Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction
Abstract Accurately predicting Antarctic sea ice on a subseasonal‐to‐seasonal scale remains a challenge for current numerical models, partly due to imperfect model parameterizations and the extensive computational resources required. Here, we have developed a lightweight machine learning model, Ice‐...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000433 |
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| author | Yongcheng Lin Qinghua Yang Xuewei Li Xiaoran Dong Hao Luo Yafei Nie Jiuke Wang Yiguo Wang Chao Min |
| author_facet | Yongcheng Lin Qinghua Yang Xuewei Li Xiaoran Dong Hao Luo Yafei Nie Jiuke Wang Yiguo Wang Chao Min |
| author_sort | Yongcheng Lin |
| collection | DOAJ |
| description | Abstract Accurately predicting Antarctic sea ice on a subseasonal‐to‐seasonal scale remains a challenge for current numerical models, partly due to imperfect model parameterizations and the extensive computational resources required. Here, we have developed a lightweight machine learning model, Ice‐k‐nearest neighbor (kNN)‐South, to predict Antarctic sea ice concentration anomaly (SICA) up to 90 days in advance. The lightweight sea ice prediction model can be executed efficiently with limited computational resources. Compared with anomaly persistence, climatology, and the European Centre for Medium‐Range Weather Forecasts predictions, the Ice‐kNN‐South exhibits improved performance for almost 90 lead days, as evaluated by the mean absolute error (MAE), anomaly correlation coefficient (ACC), and integrated ice edge error (IIEE). Besides, the improvements of Ice‐kNN‐South are more pronounced in regions with high SICA variability by implementing a multi‐scale structure that combines field‐to‐field and grid‐to‐grid predictions. Furthermore, Ice‐kNN‐South shows the most significant enhancement in predictive skill during the summer and autumn seasons, particularly in summer. In contrast, relatively smaller enhancements are observed in the West Pacific Ocean and the Ross Sea during winter and spring. Even during the years of minimum sea ice areas (SIAs) in 2017, 2022, 2023, and 2024, Ice‐kNN‐South accurately captures sea ice variations, maintaining monthly averaged SIA errors below 8.5% compared to satellite observations within a 30‐day lead time. These findings suggest a potential approach to improve the accuracy of Antarctic sea ice predictions, particularly for the use of data‐driven models. |
| format | Article |
| id | doaj-art-1aa763977e17459da5e7ed5a91c7ecb2 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-1aa763977e17459da5e7ed5a91c7ecb22025-08-20T03:44:24ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-03-0121n/an/a10.1029/2024JH000433Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice PredictionYongcheng Lin0Qinghua Yang1Xuewei Li2Xiaoran Dong3Hao Luo4Yafei Nie5Jiuke Wang6Yiguo Wang7Chao Min8School of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai ChinaSchool of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai ChinaState Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian ChinaSchool of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai ChinaSchool of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai ChinaSchool of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai ChinaSchool of Artificial Intelligence Sun Yat‐sen University Zhuhai ChinaNansen Environmental and Remote Sensing Centre and Bjerknes Centre for Climate Research Bergen NorwaySchool of Atmospheric Sciences Sun Yat‐sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai ChinaAbstract Accurately predicting Antarctic sea ice on a subseasonal‐to‐seasonal scale remains a challenge for current numerical models, partly due to imperfect model parameterizations and the extensive computational resources required. Here, we have developed a lightweight machine learning model, Ice‐k‐nearest neighbor (kNN)‐South, to predict Antarctic sea ice concentration anomaly (SICA) up to 90 days in advance. The lightweight sea ice prediction model can be executed efficiently with limited computational resources. Compared with anomaly persistence, climatology, and the European Centre for Medium‐Range Weather Forecasts predictions, the Ice‐kNN‐South exhibits improved performance for almost 90 lead days, as evaluated by the mean absolute error (MAE), anomaly correlation coefficient (ACC), and integrated ice edge error (IIEE). Besides, the improvements of Ice‐kNN‐South are more pronounced in regions with high SICA variability by implementing a multi‐scale structure that combines field‐to‐field and grid‐to‐grid predictions. Furthermore, Ice‐kNN‐South shows the most significant enhancement in predictive skill during the summer and autumn seasons, particularly in summer. In contrast, relatively smaller enhancements are observed in the West Pacific Ocean and the Ross Sea during winter and spring. Even during the years of minimum sea ice areas (SIAs) in 2017, 2022, 2023, and 2024, Ice‐kNN‐South accurately captures sea ice variations, maintaining monthly averaged SIA errors below 8.5% compared to satellite observations within a 30‐day lead time. These findings suggest a potential approach to improve the accuracy of Antarctic sea ice predictions, particularly for the use of data‐driven models.https://doi.org/10.1029/2024JH000433machine learningsea ice predictionAntarctic sea iceice‐kNN‐south |
| spellingShingle | Yongcheng Lin Qinghua Yang Xuewei Li Xiaoran Dong Hao Luo Yafei Nie Jiuke Wang Yiguo Wang Chao Min Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction Journal of Geophysical Research: Machine Learning and Computation machine learning sea ice prediction Antarctic sea ice ice‐kNN‐south |
| title | Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction |
| title_full | Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction |
| title_fullStr | Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction |
| title_full_unstemmed | Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction |
| title_short | Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction |
| title_sort | ice knn south a lightweight machine learning model for antarctic sea ice prediction |
| topic | machine learning sea ice prediction Antarctic sea ice ice‐kNN‐south |
| url | https://doi.org/10.1029/2024JH000433 |
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