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

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

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

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

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