Editing IPCC:AR6/WGI/Chapter-10 (section)

== 10.1 Foundations for Regional Climate Change Information ==

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=== 10.1.1 Introduction ===

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This chapter assesses the foundations for the distillation of regional climate change information from multiple lines of evidence. The AR5, SR1.5 and SRCCL reports underlined the relevance of assessing regional climate information that is useful and relevant to the decision scale (Box 10.1). To respond to this need, the AR6 WGI Report includes four regional chapters of which this is the first. Chapter 10 assesses the sources and methodologies used by the Chapters 11, 12 and [[IPCC:Wg1:Chapter:Atlas|Atlas]] to construct regional information. Chapter 10 builds on the assessment of methodologies considered to construct global climate change information in Chapters 2 to 4 and on the processes assessed in Chapters 5 to 9. Additionally, this chapter assesses the methodologies for the co-production of regional climate information, the role of the different actors involved in the process and the relevance of the user context and values.

Regional climate change refers to a change in climate in a given region ( [[#10.1.2.1|Section 10.1.2.1]] ) identified by changes in the mean or higher moments of the probability distribution of a climate variable and persisting for a few decades or longer. It can also refer to a change in temporal properties such as persistence and frequency of occurrence of weather and climate extreme events. Regional climate change may be caused by natural internal processes such as atmospheric internal variability and local climate response to low-frequency modes of climate variability (Technical Annex IV), as well as by changes in external forcings such as modulations of the solar cycle, orbital forcing, volcanic eruptions, and persistent anthropogenic changes in the composition of the atmosphere or in land use and land cover (Cross-Chapter Box 3.2; [[#IPCC--2018a|IPCC, 2018a]] ), in addition to the interactions and feedbacks between them. Process interaction in space is pervasive, which means that small spatial scales often have an influence on the larger scales ( [[#Palmer--2013|Palmer, 2013]] ; [[#Sandu--2016|Sandu et al., 2016]] ). Depending on the context, a region may refer to a large area such as a monsoon region, but may also be confined to smaller areas such as coastlines, mountain ranges or human settlements like cities. Users (understood as anyone incorporating climate information into their activity) often request climate information for these range of scales since their operating and adaptation decision scales range from the local to the sub-continental level.

Given the many types of regional climates, the broad range of spatial and temporal scales ( [[#10.1.2|Section 10.1.2]] ), and the diversity of user needs, a variety of methodologies and approaches have been developed to construct regional climate change information. The sources include global and regional climate model simulations, statistical downscaling and bias adjustment methods. A commonly used source is long-term (end-of-century) model projections of regional climate change, as well as near-term (next 10 years) climate predictions ( [[#Kushnir--2019|Kushnir et al., 2019]] ; [[#Rössler--2019a|Rössler et al., 2019a]] ). Regional observations, with their associated challenges, are a key source for the regional climate information construction process (Q. [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ). High-quality observations that enable monitoring of the regional aspects of climate are used to adjust inherent model biases and are the basis for assessing model performance. Process understanding and attribution of observed changes to large- and regional-scale anthropogenic and natural drivers and forcings are also important sources.

All these sources are used, when available, to distil regional climate information from multiple lines of evidence (Figure 10.1). The resulting climate information can then be integrated, following a co-production process involving both the user and the producer, into a user context that often is already taken into account when constructing the regional climate information. In fact, the distillation process leading to the climate information can consider the specific context of the question at stake, the values of both the user and the producer, and the challenge of communicating across different communities ( [[#10.5|Section 10.5]] ).

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[[File:ff09c09348f302e905a9e8b064ca5a88 IPCC_AR6_WGI_Figure_10_1.png]]

'''Figure 10.1''' '''|''' '''Diagram of the processes leading to the construction of regional climate information (blue) and user-relevant regional climate information (brown).''' The chapter sections and the other chapters of the Report involved in each step are indicated in rectangles. WGII stands for Working Group II. Literature refers to scientific and technical literature, and climate experts refers to climate scientists, practitioners and local communities, as defined in [[#10.5|Section 10.5]] .

The chapter (Figure 10.2) starts with an introduction of the concepts used in the distillation of regional climate information ( [[#10.1|Section 10.1]] ). [[#10.2|Section 10.2]] addresses the aspects associated with the access to and use of observations, while different modelling approaches are introduced and assessed in [[#10.3|Section 10.3]] . [[#10.3|Section 10.3]] also addresses the performance of models in simulating relevant climate characteristics as needed to estimate the credibility of future projections. [[#10.4|Section 10.4]] assesses the interplay between anthropogenic causes and internal variability at regional scales, and its relevance for the attribution of regional climate changes and the emergence of regional climate change signals. [[#10.5|Section 10.5]] tackles the issue of how regional climate information is distilled from different sources taking into account the context and the values of both the producer and the user. [[#10.6|Section 10.6]] illustrates the distillation approach using three comprehensive examples. Finally, [[#10.7|Section 10.7]] lists some limitations to the assessment of regional climate information.

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[[File:de330e01faaaf00282db4d87bf4f5f74 IPCC_AR6_WGI_Figure_10_2.png]]

'''Figure 10.2''' '''|''' '''Visual guide to Chapter 10.'''

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=== 10.1.2 Regional Climate Change and the Relevant Spatial and Temporal Scales ===

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The global coupled atmosphere–ocean–land–cryosphere system, including its feedbacks, shows variability over a wide spectrum of spatial and temporal scales ( [[#Hurrell--2009|Hurrell et al., 2009]] ). This section discusses concepts and definitions of what can be considered a region, the relevant temporal scales and region-specific aspects of the baselines used.

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==== 10.1.2.1 Spatial Scales and Definition of Regions ====

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Large-scale climate and the associated phenomena have been defined in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] (e.g., Cross-Chapter Box 2.2) as ranging from global and hemispheric, to ocean basin and continental scales. The definition of the regional scale is case specific in the AR6 WGI Report. [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] provides definitions of the different regional types adopted by the different chapters. In this chapter, regional scales are defined as ranging from the size of sub-continental areas (e.g., the Mediterranean basin) to local scales (e.g., coastlines, mountain ranges and cities) without prescribing any formal regional boundaries. These spatial length scales range from a few thousand down to a few kilometres and the relevant driving modes and processes at regional scales are summarized in Figure 10.3. In contrast to Chapters 11, 12 and Atlas, which make a region-by-region assessment of climate change, this chapter does not necessarily restrict itself to the use of the AR6 WGI Reference Regions ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] and Atlas.1.3). Different regional definitions have been used in sections 10.4 and 10.6, selected for their adequacy to illustrate methodological aspects (e.g., for the attribution of long-term regional trends, regions that display such trends have been selected). Typological regions ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.5|Section 1.4.5]] and Atlas.1.3) are used in Box 10.3 and Cross-Chapter Box 10.4.

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[[File:9c0604ea97c096b0d69069510c1464ed IPCC_AR6_WGI_Figure_10_3.png]]
'''Figure 10.3 | Schematic diagram''' '''to display interacting spatial and temporal scales relevant to regional climate change information.''' Figure adapted from [[#Orlanski--1975|Orlanski (1975)]] . The processes included in the different models and model components considered in Chapter 10 are indicated as a function of these scales. The various types of models (including global and regional climate models) for constructing regional climate information are assessed in [[#10.3.1|Section 10.3.1]] and Box 10.3.

