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Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario
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作者 xiaoran dong Yafei NIE +6 位作者 Jinfei WANG Hao LUO Yuchun GAO Yun WANG Jiping LIU Dake CHEN Qinghua YANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第8期1569-1573,共5页
The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the refo... The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction. 展开更多
关键词 deep learning ANTARCTIC sea ice seasonal prediction
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