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

==== 10.3.1.2 Regional Climate Models ====

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Regional climate models (RCMs) are dynamical models similar to global models that are applied over a limited area, but with a horizontal resolution higher than that of standard global models. They are the basis for dynamical downscaling to produce sub-continental climate information (e.g., Chapters 11, 12 and Atlas) but are also often used for process understanding. At lateral and, if applicable, lower boundaries, RCMs take their values from a driving dataset, which could be a global model or a reanalysis. RCMs are typically one-way nested: they do not feed back into the driving model, although two-way nested global model-RCM simulations have been performed that examine regional influence on large-scale climate, potentially improving it ( [[#Lorenz--2005|Lorenz and Jacob, 2005]] ; [[#Harris--2013|Harris and Lin, 2013]] ; [[#Junquas--2016|Junquas et al., 2016]] ). Spectral nudging ( [[#Kida--1991|Kida et al., 1991]] ; [[#Waldron--1996|Waldron et al., 1996]] ; [[#von%20Storch--2000|von Storch et al., 2000]] ; [[#Kanamaru--2007|Kanamaru and Kanamitsu, 2007]] ) can increase consistency with the driving model, whereby selected variables, such as the wind field, are forced to closely follow a prescribed large-scale field over a specified range of spatial scales. RCMs can inherit biases from the driving global model in addition to producing biases themselves ( [[#Hall--2014|Hall, 2014]] ; [[#Hong--2014|Hong and Kanamitsu, 2014]] ; [[#Dosio--2015|Dosio et al., 2015]] ; [[#Takayabu--2016|Takayabu et al., 2016]] ). The consistency between the circulation features simulated by the RCM and those inherited through the boundary conditions depends on (i) the relative importance of the large-scale forcing compared to local-scale phenomena, and (ii) the size of the RCM domain (e.g., [[#Diaconescu--2013|Diaconescu and Laprise, 2013]] ). Large domains also allow the RCM to generate much of its own internally generated unforced variability ( [[#Nikiema--2017|Nikiema et al., 2017]] , and references therein; [[#Sanchez-Gomez--2018|Sanchez-Gomez and Somot, 2018]] ).

The Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative ( [[#Giorgi--2009|Giorgi et al., 2009]] ; [[#Giorgi--2015|Giorgi and Gutowski, 2015]] ; [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ) provides ensembles of high-resolution historical (starting as early as 1950) and future climate projections for various regions. RCMs in CORDEX typically have a horizontal resolution between 10 and 50 km. But much finer spatial resolution is required to fully resolve deep convection, an important cause of precipitation in much of the world. Therefore, an emerging strand in dynamical downscaling employs simulations at convection permitting scales, at horizontal resolutions of a few kilometres, where deep-convection parametrizations can be switched off, approximately simulating deep convection ( [[#Prein--2015|Prein et al., 2015]] ; [[#Stratton--2018|Stratton et al., 2018]] ; [[#Coppola--2020|Coppola et al., 2020]] ). A recent study indicates that switching off the deep-convection parametrization may be beneficial also in simulations performed at coarser resolutions ( [[#Vergara-Temprado--2020|Vergara-Temprado et al., 2020]] ). Alternatively, some RCMs make use of scale-aware parametrizations that are able to adapt to increasing resolution without switching off the convection scheme ( [[#Hamdi--2012|Hamdi et al., 2012]] ; [[#De%20Troch--2013|De Troch et al., 2013]] ; Plant and Yano, 2015; [[#Giot--2016|Giot et al., 2016]] ; [[#Termonia--2018|Termonia et al., 2018]] ; [[#Yano--2018|Yano et al., 2018]] ).

RCMs have often consisted of atmospheric and land components that do not include all possible Earth system processes and therefore neglect important processes such as air-sea coupling (in standard RCMs sea surface temperatures, SSTs, are prescribed from global model simulations or reanalyses) or the chemistry of aerosol–cloud interaction (aerosols prescribed with a climatology), which may influence regional climate projections. Therefore, some RCMs have been extended by coupling to additional components like interactive oceans, sometimes with sea ice ( [[#Kjellström--2005|Kjellström et al., 2005]] ; [[#Somot--2008|Somot et al., 2008]] ; [[#Van%20Pham--2014|Van Pham et al., 2014]] ; [[#Sein--2015|Sein et al., 2015]] ; [[#Ruti--2016|Ruti et al., 2016]] ; [[#Zou--2016a|Zou and Zhou, 2016a]] ; [[#Zou--2017|Zou et al., 2017]] ; [[#Samanta--2018|Samanta et al., 2018]] ), rivers ( [[#Sevault--2014|Sevault et al., 2014]] ; [[#Lee--2015|Lee et al., 2015]] ; [[#Di%20Sante--2019|Di Sante et al., 2019]] ), glaciers ( [[#Kotlarski--2010|Kotlarski et al., 2010]] ), and aerosols ( [[#Zakey--2006|Zakey et al., 2006]] ; [[#Zubler--2011|Zubler et al., 2011]] ; [[#Nabat--2015|Nabat et al., 2015]] ). The coupling of these components allows for the investigation of additional climate processes such as regional sea level change ( [[#Adloff--2018|Adloff et al., 2018]] ), ocean–land interactions ( [[#Lima--2019|Lima et al., 2019]] ; [[#Soares--2019a|Soares et al., 2019a]] ), or the impact of high-frequency ocean–atmosphere coupling on the climatology of Mediterranean cyclones ( [[#Flaounas--2018|Flaounas et al., 2018]] ).

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