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ; M. [[#Liu--2019|Liu et al., 2019]] a). Projections of TC precipitation using large-ensemble experiments ( [[#Kitoh--2019|Kitoh and Endo, 2019]] ) show that the annual maximum one-day precipitation total is projected to increase, except for the western North Pacific where only a small change (or even a reduction) is projected, mainly due to a projected decrease of TC frequency. They also show that the 10-year return value of extreme Rx1day associated with TCs will greatly increase in a region extending from Hawaii to the south of Japan. TC tracks and the location of topography relative to TCs significantly affect precipitation, thus, in general, areas on the eastern and southern faces of mountains have more impacts of TC precipitation changes ( [[#Hatsuzuka--2020|Hatsuzuka et al., 2020]] ). Projection studies using variable-resolution models in the North Atlantic ( [[#Stansfield--2020|Stansfield et al., 2020]] ) indicate that TC-related precipitation rates within North Atlantic TCs and the amount of hourly precipitation due to TC are projected to increase by the end of the century compared to a historical simulation. However, the annual average TC-related Rx5day over the eastern USA is projected to decrease because of a reduction in landfalling TCs. RCM studies with around 25–50 km grid spacing are used to study projected changes in TCs. The projected changes of TCs in South East Asia simulated by RCMs are consistent with those of most GCMs, showing a decrease in TC frequency and an increase in the amount of TC-associated precipitation or an increase in the frequency of intense TCs ( [[#Redmond--2015|Redmond et al., 2015]] ; [[#Gallo--2019|Gallo et al., 2019]] ).

Projected changes in TC tracks or TC areas of occurrence in the late 21st century vary considerably among available studies, although there is better agreement in the western North Pacific. Several studies project either poleward or eastward expansion of TC occurrence over the western North Pacific region, and more TC occurrence in the central North Pacific ( [[#Yamada--2017|Yamada et al., 2017]] ; [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Wehner--2018a|Wehner et al., 2018a]] ; [[#Roberts--2020b|Roberts et al., 2020b]] ). The observed poleward expansion of the latitude of maximum TC intensity in the western North Pacific is consistently reproduced by the CMIP5 models and downscaled models, and these models show further poleward expansion in the future; the projected mean migration rate of the mean latitude where TCs reach their lifetime-maximum intensity is 0.2±0.1° from CMIP5 model results, while it is 0.13±0.04° from downscaled models in the western North Pacific ( [[#Kossin--2014|Kossin et al., 2014]] , 2016a). In the North Atlantic, while the location of TC maximum intensity does not show clear poleward migration observationally ( [[#Kossin--2014|Kossin et al., 2014]] ), it tends to migrate poleward in projections ( [[#Garner--2017|Garner et al., 2017]] ). The poleward migration is less robust among models and observations in the Indian Ocean, eastern North Pacific, and South Pacific (e.g., [[#Tauvale--2019|Tauvale and Tsuboki, 2019]] ; Ramsay et al. 2018; Cattiaux et al. 2020). There is presently no clear consensus in projected changes in TC translation speed ( [[#Knutson--2020|Knutson et al., 2020]] ), although recent studies suggest a slowdown outside of the tropics (Kossin, 2019; [[#Yamaguchi--2020|Yamaguchi et al., 2020]] ; G. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ), but regionally there can even be an acceleration of the storms ( [[#Hassanzadeh--2020|Hassanzadeh et al., 2020]] ).

The spatial extent, or ‘size’, of the TC wind field is an important determinant of storm surge and damage. No detectable anthropogenic influences on TC size have been identified to date, because TCs in observations vary in size substantially ( [[#Chan--2015|Chan and Chan, 2015]] ) and there is no definite theory on what controls TC size, although this is an area of active research ( [[#Chavas--2014|Chavas and Emanuel, 2014]] ; [[#Chan--2018|Chan and Chan, 2018]] ). However, projections by high-resolution models indicate future broadening of TC wind fields when compared to TCs of the same categories ( [[#Yamada--2017|Yamada et al., 2017]] ), while [[#Knutson--2015|Knutson et al. (2015)]] simulate a reasonable interbasin distribution of TC size climatology, but project no statistically significant change in global average TC size. A plausible mechanism is that, as the tropopause height becomes higher with global warming, the eye wall areas become wider because the eye walls are inclined outward with height to the tropopause. This effect is only reproduced in high-resolution convection-permitting models capturing eye walls, and such modelling studies are not common. Moreover, the projected TC size changes are generally on the order of 10% or less, and these size changes are still highly variable between basins and studies. Thus, the projected change in both magnitude and sign of TC size is uncertain.

The coastal effects of TCs depend on TC intensity, size, track, and translation speed. Projected increases in sea level, average TC intensity, and TC rainfall rates each generally act to further elevate future storm surge and fresh-water flooding (see [[IPCC:Wg1:Chapter:Chapter-9#9.6.4.2|Section 9.6.4.2]] ). Changes in TC frequency could contribute toward increasing or decreasing future storm surge risk, depending on the net effects of changes in weaker vs stronger storms. Several studies ( [[#McInnes--2014|McInnes et al., 2014]] , 2016; [[#Little--2015|Little et al., 2015]] ; [[#Garner--2017|Garner et al., 2017]] ; [[#Timmermans--2017|Timmermans et al., 2017]] , 2018) have explored future projections of storm surge in the context of anthropogenic climate change with the influence of both sea level rise and future TC changes. [[#Garner--2017|Garner et al. (2017)]] investigated the near-future changes in the New York City coastal flood hazard, and suggested a small change in storm-surge height because effects of TC intensification are compensated by the offshore shifts in TC tracks, but concluded that the overall effect due to the rising sea levels would increase the flood hazard. Future projection studies of storm surge in East Asia, including China, Japan and Korea, also indicate that storm surges due to TCs become more severe ( [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|J.A. Yang et al., 2018]] ; [[#Mori--2019|Mori et al., 2019]] , 2021; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b). For the Pacific Islands, [[#McInnes--2014|McInnes et al. (2014)]] found that the future projected increase in storm surge in Fiji is dominated by sea level rise, and projected TC changes make only a minor contribution. Among various storm surge factors, there is ''high confidence'' that sea level rise will lead to a higher possibility of extreme coastal water levels in most regions, with all other factors assumed equal.

In the North Atlantic, vertical wind shear, which inhibits TC genesis and intensification, varies in a quasi-dipole pattern, with one centre of action in the tropics and another along the south-east USA coast ( [[#Vimont--2007|Vimont and Kossin, 2007]] ). This pattern of variability creates a protective barrier of high shear along the USA coast during periods of heightened TC activity in the tropics ( [[#Kossin--2017|Kossin, 2017]] ), and appears to be a natural part of the Atlantic ocean–atmosphere climate system ( [[#Ting--2019|Ting et al., 2019]] ). Greenhouse gas forcing in CMIP5 and the Community Earth System Model Large Ensemble ( [[#Kay--2015|Kay et al., 2015]] ) simulations, however, erodes the pattern and degrades the natural shear barrier along the USA coast. Following the RCP8.5 emissions scenario, the magnitude of the erosion of the barrier equals the amplitude of past natural variability (time of emergence) by the mid-21st century ( [[#Ting--2019|Ting et al., 2019]] ). The projected reduction of shear along the USA East Coast with warming is consistent among studies (e.g., [[#Vecchi--2007|Vecchi and Soden, 2007]] ).

In summary, average peak TC wind speeds and the proportion of Category 4–5 TCs will ''very likely'' increase globally with warming. It is ''likely'' that the frequency of Category 4–5 TCs will increase in limited regions over the western North Pacific. It is ''very likely'' that average TC rain rates will increase with warming, and ''likely'' that the peak rain rates will increase at rate greater than the Clausius–Clapeyron scaling rate of 7% per 1°C of warming in some regions due to increased low-level moisture convergence caused by regional increases in TC wind intensity. It is ''likely'' that the average location where TCs reach their peak wind intensity will migrate poleward in the western North Pacific Ocean as the tropics expand with warming, and that the global frequency of TCs over all categories will decrease or remain unchanged.

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<span id="extratropical-storms"></span>
=== 11.7.2 Extratropical Storms ===

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This section focuses on extratropical cyclones (ETCs) that are either classified as strong or extreme by using some measure of their intensity, or by being associated with the occurrence of extremes in variables such as precipitation or near-surface wind speed ( [[#Seneviratne--2012|Seneviratne et al., 2012]] ). Since AR5, the high relevance of ETCs for extreme precipitation events has been well established ( [[#Pfahl--2012|Pfahl and Wernli, 2012]] ; [[#Catto--2013|Catto and Pfahl, 2013]] ; [[#Utsumi--2017|Utsumi et al., 2017]] ), with 80% or more of hourly and daily precipitation extremes being associated with either ETCs or fronts over oceanic mid-latitude regions, and somewhat smaller, but still very large, proportions of events over mid-latitude land regions ( [[#Utsumi--2017|Utsumi et al., 2017]] ). The emphasis in this section is on individual ETCs that have been identified using some detection and tracking algorithms. Mid-latitude atmospheric rivers are assessed in [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.8|Section 8.3.2.8]] .

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<span id="observed-trends-5"></span>
==== 11.7.2.1 Observed Trends ====

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[[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.3|Section 2.3.1.4.3]] concluded that there is overall ''low confidence'' in recent changes in the total number of ETCs over both hemispheres, and that there is ''medium confidence'' in a poleward shift of the storm tracks over both hemispheres since the 1980s. Overall, there is also ''low confidence'' in past-century trends in the number and intensity of the strongest ETCs due to the large interannual and decadal variability ( [[#Feser--2015|Feser et al., 2015]] ; [[#Reboita--2015|Reboita et al., 2015]] ; [[#Wang--2016|Wang et al., 2016]] ; [[#Varino--2019|Varino et al., 2019]] ) and due to temporal and spatial heterogeneities in the number and type of assimilated data in reanalyses, particularly before the satellite era ( [[#Krueger--2013|Krueger et al., 2013]] ; [[#Tilinina--2013|Tilinina et al., 2013]] ; [[#Befort--2016|Befort et al., 2016]] ; [[#Chang--2016|Chang and Yau, 2016]] ; [[#Wang--2016|Wang et al., 2016]] ). There is ''medium confidence'' that the agreement among reanalyses and detection and tracking algorithms is higher when considering stronger cyclones ( [[#Neu--2013|Neu et al., 2013]] ; [[#Pepler--2015|Pepler et al., 2015]] ; [[#Wang--2016|Wang et al., 2016]] ). Over the Southern Hemisphere, there is ''high confidence'' that the total number of ETCs with low central pressures (<980 hPa) has increased between 1979 and 2009, with all eight reanalyses considered by [[#Wang--2016|Wang et al. (2016)]] showing positive trends, and five of them showing statistically significant trends. Similar results were found by [[#Reboita--2015|Reboita et al. (2015)]] using a different detection and tracking algorithm and a single reanalysis product. Over the Northern Hemisphere, there is ''high agreement'' among reanalyses that the number of cyclones with low central pressures (<970 hPa) has decreased in summer and winter during the period 1979–2010 ( [[#Tilinina--2013|Tilinina et al., 2013]] ; [[#Chang--2016|Chang et al., 2016]] ). However, changes exhibit substantial decadal variability and do not show monotonic trends since the 1980s. For example, over the Arctic and North Atlantic, [[#Tilinina--2013|Tilinina et al. (2013)]] showed that the number of cyclones with very low central pressure (<960 hPa) increased from 1979 to 1990 and then declined until 2010 in all five reanalyses considered. Over the North Pacific, the number of cyclones with very low central pressure reached a peak around 2000 and then decreased until 2010 in the five reanalyses considered ( [[#Tilinina--2013|Tilinina et al., 2013]] ). Overall, however, it should be noted that characterising trends in the dynamical intensity of ETCs (e.g., wind speeds) using the absolute central pressure is problematic because the central pressure depends on the background mean sea level pressure, which varies seasonally and regionally (e.g., [[#Befort--2016|Befort et al., 2016]] ).

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<span id="model-evaluation-5"></span>
==== 11.7.2.2 Model Evaluation ====

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There is ''high confidence'' that coarse-resolution climate models (e.g., CMIP5 and CMIP6) underestimate the dynamical intensity of ETCs, including the strongest ETCs, as measured using a variety of metrics, including mean pressure gradient, mean vorticity and near-surface wind speeds, over most regions ( [[#Colle--2013|Colle et al., 2013]] ; [[#Zappa--2013a|Zappa et al., 2013a]] ; [[#Govekar--2014|Govekar et al., 2014]] ; [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Trzeciak--2016|Trzeciak et al., 2016]] ; [[#Seiler--2018|Seiler et al., 2018]] ; [[#Priestley--2020|Priestley et al., 2020]] ). There is also ''high confidence'' that most current climate models underestimate the number of explosive systems (i.e., systems showing a decrease in mean sea level pressure of at least 24 hPa in 24 hours) over both hemispheres ( [[#Seiler--2016a|Seiler and Zwiers, 2016a]] ; [[#Gao--2020|Gao et al., 2020]] ; [[#Priestley--2020|Priestley et al., 2020]] ). There is ''high confidence'' that the underestimation of the intensity of ETCs is associated with the coarse horizontal resolution of climate models, with higher horizontal resolution models, including HighResMIP and CORDEX, usually showing better performance ( [[#Colle--2013|Colle et al., 2013]] ; [[#Zappa--2013a|Zappa et al., 2013a]] ; [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Trzeciak--2016|Trzeciak et al., 2016]] ; [[#Seiler--2018|Seiler et al., 2018]] ; [[#Gao--2020|Gao et al., 2020]] ; [[#Priestley--2020|Priestley et al., 2020]] ). The improvement by higher-resolution models is found, even when comparing models and reanalyses after post-processing data to a common resolution ( [[#Zappa--2013a|Zappa et al., 2013a]] ; [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Priestley--2020|Priestley et al., 2020]] ). The systematic bias in the intensity of ETCs has also been linked to the inability of current climate models to resolve diabatic processes, particularly those related to the release of latent heat ( [[#Willison--2013|Willison et al., 2013]] ; [[#Trzeciak--2016|Trzeciak et al., 2016]] ) and the formation of clouds ( [[#Govekar--2014|Govekar et al., 2014]] ). There is ''medium confidence'' that climate models simulate well the spatial distribution of precipitation associated with ETCs over the Northern Hemisphere, together with some of the main features of the ETC life cycle, including the maximum in precipitation occurring just before the peak in dynamical intensity (e.g., vorticity) as observed in a reanalysis and observations ( [[#Hawcroft--2018|Hawcroft et al., 2018]] ). There is, however, large observational uncertainty in ETC-associated precipitation ( [[#Hawcroft--2018|Hawcroft et al., 2018]] ) and limitations in the simulation of frontal precipitation, including overly low rainfall intensity over mid-latitude oceanic areas in both hemispheres ( [[#Catto--2015|Catto et al., 2015]] ).

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<span id="detection-and-attribution-event-attribution-5"></span>
==== 11.7.2.3 Detection and Attribution, Event Attribution ====

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( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3%20|Section 3.3.3.3]] concluded that there is ''low confidence'' in the attribution of observed changes in the number of ETCs in the Northern Hemisphere and ''high confidence'' that the poleward shift of storm tracks in the Southern Hemisphere is linked to human activity, mostly due to emissions of ozone-depleting substances. Specific studies attributing changes in the most extreme ETCs are not available. The human influence on individual extreme ETC events has been considered only a few times and there is overall ''low confidence'' in the attribution of these changes ( [[#NASEM--2016|NASEM, 2016]] ; [[#Vautard--2019|Vautard et al., 2019]] ).

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<span id="projections-4"></span>
==== 11.7.2.4 Projections ====

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The frequency of ETCs is expected to change, primarily following a poleward shift of the storm tracks as discussed in [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.6|Section 4.5.1.6]] (see also Figure 4.31) and [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.8|Section 8.4.2.8]] . There is ''medium confidence'' that changes in the dynamical intensity (e.g., wind speeds) of ETCs will be small, although changes in the location of storm tracks can lead to substantial changes in local extreme wind speeds ( [[#Zappa--2013b|Zappa et al., 2013b]] ; [[#Chang--2014|Chang, 2014]] ; [[#Li--2014|Li et al., 2014]] ; [[#Seiler--2016b|Seiler and Zwiers, 2016b]] ; [[#Yettella--2017|Yettella and Kay, 2017]] ; [[#Barcikowska--2018|Barcikowska et al., 2018]] ; [[#Kar-Man%20Chang--2018|Kar-Man Chang, 2018]] ). [[#Yettella--2017|Yettella and Kay (2017)]] detected and tracked ETCs over both hemispheres in an ensemble of 30 Community Earth System Model Large Ensemble simulations, differing only in their initial conditions, and found that changes in mean wind speeds around ETC centres are often negligible between present (1986–2005) and future (2081–2100) periods. Using 19 CMIP5 models, [[#Zappa--2013b|Zappa et al. (2013b)]] found an overall reduction in the number of cyclones associated with low-troposphere (850-hPa) wind speeds larger than 25 m s <sup>–1</sup> over the North Atlantic and Europe with the number of the 10% strongest cyclones decreasing by about 8% and 6% in December–January–February and June–July–August according to the RCP4.5 scenario (2070–2099 vs. 1976–2005). Over the North Pacific, [[#Chang--2014|Chang (2014)]] showed that CMIP5 models project a decrease in the frequency of ETCs, with the largest central pressure perturbation (i.e., the depth, strongly related with low-level wind speeds) by the end of the century according to simulations using the RCP8.5 scenario. Using projections from CMIP5 GCMs under the RCP8.5 scenario (1981–2000 to 2081–2100), [[#Seiler--2016b|Seiler and Zwiers (2016b)]] projected a northward shift in the number of explosive ETCs in the northern Pacific, with fewer and weaker events south, and more frequent and stronger events north of 45°N. Using 19 CMIP5 GCMs under the RCP8.5 scenario, [[#Kar-Man%20Chang--2018|Kar-Man Chang (2018)]] found a significant decrease in the number of ETCs associated with extreme wind speeds (2081–2100 vs. 1980–99) over the Northern Hemisphere (average decrease of 17%) and over some smaller regions, including the Pacific and Atlantic regions.

