Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales

Abstract

This study evaluates the added value in the representation of surface climate variables from\ud an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM\ud simulations and their driving data over a wide range of RCM experimental setups and climate statistics.\ud The methodology is specifically designed to compare results across different variables and metrics, and it\ud incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results\ud show that the RCMs’ added value strongly depends on the type of driving data, the climate variable, and the\ud region of interest but depends rather weakly on the choice of the statistical measure, the season, and the\ud RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that\ud improvements are coming from the small scales when considering the representation of spatial patterns,\ud but from the large-scale contribution in the case of absolute values. Our results also show that a large part\ud of the added value can be attained using some simple postprocessing methods