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==== 10.1.2.2 Temporal Scales, Baselines and Dimensions of Integration ====

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The concept of a unified and seamless framework for weather and climate prediction ( [[#Brown--2012|]] [[#Brown--2012|A. Brown et al., 2012]] ; [[#Hoskins--2013|Hoskins, 2013]] ) provides the context for understanding and simulating regional climate across multiple spatial and temporal scales. This concept is embodied in the subseasonal-to-seasonal ( [[#Vitart--2017|Vitart et al., 2017]] ) and the seasonal-to-multi-annual ( [[#Smith--2020|Smith et al., 2020]] ) prediction activities that generate regional climate information across temporal scales. The seamless framework benefits from the convergence of methods traditionally used in weather forecasting and climate projections, in particular the role of the initialization in climate models and the strategies for the evaluation of physical processes relevant at different temporal scales.

The relatively short observational record ( [[#10.2|Section 10.2]] ) is a primary challenge to estimate the forced signal and to isolate low-frequency, multi-decadal and longer-term internal variability ( [[#Frankcombe--2015|Frankcombe et al., 2015]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Bathiany--2018|Bathiany et al., 2018]] ). Because only one realization of the actual climate exists, it is non-trivial to extract estimates of internal and forced variability from the available data ( [[#Frankcombe--2015|Frankcombe et al., 2015]] ). As an alternative, approaches that use large observational ensembles can be applied ( [[#10.4|Section 10.4]] ; [[#McKinnon--2018|McKinnon and Deser, 2018]] ).

There is a close relationship between spatial and temporal scales (Figure 10.3). For example, an individual convective storm may exhibit scales of variability ranging from metres and seconds to kilometres and hours, while for El Niño–Southern Oscillation (ENSO) the scales of variability are regional to hemispheric in extent and multi-year in length. These scales interact and the interactions are represented in climate models, although the ability of current models to simulate regional phenomena and even large-scale climate drivers still leaves room for improvement ( [[#10.3|Section 10.3]] ) and limits their capability to represent the interactions across spatial and temporal scales.

It is important to note that in this chapter and subsequent regional chapters, including the Interactive Atlas, the baselines and reference periods used for climate change estimates from regional models may vary from those used in Chapters 1 to 9. In these chapters three main time baselines are defined for the past, for example, pre-industrial (before 1750), early industrial (1850–1900) and recent (1995–2014), while the future reference periods are 2021–2040 (near term), 2041–2060 (mid-term) and 2081–2100 (long term) ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] and Cross-Chapter Box 1.2). Regional climate simulations used in the recent literature have been performed with different baselines. The differences are often due to the availability of the boundary conditions from global simulations, leading to periods chosen for those simulations like 1950–2005, in line with the CMIP5 historical simulations followed by projections from 2005 onwards ( [[#Vaittinada%20Ayar--2016|Vaittinada Ayar et al., 2016]] ; [[#Zhang--2017|Zhang et al., 2017]] ; L. [[#Cai--2018|]] [[#Cai--2018|Cai et al., 2018]] ). For simulations that use CMIP3 boundary conditions other periods have been used. As a consequence, these regional simulations mix for the recent period historical simulations with projections. The mismatch needs to be considered when assessing results obtained from both global and regional models in the context of the climate information distillation process, or when linking the regional chapters to the assessments performed in previous chapters. The choice of baseline provides a source of uncertainty for the assessment of climate impacts (e.g., for the response of bird species in Africa; [[#Baker--2016|Baker et al., 2016]] ). Besides, a range of different baselines may need to be considered to satisfy a variety of users, since this choice affects the perceived result ( [[#Dobor--2019|Dobor and Hlásny, 2019]] ). The influence of the different baseline periods can be explored using the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] where different baselines are available, for example, 1986–2005 (according to AR5), 1995–2014 (this Report), and both 1961–1990 and 1981–2010 (WMO).

One way of overcoming the baseline uncertainty is to define the results for a given model based on specific global mean temperature changes from the pre-industrial period (e.g., [[#Sylla--2018|Sylla et al., 2018]] for West Africa; [[#Kjellström--2018|Kjellström et al., 2018]] for Europe; [[#Taylor--2018|Taylor et al., 2018]] for the Caribbean; [[#Montroull--2018|Montroull et al., 2018]] for South America). The specific global mean temperature is known as global warming level (GWL; Sections 1.6.2 and 10.6.4, and Cross-Chapter Box 11.1). The GWL is a useful dimension of integration because important changes in regional climate, including many types of extremes, scale quasi-linearly with the GWLs, often independently of the underlying emissions scenarios (e.g., [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Beusch--2020|Beusch et al., 2020]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ), always taking into account caveats described in Cross-Chapter Box 11.1. In addition, GWLs allow a separated analysis of the global and regional climate responses associated with a warming level ( [[#10.6.4|Section 10.6.4]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). The choice of global temperature goal in the context of the 2015 Paris Agreement means that there is an increasing desire for the regional climate information to be expressed as a function of GWLs.

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=== 10.1.3 Sources of Regional Climate Variability and Change ===

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Variability in regional climate arises from natural and anthropogenic forcings, internal variability including the local expression of large-scale remote drivers (also known as teleconnections), and the feedbacks between them. Due to the many possible drivers of variability and change (Figure 10.3), quantifying the interplay between internal modes of decadal variability and any externally forced component is crucial in attempts to attribute causes of regional climate changes (e.g., [[#Hoell--2017|Hoell et al., 2017]] ; [[#Nath--2018|Nath et al., 2018]] ). A regional climate signal could arise purely due to some anthropogenic influence or conversely, entirely due to internal variability, but it is most likely the result of a combination of both ( [[#10.4|Section 10.4]] ). This section briefly introduces these sources of regional variability and should be read along with corresponding sections in Chapters 3, 6 and 7. [[#10.3|Section 10.3]] assesses their representation in climate models, [[#10.4|Section 10.4]] discusses their relevance for the attribution of multi-decadal trends and [[#10.6|Section 10.6]] refers to them as sources in specific examples where regional climate information is built. [[IPCC:Wg1:Chapter:Chapter-8#8.2|Section 8.2]] offers a companion discussion focussing on changes in the water cycle. An example of how changes in one region could act as a source for changes in a neighbouring one is assessed in the Cross-Chapter Box 10.1 for the linkages between polar and mid-latitude regions, an interaction that has led to substantial recent research. This section also introduces the sources of uncertainty in model-derived regional climate information and how the quantification of the uncertainties influences the confidence of the regional climate information.

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==== 10.1.3.1 Forcings Controlling Regional Climate ====

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There are important differences in the processes affected by greenhouse gases (GHGs) over land and ocean. Notably, this leads to preferential warming of the land regions, which are themselves skewed towards the Northern Hemisphere (NH).

Variations in solar forcing ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ) could influence regional climate through its modulation of circulation patterns, although this research field is still hampered by large observational and modelling uncertainties. The 11-year solar cycle has been suggested to affect the leading atmospheric circulation modes of the North Atlantic region in model-based studies ( [[#Gray--2013|Gray et al., 2013]] ; [[#Thiéblemont--2015|Thiéblemont et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ). In particular the solar cycle has been suggested as an important source of near-term predictability of the North Atlantic Oscillation (NAO; [[#Kushnir--2019|Kushnir et al., 2019]] ), while other studies have not found evidence for links between the solar cycle and NAO in observational records ( [[#Ortega--2015|Ortega et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ; [[#Chiodo--2019|Chiodo et al., 2019]] ). On centennial time scales, solar fluctuations were found to be correlated with the Eastern Atlantic Pattern ( [[#Sjolte--2018|Sjolte et al., 2018]] ). Possible influences on winter circulation and temperature over Eurasia ( [[#Chen--2015|Chen et al., 2015]] ) and North America ( [[#Liu--2014|Liu et al., 2014]] ; [[#Li--2018|Li and Xiao, 2018]] ) have also been identified.