Over the Southern Hemisphere, future changes (RCP8.5 scenario; 1980–1999 to 2081–2100) in extreme ETCs were studied by [[#Chang--2017|Chang (2017)]] using 26 CMIP5 models, and a variety of intensity metrics (850-hPa vorticity, 850-hPa wind speed, mean sea level pressure and near-surface wind speed). They found that the number of extreme cyclones is projected to increase by at least 20% and as much as 50%, depending on the specific metric used to define extreme ETCs. Increases in the number of strong cyclones appear to be robust across models and for most seasons, although they show strong regional variations, with increases occurring mostly over the southern flank of the storm track, consistent with a shift and intensification of the storm track. Overall, there is ''medium confidence'' that projected changes in the dynamical intensity of ETCs depend on the resolution and formulation (e.g., explicit or implicit representation of convection) of climate models ( [[#Booth--2013|Booth et al., 2013]] ; [[#Michaelis--2017|Michaelis et al., 2017]] ; [[#Zhang--2017|Zhang and Colle, 2017]] ).

As reported in AR5 and in [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.8|Section 8.4.2.8]] , despite small changes in the dynamical intensity of ETCs, there is ''high confidence'' that the precipitation associated with ETCs will increase in the future ( [[#Zappa--2013b|Zappa et al., 2013b]] ; [[#Marciano--2015|Marciano et al., 2015]] ; [[#Pepler--2016|Pepler et al., 2016]] ; [[#Michaelis--2017|Michaelis et al., 2017]] ; [[#Yettella--2017|Yettella and Kay, 2017]] ; [[#Zhang--2017|Zhang and Colle, 2017]] ; [[#Barcikowska--2018|Barcikowska et al., 2018]] ; [[#Hawcroft--2018|Hawcroft et al., 2018]] ; [[#Zarzycki--2018|Zarzycki, 2018]] ; [[#Kodama--2019|Kodama et al., 2019]] ; [[#Bevacqua--2020a|Bevacqua et al., 2020a]] ; [[#Reboita--2021|Reboita et al., 2021]] ). There is ''high confidence'' that increases in precipitation will follow increases in low-level water vapour (i.e., about 7% per 1°C of surface warming; see Box 11.1) and will be larger for higher warming levels ( [[#Zhang--2017|Zhang and Colle, 2017]] ). There is ''medium confidence'' that precipitation changes will show regional and seasonal differences due to distinct changes in atmospheric humidity and dynamical conditions ( [[#Zappa--2015|Zappa et al., 2015]] ; [[#Hawcroft--2018|Hawcroft et al., 2018]] ), with decreases in some specific regions such as the Mediterranean ( [[#Zappa--2015|Zappa et al., 2015]] ; [[#Barcikowska--2018|Barcikowska et al., 2018]] ). There is ''high confidence'' that snowfall associated with winter ETCs will decrease in the future, because increases in tropospheric temperatures lead to a lower proportion of precipitation falling as snow ( [[#O’Gorman--2014|O’Gorman, 2014]] ; [[#Rhoades--2018|Rhoades et al., 2018]] ; [[#Zarzycki--2018|Zarzycki, 2018]] ). However, there is ''medium confidence'' that extreme snowfall events associated with winter ETCs will change little in regions where snowfall will be supported in the future ( [[#O’Gorman--2014|O’Gorman, 2014]] ; [[#Zarzycki--2018|Zarzycki, 2018]] ).

In summary, there is ''low confidence'' in past changes in the dynamical intensity (e.g., maximum wind speeds) of ETCs and ''medium confidence'' that, in the future, these changes will be small, although changes in the location of storm tracks could lead to substantial changes in local extreme wind speeds. There is ''high confidence'' that average and maximum ETC precipitation-rates will increase with warming, with the magnitude of the increases associated with increases in atmospheric water vapour. There is ''medium confidence'' that projected changes in the intensity of ETCs, including wind speeds and precipitation, depend on the resolution and formulation of climate models.

<div id="11.7.3" class="h2-container"></div>

<span id="severe-convective-storms"></span>
=== 11.7.3 Severe Convective Storms ===

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Severe convective storms are convective systems that are associated with extreme phenomena such as tornadoes, hail, heavy precipitation (rain or snow), strong winds, and lightning. The assessment of changes in severe convective storms in SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) and AR5 (Chapter 12, [[#Collins--2013|Collins et al., 2013]] ) is limited and focused mainly on tornadoes and hail storms. The SREX assessed that there is ''low confidence'' in observed trends in tornadoes and hail because of data inhomogeneities and inadequacies in monitoring systems. Subsequent literature assessed in the ''Climate Science Special Report'' ( [[#Kossin--2017|Kossin et al., 2017]] ) led to the assessment of the observed tornado activity over the 2000s in the USA, with a decrease in the number of days per year with tornadoes and an increase in the number of tornadoes on these days ( ''medium confidence'' ). However, there is ''low confidence'' in past trends for hail and severe thunderstorm winds. Climate models consistently project environmental changes that would support an increase in the frequency and intensity of severe thunderstorms that combine tornadoes, hail, and winds ( ''high confidence'' ), but there is ''low confidence'' in the details of the projected increase. Regional aspects of severe convective storms and details of the assessment of tornadoes and hail are also assessed in [[IPCC:Wg1:Chapter:Chapter-12#12.3.3.2|Section 12.3.3.2]] (tornadoes), [[IPCC:Wg1:Chapter:Chapter-12#12.3.4.5|Section 12.3.4.5]] (hail), [[IPCC:Wg1:Chapter:Chapter-12#12.4.5.3|Section 12.4.5.3]] (Europe), [[IPCC:Wg1:Chapter:Chapter-12#12.4.6.3|Section 12.4.6.3]] (North America), and [[IPCC:Wg1:Chapter:Chapter-12#12.7.2|Section 12.7.2]] (regional gaps and uncertainties).

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<span id="mechanisms-and-drivers-5"></span>
==== 11.7.3.1 Mechanisms and Drivers ====

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Severe convective storms are sometimes embedded in synoptic-scale weather systems, such as TCs, ETCs, and fronts ( [[#Kunkel--2013|Kunkel et al., 2013]] ). They are also generated as individual events as mesoscale convective systems (MCSs) and mesoscale convective complexes (MCCs, a special type of a large, organized and long-lived MCS), without being clearly embedded within larger-scale weather systems. In addition to the general vigorousness of precipitation, hail, and winds associated with MCSs, characteristics of MCSs are viewed in new perspectives in recent years, probably because of both the development of dense mesoscale observing networks and advances in high-resolution mesoscale modelling (Sections 11.7.3.2 and 11.7.3.3). The horizontal scale of MCSs is discussed with their organization of the convective structure, and it is examined with a concept of ‘convective aggregation’ in recent years ( [[#Holloway--2017|Holloway et al., 2017]] ). MCSs sometimes take a linear shape and stay almost stationary with successive production of cumulonimbus on the upstream side (back-building type convection), and cause heavy rainfall ( [[#Schumacher--2005|Schumacher and Johnson, 2005]] ). Many of the recent severe rainfall events in Japan are associated with band-shaped precipitation systems ( [[#Kunii--2016|Kunii et al., 2016]] ; [[#Oizumi--2018|Oizumi et al., 2018]] ; [[#Tsuguti--2018|Tsuguti et al., 2018]] ; [[#Kato--2020|Kato, 2020]] ), suggesting common characteristics of severe precipitation, at least in East Asia. The convective modes of severe storms in the USA can be classified into rotating or linear modes and preferable environmental conditions for these modes, such as vertical shear, have been identified ( [[#Trapp--2005|Trapp et al., 2005]] ; [[#Smith--2013|Smith et al., 2013]] ; [[#Allen--2018|Allen, 2018]] ). Cloud microphysics characteristics of MCSs were examined and the roles of warm rain processes on extreme precipitation were emphasized recently ( [[#Sohn--2013|Sohn et al., 2013]] ; [[#Hamada--2015|Hamada et al., 2015]] ; [[#Hamada--2018|Hamada and Takayabu, 2018]] ). Idealized studies also suggest the importance of ice and mixed-phase processes of cloud microphysics on extreme precipitation ( [[#Sandvik--2018|Sandvik et al., 2018]] ; [[#Bao--2019|Bao and Sherwood, 2019]] ). However, it is unknown whether the types of MCS are changing in recent periods or observed ubiquitously all over the world.

Severe convective storms occur under conditions preferable for deep convection, that is, conditionally unstable stratification, sufficient moisture, both in lower and middle levels of the atmosphere, and a strong vertical shear. These large-scale environmental conditions are viewed as necessary conditions for the occurrence of severe convective systems, or the resulting tornadoes and lightning, and the relevance of these factors strongly depends on the region (e.g., [[#Antonescu--2016a|Antonescu et al., 2016a]] ; [[#Allen--2018|Allen, 2018]] ; [[#Tochimoto--2018|Tochimoto and Niino, 2018]] ). Frequently used metrics are atmospheric static stability, moisture content, convective available potential energy (CAPE) and convective inhibition, wind shear or helicity, including storm-relative environmental helicity ( [[#Tochimoto--2018|Tochimoto and Niino, 2018]] ; [[#Elsner--2019|Elsner et al., 2019]] ). These metrics, largely controlled by large-scale atmospheric circulations or synoptic weather systems, such as TCs and ETCs, are then generally used to examine severe convective systems. In particular, there is ''high confidence'' that CAPE in the tropics and the subtropics increases in response to global warming (M.S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ), as supported by theoretical studies ( [[#Singh--2013|Singh and O’Gorman, 2013]] ; [[#Seeley--2015|Seeley and Romps, 2015]] ; [[#Romps--2016|Romps, 2016]] ; [[#Agard--2017|Agard and]] [[#Emanuel--2017|Emanuel, 2017]] ). The uncertainty, however, arises from the balance between factors affecting severe storm occurrence. For example, the warming of mid-tropospheric temperatures leads to an increase in the freezing level, which leads to increased melting of smaller hailstones, while there may be some offset by stronger updrafts driven by increasing CAPE, which would favour the growth of larger hailstones, leading to less melting when falling ( [[#Allen--2018|Allen, 2018]] ; [[#Mahoney--2020|Mahoney, 2020]] ).

There are few studies on relations between changes in severe convective storms and those of the large-scale circulation patterns. Tornado outbreaks in the USA are usually associated with ETCs with their frontal systems and TCs ( [[#Fuhrmann--2014|Fuhrmann et al., 2014]] ; [[#Tochimoto--2016|Tochimoto and Niino, 2016]] ). In early June to late July in East Asia, associated with the Baiu/Changma/Mei-yu, severe precipitation events are frequently caused by MCSs. Severe precipitation events are also caused by remote effects of TCs, known as predecessor rain events ( [[#Galarneau--2010|Galarneau et al., 2010]] ). Atmospheric rivers and other coherent types of enhanced water vapour flux also have the potential to induce severe convective systems ( [[#Kamae--2017a|Kamae et al., 2017a]] ; [[#Waliser--2017|Waliser and Guan, 2017]] ; [[#Ralph--2018|Ralph et al., 2018]] ; see [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.8.2|Section 8.3.2.8.2]] ). Combined with the above drivers, topographic effects also enhance the intensity and duration of severe convective systems and the associated precipitation ( [[#Ducrocq--2008|Ducrocq et al., 2008]] ; [[#Piaget--2015|Piaget et al., 2015]] ). However, the changes in these drivers are not generally significant, so their relations to severe convective storms are unclear.

In summary, severe convective storms are sometimes embedded in synoptic-scale weather systems, such as TCs, ETCs and fronts, and modulated by large-scale atmospheric circulation patterns. The occurrence of severe convective storms and the associated severe events, including tornadoes, hail, and lightning, is affected by environmental conditions of the atmosphere, such as CAPE and vertical shear. The uncertainty, however, arises from the balance between these environmental factors affecting severe storm occurrence.

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<span id="observed-trends-6"></span>
==== 11.7.3.2 Observed Trends ====

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Observed trends in severe convective storms or MCSs are not well documented, but the climatology of MCSs has been analysed in specific regions (North America, South America, Europe, Asia; regional aspects of convective storms are separately assessed in Chapter 12). As the definition of severe convective storms varies depending on the literature, it is not straightforward to make a synthesizing view of observed trends in severe convective storms in different regions. However, analysis using satellite observations provides a global view of MCSs ( [[#Kossin--2017|Kossin et al., 2017]] ). The global distribution of thunderstorms is captured ( [[#Zipser--2006|Zipser et al., 2006]] ; [[#Liu--2015|Liu and Zipser, 2015]] ) by using the satellite precipitation measurements by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) ( [[#Hou--2014|Hou et al., 2014]] ). The climatological characteristics of MCSs are provided by satellite analyses in South America ( [[#Durkee--2010|Durkee and Mote, 2010]] ; [[#Rasmussen--2011|Rasmussen and Houze, 2011]] ; [[#Rehbein--2018|Rehbein et al., 2018]] ) and those of MCCs in the Maritime Continent by [[#Trismidianto%20and%20H.%C2%A0Satyawardhana--2018|Trismidianto and Satyawardhana (2018)]] . Analysis of the environmental conditions favourable for severe convective events indirectly indicates the climatology and trends of severe convective events ( [[#Allen--2018|Allen et al., 2018]] ; [[#Taszarek--2018|Taszarek et al., 2018]] , 2019), though favourable conditions depend on the location, such as the difference for tornadoes associated with ETCs between the USA and Japan ( [[#Tochimoto--2018|Tochimoto and Niino, 2018]] ).

Observed trends in severe convective storms are highly regionally dependent. In the USA, it is indicated that there is no significant increase in convective storms, and hail and severe thunderstorms ( [[#Kunkel--2013|Kunkel et al., 2013]] ; [[#Kossin--2017|Kossin et al., 2017]] ). There is an upward trend in the frequency and intensity of extreme precipitation events in the USA ( ''high confidence'' ) ( [[#Kunkel--2013|Kunkel et al., 2013]] ; Easterling et al., 2017), and MCSs have increased in occurrence and precipitation amounts since 1979 ( ''limited evidence'' ) ( [[#Feng--2016|Feng et al., 2016]] ). Significant interannual variability of hailstone occurrences is found in the Southern Great Plains of the USA ( [[#Jeong--2020|Jeong et al., 2020]] ). The mean annual number of tornadoes has remained relatively constant, but their variability of occurrence has increased since the 1970s,particularly over the 2000s, with a decrease in the number of days per year, but an increase in the number of tornadoes on these days ( [[#Brooks--2014|Brooks et al., 2014]] ; [[#Elsner--2015|Elsner et al., 2015]] , 2019; [[#Kossin--2017|Kossin et al., 2017]] ; [[#Allen--2018|Allen, 2018]] ). There has been a shift in the distribution of tornadoes, with increases in the mid-south of the USA and decreases over the High Plains ( [[#Gensini--2018|Gensini and Brooks, 2018]] ). Trends in MCSs are relatively more visible for particular aspects of MCSs, such as lengthening of active seasons and dependency on duration. MCSs have increased in occurrence and precipitation amounts since 1979 (Easterling et al., 2017). [[#Feng--2016|Feng et al. (2016)]] analysed that the observed increases in spring total and extreme rainfall in the central USA are dominated by MCSs, with increased frequency and intensity of long-lasting MCSs.

Studies on trends in severe convective storms and their ingredients outside of the USA are limited. [[#Westra--2014|Westra et al. (2014)]] found that there is an increase in the intensity of short-duration convective events (minutes to hours) over many regions of the world, except eastern China. In Europe, a climatology of tornadoes shows an increase in detected tornadoes between 1800 and 2014, but this trend might be affected by the density of observations ( [[#Antonescu--2016a|Antonescu et al., 2016a]] , b). An increase in the trend in extreme daily rainfall is found in south-eastern France, where MCSs play a key role in this type of event ( [[#Blanchet--2018|Blanchet et al., 2018]] ; [[#Ribes--2019|Ribes et al., 2019]] ). Trend analysis of the mean annual number of days with thunderstorms since 1979 in Europe indicates an increase over the Alps and central, south-eastern, and eastern Europe, with a decrease over the south-west ( [[#Taszarek--2019|Taszarek et al., 2019]] ). In the Sahelian region, [[#Taylor--2017|Taylor et al. (2017)]] analysed MCSs using satellite observations since 1982 and showed an increase in the frequency of extreme storms. In Bangladesh, the annual number of propagating MCSs decreased significantly during 1998–2015 based on TRMM precipitation data ( [[#Habib--2019|Habib et al., 2019]] ). [[#Prein--2018|Prein and Holland (2018)]] estimated the hail hazard from large-scale environmental conditions using a statistical approach and showed increasing trends in the USA, Europe, and Australia. However, trends in hail on regional scales are difficult to validate because of an insufficient length of observations and inhomogeneous records ( [[#Allen--2018|Allen, 2018]] ). The high spatial variability of hail suggests it is reasonable that there would be local signals of both positive and negative trends, and the trends that are occurring in hail globally are uncertain. In China, the total number of days that have either a thunderstorm or hail have decreased by about 50% from 1961 to 2010, and the reduction in these severe weather occurrences correlates strongly with the weakening of the East Asian summer monsoon (Q. [[#Zhang--2017|]] [[#Zhang--2017|]] [[#Zhang--2017|Zhang et al., 2017]] ). More regional aspects of severe convective storms are detailed in Chapter 12.

In summary, because the definition of severe convective storms varies depending on the literature and the region, it is not straightforward to make a synthesizing view of observed trends in severe convective storms in different regions. In particular, observational trends in tornadoes, hail, and lightning associated with severe convective storms are not robustly detected due to insufficient coverage of the long-term observations. There is ''medium confidence'' that the mean annual number of tornadoes in the USA has remained relatively constant, but their variability of occurrence has increased since the 1970s, particularly over the 2000s, with a decrease in the number of days per year, and an increase in the number of tornadoes on these days ( ''high confidence'' ). Detected tornadoes have also increased in Europe, but the trend depends on the density of observations.

