School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Mirong Song
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Ke Fan
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China
Rune Grand Graversen
Department of Physics and Technology, Arctic University of Norway, Tromsø 9019, Norway
Antarctic sea ice has changed rapidly in recent years. Here we developed a deep learning model trained by multiple climate variables for extended seasonal Antarctic sea ice prediction. Our model shows high predictive skills up to 6 months in advance, particularly in predicting extreme events. It also shows skillful predictions at the sea ice edge and year-to-year sea ice changes. Variable importance analyses suggest what variables are more important for prediction at different lead times.
Antarctic sea ice has changed rapidly in recent years. Here we developed a deep learning model...