Deep learning shows promise for seasonal prediction of Antarctic sea ice in a rapid decline scenario
Dong, X.R.; Nie, Y.F.; Wang, J.; Luo, H.; Gao, Y.C.; Wang, Y.; Liu, J.P.; Chen, D.K.; Yang, Q.H. (2024). Deep learning shows promise for seasonal prediction of Antarctic sea ice in a rapid decline scenario. Adv. atmos. sci. Online First: 5. https://dx.doi.org/10.1007/s00376-024-3380-y
In: Advances in Atmospheric Sciences. China Ocean Press/Springer: Beijing. ISSN 0256-1530; e-ISSN 1861-9533, more
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| Keyword |
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| Author keywords |
deep learning; Antarctic; sea ice; seasonal prediction |
| Authors | | Top |
- Dong, X.R.
- Nie, Y.F.
- Wang, J., more
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- Luo, H.
- Gao, Y.C.
- Wang, Y.
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- Liu, J.P.
- Chen, D.K.
- Yang, Q.H.
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| Abstract |
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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