Scientific and Technical Coordination of Module E, including research activities with flagship character for the systematic data-based and process-oriented validation. Apart from carrying of the coordination of Module E, EnsDiVal also pursues the following scientific questions:

The variability of extreme events’ occurrence and intensity is particularly relevant because of the significant impacts on economy and society. The scientific contribution within the Module E coordination will focus on synoptic scale winter windstorms, which are the costliest type of natural catastrophes in Europe.
Research to be carried out within EnsDiVal shall improve knowledge about the decadal variability of frequency and intensity of these extreme events, including the spatial distribution and extent. A deeper understanding of the processes shaping or modifying the characteristics of extreme winter windstorms on decadal time scales will be pursued. Precursory meteorological settings relevant on the seasonal time scale are taken as one of the starting points for identifying the respective large scale situations which could have a predictive value for the occurrence of these extremes on the decadal time scale. The existence of such precursors and their representation in observational and simulated datasets is the basis for a potential prediction of occurrence probability and intensity of extreme events from decadal scale processes. Thus, their identification in the MiKlip model system is a central part of the process oriented validation pursuit in Module E. A successful reproduction of these relations in the forecast system shall contribute to MiKlip’s potential for decadal prediction of extremes.

Goals

• Identification of processes leading to extremes in the frequency and intensity of winter wind storms on the decadal time scale
• Assessment of the reproduction of these processes within the MiKlip model system, i.e. a profound validation of the MiKlip model system regarding these processes
• Identification of relevant precursors, i.e. skillfull predictors including possible model biases and trends for winter wind storm frequency and intensity on the decadal time scale