Advancing seasonal storm surge forecasting through weather pattern–informed ensemble subselection

Abstract. This study investigates how large-scale atmospheric circulation regimes can improve seasonal storm surge predictions in the North Sea using the high-resolution Max Planck Institute Earth System Model (MPI-ESM-HR) forecast ensemble. Although the ensemble mean shows limited skill in predicting interannual variations of seasonal storm surge activity, circulation-dependent information within the ensemble can be exploited through weather-pattern-informed subselection to improve predictions beyond the full ensemble mean. The approach provides a physically interpretable framework for extending circulation-based coastal hazard forecasting towards seasonal timescales. An idealised approach using perfect knowledge of weather patterns achieves anomaly correlations of 0.78 and 0.64 for seasonal predictions of surge height and surge-event counts, respectively, substantially exceeding the forecast-based subselection (0.27 and 0.31). This gap highlights the potential to improve seasonal storm surge forecasts through better prediction of surge-relevant atmospheric regimes.