Editing IPCC:AR6/WGI/Chapter-1 (section)

== Cross-Chapter Box 1.3 | Risk Framing in IPCC AR6 ==

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'''Contributing Authors:''' Andy Reisinger (New Zealand), Maisa Rojas (Chile), Aïda Diongue-Niang (Senegal), Maarten K. van Aalst (The Netherlands), Mathias Garschagen (Germany), Mark Howden (Australia), Margot Hurlbert (Canada), Katharine Mach (United States of America), Sawsan Khair Elsied Abdel Rahim Mustafa (Sudan), Brian O’Neill (United States of America), Roque Pedace (Argentina), Jana Sillmann (Norway/Germany), Carolina Vera (Argentina), David Viner (United Kingdom)

The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; [[#IPCC--2012|IPCC, 2012]]) presented a framework for assessing risks from climate change, which linked hazards (due to changes in climate) with exposure and vulnerability ([[#Cardona--2012|Cardona et al., 2012]]). This framework was further developed by AR5 WGII ([[#IPCC--2014b|IPCC, 2014b]]), while AR5 WGI focussed only on the hazard component of risk. As part of AR6, a cross-Working Group process expanded and refined the concept of risk to allow for a consistent risk framing to be used across the three IPCC Working Groups ([[#IPCC--2019b|IPCC, 2019b]]; Box 2 in [[#Abram--2019|Abram et al., 2019]]; [[#Reisinger--2020|Reisinger et al., 2020]]).

In this revised definition, risk is defined as:

The potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with such systems. In the context of climate change, risks can arise from potential impacts of climate change as well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health and well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), ecosystems and species.

In the context of climate change impacts, risks result from dynamic interactions between climate-related hazards with the exposure and vulnerability of the affected human or ecological system to the hazards. Hazards, exposure and vulnerability may each be subject to uncertainty in terms of magnitude and likelihood of occurrence, and each may change over time and space due to socio-economic changes and human decision-making (see also risk management, adaptation and mitigation).

In the context of climate change responses, risks result from the potential for such responses not achieving the intended objective(s), or from potential trade-offs with, or negative side-effects on, other societal objectives, such as the Sustainable Development Goals (SDGs) (see also risk trade-off). Risks can arise, for example, from uncertainty in implementation, effectiveness or outcomes of climate policy, climate-related investments, technology development or adoption, and system transitions.

The following concepts are also relevant for the definition of risk (Glossary):

'''Exposure:''' The presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected.

'''Vulnerability:''' The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.

'''Hazard:''' The potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources.

'''Impacts:''' The consequences of realized risks on natural and human systems, where risks result from the interactions of climate-related hazards (including extreme weather/climate events), exposure, and vulnerability. Impacts generally refer to effects on lives, livelihoods, health and well-being, ecosystems and species, economic, social and cultural assets, services (including ecosystem services), and infrastructure. Impacts may be referred to as consequences or outcomes and can be adverse or beneficial.

'''Risk in AR6 WGI'''

The revised risk framing clarifies the role and contribution of WGI to risk assessment. ‘Risk’ in IPCC terminology applies only to human or ecological systems, not to physical systems on their own.

'''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.

InAR6, WGI uses the term ‘climatic impact-drivers’ to describe changes in physical systems rather than ‘hazards’, because the term hazard already assumes an adverse consequence. The terminology of ‘climatic impact-driver’ therefore allows WGI to provide a more value-neutral characterization of climatic changes that may be relevant for understanding potential impacts, without pre-judging whether specific climatic changes necessarily lead to adverse consequences, as some could also result in beneficial outcomes depending on the specific system and associated values. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] and the [[IPCC:Wg1:Chapter:Atlas|Atlas]] assess and provide information on climatic impact-drivers for different regions and sectors to support and link to the WGII assessment of the impacts and risks (or opportunities) related to the changes in the climatic impact-drivers. Although CIDs can lead to adverse or beneficial outcomes, focus is given to CIDs connected to hazards, and hence inform risk.

‘Extremes’ are a category of CID, corresponding to unusual events with respect to the range of observed values of the variable. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] assesses changes in weather and climate extremes, their attribution and future projections.

