This work package aims at optimizing the ensemble characteristics of the operational regional decadal prediction system towards a realistic range of uncertainty between different ensemble members. An optimal subset of the global MPI-ESM ensemble members is defined for dynamical downscaling with CCLM and the ensemble spread of decadal predictions is calibrated towards external multi-model ensembles applying a combination of dynamical and low-cost statistical-dynamical downscaling. The use of an ensemble-subset enables a more efficient regionalization of MPI-ESM decadal hindcasts in terms of computing time for the operational prediction system.
The work package is structured by several tasks:
1) By means of multivariate statistics and Bayesian approaches an optimal subset of the global MPI-ESM ensemble members representing the statistical properties of the whole ensemble is selected for the dynamical downscaling with CCLM. This approach permits a real-time application to operational predictions (C3-2.1).
2) The ensemble spread of the regional predictions in Central Europe is calibrated towards the adequate spread of the regional multi-model ensembles from the MiKlip I, EURO-CORDEX and ENSEMBLES projects for different variables, seasons and regions applying a combination of regression models, probability matching and Bayes statistics (C3-2.2).
3) Statistical-dynamical downscaling methodologies for the regionalization of the three key variables temperature, precipitation and wind are developed and applied to the full MPI-ESM hindcast ensemble. (C3-2.3 and C3-2.4).
4) A strategy is developed to combine the results from the dynamical and statistical-dynamical downscaling in terms of the optimization of the ensemble characteristics which can be applied to all regional predictions of MiKlip II (C3-2.5).
5) The resulting subset of global ensemble simulations and the optimized ensemble spread of the regional predictions define the optimal configuration and post-processing of the operational regional prediction system and the developed methods are integrated in the central evaluation system for comparison with global predictions (C3-2.6).
The deliverables of the work package are given by the milestones of the different tasks: the optimized subset of global ensemble simulations (M1, month 12), the optimized ensemble spread for different variables, seasons and regions (M2, month 30), the methodologies for the statistical-dynamical downscaling (M3, month 27), homogenous time series for temperature, precipitation, and wind as derived from the statistical-dynamical downscaling (M4, month 33), the ensemble optimization applying the combination of dynamical and statistical-dynamical downscaling (M5, month 33) and the integration of the developed methods in the central evaluation system and the operational prediction system (M6, month 36)
The statistical-dynamical downscaling methodology is based on weather type analysis. In MiKlip-II focus will be given to the Hess and Brezowsky Grosswetterlagen. So far, an objective Grosswetterlagen classification has been applied to ERA40/ERA-Interim and the full available ensemble of the MiKlip decadal prediction system (baseline0, baseline1, prototype, historical runs).