Multiscale data-driven forecasting of Sea Ice Essential Climate Variables

Short-term forecasting of Arctic essential climate variables (ECVs) requires methods that can exploit the growing diversity of forthcoming satellite observations while remaining robust to sparse and heterogeneous sampling. This study targets sea-ice concentration (SIC) and sea-ice thickness (SIT) forecasting using observations from the new Copernicus Sentinel Expansion missions: ROSE-L providing high-resolution (≈500 m) SIC, CIMR delivering intermediate-resolution (≈5 km) SIC and thin sea ice thickness, and CRISTAL altimeter supplying SIT and sea surface height at similar scales. We propose an online multiresolution neural forecasting framework designed to ingest irregular satellite swaths across resolutions and sensor types, and to produce observation-conditioned nowcasts compatible with operational constraints. The model combines multiscale forecast architectures to explicitly handle intermittency, scale disparities, and sensor-dependent information content. Beyond its operational relevance, the framework is used as a research tool to investigate predictability across scales, enabling a systematic analysis of how submesoscale ice processes impact short-term forecast skill at coarser resolutions. Forecast performance is assessed using resolution-aware metrics, revealing scale-dependent gains in ice-edge sharpness, thin-ice variability, and short-lead SIT evolution compared to baseline methods. By explicitly combining ROSE-L, CIMR, and CRISTAL observations within a unified multiresolution framework, this work enables a direct assessment of how high-resolution sea-ice variability propagates across scales and impacts short-term predictability in operational Arctic ECV forecasts.