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

== 1.3 Understanding and Evaluating Climate Risks ==

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Understanding of climate change has advanced in important ways that shape the AR6 assessment. This section describes advances in the understanding of the complex nature of climate change risks, the deep integration of social sciences, and increased utilisation of IK and LK. These multi-faceted dimensions of understanding climate change and evaluating risks are introduced here.

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=== 1.3.1 Nature of Climate Risk ===

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Since AR5, understanding of the nature of climate risk has advanced substantially. AR6 assesses the serious, complex and cascading climate risks unfolding across sectors and regions. These risks are shaped by many societal factors including cultural norms and social practice, socioeconomic development, underlying physical and social vulnerability, and societal responses themselves (Section 1.2.1.1). Throughout, there is increased attention to the important role of different forms of knowledge, especially IK and LK, in the understanding and the management of the changing climate.

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==== 1.3.1.1 The Nature of Climate Risk as Assessed in this Report ====

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Greater understanding of climate-related risks is emerging; however, there are important shortcomings in the information for some regions and sectors, and for developing versus developed countries. These risks assume significance in interaction with the cultures, values, ethics, identities, experiences, and knowledge systems of affected communities and societies, as well as their governance, finances, capabilities and resources. The key risk assessment in the IPCC AR5 informed the long-term temperature goal in the 2015 Paris Agreement—limiting the increase in global mean temperature to well below 2°C and pursuing efforts towards limiting warming to 1.5°C ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ; [[#Pachauri--2014)|Pachauri et al., 2014)]] . The IPCC Special Report on Global Warming of 1.5°C, responding to an invitation by UNFCCC, used new scientific information to provide a specific risk assessment associated with the ambitious warming levels targeted by the Paris Agreement ( [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ). The Special Reports on Oceans and Land further advanced the methods of transparent risk assessment ( [[#Zommers--2020|Zommers et al., 2020]] ). The current assessment expands significantly from the previous reports, aiming to inform and advance understanding of the following core themes: (a) the ways changes in vulnerability and exposure modulate risks of climate change impacts and risk complexity in addition to warming; (b) the knowledge basis relevant to continued refinement of temperature goals; (c) the effectiveness of adaptation solutions; (d) the management of risks at higher levels of warming, should ambitious climate change mitigation be unsuccessful, including limits to adaptation; and (e) the benefits of climate change mitigation and emissions reductions (Section 16.1).

This report evaluates key risks—potentially severe risks—meriting society’s full attention globally and regionally across sectors, in order to inform judgements about dangerous anthropogenic interference with the climate system ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ; [[#Mach--2016|Mach et al., 2016]] ; see also Sections 16.1.2; 16.4; WGI Section 1.2.4.1). As described detail in Chapter 16, evaluation of key risks is based on expert judgement applied to all relevant lines of evidence, with a focus on the role of societal values in determining the importance of a risk. Specific criteria considered relate to the magnitude of adverse consequences, including the potential for irreversibility, thresholds, or cascading effects; the likelihood of adverse consequences; the timing of the risk; and the ability to respond to the risk (Section 16.5.1).

The key risk assessment conveys increasing urgency given the growing visibility of climate change impacts in the current world (Sections 1.1; 16.1). Representative key risks emerging across sectors and regions include risks to coastal socio-ecological systems and terrestrial and ocean ecosystems; risks associated with critical infrastructure, networks and services; risks to living standards and human health; risks to food and water security; and risks to peace and migration (Section 16.5). Compared to the AR5, the emphasis on human dimensions of key climate-related risks has continued and increased, for instance, the potentially severe impacts for cultural heritage ( [[#IPCC--2014c|IPCC, 2014c]] ; Pachauri et al., 2014; see also Section 16.4). These human dimensions are essential for understanding vulnerability, impacts and risks central to ensuring human well-being, human security, sustainable development and poverty reduction in a changing climate.

To encompass the nature of climate risk, IPCC assessment since the Third Assessment Report has used five overarching domains, named ‘reasons for concern’, to assess increasing risk for societies and ecosystems under climate change ( [[#IPCC--2014b|IPCC, 2014b]] ; [[#O’Neill--2017|]] [[#O’Neill--2017|O’Neill et al., 2017]] ; see also Section 16.5; WGI Section 1.2.4.1). The reasons for concern approach has enabled evidence to be combined with expert judgement, in order to provide a holistic assessment across multiple lines of evidence ( [[#O’Neill--2017|]] [[#O’Neill--2017|O’Neill et al., 2017]] ). The approach also respects the uncertainties inherent to climate risk and highlights the ways in which values are relevant in connecting scientific knowledge to societal decision making and risk management. The different reasons for concern underscore that there is no single metric that can reflect all dimensions of climate-related risk and the diversity of consequences for lives and livelihoods, health and well-being, economic and sociocultural assets, infrastructure and ecosystems ( [[#Mach--2017|Mach and Field, 2017]] ; see also Section 1.4.1.2).

The AR6 Reasons for Concern framework enables integration across key risks and representative key risks, including how risks vary with the magnitude of global warming, socioeconomic development pathways and levels of adaptation (Section 16.6). Risk levels are determined through a formal elicitation approach for both representative key risks and reasons for concern, following the authors’ assessment of the literature. The reasons for concern consider ''unique and threatened systems'' (RFC1), such as coral reefs or Arctic Sea ice systems that have especially high vulnerability and low capacity to adapt. They also include the role of ''extreme weather events'' (RFC2), such as heat waves, heavy rain, drought, coastal flooding or wildfires. The reasons for concern address both the ''distributional'' and the ''aggregate impacts'' of climate change (RFC3, RFC4), including the unfairness factor for populations that have contributed little in terms of historic emissions but that are disproportionately vulnerable to the impacts of a changing climate. The final reason for concern relates to ''large-scale singular events'' , nonlinearities and tipping points (RFC5), including ice sheet collapse and ecosystem regime shifts.

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'''Figure 1.3 |''' '''Different interactions can decrease or increase climate-related risks.''' Key examples include interactions '''(a)''' among sectors, '''(b)''' through time, '''(c)''' across regions, or '''(d)''' between impacts and responses. The specific interactions indicated within each panel of this figure are illustrative, not comprehensive or indicative of relative importance. Source: (Simpson et. al. 2021)

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==== 1.3.1.2 The Complexities of Climate Risk ====

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The AR6 assessment incorporates the inherently complex nature of climate risk, vulnerability, exposure and impacts, which include feedbacks, cascades, non-linear behaviour and the potential for surprise (Figures 1.3; 1.4). Many different overlapping and complementary terms and methods are used to evaluate and understand complex climate risk relevant to this report, such as aggregated, compounding or cascading risks, all of which are considered here as relevant to complex climate risk ( [[#Pescaroli--2018|Pescaroli and Alexander, 2018]] ; Simpson et. al. 2021).

