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

==== 10.3.3.10 Synthesis of Model Performance at Simulating Regional Climate and Climate Change ====

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Global models reproduce many of the features of observed climate and its variability at regional scales. However, global models can show a variety of biases in, for instance, precipitation and temperature at scales ranging from continental ( [[#Prasanna--2016|Prasanna, 2016]] ) to sub-continental scales ( [[#Lovino--2018|Lovino et al., 2018]] ), both in the mean and in higher order moments of the climatological distribution of the variable (Figure 10.6; [[#Ren--2019|Ren et al., 2019]] ; [[#Xin--2020|Xin et al., 2020]] ). Regional biases could occur even if all the relevant large-scale processes are correctly represented, but not their interaction with regional features such as orography or land–sea contrasts ( [[#10.3.3.4|Section 10.3.3.4]] ). These biases have been considered an important limiting factor in model usability, especially at the regional scale ( [[#Palmer--2016|Palmer, 2016]] ). In spite of this, global model simulations have been extensively used to create regional estimates of climate change (Chapters 11, 12 and Atlas), taking into account the result of a performance assessment (Chapter 11, Sections 10.3.3.3–10.3.3.8, and Atlas; [[#Jiang--2020|Jiang et al., 2020]] ). However, their application is limited in part by the effective resolution of these models ( [[#Klaver--2020|Klaver et al., 2020]] ).

Global model performance at the regional scale is assessed in terms of the time or spatial averages of key variables (see Atlas; [[#Brunner--2019|Brunner et al., 2019]] ), the ability to reproduce their seasonal cycle ( [[#Hasson--2013|Hasson et al., 2013]] ) or a set of extreme climate indicators (Chapter 11; [[#Luo--2020|Luo et al., 2020]] ) and the representation of regional processes and phenomena, feedbacks, drivers and forcing impacts (Sections 10.3.3.4–10.3.3.6). In many cases, the performance estimates have been used to select models for either an application or a more in-depth study ( [[#Lovino--2018|Lovino et al., 2018]] ), to select the models that provide boundary conditions to perform RCM simulations ( [[#McSweeney--2015|McSweeney et al., 2015]] ) or to weight the results of the global model simulations ( [[#Sanderson--2015|Sanderson et al., 2015]] ; [[#Brunner--2020|Brunner et al., 2020]] ). While some large-scale metrics are improved between the CMIP5 and CMIP6 experiments (Chapter 3; [[#Cannon--2020|Cannon, 2020]] ), there is not yet concluding evidence of a systematic improvement for surface variables at the regional scale.

The special class of high-resolution global models (Sections 1.5.3.1 and 10.3.3.1, Chapter 3; [[#Haarsma--2016|Haarsma et al., 2016]] ; [[#Prodhomme--2016|Prodhomme et al., 2016]] ) is expected to improve some of the regional processes that are not appropriately represented in standard global models ( [[#Roberts--2018|Roberts et al., 2018]] ). There is general consensus that increasing global model resolution improves some long-standing biases (Chapter 3, [[#10.3.3.3|Section 10.3.3.3]] , and Figures 10.6 and 10.7; [[#Demory--2014|Demory et al., 2014]] , 2020; [[#Schiemann--2014|Schiemann et al., 2014]] ; [[#Dawson--2015|Dawson and Palmer, 2015]] ; [[#van%20Haren--2015|van Haren et al., 2015]] ; [[#Feng--2017|Feng et al., 2017]] ; [[#Fabiano--2020|Fabiano et al., 2020]] ), although the resolution increase is not a guarantee of overall improvement ( [[IPCC:Wg1:Chapter:Chapter-8#8.5.1|Section 8.5.1]] ; [[#Fabiano--2020|Fabiano et al., 2020]] ; [[#Hertwig--2021|Hertwig et al., 2021]] ). For instance, increasing resolution in global models has been shown to improve Asian monsoon rainfall anchored to orography and the monsoon circulation ( [[#Johnson--2016|Johnson et al., 2016]] ), but fails to solve the major dry bias. It is also difficult to disentangle the role of resolution increase and model tuning on the performance of the GCM ( [[#Anand--2018|Anand et al., 2018]] ). Some efforts have been undertaken to complement the performance improvements of resolution by using stochastic parametrizations ( [[#Palmer--2019|Palmer, 2019]] ), which explicitly acknowledge the multi-scale nature of the climate system, in standard resolution global models with some success ( [[#Dawson--2015|Dawson and Palmer, 2015]] ; [[#MacLeod--2016|MacLeod et al., 2016]] ; [[#Zanna--2017|Zanna et al., 2017]] , 2019). The expectation is to achieve a similar performance to the increase in resolution at a reduced computational cost.

Despite their known errors that affect model performance, there is ''high confidence'' that global models provide useful information for the production of regional climate information. There is ''robust evidence'' and ''high agreement'' that the increase of global model resolution helps in reducing the biases limiting performance at the regional scale, although resolution per se does not automatically solve all performance limitations shown by global models. There is ''robust evidence'' that stochastic parametrizations can help to improve some aspects of the global model performance that are relevant to regional climate information.