As examples of the use of this terminology, the term ‘flood risk’ should not be used if it only describes changes in the frequency and intensity of flood events (a hazard); the risk from flooding to human and ecological systems is caused by the flood hazard, the exposure of the system affected (e.g., topography, human settlements or infrastructure in the area potentially affected by flooding) and the vulnerability of the system (e.g., design and maintenance of infrastructure, existence of early warning systems). As another example, climate-related risk to food security can arise from both potential climate change impacts and responses to climate change and can be exacerbated by other stressors. Drivers for risks related to climate change impacts include climatic impact- drivers (e.g., drought, temperature extremes, humidity) mediated by other climatic impact-drivers (e.g., increased CO <sub>2</sub> fertilization of certain types of crops may help increase yields), the potential for indirect climate-related impacts (e.g., pest outbreaks triggered by ecosystem responses to weather patterns), exposure of people (e.g., how many people depend on a particular crop) and vulnerability or adaptability (how able are affected people to substitute other sources of food, which may be related to financial access and markets).

Information provided by WGI may or may not be relevant to understand risks related to climate change responses. For example, the risk to a company arising from emissions pricing, or the societal risk from reliance on an unproven mitigation technology, is not directly dependent on actual or projected changes in climate but arise largely from human choices. However, WGI climate information may be relevant to understand the potential for maladaptation, such as the potential for specific adaptation responses not achieving the desired outcome or having negative side effects. For example, WGI information about the range of sea level rise can help inform understanding of whether coastal protection, accommodation, or retreat would be the most effective risk management strategy in a particular context.

From a WGI perspective, low-likelihood, high-impact outcomes and the concept of deep uncertainty are also relevant for risk assessment.

'''Low-likelihood, high-impact (LLHI) outcomes:''' Outcomes/events whose probability of occurrence is low or not well known (as in the context of deep uncertainty) but whose potential impacts on society and ecosystems could be high. To better inform risk assessment and decision-making, such low-likelihood outcomes are considered if they are associated with very large consequences and may therefore constitute material risks, even though those consequences do not necessarily represent the most likely outcome.

The AR6 WGI Report provides more detailed information about these types of events compared to AR5 (Table 1.1, [[#1.4.4|Section 1.4.4]]).

Recognizing the need for assessing and managing risk in situations of high uncertainty, SROCC advanced the treatment of situations with deep uncertainty ([[#1.2.3|Section 1.2.3]]; [[#IPCC--2019b|IPCC, 2019b]]; Box 5 in [[#Abram--2019|Abram et al., 2019]]). A situation of deep uncertainty exists when experts or stakeholders do not know or cannot agree on: (i) appropriate conceptual models that describe relationships among key driving forces in a system; (ii) the probability distributions used to represent uncertainty about key variables and parameters; and/or (iii) how to weigh and value desirable alternative outcomes ([[#Abram--2019|Abram et al., 2019]]). The concept of deep uncertainty can complement the IPCC calibrated uncertainty language and thereby broaden the communication of risk.
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'''Cross-Working Group Box | Attribution'''

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'''Contributing Authors:''' Pandora Hope (Australia), Wolfgang Cramer (France/Germany), Gregory M. Flato (Canada), Katja Frieler (Germany), Nathan P. Gillett (Canada), Christian Huggel (Switzerland), Jan Minx (Germany), Friederike Otto (United Kingdom/Germany), Camille Parmesan (France, United Kingdom/United States of America), Joeri Rogelj (United Kingdom/Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Aimée B.A. Slangen (The Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), Maarten K. van Aalst (The Netherlands), Robert Vautard (France), Xuebin Zhang (Canada)

'''Introduction'''

Changes in the climate system are becoming increasingly apparent, as are the climate-related impacts on natural and human systems. Attribution is the process of evaluating the contribution of one or more causal factors to such observed changes or events. Typical questions addressed by the IPCC include: ‘To what extent is an observed change in global temperature induced by anthropogenic GHG and aerosol concentration changes, or influenced by natural variability?’ and ‘What is the contribution of climate change to observed changes in crop yields, which are also influenced by changes in agricultural management?’ Changes in the occurrence and intensity of extreme events can also be attributed, addressing questions such as: ‘Have human GHG emissions increased the likelihood or intensity of an observed heatwave?’