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<span id="model-evaluation-6"></span>
==== 11.7.3.3 Model Evaluation ====

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The explicit representation of severe convective storms requires non-hydrostatic models with horizontal grid spacings finer than 4 km, denoted as convection-permitting models or storm-resolving models ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.1|Section 10.3.1]] ). Convection-permitting models are becoming available to run over a wide domain, such as a continental scale or even over the global area, and show realistic climatological characteristics of MCSs ( [[#Prein--2015|Prein et al., 2015]] ; [[#Guichard--2017|Guichard and Couvreux, 2017]] ; [[#Satoh--2019|Satoh et al., 2019]] ). Such high-resolution simulations are computationally too expensive to perform at the larger domain and for long periods, and alternative methods by using an RCM with dynamical downscaling are generally used ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.1|Section 10.3.1]] ). Convection-permitting models are used as the flagship project of CORDEX to particularly study projections of thunderstorms ( [[IPCC:Wg1:Chapter:Chapter-10#10.3.3|Section 10.3.3]] ). Simulations of North American MCSs by a convection-permitting model conducted by [[#Prein--2020|Prein et al. (2020)]] were able to capture the main characteristics of the observed MCSs, such as their size, precipitation rate, propagation speed, and lifetime. Cloud-permitting model simulations in Europe also showed sub-daily precipitation realistically ( [[#Ban--2014|Ban et al., 2014]] ; [[#Kendon--2014|Kendon et al., 2014]] ). Evaluation of precipitation conducted using convection-permitting simulations around Japan showed that finer resolution improves intense precipitation ( [[#Murata--2017|Murata et al., 2017]] ). MCSs over Africa simulated using convection-permitting models showed better extreme rainfall ( [[#Kendon--2019|Kendon et al., 2019]] ) and diurnal cycles and convective rainfall over land than the coarser-resolution RCMs or GCMs ( [[#Stratton--2018|Stratton et al., 2018]] ; [[#Crook--2019|Crook et al., 2019]] ).

The other modelling approach is the analysis of the environmental conditions that control characteristics of severe convective storms using the typical climate model results in CMIP5/6 ( [[#Allen--2018|Allen, 2018]] ). Severe convective storms are generally formed in environments with large CAPE and tornadic storms are, in particular, formed with a combination of large CAPE and strong vertical wind shear. As the processes associated with severe convective storms occur over a wide range of spatial and temporal scales, some of which are poorly understood and are inadequately sampled by observational networks, the model calibration approaches are generally difficult and insufficiently validated. Therefore, model simulations and their interpretations should be done with much caution.

In summary, there are typically two kinds of modelling approaches for studying changes in severe convective storms. One is to use convection-permitting models in wider regions or the global domain in time-sliced downscaling methods to directly simulate severe convective storms. The other is the analysis of the environmental conditions that control characteristics of severe convective storms by using coarse-resolution GCMs. Even in finer-resolution convection-permitting models, it is difficult to directly simulate tornadoes, hail storms, and lightning, so modelling studies of these changes are limited.

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<span id="detection-and-attribution-event-attribution-6"></span>
==== 11.7.3.4 Detection and Attribution, Event Attribution ====

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It is extremely difficult to detect differences in time and space of severe convective storms ( [[#Kunkel--2013|Kunkel et al., 2013]] ). Although some ingredients that are favourable for severe thunderstorms have increased over the years, others have not; thus, overall, changes in the frequency of environments favourable for severe thunderstorms have not been statistically significant. Event attribution studies on severe convective events have now been undertaken for some cases. For the case of the heavy rain event of July 2018 in Japan (Box 11.4), [[#Kawase--2020|Kawase et al. (2020)]] took a storyline approach to show that the rainfall during this event in Japan was increased by approximately 7% due to recent rapid warming around Japan. For the case of the December 2015 extreme rainfall event in Chennai, India, the extremity of the event was equally caused by the warming trend in the Bay of Bengal SSTs and the strong El Niño conditions ( [[#van%20Oldenborgh--2016|van Oldenborgh et al., 2016]] ; [[#Boyaj--2018|Boyaj et al., 2018]] ). For hailstorms, such as those that caused disasters in the USA in 2018, detection of the role of climate change in changing hail storms is more difficult, because hail storms are not, in general, directly simulated by convection-permitting models and not adequately represented by the environmental parameters of coarse-resolution GCMs ( [[#Mahoney--2020|Mahoney, 2020]] ).

In summary, it is extremely difficult to detect and attribute changes in severe convective storms. There is ''limited evidence'' that extreme precipitation associated with severe convective storms has increased in some cases.

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<span id="projections-5"></span>
==== 11.7.3.5 Projections ====

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Future projections of severe convective storms are usually studied either by analysing the environmental conditions simulated by climate models, or by a time-slice approach with higher-resolution convection-permitting models by comparing simulations downscaled with climate model results under historical conditions and those under hypothesized future conditions ( [[#Kendon--2017|Kendon et al., 2017]] ; [[#Allen--2018|Allen, 2018]] ). Up to now, individual studies using convection-permitting models gave projections of extreme events associated with severe convective storms in local regions, and it is not generally possible to obtain global or general views of projected changes of severe convective storms. [[#Prein--2017|Prein et al. (2017)]] investigated future projections of North American MCS simulations and showed an increase in MCS frequency and an increase in total MCS precipitation volume by the combined effect of increases in maximum precipitation rates associated with MCSs and increases in their size. [[#Rasmussen--2020|Rasmussen et al. (2020)]] investigated future changes in the diurnal cycle of precipitation by capturing organized and propagating convection and showed that weak-to-moderate convection will decrease, and strong convection will increase in frequency in the future. [[#Ban--2015|Ban et al. (2015)]] found that the day-long and hour-long precipitation events in summer intensify in the European region covering the Alps. [[#Kendon--2019|Kendon et al. (2019)]] showed future increases in extreme three-hourly precipitation in Africa. [[#Murata--2015|Murata et al. (2015)]] investigated future projections of precipitation around Japan and showed a decrease in monthly mean precipitation in the eastern Japan Sea region in December, suggesting that convective clouds become shallower in the future in the winter over the Japan Sea.

The other approach is the projection of the environmental conditions that control characteristics of severe convective storms by analysing climate model results. There is ''high confidence'' that CAPE, particularly summer mean CAPE and high percentiles of the CAPE in the tropics and subtropics, increases in response to global warming in an ensemble of climate models including those of CMIP5, mainly from increased low-level specific humidity ( [[#Sobel--2011|Sobel and Camargo, 2011]] ; M.S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a). Convective inhibition becomes stronger over most land areas under global warming, resulting mainly from reduced low-level relative humidity over land (J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] a). However, there are large differences within the CMIP5 ensemble for environmental conditions, which contribute to some degree of uncertainty ( [[#Allen--2018|Allen, 2018]] ). Because the relation between simulated environments in models and the occurrence of severe convective storms are, in general, insufficiently validated, there is generally ''low confidence'' in the projection of severe convective storms with the approach of the environmental conditions.

In the USA, projected changes in the environmental conditions show an increase in CAPE and no changes or decreases in the vertical wind shear, suggesting favourable conditions for an increase in severe convective storms in the future, but the interpretation of how tornadoes or hail will change is an open question because of the strong dependence on shear ( [[#Brooks--2013|Brooks, 2013]] ). [[#Diffenbaugh--2013|Diffenbaugh et al. (2013)]] showed robust increases in the occurrence of the favourable environments for severe convective storms with increased CAPE and stronger low-level wind shear in response to future global warming. A downscaling approach showed that the variability of the occurrence of severe convective storms increases in spring in late 21st-century simulations ( [[#Gensini--2015|Gensini and Mote, 2015]] ). Future changes in hail occurrence in the USA examined through convection-permitting dynamical downscaling suggested that the hail season may begin earlier in the year and exhibit more interannual variability, with increases in the frequency of large hail in broad areas over the USA ( [[#Trapp--2019|Trapp et al., 2019]] ). There is ''medium confidence'' that the frequency and variability of the favourable environments for severe convective storms will increase in spring, and ''low confidence'' for summer and autumn ( [[#Diffenbaugh--2013|Diffenbaugh et al., 2013]] ; [[#Gensini--2015|Gensini and Mote, 2015]] ; [[#Hoogewind--2017|Hoogewind et al., 2017]] ). The occurrence of hail events in Colorado in the USA was examined by comparing both present-day and projected future climates using high-resolution model simulations capable of resolving hailstorms ( [[#Mahoney--2012|Mahoney et al., 2012]] ), which showed that hail is almost eliminated at the surface in the future in most of the simulations, despite more intense future storms and significantly larger amounts of hail generated in-cloud.

Future changes in severe convection environments show enhancement of instability with less robust changes in the frequency of strong vertical wind shear in Europe ( [[#Púčik--2017|Púčik et al., 2017]] ) and in Japan ( [[#Muramatsu--2016|Muramatsu et al., 2016]] ). In Japan, the frequency of conditions favourable for strong tornadoes increases in spring, and partly in summer.

In summary, the average and maximum rain rates associated with severe convective storms increase in a warming world in some regions, including the USA ( ''high confidence'' ). There is ''high confidence'' from climate models that CAPE increases in response to global warming in the tropics and subtropics, suggesting more favourable environments for severe convective storms. The frequency of severe convective storms in spring is projected to increase in the USA, leading to a lengthening of the severe convective storm season ( ''medium confidence'' ); evidence in other regions is limited. There is significant uncertainty about projected regional changes in tornadoes, hail, and lightning due to limited analysis of simulations using convection-permitting models ( ''hi'' ''gh confidence'' ).

<div id="11.7.4" class="h2-container"></div>

<span id="extreme-winds"></span>
=== 11.7.4 Extreme Winds ===

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Extreme winds are defined here in terms of the strongest near-surface wind speeds that are generally associated with extreme storms, such as TCs, ETCs, and severe convective storms. In previous IPCC reports, near-surface wind speed (including extremes), has not been assessed as a variable in its own right, but rather in the context of other extreme atmospheric or oceanic phenomena. The exception was the SREX report ( [[#Seneviratne--2012|Seneviratne et al., 2012]] ), which specifically examined past changes and projections of mean and extreme near-surface wind speeds. A strong decline in extreme winds compared to mean winds was reported for the continental northern mid-latitudes. Due to the small number of studies and uncertainties in terrestrial-based surface wind measurements, the findings were assigned ''low confidence'' in SREX. The AR5 reported a weakening of mean and maximum winds from the 1960s or 1970s to the early 2000s in the tropics and mid-latitudes, and increases in high latitudes, but with ''low confidence'' in changes in the observed surface winds over land ( [[#Hartmann--2013|Hartmann et al., 2013]] ). Observed trends in mean wind speed over land and the ocean are assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.4|Section 2.3.1.4.4]] . Aspects of climate impact-drivers for winds are addressed in Sections 12.3.3 and 12.5.2, and their regional changes are assessed in [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] .

Observationally, although not specifically addressing extreme wind speed changes, negative surface wind speed trends (stilling) were found in the tropics and mid-latitudes of both hemispheres of –0.014 m s <sup>–1</sup> yr <sup>–1</sup> , while positive trends were reported at high latitudes poleward of 70 degrees, based on a review of 148 studies ( [[#McVicar--2012b|McVicar et al., 2012b]] ). An earlier study attributed the stilling to both changes in atmospheric circulation and an increase in surface roughness due to an overall increase in vegetation cover ( [[#Vautard--2010|Vautard et al., 2010]] ). Since then, a number of studies have mostly confirmed these general negative mean-wind trends based on anemometer data for Spain ( [[#Azorin-Molina--2017|Azorin-Molina et al., 2017]] ), Turkey, ( [[#Dadaser-Celik--2014|Dadaser-Celik and Cengiz, 2014]] ), the Netherlands, ( [[#Wever--2012|Wever, 2012]] ), Saudi Arabia, ( [[#Rehman--2013|Rehman, 2013]] ), Romania, ( [[#Marin--2014|Marin et al., 2014]] ), and China ( [[#Chen--2013|Chen et al., 2013]] ). [[#Lin--2013|Lin et al. (2013)]] note that wind speed variability over China is greater at high-elevation locations compared to those closer to mean sea level. [[#Hande--2012|Hande et al. (2012)]] , using radiosonde data, found an increase in surface wind speed on Macquarie Island of Australia.

A number of new studies have examined surface wind speeds over the ocean using ship-based measurements, satellite altimeters, and Special Sensor Microwave/Imagers ( [[#Tokinaga--2011|Tokinaga and Xie, 2011]] ; [[#Zieger--2014|Zieger et al., 2014]] ). It has been noted that wind speed trends tend to be stronger in altimeter measurements, although the spatial patterns of change are qualitatively similar in both instruments ( [[#Zieger--2014|Zieger et al., 2014]] ). Q. [[#Liu--2016|]] [[#Liu--2016|Liu et al. (2016)]] found positive trends in surface wind speeds over the Arctic Ocean in 20 years of satellite observations. Small positive trends in mean wind speed were found in 33 years of satellite data, together with larger trends in the 90th percentile values over global oceans ( [[#Ribal--2019|Ribal and Young, 2019]] ). These results were consistent with an earlier study that found a positive trend in 1-in-100-year wind speeds ( [[#Young--2012|Young et al., 2012]] ). A positive change in mean wind speeds was found for the Arabian Sea and the Bay of Bengal ( [[#Shanas--2015|Shanas and Kumar, 2015]] ) and [[#Zheng--2017|Zheng et al. (2017)]] found that positive wind speed trends over the ocean were larger during winter seasons than summer seasons.

Changes in extreme winds are associated with changes in the characteristics (locations, frequencies, and intensities) of extreme storms, including TCs, ETCs, and severe convective storms. For TCs, as assessed in [[#11.7.1.5|Section 11.7.1.5]] , it is projected that the average peak TC wind speeds will increase globally with warming, while the global frequency of TCs over all categories will decrease or remain unchanged; the average location where TCs reach their peak wind intensity will migrate poleward in the western North Pacific Ocean as the tropics expand with warming. Frequency, intensities, and geographical distributions of extreme wind events associated with TCs will change according to these TC changes. For ETCs, by the end of the century, CMIP5 models show that the number of ETCs associated with extreme winds will significantly decrease in the mid- and high latitudes of the Northern Hemisphere in winter, with the projected decrease being larger over the Atlantic ( [[#Kar-Man%20Chang--2018|Kar-Man Chang, 2018]] ), while it will significantly increase irrespective of the season in the Southern Hemisphere ( [[#11.7.2.4|Section 11.7.2.4]] ; [[#Chang--2017|Chang, 2017]] ). Over the ocean in the subtropics, a large ensemble of 60-km global model simulations indicated that extreme winds associated with storm surges will intensify over 15–35°N in the Northern Hemisphere ( [[#Mori--2019|Mori et al., 2019]] ). However, extreme surface wind speeds will mostly decrease due to decreases in the number and intensity of TCs over most tropical areas of the Southern Hemisphere ( [[#Mori--2019|Mori et al., 2019]] ). The projected changes in the frequency of extreme winds are associated with the future changes in TCs and ETCs.

Extreme cyclonic windstorms that share some characteristics with both TCs and ETCs occur regularly over the Mediterranean Sea and are often referred to as ‘medicanes’ (Ragone et al., 2018; [[#Miglietta--2019|Miglietta and Rotunno, 2019]] ; [[#Zhang--2021|Zhang et al., 2021]] ). Medicanes pose substantial threats to regional islands and coastal zones. A growing body of literature consistently found that the frequency of medicanes decreases under warming, while the strongest medicanes become stronger (Gaertner et al., 2007; Romero and [[#Emanuel--2013|Emanuel, 2013]] , 2017; [[#Cavicchia--2014|Cavicchia et al., 2014]] ; [[#Tous--2016|Tous et al., 2016]] ; [[#Romera--2017|Romera et al., 2017]] ; [[#González-Alemán--2019|González-Alemán et al., 2019]] ). This is also consistent with expected global changes in TCs under warming ( [[#11.7.1|Section 11.7.1]] ). Based on the consistency of these studies, it is ''likely'' that medicanes will decrease in frequency, while the strongest medicanes become stronger under warming scenario projections ( ''medi'' ''um confidence'' ).

In summary, the observed intensity of extreme winds is becoming less severe in the low to mid-latitudes, while becoming more severe in high latitudes poleward of 60 degrees ( ''low confidence'' ). Projected changes in the frequency and intensity of extreme winds are associated with projected changes in the frequency and intensity of TCs and ETCs ( ''medi'' ''um confidence'' ).

<div id="11.8" class="h1-container"></div>

<span id="compound-events"></span>
== 11.8 Compound Events ==

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The SREX (SREX Chapter 3) first defined compound events as: (i) two or more extreme events occurring simultaneously or successively, (ii) combinations of extreme events with underlying conditions that amplify the impact of the events, or (iii) combinations of events that are not themselves extremes but lead to an extreme event or impact when combined.

Further definitions of compound events have emerged since SREX. [[#Zscheischler--2018|Zscheischler et al. (2018)]] defined compound events broadly as ‘the combination of multiple drivers and/or hazards that contributes to societal or environmental risk’. This definition is used in the present assessment, because of its clear focus on the risk framework established by the IPCC, and also highlighting that compound events may not necessarily result from dependent drivers. Compound events have been classified into: preconditioned events, where a weather-driven or climate-driven precondition aggravates the impacts of a climatic impact-driver; multivariate events, where multiple drivers and/or climatic impact-drivers lead to an impact; temporally compounding events, where a succession of hazards leads to an impact; and spatially compounding events, where hazards in multiple connected locations cause an aggregated impact ( [[#Zscheischler--2020|Zscheischler et al., 2020]] ). Drivers include processes, variables, and phenomena in the climate and weather domain that may span over multiple spatial and temporal scales. Hazards (such as floods, heatwaves, wildfires; also termed ´climatic impact-drivers´ in this report, see Chapter 12) are usually the immediate physical precursors to negative impacts, but can occasionally have positive outcomes ( [[#Flach--2018|Flach et al., 2018]] ). The present assessment focuses on the physical dimension of changes in compound events, as it is part of the IPCC AR6 Working Group I Report.

