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

Journal: Climate Dynamics
Year: 2016  
Status: Published
In this status since: 16 Oct 2015
PDF file: 2015_Casanueva_ClimDyn_finaldraft.pdf
Link to PDF: 2015_Casanueva_ClimDyn
DOI: 10.1007/s00382-015-2865-x
, Kotlarski, S., , , , Boberg, F., Colette, A., Christensen, O.B., Goergen, K., Jacob, D., Keuler, K., Nikulin, G., Teichmann, C., Vautard, R.

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 (12 km 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. Regarding seasonal biases, we find only limited evidence for an added value in the precipitation intensity and frequency or in the derived indicators. Evaluation results considerably depend on the RCM, season and indicator considered. To adequately represent daily precipitation statistics, bias correction techniques are needed at both resolutions. Three simple bias correction methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Percentiles are more sensitive to changes in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This implies 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. 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 bias correction methods.