An updated assessment of past changes in stratospheric ozone can be found in [[IPCC:Wg1:Chapter:Chapter-2#2.2.5.2|Section 2.2.5.2]] . The AR6 assesses that both GHG and stratospheric ozone depletion have contributed to the expansion of the zonal mean Hadley cell in the Southern Hemisphere (SH) for the period 1981–2000 with ''medium confidence'' [[IPCC:Wg1:Chapter:Chapter-3#3.3.3|Section 3.3.3]] ; [[#Garfinkel--2015|Garfinkel et al., 2015]] ; [[#Waugh--2015|Waugh et al., 2015]] ; [[#Grise--2019|Grise et al., 2019]] ). There is ''medium confidence'' that stratospheric ozone depletion contributed to the strengthening trend of the summer Southern Annular Mode (SAM) for the period 1970–1990, but this influence has been weaker since 2000 ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.2|Section 3.7.2]] ). The poleward shift of the SH westerlies has also been explained by stratospheric ozone depletion ( [[#Solman--2016|Solman and Orlanski, 2016]] ). [[#10.4|Section 10.4]] assesses its role in the multi-decadal increase of rainfall in south-eastern South America and [[#10.6.2|Section 10.6.2]] does so for the occurrence of the Cape Town drought.

Both natural and anthropogenic aerosols are often emitted at a regional scale, have a short atmospheric lifetime (from a few hours to several days; Section 6.1), are dispersed regionally and affect climate at a regional scale through radiative cooling/heating and cloud microphysical effects (Chapter 8; [[#Rotstayn--2015|Rotstayn et al., 2015]] ; [[#Sherwood--2015|Sherwood et al., 2015]] ). The majority of aerosols scatter solar radiation, but with strong regional variations ( [[#Shindell--2009|Shindell and Faluvegi, 2009]] ) that lead to regional radiative effects of up to two orders of magnitude larger than the global average ( [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|B. Li et al., 2016]] ; K. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Mallet--2016|Mallet et al., 2016]] ). Black carbon, instead, is known to absorb solar radiation, leading to regional atmospheric warming patterns due to its inhomogeneous spatial distribution ( [[#Gustafsson--2016|Gustafsson and Ramanathan, 2016]] ). Patterns of forcing generally follow those of aerosol burden. However, temperature and precipitation responses are both local and remote (Z. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Kasoar--2018|Kasoar et al., 2018]] ; L. [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Samset--2018|Samset et al., 2018]] ; [[#Thornhill--2018|Thornhill et al., 2018]] ; [[#Westervelt--2018|Westervelt et al., 2018]] ). For instance, changes in aerosol concentrations in the NH have been reported to modulate monsoon precipitation in West Africa and the Sahel ( [[#Undorf--2018|Undorf et al., 2018]] ; [[#10.4.2.1|Section 10.4.2.1]] ) and in Asia (H. [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ; [[#10.6.3|Section 10.6.3]] ).

Natural aerosols include mineral dust, volcanic aerosol and sea salt. The feedback processes between climate and mineral dust as well as sea salt are assessed in Section 6.4, while the volcanic aerosol is dealt with in Cross-Chapter Box 4.1. Mineral dust created by wind erosion of arid and semi-arid surfaces dominates the aerosol load over some areas. The major sources of contemporary dust are located in the arid topographic basins of northern Africa, Middle East, Central and south-west Asia, the Indian subcontinent, and East Asia ( [[#Prospero--2002|Prospero et al., 2002]] ; [[#Ginoux--2012|Ginoux et al., 2012]] ) and emissions are controlled by changes in surface winds, precipitation, and vegetation ( [[#Ridley--2014|Ridley et al., 2014]] ; W. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#DeFlorio--2016|DeFlorio et al., 2016]] ; [[#Evan--2016|Evan et al., 2016]] ; [[#Pu--2018|Pu and Ginoux, 2018]] ). Dust both scatters and absorbs radiation and serves as a nuclei of warm and cold clouds (Chapter 6). The surface direct radiative effect is likely negative over land and ocean, especially when the assumed solar absorption by dust is large ( [[#Miller--2014|Miller et al., 2014]] ; [[#Strong--2015|Strong et al., 2015]] ). Surface temperature and precipitation adjust to the direct radiative effect with both sign and magnitude depending on the dust absorptive properties. Dust often cools the surface, but in regions such as the Sahara surface air temperature increases as the shortwave absorption by dust is increased, leading to increases of surface temperature over the major reflective dust sources ( [[#Miller--2014|Miller et al., 2014]] ; [[#Solmon--2015|Solmon et al., 2015]] ; [[#Strong--2015|Strong et al., 2015]] ; [[#Jin--2016|Jin et al., 2016]] ; [[#Sharma--2017|Sharma and Miller, 2017]] ).

Volcanic eruptions load the atmosphere with large amounts of sulphur, which is transformed through chemical reactions and micro-physics processes into sulphate aerosols (Cross-Chapter Box 4.1; [[#Stoffel--2015|Stoffel et al., 2015]] ; [[#LeGrande--2016|LeGrande et al., 2016]] ). If the plume reaches the stratosphere, sulphate aerosols can remain there for months or years (about two to three for large eruptions) and can then be transported to other areas by the Brewer-Dobson circulation. If the eruption occurs in the tropics, its plume is dispersed across the Earth in a few years, while if the eruption occurs in the high latitudes, aerosols mainly remain in the same hemisphere ( [[#Pausata--2015|Pausata et al., 2015]] ). The global temperature response observed after the last five major eruptions of the last two centuries is estimated to be around –0.2°C ( [[#Swingedouw--2017|Swingedouw et al., 2017]] ), in association with a general decrease of precipitation ( [[#Iles--2017|Iles and Hegerl, 2017]] ). Nevertheless, the statistical significance of the regional response remains difficult to evaluate over the historical era ( [[#Bittner--2016|Bittner et al., 2016]] ; [[#Swingedouw--2017|Swingedouw et al., 2017]] ) due to the small sampling of large volcanic eruptions over this period and the fact that the signal is superimposed upon relatively large internal variability ( [[#Gao--2018|Gao and Gao, 2018]] ; [[#Dogar--2019|Dogar and Sato, 2019]] ). Evidence from paleoclimate observations is therefore crucial to obtain a sufficient signal-to-noise ratio ( [[#Sigl--2015|Sigl et al., 2015]] ). Reconstructed modes of climate variability based on proxy records allow evaluation of the influence on those modes ( [[#Zanchettin--2013|Zanchettin et al., 2013]] ; [[#Ortega--2015|Ortega et al., 2015]] ; [[#Sjolte--2018|Sjolte et al., 2018]] ; [[#Michel--2020|Michel et al., 2020]] ).

Anthropogenic aerosols play a key role in climate change (Chapter 6). Although the global mean optical depth caused by anthropogenic aerosols did not change from 1975 to 2005 (Chapter 6), the regional pattern changed dramatically between Europe and eastern Asia ( [[#Fiedler--2017|Fiedler et al., 2017]] , 2019; [[#Stevens--2017|Stevens et al., 2017]] ). Large regional differences in present-day aerosol forcing exist with consequences for regional temperature, hydrological cycle and modes of variability (Chapter 8, [[#10.6|Section 10.6]] ). Examples of regions with a notable role for anthropogenic aerosol forcing are the Indian monsoon region ( [[#10.6.3|Section 10.6.3]] ) and the Mediterranean basin [[#10.6.4|Section 10.6.4]] ). Anthropogenic aerosols are also very relevant in many urban areas (Box 10.3; [[#Gao--2016|Gao et al., 2016]] ; [[#Kajino--2017|Kajino et al., 2017]] ).

The SRCCL assessed that nearly three-quarters of the land surface is under some form of land use, particularly in agriculture and forest management ( [[#Jia--2019|Jia et al., 2019]] ). The effects of land management on climate are much less studied than land cover effects although net cropland has changed little over the past 50 years, while land management has continuously changed ( [[#Jia--2019|Jia et al., 2019]] ). [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.1|Section 7.3.4.1]] assesses the global influence of both land use and irrigation on the effective radiative forcings. Land cover changes and land management can influence climate locally, such as the urban heat island and non-locally as in the case of increased rainfall downwind of a city ( [[#Jia--2019|Jia et al., 2019]] ; Box 10.3) or the monsoon circulation affected by irrigation ( [[#10.6.3|Section 10.6.3]] ). The influence of land cover changes and land management on regional climate extremes is assessed in [[IPCC:Wg1:Chapter:Chapter-11#11.1.6|Section 11.1.6]] .

It is ''very likely'' that the global land surface air temperature response to urbanization is negligible ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.3|Section 2.3.1.1.3]] ). However, there is evidence that urbanization may regionally amplify the air temperature response to climate change in different climatic zones ( [[#Mahmood--2014|Mahmood et al., 2014]] ), either under present ( [[#Doan--2016|Doan et al., 2016]] ; [[#Kaplan--2017|Kaplan et al., 2017]] ; X. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ) or future climate conditions ( [[#Argüeso--2014|Argüeso et al., 2014]] ; [[#Kim--2016|Kim et al., 2016]] ; [[#Kusaka--2016|Kusaka et al., 2016]] ; [[#Grossman-Clarke--2017|Grossman-Clarke et al., 2017]] ; [[#Krayenhoff--2018|Krayenhoff et al., 2018]] ). For instance, in northern Belgium, [[#Berckmans--2019|Berckmans et al. (2019)]] found that including urbanization scenarios for the near future (up to 2035) have a comparable influence on minimum temperature (increasing it by 0.6°C) to that of the GHG-induced warming under RCP8.5.

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==== 10.1.3.2 Internal Drivers of Regional Climate Variability ====

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Internal climate variability on seasonal to multi-decadal temporal scales is substantial at regional scales. This variability arises from internal modes of atmospheric and oceanic variability, intrinsically coupled climate modes, and may additionally be driven by processes other than those originating the modes. It also interacts with the response of the climate system to external forcing. The teleconnections associated with the modes are useful to understand the relationship between large and regional scales (Annex IV: Modes of Variability). A description of various large-scale modes of variability can be found in Chapters 2, 3 and 8, and in Annex IV, while their future projections are assessed in Chapter 4. The specificities of their regional influence are briefly discussed here. More details of their typical temporal scales and regional influences can be found in Annex IV.

Atmospheric modes of variability may have seasonally-dependent regional effects like the North Atlantic Oscillation (NAO) in European winter ( [[#Tsanis--2019|Tsanis and Tapoglou, 2019]] ) and summer ( [[#Bladé--2012|Bladé et al., 2012]] ; [[#Dong--2013|Dong et al., 2013]] ). Even though these modes are internal to the climate system, their variability can be affected by anthropogenic forcings. For instance, the SAM ( [[#Hendon--2014|Hendon et al., 2014]] ) is both internally driven ( [[#Smith--2017|Smith and Polvani, 2017]] ), but also affected by recent stratospheric ozone changes ( [[#Bandoro--2014|Bandoro et al., 2014]] ). The teleconnections between these modes of variability and surface weather often exhibit considerable non-stationarity ( [[#Hertig--2015|Hertig et al., 2015]] ).

Due to the large ocean heat capacity and their long temporal scales, multi-annual to multi-decadal modes of ocean variability such as the Pacific Decadal Variability (PDV; [[#Newman--2016|Newman et al., 2016]] ) and the Atlantic Multi-decadal Variability (AMV; [[#Buckley--2016|Buckley and Marshall, 2016]] ) are key drivers of regional climate change. In the case of the AMV both natural (volcanic) and anthropogenic (aerosol) external forcings are thought to be involved in its timing and intensity ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ). These modes not only affect nearby regions but also remote parts of the globe through atmospheric teleconnections ( [[#Meehl--2013|Meehl et al., 2013]] ; [[#Dong--2015|Dong and Dai, 2015]] ) and can act to modulate the influence of natural and anthropogenic forcings ( [[#Davini--2015|Davini et al., 2015]] ; [[#Ghosh--2017|Ghosh et al., 2017]] ; [[#Ménégoz--2018b|Ménégoz et al., 2018b]] ). The dynamics of the ocean modes is simultaneously affected by other modes of variability spanning the full range of spatial and temporal scales due to non-linear interactions (Figure 10.3; [[#Kucharski--2010|Kucharski et al., 2010]] ; [[#Dong--2018|Dong et al., 2018]] ). This mutual interdependence can result in changing characteristics of the connection over time ( [[#Gallant--2013|Gallant et al., 2013]] ; [[#Brands--2017|Brands, 2017]] ; [[#Dong--2017|Dong and McPhaden, 2017]] ), and of their regional climate impact ( [[#Martín-Gómez--2016|Martín-Gómez and Barreiro, 2016]] , 2017). As with atmospheric modes of variability, the regional influence of ocean modes of variability on regional climates can be seasonally dependent ( [[#Haarsma--2015|Haarsma et al., 2015]] ).

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==== 10.1.3.3 Uncertainty and Confidence ====

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Uncertainty and confidence are treated in the same way in regional climate change information as in larger-scale (continental and global) climate problems ( [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] and [[#10.3.4|Section 10.3.4]] ). The degree of confidence in climate simulations and in the resulting climate information typically depends on the identification of the role of the uncertainties ( [[#10.3.4|Section 10.3.4]] ). Since the direct verification of simulations of future climate changes is not possible, model performance and reliable (i.e., trustworthy) uncertainty estimates need to be assessed indirectly through process understanding and a systematic comparison with observations of past and current climate ( [[#10.3.3|Section 10.3.3]] ; [[#Knutti--2010|Knutti et al., 2010]] ; [[#Eyring--2019|Eyring et al., 2019]] ). The observational uncertainty, which is particularly large at regional scales, also has to be taken into account ( [[#10.2|Section 10.2]] ). These uncertainty estimates are then propagated in the distillation process to generate climate information.

Uncertainties in model-based future regional climate information arise from different sources and are introduced at various stages in the process ( [[#Lehner--2020|Lehner et al., 2020]] ): (i) forcing uncertainties associated with the future scenario or pathway that is assumed; (ii) internal variability; and (iii) uncertainties related to imperfections in climate models, also referred to as structural or model uncertainty. However, the relative role of each of these sources of uncertainty differs between the global and the regional scales as well as between variables and also between different regions ( [[#Lehner--2020|Lehner et al., 2020]] ). One way to address the internal variability and model uncertainties is to consider results from both multiple models and multiple realizations of the same model ( [[#Eyring--2016a|Eyring et al., 2016a]] ; [[#Lehner--2020|Lehner et al., 2020]] ; [[#Díaz--2021|Díaz et al., 2021]] ). These models are at times also combined with different weights that are a function of their performance and independence to increase the confidence of the multi-model ensemble ( [[#Abramowitz--2019|Abramowitz et al., 2019]] ; [[#Brunner--2019|Brunner et al., 2019]] ).

