AbstractIn the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate
Downscaling Experiment) domain with different configurations
in microphysics, convection and radiation for the time period 1990–2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same
spatial resolution (0.44). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover.
The analysis of the WRF ensemble indicates systematic temperature
and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell–Devenyi convection and CAM (Community
Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max -2.5 C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40–60 %) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation.
Maximum winter cold biases are over northeastern Europe (-2.8 C); this location suggests that land–atmosphere rather than cloud–radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain–Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15–30 %). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.