The dynamic nature of risk and its determinants is one important dimension of complexity. The risk of climate change impacts can be usefully understood as resulting from dynamic interactions among climate-related hazards, the exposure and vulnerability of affected human and ecological systems, and also responses (see Section 1.2.1; AR6 Glossary, [[#IPCC--2021b|IPCC, 2021b]] ; WGI AR6 Cross-Chapter Box 2 in Chapter 1, [[#Chen--2021|Chen et al., 2021]] ; [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ). The determinants of risk all can vary and change through space and time in response to socioeconomic development and decision making (Figures 1.4; 1.5; Section 16.1). Hazards are affected by current and future changes in climate, including altered climate variability and shifts in frequency and intensity of extreme events (WGI AR6 Chapter 12, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Such hazards can be sudden, for example, a heat wave or heavy rain event, or slower onset, for example, land loss, degradation and erosion linked to multiple climate hazards compounding. The severity of climate change impacts will depend strongly on vulnerability, which is also dynamic and includes the sensitivity and adaptive capacity of affected human and ecological systems (McDowell et al., 2016; [[#Jurgilevich--2017|Jurgilevich et al., 2017]] ; [[#Ford--2018|Ford et al., 2018]] ; [[#Viner--2020|Viner et al., 2020]] ). As a result, risks vary at fine scale across communities and societies and also among people within societies, depending, for example, on intersecting inequalities and context-specific factors such as culture, gender, religion, ability and disability, or ethnicity ( [[#Kuruppu--2009|Kuruppu, 2009]] ; [[#Jones--2011|Jones and Boyd, 2011]] ; [[#Carr--2014|Carr and Thompson, 2014]] ; also Section 16.1.4). The dynamic social distribution of impacts is the subject of increasing attention within climate assessment and responses, including the role of adaptation, iterative risk management and climate resilient sustainable development (Section 16.1).

Another core area of complexity in climate risk is the behaviour of complex systems, which includes multiple stressors unfolding together, cascading or compounding interactions, and non-linear responses and the potential for surprises ( [[#Kopp--2017|Kopp et al., 2017]] ; [[#Clarke--2018|Clarke et al., 2018]] ; [[#Yokohata--2019|Yokohata et al., 2019]] ). Risks and responses, including their determinants, can all interact dynamically in shaping the complexity of climate risk (Figure 1.4). The combined effects of multiple stressors or compound hazards and risks are unlikely to be assessed through simple addition of the independent effects and instead require system approaches to understanding risk. While some components may cancel each other out, others may nonlinearly increase risk. Nonlinearities can result from abrupt climate changes, tipping points or thresholds in responses, alternative stable states, low-probability/high-consequence outcomes or events that cannot be predicted based on current understanding (WGI Section 1.4.4.3).

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'''Figure 1.4 |''' '''Increasingly complex climate-related risks.''' Risk results from interactions among the determinants of risk—hazard, vulnerability, and exposure, shaped by responses—which can interact in complex ways. Different risks and responses can compound in single '''a''' ) or multiple '''b''' ) directions, cascade (e.g., with one event triggering another; '''c''' ), and aggregate (e.g., with independent determinants of risks co-occurring; '''d''' ). This complex nature of risk is central in the AR6 assessment. Figure adapted from Simpson et al. (2021).

The nature of climate risk also involves risks from responses themselves (Figure 1.5c). The risks of climate change responses include the possibility of responses not achieving their intended objectives or having trade-offs or adverse side effects for other societal objectives (Annex II: Glossary; Section 16.1). In particular, human responses may create novel hazards and unexpected side effects and entail opportunity costs and path dependencies ( [[#Boonstra--2016|Boonstra, 2016]] ). Such feedback loops can unfold at local and global scales, including large-scale interactions among climate, ecological and human systems with human behaviour and decision making affecting such interactions. Response risks can originate from uncertainty in implementation, maladaptation, action effectiveness, technology development or adoption, or transitions in systems (see Sections 1.4 and 1.5). Typical risks may be related to regulation, litigation, competition, sociopolitics or reputation. Interactions across responses can importantly involve co-benefits for other objectives, such as for human health and well-being which may be improved from both reduced air pollution (e.g., AR6 WGI Chapter 6, Szope et al., 2021; WGIII, IPCC, 2022) and enhanced adaptation to climate change. The nature of risk also entails residual impacts that will occur even with ambitious societal responses, given limits to adaptation at sectoral and regional levels (Section 1.4, 16.1, 16.4). In some cases, the losses will be irreversible.

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'''Figure 1.5 |''' '''Risk in IPCC assessment through time.''' '''(a)''' An explicit risk framing emerged in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) and WGII AR5 ( [[#IPCC--2014a|IPCC, 2014a]] , b). '''(b)''' In the current assessment, the role of responses in modulating the determinants of risk is a new emphasis (the ‘wings’ of the hazard, vulnerability, and exposure ‘propellers’ represents the ways in which responses modulate each of these risk determinants). '''(c)''' As the risk assessment spans Working Groups, the differential role of risk determinants for risk related to impacts, adaptation, and vulnerability versus risk related to mitigation becomes an increasingly important feature of climate risk assessment as well as management.

Due to these complexities, the challenge of assessing risks of climate change is not well bounded, will be framed differently by individuals and groups, involves large and deep uncertainties, and will have unclear solutions and pathways to solutions ( [[#Rittel--1973|Rittel and Webber, 1973]] ; [[#Renn--2008|Renn, 2008]] ; see also Sections 1.5.2; 17.2.1). Challenges also include the degree to which time is running out, there is no central authority, those seeking the solutions are also causing the problem, and the present is favoured over the future ( [[#Sun--2016|Sun and Yang, 2016]] ; see also Section 17.2.1). Both the needs for and the limits to adaptation responses fundamentally depend on progress achieved in reducing GHG emissions and limiting the magnitude of climate change that occurs, interlinked with socioeconomic development trajectories and the many social and political factors shaping climate risks and responses.

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'''Cross-Working Group Box ATTRIBUTION | Attribution in the IPCC Sixth Assessment Report'''
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Authors: Pandora Hope (Australia), Wolfgang Cramer (France/Germany), Maarten van Aalst (Netherlands), Greg Flato (Canada), Katja Frieler (Germany), Nathan Gillett (Canada/UK), Christian Huggel (Switzerland), Jan Minx (Germany), Friederike Otto (UK /Germany), Camille Parmesan (France/ UK /USA), Joeri Rogelj (UK /Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Aimee Slangen (the Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), 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. Attr ibution 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 are, for example: ‘To what degree is an observed change in global temperature induced by anthropogenic GHG and aerosol concentration changes or influenced by natural variability?’ or ‘What is the contribution of climate change to observed changes in crop yields that 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 heat wave?’

This Cross-Working Group Box briefly describes why attribution studies are important. It also describes some new developments in the methods used 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 (WGI Chapters 3, 10 and 11); or between observed changes in climate and changing species distributions and food production (e.g., [[#Verschuur--2021|Verschuur et al., 2021]] ; WGII [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] and others, summarised in Chapter 16) or between climate change mitigation policies and atmospheric GHG concentrations (WGI Chapter 5; WGIII Chapter 14). As such, they support numerous statements made by the IPCC ( [[#IPCC--2013|IPCC, 2013]] ; [[#IPCC--2014c|IPCC, 2014c]] ; WGI Section 1.3, Appendix 1A).

Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate protection policies (e.g., [[#Nauels--2019|Nauels et al., 2019]] ; [[#Banerjee--2020|Banerjee et al., 2020]] ; WGI Section 4.6.3), inform and constrain projections ( [[#Gillett--2021|Gillett et al., 2021]] ; Ribes et al., 2021; WGI Section 4.2.3) or inform loss and damages estimates and potential climate litigation cases by estimating the costs of climate change ( [[#Huggel--2015|Huggel et al., 2015]] ; 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., Climate &amp; Development Knowledge Network, 2017).

Steps towards an attribution assessment

The unambiguous framing of what is being attributed to what is a crucial first step for an assessment ( [[#Easterling--2016|Easterling et al., 2016]] ; [[#Hansen--2016|Hansen et al., 2016]] ; 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 (see Figure ATTRIBUTION.1 in Chapter 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 ( [[#Poloczanska--2013|Poloczanska et al., 2013]] ; [[#Ray--2015|Ray et al., 2015]] ; Cohen et al., 2018; WGI Section 1.5). The quality of the observational record of drivers should also be considered (e.g., volcanic eruptions: WGI 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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'''Figure Cross-Working Group Box ATTRIBUTION.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 ( [[#Hegerl--2010|Hegerl et al., 2010]] ; [[#Vautard--2019|Vautard et al., 2019]] ; [[#Otto--2020a|Otto et al., 2020a]] ; [[#Philip--2020|Philip et al., 2020]] ; WGI Section 1.5). Finally, appropriate communication of the attribution assessment and the accompanying confidence in the result is needed (e.g., [[#Lewis--2019|Lewis et al., 2019]] ).

Attribution methods

'''Attribution of changes in atmospheric GHG concentrations to anthropogenic activity'''
AR6 WGI [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] ( [[#Canadell--2021|Canadell et al., 2021]] ) presents multiple lines of evidence that unequivocally establish the dominant role of human activities in the growth of atmospheric CO 2 , including through analysing changes in atmospheric carbon isotope ratios and the atmospheric O 2 :N 2 ratio (WGI Section 5.2.1.1, [[#Canadell--2021|Canadell et al., 2021]] ). 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).

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 (e.g., [[#Hegerl--2010|Hegerl et al., 2010]] ; [[#bindoff--2014|Bindoff and et al., 2014]] ; WGI Section 1.3.4). 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 (e.g., [[#Naveau--2018|Naveau et al., 2018]] ; [[#Santer--2019|Santer et al., 2019]] ; WGI Section 3.2). 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; 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 ( [[#Lejeune--2018|Lejeune et al., 2018]] ; [[#Undorf--2018|Undorf et al., 2018]] ; [[#Boé--2020|Boé et al., 2020]] ; [[#Thiery--2020|Thiery et al., 2020]] ; see also WGI Sections 10.4.2; 11.1.6; 11.2.2). In general, regional climate variations are larger than the variations in 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).

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 ( [[#National%20Academies%20of%20Sciences--2016|National Academies of Sciences, 2016]] ; [[#Stott--2016|Stott et al., 2016]] ; [[#Jézéquel--2018|Jézéquel et al., 2018]] ; Wehner et al., 2019; [[#Wang--2020|Wang et al., 2020]] ; WGI Sections 10.4.1; 11.2.2). 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 ( [[#hauser--2016|Hauser et al., 2016]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ; [[#Grose--2020|Grose et al., 2020]] ; WGI Section 10.4.1; WGI Chapter 11). Events where attributable human influences have been found include hot and cold temperature extremes (including some with widespread impacts), heavy precipitation, and certain types of droughts and tropical cyclones (e.g., [[#Vogel--2019|Vogel et al., 2019]] ; [[#Herring--2021|Herring et al., 2021]] ; WGI Section 11.9). 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 et al., 2017]] ; examples in Table 16.1; Section 16.2).

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Cross-Working Group Box

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 the AR6 and is synthesised 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 socioeconomic 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 LK, process understanding and empirical or dynamical modelling (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 and 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--2019b|IPBES, 2019b]] ). 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 (Section 16.2; [[#Cramer--2014|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 palaeodata, 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 not always involve attribution to anthropogenic climate forcing. However, a growing number of studies include this aspect (e.g., [[#Diffenbaugh--2019|Diffenbaugh and Burke, 2019]] , for the attribution of economic inequality between countries; [[#Frame--2020|Frame et al., 2020]] , for the attribution of damages induced by Hurricane Harvey; or [[#Schaller--2016|Schaller et al., 2016]] , for flood damages).

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=== 1.3.2 Assessing, Evaluating, and Understanding Climate Impacts and Risks ===

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Multiple, diverse sources of information underlie our understanding of climate risks and response, including climate change science, diverse social sciences and IK and LK.

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==== 1.3.2.1 Detection and Attribution of Climate Change and its Impacts ====

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Anthropogenic climate change is unequivocal and ongoing. The detection of specific changes in the climate and their diverse impacts on people and nature is advancing, with robust attribution of climate change to GHG emissions as well as to other contributing factors (e.g., socioeconomic development, land use change). In the AR6, advances include an increasing ability to link individual extreme weather and climate events to emissions of GHGs, increasing identification of impacts for societies and economies and strong linkages in the attribution methods across Working Groups (Cross-Working Group Box: ATTRIBUTION in Chapter 1).

Impacts occurring today can be put into context through understanding of long-term changes on Earth, introduced in Cross-Chapter Box PALEO in Chapter 1. Climate has always varied and changed in the past, and this change often caused substantial ecological, evolutionary and socioeconomic impacts. Adaptation of ecosystems and societies occurred through responses as diverse as migration to mass extinction. Humankind is at the verge of leaving the Holocene climatic envelope, in which all human achievement since the advent of agriculture has occurred. In some systems, the changes and losses will be irreversible.

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==== 1.3.2.2 Perceiving Climate Risk and Human Response ====

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Since AR5, social science literature on how individuals and societies perceive and respond to climate risk has advanced dramatically ( [[#Renn--2008|Renn, 2008]] ; [[#Jones--2014|Jones et al., 2014]] ; [[#taylor--2014|Taylor et al., 2014]] ; [[#Neaves--2017|Neaves and Royer, 2017]] ; [[#Van%20Valkengoed--2019|Van Valkengoed and Steg, 2019]] ). The literature is increasingly integrating and advancing long-standing scholarship on environmental and social governance, human dimensions of environmental change, risk perception and communication, and enabling conditions for effective policymaking. These emergent literatures on climate risk, human action and solution are reflected in three broad areas of analysis: (a) root drivers (i.e., role of cultural norms and social practice, social structures and economic development status that shape physical and social vulnerability; (b) context-specific barriers and enablers (i.e., governance structures, institutional structure and function, risk perceptions, access to financing and knowledge availability and needs) and (c) the solution-proximate decision space (i.e., climate urgency and catalysing conditions, risk communication strategies, M&amp;E strategies) (see [[#Solecki--2017|Solecki et al., 2017]] ; [[#Jorgenson--2019|Jorgenson et al., 2019]] ).