Other elements that play a role are the inconsistency between the global and regional models in dynamical downscaling or the observational and methodological uncertainty in bias-adjustment methods ( [[#Sørland--2018|Sørland et al., 2018]] ). These elements, in addition to those typical of the uncertainty in global and large-scale phenomena (Chapters 1–9), affect the overall confidence of regional climate information. This complex scene with different sources of uncertainty makes the collection of results available from multi-model, multi-member simulations most useful when synthesized through a distillation process ( [[#10.5.3|Section 10.5.3]] ).

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<span id="distillation-of-regional-climate-information-1"></span>
=== 10.1.4 Distillation of Regional Climate Information ===

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Regional climate information is synthesized from different lines of evidence from a number of sources (Sections 10.2–10.4) taking into account the context of a user vulnerable to climate variability and change at regional scales ( [[#Baztan--2017|Baztan et al., 2017]] ) and the values of all relevant actors ( [[#Corner--2014|Corner et al., 2014]] ; [[#Bessette--2017|Bessette et al., 2017]] ) in a process called distillation ( [[#10.5|Section 10.5]] ). Distillation, understood as the process of synthesizing information about climate change from different lines of evidence obtained from a variety of sources and taking into account the user context and the values of all relevant actors, allows the connection of global climate change to the local and regional scales, where adaptation responses and policy decisions take place. Climate information is translated into the user context in a co-production process that introduces further user-relevant elements leading to user-relevant climate information (Figure 10.1; [[#Pettenger--2016|Pettenger, 2016]] ; [[#Verrax--2017|Verrax, 2017]] ) for a specific demand like, for instance, guiding climate-resilient development ( [[#Kruk--2017|Kruk et al., 2017]] ; [[#Parker--2019|Parker and Lusk, 2019]] ).

The approaches adopted in the distillation of regional climate information are diverse and range from the simple delivery of data as information to co-production with the user using as many lines of evidence as possible ( [[#Lourenço--2016|Lourenço et al., 2016]] ). The availability and selection of the sources and the approach followed has implications for the usefulness of the information. For instance, it is well-established that it is invalid to take a time series from a gridcell of a model simulation as equivalent to an observational estimate of a point within the cell, due to the lack of representativeness ( [[#10.3|Section 10.3]] ), and consequently the information building solely on this type of data source is of limited use. Relevant decisions are made during the distillation process, such as what method is most suitable to a specific user context and the question being addressed. The information may be provided in the form of summarized raw data, a set of user-oriented indicators, a set of figures and maps with either a brief description, in the form of a storyline, or formulated as rich and complex climate adaptation plans. The information typically includes a description of the sources and assumptions, estimates of the associated uncertainty and its sources, and guidance to prevent possible misunderstandings in its communication.

The choices made for the distillation have typically been part of a linear supply chain, starting from the access to climate data that are transformed into maps or derived climate data products, and finally formulating statements that are communicated and delivered to a broad range of users ( [[#Hewitt--2012|Hewitt et al., 2012]] ; [[#Hewitson--2017|Hewitson et al., 2017]] ). This methodology has proven to be valuable in many cases, but it is equally fraught with dangers of not communicating important assumptions, not estimating the impact of relevant uncertainties, and possibly causing misunderstandings in the handover to the user community. This has led to the emergence of new pathways to generate user-oriented climate information, many in the context of emerging climate services ( [[#Buontempo--2018|Buontempo et al., 2018]] ; [[#Hewitt--2020|Hewitt et al., 2020]] ), which are assessed in [[#10.5|Section 10.5]] and in Chapter 12.

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=== 10.1.5 Regional Climate Information in the AR6 WGI Report ===

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This chapter is part of a cluster devoted to regional climate (Chapters 10, 11, 12 and Atlas). It introduces many of the aspects relevant to the generation of regional climate information that are dealt with in detail elsewhere. Figure 10.4 summarizes how these chapters relate to one another and to the rest of the report.

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[[File:513dc0021345b749c3ccc022275b52d5 IPCC_AR6_WGI_Figure_10_4.png]]

'''Figure 10.4''' '''|''' '''Schematic diagram that illustrates the treatment of regional climate change in the different parts of the WGI Report and how the chapters relate to each other.'''

( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses observed, attributed and projected changes in weather and climate extremes, provides a mechanistic understanding on how changes in extremes are related to human-induced climate change and provides regional, continental and global-scale assessments on changes in extremes, including compound events. [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] identifies elements of the climate system relevant for sectoral impacts referred to as climatic impact-drivers (CIDs), assesses past and future evolutions of sector-relevant CIDs for each AR6 region, synthesizes such evolutions for different time periods and by GWL, and assesses how CIDs are used in climate services. The ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] assesses observed, attributed and projected changes in mean climate, performs a comparison of CMIP5, CMIP6 and CORDEX simulations, evaluates downscaling performance and assesses approaches to communicate climate information. The Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] facilitates the exploration of datasets assessed in all chapters through a wide range of maps, graphs and tables generated in an interactive manner. This allows for the comparison of changes at warming levels and scenario/time-period combinations, display of indices for extremes and CIDs, and serves all chapters in the report to facilitate synthesis information and support the Technical Summary and the Summary for Policymakers.

Other chapters also include a strong regional component and provide context for the assessment of regional climate. [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] introduces the different types of climatic regions used in the AR6 WGI Report and the main types of climatic models. [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] describes the recent and current state of the climate from observations, most of which are key for the production of regional information. [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] assesses human influence on the climate system and [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] assesses climate change projections, with a global focus. These three chapters include phenomena that are important for shaping regional climate such as general circulation, jets, storm tracks, blocking and modes of variability. At the same time, the visualization of information in global maps in these chapters provides valuable information for the sub-continental scale. [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] assesses the knowledge about the carbon and biogoechemical cycles, whose fluxes and responses show variability that is strongly regional in nature. [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] assesses the regional evolution of short-lived climate forcers as well as their influence on regional climate and air quality. [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] assesses observed and projected changes in the variability of the regional water cycle, including monsoons, while changes of the regional oceans, changes in cryosphere and regional sea level change are assessed in Chapter 9.

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'''Box 10.1 | Regional Climate in AR5 and the Special Reports SRCCL, SROCC and SR1.5'''

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This box summarizes the information on linking global and regional climate change information in the Fifth Assessment Report (AR5) and the three Special Reports of the IPCC Sixth Assessment Cycle. This information frames the treatment of the production of regional climate information in previous reports and identifies some of the gaps that the AR6 WGI Report needs to address.

'''Fifth Assessment Report, AR5'''

In WGI [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] ( [[#Flato--2014|Flato et al., 2014]] ), regional downscaling methods were addressed as tools to provide climate information at the scales needed for many climate impact studies. The assessment found ''high confidence'' that downscaling adds value both in regions with highly variable topography and for various small-scale phenomena. Regional models necessarily inherit biases from the global models used to provide boundary conditions. Furthermore, the ability of AR5 to systematically evaluate regional climate models (RCMs), and statistical downscaling schemes, were hampered because coordinated intercomparison studies were still emerging. However, several studies demonstrated that added value arises from higher resolution in regions where stationary small-scale features like topography and complex coastlines are present, and from improved representation of small-scale processes like convective precipitation.

