High-Resolution Climate Predictions and Short-Range Forecasts to Improve the Process Understanding and the Representation of Land-Surface Interactions in the WRF Model in Southwest Germany (WRFCLIM)

  • Hans-Stefan Bauer
  • Kirsten Warrach-Sagi
  • Volker Wulfmeyer
  • Thomas Schwitalla
  • Martin Kirn
Conference paper


The use of numerical modeling for climate projections is an important task in scientific research since they are the most promising means to gain insight in possible climate changes. The quality of the simulations and the understanding of the interaction of anthropogenic climate change with natural climate variability have been constantly improved in recent years. This is especially true for the global scale. In the meantime, regional climate simulations with grid resolutions of 10–50 km became available in the climate modeling community. However, several effects as e.g. boundary problems or inconsistencies in model physics introduced by the driving models limit the improvement of the simulation skill on the mesoscale.

Within the CORDEX framework, the Weather Research and Forecasting (WRF) model is applied for the first time to perform a 20-year simulation for the period 1989–2009 for Europe driven by the ERA-Interim analysis. The selected resolutions are 0.33°and 0.11°. Aim of this simulation is to investigate the capability of the selected WRF configuration to represent the climate in the recent two decades and to study the effect of resolution on the results. Furthermore a first climate projection driven by the ECHAM5/MPI OM model was done for the period 1989–2030.

First results from evaluating the annual and seasonal precipitation from 1990 to 2009 against the German REGNIE observations show a wet bias of the model in the order of 30.


Regional Climate Model Climate Projection Numerical Weather Prediction International Satellite Cloud Climatology Project Regional Climate Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hans-Stefan Bauer
    • 1
  • Kirsten Warrach-Sagi
    • 1
  • Volker Wulfmeyer
    • 1
  • Thomas Schwitalla
    • 1
  • Martin Kirn
    • 1
  1. 1.Institute of Physics and MeteorologyUniversity of HohenheimStuttgartGermany

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