Seasonal Predictability of Sea Ice and Bottom Temperature Across the Eastern Bering Sea Shelf

被引:14
作者
Kearney, K. A. [1 ,2 ]
Alexander, M. [3 ]
Aydin, K. [2 ]
Cheng, W. [1 ,4 ]
Hermann, A. J. [1 ,4 ]
Hervieux, G. [3 ,5 ]
Ortiz, I [1 ,2 ]
机构
[1] Univ Washington, Cooperat Inst Climate Ocean & Ecosyst Studies, Seattle, WA 98195 USA
[2] NOAA, Alaska Fisheries Sci Ctr, Seattle, WA 98115 USA
[3] NOAA, Earth Syst Res Lab, Boulder, CO USA
[4] NOAA, Pacific Marine Environm Lab, 7600 Sand Point Way Ne, Seattle, WA 98115 USA
[5] Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO USA
关键词
seasonal forecast; Bering Sea; cold pool; Integrated Ecosystem Assessment; CLIMATE-CHANGE; PREDICTION; VARIABILITY; NORTHERN; SURFACE; SYSTEM; FISHERIES; FORECASTS; ENSEMBLE; FLUXES;
D O I
10.1029/2021JC017545
中图分类号
P7 [海洋学];
学科分类号
0707 ;
摘要
Seasonal sea ice plays a key role in shaping the ecosystem dynamics of the eastern Bering Sea shelf. In particular, it leads to the formation of a characteristic pool of cold water that covers the bottom of the shelf from winter through summer; the extent of this cold pool is often used as a management index for distribution, productivity, recruitment, and survival of commercially important fish and shellfish species. Here, we quantify our ability to seasonally forecast interannual variability in Bering Sea bottom temperature and sea ice extent. Retrospective forecast simulations from two global forecast models are downscaled using a regional ocean model; the retrospective forecast simulations include 9-month to 12-month forecasts spanning 1982-2010. We find that dynamic forecasting can predict summer bottom temperatures across the eastern Bering Sea shelf with lead times of up to 4 months. The majority of the prediction skill derives from the persistence signal, and a persistence forecast is comparably skillful to the dynamic forecast at these lead times. However, forecast skill of sea ice advance and retreat is low when a forecast model is initialized before or during the ice season (October-February); this limits the ability of either dynamic or persistence models to predict summer bottom temperatures when initialized across the late fall to early spring months.
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页数:21
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