WGI Chapter 14 ( [[#Christensen--2013|Christensen et al., 2013]] ) stressed that credibility in regional climate change projections increases when key drivers of the change are known to be well-simulated and well-projected by climate models.

Working Group II (WGII) Chapter 21 ( [[#Hewitson--2014b|Hewitson et al., 2014b]] ) addressed the regional climate change context from the perspective of impacts, vulnerability and adaptation. This chapter emphasized that a good understanding of decision-making contexts is essential to define the type and scale of information required from physical climate. Further, the chapter identified that the regional climate information was limited by the paucity of comprehensive observations and their analysis along with the different levels of confidence in projections ( ''high confidence'' ). Notably, at the time of AR5, many studies still relied on global datasets, models, and assessment methods to inform regional decisions, which were not considered as effective as tailored regional approaches. The regional scale was not defined but instead it was emphasized that climate change responses play out on a range of scales, and the relevance and limitations of information differ strongly from global to local scales, and from one region to another.

Chapter 21 noted that the production of downscaled datasets (by both dynamical and statistical methods) remains weakly coordinated, and that results indicate that high-resolution downscaled reconstructions of the current climate can have significant errors. Key in this was that the increase in downscaled datasets has not narrowed the uncertainty range, and that integrating these data with historical change and process-based understanding remains an important challenge.

The chapter identified the common perception that higher resolution (i.e., more spatial detail) equates to more usable and robust information, which is not necessarily true. Instead, it is through the integration of multiple sources of information that robust understanding of change is developed.

WGII Chapter 21 highlighted that the different contexts of an impact study are defining features for how climate risk is perceived. Perspectives were characterized as top-down (physical vulnerability) and bottom-up perspectives (social vulnerability). The top-down perspective uses climate change impacts as the starting point of how people and/or ecosystems are vulnerable to climate change, and commonly applies global-scale scenario information or refines this to the region of interest through downscaling procedures. Conversely, in the ‘bottom-up’ approach the development context is the starting point, focusing on local scales, and layers climate change on top of this. An impact focus tends to look to the future to see how to adjust to expected changes, whereas a vulnerability-focused approach is centred on addressing the drivers of current vulnerability.

Box 10.1

'''Special Report on Climate Change and Land (SRCCL; [[#IPCC--2019a|IPCC, 2019a]] )'''

The SRCCL ( [[#Jia--2019|Jia et al., 2019]] ) assessed that there is ''robust evidence'' and ''high agreement'' that land cover and land use or management exert significant influence on atmospheric states (e.g., temperature, rainfall, wind intensity) and phenomena (e.g., monsoons), at various spatial and temporal scales, through their biophysical influences on climate. There is ''robust evidence'' that dry soil moisture anomalies favour summer heatwaves. Part of the projected increase in heatwaves and droughts can be attributed to soil moisture feedbacks in regions where evapotranspiration is limited by moisture availability ( ''medium confidence'' ). Vegetation changes can also amplify or dampen extreme events through changes in albedo and evapotranspiration, which will influence future trends in extreme events ( ''medium confidence'' ).

The influence of different changes in land use (e.g., afforestation, urbanization), on the local climate depends on the background climate ( ''robust evidence'' , ''high agreement'' ). There is ''high confidence'' that regional climate change can be dampened or enhanced by changes in local land cover and land use, with sign and magnitude depending on region and season.

Water management and irrigation were generally not accounted for by CMIP5 global models available at the time of SRCCL. Additional water can modify regional energy and moisture balance particularly in areas with highly productive agricultural crops with high rate of evapotranspiration. Urbanization increases the risks associated with extreme events ( ''high confidence'' ). Urbanization suppresses evaporative cooling and amplifies heatwave intensity ( ''high confidence'' ) with a strong influence on minimum temperatures ( ''high confidence'' ).

'''Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; [[#IPCC--2019b|IPCC, 2019b]] )'''

The SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ) stated that observations and models for assessing changes in the ocean and the cryosphere have been developed considerably during the past century but observations in some key regions remain under-sampled and were very short relative to the time scales of natural variability and anthropogenic changes. Retreat of mountain glaciers and thawing of mountain permafrost continues and will continue due to significant warming in those regions, where it is ''likely'' to exceed global temperature increase.

The SROCC assessed that it is ''virtually certain'' that Antarctica and Greenland have lost mass over the past decade and observed glacier mass loss over the last decades is attributable to anthropogenic climate change ( ''high confidence'' ). It is ''virtually certain'' that projected warming will result in continued loss in Arctic sea ice in summer, but there is ''low confidence'' in climate model projections of Antarctic sea ice change because of model biases and disagreement with observed trends. Knowledge and observations of the polar regions were sparse compared to many other regions, due to remoteness and challenges of operating in them.

The sensitivity of small islands and coastal areas to increased sea levels differs between emissions scenarios and regionally, and a consideration of local processes is critical for projections of sea level influences at local scales.

'''Special Report on Global Warming of 1.5°C (SR1.5; [[#IPCC--2018b|IPCC, 2018b]] )'''

The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessed that most land regions were experiencing greater warming than the global average, with annual average warming already exceeding 1.5°C in many regions. Over one quarter of the global population live in regions that have already experienced more than 1.5°C of warming in at least one season. Land regions will warm more than ocean regions over the coming decades (transient climate conditions).

Transient climate projections reveal observable differences between 1.5°C and 2°C global warming in terms of mean temperature and extremes, both at a global scale and for most land regions. Such studies also reveal detectable differences between 1.5°C and 2°C precipitation extremes in many land regions. For mean precipitation and various drought measures there is substantially lower risk for human systems and ecosystems in the Mediterranean region at 1.5°C compared to 2°C.

The different pathways to a 1.5°C warmer world may involve a transition through 1.5°C, with both short- and long-term stabilization (without overshoot), or a temporary rise and fall over decades and centuries (overshoot). The influence of these pathways is small for some climate variables at the regional scale (e.g., regional temperature and precipitation extremes) but can be very large for others (e.g., sea level).

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'''Cross-Chapter Box 10.1 | Influence of the Arctic on Mid-latitude Climate'''

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'''Coordinator:''' Rein Haarsma (The Netherlands)

'''Contributors:''' Francisco J. Doblas-Reyes (Spain), Hervé Douville (France), Nathan P. Gillett (Canada), Gerhard Krinner (France/Germany, France), Dirk Notz (Germany), Krishnan Raghavan (India), Alex C. Ruane (United States of America), Sonia I. Seneviratne (Switzerland), Laurent Terray (France), Cunde Xiao (China)

The Arctic has ''very likely'' warmed more than twice the global rate over the past 50 years with the greatest increase during the cold season (Atlas.11.2). Several mechanisms are responsible for the enhanced lower troposphere warming of the Arctic, including ice albedo, lapse rate, Planck and cloud feedbacks ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.1|Section 7.4.4.1]] ). The rapid Arctic warming strongly affects the ocean, atmosphere, and cryosphere in that region ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1|Section 2.3.2.1]] and Atlas.11.2). Averaged over the decade 2010–2019, monthly average sea ice area in August, September and October has been about 25% smaller than during 1979–1988 ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.3.1.1|Section 9.3.1.1]] ). It is ''very likely'' that anthropogenic forcings mainly due to greenhouse gas increases have contributed substantially to Arctic sea ice loss since 1979, explaining at least half of the observed long-term decrease in summer sea ice extent ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.1.1|Section 3.4.1.1]] ).

