Multidecadal variability and predictability of Antarctic sea ice in the GFDL SPEAR_LO model

• Published in: The Cryosphere
• Other Authors: Morioka, Yushi (author), Zhang, Liping (author), Delworth, Thomas L. (author), Yang, Xiaosong (author), Zeng, Fanrong (author), Nonaka, Masami (author), Behera, Swadhin K. (author)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2023
• Subjects: Antarctic; The Antarctic; Weddell; Weddell Sea; Antarc*; Sea ice; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-17-5219-2023

Using a state-of-the-art coupled general circulation model, physical processes underlying Antarctic sea ice multidecadal variability and predictability are investigated. Our model simulations constrained by atmospheric reanalysis and observed sea surface temperature broadly capture a multidecadal variability in the observed sea ice extent (SIE) with a low sea ice state (late 1970s-1990s) and a high sea ice state (2000s-early 2010s), although the model overestimates the SIE decrease in the Weddell Sea around the 1980s. The low sea ice state is largely due to the deepening of the mixed layer and the associated deep convection that brings subsurface warm water to the surface. During the high sea ice period (post-2000s), the deep convection substantially weakens, so surface wind variability plays a greater role in the SIE variability. Decadal retrospective forecasts started from the above model simulations demonstrate that the Antarctic sea ice multidecadal variability can be skillfully predicted 6-10 years in advance, showing a moderate correlation with the observation. Ensemble members with a deeper mixed layer and stronger deep convection tend to predict a larger sea ice decrease in the 1980s, whereas members with a larger surface wind variability tend to predict a larger sea ice increase after the 2000s. Therefore, skillful simulation and prediction of the Antarctic sea ice multidecadal variability require accurate simulation and prediction of the mixed layer, deep convection, and surface wind variability in the model.