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

==== 1.5.4.7 Emergent Constraints on Climate Feedbacks, Sensitivities and Projections ====

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An emergent constraint is the relationship between an uncertain aspect of future climate change and an observable feature of the Earth System, evident across an ensemble of models ( [[#Allen--2002|Allen and Ingram, 2002]] ; [[#Mystakidis--2016|Mystakidis et al., 2016]] ; [[#Wenzel--2016|Wenzel et al., 2016]] ; [[#Hall--2019|Hall et al., 2019]] ; [[#Winkler--2019|Winkler et al., 2019]] ). Complex Earth system models (ESMs) simulate variations on time scales from hours to centuries, telling us how aspects of the current climate relate to its sensitivity to anthropogenic forcing. Where an ensemble of different ESMs displays a relationship between a short-term observable variation and a longer-term sensitivity, an observation of the short-term variation in the real world can be converted, via the model-based relationship, into an ‘emergent constraint’ on the sensitivity. This is shown schematically in Figure 1.23 (see Glossary; [[#Eyring--2019|Eyring et al., 2019]] ).

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'''Figure 1.23 |''' '''The principle of emergent constraints''' . An ensemble of models (blue dots) defines a relationship between an observable mean, trend or variation in the climate (x-axis) and an uncertain projection, climate sensitivity or feedback (y-axis). An observation of the x-axis variable can then be combined with the model-derived relationship to provide a tighter estimate of the climate projection, sensitivity or feedback on the y-axis. Figure adapted from [[#Eyring--2019|Eyring et al. (2019)]] .
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Emergent constraints use the spread in model projections to estimate the sensitivities of the climate system to anthropogenic forcing, providing another type of ensemble-wide information that is not readily available from simulations with one ESM alone. As emergent constraints depend on identifying those observable aspects of the climate system that are most related to climate projections, they also help to focus model evaluation on the most relevant observations ( [[#Hall--2019|Hall et al., 2019]] ). However, there is a chance that indiscriminate data-mining of the multi-dimensional outputs from ESMs could lead to spurious correlations ( [[#Caldwell--2014|Caldwell et al., 2014]] ; [[#Wagman--2018|Wagman and Jackson, 2018]] ) and less-than-robust emergent constraints on future changes ( [[#Bracegirdle--2013|Bracegirdle and Stephenson, 2013]] ). To avoid this, emergent constraints need to be tested ‘out of sample’ on parts of the dataset that were not included in its construction ( [[#Caldwell--2018|Caldwell et al., 2018]] ) and should also always be based on sound physical understanding and mathematical theory ( [[#Hall--2019|Hall et al., 2019]] ). Their conclusions should also be reassessed when a new generation of MMEs becomes available, such as CMIP6. As an example, [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.5.4) discusses and assesses recent studies where equilibrium climate sensitivities (ECS) diagnosed in a multi-model ensemble are compared with the same models’ estimates of an observable quantity, such as post-1970s global warming or tropical sea surface temperatures of past climates like the Last Glacial Maximum or the Pliocene. Assessments of other emergent constraints appear throughout later chapters, such as [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.5|Section 4.2.5]] ), [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Section 5.4.6) and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.5.4).

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