<div id="11.8.1" class="h2-container"></div>

<span id="overview"></span>
=== 11.8.1 Overview ===

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The combination of two or more – not necessarily extreme – weather or climate events that occur: i) at the same time; ii) in close succession; or iii) concurrently in different regions, can lead to extreme impacts that are much larger than the sum of the impacts due to the occurrence of individual extremes alone. This is because multiple stressors can exceed the coping capacity of a system more quickly. The contributing events can be of similar types (clustered multiple events) or of different types ( [[#Zscheischler--2020|Zscheischler et al., 2020]] ). Many major weather- and climate-related catastrophes are inherently of a compound nature ( [[#Zscheischler--2019|Zscheischler et al., 2019]] ).This has been highlighted for a broad range of hazards, such as droughts, heatwaves, wildfires, coastal extremes, and floods ( [[#Westra--2016|Westra et al., 2016]] ; [[#AghaKouchak--2020|AghaKouchak et al., 2020]] ; [[#Ridder--2020|Ridder et al., 2020]] ). Co-occurring extreme precipitation and extreme winds can result in infrastructural damage ( [[#Martius--2016|Martius et al., 2016]] ); the compounding of storm surge and precipitation extremes can cause coastal floods ( [[#Wahl--2015|Wahl et al., 2015]] ); the combination of drought and heat can lead to tree mortality ( [[#11.6|Section 11.6]] ; [[#Allen--2015|Allen et al., 2015]] ); and wildfires increase occurrences of hailstorms and lightning (Y. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] a). Compound storm types consisting of co-located cyclone, front and thunderstorm systems have a higher chance of causing extreme rainfall and extreme winds than individual storm types ( [[#Dowdy--2017|Dowdy and Catto, 2017]] ). Extremes may occur at similar times at different locations ( [[#De%20Luca--2020a|De Luca et al., 2020a]] , b) but affect the same system, for instance, spatially concurrent climate extremes affecting crop yields and food prices ( [[#Singh--2018|Singh et al., 2018]] ; [[#Anderson--2019|Anderson et al., 2019]] ). Studies also show an increasing likelihood for breadbasket regions to be concurrently affected by climate extremes with increasing global warming, even between 1.5°C and 2°C of global warming (Box 11.2; [[#Gaupp--2019|Gaupp et al., 2019]] ). Concomitant extreme conditions at different locations become more probable as changes in climate extremes are emerging over an increasing fraction of the land area (Sections 11.2.3, 11.2.4, 11.8.2 and 11.8.3, and Box 11.4).

Finally, impacts may occur because of large multivariate anomalies in the climate drivers, if systems are adapted to historical multivariate climate variability ( [[#Flach--2017|Flach et al., 2017]] ). For instance, ecosystems are typically adapted to the local covariability of temperature and precipitation such that a bivariate anomaly may have a large impact, even though neither temperature nor precipitation may be extreme based on a univariate assessment ( [[#Mahony--2018|Mahony and Cannon, 2018]] ). Given that almost all systems are affected by weather and climate phenomena at multiple space-time scales ( [[#Raymond--2020|Raymond et al., 2020]] ), it is natural to consider extremes in a compound event framework. It should be noted, however, that multi-hazard dependencies can also decrease risk, for instance when hazards are negatively correlated ( [[#Hillier--2020|Hillier et al., 2020]] ). Despite this recognition, the literature on past and future changes in compound events has been limited, but is growing. This section assesses examples of types of compound events in available literature.

In summary, compound events include the combination of two or more – not necessarily extreme – weather or climate events that occur (i) at the same time, (ii) in close succession, or (iii) concurrently in different regions. The land area affected by concurrent extremes has increased ( ''high confidence'' ). Concurrent extreme events at different locations, but possibly affecting similar sectors (e.g., breadbaskets) in different regions, will become more frequent with increasing global warming, in particular above +2°C of global warming ( ''high'' ''confidence'' ).

<div id="11.8.2" class="h2-container"></div>

<span id="concurrent-extremes-in-coastal-and-estuarine-regions"></span>
=== 11.8.2 Concurrent Extremes in Coastal and Estuarine Regions ===

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Coastal and estuarine zones are prone to a number of meteorological extreme events and also to concurrent extremes (see also Section 6.8.2 in SROCC). Floods are a major climatic impact-driver in coastal regions around the world (Chapter 12), and flood occurrence may be influenced by the dependence between storm surge, extreme rainfall, and river flow, but also by sea level rise, waves and tides, as well as groundwater for estuaries. Floods with multiple drivers are often referred to as ‘compound floods’ ( [[#Wahl--2015|Wahl et al., 2015]] ; [[#Moftakhari--2017|Moftakhari et al., 2017]] ; [[#Bevacqua--2020c|Bevacqua et al., 2020c]] ).

At USA coasts, the probability of co-occurring storm surge and heavy precipitation is higher for the Atlantic/Gulf coast relative to the Pacific coast ( [[#Wahl--2015|Wahl et al., 2015]] ). Furthermore, six studied locations on the USA coast with long overlapping time series show an increase in the dependence between heavy precipitation and storm surge over the last century, leading to more frequent co-occurring storm surge and heavy precipitation events at the present day ( [[#Wahl--2015|Wahl et al., 2015]] ). Storm surge and extreme rainfall are also dependent in most locations on the Australian coasts ( [[#Zheng--2013|Zheng et al., 2013]] ) and in Europe along the Dutch coasts ( [[#Ridder--2018|Ridder et al., 2018]] ), along the Mediterranean Sea, the Atlantic coast and the North Sea ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ). The probability of flood occurrence can be assessed via the dependence between storm surge and river flow ( [[#Bevacqua--2020b|Bevacqua et al., 2020b]] , c). For instance, the occurrence of a North Sea storm surge in close succession with an extreme Rhine or Meuse river discharge is much more probable due to their dependence, compared to if both events were independent ( [[#Kew--2013|Kew et al., 2013]] ; [[#Klerk--2015|Klerk et al., 2015]] ). Significant dependence between high sea levels and high river discharge are found for more than half of the available station observations, which are mostly located around the coasts of North America, Europe, Australia, and Japan ( [[#Ward--2018|Ward et al., 2018]] ). Combining global river discharge with a global storm surge model, hotspots of compound flooding have been discovered that are not well covered by observations in some regions, including Madagascar, Northern Morocco, Vietnam, and Taiwan of China ( [[#Couasnon--2020|Couasnon et al., 2020]] ). In the Dutch Noorderzijlvest area, there is more than a two-fold increase in the frequency of exceeding the highest warning level compared to the case if storm surge and heavy precipitation were independent ( [[#van%20den%20Hurk--2015|van den Hurk et al., 2015]] ). In other regions and seasons, the dependence can be insignificant (W. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ) and there can be significant seasonal and regional differences in the storm surge–heavy precipitation relationship. Assessments of flood probabilities are often not based on actual flood measurements; instead, they are estimated from its main drivers, including astronomical tides, storm surge, heavy precipitation, and high streamflow. Such single driver analyses might underestimate flood probabilities if multiple correlated drivers contribute to flood occurrence (e.g., [[#van%20den%20Hurk--2015|van den Hurk et al., 2015]] ).

Many coastal areas are also prone to the occurrence of compound precipitation and wind extremes, which can cause damage, including to infrastructure and natural environments. A high percentage of co-occurring wind and precipitation extremes are found in coastal regions and in areas with frequent tropical cyclones. Finally, the combination of extreme wave height and duration is also shown to influence coastal erosion processes ( [[#Corbella--2012|Corbella and Stretch, 2012]] ).

Aspects of concurrent extremes in coastal and estuarine environments have increased in frequency and/or magnitude over the last century in some regions. These include an increase in the dependence between heavy precipitation and storm surge over the last century, leading to more frequent co-occurring storm surge and heavy precipitation events in the present day along USA coastlines ( [[#Wahl--2015|Wahl et al., 2015]] ). In Europe, the probability of compound flooding occurrence increases most strongly along the Atlantic coast and the North Sea under strong warming. This increase is mostly driven by an intensification of precipitation extremes and aggravated flooding probability due to sea level rise ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ). At the global scale and under a high-emissions scenario, the concurrence probability of meteorological conditions driving compound flooding would increase by more than 25%, on average, along coastlines worldwide by 2100, compared to the present ( [[#Bevacqua--2020c|Bevacqua et al., 2020c]] ). Sea level extremes and their physical impacts in the coastal zone arise from a complex set of atmospheric, oceanic, and terrestrial processes that interact on a range of spatial and temporal scales and will be modified by a changing climate, including sea level rise ( [[#McInnes--2016|McInnes et al., 2016]] ). Interactions between sea level rise and storm surges ( [[#Little--2015|Little et al., 2015]] ), and sea level and fluvial flooding ( [[#Moftakhari--2017|Moftakhari et al., 2017]] ) are projected to lead to more frequent and intense compound coastal flooding events as sea levels continue to rise.

In summary, there is ''medium confidence'' that, over the last century, the probability of compound flooding has increased in some locations, including along the USA coastline. There is ''high confidence'' that the occurrence and magnitude of compound flooding in coastal regions will increase in the future due to both sea level rise and increases in heavy precipitation.

<div id="11.8.3" class="h2-container"></div>

<span id="concurrent-droughts-and-heatwaves"></span>
=== 11.8.3 Concurrent Droughts and Heatwaves ===

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Concurrent droughts and heatwaves have a number of negative impacts on human society and natural ecosystems. Studies since SREX and AR5 show several occurrences of observed combinations of drought and heatwaves in various regions.

Over most land regions, temperature and precipitation are strongly negatively correlated during summer ( [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ), mostly due to land–atmosphere feedbacks (Sections 11.1.6 and 11.3.2), but also because synoptic-scale weather systems favourable for extreme heat are also unfavourable for rain ( [[#Berg--2015|Berg et al., 2015]] ). This leads to a strong correlation between droughts and heatwaves ( [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ). Drought events characterized by low precipitation and extreme high temperatures have occurred, for example, in California ( [[#AghaKouchak--2014|AghaKouchak et al., 2014]] ), inland Eastern Australia ( [[#King--2014|King et al., 2014]] ), and large parts of Europe ( [[#Orth--2016a|Orth et al., 2016a]] ). The 2018 growing season was both record-breaking dry and hot in Germany ( [[#Zscheischler--2020|Zscheischler and Fischer, 2020]] ).

The probability of co-occurring meteorological droughts and heatwaves has increased in the observational period in many regions and will continue to do so under unabated warming ( [[#Herrera-Estrada--2017|Herrera-Estrada and Sheffield, 2017]] ; [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ; [[#Hao--2018|Hao et al., 2018]] ; [[#Sarhadi--2018|Sarhadi et al., 2018]] ; [[#Alizadeh--2020|Alizadeh et al., 2020]] ; [[#Wu--2021|Wu et al., 2021]] ). Overall, projections of increases in co-occurring drought and heatwaves are reported in northern Eurasia ( [[#Schubert--2014|Schubert et al., 2014]] ), Europe ( [[#Orth--2016a|Orth et al., 2016a]] ; [[#Sedlmeier--2018|Sedlmeier et al., 2018]] ), south-east Australia ( [[#Kirono--2017|Kirono et al., 2017]] ), multiple regions of the USA ( [[#Diffenbaugh--2015|Diffenbaugh et al., 2015]] ; [[#Herrera-Estrada--2017|Herrera-Estrada and Sheffield, 2017]] ), north-west China (X. [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Kong--2020|Kong et al., 2020]] ) and India ( [[#Sharma--2017|Sharma and Mujumdar, 2017]] ). The dominant signal is related to the increase in heatwave occurrence, which has been attributed to anthropogenic forcing ( [[#11.3.4|Section 11.3.4]] ). This means that, even if drought occurrence is unaffected, compound hot and dry events will be more frequent ( [[#Sarhadi--2018|Sarhadi et al., 2018]] ; [[#Yu--2020|Yu and Zhai, 2020]] ).

Drought and heatwaves are also associated with fire weather, related through high temperatures, low soil moisture, and low humidity. Fire weather refers to weather conditions conducive to triggering and sustaining wildfires, which generally include temperature, soil moisture, humidity, and wind (Chapter 12). Concurrent hot and dry conditions amplify conditions that promote wildfires ( [[#Schubert--2014|Schubert et al., 2014]] ; [[#Littell--2016|Littell et al., 2016]] ; [[#Dowdy--2018|Dowdy, 2018]] ; [[#Hope--2019|Hope et al., 2019]] ). Burnt area extent in western USA forests ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ) and particularly in California ( [[#Williams--2019|Williams et al., 2019]] ) has been linked to anthropogenic climate change via a significant increase in vapour pressure deficit, a primary driver of wildfires. A study of the western USA examined the correlation between historical water-balance deficits and annual area burned, across a range of vegetation types, from temperate rainforest to desert ( [[#McKenzie--2017|McKenzie and Littell, 2017]] ). The relationship between temperature and dryness, and wildfire, varied with ecosystem type, and the fire–climate relationship was nonstationary and vegetation-dependent. In many fire-prone regions, such as the Mediterranean and China’s Daxing’anling region, projections for increased severity of future drought and heatwaves may lead to an increased frequency of wildfires relative to observed climatology (Tian et al., 2017; [[#Ruffault--2018|Ruffault et al., 2018]] ). Observations show a long-term trend towards more dangerous weather conditions for bushfires in many regions of Australia, which is attributable (at least in part) to anthropogenic climate change ( [[#Dowdy--2018|Dowdy, 2018]] ). There is emerging evidence that recent regional surges in wildland fires are being driven by changing weather extremes (Cross-Chapter Box 3; [[#Jia--2019|Jia et al., 2019]] ; SRCCL Chapter 2). Between 1979 and 2013, the global burnable area affected by long fire weather seasons doubled, and the mean length of the fire weather season increased by 19% ( [[#Jolly--2015|Jolly et al., 2015]] ). However, at the global scale, the total burned area has been decreasing between 1998 and 2015 due to human activities mostly related to changes in land use ( [[#Andela--2017|Andela et al., 2017]] ). Given the projected ''high confidence'' increase in compound hot and dry conditions, there is ''high confidence'' that fire weather conditions will become more frequent at higher levels of global warming in some regions. This assessment is also consistent with Chapter 12’s examination of regional projected changes in fire weather. The SRCCL (Chapter 2) assessed with ''high confidence'' that future climate variability is expected to enhance the risk and severity of wildfires in many biomes such as tropical rainforests.

In summary, there is ''high confidence'' that concurrent heatwaves and droughts have increased in frequency over the last century at the global scale due to human influence. There is ''medium confidence'' that weather conditions that promote wildfires (fire weather) have become more probable in southern Europe, northern Eurasia, the USA, and Australia over the last century. There is ''high confidence'' that compound hot and dry conditions become more probable in nearly all land regions as global mean temperature increases. There is ''high confidence'' that fire weather conditions will become more frequent at higher levels of global warming in some regions.

<div id="box-11.4" class="h2-container box-container"></div>

Box 11.4 | Case Study: Global-scale Concurrent Climate Anomalies – the 2015–2016 Extreme El Niño and 2018 Boreal Spring–Summer

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Occurrence of concurrent or near-concurrent extremes in different parts of a region, or in different locations around the world, challenges adaptation and risk management capacity. This can occur as a result of natural climate variability, as climates in different parts of the world are interconnected through large-scale atmospheric–oceanic teleconnections. In addition, in a warming climate, the probability of having several locations being affected simultaneously by, for example, hot extremes and heatwaves increases strongly as a function of global warming, with detectable changes even for changes as small as +0.5°C of additional global warming (Sections 11.2.4 and 11.3, Cross-chapter Box 11.1). Recent articles have highlighted the risks associated with concurrent extremes over large spatial scales (e.g., [[#Lehner--2015|Lehner and Stocker, 2015]] ; [[#Boers--2019|Boers et al., 2019]] ; [[#Gaupp--2019|Gaupp et al., 2019]] ). There is evidence that such global-scale extremes associated with hot temperature extremes are increasing in occurrence ( [[#Sippel--2015|Sippel et al., 2015]] ; [[#Vogel--2019|Vogel et al., 2019]] ). Hereafter, the focus is on two case studies of recent global-scale events that featured concurrent extremes in several regions across the world. The first focuses on concurrent extremes driven by variability in tropical Pacific sea surface temperatures (SSTs) associated with the 2015–2016 extreme El Niño, while the second addresses the impacts of global warming combined with abnormal atmospheric circulation patterns in the 2018 boreal spring/summer.

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[[File:e0d9c9cd7d9b726fa43bbde1a27c248d IPCC_AR6_WGI_Box_11_4_Figure_1.png]]

'''Box 11.4, Figure 1 |'''

'''| Analysis of the percentage of land area affected by temperature extremes larger than two (blue) or three (orange) standard deviations in June–July–August (JJA) between 30°N and 80°N using a normalization.''' This figure shows a substantial increase in the overall land area affected by very strong hot extremes since 1990. Adapted from Sippel et al. (2015).

''''''The extreme El Ni''' '''ño in 2015–2016''''''
El Niño–Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different parts of the world, especially in extreme El Niño (Annex IV.2.3) cases. Additionally, the background climate warming associated with greenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Niño conditions. The 2015–2016 extreme El Niño event was one of the three extreme El Niño events since the 1980s and the availability of satellite rainfall observations. According to some measures, it was the strongest El Niño in the past 145 years ( [[#Barnard--2017|Barnard et al., 2017]] ). The 2015–2016 warmth was unprecedented at the central equatorial Pacific (Niño4: 5°N–5°S, 150°E–150°W), and this exceptional warmth was ''unlikely'' to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend ( [[#Newman--2018|Newman et al., 2018]] ). In particular, its signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing to the highest record of GMST in 2016 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ). Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. A brief summary of extreme events that happened in 2015–2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of the world during the 2015–2016 extreme El Niño in Box 11.4, Table 1, as well as in the short summary that follows.

