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Evaluation of agricultural-related extreme events in hindcast COSMO-CLM simulations over Central Europe
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1  Center for Environmental Systems Research, University of Kassel, Kassel, Germany


High horizontal resolution regional climate model simulations serve as forcing data for crop and dynamic vegetation models to generate possible scenarios for the future concerning the effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM9 (CCLM) from 1980 to 2019 with a spin-up starting at 1979. The model was driven with hourly ERA5 data, which is the latest climate reanalysis product by ECMWF and directly downscaled to 3 km horizontal resolution over central Europe. The land-use classes are described by ECOCLIMAP, and the soil type and depth by HWSD. The evaluation is carried out in terms of temperature, precipitation, and extreme weather indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm and dry summer bias found in its parent model, it reproduces the main features of the recent past climate of central Europe, including the seasonal mean climate patterns and probability density distributions. Furthermore, the model catches extreme weather events related to heat, drought, heavy rains, flooding, and spring frost events. The results highlight the possibility to directly downscale ERA5 data with regional climate models avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate projections of future changes in agricultural extreme events.

Keywords: Hindcast run; Dynamical downscaling; Model bias; Agricultural related extreme events