Bias adjustment for decadal predictions of precipitation in Europe from CCLM
A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment approach. In step one, we estimate model errors based on a long-term ‘CCLM assimilation run’ (regionalizing data from a global assimilation run) and observational data. In step two, the resulting transfer function is applied to the complete set of decadal hindcast simulations (285 individual runs). In contrast to lead-time-dependent bias-adjustment approaches, this one is designed for variables with poor decadal prediction skill and without dominant lead-time-dependent bias. In terms of the CCLM assimilation run, MOS is shown to be effective in predictor selection, model skill improvement, and model bias reduction. Yet, the positive effect of MOS correction is accompanied with an underestimation of precipitation variability. After MOS application, an estimated mean square skill score of more than 0.5 is observed regionally. Simulated precipitation in decadal hindcasts is further improved when the MOS is trained on the basis of other decadal hindcasts from the same regional climate model but with a large underestimation in forecast uncertainty. Our results suggest that the MOS system derived from the assimilation run is less effective but allows the potential climate predictability in decadal hindcasts and forecasts to be retained. Using hindcasts itself for training is recommended unless a statistical method is capable of distinguishing biases and predictions within a hindcasts dataset.
KeywordsBias-adjustment CCLM Hindcasts Decadal prediction Precipitation Model output statistics
This work was conducted in the framework of the German MiKlip project and supported by the German Ministry of Education and Research (BMBF) under Grant no. 01LP1129A-F. We thank the Max-Planck Institute for Meteorology for providing the MPI-ESM decadal predictions. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com). Support for the Twentieth Century reanalysis Project dataset is provided by the US Department of Energy, Office of Science Innovative and Noval Computational Impact on Theory and Experiment (DOE INCITE) Program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. We thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing Ocean Reanalysis, ERA-20C and ERA-Interim datasets. All MiKlip simulations were performed at the German climate computing center (Deutsches Klimarechenzentrum, DKRZ).
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