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'''Box 11.4, Table 1 |'''

'''List of events related to the 2015–2016 Extreme El Niño in''' '''the literature.'''
{| class="wikitable"
|-
! Region

! Period

! Events

! References

|-
| Indonesia

| July 2015 to June 2016

| Droughts, forest fire

| [[#Field--2016|Field et al. (2016)]] ; [[#Huijnen--2016|Huijnen et al. (2016)]] ; [[#Patra--2017|Patra et al. (2017)]] ; [[#Hartmann--2018|Hartmann et al. (2018)]]

|-
| Northern Australia

| Between late 2015 and early 2016

| High temperature and drought

| [[#Duke--2017|Duke et al. (2017)]]

|-
| Amazon

| September 2015 to May 2016

| Droughts, forest fire

| [[#Jiménez-Muñoz--2016|Jiménez-Muñoz et al. (2016)]] ; [[#Erfanian--2017|Erfanian et al. (2017)]] ; [[#Aragão--2018|Aragão et al. (2018)]] ; [[#Panisset--2018|Panisset et al. (2018)]] ; [[#Ribeiro--2018|Ribeiro et al. (2018)]]

|-
| The entirety of South America north of 20°S

| Austral spring and 2015–2016 summer

| Droughts

| [[#Erfanian--2017|Erfanian et al. (2017)]]

|-
| Ethiopia

| February-September 2015

| Droughts

| [[#Blunden--2016|Blunden and Arndt (2016)]] ; [[#Philip--2018b|Philip et al. (2018b)]]

|-
| Southern Africa

| November 2015–April 2016

| Droughts

| Funk et al. (2016, 2018a); [[#Blamey--2018|Blamey et al. (2018)]] ; [[#Yuan--2018a|Yuan et al. (2018a)]]

|-
| Europe

| Boreal 2015–2016 winter

| Effects on circulation patterns

| [[#Geng--2017|Geng et al. (2017)]] ; [[#Scaife--2017|Scaife et al. (2017)]]

|-
| India

| May 2016

| High temperature

| [[#van%20Oldenborgh--2018|van Oldenborgh et al. (2018)]]

|-
| India

| December 2015

| Extreme rainfall

| [[#van%20Oldenborgh--2016|van Oldenborgh et al. (2016)]] ; [[#Boyaj--2018|Boyaj et al. (2018)]]

|-
| China

| June–July 2016

| Extreme rainfall

| [[#Sun--2018|Sun and Miao (2018)]] ; [[#Yuan--2018b|Yuan et al. (2018b)]] ; [[#Zhou--2018|Zhou et al. (2018)]]

|-
| Western North Pacific

| Boreal summer 2015

| The large number (13) of Category 4 and 5 tropical cyclones

| [[#Blunden--2016|Blunden and Arndt (2016)]] ; [[#Mueller--2016|]] [[#Mueller--2016|B. Mueller et al. (2016)]] ; W. [[#Zhang--2016a|Zhang et al. (2016a)]] ; Hong et al., (2018); [[#Yamada--2019|Yamada et al. (2019)]]

|-
| Eastern North Pacific

| Boreal summer 2015

| A record-breaking number of tropical cyclones

| [[#Collins--2016|Collins et al. (2016)]] ; [[#Murakami--2017b|Murakami et al. (2017b)]]

|-
| Global

| 2015–2016 El Niño

| High CO <sub>2</sub> release to the atmosphere associated with droughts and fires in several affected regions

| [[#Humphrey--2018|Humphrey et al. (2018)]] ; [[#Brando--2019|Brando et al. (2019)]]

|}

Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa, and Europe. As a result, global measurements of land water anomalies were particularly low in that year ( [[#Humphrey--2018|Humphrey et al., 2018]] ). In 2015, Indonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze crisis (Field et al. , 2016; Huijnen et al. , 2016; Patra et al. , 2017; Hartmann et al. , 2018). The northern part of Australia experienced high temperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the Gulf of Carpentaria in Northern Australia ( [[#Duke--2017|Duke et al., 2017]] ). The Amazon region experienced the most intense droughts of this century in 2015–2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 ( [[#Lewis--2011|Lewis et al., 2011]] ; [[#Erfanian--2017|Erfanian et al., 2017]] ; [[#Panisset--2018|Panisset et al., 2018]] ). The 2015–2016 Amazon drought impacted the entirety of South America north of 20°S during the austral spring and summer ( [[#Erfanian--2017|Erfanian et al., 2017]] ). It also increased forest fire incidence by 36% compared to the preceding 12 years ( [[#Aragão--2018|Aragão et al., 2018]] ) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) concentration in the area, affecting air quality ( [[#Ribeiro--2018|Ribeiro et al., 2018]] ). This out-of-season drought affected the water availability for human consumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation of food, medicines, and fuels ( [[#INMET--2017|INMET, 2017]] ). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the worst in several decades and was associated with the 2015–2016 extreme El Niño ( [[#Blunden--2016|Blunden and Arndt, 2016]] ; [[#Philip--2018b|Philip et al., 2018b]] ). It was suggested that anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs and local air temperatures ( [[#Funk--2016|Funk et al., 2016]] , 2018b; [[#Yuan--2018a|Yuan et al., 2018a]] ). It has also been suggested that the 2015–2016 extreme El Niño affected circulation patterns in Europe during the 2015–2016 winter ( [[#Geng--2017|Geng et al., 2017]] ; [[#Scaife--2017|Scaife et al., 2017]] ).

The atmospheric CO <sub>2</sub> growth rate was particularly high in 2015, possibly related to some of the mentioned droughts, in particular in Indonesia and the Amazon region, leading to higher CO <sub>2</sub> release in combination with less CO <sub>2</sub> uptake from land areas ( [[#Humphrey--2018|Humphrey et al., 2018]] ). The impact of the 2015–2016 extreme El Niño on vegetation systems via drought was also shown from satellite data ( [[#Kogan--2017|Kogan and Guo, 2017]] ). Overall, tropical forests were a carbon source to the atmosphere during the 2015–2016 El Niño-related drought, with some estimates suggesting that up to 2.3 PgC were released ( [[#Brando--2019|Brando et al., 2019]] ).

The 2015–2016 extreme El Niño has induced extreme precipitation in some regions. Severe rainfall events were observed in Chennai city in India in Devember 2015, and the Yangtze river region in China in June–July 2016, and it was shown that these rainfall events are partly attributed to the 2015–2016 extreme El Niño (van Oldenborgh et al. , 2016; Boyaj et al. , 2018; [[#Sun--2018|Sun and Miao, 2018]] ; Yuan et al. , 2018b; Zhou et al., 2018).

In 2015, tropical cyclone activity was notably high in the North Pacific ( [[#Blunden--2016|Blunden and Arndt, 2016]] ). Over the western North Pacific, there were 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area’s typical annual value of 6.3 (W. [[#Zhang--2016b|]] [[#Zhang--2016|Zhang et al., 2016]] b ). Similarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain ( [[#Collins--2016|Collins et al., 2016]] ; [[#Murakami--2017b|Murakami et al., 2017b]] ). These extraordinary TC activities were related to the average SST anomaly during that year, which were associated with the 2015–2016 extreme El Niño and the positive phase of the Pacific Meridional Mode ( [[#Murakami--2017b|Murakami et al., 2017b]] ; [[#Hong--2018|Hong et al., 2018]] ; [[#Yamada--2019|Yamada et al., 2019]] ). However, it has been suggested that the intense TC activities in both the western and the eastern North Pacific in 2015 were not only due to the El Niño, but also to a contribution of anthropogenic forcing ( [[#Murakami--2017b|Murakami et al., 2017b]] ; S.-H. [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ). The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity in the western North Pacific in 2015 ( [[#Zhan--2018|Zhan et al., 2018]] ). In contrast, in Australia, it was the least active TC season since satellite records began in 1969–1970 ( [[#Blunden--2017|Blunden and Arndt, 2017]] ).

''''''Global-scale temperature extremes and concurrent precipitation extremes in boreal 2018 sp''' '''ring and summer''''''
In the 2018 boreal spring–summer season (May–August), wide areas of the mid-latitudes in the Northern Hemisphere experienced heat extremes and (in part) enhanced drought (Box 11.4, Figure 2; Kornhuber et al. , 2019; Vogel et al. , 2019). The reported impacts included ( [[#Vogel--2019|Vogel et al., 2019]] ): 90 deaths from heat strokes in Quebec (Canada); 1469 deaths from heat strokes in Japan ( [[#Shimpo--2019|Shimpo et al., 2019]] ); 48 heat-related deaths in the Republic of Korea ( [[#Min--2020|Min et al., 2020]] ); heat warnings affecting 90,000 students in the USA; fires in numerous countries (Canada (British Columbia), USA (California), Finland (Lapland), Latvia); crop losses in the UK, Germany and Switzerland ( [[#Vogel--2019|Vogel et al., 2019]] ) and overall in central and Northern Europe (leading to yield reductions of up to 50% for the main crops ( [[#Toreti--2019|Toreti et al., 2019]] ); fish deaths in Switzerland; and melting of roads in the Netherlands and the UK, among others. In addition to the numerous hot and dry extremes, an extremely heavy rainfall event occurred over wide areas of Japan from 28 June to 8 July 2018 ( [[#Tsuguti--2018|Tsuguti et al., 2018]] ), which was followed by a heatwave ( [[#Shimpo--2019|Shimpo et al., 2019]] ). The heavy precipitation event caused more than 230 deaths in Japan, and was named ‘the Heavy Rain Event of July 2018’.

The heavy precipitation event was characterized by unusually widespread and persistent rainfall and locally anomalous total precipitation led by band-shaped precipitation systems, which are frequently associated with heavy precipitation events in East Asia ( [[#11.7.3|Section 11.7.3]] ; [[#Kato--2020|Kato, 2020]] ). The extreme rainfall in Japan was caused by anomalous moisture transport with a combination of abnormal jet condition ( [[#Takemi--2019|Takemi and Unuma, 2019]] ; [[#Takemura--2019|Takemura et al., 2019]] ; [[#Tsuji--2020|Tsuji et al., 2020]] ; [[#Yokoyama--2020|Yokoyama et al., 2020]] ), which can be viewed as an atmospheric river (Sections 8.2.2.8 and 11.7.2; [[#Yatagai--2019|Yatagai et al., 2019]] ) caused by intensified inflow velocity and high SST around Japan ( [[#Sekizawa--2019|Sekizawa et al., 2019]] ; [[#Kawase--2020|Kawase et al., 2020]] ).

This precipitation event and the subsequent heatwave are related to abnormal condition of the jet stream and North Pacific Subtropical High in this month ( [[#Shimpo--2019|Shimpo et al., 2019]] ; [[#Ren--2020|Ren et al., 2020]] ), which caused extreme conditions from Europe, Eurasia, and North America (Box 11.4, Figure 2; [[#Kornhuber--2019|Kornhuber et al., 2019]] ). A combination of the positive anomaly of the North Atlantic Oscillation (NAO, Annex IV.2.1) and the meandering jets is necessary to explain the pattern of the observed anomalies (Drouard et al. , 2019) . A role of Atlantic SST anomaly on the meandering jets and the subtropical high have been suggested ( [[#Liu--2019|B. Liu et al., 2019]] ). These dynamic and thermodynamic components generally have substantial influence on extreme rainfall in East Asia ( [[#Oh--2018|Oh et al., 2018]] ), but it is under investigation whether these factors were due to anthropogenic forcing.

Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May–July period, [[#Vogel--2019|Vogel et al. (2019)]] found that the event was unprecedented in terms of the total area affected by hot extremes (on average, about 22% of populated and agricultural areas in the Northern Hemisphere) for that period, but was consistent with a +1°C climate which was the estimated global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May–July temperature extremes would approximately occur two out of three years under +1.5°C of global warming, and every year under +2°C of global warming. [[#Imada--2019|Imada et al. (2019)]] also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C above pre-industrial levels. [[#Kawase--2020|Kawase et al. (2020)]] showed that the extreme rainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) showed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic warming. Hence, it is ''virtually certain'' that these 2018 concurrent events would not have occurred without human-induced global warming. Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, there is currently ''low confidence'' in projected changes in the frequency or strength of the anomalous circulation patterns leading to concurrent extremes (e.g., Cross-Chapter Box 10.1).

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[[File:8b960021e628718fccebc033dea879cd IPCC_AR6_WGI_Box_11_4_Figure_2.png]]

'''Box 11.4, Figure 2 |'''

'''Meteorological conditions in July 2018.''' The colour shading shows the monthly mean near-surface air temperature anomaly with respect to 1981–2010. Contour lines indicate the geopotential height in m, highlighted are the isolines on 12,000 m and 12,300 m, which indicate the approximate positions of the polar-front jet and subtropical jet, respectively. The light blue-green ellipse shows the approximate extent of the strong precipitation event that occurred at the beginning of July in the region of Japan and Korea. All data is from the global European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5, [[#Hersbach--2020|Hersbach et al., 2020]] ).

The case studies presented in this Box illustrate the current state of knowledge regarding the contribution of human-induced climate change to recent concurrent extremes in the global domain. Recent years have seen a more frequent occurrence of such events. The heatwave in Europe in the 2019 boreal summer and its coverage in the global domain is an additional example ( [[#Vautard--2020|Vautard et al., 2020]] ). However, very few studies investigate which types of concurrent extreme events could occur under increasing global warming. It has been noted that such events could also be of particular risk for concurrent impacts in the world’s breadbaskets ( [[#Zampieri--2017|Zampieri et al., 2017]] ; [[#Kornhuber--2020|Kornhuber et al., 2020]] ; see also [[#11.8.1|Section 11.8.1]] ).

In summary, the 2015–2016 extreme El Niño and the 2018 boreal spring/summer extremes were two examples of recent concurrent extremes. The El Niño event in 2015–2016 was one of the three extreme El Niño events since the 1980s, and there are many extreme events concurrently observed in this period including droughts, heavy precipitation, and more frequent intense tropical cyclones. Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period ( ''medium confidence'' ), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. The 2018 boreal spring/summer extremes were characterized by heat extremes and enhanced droughts in wide areas of the mid-latitudes in the Northern Hemisphere and extremely heavy rainfall in East Asia. These concurrent events were generally related to the abnormal condition of the jet and North Pacific Subtropical High, but also amplified by background global warming. It is ''virtually certain'' that these 2018 concurrent extreme events would not have occurred without human-induced global warming. Recent years have seen a more frequent occurrence of such concurrent events. However, it is still unknown which types of concurrent extreme events could occur under increasing global warming.

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<span id="regional-information-on-extremes"></span>
== 11.9 Regional Information on Extremes ==

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This section complements the assessments of changes in temperature extremes ( [[#11.3|Section 11.3]] ), heavy precipitation ( [[#11.4|Section 11.4]] ), and droughts ( [[#11.6|Section 11.6]] ), by providing additional regional details. Regional changes in floods are assessed in Chapter 12. Owing to the large number of regions and space limitations, the regional assessment for each of the AR6 reference regions (see [[IPCC:Wg1:Chapter:Chapter-1#1.5.2.2|Section 1.5.2.2]] for a description) is presented here in a set of tables. The tables are organized according to types of extremes (temperature, heavy precipitation, droughts) for Africa (Tables 11.4–11.6), Asia (Tables 11.7–11.9), Australasia (Tables 11.10–11.12), Central and South America (Tables 11.13–11.15), Europe (Tables 11.16–11.18), and North America (Tables 11.19–11.21). Each table contains regional assessments for observed changes, the human contribution to the observed changes, and projections of changes in these extremes at 1.5°C, 2°C and 4°C of global warming. A synthesis of regional changes in hot extremes, heavy precipitation, agricultural and ecological droughts, and hydrological droughts can be found in the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Appendix in Table 11.A.2.

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<span id="overview-1"></span>
=== 11.9.1 Overview ===

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Sections 11.9.2, 11.9.3 and 11.9.4 provide brief summaries of the underlying evidence used to derive the regional assessments for temperature extremes, heavy precipitation events, and droughts, respectively. The assessments take into account evidence from studies based on global datasets (global studies), as well as regional studies. Global studies include analyses for all continents and AR6 regions with sufficient data coverage, and provide an important basis for cross-region consistency, as the same data and methods are used for all regions. However, individual regional studies may include additional information that is missed in global studies, and thus provide an important regional calibration for the assessment.

The assessments are presented using the calibrated confidence and likelihood language (Box 1.1). ''Low confidence'' is assessed when there is ''limited evidence'' , either because of a lack of available data in the region and/or a lack of relevant studies. ''Low confidence'' is also assessed when there is a lack of agreement on the evidence of a change, which may be due to large variability or inconsistent changes depending on the considered sub-regions, time frame, models, assessed metrics, or studies. In cases when the evidence is strongly contradictory, for example, with substantial regional changes of opposite sign, ‘mixed signal’ is indicated. With an assessment of ''low confidence'' , the direction of change is not indicated in the tables. A direction of change (increase or decrease) is provided with an assessment of ''medium confidence'' , ''high confidence'' , ''likely'' , or higher likelihood levels. Likelihood assessments are only provided in the case of ''high confidence'' . In some cases, there may be confidence in a small or no change.

For projections, changes are assessed at three global warming levels (GWLs; Cross-Chapter Box 11.1): 1.5°C, 2°C and 4°C. The assessments use literature based both on GWL projections and scenario-based projections. In the case of literature on scenario-based projections, a mapping between scenarios/time frames and GWLs was performed, as documented in Cross-Chapter Box 11.1. Projections of changes in temperature and precipitation extremes are assessed relative to two different baselines: the recent past (1995–2014) and pre-industrial (1850–1900). With smaller changes relative to the variability, in particular because droughts happen on longer timescales compared to extremes of daily temperature and precipitation, it is more difficult to distinguish changes in drought relative to the recent past. As such, changes in droughts are assessed relative to the pre-industrial baseline, unless indicated otherwise.

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<span id="temperature-extremes-2"></span>
=== 11.9.2 Temperature Extremes ===

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Tables 11.4, 11.7, 11.10, 11.13, 11.16, and 11.19 include assessments for past temperature extremes and their attribution, as well as future projections. The evidence is mostly drawn from changes in metrics based on daily maximum and minimum temperatures, similar to those used in [[#11.3|Section 11.3]] . The regional assessments start from global studies that used consistent analyses for all regions globally with sufficient data. This includes [[#Dunn--2020|Dunn et al. (2020)]] for observed changes, and [[#Li--2021|Li et al. (2021)]] and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) for projections with the CMIP6 multi-model ensemble. Evidence from regional studies, and those based on the CMIP5 multi-model ensemble or CORDEX simulations, are then used to refine the confidence assessments. For attribution, Seong et al. (2020) provide a consistent analysis for AR6 regions, and Z. [[#Wang--2017a|Wang et al. (2017a)]] for SREX regions. Additional regional studies, including event attribution analyses ( [[#11.2|Section 11.2]] ), are used when available. In some regions that were not analysed in Seong et al. (2020), and those with no known event attribution studies, ''medium confidence'' of a human contribution is assessed: when there is strong evidence of changes from observations that are in the direction of model-projected changes for the future; when the magnitude of projected changes increases with global warming; and where there is no other evidence to the contrary. This assessment is further supported by an understanding of how temperature extremes change with the mean temperature and overwhelming evidence of a human contribution to the observed larger-scale changes in the mean temperature and temperature extremes.

