4.6 Article

Evaluation of the Large EURO-CORDEX Regional Climate Model Ensemble

Journal

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
Volume 126, Issue 17, Pages -

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2019JD032344

Keywords

climate change; regional climate modeling; EURO-CORDEX; climate model evaluation; model biases; European climate

Funding

  1. Copernicus Climate Change Service
  2. European Union's Horizon 2020 research and innovation program [820655]
  3. ERANET-SusCROP Cofund Action (RCN) [299600/E50]
  4. H2020 Societal Challenges Programme [820655] Funding Source: H2020 Societal Challenges Programme

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The use of regional climate model (RCM) projections in providing regional climate information is expanding rapidly, particularly in Europe. While model simulations generally agree with observations and reanalyses, there are systematic biases identified related to temperature, precipitation, and dynamical variables.
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.

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