These three areas are deeply embedded in the social sciences and reflect fundamental questions of how and why humans and their institutions act and respond (Chapter 17). In the past two decades, these basic issues have been applied to research of climate change, dynamic risk and adaptation. Underlying this analysis, particularly of root drivers, barriers and enablers, are assertions regarding the foundational properties of individual and collective behaviour (i.e., self-interest, optimisation, rationality, bounded rationality), how they are structured and how these properties can be revealed. This literature draws on several academic disciplines, including anthropology, economics, geography, political science, psychology, sociology and urban studies. Climate change social science research is often interdisciplinary or transdisciplinary and hence utilises a variety of methods to derive new knowledge ( [[#Orlove--2020|Orlove et al., 2020]] ).

In contrast to previous assessments, AR6 is increasingly focused on the needs for and challenges of assessing the societal response to climate change. The accurate tabulation of adaptation, a key question for examining the solution space, is difficult (Chapter 16; Cross-Chapter Box ADAPT in Chapter 1), since many forms of adaptation activity are under-represented in the peer-reviewed and grey literature. Moreover, the related question of assessing the effectiveness of adaptation, that is, the extent to which it reduces risk, is also difficult. Estimating risk reduction often involves counterfactuals, for instance, quantifying the damage a flood would have caused had a community not adapted prior to a storm or projecting the damage averted by today’s adaptation in some future storm (see Cross-Chapter Box PROGRESS in Chapter 17). Many socioeconomic drivers affect risk, so attribution for any observed or projected changes must be allocated among those that are due to adaptation and those due to economic development, cultural changes and other types of policies and trends. For instance, many measures of sustainable development overlap with those for adaptive capacity and both can reduce climate risk while also yielding benefits irrespective of future climate regimes ( [[#UNEP--2018|UNEP, 2018]] ). There are also many different goals for adaptation both among and within different jurisdictions, so that adaptation efforts deemed effective by some individuals may not be deemed effective by others ( [[#Dilling--2019|Dilling et al., 2019]] ).

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==== 1.3.2.3 Indigenous Knowledge and Local Knowledge ====

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While scientific knowledge is vital, IK and LK are also necessary for understanding and acting effectively on climate risk ( [[#IPCC--2014a|IPCC, 2014a]] ; [[#IPCC--2019b|IPCC, 2019b]] , SROCC Chapter 1; see also Section 2.4). '''Indigenous knowledge''' refers to the understandings, skills and philosophies developed by societies with long histories of interaction with their natural surroundings ( [[#IPCC--2019a|IPCC, 2019a]] ). '''Local knowledge''' is defined as the understandings and skills developed by individuals and populations, specific to the places where they live ( [[#IPCC--2019a|IPCC, 2019a]] ). These definitions relate to the debates on the world’s cultural diversity ( [[#UNESCO--2018a|UNESCO, 2018a]] ), which are increasingly connected to climate change debates ( [[#UNESCO--2018b|UNESCO, 2018b]] ). However, there is agreement that, in the same way that there is not a unique definition of Indigenous Peoples because it depends on self-determination (see below), there is not a single definition of neither IK and LK. Therefore, contextualisation is greatly needed. IK and LK will shape perceptions which are vital to managing climate risk in day-to-day activities and longer-term actions.

Such experience-based and practical knowledge is obtained over generations through observing and working directly within various environments. Knowledge may be place based and rooted in local cultures, especially when it reflects the beliefs of long-settled communities who have strong ties to their natural environments ( [[#Orlove--2010|Orlove et al., 2010]] ). Other times, knowledge may be embedded in institutions or oral traditions that mobilise them across contexts, for example, as migrant populations bring their knowledge across different regions, and have global relevance. Scientific insights often confirm IK and LK ( [[#Ignatowski--2013|Ignatowski and Rosales, 2013]] ), but IK and LK also provides specific, alternative ways to understand environmental change. This includes tacit and embodied aspects of knowledge ( [[#Mellegård--2020|Mellegård and Boonstra, 2020]] ) that may be crucial to foster local action and that are not easily captured in scientific knowledge (including cultural indicators, scales and interconnectedness between ecosystems). Multiple knowledge systems (i.e. IK, LK, disciplinary knowledge, technical expertise) may coevolve in iterative and interactive processes whereby they influence each other. However, at the same time, they may have specific characteristics so that they cannot be reduced to each other or subsumed by each other, and they all have relevance to understanding the interactions between society and climate ( [[#Bremer--2019|Bremer et al., 2019]] ).

Moreover, IK and LK may be particularly relevant to ensuring that climate action does not cause further harm, and also addresses historical injustices committed against Indigenous Peoples and other marginalised social groups, recognising them as active agents of their own change ( [[#Nursey-Bray--2019|Nursey-Bray et al., 2019]] ). There are between 370 and 500 million people in at least 90 countries belonging to about 5,000 different ethnic groups that are classified as ‘Indigenous’ ( [[#Sangha--2019|Sangha et al., 2019]] ). Although there is no single, universal definition of Indigenous Peoples, core criterion within both the ILO Convention on Indigenous and Tribal Peoples (1989) and the UN Declaration on the Rights of Indigenous Peoples ( [[#UN--2007|UN, 2007]] ) include: (a) self-determination and (b) the recognition that Indigenous Peoples as distinct social and cultural groups that retain collective ancestral ties to the lands they inhabited or to the lands from which they have been displaced. Indigenous Peoples attribute cultural and spiritual values to land, environmental features and landscapes ( [[#ILO--2013|ILO, 2013]] ; [[#ILO--2019|ILO, 2019]] ). Indigenous Peoples suffer disproportionally. For example, they are three times more likely to live in extreme poverty than non-Indigenous Peoples; they are also more likely to suffer discrimination and violence ( [[#UN--2020|UN, 2020]] ). At the same time, Indigenous Peoples have long led climate change and environmental protection agendas.

Indigenous Peoples have been faced with adaptation challenges for centuries and have developed coping strategies in changing environments ( [[#Coates--2004|Coates, 2004]] ). Along with other local groups, they hold relevant knowledge about the environment and environmental change, the impact of those changes on ecosystems and livelihoods, and possible effective adaptive responses (see Cross-Chapter Box INDIG in Chapter 18). Therefore, the participation of Indigenous Peoples in climate change decisions and the inclusion of Indigenous knowledge in the IPCC assessment process should be of high priority (following recommendations in [[#UNESCO--2018b|UNESCO, 2018b]] , and [[#UN--2020|UN, 2020]] ). Furthermore, the participation of scientifically trained climate specialists with indigenous backgrounds is valuable to the work of IPCC because the assessment must reflect a diverse range of views and expertise (for examples of IK, see Cross-Chapter Box INDIG in Chapter 18). Article 31 of the UN Declaration on the Rights of Indigenous Peoples (2007) supports the inclusion of IK and LK in the IPCC assessment process, calling for the use of IK and LK to be protected and validated by Indigenous Peoples themselves and their inclusion as active participants in the assessment ( [[#Klenk--2017|Klenk et al., 2017]] ). Paying special attention to the mechanism whereby some forms of knowledge have been excluded in previous reports—such as the use of technical knowledge or acronyms, or the deployment of discipline-specific validation mechanism—is a first step towards developing an inclusive assessment that reflects a wide range of voices.

