Assessment of High-Resolution Climate Extreme Indices over Europe Based on Daily Precipitation and Temperature Data (Raw and Bias-Adjusted) as Issued from an Ensemble of RCM CORDEX

The objective is to provide evaluation of data quality and associated uncertainties within the scope of the European FP7 Climate Information Portal for Copernicus (CLIPC) project. The portal focuses on building harmonized access to a variety of high-resolution climate-change and underlying decision-making products including climate indices. Climate change dataset is based on the Regional Climate Model (RCM) simulations with spatial resolution of 0.11º over Europe (EUR-11), available from the Coordinated Regional climate Downscaling Experiment (CORDEX). Due to systematic model errors, RCM output often requires use of bias adjustment techniques in impact studies. The Distribution Based Scaling (DBS) method, developed at the Swedish Meteorological and Hydrological Institute (SMHI), has been applied to the RCM EUR-11 output under the RCP45 and RCP85 scenarios using the 5km EURO4M MESAN (1989-2010) regional reanalysis as a reference dataset. Bias correction is done in conjunction with the Bias Correction Intercomparison Project (BCIP). To estimate level of uncertainty that bias adjustment might introduce, an analysis of extremes in changing climate is thus being evaluated. Climate indices for monitoring changes in extremes were defined by the Ccl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). A generic open-source python package (icclim) is used to calculate a selection of ETCCDI climate indices from daily precipitation and 2 m air temperature for both raw and bias-adjusted simulations. Preliminary results show that effects of bias-correction depend on (i) selected indice, (ii) climatic zone, (iii) topography, (iv) land or ocean surface, (v) time frame in projected climate.