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

==== 10.3.3.9 Fitness of Climate Models for Projecting Regional Climate ====

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AR5 stated that confidence in climate model projections is based on the physical understanding of the climate system and its representation in climate models. A climate model’s credibility for future projections may be increased if the model is able to simulate past variations in climate (Sections 10.3.3.8, 10.4.1 and 10.6; [[#Flato--2014|Flato et al., 2014]] ). In particular, the credibility of downscaled information depends on the quality of both the downscaling method and of the global model providing the large-scale boundary conditions ( [[#Flato--2014|Flato et al., 2014]] ). Credibility is closely linked to the concept of adequacy or fitness-for-purpose ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.4.1|Section 1.5.4.1]] ; [[#Parker--2009|Parker, 2009]] ). From a regional perspective, one may ask about the fitness of a climate model for simulating future changes of specific aspects of a specific regional climate. The required level of model fitness may depend on the user context ( [[#10.5|Section 10.5]] ). A key challenge is to link performance at representing present and past climate (Sections 10.3.3.3–10.3.3.8) to the confidence in future projections ( [[IPCC:Wg1:Chapter:Chapter-1#1.3.5|Section 1.3.5]] ; [[#Baumberger--2017|Baumberger et al., 2017]] ) and it is addressed in this subsection.

A general idea of model fitness for a given application may be obtained by checking whether relevant large- ( [[#10.3.3.4|Section 10.3.3.4]] ) and regional-scale (Sections 10.3.3.5 and 10.3.3.6) processes are explicitly resolved (Figure 10.3). The basis for confidence in climate projection is a solid process understanding ( [[#Flato--2014|Flato et al., 2014]] ; [[#Baumberger--2017|Baumberger et al., 2017]] ). Thus, the key to assessing the fitness-for-purpose of a model is the evaluation of how relevant processes controlling regional climate are represented ( [[#Collins--2018|Collins et al., 2018]] ). A process-based evaluation may be more appropriate than an evaluation of the variables of interest (e.g., temperature, precipitation), because biases in the latter may in principle be reduced if the underlying processes are realistically simulated (Cross-Chapter Box 10.2), while individual variables may appear as well-represented because of compensating errors ( [[#Flato--2014|Flato et al., 2014]] ; [[#Baumberger--2017|Baumberger et al., 2017]] ). Combining a process-based evaluation with a mechanistic explanation of projected changes further increases confidence in projections ( [[#Bukovsky--2017|Bukovsky et al., 2017]] ). Fitness-for-purpose can also be assessed by comparing the simulated response of a model with simulations of higher resolution models that better represent relevant processes ( [[#Baumberger--2017|Baumberger et al., 2017]] ). For instance, [[#Giorgi--2016|Giorgi et al. (2016)]] have corroborated their findings on precipitation changes comparing standard RCM simulations with convection-permitting simulations.

The evaluation of model performance at historical variability and long-term changes provides further relevant information ( [[#Flato--2014|Flato et al., 2014]] ). Trend evaluation may provide very useful insight, but has limitations, in particular at the regional scale, mainly due to multi-decadal internal climate variability ( [[#10.3.3.8|Section 10.3.3.8]] ), observational uncertainty (in both driving reanalysis and local trends; [[#10.2|Section 10.2]] ), and the fact that often not all regional forcings are known, and that past trends may be driven by forcings other than those driving future trends (Sections 10.4.1 and 10.6.3).

Increasing resolution ( [[#Haarsma--2016|Haarsma et al., 2016]] ) or performing downscaling may be particularly important when it modifies the climate change signal of a lower resolution model in a physically plausible way ( [[#Hall--2014|Hall, 2014]] ). Improvements may result from a better representation of regional processes, upscale effects, as well as the possibility of a region-specific model tuning ( [[#Sørland--2018|Sørland et al., 2018]] ). For instance, [[#Gula--2012|Gula and Peltier (2012)]] showed that a higher resolution allows for a more realistic simulation of lake-induced precipitation, resulting in a more credible projection of changes in the snow belts of the North American Great Lakes. Similarly, [[#Giorgi--2016|Giorgi et al. (2016)]] demonstrated that an ensemble of RCMs better represents high-elevation surface heating and in turn increased convective instability. As a result, the summer convective precipitation response was opposite to that simulated by the driving global models (Figure 10.9). Similarly, [[#Walton--2015|Walton et al. (2015)]] showed that a kilometre-scale RCM enables a more realistic representation of the snow-albedo feedback in mountainous terrain compared to standard resolution global models, leading to a more plausible simulation of elevation-dependent warming. [[#Bukovsky--2017|Bukovsky et al. (2017)]] argue that strong seasonal changes in warm-season precipitation in the Southern Great Plains of the USA, projected by RCMs, are more credible than the weaker global model changes because precipitation is better simulated in the RCMs.

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[[File:922aad415db36de19ede6fb50098babc IPCC_AR6_WGI_Figure_10_9.png]]

'''Figure 10.''' '''9 |''' '''Projected changes in summer (June to August) precipitation (in percent with respect to the mean precipitation) over the Alps between the periods 2070–2099 and 1975–2004. (a)''' Mean of four global climate models (GCMs) regridded to a common 1.32° × 1.32° grid resolution; '''(b)''' mean of six regional climate models (RCMs) driven with these GCMs. The grey isolines show elevation at 200 m intervals of the underlying model data. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11). Figure adapted from [[#Giorgi--2016|Giorgi et al. (2016)]] .

