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

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

<div id="h3-6-siblings" class="h3-siblings"></div>

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).

<div id="_idContainer027" class="Figure"></div>

[[File:34b0c54375c7f63a9407425e22fbe4a7 IPCC_AR6_WGII_Figure_1_004.png]]

'''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.

<div id="_idContainer030" class="Figure"></div>

[[File:d3fba60f732f00f2f746946545c45d19 IPCC_AR6_WGII_Figure_1_005a.png]]

[[File:4cb97f4053ba8299d401335d32e3103c IPCC_AR6_WGII_Figure_1_005b.png]]

'''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.

<div id="cross-working-group-box-attribution" class="h2-container box-container"></div>

'''Cross-Working Group Box ATTRIBUTION | Attribution in the IPCC Sixth Assessment Report'''
<div id="h2-20-siblings" class="h2-siblings"></div>

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.

[[File:2e37fcdef1dd00dba569131b9e07fde7 IPCC_AR6_WGII_Figure_1_Cross-Working_Group_Box_ATTRIBUTION_1.png]]

'''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).

<div id="_idContainer031" class="Box_Header-continued"></div>

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).

<div id="1.3.2" class="h2-container"></div>

<span id="assessing-evaluating-and-understanding-climate-impacts-and-risks"></span>