The AR4 was the first IPCC report to explicitly discuss the value of IK and LK in adaptation and mitigation processes. AR5 recognised the importance of creating synergies across disciplines in the production of knowledge, acknowledging the importance of ‘non-scientific’ sources such as IK, which may not follow discipline conventions but nevertheless reflects the outcomes of learning across generations ( [[#Burkett--2014|Burkett et al., 2014]] ). This also explains the importance of including IK and LK and diverse stakeholder interests and values in local decision making processes ( [[#Jones--2014|Jones et al., 2014]] ). Such processes should be done in partnership with IK and LK knowledge holders and, when possible, be led by them ( [[#Inuit%20Tapiriit%20Kanatami--2018|Inuit Tapiriit Kanatami, 2018]] ). Recent IPCC reports have included distinct sections dedicated to IK and LK (e.g., [[#IPCC--2019b|IPCC, 2019b]] ). The IPCC Special Report on Climate Change and Land (SRCCL) includes a section on ‘Local and Indigenous knowledge for addressing land degradation’ (2019a) and the IPCC Special Report on Ocean and Cryosphere (SROCC) describes LK as ‘what non-Indigenous communities, both rural and urban, use on a daily and lifelong basis,’ a type of knowledge which is recognised as ‘multi-generational, embedded in community practices and cultures, and adaptive to changing conditions’ (2019b). The IPCC Special Report on Global Warming of 1.5°C emphasised the high vulnerability of Indigenous Peoples to climate change. It stated that disadvantaged and vulnerable populations, including Indigenous Peoples and certain local communities, are at disproportionately higher risk of suffering adverse consequences with global warming of 1.5°C and beyond ( [[#IPCC--2018b|IPCC, 2018b]] ). The report also assessed evidence in relation to the importance of including IK and LK in adaptation options, explaining their role in early warning systems and arguing that they are part of a range of approaches to catalyse wide-scale values and are consistent with adapting to and limiting global warming to 1.5°C ( [[#IPCC--2018b|IPCC, 2018b]] ).

Since AR5, several academic publications have directly addressed the challenges of including IK and LK in climate research ( [[#Ford--2016|Ford et al., 2016]] ; [[#Yeh--2016|Yeh, 2016]] ; [[#David-Chavez--2018|David-Chavez and Gavin, 2018]] ) and demonstrated its value in building resilience to extreme events related to climate change ( [[#Janif--2016|Janif et al., 2016]] ; [[#Olazabal--2021|Olazabal et al., 2021]] ). For instance, IK and LK has proved useful in land management methods that reduce wildfire risk ( [[#Nepstad--2006|Nepstad et al., 2006]] ; Cook et al., 2012; [[#Welch--2013|Welch et al., 2013]] ; Mistry et al., 2016). Since IK is traditionally communicated through storytelling and oral history, there are practical challenges to integrating it into an assessment that prioritises scientific knowledge. There is a need for increased critical engagement towards the co-production of knowledge ( [[#Ford--2016|Ford et al., 2016]] ). Scholars now recognise the ontological and epistemological differences in approaches, understandings and effects of climate change ( [[#Yeh--2016|Yeh, 2016]] ). One common strategy has been assessing Indigenous observations of climate change alongside scientific data ( [[#Klein--2014a|Klein et al., 2014a]] ) as a means to bridge the gap between scientific inquiry and Indigenous knowledge systems ( [[#Fernández-Llamazares--2017|Fernández-Llamazares et al., 2017]] ). The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the CBD have helped illustrate how to bridge multiple knowledge systems, particularly those conceived from different ontologies. Rather than viewing IK as a single source of knowledge to be compared with scientific data, recent scholarship suggests assessments, such as the IPCC, directly involve Indigenous researchers ( [[#Yumagulova--2019|Yumagulova et al., 2019]] ) to ensure ethical and equitable engagement with IK. Such partnership with and leadership of Indigenous Peoples on climate research is also consistent with the UN Declaration on the Rights of Indigenous Peoples (e.g., [[#Bawaka%20Country--2015|Bawaka Country et al., 2015]] ; [[#Inuit%20Tapiriit%20Kanatami--2018|Inuit Tapiriit Kanatami, 2018]] ; Cross-Chapter Box INDIG in Chapter 18).

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'''Cross-Chapter Box PALEO | Vulnerability and Adaptation to Past Climate Changes'''
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Authors: Wolfgang Kiessling (Germany, Chapter 3, Cross-Chapter Paper 1), Timothy A. Kohler (USA, Chapter 14), Wolfgang Cramer (France, Chapter 1, Cross-Chapter Paper 4), Gusti Anshari (Indonesia, Chapter 2), Jo Skeie Hermansen (Norway, CA Chapter 1), Darrell S. Kaufman (USA, WG 1, Chapter 2), Guy Midgley (South Africa, Chapter 16), Nussaïbah Raja (Mauritius, CA Chapter 3), Daniela N. Schmidt (UK/Germany, Chapter 13), Nils Chr. Stenseth (Norway, Chapter 1), Sukumar Raman (India, Chapter 1)

Understanding how Earth’s biota have responded to past climate dynamics is essential to understanding current and future climate-related risks, as well as the adaptive capacity and vulnerabilities of ecosystems and the human livelihoods depending on them. Here we assess climate impacts on long geological time scales (Cross-Chapter Box PALEO in Chapter 1, Figure PALEO.1), as well as for the last 70 kyr of ''Homo sapiens'' ’ existence (Cross-Chapter Box PALEO in Chapter 1, Figure PALEO.2). Climate responses of natural and human systems are intertwined through the physiological limits of wild animals, livestock, plants and humans, subject to a slow evolutionary dynamic ( [[#Poertner--2021|Pörtner, 2021]] ; Sections 2.6.1; 3.3).