Cross-Chapter Box 10.1

In this box, the possible influences of the Arctic warming on the lower latitudes are assessed. This linkage was also the topic of Box 3.2 of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; [[#IPCC--2019b|IPCC, 2019b]] ). It is a topic that has been strongly debated ( [[#Ogawa--2018|Ogawa et al., 2018]] ; K. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ). Separate hypotheses have emerged for winter and summer that describe possible mechanisms of how the Arctic can influence the weather and climate at lower latitudes. They involve changes in the polar vortex, storm tracks, jet stream, planetary waves, stratosphere-troposphere coupling, and eddy-mean flow interactions, thereby affecting the mid-latitude atmospheric circulation, and the frequency, intensity, duration, seasonality and spatial extent of extremes and climatic impact-drivers like cold spells, heatwaves, and floods (Cross-Chapter Box 10.1, Figure 1). However, we note that a decrease in the intensity of cold extremes has been observed in the Northern Hemisphere mid-latitudes in winter since 1950 ( [[IPCC:Wg1:Chapter:Chapter-11#11.3.2|Section 11.3.2]] ; [[#van%20Oldenborgh--2019|van Oldenborgh et al., 2019]] ). Since SROCC, new literature has appeared, and the mechanisms and their criticisms are assessed here as an update and extension to the SROCC box.

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[[File:3895a2ee1911eda820e3b9bf49db418b IPCC_AR6_WGI_CCBox_10_1_Figure_1.png]]

'''Cross-Chapter Box 10.1, Figure 1''' 

'''|'''

'''Mechanisms of potential influences of recent and future Arctic warming on mid-latitude climate and variability.''' Mechanisms are different for winter and summer with different associated influences on mid-latitudes. The mechanisms involve changes in the polar vortex, storm tracks, planetary waves and jet stream.

'''Mechanisms for a potential influence in winter'''

It has been proposed that Arctic amplification, by reducing the equator–pole temperature contrast, could result in a weaker and more meandering jet with Rossby waves of larger amplitude ( [[#Francis--2017|Francis et al., 2017]] ; [[#Zhang--2020|Zhang and Luo, 2020]] ). This may cause weather systems to travel eastward more slowly and thus, all other things being equal, Arctic amplification could lead to more persistent weather patterns over the mid-latitudes ( [[#Francis--2012|Francis and Vavrus, 2012]] ). The persistent large meandering flow may increase the likelihood of connected patterns of temperature and precipitation climatic impact-drivers because they frequently occur when atmospheric circulation patterns are persistent, which tends to occur with a strong meridional wind component. Another possible consequence of Arctic warming is on the NAO/AO that shows a negative trend over the 1990s and early 2000s ( [[#Robson--2016|Robson et al., 2016]] ; [[#Iles--2017|Iles and Hegerl, 2017]] ), and has been linked to the reduction of sea ice in the Barents and Kara seas, and the increase in Eurasian snow cover ( [[#Cohen--2012|Cohen et al., 2012]] ; [[#Nakamura--2015|Nakamura et al., 2015]] ; [[#Yang--2016|Yang et al., 2016]] ). During negative NAO/AO the storm tracks shift equatorward and winters are predominantly more severe across northern Eurasia and the eastern United States, but relatively mild in the Arctic. This temperature pattern is sometimes referred to as the ‘warm Arctic–cold continents (WACC)’ pattern ( [[#Chen--2018|Chen et al., 2018]] ). However, L. [[#Sun--2016|]] [[#Sun--2016|Sun et al. (2016)]] noticed that the WACC is a manifestation of natural variability. Enhanced sea ice loss in the Barents-Kara Sea has also been related to a weakening of the stratospheric polar vortex ( [[#Kretschmer--2020|Kretschmer et al., 2020]] ) and its increased variability ( [[#Kretschmer--2016|Kretschmer et al., 2016]] ) that would induce a negative NAO/AO ( [[#Kim--2014|Kim et al., 2014]] ), the WACC pattern ( [[#Kim--2014|Kim et al., 2014]] ), and an increase in cold air outbreaks (CAO) in mid-latitudes ( [[#Kretschmer--2018|Kretschmer et al., 2018]] ). Arctic warming might also increase Eurasian snow cover in autumn caused by the moister air that is advected into Eurasia from the Arctic with reduced sea ice cover ( [[#Cohen--2014|Cohen et al., 2014]] ; [[#Jaiser--2016|Jaiser et al., 2016]] ), although [[#Peings--2019|Peings (2019)]] suggests a possible influence of Ural blockings on both the autumn snow cover and the early winter polar stratosphere. The circulation changes over the Ural-Siberian region are also suggested to provide a link between Barents-Kara sea ice and the NAO ( [[#Santolaria-Otín--2021|Santolaria-Otín et al., 2021]] ).

'''Mechanisms for a potential influence in summer'''

As in winter, Arctic summer warming may result in a weakening of the westerly jet and mid-latitude storm tracks, as suggested for the recent period of Arctic warming ( [[#Coumou--2015|Coumou et al., 2015]] ; [[#Petrie--2015|Petrie et al., 2015]] ; [[#Chang--2016|Chang et al., 2016]] ). Additional proposed consequences are a southward shift of the jet ( [[#Butler--2010|Butler et al., 2010]] ) and a double jet structure associated with an increase of the land–ocean thermal gradient at the coastal boundary ( [[#Coumou--2018|Coumou et al., 2018]] ). It is hypothesized that weaker jets, diminished meridional temperature contrast, and reduced baroclinicity might induce a larger amplitude in stationary wave response to stationary forcings ( [[#Zappa--2011|Zappa et al., 2011]] ; [[#Petoukhov--2013|Petoukhov et al., 2013]] ; [[#Hoskins--2015|Hoskins and Woollings, 2015]] ; [[#Coumou--2018|Coumou et al., 2018]] ; [[#Mann--2018|Mann et al., 2018]] ; R. [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ), and also that a double jet structure would favour wave resonance ( [[#Kornhuber--2017|Kornhuber et al., 2017]] ; [[#Mann--2017|Mann et al., 2017]] ). Some studies suggest that this is corroborated by an observed increase of quasi-stationary waves ( [[#Di%20Capua--2016|Di Capua and Coumou, 2016]] ; [[#Vavrus--2017|Vavrus et al., 2017]] ; [[#Coumou--2018|Coumou et al., 2018]] ).

'''Assessment'''

The above proposed hypotheses are based on concepts of geophysical fluid dynamics and surface coupling and can, in principle, help explain the existence of a link between the Arctic changes and the mid-latitudes with the potential to affect many impact sectors ( [[#Barnes--2015|Barnes and Screen, 2015]] ). However, the validity of some dynamical underlying mechanisms, such as a reduced meridional temperature contrast inducing enhanced wave amplitude, has been questioned ( [[#Hassanzadeh--2014|Hassanzadeh et al., 2014]] ; [[#Hoskins--2015|Hoskins and Woollings, 2015]] ). On the contrary, the reduced meridional temperature contrast has been related to reduced meridional temperature advection and thereby reduced winter temperature variability ( [[#Collow--2019|Collow et al., 2019]] ).

