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

==== 10.3.4.2 Representing and Reducing Uncertainties ====

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Climate response uncertainties (Chapter 1) can be represented by multi-model ensembles, although the sampled uncertainty typically underestimates the full range of uncertainty ( [[#Collins--2013b|Collins et al., 2013b]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ; [[#Almazroui--2021|Almazroui et al., 2021]] ). Traditionally, climate response uncertainty has been characterized by the ensemble spread around the multi-model mean change. The change has then further been qualified in terms of the agreement across models and compared to estimates of internal climate variability ( [[#Collins--2013b|Collins et al., 2013b]] ). Since AR5, several limitations of this approach have been identified ( [[#Madsen--2017|Madsen et al., 2017]] ) such as the failure to address physically plausible, but low-likelihood, high-impact scenarios (Chapters 1, 4, 8 and 9; [[#Sutton--2018|Sutton, 2018]] ) or that qualitatively different or even opposite changes may be equally plausible at the regional scale ( [[#Shepherd--2014|Shepherd, 2014]] ). In a multi-model mean these different responses would be lumped together, strongly dampened, and qualified as non-robust, whereas in fact high impacts might occur. Further, the multi-model mean itself is often implausible because it is a statistical construct ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ). Overall, there is ''high confidence'' that some regional future climate changes are not well-characterized by multi-model mean and spread.

Since AR5, physical climate storyline approaches (see also Chapter 1, [[#10.5.3|Section 10.5.3]] , Box 10.2, and Atlas.2.5.2) have been developed to better characterize and communicate uncertainties in regional climate projections ( [[#Shepherd--2019|Shepherd, 2019]] ). A special class of such storylines attempts to attribute regional uncertainties to uncertainties in remote drivers. For instance, the Dutch Meteorological Service has presented climate projections for the Netherlands for different plausible changes of the mid-latitude atmospheric circulation and different levels of European warming ( [[#van%20den%20Hurk--2014|van den Hurk et al., 2014]] ). [[#Manzini--2014|Manzini et al. (2014)]] have quantified the impact of uncertainties in tropical upper troposphere warming, polar amplification, and stratospheric wind change on Northern Hemisphere winter climate change. Based on these results, [[#Zappa--2017|Zappa and Shepherd (2017)]] separated the multi-model ensemble into physically consistent sub-groups or storylines of qualitatively different projections in relevant remote drivers of the atmospheric circulation. In a similar vein, ( [[#Ose--2020|Ose et al., 2020]] ) trace uncertainties in projections of the East Asian summer monsoon and [[#Mindlin--2020|Mindlin et al. (2020)]] conditioned the response of Southern Hemisphere mid-latitude circulation and precipitation to greenhouse gas forcing on large-scale climate indicators ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.9.2|Section 8.4.2.9.2]] ).

These physical climate storylines help to physically explain contradicting regional projections and thus make the conveyed information a better representation of the true uncertainty ( [[#Hewitson--2014a|Hewitson et al., 2014a]] ). Additionally, the attribution of regional uncertainties to drivers may in principle help reduce uncertainty in the case where some storylines can be ruled out because the projected changes in the driving processes appear to be physically implausible ( [[#Zappa--2017|Zappa and Shepherd, 2017]] ). There is thus ''high confidence'' that storylines attributing uncertainties in regional projections to uncertainties in changes of remote drivers aid the interpretation of uncertainties in climate projections.

Another approach that has continued to develop for characterising and reducing projection uncertainties is the use of emergent constraints (Chapters 1, 4, 5 and 7; [[#Hall--2019|Hall et al., 2019]] ). The idea is to link the spread in climate model projections via regression to the spread in present climate model biases for relevant driving processes. Models with lower biases are assigned higher weight in the projections, which in turn reduces the spread of the projections in a physical way and may additionally reduce projection uncertainty. For instance, [[#Simpson--2016|Simpson et al. (2016)]] have reduced the spread in projections of North American winter hydroclimate by linking this spread to model biases in the representation of relevant stationary wave patterns. Other examples of using emergent constraints in a regional context are Brown et al. (2016), G. [[#Li--2017|]] [[#Li--2017|Li et al. (2017)]] , [[#Giannini--2019|Giannini and Kaplan (2019)]] , [[#Ose--2019|Ose (2019)]] and [[#Zhou--2019|Zhou et al. (2019)]] .

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