Using seasonal forecast ensembles to estimate of low-probability high-impact events and unprecedented extremes

Extratropical cyclones (ETCs) can cause severe storm surges, leading to extreme sea levels, coastal flooding and significant economic losses. Accurate estimates of storm surge frequency and intensity are crucial for flood hazard assessments and effective risk mitigation. However, limited observational records pose a challenge for predicting low-probability high-impact events and unprecedented extreme surges, particularly in regions yet to experience such events.

Global synthetic datasets have demonstrated to be crucial in addressing these limitations by providing larger datasets that reduce uncertainties in risk estimates and capture unprecedented events. Despite their potential, a comprehensive large-scale dataset for ETC-induced storm surges is currently lacking.

In this study, we explore the feasibility of pooling ensembles from ECMWF’s SEAS5 seasonal forecasting system and integrating them with the Global Tide and Surge Model (GTSM) to generate realistic synthetic storm surge events. Using the resulting extended storm surge time series, we assess the storm surge risk for Europe, identify unprecedented surge events, and advance our understanding of their underlying large-scale physical drivers.