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<span id="heavy-precipitation-1"></span>
=== 11.9.3 Heavy Precipitation ===

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Tables 11.5, 11.8, 11.11, 11.14, 11.17, and 11.20 include assessments for past changes in heavy precipitation events and their attribution, as well as future projections. The evidence is mostly drawn from changes in metrics based on one-day or five-day precipitation amounts, as addressed in [[#11.4|Section 11.4]] . Similar to temperature extremes, the assessment of changes in heavy precipitation uses global studies, including [[#Dunn--2020|Dunn et al. (2020)]] and [[#Sun--2021|Sun et al. (2021)]] for observed changes, and [[#Li--2021|Li et al. (2021)]] and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) for projected changes using the CMIP6 multi-model ensemble. For attribution, [[#Paik--2020|Paik et al. (2020)]] provided continental analyses where data coverage was sufficient, but no attribution studies based on global data are available for the regional scale. For each region, regional studies, and studies based on the CMIP5 multi-model ensemble or CORDEX simulations, are also considered in the assessments for past changes, attribution, and projections.

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<span id="droughts-2"></span>
=== 11.9.4 Droughts ===

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Tables 11.6, 11.9, 11.12, 11.15, 11.18, and 11.21 provide regional assessments on past, attributed and projected changes in droughts. The assessment is subdivided in three drought categories corresponding to four drought types: i) meteorological droughts, ii) agricultural and ecological droughts, and iii) hydrological droughts (see [[#11.6|Section 11.6]] ). A list of metrics and global studies used for the assessments is provided below. The evidence from global studies is complemented in each continent with evidence from regional studies. An overview of studies considered for the assessments in projections is provided in Table 11.3.

Meteorological droughts are assessed based on observed and projected changes in precipitation-only metrics such as the Standardized Precipitation Index (SPI) and Consecutive Dry Days (CDD). Observed changes are assessed based on two global studies, [[#Dunn--2020|Dunn et al. (2020)]] for CDD, and [[#Spinoni--2019|Spinoni et al. (2019)]] for SPI. For projections, evidence for changes at 1.5°C and 2°C of global warming is drawn from L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]] and [[#Touma--2015|Touma et al. (2015)]] (based on RCP8.5 for 2010–2054 compared to 1961–2005) for SPI (CMIP5) and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) for CDD (CMIP6). For projections at 4°C of global warming, evidence is drawn from several sources, including [[#Touma--2015|Touma et al. (2015)]] and [[#Spinoni--2020|Spinoni et al. (2020)]] for SPI (from CMIP5 and CORDEX, respectively), and 11.SM for CDD (CMIP6). No global-scale studies are available for the attribution of meteorological drought, so this assessment is based on regional detection and attribution or event attribution studies.

Agricultural and ecological droughts are primarily assessed based on observed and projected changes in total column soil moisture, complemented by evidence on changes in surface soil moisture, water-balance (precipitation minus evapotranspiration (ET)) and metrics driven by precipitation and atmospheric evaporative demand (AED) such as the SPEI and PDSI ( [[#11.6|Section 11.6]] ). In the latter, only studies including estimates based on the Penman–Monteith equation (SPEI-PM and PDSI-PM) are considered because of biases associated with temperature-only approaches ( [[#11.6|Section 11.6]] ). ''Medium'' to ''high confidence'' in drying was assigned in the assessment for arid regions if a signal was also identifiable in total soil moisture in addition to surface soil moisture or metrics that combine AED and precipitation, which tend to dry more in these regions. For observed changes, evidence is drawn from several sources: [[#Padrón--2020|Padrón et al. (2020)]] for changes in precipitation minus ET, as well as soil moisture from the multi-model Land Surface Snow and Soil Moisture Model Intercomparison Project within CMIP6 (11.SM; [[#van%20Den%20Hurk--2016|van Den Hurk et al., 2016]] ); [[#Greve--2014|Greve et al. (2014)]] for changes in precipitation minus ET, and precipitation minus AED; [[#Spinoni--2019|Spinoni et al. (2019)]] for changes in SPEI-PM; and [[#Dai--2017|Dai and Zhao (2017)]] for changes in PDSI-PM.

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'''Table 11.3 |''' '''Global analyses considered for the assessments of drought projections.''' MET refers to meteorological droughts, AGR/ECOL to agricultural and ecological droughts, and HYDR to hydrological droughts.

{| class="wikitable"
|-
! Reference

! Model Data <sup>a</sup>

! Index <sup>a</sup>

! Drought Type

! Projection Horizons

! Baseline

|-
| 11.SM

| CMIP6

| CDD, Soil moisture (total, surface)

| MET

| 1.5°C, 2°C, 4°C

| 1850–1900

|-
| [[#Cook--2020|Cook et al. (2020)]]

| CMIP6

| Soil moisture (total, surface), runoff (total, surface)

| AGR/ECOL, HYDR

| 2071–2011, SSP1-2.6 (about 2°C, Cross-Chapter Box 11.1; Table 4.2)

2071–2011, SSP3-7-3 (about 4°C, Cross-Chapter Box 11.1; Table 4.2)

| 1850–1900

|-
| L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]]

| CMIP5

| SPI, soil moisture (total, surface)

| MET, AGR/ECOL

| 1.5°C, 2°C

| 1971–2000

|-
| [[#Touma--2015|Touma et al. (2015)]]

| CMIP5

| SPI, SRI

| MET, HYDR

| 2010–2054, RCP8.5 (about 1.5°C; Cross-Chapter Box 11.1 and 11.SM.1)

2055–2099, RCP8.5 (about 3.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1961–2005

|-
| [[#Spinoni--2020|Spinoni et al. (2020)]]

| CORDEX (CMIP5 driving GCMs, RCMs)

| SPI

| MET

| 2071–2100, RCP4.5 (about 2.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

2071–2100, RCP8.5 (about 4.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1981–2010

|-
| [[#Naumann--2018|Naumann et al. (2018)]]

| One GCM (EC-EARTH3-HR v3.1) driven with SST fields from seven CMIP5 GCMs

| SPEI-PM

| AGR/ECOL

| 1.5°C, 2°C, (3°C)

| 0.6°C

|-
| [[#Vicente-Serrano--2020c|Vicente-Serrano et al. (2020c)]]

| CMIP5

| SPEI-PM

| AGR/ECOL

| 2070–2100, RCP8.5 (about 4.5°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1970–2000

|-
| [[#Giuntoli--2015|Giuntoli et al. (2015)]]

| ISI-MIP (six GHMs and five CMIP5 GCMs)

| Low-flows days

| HYDR

| 2066–2099, RCP8-5 (about 4°C, Cross-Chapter Box 11.1 and 11.SM.1)

| 1972–2005

|-
| J. [[#Zhai--2020|]] [[#Zhai--2020|Zhai et al. (2020)]]

| One GHM (VIC) driven by four CMIP5 GCMs

| Extreme low runoff

| HYDR

| 1.5°C, 2°C

| 2006–2015

|}

<sup>a</sup> CMIP5 and CMIP6: Coupled Model Intercomparison Project Phases 5/6; CORDEX: Coordinated Regional Downscaling Experiment; GCMs: global climate models; RCMs: regional climate models; SST: sea surface temperatures; ISI-MIP: Inter-Sectoral Impact Model Intercomparison Project; GHMs: Global Hydrological Models; CDD: consecutive dry days index; SPI: Standardized Precipitation Index; SRI: Standardized Runoff Index; SPEI-PM: Penman–Monteith-based Standardized Precipitation Evapotranspiration Index.

For projections at 1.5°C of global warming, evidence is drawn from: L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]] , based on CMIP5; 11.SM based on CMIP6 for changes in total column and surface soil moisture; and from [[#Naumann--2018|Naumann et al. (2018)]] for changes in SPEI-PM, based on EC-Earth simulations driven with SSTs from seven CMIP5 Earth system models. For projections at 2°C of global warming, evidence is drawn from L. [[#Xu--2019|]] [[#Xu--2019|Xu et al. (2019)]] based on CMIP5, and [[#Cook--2020|Cook et al. (2020)]] (SSP1-2.6, 2071–2100 compared to pre-industrial) and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) based on CMIP6, for changes in total column and surface soil moisture; evidence is also drawn from [[#Naumann--2018|Naumann et al. (2018)]] for changes in SPEI-PM. For projections at 4°C of global warming, evidence is mostly drawn from: [[#Cook--2020|Cook et al. (2020)]] (SSP3-7.0, 2071–2100) and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM) based on CMIP6 for changes in total column and surface soil moisture; and from [[#Vicente-Serrano--2020c|Vicente-Serrano et al. (2020c)]] for changes in SPEI-PM based on CMIP5. No global-scale studies with regional-scale information are available for the attribution of agricultural and ecological droughts, so this assessment is based on regional detection and attribution or event attribution studies.

Hydrological droughts are assessed based on observed and projected changes in low flows, complemented by information on changes in mean runoff. For observed changes, evidence is drawn from three studies ( [[#Dai--2017|Dai and Zhao, 2017]] ; [[#Gudmundsson--2019|Gudmundsson et al., 2019]] , 2021). For projected changes at 1.5°C of global warming, evidence is drawn from [[#Touma--2015|Touma et al. (2015)]] based on analyses of the Standardized Runoff Index (SRI) (CMIP5, based on 2010–2054 compared to 1961–2005), complemented with regional studies when available. For projected changes at 2°C of global warming, evidence is also drawn from [[#Cook--2020|Cook et al. (2020)]] for changes in runoff in CMIP6 (Scenario SSP1-2.6, 2071–2100), and from J. [[#Zhai--2020|]] [[#Zhai--2020|Zhai et al. (2020)]] for changes in low flows based on simulations with a single model. For projected changes at 4°C of global warming, evidence is drawn from: [[#Touma--2015|Touma et al. (2015)]] based on CMIP5 analyses of SRI; [[#Cook--2020|Cook et al. (2020)]] for changes in surface and total runoff based on CMIP6; and [[#Giuntoli--2015|Giuntoli et al. (2015)]] for changes in low flows based on the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) based on six Global Hydrological Models (GHMs) and five GCMs, including an analysis of inter-model signal-to-noise ratio. One global-scale study with regional-scale information is available for the attribution of hydrological droughts ( [[#Gudmundsson--2021|Gudmundsson et al., 2021]] ), but only in a few AR6 regions. This information was complemented with evidence from regional detection and attribution, and event attribution studies when available.

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== Acknowledgements ==

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Alexis Berg, Tim Brodribb, Jamie Hannaford, Nate McDowell, Jack Scheff, Peter Stott, Lena Tallaksen, Peter Thorne, Francis Zwiers '''.'''

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== Frequently Asked Questions ==

<span id="faq-11.1-how-do-changes-in-climate-extremes-compare-with-changes-in-climate-averages"></span>
=== FAQ 11.1 | How Do Changes In Climate Extremes Compare With Changes In Climate Averages? ===

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''Human-caused climate change alters the frequency and intensity of climate variables (e.g., surface temperature) and phenomena (e.g., tropical cyclones) in a variety of ways. We now know that the ways in which average and extreme conditions have changed (and will continue to change) depend on the variable and the phenomenon being considered. Changes in local surface temperature extremes closely follow the corresponding changes in local average surface temperatures. On the contrary, changes in precipitation extremes (heavy precipitation) generally do not follow those in average precipitation, and can even move in the opposite direction (e.g., with average precipitation decreasing but extreme precipitati'' ''on increasing).''

Climate change will manifest very differently depending on which region, season and variable we are interested in. For example, over some parts of the Arctic, temperatures will warm at rates about three to four times higher during winter compared to summer months. And in summer, most of northern Europe will experience larger temperatures increases than most places in south-east South America and Australasia, with differences that can be larger than 1°C, depending on the level of global warming. In general, differences across regions and seasons arise because the underlying physical processes differ drastically across regions and seasons.

Climate change will also manifest differently for different weather regimes and can lead to contrasting changes in average and extreme conditions. Observations of the recent past and climate model projections show that, in most places, changes in daily temperatures are dominated by a general warming where the climatological average and extreme values are shifted towards higher temperatures, making warm extremes more frequent and cold extremes less frequent. The top panels in FAQ 11.1, Figure 1 show projected changes in surface temperature for long-term average conditions (left) and for extreme hot days (right) during the warm season (summer in mid- to high latitudes). Projected increases in long-term average temperature differ substantially between different places, varying from less than 3°C in some places in central South Asia and southern South America to over 7°C in some places in North America, North Africa and the Middle East. Changes in extreme hot days follow changes in average conditions quite closely, although, in some places, the warming rates for extremes can be intensified (e.g., southern Europe and the Amazon basin) or weakened (e.g., northern Asia and Greenland) compared to average values.

Recent observations and global and regional climate model projections point to changes in precipitation extremes (including both rainfall and snowfall extremes) differing drastically from those in average precipitation. The bottom panels in FAQ 11.1, Figure 1 show projected changes in the long-term average precipitation (left) and in heavy precipitation (right). Averaged precipitation changes show striking regional differences, with substantial drying in places such as southern Europe and northern South America and wetting in places such as the Middle East and southern South America. Changes in extreme precipitation are much more uniform, with systematic increases over nearly all land regions. The physical reasons behind the different responses of averaged and extreme precipitation are now well understood. The intensification of extreme precipitation is driven by the increase in atmospheric water vapour (about 7% per 1°C of warming near the surface), although this is modulated by various dynamical changes. In contrast, changes in average precipitation are driven not only by moisture increases but also by slower processes that constrain future changes over the globe to only 2–3% per 1°C of warming near the surface.

In summary, the specific relationship between changes in average and extreme conditions strongly depends on the variable or phenomenon being considered. At the local scale, average and extreme surface temperature changes are strongly related, while average and extreme precipitation changes are often weakly related. For both variables, the changes in average and extreme conditions vary strongly across different places due to the effect of local and regional processes.

[[File:d3f028f51198019b091524c9b84900c5 IPCC_AR6_WGI_FAQ_11_1_Figure_1.png]]

'''FAQ 11.1, Figure 1 |''' '''Global maps of future changes in surface temperature (top panels) and precipitation (bottom panels) for long-term average (left) and extreme conditions (right).''' All changes were estimated using the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble median for a scenario with a global warming of 4°C relative to 1850–1900 temperatures. Average surface temperatures refer to the warmest three-month season (summer in mid- to high latitudes) and extreme temperatures refer to the hottest day in a year. Precipitation changes, which can include both rainfall and snowfall changes, are normalized by 1850–1900 values and shown as a percentage; extreme precipitation refers to the largest daily precipitation in a year.

<span id="faq-11.2-will-unprecedented-extremes-occur-as-a-result-of-human-induced-climate-change"></span>
=== FAQ 11.2 | Will Unprecedented Extremes Occur As a Result Of Human-Induced Climate Change? ===

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''Climate change has already increased the magnitude and frequency of extreme hot events and decreased the magnitude and frequency of extreme cold events, and, in some regions, intensified extreme precipitation events. As the climate moves away from its past and current states, we will experience extreme events that are unprecedented, either in magnitude, frequency, timing or location. The frequency of these unprecedented extreme events will rise with increasing global warming. Additionally, the combined occurrence of multiple unprecedented extremes may result in large and unprece'' ''dented impacts.''

Human-induced climate change has already affected many aspects of the climate system. In addition to the increase in global surface temperature, many types of weather and climate extremes have changed. In most regions, the frequency and intensity of hot extremes have increased and those of cold extremes have decreased. The frequency and intensity of heavy precipitation events have increased at the global scale and over a majority of land regions. Although extreme events such as land and marine heatwaves, heavy precipitation, drought, tropical cyclones, and associated wildfires and coastal flooding have occurred in the past and will continue to occur in the future, they often come with different magnitudes or frequencies in a warmer world. For example, future heatwaves will last longer and have higher temperatures, and future extreme precipitation events will be more intense in several regions. Certain extremes, such as extreme cold, will be less intense and less frequent with increasing warming.

Unprecedented extremes – that is, events not experienced in the past – will occur in the future in five different ways (FAQ 11.2, Figure 1). First, events that are considered to be extreme in the current climate will occur in the future with unprecedented magnitudes. Second, future extreme events will also occur with unprecedented frequency. Third, certain types of extremes may occur in regions that have not previously encountered those types of events. For example, as the sea level rises, coastal flooding may occur in new locations, and wildfires are already occurring in areas, such as parts of the Arctic, where the probability of such events was previously low. Fourth, extreme events may also be unprecedented in their timing. For example, extremely hot temperatures may occur either earlier or later in the year than they have in the past.

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[[File:e92ea7cde08eb4d4cd4c490b582863e5 IPCC_AR6_WGI_FAQ_11_2_Figure_1.png]]

'''FAQ 11.2, Figure 1 |''' '''New types of unprecedented extremes that will occur as a result of''' '''climate change.'''

Finally, compound events – where multiple extreme events of either different or similar types occur simultaneously and/or in succession – may be more probable or severe in the future. These compound events can often impact ecosystems and societies more strongly than when such events occur in isolation. For example, a drought along with extreme heat will increase the risk of wildfires and agriculture damages or losses. As individual extreme events become more severe as a result of climate change, the combined occurrence of these events will create unprecedented compound events. This could exacerbate the intensity and associated impacts of these extreme events.

Unprecedented extremes have already occurred in recent years, relative to the 20th century climate. Some recent extreme hot events would have had very little chance of occurring without human influence on the climate (see FAQ 11.3). In the future, unprecedented extremes will occur as the climate continues to warm. Those extremes will happen with larger magnitudes and at higher frequencies than previously experienced. Extreme events may also appear in new locations, at new times of the year, or as unprecedented compound events. Moreover, unprecedented events will become more frequent with higher levels of warming, for example at 3°C of global warming compared to 2°C of global warming.

<span id="faq-11.3-did-climate-change-cause-that-recent-extreme-event-in-my-country"></span>

=== FAQ 11.3 | Did Climate Change Cause That Recent Extreme Event In My Country? ===

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''While it is difficult to identify the exact causes of a particular extreme event, the relatively new science of event attribution is able to quantify the role of climate change in altering the probability and magnitude of some types of weather and climate extremes. There is strong evidence that characteristics of many individual extreme events have already changed because of human-driven changes to the climate system. Some types of highly impactful extreme weather events have occurred more often and have become more severe due to these human influences. As the climate continues to warm, the observed changes in the probability and/or magnitude of some extreme weather events will continue as the human influences on these eve'' ''nts increase.''

It is common to question whether human-caused climate change caused a major weather- and climate-related disaster. When extreme weather and climate events do occur, both exposure and vulnerability play an important role in determining the magnitude and impacts of the resulting disaster. As such, it is difficult to attribute a specific disaster directly to climate change. However, the relatively new science of event attribution enables scientists to attribute aspects of specific extreme weather and climate events to certain causes. Scientists cannot answer directly whether a particular event was caused by climate change, as extremes do occur naturally, and any specific weather and climate event is the result of a complex mix of human and natural factors. Instead, scientists quantify the relative importance of human and natural influences on the magnitude and/or probability of specific extreme weather events. Such information is important for disaster risk reduction planning, because improved knowledge about changes in the probability and magnitude of relevant extreme events enables better quantification of disaster risks.