This Cross-Working Group Box briefly describes why attribution studies are important. It also describes some new developments in the methods used in those studies and provides recommendations for interpretation.

Attribution studies serve to evaluate and communicate linkages associated with climate change, for example: between the human-induced increase in GHG concentrations and the observed increase in air temperature or extreme weather events (AR6 WGI Chapters 3, 10 and 11); or between observed changes in climate and changing species distributions and food production (AR6 WGII Chapters 2 and others, summarized in WGII Chapter 16; e.g., [[#Verschuur--2021|Verschuur et al., 2021]]); or between climate change mitigation policies and atmospheric GHG concentrations (AR6 WGI Chapter 5; AR6 WGIII Chapter 14). As such, they support numerous statements made by the IPCC (AR6 WGI [[#1.3|Section 1.3]] and Appendix 1A; [[#IPCC--2013b|IPCC, 2013b]], 2014b).

Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate protection policies (AR6 WGI [[IPCC:Wg1:Chapter:Chapter-4#4.6.3|Section 4.6.3]]; e.g., [[#Nauels--2019|Nauels et al., 2019]]; [[#Banerjee--2020|Banerjee et al., 2020]]), inform and constrain projections (WGI [[IPCC:Wg1:Chapter:Chapter-4#4.2.3|Section 4.2.3]]; [[#Gillett--2021|Gillett et al., 2021]]; [[#Ribes--2021|Ribes et al., 2021]]) or inform the loss and damages estimates and potential climate litigation cases by estimating the costs of climate change ([[#Huggel--2015|Huggel et al., 2015]]; [[#Marjanac--2017|Marjanac et al., 2017]]; [[#Frame--2020|Frame et al., 2020]]). These findings can thus inform mitigation decisions as well as risk management and adaptation planning (e.g., [[#CDKN--2017|CDKN, 2017]]).

'''Steps towards an attribution assessment'''

The unambiguous framing of what changes are being attributed to what causes is a crucial first step for an assessment ([[#Easterling--2016|Easterling et al., 2016]]; [[#Hansen--2016|Hansen et al., 2016]]; [[#Stone--2021|Stone et al., 2021]]), followed by the identification of the possible and plausible drivers of change and the development of a hypothesis or theory for the linkage (Cross-Working Group Box: Attribution, Figure 1). The next step is to clearly define the indicators of the observed change or event and note the quality of the observations. There has been significant progress in the compilation of fragmented and distributed observational data, broadening and deepening the data basis for attribution research (WGI [[#1.5|Section 1.5]]; e.g., [[#Poloczanska--2013|Poloczanska et al., 2013]]; [[#Ray--2015|Ray et al., 2015]]; [[#Cohen--2018|Cohen et al., 2018]]). The quality ofthe observational record of drivers should also be considered (e.g., volcanic eruptions: WGI [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]]). Impacted systems also change in the absence of climate change; this baseline and its associated modifiers – such as agricultural developments or population growth – need to be considered, alongside the exposure and vulnerability of people depending on these systems.

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[[File:638aa4fa277b50207bb63cce1961b263 IPCC_AR6_WGI_CCBOX_Attribution_Figure_1.png]]

'''Cross-Working Group Box: Attribution, Figure 1 |''' '''Schematic of the steps to develop an attribution assessment, and the purposes of such assessments. Methods and systems used to test the attribution hypothesis or theory include: model-based fingerprinting; other model-based methods; evidence-based fingerprinting; process-based approaches; empirical or decomposition methods; and the use of multiple lines of evidence.''' Many of the methods are based on the comparison of the observed state of a system to a hypothetical counterfactual world that does not include the driver of interest to help estimate the causes of the observed response.

There are many attribution approaches, and several methods are detailed below. In physical and biological systems, attribution often builds on the understanding of the mechanisms behind the observed changes and numerical models are used, while in human systems other methods of evidence-building are employed. Confidence in the attribution can be increased if more than one approach is used and the model is evaluated as fit-for-purpose (WGI [[#1.5|Section 1.5]], WGI Section 3.8, WGI Section 10.3.3.4 ; Hegerl et al. , 2010; Vautard et al. , 2019; Otto et al. , 2020; Philip et al. , 2020) . The final step includes appropriate communication of the attribution assessment and the accompanying confidence in the result (e.g., [[#Lewis--2019|Lewis et al., 2019]]).

