Observational uncertainty and regional climate model evaluation: A pan-European perspective

Abstract

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan-European scale. Three different reference data sets are considered: the coarse-resolved E-OBS data set, a compilation of regional high-resolution gridded products (HR) and the European-scale MESAN reanalysis. Five high-resolution ERA-Interim-driven RCM experiments of the EURO-CORDEX initiative are evaluated against each of these references over eight European sub-regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, i.e. the grid spacing of the coarsest data set in the exercise (E-OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub-regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet-day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub-domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference.The present work has been carried out as part of the EU-COST Action VALUE (Validating and Integrating Downscaling Methods for Climate Change Research; ES1102). The authors gratefully acknowledge the providers of RCM and observational data. For the high-resolution national/regional grids these are the University of Cantabria (SP), the Institute of Meteorology and Water Management – National Research Institute (PO), Météo-France/CERFACS (FR), The Swedish Meteorological and Hydrological Institute (SE), Deutscher Wetterdienst (GE), the Hungarian Meteorological Service (CA), the Norwegian Meteorological Institute (NO) and Federal Office of Meteorology and Climatology MeteoSwiss (CH). Furthermore, we acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://eca.knmi.nl). The MESAN data set was provided by the Swedish Meteorological and Hydrological Institute. All analysis were performed on the computing infrastructure of the Swiss National Supercomputing Centre CSCS. They furthermore thank the climate modelling groups of the EURO-CORDEX initiative for producing and making available their model output. The contribution of Olle Räty was partly funded by the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters