Subseasonal prediction of regional Antarctic sea ice by a deep learning model
- 1CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
- 2Lamont-Doherty Earth Observatory of Columbia University, New York, USA
- 3National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
- 4First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China
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.
How to cite: Wang, Y., Yuan, X., Ren, Y., Bushuk, M., Shu, Q., Li, C., and Li, X.: Subseasonal prediction of regional Antarctic sea ice by a deep learning model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14303, https://doi.org/10.5194/egusphere-egu24-14303, 2024.
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