Data Driven modelling of Sea Ice Drift

Abstract

We develop a realistic simulation of sea ice drift and deformation using machine learning (ML) techniques as an alternative to traditional physics-based numerical models. Traditional numerical sea ice models like neXtSIM or CICE are based on complex dynamic equations with parametrisations for air and ocean drag, Cori- olis effects, and internal ice stress, treating ice as either a viscous-plastic liquid or an elastic-brittle solid body. We propose a novel approach using data-driven techniques to replace these complex equations, reducing computational time and enhancing model flexibility and observational data integration. Our deep learning algorithms are based on graph neural networks (GNNs) to emulate neXTSIM dynamics. GNNs handle non-gridded and unstructured data, enabling fully Lagrangian modeling within neXTSIM’s triangular mesh. Trained on climate models with realistic wind and ocean forcing and sea ice conditions, these algorithms are evaluated against neXTSIM simulations. Two setups are evaluated: the central Arctic and the entire Arctic basin, using Graph-Unet and Mesh-Graph-Nets (MGN) GNNs. The MGN model achieves better performance than the Graph-Unet model, with predictions over the central Arctic being more accurate. The ML models accurately predict velocities and dis- placements for each mesh node for lead times of 1, 2, and 3 hours, capturing sea ice drift and deformation. They can also be iteratively applied for longer predictions using existing weather data, updating sea ice concentration and thickness based on predicted dynamics. Preliminary results indicate significant potential for GNNs to emulate mesh-based sea ice simulations, reducing computational time by orders of magnitude and en- abling new data assimilation techniques and observational data integration