Abstract
In recent years, the Weddell Sea ice loss accounted for nearly half of Antarctic sea ice loss, whose prediction has attracted a growing number of attentions. In this study, we developed two types of monthly spatial statistical prediction models for sea ice concentration (SIC) in the Weddell Sea, specifically the Multiple Linear Regression (MLR) and Multivariate Empirical Orthogonal Function (MEOF) prediction models. Both prediction models shared a common set of advanced oceanic and atmospheric variables, including the Pacific Decadal Oscillation, eastern tropical Pacific ocean sea surface temperatures (SST), western tropical Indian ocean SST, southern tropical Atlantic ocean SST, Antarctica sea level pressure, and Amundsen-Weddell Dipole of surface air temperatures with the leading times of 1-57 month(s). The 1st-month Weddell SIC ahead of the predicting time was also individually incorporated or together with the 12th-month advanced one as the predicting factors. Although the MEOF method is excellent at extracting the spatial mode of meteorological variables, it is unexpected that the MLR prediction model demonstrated enhanced performances. The median of average ACC of MLR prediction model was up to 0.45 and that of average RMSE was as low as 0.8, which was significantly better than these of 0.34 and 1.12 of MEOF prediction model. This study highlights the advantages of MLR prediction model and the importance of employing advanced sea ice to improve the spatial predictive performance of statistical models.
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