Climate has always changed, often with severe effects on nature, including species loss

Observations provided by the historical, archaeological, and palaeontological records, together with paleoclimatic data, demonstrate that climatic variability has high potential to affect biodiversity and human society ( ''high confidence'' ). The evolution of the Earth’s biota has been punctuated by global biodiversity crises often triggered by rapid warming ( ''high confidence'' ) (Figure PALEO.1; [[#Bond--2017|Bond and Grasby, 2017]] ; [[#Benton--2018|Benton, 2018]] ; [[#Foster--2018|Foster et al., 2018]] ;). These so-called hyperthermal events were marked by rapid warming of &gt;1°C, which coincided with global disturbances of the carbon and water cycles, and by reduced oxygen and pH in seawater ( [[#Foster--2018|Foster et al., 2018]] ; [[#Clapham--2019|Clapham and Renne, 2019]] ). Magnitudes of global temperature shifts in hyperthermal events were sometimes greater than those predicted for the current century but extended over longer periods of time. Rates inferred from paleo records that are coarsely resolved are inevitably lower than those from direct observations during recent decades, and caution must be exercised when describing the rate of recent temperature changes as unprecedented (Kemp et al., 2015). Mass extinctions, each with greater than 70% marine species extinctions, occurred when the magnitude of temperature change exceeded 5.2°C ( [[#Song--2021|Song et al., 2021]] ), albeit species extinctions occurred at lower magnitudes of warming ( ''medium confidence'' ).

Adaptation options to rapid climate change are limited

Responses of biota to rapid climate change have included range shifts ( ''very high confidence'' ), phenotypic plasticity ( ''high confidence'' ), evolutionary adaptation ( ''medium confidence'' ), and species extinctions, including mass extinctions ( ''very high confidence'' ). While knowledge about the relative roles of these processes in promoting survival during times of climate change is still limited ( [[#Nogués-Bravo--2018|Nogués-Bravo et al., 2018]] ), they have influenced the evolutionary trajectories of species and entire ecosystems ( ''high confidence'' ), and also the course of human history ( ''medium confidence'' ). The combined ecological and evolutionary responses to ancient rapid warming events ranged from extinction of 81% of marine animal species and 70% of terrestrial tetrapod species on land at the end of the Permian period (~ 252 million years ago, Ma) ( [[#Smith--2005|Smith and Botha, 2005]] ; [[#Stanley--2016|Stanley, 2016]] ) to low rates of species extinctions but biome- and range shifts on land and in the ocean at the Palaeocene-Eocene Thermal Maximum (PETM, ~ 56 Ma) (Figure PALEO.1; [[#Ivany--2018|Ivany et al., 2018]] ; [[#Fraser--2020|Fraser and Lyons, 2020]] ; [[#Huurdeman--2021|Huurdeman et al., 2021]] ). Temperature and deoxygenation were key drivers of past biotic responses in the oceans ( [[#Gibbs--2016|Gibbs et al., 2016]] ; [[#Penn--2018|Penn et al., 2018]] ; Section 3.3) ( ''high confidence'' ), whereas on land the interplay between temperature and precipitation is less well established in ancient hyperthermals ( [[#Frank--2021|Frank et al., 2021]] ) ( ''medium confidence'' ). Climate-driven extinction risk increased by up to 40% when a short-term climate change added to a long-term trend in the same direction, for example when a long-term warming trend was followed by rapid warming ( [[#Mathes--2021|Mathes et al., 2021]] ).

Organismic traits associated with extinctions during ancient climate changes help identify present-day vulnerabilities and conservation priorities ( [[#Barnosky--2017|Barnosky et al., 2017]] ; [[#Calosi--2019|Calosi et al., 2019]] ; [[#Reddin--2020|Reddin et al., 2020]] ; Chapters 2; 3; Cross-Chapter Paper 1). Marine invertebrates and fishes are at greater extinction risk in response to warming than terrestrial ones because of reduced availability of thermal refugia in the sea ( [[#Pinsky--2019|Pinsky et al., 2019]] ) ( ''high confidence'' ). Terrestrial plants showed reduced extinction during past rapid warming compared to animals ( ''high confidence'' ), although they readily adjusted their ranges and reorganised vegetation types ( [[#Yu--2015|Yu et al., 2015]] ; [[#Lindström--2016|Lindström, 2016]] ; [[#Heimhofer--2018|Heimhofer et al., 2018]] ; [[#Slater--2019|Slater et al., 2019]] ; [[#Huurdeman--2021|Huurdeman et al., 2021]] ).

Population range shifts including migrations are common adaptations to climate changes across multiple time scales and ecological systems in the past and in response to current warming ( ''high confidence'' ). Poleward expansions and retractions ( [[#Redding--2018|Reddin et al., 2018]] ; [[#Williams--2018|Williams et al., 2018]] ; [[#Fordham--2020|Fordham et al., 2020]] ) as well as migration upslope and downslope in response to warming and cooling were common adaptations ( [[#Ortega-Rosas--2008|Ortega-Rosas et al., 2008]] ; [[#Iglesias--2018|Iglesias et al., 2018]] ;). During warming periods, diversity loss was common near the equator ( ''medium confidence'' ) ( [[#Kiessling--2012|Kiessling et al., 2012]] ; [[#Kröger--2017|Kröger, 2017]] ; [[#Yasuhara--2020|Yasuhara et al., 2020]] ), while diversity gains and forest expansion occurred in high latitudes ( [[#Brovkin--2021|Brovkin et al., 2021]] ). Comparison of contemporary shells and skeletons with historical collections in museums ( [[#Barnes--2011|Barnes et al., 2011]] ) and the analysis of skeletons of long-lived organisms ( [[#Cantin--2010|Cantin et al., 2010]] ) indicate significant climate-induced change in organismic growth rates today ( ''high agreement, medium confidence'' ).

Humankind has responded to regional climate variability within a narrow Holocene climatic envelope

Early human evolution (beginning ~2.1 Ma) occurred in a highly variable climate characterised by glacial-interglacial cycles. This variability may have favoured key hominin adaptations such as bipedality, increased brain size, complex sociality, and more diverse tools ( [[#Potts--1998|Potts, 1998]] ; [[#Potts--2020|Potts et al., 2020]] ) ( ''medium confidence'' ), but extinctions of five species of ''Homo'' have also been attributed partly to climate change ( [[#Raia--2020|Raia et al., 2020]] ) ( ''low confidence'' ). The ‘out-of-Africa’ dispersal of anatomically modern humans may have been driven by climate variability ( [[#Timmermann--2016|Timmermann and Friedrich, 2016]] ; Tierney et al., 2017) ( ''medium confidence, low agreement'' ). Most late Pleistocene megafaunal extinctions are attributed to direct and indirect human impacts ( [[#Sandom--2014|Sandom et al., 2014]] ), although some were likely accelerated by climate change ( [[#Wan--2017|Wan and Zhang, 2017]] ; Westaway et al., 2017; [[#Carotenuto--2018|Carotenuto et al., 2018]] ; [[#Saltré--2019|Saltré et al., 2019]] ) ( ''low confidence'' ).

