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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
Peer reviewed article  
Beschikbaar in  Auteurs 
Trefwoord
    Marien/Kust
Author keywords
    deep learning; Antarctic; sea ice; seasonal prediction
Auteurs  Top 
  • Dong, X.R.
  • Nie, Y.F.
  • Wang, J.
  • Luo, H.
  • Gao, Y.C.
  • Wang, Y.
  • Liu, J.P.
  • Chen, D.K.
  • Yang, Q.H.
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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