Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model

Wang, Yunhe; Yuan, Xiaojun; Ren, Yibin; Bushuk, Mitchell; Shu, Qi; Li, Cuihua; Li, Xiaofeng

Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1-8 weeks) due to limited understanding of ice-related physical mechanisms. To overcome this limitation, we developed a deep learning model named SIPNet that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like ECMWF, NCEP, and GFDL-SPEAR, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice.

Geographic Areas


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Geophysical Research Letters

More About This Work

Academic Units
Lamont-Doherty Earth Observatory
Ocean and Climate Physics
Published Here
September 6, 2023