Lin, Y., Yang, Q., Li, X., Dong, X., Luo, H., Nie, Y., et al. 2025: Ice-kNN-south: A lightweight machine learning model for Antarctic sea ice prediction. JAMES. https://doi.org/10.1029/2024JH000433

Summary: Antarctic sea ice has undergone a transition, with more frequent extreme minimum events observed since 2014, emphasizing the ongoing need for accurate predictions. We developed a lightweight machine learning model called Ice-k-nearest neighbor (kNN)-South to improve Antarctic sea ice prediction. Compared with commonly used benchmarks, such as anomaly persistence, climatology, and the European Centre for Medium-Range Weather Forecasts predictions, the Ice-kNN-South shows skillful predictions for almost 90 lead days, especially in summer. Even in years with extreme minimum sea ice areas, Ice-kNN-South shows strong skill in predicting Antarctic sea ice cover. Additionally, due to its minimal computational resource requirements, Ice-kNN-South shows promise for operational and real-time Antarctic sea ice prediction applications.

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