C3-WP1 - Post-Processing

The aim of this work package is to identify the optimal post-processing of the operational regional decadal prediction system for Central Europe which removes systematic errors in the regional model output applying different approaches of bias and drift correction. The statistical post-processing represents an efficient and low-cost tool to calibrate climate model output before being communicated to users.

The work package is structured by several tasks: First, different approaches of statistical post-processing are applied based on observational data for Central Europe and the multi-decadal assimilation runs nudged to observations for bias correction or rather the decadal predictions for drift correction. The developed statistical transfer functions are compared for different variables and applied for the calibration of the decadal predictions which are then evaluated by cross validation with independent observations (C3-1.1, Fig. 1).

Fig. 1: Research concept and possible approaches of the statistical post-processing

The optimal combination of the individual approaches for the statistical post-processing of the decadal predictions is analyzed in detail and separately for different seasons, regions in Central Europe and variables or climate phenomena (C3-1.2). The developed metric of combined statistical post-processing is integrated in the central evaluation system CES of MiKlip II in order to be applied for the calibration of different regional decadal predictions and for the comparison with that of global decadal predictions (C3-1.3). Finally, this metric is as well applied as efficient calibration approach with minor computational requirements for the operational regional decadal prediction system with CCLM (C3-1.4).

The deliverables of the work package are given by the milestones of the different tasks: the comparison of the individual approaches of the statistical post-processing (M1, month 24), the optimal combination of the individual approaches of the statistical post-processing for different seasons, regions and variables (M2, month 30) and the integration of the statistical post-processing in the central evaluation system (M3, month 33) and in the operational prediction system (M4, month 36).

Until now the literature has been reviewed for different approaches of the statistical post-processing and first realization steps have been planned.