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

=== Atlas.8.3 Assessment of Model Performance ===

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A globalevaluation of annual mean temperature and precipitation from the CMIP6 ensemble is presented in Sections 3.3.1 and 3.3.2 respectively. In general, annual mean temperature is slightly underestimated at high latitudes and overestimated in the MED area. Temporal evolution of decadal temperature oscillations in Europe simulated by the CMIP6 historical simulations is well reproduced ( [[#Fan--2020|Fan et al., 2020]] ). [[#Fernandez-Granja--2021|Fernandez-Granja et al. (2021)]] report an overall improvement of CMIP6 compared to CMIP5 to reproduce atmospheric weather patterns over Europe.

Regional climate models (RCMs; [[IPCC:Wg1:Chapter:Chapter-10#10.3.1.2|Section 10.3.1.2]] ) have been extensively evaluated for a range of climate features over Europe ( [[#Casanueva--2016|Casanueva et al., 2016]] ; [[#Vaittinada%20Ayar--2016|Vaittinada Ayar et al., 2016]] ; [[#Krakovska--2017|Krakovska et al., 2017]] ; [[#Terzago--2017|Terzago et al., 2017]] ; [[#Cavicchia--2018|Cavicchia et al., 2018]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ; [[#Fantini--2018|Fantini et al., 2018]] ; [[#Harzallah--2018|Harzallah et al., 2018]] ; [[#Ivanov--2018|Ivanov et al., 2018]] ; [[#Panthou--2018a|Panthou et al., 2018a]] ). Standard assessments of RCMs driven by reanalyses, typically run at 12–25 km spatial resolution, confirm that the Euro-CORDEX and Med-CORDEX ensembles are capable of reproducing the salient features of European climate ( [[#Kotlarski--2014|Kotlarski et al., 2014]] ; [[#Krakovska--2018|Krakovska, 2018]] ) and represent European circulation features realistically ( [[#Cardoso--2016|Cardoso et al., 2016]] ; [[#Drobinski--2018|Drobinski et al., 2018]] ; [[#Flaounas--2018|Flaounas et al., 2018]] ; [[#Sanchez-Gomez--2018|Sanchez-Gomez and Somot, 2018]] ). Seasonal and regionally averaged temperature biases generally do not exceed 1.5°C, while precipitation biases can be up to ±40% ( [[#Kotlarski--2014|Kotlarski et al., 2014]] ). Extensive evaluation of a large collection of RCM–GCM combinations show a general wet, cold and windy bias compared to observations and reanalyses, but none of the models is systematically performing best or worst ( [[#Vautard--2021|Vautard et al., 2021]] ). Higher-resolution simulations do show improved performance in reproducing the spatial patterns and seasonal cycle of not only extreme precipitation but also mean precipitation over all European regions (see Sections 10.3.3.4 and 10.3.3.5 for an extensive evaluation of the added value of increased simulation resolution; [[#Mayer--2015|Mayer et al., 2015]] ; [[#Fantini--2018|Fantini et al., 2018]] ; [[#Soares--2018|Soares and Cardoso, 2018]] ; [[#Ciarlo%60--2021|Ciarlo` et al., 2021]] ).

In line with findings reported in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.8|Section 10.3.3.8]] , several studies argue that both GCMs and RCMs underestimate the observed trend in European summer temperature ( [[#Dosio--2016|Dosio, 2016]] ; [[#Boé--2020b|Boé et al., 2020b]] ), indicating that essential processes are missing or that the natural variability is not correctly sampled ( [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ). [[#Nabat--2014|Nabat et al. (2014)]] argued that including realistic aerosol variations enables climate models to correctly reproduce the summer warming trend (as is required for attributing continental annual temperature trends, [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.1|Section 3.3.1.1]] ). However, other studies showed models to be sensitive also to local effects, such as land surface processes, convection, microphysics and snow albedo ( [[#Vautard--2013|Vautard et al., 2013]] ; [[#Davin--2016|Davin et al., 2016]] ). In Euro-CORDEX the warm and dry summer bias over southern and south-eastern Europe is reduced compared to the previous ENSEMBLES simulations ( [[#Katragkou--2015|Katragkou et al., 2015]] ; [[#Giot--2016|Giot et al., 2016]] ; [[#Prein--2017|Prein and Gobiet, 2017]] ; [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ). Natural variability has strongly affected the historical warming and large ensembles are necessary for a correct estimation of the forced signal versus natural variability ( [[#Aalbers--2018|Aalbers et al., 2018]] ; [[#Lehner--2020|Lehner et al., 2020]] ).

Specific assessments of convection-permitting RCMs (CPRCMs, running at a resolution of typically 1 to 3 km and designed for extreme precipitation characteristics) is undertaken in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.4.1|Section 10.3.3.4.1]] . A unique CPRCM ensemble has been applied over the great Alpine domain and improves representation of mean and extreme precipitation compared to coarser resolution models ( [[#Ban--2021|Ban et al., 2021]] ; [[#Pichelli--2021|Pichelli et al., 2021]] ). The role of aerosol forcing is increasingly analysed as new and more realistic aerosol datasets become available ( [[#Nabat--2013|Nabat et al., 2013]] ; [[#Pavlidis--2020|Pavlidis et al., 2020]] ), and as RCMs begin to include interactive aerosols ( [[#Nabat--2012|Nabat et al., 2012]] , 2015, 2020; [[#Drugé--2019|Drugé et al., 2019]] ). Explicitly accounting for aerosol effects in RCMs leads to improved representation of the surface shortwave radiation at various scales: long-term means ( [[#Gutiérrez--2018|Gutiérrez et al., 2018]] ), day-to-day variability ( [[#Nabat--2015|Nabat et al., 2015]] ), and long-term trends ( [[#Nabat--2014|Nabat et al., 2014]] ).

New, or updated, higher-resolution, coupled atmosphere-ocean-ice model systems have been found to improve simulations of observed climate features over the Baltic area compared to atmosphere-only model versions, including correlation between precipitation and SST, between surface heat-flux components and SST, and weather events like convective snow bands over the Baltic Sea (e.g., [[#Tian--2013|Tian et al., 2013]] ; [[#Van%20Pham--2014|Van Pham et al., 2014]] ; [[#Gröger--2015|Gröger et al., 2015]] ; S. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#Pham--2017|Pham et al., 2017]] ). Coupled atmosphere–land–river–ocean regional climate system models (RCSMs) from Med-CORDEX have similar skill as the ENSEMBLES and the Euro-CORDEX ensembles to represent decadal variability of Mediterranean climate and its extremes ( [[#Cavicchia--2018|Cavicchia et al., 2018]] ; [[#Dell’Aquila--2018|Dell’Aquila et al., 2018]] ; [[#Gaertner--2018|Gaertner et al., 2018]] ). [[#Panthou--2018a|Panthou et al. (2018a)]] showed that, over land, differences between atmosphere-only and coupled RCMs are confined to coastal areas that are directly influenced by SST anomalies. In contrast, [[#Van%20Pham--2014|Van Pham et al. (2014)]] showed significant differences in seasonal mean temperature across a widespread continental domain.

Statistical downscaling methods are assessed in [[IPCC:Wg1:Chapter:Chapter-10#10.3.3.7|Section 10.3.3.7]] , including the intercomparison and evaluation activities performed in the framework of VALUE and Euro-CORDEX over Europe.

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