An Interactive Hybrid Framework Coupling Machine Learning and Dynamical Models for Arctic Sea Ice Prediction

Arctic sea ice has declined markedly over the past four decades, bringing new opportunities for commercial shipping and eco-tourism while elevating risks to shipping safety, which highlights the urgent demand for a seamless and skillful sea-ice prediction system. Current sea-ice prediction approaches fall into two main categories: statistical and dynamical models. The former (including machine learning) show promising performance in sea-ice concentration prediction but lack sufficient physical constraints due to their pure data-driven nature; the latter (including Earth system models) are physically grounded with strong consistency and multivariable coherence, yet suffer from high computational costs, imperfect parameterizations and accumulated forecast errors. To overcome these limitations, we propose a hybrid framework that integrates the complementary strengths of machine learning and dynamical models. Specifically, we develop an interactive ensemble prediction system, termed SUPER, which couples the machine learning model Ice-kNN with the Norwegian Climate Prediction Model (NorCPM) to enable recursive information exchange between the two models. Within SUPER, Ice-kNN and NorCPM run in parallel and exchange information at regular intervals (e.g., weekly), allowing mutual adjustment of their predictions. We conduct seasonal hindcasts for the period 2000–2023 and evaluate the daily sea-ice prediction skill of SUPER against that of the standalone models. Preliminary results indicate that the hybrid system substantially reduces errors in sea-ice concentration and sea-ice edge predictions for NorCPM, while yielding improvements for Ice-kNN. Further tuning and evaluation are ongoing, and updated results will be presented.