Dong, X. R., and Coauthors, 2024: Deep learning shows promise for seasonal prediction of Antarctic sea ice in a rapid decline scenario. Adv. Atmos. Sci., 41(8), 1569−1573, https://doi.org/10.1007/s00376-024-3380-y.
Citation: Dong, X. R., and Coauthors, 2024: Deep learning shows promise for seasonal prediction of Antarctic sea ice in a rapid decline scenario. Adv. Atmos. Sci., 41(8), 1569−1573, https://doi.org/10.1007/s00376-024-3380-y.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

  • 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 (ConvLSTM) Network. The reforecast experiments demonstrate that ConvLSTM 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.
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