Global models tend to have difficulties in simulating climate over regions where unresolved local scale processes, feedbacks and non-linear scale interactions result in a degradation of the model performance compared to models with higher resolution. In this case, RCMs and variable resolution global models can resolve part of these processes in the regions of interest at an acceptable computational cost ( [[#Rummukainen--2016|Rummukainen, 2016]] ; [[#Giorgi--2019|Giorgi, 2019]] ; [[#Gutowski%20Jr.--2020|Gutowski Jr. et al., 2020]] ).

The assessment of RCM performance needs to focus not only on mean climatology (Atlas), but also trends ( [[#10.3.3.8|Section 10.3.3.8]] ) and extremes (Chapter 11), and the RCM’s ability at correctly reproducing relevant processes, forcings and feedbacks including aerosols, plant responses to increasing CO <sub>2</sub> , and so on, ( [[#Schwingshackl--2019|Schwingshackl et al., 2019]] ; [[#Boé--2020a|Boé et al., 2020a]] ; Sections 11.2. and 10.3.3.3 to 10.3.3.8) to be fit for future projections ( [[#10.3.3.9|Section 10.3.3.9]] ).

When RCMs are driven by global models, part of the uncertainty in the RCM simulation is introduced by the global model biases ( [[#Kjellström--2018|Kjellström et al., 2018]] ; [[#Sørland--2018|Sørland et al., 2018]] ; [[#Christensen--2020|Christensen and Kjellström, 2020]] ). As RCMs are typically not able to mitigate global model biases in large-scale dynamical processes, if such biases are substantial, and if the corresponding large-scale processes are important drivers of regional climate, downscaling is questionable ( [[#10.3.3.3|Section 10.3.3.3]] ). However, when global models have weak circulation biases and regional climate change is controlled mainly by regional-scale processes and feedbacks, dynamical downscaling has the potential to add substantial value to global model simulations ( [[#10.3.3.4|Section 10.3.3.4]] and Atlas; [[#Hall--2014|Hall, 2014]] ; [[#Rummukainen--2016|Rummukainen, 2016]] ; [[#Giorgi--2019|Giorgi, 2019]] ; [[#Schwingshackl--2019|Schwingshackl et al., 2019]] ; [[#Boé--2020a|Boé et al., 2020a]] ; [[#Lloyd--2021|Lloyd et al., 2021]] ).

There is ''very high confidence'' ( ''robust evidence'' and ''high agreement'' ) that RCMs add value to global simulations in representing many regional weather and climate phenomena, especially over regions of complex orography or with heterogeneous surface characteristics and for local-scale phenomena. Realistically representing local-scale phenomena such as land–sea breezes requires simulations at a resolution of the order of 10 km ( ''high confidence'' ). Simulations at kilometre-scale resolution add value in particular to the representation of convection, sub-daily summer precipitation extremes ( ''high confidence'' ) and soil-moisture–precipitation feedbacks ( ''medium confidence'' ). Resolving regional processes may be required to correctly represent the sign of regional climate change ( ''medium confidence'' ). However, the performance of RCMs and their fitness for future projections depend on their representation of relevant processes, forcings and drivers in the specific context (Sections 10.3.3.4–10.3.3.8).

Statistical downscaling, bias adjustment and weather generators outperform uncorrected output of global and regional models for a range of statistical aspects at single locations due to their calibration ( [[#Casanueva--2016|Casanueva et al., 2016]] ), but RCMs are superior when spatial fields are relevant ( [[#Mehrotra--2014|Mehrotra et al., 2014]] ; [[#Vaittinada%20Ayar--2016|Vaittinada Ayar et al., 2016]] ; [[#Maraun--2019a|Maraun et al., 2019a]] ). Similarly, there is some evidence that bias adjustment is comparable in performance when applied to global models and dynamically downscaled global models only for single locations, but dynamical downscaling prior to bias adjustment clearly adds value once spatial dependence is relevant ( [[#Maraun--2019a|Maraun et al., 2019a]] ). These results may explain why dynamical downscaling does not add value to global model simulations for (single-site) agricultural modelling, when both global and regional models are bias adjusted ( [[#Glotter--2014|Glotter et al., 2014]] ), but dynamical downscaling adds value compared to bias-adjusted global model output for spatially distributed hydrological models ( [[#Qiao--2014|Qiao et al., 2014]] ).

Overall, statistical downscaling methods with carefully chosen predictors and an appropriate model structure for a given application realistically represent many statistical aspects of present-day daily temperature and precipitation ( ''high confidence'' , [[#10.3.3.7|Section 10.3.3.7]] ). Bias adjustment has proven beneficial as an interface between climate model projections and impact modelling in many different contexts ( ''high confidence'' ) ( [[#10.3.3.7|Section 10.3.3.7]] ). Weather generators realistically simulate many statistical aspects of present-day daily temperature and precipitation ( ''high confidence'' ) ( [[#10.3.3.7|Section 10.3.3.7]] ). The performance of these approaches and their fitness for future projections also depends on predictors and change factors taken from the driving dynamical models ( ''high confidence'' ) ( [[#10.3.3.9|Section 10.3.3.9]] ).

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