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

==== 1.4.3.2 Uncertainty Quantification ====

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Not all of these listed sources of uncertainty are of the same type. For example, internal climate variations are an intrinsic uncertainty that can be estimated probabilistically, and could be more precisely quantified, but cannot usually be reduced. However, advances in decadal prediction offer the prospect of narrowing uncertainties in the trajectory of the climate for a few years ahead ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.3|Section 4.2.3]] ; e.g., [[#Meehl--2014|Meehl et al., 2014]] ; [[#Yeager--2017|Yeager and Robson, 2017]] ).

Other sources of uncertainty, such as model response uncertainty, can in principle be reduced, but are not amenable to a frequency-based interpretation of probability, and Bayesian methods to quantify the uncertainty have been considered instead (e.g., [[#Tebaldi--2004|Tebaldi, 2004]] ; [[#Rougier--2007|Rougier, 2007]] ; [[#Sexton--2012|Sexton et al., 2012]] ). The scenario uncertainty component is distinct from other uncertainties, given that future anthropogenic emissions can be considered as the outcome of a set of societal choices ( [[#1.6.1|Section 1.6.1]] ).

For climate model projections it is possible to approximately quantify the relative amplitude of various sources of uncertainty (e.g., [[#Hawkins--2009|Hawkins and Sutton, 2009]] ; [[#Lehner--2020|Lehner et al., 2020]] ). A range of different climate models are used to estimate the model response uncertainty to a particular emissions pathway, and multiple pathways are used to estimate the scenario uncertainty. The unforced component of internal variability can be estimated from individual ensemble members of the same climate model ( [[#1.5.4.8|Section 1.5.4.8]] ; e.g., [[#Deser--2012|Deser et al., 2012]] ; [[#Maher--2019|Maher et al., 2019]] ).

Figure 1.15 illustrates the relative size of these different uncertainty components using a ‘cascade of uncertainty’ ( [[#Wilby--2010|Wilby and Dessai, 2010]] ), with examples shown for global mean temperature, Northern South American annual temperatures and East Asian summer precipitation changes. For global mean temperature, the role of internal variability is small, and the total uncertainty is dominated by emissions scenario and model response uncertainties. Note that there is considerable overlap between individual simulations for different emissions scenarios, even for the mid-term (2041–2060). For example, the slowest-warming simulation for SSP5-8.5 produces less mid-term warming than the fastest-warming simulation for SSP1-1.9. For the long term, emissions scenario uncertainty becomes dominant.

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'''Figure 1.15 |''' '''The ‘cascade of uncertainties’ in CMIP6 projections.''' Changes in: GSAT '''(left)''' ; Northern South America temperature '''(middle)''' ; and East Asia summer (June–July–August, JJA) precipitation '''(right)''' . These are shown for two time periods: 2041–2060 '''(top)''' and 2081–2100 '''(bottom)''' . The SSP–radiative forcing combination is indicated at the top of each cascade at the value of the multi-model mean for each scenario. This branches downwards to show the ensemble mean for each model, and further branches into the individual ensemble members, although often only a single member is available. These diagrams highlight the relative importance of different sources of uncertainty in climate projections, which varies for different time periods, regions and climate variables. See ( [[#1.4.5|Section 1.4.5]] for the definition of the regions used. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).
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Therelative uncertainty due to internal variability and model uncertainty increases for smaller spatial scales. In the regional example shown in Figure 1.15 for changes in temperature, the same scenario and model combination has produced two simulations which differ by 1°C in their projected 2081–2100 averages due solely to internal climate variability. For regional precipitation changes, emissions scenario uncertainty is often small relative to model response uncertainty. In the example shown in Figure 1.15, the SSPs overlap considerably, but SSP1-1.9 shows the largest precipitation change in the near term, even though global mean temperature warms the least; this is due to differences between regional aerosol emissions projected in this and other scenarios ( [[#Wilcox--2020|Wilcox et al., 2020]] ). These cascades of uncertainty would branch out further if applying the projections to derive estimates of changes in hazard (e.g., [[#Wilby--2010|Wilby and Dessai, 2010]] ; [[#Halsnæs--2018|Halsnæs and Kaspersen, 2018]] ; [[#Hattermann--2018|Hattermann et al., 2018]] ).

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