Editing IPCC:Wg1:Chapter:Chapter-1-comments (section)

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

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

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

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

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