Projecting storm surge extremes with a deep learning surrogate model

Sea level rise and increasing coastal flood risks demand the development of accurate and efficient coastal risk models capable of generating large ensembles of projections to support robust adaptation strategies. The latest IPCC report emphasizes the importance of projecting storm surge changes and their associated uncertainties, alongside mean sea level rise. However, the high computational cost of storm surge simulations continues to limit the feasibility of generating large ensembles.
Artificial Intelligence (AI) is emerging as a promising alternative to simulate storm surge scenarios with significantly reduced computational costs. Despite recent advancements, key challenges remain in accurately representing extreme events and ensuring robust model extrapolation under changing climate conditions. While AI-based surrogate models have been proposed in the literature, gaps persist in understanding their performance limits for extreme events in future scenarios, hindering their application in climate adaptation planning.
To address these challenges, we developed a deep learning (DL) surrogate model of the physics-based Global Tide and Surge Model (GTSM). The DL model is trained using reanalysis data (ERA5) and historical scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) High Resolution Model Intercomparison Project (HighResMIP). Our analysis focuses on the DL model's performance in simulating extreme storm surge events, validated against GTSM outputs for both historical reanalysis and future projections, with a case study along the New York coastline.
To enhance the surrogate model’s performance for extreme events, we explore various loss functions, including a customized quantile loss function, and test alternative DL architectures with different input configurations. Results demonstrate that the quantile loss improves the model's accuracy for extremes compared to standard loss functions such as mean square error. Additionally, fine-tuning DL models with specific Global Climate Model forcing fields improves the alignment of AI-predicted storm surge trajectories with GTSM outputs, even under diverse spatiotemporal resolutions and model setups.
These findings highlight the critical importance of selecting appropriate loss functions and training datasets to ensure robust performance over extreme events and projected future scenarios. Our globally applicable framework, relying solely on open-source data, offers a promising pathway to scalable and efficient storm surge projections, with implications for robust coastal adaptation planning.