On a case-by-case basis, scientists can now quantify the contribution of human influences to the magnitude and probability of many extreme events. This is done by estimating and comparing the probability or magnitude of the same type of event between the current climate – including the increases in greenhouse gas concentrations and other human influences – and an alternate world where the atmospheric greenhouse gases remained at pre-industrial levels. FAQ 11.3 Figure 1 illustrates this approach using differences in temperature and probability between the two scenarios as an example. Both the pre-industrial (blue) and current (red) climates experience hot extremes, but with different probabilities and magnitudes. Hot extremes of a given temperature have a higher probability of occurrence in the warmer current climate than in the cooler pre-industrial climate. Additionally, an extreme hot event of a particular probability will be warmer in the current climate than in the pre-industrial climate. Climate model simulations are often used to estimate the occurrence of a specific event in both climates. The change in the magnitude and/or probability of the extreme event in the current climate compared to the pre-industrial climate is attributed to the difference between the two scenarios, which is the human influence.

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[[File:0ea2f948acfd1608b1ef5abbb385d492 IPCC_AR6_WGI_FAQ_11_3_Figure_1.png]]

'''FAQ 11.3, Figure 1 |''' '''Changes in climate result in changes in the magnitude and probability of extremes.''' Example of how temperature extremes differ between a climate with pre-industrial greenhouse gases (shown in blue) and the current climate (shown in orange) for a representative region. The horizontal axis shows the range of extreme temperatures, while the vertical axis shows the annual chance of each temperature event’s occurrence. Moving towards the right indicates increasingly hotter extremes that are more rare (less probable). For hot extremes, an extreme event of a particular temperature in the pre-industrial climate would be more probable (vertical arrow) in the current climate. An event of a certain probability in the pre-industrial climate would be warmer (horizontal arrow) in the current climate. While the climate under greenhouse gases at the pre-industrial level experiences a range of hot extremes, such events are hotter and more frequent in the current climate.

Attributable increases in probability and magnitude have been identified consistently for many hot extremes. Attributable increases have also been found for some extreme precipitation events, including hurricane rainfall events, but these results can vary among events. In some cases, large natural variations in the climate system prevent attributing changes in the probability or magnitude of a specific extreme to human influence. Additionally, attribution of certain classes of extreme weather (e.g., tornadoes) is beyond current modelling and theoretical capabilities. As the climate continues to warm, larger changes in probability and magnitude are expected and, as a result, it will be possible to attribute future temperature and precipitation extremes in many locations to human influences. Attributable changes may emerge for other types of extremes as the warming signal increases.

In conclusion, human-caused global warming has resulted in changes in a wide variety of recent extreme weather events. Strong increases in probability and magnitude, attributable to human influence, have been found for many heatwaves and hot extremes around the world.

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== Large Tables ==

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'''Table 11.4 |''' '''Observed trends, human contribution to observed trends, and projected chang''' '''es at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Africa, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.2 for details. CMIP6: Coupled Model Intercomparison Project Phase 6; TXx: hottest daily maximum temperature; TNn: coldest daily minimum temperature; CORDEX: Coordinated Regional Downscaling Experiment; RCM: regional climate model.

[[File:14f4581ae8db67365473dbc72572e1c2 IPCC_AR6_WGI_Chapter11_Table_11_4_1.jpg]] [[File:2ce445b30d96ae1c1740faf678b177b8 IPCC_AR6_WGI_Chapter11_Table_11_4_2.jpg]] [[File:b357339bae536664708b966b99444e6d IPCC_AR6_WGI_Chapter11_Table_11_4_3.jpg]] [[File:58f58267afd6423842b1239d0ed05cc2 IPCC_AR6_WGI_Chapter11_Table_11_4_4.jpg]] [[File:e4e718fed873c102e81d2680f632fe81 IPCC_AR6_WGI_Chapter11_Table_11_4_6.jpg]] [[File:87baf1c107ad419465f91aa13744008c IPCC_AR6_WGI_Chapter11_Table_11_4_7.jpg]]

'''Table 11.5 |''' '''Observed trends, human contribution to observed trends, and projected changes''' '''at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Africa, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.3 for details.

[[File:b54b1be850bed175e8ed66cd5a4d49c4 IPCC_AR6_WGI_Chapter11_Table_11_5_1.jpg]] [[File:a7c36d75ac35396db5dc0ee39630fbfe IPCC_AR6_WGI_Chapter11_Table_11_5_2.jpg]] [[File:7d7e084f3b60f408a6acc9896a04ae27 IPCC_AR6_WGI_Chapter11_Table_11_5_3.jpg]] [[File:340f57336f309f9837e5941fd24b831b IPCC_AR6_WGI_Chapter11_Table_11_5_4.jpg]] [[File:ed743d6ed09ee7b0716459c46cdf80ea IPCC_AR6_WGI_Chapter11_Table_11_5_5.jpg]] [[File:99ddf3e3ec06a487051169729d5fa89b IPCC_AR6_WGI_Chapter11_Table_11_5_6.jpg]]

'''Table 11.6 |''' '''Observed trends, human contribution to observed trends,''' '''and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Africa, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.4 for details.

[[File:0b999715cf4bd5a36e48f58d2ced793b IPCC_AR6_WGI_Chapter11_Table_11_6_1.jpg]] [[File:c3cfb758d9042dad031984927ad0d359 IPCC_AR6_WGI_Chapter11_Table_11_6_2.jpg]] [[File:7ae968d19271c88ffe34680b8d135494 IPCC_AR6_WGI_Chapter11_Table_11_6_3.jpg]] [[File:e9abbb4352521dd782610080b491afbd IPCC_AR6_WGI_Chapter11_Table_11_6_4.jpg]] [[File:cc3f65ba4b64e2d879fc5249a322a3dc IPCC_AR6_WGI_Chapter11_Table_11_6_5.jpg]] [[File:b93c869f8dad713504b19be9df15fc97 IPCC_AR6_WGI_Chapter11_Table_11_6_6.jpg]] [[File:48aa01bc243ff1523e014b98159f5855 IPCC_AR6_WGI_Chapter11_Table_11_6_7.jpg]] [[File:8b80153e9d054723cdfa97536941a220 IPCC_AR6_WGI_Chapter11_Table_11_6_8.jpg]] [[File:e5bad4b3b911c7a1c547af7210add10f IPCC_AR6_WGI_Chapter11_Table_11_6_9.jpg]] [[File:8bf5259ea50d6e0b58697ea03268dffa IPCC_AR6_WGI_Chapter11_Table_11_6_10.jpg]] [[File:d8b2f773a36773697babf36d9017f657 IPCC_AR6_WGI_Chapter11_Table_11_6_11.jpg]] [[File:8a03a4c011d6869b9badd35c57c9973b IPCC_AR6_WGI_Chapter11_Table_11_6_12.jpg]] [[File:b730acf7c27fad283741c1af4cfb8868 IPCC_AR6_WGI_Chapter11_Table_11_6_13.jpg]]

'''Table 11.7 |''' '''Observed trends, human contr''' '''ibution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Asia, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.2 for details.

[[File:38b852a0aad07768c553468a609eb79e IPCC_AR6_WGI_Chapter11_Table_11_7_1.jpg]] [[File:b0cfa666377dd10c015dc92a05ae9558 IPCC_AR6_WGI_Chapter11_Table_11_7_2.jpg]] [[File:53f58343c292c0e6acc8b320f9544296 IPCC_AR6_WGI_Chapter11_Table_11_7_3.jpg]] [[File:a910a645f3bbadec7432ba44ad981818 IPCC_AR6_WGI_Chapter11_Table_11_7_4.jpg]] [[File:a082fb944621fe6caa01a831f6008d90 IPCC_AR6_WGI_Chapter11_Table_11_7_5.jpg]] [[File:fd5358a1ea6c06ebfe07f55ab5356b36 IPCC_AR6_WGI_Chapter11_Table_11_7_6.jpg]] [[File:080550c205220848090185483188b625 IPCC_AR6_WGI_Chapter11_Table_11_7_7.jpg]] [[File:cfe750b23cc0ad9645bf892f812ce2cd IPCC_AR6_WGI_Chapter11_Table_11_7_8.jpg]] [[File:b09854999feada9af95cb4c3094f94e9 IPCC_AR6_WGI_Chapter11_Table_11_7_9.jpg]] [[File:3c88ad20e45d39167a510a80ea4469fc IPCC_AR6_WGI_Chapter11_Table_11_7_10.jpg]] [[File:677760075854d37eb4cf958b800efa78 IPCC_AR6_WGI_Chapter11_Table_11_7_11.jpg]] [[File:3873320c2d68920825e3c25ee76421c4 IPCC_AR6_WGI_Chapter11_Table_11_7_12.jpg]] [[File:d47d3840e9ffb355576b5b265ade7d3f IPCC_AR6_WGI_Chapter11_Table_11_7_13.jpg]] [[File:dbf6ca8de79e4693a791367fb5b8a9ef IPCC_AR6_WGI_Chapter11_Table_11_7_14.jpg]] [[File:d22b35cec307d529220b5b4009e886dd IPCC_AR6_WGI_Chapter11_Table_11_7_15.jpg]]

'''Table 11.8 |''' '''Observed trends, hu''' '''man contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Asia, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.3 for details.

[[File:0ce1dbdd0ef9a98ff950689aaa5c5555 IPCC_AR6_WGI_Chapter11_Table_11_8_1.jpg]] [[File:1923b32c508ec9020e85b7470af45ee7 IPCC_AR6_WGI_Chapter11_Table_11_8_2.jpg]] [[File:3e459beb8fde1ca2562d4e8ac7763eae IPCC_AR6_WGI_Chapter11_Table_11_8_3.jpg]] [[File:2881f0028c46c00e8f5acc68301b03ac IPCC_AR6_WGI_Chapter11_Table_11_8_4.jpg]] [[File:b3329b8fad05c0dac8f86e7f70c6f2d7 IPCC_AR6_WGI_Chapter11_Table_11_8_5.jpg]] [[File:7d0ef9e71e9c6f34d085b22909a5c7af IPCC_AR6_WGI_Chapter11_Table_11_8_6.jpg]] [[File:72feb607733bf8a97a3d95b427107c7b IPCC_AR6_WGI_Chapter11_Table_11_8_7.jpg]] [[File:4e7e2cc67b75c524866f0f639c5083a4 IPCC_AR6_WGI_Chapter11_Table_11_8_8.jpg]] [[File:c0dc742224e5f2e5fc7a2bfaf37adf61 IPCC_AR6_WGI_Chapter11_Table_11_8_9.jpg]] [[File:e256c30bcb86269f1f4a274a82965b51 IPCC_AR6_WGI_Chapter11_Table_11_8_10.jpg]] [[File:34362cf1e3f6ce7c3d9630146f65296c IPCC_AR6_WGI_Chapter11_Table_11_8_11.jpg]] [[File:1643dc79b99ca67b94d74e0471711f49 IPCC_AR6_WGI_Chapter11_Table_11_8_12.jpg]]

'''Table 11.9 |''' '''Observed trends, human contribution to observed trends, and projecte''' '''d changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Asia, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.4 for details.

[[File:b7f5f8c9e5f6759c9b35fa1b547ca666 IPCC_AR6_WGI_Chapter11_Table_11_9_1.jpg]] [[File:374b62bca1fca50a904365b6c5491138 IPCC_AR6_WGI_Chapter11_Table_11_9_2.jpg]] [[File:23f5a9d7a19a0fc5c7fc61676b276a6e IPCC_AR6_WGI_Chapter11_Table_11_9_3.jpg]] [[File:5624d5393546a85d0705d285c34011c8 IPCC_AR6_WGI_Chapter11_Table_11_9_4.jpg]] [[File:15608536c10d2b12a568d6aeea2e5f4e IPCC_AR6_WGI_Chapter11_Table_11_9_5.jpg]] [[File:c016c3a34ab89a3ee0ca1e3b689de068 IPCC_AR6_WGI_Chapter11_Table_11_9_6.jpg]] [[File:e88044ff38a5e04f4c64be44f4b31516 IPCC_AR6_WGI_Chapter11_Table_11_9_7.jpg]] [[File:a44645f7f87d2250ef174f81a3215a2e IPCC_AR6_WGI_Chapter11_Table_11_9_8.jpg]] [[File:bf1a5c9318e1e687e54a6904cf1a7b58 IPCC_AR6_WGI_Chapter11_Table_11_9_9.jpg]] [[File:617059db84169d91adf6eac690493a74 IPCC_AR6_WGI_Chapter11_Table_11_9_10.jpg]] [[File:58fd2306e09edd4d955e2d14cd98119e IPCC_AR6_WGI_Chapter11_Table_11_9_11.jpg]] [[File:494d331f289a2b8452e5d45e7dad4cb3 IPCC_AR6_WGI_Chapter11_Table_11_9_12.jpg]] [[File:ea6ef2bad86cfe5245a9f09019494204 IPCC_AR6_WGI_Chapter11_Table_11_9_13.jpg]] [[File:7546aac9d006081a13fdf6933acfd148 IPCC_AR6_WGI_Chapter11_Table_11_9_14.jpg]] [[File:ae9f8900c8f3734722f6c2e2453308a3 IPCC_AR6_WGI_Chapter11_Table_11_9_15.jpg]] [[File:da1e03cfc5a1f319391221bf357cbbad IPCC_AR6_WGI_Chapter11_Table_11_9_16.jpg]] [[File:b1c8be40831a9c578eabbdc35ba461f3 IPCC_AR6_WGI_Chapter11_Table_11_9_17.jpg]] [[File:7f0a74c7f2103a02719f1ef971d8f7c3 IPCC_AR6_WGI_Chapter11_Table_11_9_18.jpg]] [[File:e38081bd7b1abe4789f59e7df07e67f0 IPCC_AR6_WGI_Chapter11_Table_11_9_19.jpg]] [[File:5d727569adda4aa89becb836b057cada IPCC_AR6_WGI_Chapter11_Table_11_9_20.jpg]] [[File:684a190ef525ecf36a00e8249e821008 IPCC_AR6_WGI_Chapter11_Table_11_9_21.jpg]]

'''Table 11.10 |''' '''Observed trends, hum''' '''an contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Australasia, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.2 for details.

[[File:49587d882c98bc3045746338236eb8a0 IPCC_AR6_WGI_Chapter11_Table_11_10_1.jpg]] [[File:384f41938906312a02690d99e2b584cc IPCC_AR6_WGI_Chapter11_Table_11_10_2.jpg]] [[File:ee12137d0e8a7fbddf6b55110ec1b42e IPCC_AR6_WGI_Chapter11_Table_11_10_3.jpg]] [[File:383237be727879b60b8810cc0eb67f55 IPCC_AR6_WGI_Chapter11_Table_11_10_4.jpg]] [[File:28460e0175ecab023ddc0e6b9c9409a4 IPCC_AR6_WGI_Chapter11_Table_11_10_5.jpg]] [[File:96d95b0e5c8e951b04d69aa19eb43758 IPCC_AR6_WGI_Chapter11_Table_11_10_6.jpg]] [[File:5c22826d0b6059f813162856a112f46e IPCC_AR6_WGI_Chapter11_Table_11_10_7.jpg]] [[File:b40beba6971f7889342bba76dc979009 IPCC_AR6_WGI_Chapter11_Table_11_10_8.jpg]]

'''Table 11.11 |''' '''Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Australasia, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.3 for details.

[[File:eb72027dce5c5e88d2a315532cbe45f3 IPCC_AR6_WGI_Chapter11_Table_11_11_1.jpg]] [[File:87308d53b4070eceb9f5d3db7d8daaa3 IPCC_AR6_WGI_Chapter11_Table_11_11_2.jpg]] [[File:c308ad8f1e79a83070413635c2fe648a IPCC_AR6_WGI_Chapter11_Table_11_11_3.jpg]] [[File:75615eb2461648b83d7bae498252828c IPCC_AR6_WGI_Chapter11_Table_11_11_4.jpg]]

'''Table 11.12 |''' '''Observed trends, human contribution to observed trends, and projected changes''' '''at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Australasia, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.4 for details.

[[File:0c51f4eb8c79262ecd18d3e301d9d0cf IPCC_AR6_WGI_Chapter11_Table_11_12_1.jpg]] [[File:75f39fe3fd491e4c33dc6aa7a93f97b5 IPCC_AR6_WGI_Chapter11_Table_11_12_2.jpg]] [[File:7c1c52064e54b471f5140588f17fb512 IPCC_AR6_WGI_Chapter11_Table_11_12_3.jpg]] [[File:4488829be106f0534da2b0c43d5290c9 IPCC_AR6_WGI_Chapter11_Table_11_12_4.jpg]] [[File:21dd678ff4bfc70ed534baee848a8737 IPCC_AR6_WGI_Chapter11_Table_11_12_5.jpg]] [[File:882510f66ccea18b11c95c40a7baab06 IPCC_AR6_WGI_Chapter11_Table_11_12_6.jpg]] [[File:4909ec67514856e96264c1ebda60021c IPCC_AR6_WGI_Chapter11_Table_11_12_7.jpg]]

'''Table 11.13 |''' '''Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Central and South America, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.2 for details.

