Authors:
Xi Liang,a Qinghua Yang,a Lars Nerger,b Svetlana N. Losa,b Biao Zhao,c Fei Zheng,d Lin Zhang,a and Lixin Wue,f
a Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing, China
b Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research,Bremerhaven, Germany
c First Institute of Oceanography, State Oceanic Administration, Qingdao, China
d International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing, China
e Physical Oceanography Laboratory, Qingdao Collaborative Innovation Center of Marine Science and Technology (CIMST), Ocean University of China, Qingdao, China
f Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
Abstract:
Sea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2℃ at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.
Citation:
Xi Liang, Qinghua Yang, Lars Nerger, Svetlana N. Losa, Biao Zhao, Fei Zhang, Lin Zhang, and Lixin Wu: Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter, Journal of Atmospheric and Oceanic Technology, Volume 34, DOI: 10.1175/JTECH-D-16-0166.1