Including additional components, feedbacks and drivers can substantially modify the simulated future climate. For example, Kjellström et al. (2005) and [[#Somot--2008|Somot et al. (2008)]] have shown that a regional ESM can significantly modify the SST response to climate change of its driving global model with implications for the climate change signal over both the sea and land. In particular, coupled ocean–atmosphere RCMs may increase the credibility of projections in regions of strong air-sea coupling such as the East Asia–western North Pacific domain ( [[#Zou--2016b|Zou and Zhou, 2016b]] , 2017). Recent studies demonstrate the importance of including regional patterns of evolving aerosols in RCMs for simulating regional climate change ( [[#Boé--2020a|Boé et al., 2020a]] ; [[#Gutiérrez--2020|Gutiérrez et al., 2020]] ). RCMs not including the plant physiological response to increasing CO <sub>2</sub> concentrations have been shown to substantially underestimate projected increases in extreme temperatures across Europe compared to global models that explicitly model this effect ( [[#Schwingshackl--2019|Schwingshackl et al., 2019]] ).

A difference between the climate changes simulated by two models does not automatically imply the more complex or higher resolution model is superior (e.g., [[#Dosio--2019|Dosio et al., 2019]] ). Studies comparing convection-permitting RCM simulations to simulations of climate models with parametrized convection find, depending on the considered models, regions and seasons, either similar or qualitatively different projected changes in short duration extreme precipitation ( [[#Chan--2014a|Chan et al., 2014a]] , b, 2020; [[#Ban--2015|Ban et al., 2015]] ; [[#Tabari--2016|Tabari et al., 2016]] ; [[#Fosser--2017|Fosser et al., 2017]] ; [[#Kendon--2017|Kendon et al., 2017]] , 2019; [[#Vanden%20Broucke--2018|Vanden Broucke et al., 2018]] ). Process studies provide evidence that convection-permitting simulations better represent crucial local and mesoscale features of convective storms and thus simulate more plausible changes ( [[#Meredith--2015a|Meredith et al., 2015a]] ; [[#Prein--2017|Prein et al., 2017]] ; [[#Fitzpatrick--2020|Fitzpatrick et al., 2020]] ), but further research is required to confirm and reconcile the different findings.

Studies assessing the fitness of statistical approaches for regional climate projections are still very limited in number. For statistical downscaling, a key issue is to include predictors that control long-term changes in regional climate. Models differing only in the choice of predictors may perform similarly in the present climate, but may project opposite precipitation changes ( [[#Fu--2018|Fu et al., 2018]] ; [[#Manzanas--2020|Manzanas et al., 2020]] ). In addition to trend-evaluation studies ( [[#10.3.3.8|Section 10.3.3.8]] ), perfect-model experiments ( [[#10.3.2.5|Section 10.3.2.5]] ) have been used to assess whether a given model structure with a chosen set of predictors is capable of reproducing the simulated future climates ( [[#Gutiérrez--2013|Gutiérrez et al., 2013]] ; [[#Räty--2014|Räty et al., 2014]] ; [[#Dayon--2015|Dayon et al., 2015]] ; [[#Dixon--2016|Dixon et al., 2016]] ; [[#San-Martín--2017|San-Martín et al., 2017]] ). Importantly, it is found that standard analogue methods inherently underestimate future warming trends because of missing analogues for a warmer climate ( [[#Gutiérrez--2013|Gutiérrez et al., 2013]] ).

Bias adjustment assumes that model biases are time invariant (or more precisely, independent of the climate state), such that the adjustment made to present climate simulations is still applicable to future climate simulations. Many findings challenge the validity of this assumption, as already assessed in AR5 ( [[#Flato--2014|Flato et al., 2014]] ). Further research has addressed this issue by means of perfect model experiments ( [[#10.3.2.5|Section 10.3.2.5]] ) and process understanding. Perfect-model studies with GCMs found that circulation, energy, and water-cycle biases are roughly state-independent ( [[#Krinner--2018|Krinner and Flanner, 2018]] ), whereas temperature biases depend linearly on temperature ( [[#Kerkhoff--2014|Kerkhoff et al., 2014]] ). Others show that regional temperature biases may depend on soil moisture and albedo, and may thus be state-dependent ( [[#Maraun--2012|Maraun, 2012]] ; [[#Bellprat--2013|Bellprat et al., 2013]] ; [[#Maraun--2017|Maraun et al., 2017]] ; see Cross-Chapter Box 10.2 for further limitations of bias adjustment). The fitness of weather generators for future projections depends on whether they account for all relevant changes in their parameters, either by predictors or change factors ( [[#Maraun--2018b|Maraun and Widmann, 2018b]] ).

In any case, the fitness of regional climate projections based on dynamical downscaling or statistical approaches depends on the fitness of the driving models in projecting boundary conditions, predictors and change factors ( [[#Hall--2014|Hall, 2014]] ; [[#Maraun--2018b|Maraun and Widmann, 2018b]] ).

Overall, there is ''high confidence'' that an assessment of model fitness for projections applying process-based evaluation, process-based plausibility checks of projections and a comparison of different model types, increases the confidence in climate projections. There is ''high confidence'' that increasing model resolution, dynamical downscaling, statistical downscaling with well-simulated predictors controlling regional climate change, and adding relevant model components can increase the fitness for projecting some aspects of regional climate when accompanied by a process-understanding analysis.

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