'''Attribution methods'''

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=== Attribution of changes in atmospheric greenhouse gas concentrations to anthropogenic activity ===

The AR6 WGI [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] presents multiple lines of evidence that unequivocally establish the dominant role of human activities in the growth of atmospheric CO <sub>2</sub> , including through analysing changes in atmospheric carbon isotope ratios and the atmospheric O <sub>2</sub> –N <sub>2</sub> ratio (WGI Section 5.2.1.1). Decomposition approaches can be used to attribute emissions underlying those changes to various drivers such as population, energy efficiency, consumption or carbon intensity ([[#Hoekstra--2003|Hoekstra and van den Bergh, 2003]]; [[#Raupach--2007|Raupach et al., 2007]]; [[#Rosa--2012|Rosa and Dietz, 2012]]). Combined with attribution of their climate outcomes, the attribution of the sources of GHG emissions can inform the attribution of anthropogenic climate change to specific countries or actors ([[#Matthews--2016|Matthews, 2016]]; [[#Otto--2017|Otto et al., 2017]]; [[#Skeie--2017|Skeie et al., 2017]]; [[#Nauels--2019|Nauels et al., 2019]]), and in turn inform discussions on fairness and burden sharing (WGIII Chapter 14).

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=== Attribution of observed climate change to anthropogenic forcing ===

Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to anthropogenic and natural forcings (WGI [[#1.3.4|Section 1.3.4]]; e.g., [[#Hegerl--2010|Hegerl et al., 2010]]; [[#Bindoff--2013|Bindoff et al., 2013]]). The most established method is to identify the ‘fingerprint’ of the expected space-time response to a particular climate forcing agent such as the concentration of anthropogenically induced GHGs or aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual forcing agents to an observed change (e.g., [[#Gillett--2021|Gillett et al., 2021]]). New statistical approaches have been applied to better account for internal climate variability and the uncertainties in models and observations (WGI [[IPCC:Wg1:Chapter:Chapter-3#3.2|Section 3.2]]; e.g., Naveau et al. , 2018; Santer et al. , 2019) . There are many other approaches, for example, global mean sea level change has been attributed to anthropogenic climate forcing by attributing the individual contributions from, for example, glacier melt or thermal expansion, while also examining which aspects of the observed change are inconsistent with internal variability (WGI Sections 3.5.2 and 9.6.1.4).

Specific regional conditions and responses may simplify or complicate attribution on those scales. For example, some human forcings, such as regional land-use change or aerosols, may enhance or reduce regional signals of change (WGI Sections 10.4.2, 11.1.6 and 11.2.2; Lejeune et al. , 2018; Undorf et al. , 2018; Boé et al. , 2020; Thiery et al. , 2020) . In general, regional climate variations are larger than the global mean climate, adding additional uncertainty to attribution (e.g., in regional sea level change, WGI Section 9.6.1). These statistical limitations may be reduced by ‘process-based attribution’, focusing on the physical processes known to influence the response to external forcing and internal variability (WGI Section 10.4.2).

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=== Attribution of weather and climate events to anthropogenic forcing ===

New methods have emerged since AR5 to attribute the change in likelihood or characteristics of weather or climate events or classes of events to underlying drivers (WGI Sections 10.4.1 and 11.2.2; [[#NA%20SEM--2016|NA SEM, 2016]]; Stott et al. , 2016; Jézéquel et al. , 2018; Wehner et al. , 2018; Wang et al. , 2021) . Typically, historical changes, simulated under observed forcings, are compared to a counterfactual climate simulated in the absence of anthropogenic forcing. Another approach examines facets of the weather and thermodynamic status of an event through process-based attribution (WGI [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] and Section 10.4.1; Hauser et al. , 2016; Shepherd et al. , 2018; Grose et al. , 2019) . Events where attributable human influence have been found include hot and cold temperature extremes (including some with widespread impacts), heavy precipitation, and certain types of droughts and tropical cyclones (AR6 WGI Section 11.9; e.g., [[#Vogel--2019|Vogel et al., 2019]]; [[#Herring--2021|Herring et al., 2021]]). Event attribution techniques have sometimes been extended to ‘end-to-end’ assessments from climate forcing to the impacts of events on natural or human systems ([[#Otto--2017|Otto, 2017]]).