The emergence of agriculture (~10.2 ka) in southwest Asia was associated with stable (within ±1°C global mean annual on multi-century time scale; WGI Chapter 2) warm and moist conditions (Richerson et al., 2001; [[#Rohling--2019|Rohling et al., 2019]] ; [[#Palmisano--2021|Palmisano et al., 2021]] ). Variability in resource availability and agricultural production, entrained by climatic variability, is implicated in the disruption and decline of numerous past human societies ( ''medium confidence'' ) ( [[#d’Alpoim%20Guedes--2018|d’Alpoim Guedes and Bocinsky, 2018]] ; [[#Cookson--2019|Cookson et al., 2019]] ; [[#Jones--2019|Jones, 2019]] ; [[#Park--2019|Park et al., 2019]] ). These crises are partially caused by regional climate anomalies including Holocene ‘Rapid Climate Change Events’ ( [[#Rohling--2019|Rohling et al., 2019]] ) not visible in the globally averaged conditions shown in Figure PALAEO.2. Such anomalies affected human population size ( [[#Clark--2019|Clark et al., 2019]] ; [[#Kuil--2019|Kuil et al., 2019]] ; [[#Riris--2019|Riris and Arroyo-Kalin, 2019]] ), health ( [[#Campbell--2020|Campbell and Ludlow, 2020]] ) and social stability/conflict ( [[#Büntgen--2011|Büntgen et al., 2011]] ; [[#Kohler--2014|Kohler et al., 2014]] ), and triggered migrations ( [[#D’Andrea--2011|D’Andrea et al., 2011]] ; [[#Schwindt--2016|Schwindt et al., 2016]] ; [[#Chiotis--2018|Chiotis, 2018]] ; [[#Prei--2018|Pei et al., 2018]] ) or retarded them ( [[#Betti--2020|Betti et al., 2020]] ; FAQ 14.2). Populations have also been impacted by sea level change in coastal areas ( [[#Turney--2007|Turney and Brown, 2007]] ; Cross-Chapter Box SLR in Chapter 3).

Evidence for widespread droughts ~4.2 ka, lasting for several centuries in some regions, has been tentatively linked to declines of the Akkadian Empire ( [[#Weiss--2017|Weiss, 2017]] ; [[#Carolin--2019|Carolin et al., 2019]] ), the Indus Valley ( [[#Giosan--2018|Giosan et al., 2018]] ; [[#Sengupta--2020|Sengupta et al., 2020]] ), and the Egyptian Old Kingdom and Yangtze River Valley ( [[#Ran--2019|Ran and Chen, 2019]] ). Deteriorating climates often exacerbate accumulating weaknesses in social systems to which population growth and urban expansion contribute ( [[#Knapp--2016|Knapp and Manning, 2016]] ; [[#Lawrence--2021|Lawrence et al., 2021]] ; [[#Scheffer--2021|Scheffer et al., 2021]] ). The rather narrow climatic niche favoured by human societies over the last 6000 years is poised to move on the Earth’s surface at speeds unprecedented in this time span ( [[#IPCC--2021a|IPCC, 2021a]] ), with consequences for human well-being and migration that could be profound under high-emission scenarios ( [[#Xu--2020|Xu et al., 2020]] ). This will overturn the long-lasting stability of interactions between humans and domesticated plants and animals as well as challenge the habitability for humans in several world regions ( [[#Horton--2021|Horton et al., 2021]] ) ( ''medium confidence'' ).

Climate change destroys unique natural archives and important cultural heritage sites

Climate change not only impacts past ecosystems and societies but also the remains they have left. The progressive loss of archaeological and historical sites and natural archives of paleo environmental data (WGI Chapter 2) constitutes often-overlooked impacts of climate change (Cross-Chapter Box SLR in Chapter 3; [[#Anderson--2017|Anderson et al., 2017]] ; [[#Hollesen--2018|Hollesen et al., 2018]] ; [[#Climate%20Change%20Cultural%20Heritage%20Working%20Group%20International--2019|Climate Change Cultural Heritage Working Group International, 2019]] ). These archives include peat bogs and coastal archives lost to sea level rise, droughts and fires, degradation through permafrost thaw, and dissolution. The ancient cultural diversity documented by such sites is an important resource for future adaptation ( [[#Rockman--2020|Rockman and Hritz, 2020]] ; [[#Burke--2021|Burke et al., 2021]] ). Since many of these sites constitute anchors for IK, their loss is not just data lost to science, it also interrupts intergenerational transmission of knowledge (Green et al., 2009).

[[File:73db2a1a7f8e7e3435caeaa91d226015 IPCC_AR6_WGII_Figure_1_Cross-Chapter_Box_PALEO_1.png]]

'''Figure Cross-Chapter Box PALEO.1 |''' '''Biological responses to six well-known ancient rapid warming events (hyperthermals) over the last 300 million years.''' Temperature anomalies (mean temperature difference to pre-industrial 1850–1900, solid orange curve) derived from climate modelling (300–66 Ma) ( [[#Haywood--2019|Haywood et al., 2019]] ) and deep-sea proxy data (66–0.1 Ma) ( [[#Hansen--2013|Hansen et al., 2013]] ). Temperature peaks underneath the grey bars indicate well-known hyperthermals with temperature anomalies derived from temperature-sensitive proxy data ( [[#Foster--2018|Foster et al., 2018]] ). Error bars indicate uncertainties in peak warming events (ranges in the literature). Insets show observed impacts to the biosphere. Q, Quaternary.

[[File:fab8f3f937abe3f8b83bd4a753405ce1 IPCC_AR6_WGII_Figure_1_Cross-Chapter_Box_PALEO_2.png]]

'''Figure Cross-Chapter Box PALEO.2 |''' '''Humankind is embarking on a trajectory beyond the global temperatures experienced since at least the advent of agriculture.''' Global surface temperature change for the last 70,000 years (relative to 1850–1900; data from WGI Chapter 2) alongside projections (with 5–95% range; WGI Chapter 4) and major events in human societies. Global climatic parameters do not always capture regional variability of importance to specific societies. The ‘Orbis Spike’ represents a pronounced dip in atmospheric CO 2 from the Law Dome ice core (Antarctica) ( [[#MacFarling%20Meure--2006|MacFarling Meure et al., 2006]] ) marking the globalisation in biota and trade of the Columbian Exchange and population declines and afforestation in the Americas. This, and the 1964 14 C peak, have been suggested as possible markers for the onset of the Anthropocene (Lewis and Maslin, 2015). Population trends from United Nations (2019).

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=== 1.3.3 Regional Assessment ===

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As climate change is a multi-scale phenomenon, from the local to the global, the assessment of climate risks and climate change impacts is strongly spatial, with a focus on regional climate change. The term ‘regions’ is used in different ways throughout the AR6 assessment as the use of the term varies across disciplines and context.

First, there are chapters dedicated to regional assessment in AR6 WGII (Chapters 9–14 and Cross-Chapter Papers 4 and 6). Within the content of these and other chapters of AR6, the term region is often used to describe continental and sub-continental regions, oceanic regions, hemispheres, or more specific localities within these geographic areas. Building on the continental domains defined in AR5 WGII and to ensure consistency with WGI [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-12 Chapter 12] ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ) and the WGI ''Atlas'' ( [[#Gutiérrez--2021|Gutiérrez et al., 2021]] ), AR6 WGII uses a continental set of regions, namely Africa, Asia, Australasia, Europe, North America, Central and South America, Small Islands, Polar Regions and the Ocean.

