Automated Classification of Atmospheric Circulation Types for Compound Flood Risk Assessment: CMIP6 Model Analysis Utilising a Deep Learning Ensemble

Abstract

The simultaneous occurrence of high river discharges and storm surges represents a substantial hazard for many low-lying coastal areas. Potential future changes in the frequency or intensity of such compound flood events are therefore of utmost importance. To assess such changes, large and consistent ensembles with storm surge and hydrological models are needed, but are hardly available. Often the occurrence of compound flood events is linked to the presence of certain atmospheric circulation types. Future changes in the frequency of such patterns can be directly inferred from available climate simulations. A frequently used classification of atmospheric circulation types are the so-called 'Großwetterlagen' by Hess and Brezowsky. In this study, possible future changes in the occurrence of these 'Großwetterlagen' were analysed using data from 31 realisations of CMIP6 climate simulations for the emission scenarios SSP1-2.6, SSP3-7.0, and SSP5-8.5. Given the subjective nature of the classification, a deep learning ensemble for the automatic classification was developed and applied. In winter, a higher frequency of the atmospheric pattern Cyclonic Westerly towards 2100 could be inferred as a robust result among all models and scenarios. As this circulation type is potentially associated with compound flooding in some parts of the European coasts, this points towards potentially increasing risks from compound flooding in the future.