This work package aims at optimising 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 regionalisation 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 regionalisation 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 optimisation 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 optimised 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 optimised ensemble spread for different variables, seasons and regions (M2, month 30), the methodologies for the statistical-dynamical downscaling (M3, month 27), homogeneous time series for temperature, precipitation, and wind as derived from the statistical-dynamical downscaling (M4, month 33), the ensemble optimisation 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).
We have developed a statistical approach of drift-correction in a multivariate context for adjusting the drift in the prototype decadal prediction system (MPI-ESM), and demonstrated the effect of this drift correction on regional climate model (CCLM) simulations over Europe. This approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperature (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. Drift correction in the input data of CCLM removes the cooling trends in most western European land regions and systematically reduced the discrepancy of model prediction and observations. A paper with the title ‘An Effective drift correction for dynamical downscaling of decadal global climate predictions’ is accepted for publication in ‘Climate Dynamics’. [Paeth et al., 2018].
Institute for Geography and Geology, University Würzburg
Prof. Dr. Heiko Paeth
Institute for Geophysics and Meteorology, University of Cologne
PD Dr. Joaquim G. Pinto
Li, J. | Pollinger, F., Panitz, HJ., Feldmann, H., Paeth, H.
Paeth, H. | J. Li, F. Pollinger, W. A. Müller, H. Pohlmann, H. Feldmann, H.-J. Panitz