Second, the term regions is used to categorise areas around the globe with common topographical characteristics or biological characteristics. For example, [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] introduces regions in its discussion of biomes, as in arid, grassland, savanna, tundra, tropical, temperate and boreal forested regions. [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] adds reference to an area’s orientation with bodies of water, using terms such as deltaic, coastal, intercoastal, freshwater and salty. In addition, Cross-Chapter Paper 2 uses a coastal region typology based on physical geomorphology considering elevation, coastal type and topography (see Cross-Chapter Paper 2, p. 5; [[#Barragán--2015|Barragán and de Andrés, 2015]] ; [[#Kay--2017|Kay and and Adler, 2017]] ; [[#Haasnoot--2019a|Haasnoot et al., 2019a]] ).

Third, CCPs are dedicated to ''typological regions'' , defined in the Annex II: Glossary as regions that share one or more specific features (known as ‘typologies’), such as geographic location (e.g., ''coastal'' ), physical processes (e.g., ''monsoons'' ), biological (e.g., coral reefs, tropical forests, deserts), geological (e.g., mountains) or ''anthropogenic'' (e.g., megacities), and for which it is useful to consider the common climate features. Typological regions are generally discontinuous (such as monsoon areas, mountains, deserts and megacities) and are specifically used to integrate across similar climatological, geological and human domains.

Understanding climate risks across regions also requires consideration of the capabilities of developing countries and scientists across country contexts in conducting climate assessments. Substantial unevenness of available climate observations, risks assessments and scientific literature across regions and country capacities substantially challenges a globally comprehensive assessment ( [[#Connelly--2018|Connelly et al., 2018]] ).

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=== 1.3.4 Evaluating and Characterising the Degree of Certainty in Assessment Findings ===

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Since 1990, IPCC assessments have included designated terms and other approaches for communicating the expert judgements made by authors ( [[#Mastrandrea--2011|Mastrandrea and Mach, 2011]] ). The goal of such methods has been consistent treatment of uncertainties in assessing and communicating the current state of knowledge. Because terms such as ‘probable’ or ‘likely ’ hold very different meanings to different people, a standardised approach is essential for enabling consistent interpretation (WGI Section 1.2.3.1). Since its 2001 assessment, IPCC authors have applied common guidance on expert judgement across the Working Groups ( [[#Moss--2000|Moss and Schneider, 2000]] ; [[#IPCC--2005|IPCC, 2005]] ). The AR5, iteratively building from past IPCC guidance, was the first report to apply a single framework consistently across the Working Groups and their diverse topics and associated disciplines (Figure 1.3; [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ; [[#Mastrandrea--2011|Mastrandrea and Mach, 2011]] ). The outcome was increased comparability of assessment conclusions across the full spectrum of the physical science basis of climate change and resulting impacts, risks and responses ( [[#Mach--2017|Mach et al., 2017]] ).

This framework for expert judgement is again being applied in the AR6 and associated special reports in the assessment cycle ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ; see also WGI Box 1.1). Under the framework, the assessment of scientific understanding and uncertainties begins with evaluation of '''evidence''' and '''agreement''' —especially the type, amount, quality and consistency of evidence and the degree of agreement (steps 1–3 in Figure 1.6). Evidence assessed can reflect observations, experimental results, process-based understanding, statistical analyses or model outputs. Evidence is most robust when it consists of multiple lines of consistent, independent and high-quality evidence. The degree of agreement considers the extent of established, competing or speculative explanations for a given topic or phenomenon across the scientific community. Together, this evaluation of evidence and agreement forms a traceable account for each key finding in the assessment. Subsequently, the framework proceeds to evaluation of levels of '''confidence''' , which integrate evidence and agreement (steps 3–5 in Figure 1.6). Confidence reflects qualitative judgements of the validity of findings. It thereby facilitates, more readily, comparisons across assessment conclusions. Increasing evidence and agreement corresponds to increasing confidence (step 4 in Figure 1.6).

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[[File:18cfd8260d3f25dea99d12b5a40cbba6 IPCC_AR6_WGII_Figure_1_006.png]]

'''Figure 1.6 |''' '''The IPCC AR5 and AR6 framework for applying expert judgement in the evaluation and characterisation of assessment findings.''' This illustration depicts the process assessment authors apply in evaluating and communicating the current state of knowledge. Guidance for the application of this framework is described in full detail in [[#Mastrandrea--2010|Mastrandrea et al. (2010)]] . In addition to scientific knowledge, IK and LK is central to understanding and acting effectively on climate risk (Section 1.3.2.3). The diagram in this figure is reproduced from [[#Mach--2017|Mach et al. (2017)]] .

If uncertainties can be quantified, the framework involves a further option of characterising assessment findings with '''likelihood''' terms or more precise presentations of probability (steps 5–6 in Figure 1.6). The relevant probabilities can pertain to single events or broader outcomes. Probabilistic judgements can be based on statistical or modelling analyses, elicitation of expert views or other quantitative analyses. Where appropriate, authors can present probability more precisely with complete probability distributions or percentile ranges, also considering tails of distributions important for risk management. Usually, likelihood assignments are underpinned by high or very high confidence in the findings.

Confidence is often most applicable in characterising key findings in WGII assessment ( [[#Mach--2017|Mach et al., 2017]] ). This tendency results from the diverse lines of evidence across disciplines relevant to climate change impacts, adaptation and vulnerability. By contrast, likelihood is more common in WGI assessment.

The guidance to authors additionally identifies other practices and approaches relevant in applying expert judgement and developing assessment findings ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ; [[#Mastrandrea--2011|Mastrandrea and Mach, 2011]] ; [[#Mach--2017|Mach and Field, 2017]] ). First, authors are encouraged to carefully consider appropriate generalisation within assessment findings, emphasising insights that are integrative, nuanced and rigorous ( [[#IAC--2010|IAC, 2010]] ; [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ; [[#NEAA--2010|NEAA, 2010]] ). Second, authors are instructed to attend to potential biases, including in group dynamics, such as tendencies towards overconfidence and anchoring or Type I (false positive) error aversion ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ; [[#Brysse--2013|Brysse et al., 2013]] ; [[#Anderegg--2014|Anderegg et al., 2014]] ; [[#Morgan--2014|Morgan, 2014]] ). Third, particular attention is drawn to the importance of evaluating and communicating ranges of potential outcomes to inform decision making and risk management ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ). In some cases, deep uncertainties related to parameters or processes that are unknown or disagreed upon strongly benefit from dedicated methods of assessment and decision support (see Cross-Chapter Box DEEP in Chapter 17). Fourth, the guidance explores the different ways that framings of conclusions can shape their interpretation by readers. Finally, the guidance underscores the importance of reflecting upon all sources of uncertainty, which can include deep, difficult-to-quantify and easy-to-underestimate uncertainties arising from incomplete understanding of relevant processes or competing conceptualisations across the literature ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ). A detailed review of literature assessing IPCC uncertainty characterisation methods is provided in WGI 1.2.3.1.

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