Evaluation of the Large EURO-CORDEX Regional Climate Model Ensemble
Vautard, Robert; Kadygrov, Nikolay; Iles, Carley; Boberg, Fredrik; Buonomo, Erasmo; Bülow, Katharina; Coppola, Erika; Corre, Lola; van Meijgaard, Erik; Nogherotto, Rita; Sandstad, Marit; Schwingshackl, Clemens; Somot, Samuel; Aalbers, Emma; Christensen, Ole Bøssing; Ciarlo, James M.; Demory, Marie-Estelle; Giorgi, Filippo; Jacob, Daniela; Jones, Richard G.; Keuler, Klaus; Kjellström, Erik; Lenderink, Geert; Levavasseur, Guillaume; Nikulin, Grigory; Sillmann, Jana; Solidoro, Cosimo; Sørland, Silje Lund; Steger, Christian; Teichmann, Claas; Warrach-Sagi, Kirsten; Wulfmeyer, Volker
Journal article, Peer reviewed
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- Journal articles 
Original versionJournal of Geophysical Research (JGR): Atmospheres. 2021, 126 (17), . 10.1029/2019JD032344
The use of regional climate model (RCM)-based projections for providing regional climate information in a research and climate service contexts is currently expanding very fast. This has been possible thanks to a considerable effort in developing comprehensive ensembles of RCM projections, especially for Europe, in the EURO-CORDEX community (Jacob et al., 2014, 2020). As of end of 2019, EURO-CORDEX has developed a set of 55 historical and scenario projections (RCP8.5) using 8 driving global climate models (GCMs) and 11 RCMs. This article presents the ensemble including its design. We target the analysis to better characterize the quality of the RCMs by providing an evaluation of these RCM simulations over a number of classical climate variables and extreme and impact-oriented indices for the period 1981–2010. For the main variables, the model simulations generally agree with observations and reanalyses. However, several systematic biases are found as well, with shared responsibilities among RCMs and GCMs: Simulations are overall too cold, too wet, and too windy compared to available observations or reanalyses. Some simulations show strong systematic biases on temperature, others on precipitation or dynamical variables, but none of the models/simulations can be defined as the best or the worst on all criteria. The article aims at supporting a proper use of these simulations within a climate services context.