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=== Attribution of observed changes in natural or human systems to climate-related drivers ===

The attribution of observed changes to climate-related drivers across a diverse set of sectors, regions and systems is part of each chapter in the WGII contribution to AR6 and is synthesized in WGII Chapter 16 (Section 16.2). The number of attribution studies on climate change impacts has grown substantially since AR5, generally leading to higher confidence levels in attributing the causes of specific impacts. New studies include the attribution of changes in socio-economic indicators such as economic damages due to river floods (e.g., [[#Schaller--2016|Schaller et al., 2016]]; [[#Sauer--2021|Sauer et al., 2021]]), the occurrence of heat-related human mortality (e.g., [[#Vicedo-Cabrera--2018|Vicedo-Cabrera et al., 2018]]; [[#Sera--2020|Sera et al., 2020]]) or economic inequality (e.g., [[#Diffenbaugh--2019|Diffenbaugh and Burke, 2019]]).

Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations from remote sensing, long-term in situ observations, and monitoring efforts, teamed with local knowledge, process understanding and empirical or dynamical modelling (WGII Section 16.2; [[#Stone--2013|Stone et al., 2013]]; [[#Cramer--2014|Cramer et al., 2014]]). The attribution of a change in a natural or human system (e.g., wild species, natural ecosystems, crop yields, economic development, infrastructure or human health) to changes in climate-related systems (i.e., climate, ocean acidification, permafrost thawing or sea level rise) requires accounting for other potential drivers of change, such as technological and economic changes in agriculture affecting crop production ([[#Hochman--2017|Hochman et al., 2017]]; [[#Butler--2018|Butler et al., 2018]]), changes in human population patterns and vulnerability affecting flood- or wildfire-induced damages ([[#Huggel--2015|Huggel et al., 2015]]; [[#Sauer--2021|Sauer et al., 2021]]), or habitat loss driving declines in wild species ([[#IPBES--2019|IPBES, 2019]]). These drivers are accounted for by estimating a baseline condition that would exist in the absence of climate change. The baseline might be stationary and be approximated by observations from the past, or it may change over time and be simulated by statistical or process-based impact models (WGII Section 16.2; Cramer et al. , 2014) . Assessment of multiple independent lines of evidence, taken together, can provide rigorous attribution when more quantitative approaches are not available ([[#Parmesan--2013|Parmesan et al., 2013]]). These include paleodata, physiological and ecological experiments, natural ‘experiments’ from very long-term datasets indicating consistent responses to the same climate trend/event, and ‘fingerprints’ in species’ responses that are uniquely expected from climate change (e.g. poleward range boundaries expanding and equatorial range boundaries contracting in a coherent pattern worldwide; [[#Parmesan--2003|Parmesan and Yohe, 2003]]) . Meta-analyses of species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of climate change at an appropriate scale for attribution to anthropogenic climate change ([[#Parmesan--2003|Parmesan and Yohe, 2003]]; [[#Parmesan--2013|Parmesan et al., 2013]]).

Impact attribution does notalways involve attribution to anthropogenic climate forcing. However, a growing number of studies include this aspect (e.g., [[#Frame--2020|Frame et al. (2020)]] for the attribution of damages induced by Hurricane Harvey; or [[#Diffenbaugh--2019|Diffenbaugh and Burke (2019)]] for the attribution of economic inequality between countries; or [[#Schaller--2016|Schaller et al. (2016)]] for flood damages).
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=== 1.4.5 Climate Regions Used in AR6 ===

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==== 1.4.5.1 Defining Climate Regions ====

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The AR5 assessed regional-scale detection and attribution and assessed key regional climate phenomena and their relevance for future regional climate projections. This report shows that past and future climate changes and extreme weather events can be substantial on local and regional scales (Chapters 8–12 and Atlas), where they may differ considerably from global trends, not only in intensity but even in the direction of change (e.g., [[#Fischer--2013|Fischer et al., 2013]]).

