Daily high-resolution Arctic sea ice 3D structure inference based on a satellite-driven physics-aware machine learning framework

This study proposes a satellite-driven machine learning (ML) framework (ICE-3D) that performs real-time inference of pan-Arctic sea ice thickness (SIT) based on ocean-atmosphere-ice coupling physical information. ICE-3D establishes a physics-aware indirect retrieval paradigm that generates pan-Arctic daily SIT at a 25 km resolution from 1979 to 2023, without relying on any empirical parameters or SIT observations. We design an innovative training strategy called spatiotemporal multiscale fusion window (STMFW) for fusing physical evolution at different scales in real time to improve targeted inference, especially for summer SIT. ICE-3D employs five decision tree-based ML models that are trained by the reanalysis and then applied to satellite observations through transfer learning. The evaluation results show that AdaBoost outperforms the other four models (MAE=0.11m, PCC=0.97). Compared to the reanalysis products (PIOMAS and TOPAZ4), ICE-3D provides a more accurate summer SIT and a more reasonable estimate of the long-term trend in sea ice volume (SIV). ICE-3D is highly consistent with SIT observations from satellite and in-situ, especially in capturing high-frequency signals and seasonal SIV. In addition, the analysis results show that approximately 38% of the Arctic sea ice has disappeared from 1991 to 2020. Over the past decade, the rate of Arctic sea ice melting has stabilized, indicating that Arctic sea ice reserves may be critically insufficient and the loss is irreversible. ICE-3D provide an opportunity to understand the pan-Arctic climate across different timescales and can play a crucial role during peak periods of the Arctic shipping season.