[[File:fb521515e2f61a1799591be64c852b2b IPCC_AR6_WGI_Chapter11_Table_11_13_1.jpg]] [[File:3df003ea01c75e8ad39a5d4bbfb4c4a6 IPCC_AR6_WGI_Chapter11_Table_11_13_2.jpg]] [[File:e28d8fed4cab29fc243683f7d6e9714f IPCC_AR6_WGI_Chapter11_Table_11_13_3.jpg]] [[File:25e986f04ebfc7da3c48cfb868f3b9cb IPCC_AR6_WGI_Chapter11_Table_11_13_4.jpg]] [[File:fb20c71d2cdbb4a88316056282459112 IPCC_AR6_WGI_Chapter11_Table_11_13_5.jpg]] [[File:58a6159edad8a44d12a95c33a950c0c0 IPCC_AR6_WGI_Chapter11_Table_11_13_6.jpg]] [[File:5bc9da37d78f724043a84430f9427462 IPCC_AR6_WGI_Chapter11_Table_11_13_7.jpg]] [[File:b384331902cbcfdf3fe78e835364a157 IPCC_AR6_WGI_Chapter11_Table_11_13_8.jpg]] [[File:743a93b98614a96b865794064bbc8784 IPCC_AR6_WGI_Chapter11_Table_11_13_9.jpg]] [[File:e171efc55e3386fe410056a936afe67d IPCC_AR6_WGI_Chapter11_Table_11_13_10.jpg]] [[File:b0d383232bc70fbb1e853d1c6d6ef65e IPCC_AR6_WGI_Chapter11_Table_11_13_11.jpg]] [[File:fe99af6146998a62f7f677839c462ce3 IPCC_AR6_WGI_Chapter11_Table_11_13_12.jpg]] [[File:fdfc381512dbdd0bccb9cab51081064f IPCC_AR6_WGI_Chapter11_Table_11_13_13.jpg]] [[File:4da45d42f3334fcda99819940bb2e4ab IPCC_AR6_WGI_Chapter11_Table_11_13_14.jpg]]

'''Table 11.14 |''' '''Observed trends, human contribution to observed''' '''trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Central and South America, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.3 for details.

[[File:6597e3ca495eb7a1733d8eabc6c5668c IPCC_AR6_WGI_Chapter11_Table_11_14_1.jpg]] [[File:a91e5a94b025485cda286ecf42b9f26f IPCC_AR6_WGI_Chapter11_Table_11_14_2.jpg]] [[File:927442fa9b9663bbd3aae861368dcd86 IPCC_AR6_WGI_Chapter11_Table_11_14_3.jpg]] [[File:58f29850f7acca1cb8a7d417a2b35357 IPCC_AR6_WGI_Chapter11_Table_11_14_4.jpg]] [[File:4c7725374a498e1c53575c65cd80245e IPCC_AR6_WGI_Chapter11_Table_11_14_5.jpg]] [[File:a03288e780e7177b9f8aaca20df42358 IPCC_AR6_WGI_Chapter11_Table_11_14_6.jpg]] [[File:8caaab4617c4f694ae833311cbbeebec IPCC_AR6_WGI_Chapter11_Table_11_14_7.jpg]]

'''Table 11.15 |''' '''Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Central and South America, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.4 for details.

[[File:f8d08c0b1da7652029b7a5ae31869131 IPCC_AR6_WGI_Chapter11_Table_11_15_1.jpg]] [[File:c4cf219790579dcf5491259d948eadbe IPCC_AR6_WGI_Chapter11_Table_11_15_2.jpg]] [[File:b4faf5303d6dfebea8e3313f2bf0699f IPCC_AR6_WGI_Chapter11_Table_11_15_3.jpg]] [[File:8056770f9b30e970c03f0b3d8c461c2e IPCC_AR6_WGI_Chapter11_Table_11_15_4.jpg]] [[File:557f04fdc7117f6a547f9b71767e6a24 IPCC_AR6_WGI_Chapter11_Table_11_15_5.jpg]] [[File:7341fae8b0177c4395e437a2d79e73e4 IPCC_AR6_WGI_Chapter11_Table_11_15_6.jpg]] [[File:56e9f05ab45b72ca162c153dc889cfca IPCC_AR6_WGI_Chapter11_Table_11_15_7.jpg]] [[File:0bacc53c2d599f66612b8e2b9bc5f992 IPCC_AR6_WGI_Chapter11_Table_11_15_8.jpg]] [[File:2043063dcab461bd0d03f7a9f3e66cc1 IPCC_AR6_WGI_Chapter11_Table_11_15_9.jpg]] [[File:f752b3913f752fa5c75dab9c332a25ea IPCC_AR6_WGI_Chapter11_Table_11_15_10.jpg]]

'''Table 11.16 |''' '''Observed trends, human contri''' '''bution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Europe, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.2 for details.

[[File:2c0cefbf0c8bf438cf1ebdf17b7c4429 IPCC_AR6_WGI_Chapter11_Table_11_16_1.jpg]] [[File:e842ae39cb4055694c6eab9e111dfbf9 IPCC_AR6_WGI_Chapter11_Table_11_16_2.jpg]] [[File:37387d139281b6510b81c66d889d4a12 IPCC_AR6_WGI_Chapter11_Table_11_16_3.jpg]] [[File:0963ace2d2d993635981f3d2e556c5ca IPCC_AR6_WGI_Chapter11_Table_11_16_4.jpg]] [[File:e384ed1941c8dad4d2bcc2080e230128 IPCC_AR6_WGI_Chapter11_Table_11_16_5.jpg]] [[File:000b7c68a452035b761adf1b8ae2e79f IPCC_AR6_WGI_Chapter11_Table_11_16_6.jpg]] [[File:f5e932592126d77806c10c9157cb88f9 IPCC_AR6_WGI_Chapter11_Table_11_16_7.jpg]] [[File:bc88592730736d79c5db3fd78fc77c6d IPCC_AR6_WGI_Chapter11_Table_11_16_8.jpg]]

'''Table 11.17 |''' '''Observed trends, human contributio''' '''n to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Europe, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.3 for details.

[[File:221b69e373365880692f2688be736314 IPCC_AR6_WGI_Chapter11_Table_11_17_1.jpg]] [[File:6103c75fe0472e48746834734da856ee IPCC_AR6_WGI_Chapter11_Table_11_17_2.jpg]] [[File:c118654b95ca59983325c0639d86a944 IPCC_AR6_WGI_Chapter11_Table_11_17_3.jpg]] [[File:446bcba9d5ab70c2737ed588cb727fbf IPCC_AR6_WGI_Chapter11_Table_11_17_4.jpg]] [[File:a181e19082ed77d0ec3e6ebd17ca7ff3 IPCC_AR6_WGI_Chapter11_Table_11_17_5.jpg]]

'''Table 11.18 |''' '''Observed trends, human contribution to observed trends, and projected c''' '''hanges at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Europe, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.4 for details.

[[File:a707336e2df3f3688f9a3e7110fce694 IPCC_AR6_WGI_Chapter11_Table_11_18_1.jpg]] [[File:7c2ded851e52b1c5fb5402eb40a7138d IPCC_AR6_WGI_Chapter11_Table_11_18_2.jpg]] [[File:bb8cd6e33547f38eb73f405d754e5217 IPCC_AR6_WGI_Chapter11_Table_11_18_3.jpg]] [[File:8c9566fbe917af95c6892dca5b41a2bd IPCC_AR6_WGI_Chapter11_Table_11_18_4.jpg]] [[File:9f95a25445799a96cbc86e3fb3326f96 IPCC_AR6_WGI_Chapter11_Table_11_18_5.jpg]] [[File:b0c527ff0d5054e7896ead7752e65794 IPCC_AR6_WGI_Chapter11_Table_11_18_6.jpg]] [[File:9b1891266a31170d148f861c07a48488 IPCC_AR6_WGI_Chapter11_Table_11_18_7.jpg]] [[File:26984adc3cfc4ed1e1241e7b66a5b852 IPCC_AR6_WGI_Chapter11_Table_11_18_8.jpg]] [[File:c236a9cd796811cf889d789c10554159 IPCC_AR6_WGI_Chapter11_Table_11_18_9.jpg]] [[File:0c8da91d20a8d4c3792be0b8350c7a51 IPCC_AR6_WGI_Chapter11_Table_11_18_10.jpg]] [[File:45b0aceeeece298f4ab0396c104f829f IPCC_AR6_WGI_Chapter11_Table_11_18_11.jpg]] [[File:5bd346237b5e77460cf6db1757f34531 IPCC_AR6_WGI_Chapter11_Table_11_18_12.jpg]]

'''Table 11.19 |''' '''Observed trends, human contribution to observ''' '''ed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in North America, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.2 for details.

[[File:dc5a23530beb46f919964f07259d78ea IPCC_AR6_WGI_Chapter11_Table_11_19_1.jpg]] [[File:f64394adf980da225e0e3e735adfd721 IPCC_AR6_WGI_Chapter11_Table_11_19_2.jpg]] [[File:8159ba2e0892aa18dc8d4b241b51d97c IPCC_AR6_WGI_Chapter11_Table_11_19_3.jpg]] [[File:a5127d234ee206998bfbd1e79233c86c IPCC_AR6_WGI_Chapter11_Table_11_19_4.jpg]] [[File:11693be3c23435fed38dccff9dc2683c IPCC_AR6_WGI_Chapter11_Table_11_19_5.jpg]] [[File:97483e14aa3fae1abde0039d16d61401 IPCC_AR6_WGI_Chapter11_Table_11_19_6.jpg]] [[File:524e5ed74c734c5e197e5a7d44c0d38f IPCC_AR6_WGI_Chapter11_Table_11_19_7.jpg]] [[File:39c15847dbfdd6dd3332c7391a84e92a IPCC_AR6_WGI_Chapter11_Table_11_19_8.jpg]] [[File:510c00203c49686025f431dc6c1fdb33 IPCC_AR6_WGI_Chapter11_Table_11_19_9.jpg]]

'''Table 11.20 |''' '''Observed trends, human co''' '''ntribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in North America, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.3 for details.

[[File:ab1f650c82fc60f130d113ab60bc5311 IPCC_AR6_WGI_Chapter11_Table_11_20_1.jpg]] [[File:19d5951adaeccd509f855dc79e09c177 IPCC_AR6_WGI_Chapter11_Table_11_20_2.jpg]] [[File:5c2acaf4644c6a22f8ac9b3b9d9fe139 IPCC_AR6_WGI_Chapter11_Table_11_20_3.jpg]] [[File:7d4a254b6af02338c9d64133dc5c6f2b IPCC_AR6_WGI_Chapter11_Table_11_20_4.jpg]] [[File:8a3021d30ffd30bfd7f97670f3fd7735 IPCC_AR6_WGI_Chapter11_Table_11_20_5.jpg]] [[File:f0a10a0fe915152a9e161b9fdbea5e77 IPCC_AR6_WGI_Chapter11_Table_11_20_6.jpg]] [[File:612051af53dc242cbc23c404ba94f20e IPCC_AR6_WGI_Chapter11_Table_11_20_7.jpg]]

'''Table 11.21 |''' '''Observed trends, human contribution to observed trends, and projected chang''' '''es at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET), agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in North America, subdivided by AR6 regions.''' See Sections 11.9.1 and 11.9.4 for details.

[[File:1572d1b57e9aa684f97a3735451358a6 IPCC_AR6_WGI_Chapter11_Table_11_21_1.jpg]] [[File:7bb6258c5513522202356b9107ce7bef IPCC_AR6_WGI_Chapter11_Table_11_21_2.jpg]] [[File:7d66d89de6ca647165e55221026e7659 IPCC_AR6_WGI_Chapter11_Table_11_21_3.jpg]] [[File:c130be57a6126c5afff6f863b22c3da3 IPCC_AR6_WGI_Chapter11_Table_11_21_4.jpg]] [[File:e47b670f8149948a5f3498b3d4f31f1a IPCC_AR6_WGI_Chapter11_Table_11_21_5.jpg]] [[File:93706963fd133cce56c07fbdeaae30ee IPCC_AR6_WGI_Chapter11_Table_11_21_6.jpg]] [[File:3acd6f5cecbed134b203373eefbd3d68 IPCC_AR6_WGI_Chapter11_Table_11_21_7.jpg]] [[File:bf10b3bc901819e0b95e76f8f5f2c310 IPCC_AR6_WGI_Chapter11_Table_11_21_8.jpg]] [[File:26efb76b315c171c9ea343f7b5de8fd5 IPCC_AR6_WGI_Chapter11_Table_11_21_9.jpg]]

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<div id="Appendix" class="h1-container"></div>

<span id="appendix-11.a"></span>
== Appendix 11.A ==

<div id="h1-15-siblings" class="h1-siblings"></div>

[[File:49952450d7a709286df461b7d48f01f7 IPCC_AR6_WGI_AX_11_1_Figure_1.png]]

'''Figure 11.''' '''A.1 |''' '''As Figure 11.3 but for the annual minimum temperature (TNn).'''

'''Table 11.A.1 |''' '''Common drought metrics, associated drought types, drought indices, general description and associated references.'''

[[File:8f69b2b9c993ea0e30f1ecfb79308b0e IPCC_AR6_WGI_Chapter11_Table_11_A_1_1.jpg]] [[File:2f29e3c1d72cceeab80ab78fec6e6365 IPCC_AR6_WGI_Chapter11_Table_11_A_1_2.jpg]]

'''Table''' '''11.A.2 |''' '''Synthesis table summarising assessments presented in Tables 11.4-11.21 for hot extremes (HOT EXT.), heavy precipitation (HEAVY PRECIP.), agriculture and ecological droughts (AGR./ECOL. DROUGHT), and hydrological droughts (HYDR. DROUGHT).''' It shows the direction of change and level of confidence in the observed trends (column OBS.), human contribution to observed trends (ATTR.), and projected changes at 1.5°C, 2°C and 4°C of global warming for each AR6 region. Projections are shown for two different baseline periods: 1850–1900 (pre-industrial) and 1995–2014 (modern or recent past) – see [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] for more details. Direction of change is represented by an upward arrow (increase) and a downward arrow (decrease). Level of confidence is reported for LOW: ''low'' , MED.: ''medium'' , HIGH: ''high'' ; levels of likelihood (only in cases of ''high confidence'' ) include: L: ''likely'' , VL: ''very likely'' , EL: ''extremely likely'' , VC: ''virtual certain'' . See ( [[#11.9|Section 11.9]] , Tables 11.4–11.21 for details. Dark orange shading highlights ''high confidence'' (also including ''likely'' , ''very likely'' , ''extremely likely'' and ''virtually certain'' changes) increases in hot temperature extremes, agricultural and ecological drought, or hydrological droughts. Yellow indicates ''medium confidence'' increases in these extremes, and blue shadings indicate decreases in these extremes. ''High confidence'' increases in heavy precipitation are highlighted in dark blue, while ''medium confidence'' increases are highlighted in light blue. No assessment for changes in drought with respect to the 1995–2014 baseline is provided, which is why the respective cells are empty.

[[File:fb4b6c8986468437b7aad92af157845b IPCC_AR6_WGI_Chapter11_Table_11_A_2_1.jpg]] [[File:dff873de0605020c52f7dc30c8fab70e IPCC_AR6_WGI_Chapter11_Table_11_A_2_2.jpg]] [[File:4ccb9e14007992d573de57581f6f7c80 IPCC_AR6_WGI_Chapter11_Table_11_A_2_3.jpg]] [[File:05a7660a2eb9184fa741f85c5f704a2d IPCC_AR6_WGI_Chapter11_Table_11_A_2_4.jpg]] [[File:d9ffe7df50514c63ccef4d63cf6461f8 IPCC_AR6_WGI_Chapter11_Table_11_A_2_5.jpg]] [[File:9965c3d15729b5671f66005be0dccbd3 IPCC_AR6_WGI_Chapter11_Table_11_A_2_6.jpg]] [[File:8dd39b2c88da8dc8c7d906c7660c68a1 IPCC_AR6_WGI_Chapter11_Table_11_A_2_7.jpg]] [[File:be93c60e03451ecbc80102eba618b7e6 IPCC_AR6_WGI_Chapter11_Table_11_A_2_8.jpg]] [[File:824fdff04ef98b7fcfbcd43dbe205244 IPCC_AR6_WGI_Chapter11_Table_11_A_2_9.jpg]] [[File:8695eacae09755524383c70117c3a481 IPCC_AR6_WGI_Chapter11_Table_11_A_2_10.jpg]]

-----

<div id="footnote-011" class="_idFootnote"></div>

[[#footnote-011-backlink|1]] See Figure 1.18 for definition of AR6 regions. Acronyms for inhabited regions: ARP: Arabian Peninsula; CAF: Central Africa; CAR: Caribbean; CAU: Central Australia; CNA: Central North America; EAS: East Asia; EAU: Eastern Australia; ECA: East Central Asia; EEU: Eastern Europe; ENA: Eastern North America; ESAF: East Southern Africa; ESB: East Siberia; GIC: Greenland/Iceland; MDG: Madagascar; MED: Mediterranean; NAU: Northern Australia; NCA: Northern Central America; NEAF: North Eastern Africa; NEN: North-Eastern North America; NES: North-Eastern South America; NEU: Northern Europe; NSA: Northern South America; NWN: North-Western North America; NWS: North-Western South America; NZ: New Zealand; RAR: Russian Arctic; RFE: Russian Far East; SAH: Sahara; SAM: South American Monsoon; SAS: South Asia; SAU: Southern Australia; SCA: Southern Central America; SEA: Southeast Asia; SEAF: South Eastern Africa; SES: South-Eastern South America; SSA: Southern South America; SWS: South-Western South America; TIB: Tibetan Plateau; WAF: Western Africa; WCA: West Central Asia; WCE: Western and Central Europe; WNA: Western North America; WSAF: West Southern Africa; WSB: West Siberia.

<div id="footnote-010" class="_idFootnote"></div>

[[#footnote-010-backlink|2]] Six-hourly intensity estimates during the lifetime of each TC.

<div id="footnote-009" class="_idFootnote"></div>

[[#footnote-009-backlink|3]] This region includes northern Africa and southern Europe.

<div id="footnote-008" class="_idFootnote"></div>

[[#footnote-008-backlink|4]] This region includes northern Africa and southern Europe.

<div id="footnote-007" class="_idFootnote"></div>

[[#footnote-007-backlink|5]] This region includes northern Africa and southern Europe.

<div id="footnote-006" class="_idFootnote"></div>

[[#footnote-006-backlink|6]] This region includes northern Africa and southern Europe.

<div id="footnote-005" class="_idFootnote"></div>

[[#footnote-005-backlink|7]] This region includes northern Africa and southern Europe.

<div id="footnote-004" class="_idFootnote"></div>

[[#footnote-004-backlink|8]] This region includes northern Africa and southern Europe.

<div id="footnote-003" class="_idFootnote"></div>

[[#footnote-003-backlink|9]] This region includes northern Africa and southern Europe.

<div id="footnote-002" class="_idFootnote"></div>

[[#footnote-002-backlink|10]] This region includes northern Africa and southern Europe.

<div id="footnote-001" class="_idFootnote"></div>

[[#footnote-001-backlink|11]] This region includes northern Africa and southern Europe.

<div id="footnote-000" class="_idFootnote"></div>

[[#footnote-000-backlink|12]] This region includes northern Africa and southern Europe.