Although the evolution of global climate trends emerges as the net result of regional phenomena, average or aggregate estimates often do not reflect the intensity, variability and complexity of regional climate changes ([[#Stammer--2018|Stammer et al., 2018]]; [[#Shepherd--2019|Shepherd, 2019]]). A fundamental aspect of the study of regional climate changes is the definition of characteristic climate zones, clusters or regions, across which the emergent climate change signal can be properly analysed and projected (see Atlas). Suitable sizes and shapes of such zones strongly depend not only on the climate variable and process of interest, but also on relevant multi-scale feedbacks.

There are several approaches to the classification of climate regions. When climate observation data was sparse and limited, the aggregation of climate variables was implicitly achieved through the consideration of biomes, giving rise to the traditional vegetation-based classification of [[#Köppen--1936|Köppen (1936)]]. In the last decades, the substantial increases in climate observations, climate modelling, and data processing capabilities have allowed new approaches to climate classification, for example through interpolation of aggregated global data from thousands of stations ([[#Peel--2007|Peel et al., 2007]]; [[#Belda--2014|Belda et al., 2014]]; [[#Beck--2018|]] [[#Beck--2018|Beck et al., 2018]]) or through data-driven approaches applied to delineate ecoregions that behave in a coherent manner in response to climate variability ([[#Papagiannopoulou--2018|Papagiannopoulou et al., 2018]]). Experience shows that each method has strengths and weaknesses through trade-offs between detail and convenience. For instance, a very detailed classification, with numerous complexly shaped regions derived from a large set of variables, may be most useful for the evaluation of climate models ([[#Rubel--2010|Rubel and Kottek, 2010]]; [[#Belda--2015|Belda et al., 2015]]; [[#Beck--2018|]] [[#Beck--2018|Beck et al., 2018]]) and climate projections ([[#Feng--2014|Feng et al., 2014]]; [[#Belda--2016|Belda et al., 2016]]). In contrast, geometrically simple regions are often best suited for regional climate modelling and downscaling (e.g., the Coordinated Regional Climate Downscaling Experiment (CORDEX) domains; [[#1.5.3|Section 1.5.3]]; [[#Giorgi--2015|Giorgi and Gutowski, 2015]]).

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==== 1.4.5.2 Types of Regions Used in AR6 ====

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IPCC’s recognition of the importance of regional climates can be traced back to its First Assessment Report (FAR; [[#IPCC--1990a|IPCC, 1990a]]), where climate projections for 2030 were presented for five sub-continental regions (see [[#1.3.6|Section 1.3.6]] for an assessment of those projections). In subsequent reports, there has been a growing emphasis on the analysis of regional climate, including two special reports: one on regional impacts ([[#IPCC--1998|IPCC, 1998]]) and another on extreme events (SREX, [[#IPCC--2012|IPCC, 2012]]). A general feature of previous IPCC reports is that the number and coverage of climate regions vary according to the subject and across Working Groups. Such varied definitions have the advantage of optimizing the results for a particular application (e.g., national boundaries are crucial for decision-making, but they rarely delimit distinctive climate regions), whereas variable definitions of regions may have the disadvantage of hindering multidisciplinary assessments and comparisons between studies or Working Groups.

In this Report, regional climate change is primarily addressed through the introduction of four classes of regions (unless otherwise explicitly mentioned and justified). The first two are the unified WGI Reference Sets of (i) Land Regions and (ii) Ocean Regions, which are used throughout the Report. These are supplemented by additional sets of (iii) Typological Regions – used in Chapters 5, 8–12 and [[IPCC:Wg1:Chapter:Atlas|Atlas]] – and (iv) Continental Regions, which are mainly used for linking Chapters 11, 12 and [[IPCC:Wg1:Chapter:Atlas|Atlas]] with Working Group II (Figure 1.18). All four classes of regions are defined and described in detail in the Atlas. Here we summarize their basic features.

