Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The...Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns （WASWP1 and WASWP2）. Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWPI and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland--Barents--Kara seas from autunm to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns （WASWP3 and WASWP4）. Their time evolutions are significantly correlated with the North Atlantic Oscillation （NAO） index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure （SLP） north of 70~N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations： Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation （or NAO）, effectively affect the spatial distribution of Arctic sea ice anomalies.展开更多
基金supported by the National Key Basic Research Project of China (Grant nos.2013CBA01804,2015CB453200)the National Natural Science Foundation of China (Grant nos.41475080,41221064)the Ocean Public Welfare Scientific Research Project of China (Grant no.201205007)
文摘Dominant statistical patterns of winter Arctic surface wind （WASW） variability and their impacts on Arctic sea ice motion are investigated using the complex vector empirical orthogonal function （CVEOF） method. The results indicate that the leading CVEOF of Arctic surface wind variability, which accounts for 33% of the covariance, is characterized by two different and alternating spatial patterns （WASWP1 and WASWP2）. Both WASWP1 and WASWP2 show strong interannual and decadal variations, superposed on their declining trends over past decades. Atmospheric circulation anomalies associated with WASWPI and WASWP2 exhibit, respectively, equivalent barotropic and some baroclinic characteristics, differing from the Arctic dipole anomaly and the seesaw structure anomaly between the Barents Sea and the Beaufort Sea. On decadal time scales, the decline trend of WASWP2 can be attributed to persistent warming of sea surface temperature in the Greenland--Barents--Kara seas from autunm to winter, reflecting the effect of the Arctic warming. The second CVEOF, which accounts for 18% of the covariance, also contains two different spatial patterns （WASWP3 and WASWP4）. Their time evolutions are significantly correlated with the North Atlantic Oscillation （NAO） index and the central Arctic Pattern, respectively, measured by the leading EOF of winter sea level pressure （SLP） north of 70~N. Thus, winter anomalous surface wind pattern associated with the NAO is not the most important surface wind pattern. WASWP3 and WASWP4 primarily reflect natural variability of winter surface wind and neither exhibits an apparent trend that differs from WASWP1 or WASWP2. These dominant surface wind patterns strongly influence Arctic sea ice motion and sea ice exchange between the western and eastern Arctic. Furthermore, the Fram Strait sea ice volume flux is only significantly correlated with WASWP3. The results demonstrate that surface and geostrophic winds are not interchangeable in terms of describing wind field variability over the Arctic Ocean. The results have important implications for understanding and investigating Arctic sea ice variations： Dominant patterns of Arctic surface wind variability, rather than simply whether there are the Arctic dipole anomaly and the Arctic Oscillation （or NAO）, effectively affect the spatial distribution of Arctic sea ice anomalies.