Studies that support the Arctic influence are mostly based on observational relationships between the Arctic temperature or sea ice extent and mid-latitude anomalies or extremes ( [[#Cohen--2012|Cohen et al., 2012]] ; [[#Francis--2012|Francis and Vavrus, 2012]] , 2015; [[#Budikova--2017|Budikova et al., 2017]] ). They are often criticized for the lack of statistical significance and the inability to disentangle cause and effect ( [[#Barnes--2013|Barnes, 2013]] ; [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Screen--2013|Screen and Simmonds, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Hassanzadeh--2014|Hassanzadeh et al., 2014]] ; [[#Barnes--2015|Barnes and Screen, 2015]] ; [[#Sorokina--2016|Sorokina et al., 2016]] ; [[#Douville--2017|Douville et al., 2017]] ; [[#Gastineau--2017|Gastineau et al., 2017]] ; [[#Blackport--2020a|Blackport and Screen, 2020a]] ; [[#Oudar--2020|Oudar et al., 2020]] ; [[#Riboldi--2020|Riboldi et al., 2020]] ). The role of the Barents-Kara sea ice loss is challenged by [[#Blackport--2019|Blackport et al. (2019)]] who find a minimal influence of reduced sea ice on severe mid-latitude winters, and by [[#Warner--2020|Warner et al. (2020)]] who suggest thatthe apparent winter NAO response to the Barents-Kara sea ice variability is mainly an artefact of the Aleutian Low internal variability and of the co-variability between sea ice and the Aleutian Low originating from tropical-extratropical teleconnections. Also [[#Gong--2020|Gong et al. (2020)]] do not find a link between Rossby wave propagation into the mid-latitudes and Arctic sea ice loss. [[#Mori--2019|Mori et al. (2019)]] argue that models underestimate the influence of the Barents-Kara Sea ice loss on the atmosphere, which is disputed by [[#Screen--2019|Screen and Blackport (2019)]] . Other studies have stressed the importance of atmospheric variability as a driver of Arctic variability ( [[#Lee--2014|Lee, 2014]] ; [[#Woods--2016|Woods and Caballero, 2016]] ; [[#Praetorius--2018|Praetorius et al., 2018]] ; [[#Olonscheck--2019|Olonscheck et al., 2019]] ). Analysing observed key variables of mid-latitude climate for 1980–2020, [[#Blackport--2020b|Blackport and Screen (2020b)]] and [[#Riboldi--2020|Riboldi et al. (2020)]] argue that the Arctic influence on mid-latitudes is small compared to other aspects of climate variability, and that observed periods of strong correlation are an artefact of internal variability or intermittency ( [[#Kolstad--2019|Kolstad and Screen, 2019]] ; [[#Siew--2020|Siew et al., 2020]] ; [[#Warner--2020|Warner et al., 2020]] ).

An additional argument in the criticism is the inability of climate models to simulate a significant response to Arctic sea ice loss, larger than the natural variability (Screen et al., 2014; [[#Walsh--2014|Walsh, 2014]] ; H.W. [[#Chen--2016|Chen et al., 2016]] ; [[#Peings--2017|Peings et al., 2017]] ; [[#Dai--2020|Dai and Song, 2020]] ), or that a very large multi-model ensemble is needed ( [[#Liang--2020|Liang et al., 2020]] ), although some studies find a significant response in summer, because then the internal variability is weaker ( [[#Petrie--2015|Petrie et al., 2015]] ).

Finally, a warmer Arctic climate can, without any additional changes in atmospheric dynamics, reduce cold extremes in winter due to advection of increasingly warmer air from the Arctic into the mid-latitudes ( [[#Screen--2014|Screen, 2014]] ; [[#Ayarzagüena--2016|Ayarzagüena and Screen, 2016]] ; [[#Ayarzagüena--2018|Ayarzagüena et al., 2018]] ).

Summarizing, different hypotheses have been developed about the influence of recent Arctic warming on the mid-latitudes in both winter and summer. Although some of the proposed mechanisms seem to be supported by various studies, the underlying mechanisms and relative strength compared to internal climate variability have been questioned. A recent review ( [[#Cohen--2020|Cohen et al., 2020]] ) states that divergent conclusions between model and observational studies, and also between different model studies, continue to obfuscate a clear understanding of how Arctic warming is influencing mid-latitude weather. In this context, [[#Shepherd--2016b|Shepherd (2016b)]] stresses the need for collaboration between scientists with different viewpoints for further understanding that could be achieved by carefully designed, multi-investigator, coordinated, multi-model simulations, data analyses and diagnostics ( [[#Overland--2016|Overland et al., 2016]] ). In agreement with Box 3.2 of SROCC, there is ''low to medium confidence'' in the exact role and quantitative effect of historical Arctic warming and sea ice loss on mid-latitude atmospheric variability.

Regarding future climate, it is important to note that mid-latitude variability is also affected by many drivers other than the Arctic changes and that those drivers as well as the linkages to mid-latitude variability might change in a warmer world. The AMV, PDV, ENSO (see Annex IV), upper tropospheric tropical heating, polar stratospheric vortex, and land surface processes associated with soil moisture ( [[#Miralles--2014|Miralles et al., 2014]] ; [[#Hauser--2016|Hauser et al., 2016]] ) and snow cover ( [[#Nakamura--2019|Nakamura et al., 2019]] ; Sato and Nakamura, 2019) are a few examples. A considerable body of literature has shown that changes to the NAO/AO on seasonal and climate change time scales can be driven by variations in the wavelength and amplitude of Rossby waves, mainly of tropical origin ( [[#Fletcher--2011|Fletcher and Kushner, 2011]] ; [[#Cattiaux--2013|Cattiaux and Cassou, 2013]] ; [[#Ding--2014|Ding et al., 2014]] ; [[#Goss--2016|Goss et al., 2016]] ). The influence of future Arctic warming on mid-latitude circulation is difficult to disentangle from the effect of such a plethora of drivers ( [[#Blackport--2017|Blackport and Kushner, 2017]] ; F. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). One of the consequences of climate change is a poleward shift of the jet induced by the tropical warming ( [[#Barnes--2013|Barnes and Polvani, 2013]] ), which is less obvious in winter especially over the North Atlantic ( [[#Peings--2018|Peings et al., 2018]] ; [[#Oudar--2020|Oudar et al., 2020]] ), and the increase of the meridional temperaturegradient in the upper troposphere, which increases storm track activity ( [[#Barnes--2015|Barnes and Screen, 2015]] ; [[#Parding--2019|Parding et al., 2019]] ). Although climate models indicate that future Arctic warming and the associated equator–pole temperature gradient decrease could affect mid-latitude climate and variability ( [[#Haarsma--2013a|Haarsma et al., 2013a]] ; [[#McCusker--2017|McCusker et al., 2017]] ; [[#Zappa--2018|Zappa et al., 2018]] ), and even the tropics and subtropics ( [[#Deser--2015|Deser et al., 2015]] ; [[#Cvijanovic--2017|Cvijanovic et al., 2017]] ; K. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ; [[#England--2020|England et al., 2020]] ; [[#Kennel--2020|Kennel and Yulaeva, 2020]] ), they do not reveal a strong influence on extreme weather ( [[#Woollings--2014|Woollings et al., 2014]] ).

In conclusion, there is ''low confidence'' in the relative contribution of Arctic warming to mid-latitude atmospheric changes compared to other drivers. Future climate change could affect mid-latitude variability in a number of ways that are still to be clarified, and which may also include the influence of Arctic warming. The linkages between the Arctic warming and the mid-latitude circulation are an example of contrasting lines of evidence that cannot yet be reconciled ( [[#10.5|Section 10.5]] ).

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