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'''Figure 1.18 |''' '''Main region types used in this report.''' '''(a)''' AR6 WGI Reference Set of Land and Ocean Regions ([[#Iturbide--2020|Iturbide et al., 2020]]), consisting of 46 land regions and 15 ocean regions, including 3 hybrid regions (CAR, MED, SEA) that are both land and ocean regions. Abbreviations are explained to the right of the map. Notice that RAR, SPO, NPO and EPO extend beyond the 180º meridian, therefore appearing at both sides of the map (indicated by dashed lines). A comparison with the previous reference regions of AR5 WGI ([[#IPCC--2013a|IPCC, 2013a]]) is presented in the Atlas. '''(b)''' Example of typological regions: monsoon domains (see Chapter 8). Abbreviations are explained to the right of the map. The black contour lines represent the global monsoon zones, while the coloured regions denote the regional monsoon domains. The two stippled regions (EqAmer and SAfri) do receive seasonal rainfall, but their classification as monsoon regions is still under discussion. '''(c)''' Continental Regions used mainly in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] and the Atlas. Stippled zones define areas that are assessed in both regions (e.g., the Caribbean is assessed as Small Islands and also as part of Central America). Small Islands are ocean regions containing small islands with consistent climate signals and/or climatological coherence.

The Reference Sets of Land and Ocean Regions are polygonal, sub-continental domains, defined through a combination of environmental, climatic and non-climatic (e.g., pragmatic, technical, historical) factors, in accordance with the literature and climatological reasoning based on observed and projected future climate. Merging the diverse functions and purposes of the regions assessed in the literature into a common reference set implies a certain degree of compromise between simplicity, practicality and climate consistency. For instance, Spain is fully included in the Mediterranean (MED) Reference Region, but is one of the most climatically diverse countries in the world. Likewise, a careful comparison of panels a and b of Figure 1.18 reveals that the simplified southern boundary of the Sahara (SAH) Reference Region slightly overlaps the northern boundary of the West African Monsoon Typological Region. As such, the resulting Reference Regions are not intended to precisely represent climates, but rather to provide simple domains suitable for regional synthesis of observed and modelled climate and climate change information ([[#Iturbide--2020|Iturbide et al., 2020]]). In particular, CMIP6 model results averaged over Reference Regions are presented in the Atlas.

The starting point for defining the AR6 Reference Sets of Land Regions was the collection of 26 regions introduced in SREX ([[#IPCC--2012|IPCC, 2012]]). The SREX collection was then revised, reshaped, complemented and optimized to reflect the recent scientific literature and observed climate-change trends, giving rise to the novel AR6 Reference Set of 46 Land Regions. Additionally, AR6 introduces a new Reference Set of 15 Ocean Regions (including 3 Hybrid Regions that are treated as both: land and ocean), which complete the coverage of the whole Earth ([[#Iturbide--2020|Iturbide et al., 2020]]).

Particular aspects of regional climate change are described by specialized domains called Typological Regions (Figure 1.18b). These regions cover a wide range of spatial scales and are defined by specificfeatures, called typologies. Examples of typologies include: tropical forests, deserts, mountains, monsoon regions and megacities, among others. Typological Regions are powerful tools to summarize complex aspects of climate defined by a combination of multiple variables. For this reason, they are used in many chapters of AR6 WGI and WGII (e.g., Chapters 8–12 and Atlas).

Finally, consistency with WGII is also pursued in Chapters 11, 12 and the [[IPCC:Wg1:Chapter:Atlas|Atlas]] through the use of a set of Continental Regions (Figure 1.18c), based on the nine continental domains defined in AR5 WGII Part B ([[#Hewitson--2014|Hewitson et al., 2014]]). These are classical geopolitical divisions of Africa, Asia, Australasia, Europe, North America, Central and South America, plus Small Islands, Polar Regions, and the Ocean. In AR6 WGI, five hybrid zones (Caribbean–Small Islands, East Europe–Asia, European Arctic, North American Arctic, and Northern Central America) are also identified, which are assessed in more than one Continental Region. Additional consistency with WGIII is pursued in [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] through the use of sub-continental domains which essentially form a subset of the Continental Set of Regions (Figure 1.18c and Section 6.1).

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