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Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations

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Abstract

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days—90pWET, contribution of the very wet days to total precipitation—R95pTOT and number of consecutive dry days—CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators’ spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods.

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Acknowledgments

The authors are grateful to Prof. C. Schär for his helpful comments and E. van Meijgaard for making available the RACMO model data. We acknowledge the observational data providers. Calculations for WRF311F were made using the TGCC super computers under the GENCI time allocation GEN6877. The WRF331A from CRP-GL (now LIST) was funded by the Luxembourg National Research Fund (FNR) through Grant FNR C09/SR/16 (CLIMPACT). The KNMI-RACMO2 simulations were supported by the Dutch Ministry of Infrastructure and the Environment. The CCLM and REMO simulations were supported by the Federal Ministry of Education and Research (BMBF) and performed under the Konsortial share at the German Climate Computing Centre (DKRZ). The CCLM simulations were furthermore supported by the Swiss National Supercomputing Centre (CSCS) under project ID s78. Part of the SMHI contribution was carried out in the Swedish Mistra-SWECIA programme founded by Mistra (the Foundation for Strategic Environmental Research). This work is supported by CORWES (CGL2010-22158-C02) and EXTREMBLES (CGL2010-21869) projects funded by the Spanish R&D programme and the European COST ACTION VALUE (ES1102). A. C. thanks the Spanish Ministry of Economy and Competitiveness for the funding provided within the FPI programme (BES-2011-047612 and EEBB-I-13-06354). We also thank two anonymous referees for their useful comments that helped to improve the original manuscript.

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Authors and Affiliations

  1. Grupo de Meteorología, Dpto. Matemática Aplicada y Ciencias de la Computación, Univ. de Cantabria, Avda. de los Castros, s/n, 39005, Santander, Spain

    A. Casanueva, S. Herrera & J. Fernández

  2. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

    S. Kotlarski

  3. Grupo de Meteorología, Instituto de Física de Cantabria CSIC-Univ. de Cantabria, Avda. de los Castros, s/n, 39005, Santander, Spain

    J. M. Gutiérrez

  4. Climate and Arctic Research, Danish Meteorological Institute, Lyngbyvej 100, 2100, Copenhagen Ø, Denmark

    F. Boberg & O. B. Christensen

  5. Institut National de l’Environnement industriel et des risques (INERIS), Verneuil en Halatte, France

    A. Colette

  6. Meteorologiocal Institute, University of Bonn, Auf dem Hügel 20, 53121, Bonn, Germany

    K. Goergen

  7. Climate Service Center Germany (GERICS), Hamburg, Germany

    D. Jacob & C. Teichmann

  8. Max Planck Institute for Meteorology, Hamburg, Germany

    D. Jacob

  9. Chair of Environmental Meteorology, Brandenburg University of Technology (BTU), Cottbus-Senftenberg, Germany

    K. Keuler

  10. Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

    G. Nikulin

  11. LSCE-IPSL CEA/CNRS/UVSQ, 91191, Gif sur Yvette Cedex, France

    R. Vautard

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  1. A. Casanueva
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Correspondence to A. Casanueva.

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K. Goergen: (Former Département Environnement et Agro-Biotechnologies, Centre de Recherche Public Gabriel Lippmann, 41 Rue du Brill, 4422 Belvaux, Luxembourg, since January 2015 renamed to Luxembourg Science and Technology, LIST).

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Casanueva, A., Kotlarski, S., Herrera, S. et al. Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations. Clim Dyn 47, 719–737 (2016). https://doi.org/10.1007/s00382-015-2865-x

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