Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.1.3.3 Uncertainty and Confidence ====

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Uncertainty and confidence are treated in the same way in regional climate change information as in larger-scale (continental and global) climate problems ( [[IPCC:Wg1:Chapter:Chapter-1|Chapter 1]] and [[#10.3.4|Section 10.3.4]] ). The degree of confidence in climate simulations and in the resulting climate information typically depends on the identification of the role of the uncertainties ( [[#10.3.4|Section 10.3.4]] ). Since the direct verification of simulations of future climate changes is not possible, model performance and reliable (i.e., trustworthy) uncertainty estimates need to be assessed indirectly through process understanding and a systematic comparison with observations of past and current climate ( [[#10.3.3|Section 10.3.3]] ; [[#Knutti--2010|Knutti et al., 2010]] ; [[#Eyring--2019|Eyring et al., 2019]] ). The observational uncertainty, which is particularly large at regional scales, also has to be taken into account ( [[#10.2|Section 10.2]] ). These uncertainty estimates are then propagated in the distillation process to generate climate information.

Uncertainties in model-based future regional climate information arise from different sources and are introduced at various stages in the process ( [[#Lehner--2020|Lehner et al., 2020]] ): (i) forcing uncertainties associated with the future scenario or pathway that is assumed; (ii) internal variability; and (iii) uncertainties related to imperfections in climate models, also referred to as structural or model uncertainty. However, the relative role of each of these sources of uncertainty differs between the global and the regional scales as well as between variables and also between different regions ( [[#Lehner--2020|Lehner et al., 2020]] ). One way to address the internal variability and model uncertainties is to consider results from both multiple models and multiple realizations of the same model ( [[#Eyring--2016a|Eyring et al., 2016a]] ; [[#Lehner--2020|Lehner et al., 2020]] ; [[#Díaz--2021|Díaz et al., 2021]] ). These models are at times also combined with different weights that are a function of their performance and independence to increase the confidence of the multi-model ensemble ( [[#Abramowitz--2019|Abramowitz et al., 2019]] ; [[#Brunner--2019|Brunner et al., 2019]] ).

Other elements that play a role are the inconsistency between the global and regional models in dynamical downscaling or the observational and methodological uncertainty in bias-adjustment methods ( [[#Sørland--2018|Sørland et al., 2018]] ). These elements, in addition to those typical of the uncertainty in global and large-scale phenomena (Chapters 1–9), affect the overall confidence of regional climate information. This complex scene with different sources of uncertainty makes the collection of results available from multi-model, multi-member simulations most useful when synthesized through a distillation process ( [[#10.5.3|Section 10.5.3]] ).

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