Satellite-Driven Spatiotemporal Multiscale Perception Learning for Estimating Daily Arctic Sea Ice Thickness

Abstract

Sea ice thickness (SIT) is a key variable in improving the accuracy and lead time of sea ice forecasts. However, obtaining continuous gridded Arctic spatiotemporal SIT fields remains a significant challenge. This study proposes a machine learning framework (ICE-3D), which directly produces daily gridded pan-Arctic SIT. ICE-3D establishes a spatiotemporal, multiscale, perception learning-based estimation paradigm. We design an innovative training strategy called spatiotemporal multiscale fusion window (STMFW). The strategy fuses physical covariations at different scales to improve targeted inference, especially for summer SIT. Validated against CryoSat-2 satellite altimetry and BGEP in situ measurements, ICE-3D achieves an overall correlation coefficient exceeding 0.75 for basin-scale SIT. Its summer SIT MAE is less than 0.38 m, outperforming TOPAZ4 reanalysis and showing comparable precision to PIOMAS within validated regions. Compared with static offline training, the online learning module reduces SIT overestimation bias in marginal ice zones by approximately 16%, and the model reliably captures high-frequency regional ice variations and seasonal sea ice volume (SIV) cycles. Our derived SIV results reveal a 38% loss of pan-Arctic sea ice volume from 1991 to 2020, accompanied by a stabilized melting rate over the past decade, indicating severely depleted perennial ice reserves and largely irreversible Arctic sea ice decline under the current climate regime. The ICE-3D SIT with quantified uncertainties supports multiscale Arctic climate analysis and provides critical data support for risk assessment during the peak Arctic shipping season.