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Geosci. Model Dev. Discuss., 6, 5711-5745, 2013
www.geosci-model-dev-discuss.net/6/5711/2013/
doi:10.5194/gmdd-6-5711-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
Review Status
This discussion paper is under review for the journal Geoscientific Model Development (GMD).
The regional MiKlip decadal forecast ensemble for Europe
S. Mieruch1, H. Feldmann1, G. Schädler1, C.-J. Lenz2, S. Kothe3, and C. Kottmeier1
1Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
2Deutscher Wetterdienst, Offenbach, Germany
3Institute for Atmospheric and Environmental Sciences, Goethe-University Frankfurt, Frankfurt am Main, Germany

Abstract. Funded by the German Ministry for Education and Research (BMBF) a major research project called MiKlip (Mittelfristige Klimaprognose, Decadal Climate Prediction) was launched and global as well as regional predictive ensemble hindcasts have been generated. The aim of the project is to demonstrate for past climate change whether predictive models have the capability of predicting climate on time scales of decades. This includes the development of a decadal forecast system, on the one hand to support decision making for economy, politics and society for decadal time spans. On the other hand, the scientific aspect is to explore the feasibility and prospects of global and regional forecasts on decadal time scales. The focus of this paper lies on the description of the regional hindcast ensemble for Europe generated by COSMO-CLM and on the assessment of the decadal variability and predictability against observations. To measure decadal variability we remove the long term bias as well as the long term linear trend from the data. Further, we applied low pass filters to the original data to separate the decadal climate signal from high frequency noise. The decadal variability and predictability assessment is applied to temperature and precipitation data for the summer and winter half-year averages/sums. The best results have been found for the prediction of decadal temperature anomalies, i.e. we have detected a distinct predictive skill and reasonable reliability. Hence it is possible to predict regional temperature variability on decadal timescales, However, the situation is less satisfactory for precipitation. Here we have found regions showing good predictability, but also regions without any predictive skill.

Citation: Mieruch, S., Feldmann, H., Schädler, G., Lenz, C.-J., Kothe, S., and Kottmeier, C.: The regional MiKlip decadal forecast ensemble for Europe, Geosci. Model Dev. Discuss., 6, 5711-5745, doi:10.5194/gmdd-6-5711-2013, 2013.
 
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