Ocean-Aware Sea-ice Emulator for Future Hybrid Coupled Prediction Systems
Year: 2026
Type: preprint
Abstract: Neural network–based emulators can reproduce atmospheric dynamics at a far lower computational cost than physics based models, and similar advances are now emerging for sea ice. We develop and assess a deterministic sea-ice emulator, intended for future coupling to an ocean model. It uses a U-Net architecture with physics-informed loss enforcing cross-variable consistency and is trained on coupled sea ice–ocean simulations. We examine emulator performance across lead times from hourly to daily and under different ocean forcings. Forcing with surface currents consistently causes instabilities, likely because the sea-ice fields already encode an implicit representation of ocean state. In contrast, forcing with sub-surface currents plus sea surface height, salinity, and temperature greatly improves stability and skill for sea-ice volume, concentration, drift, and snow thickness. Among the temporal resolutions tested, the 6-hourly emulator best balances accuracy and stability, outperforming both finer and coarser configurations as well as previously developed sea-ice emulators relying on atmospheric forcing alone. This configuration remains stable over multiple years, faithfully reproduces the seasonal cycle, large-scale drift patterns, Fram Strait export, and achieves a correlation with satellite-derived sea-ice concentration of 77.9%, compared to 78.6% for the NANUQ reference itself. We further test robustness to changes in atmospheric forcing and find degraded performance—especially for sea-ice drift and in regions with strong atmospheric variability—when switching reanalysis products, driven by differences in forcing statistics. Our results highlight the critical role of ocean forcing choice and temporal resolution in building stable sea-ice emulators suitable for coupled short-term and climate prediction systems.
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7202123850