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Sea ice data assimilation can greatly improve forecasts of Arctic sea ice evolution. Many previous sea ice data assimilation studies were conducted without assimilating ocean state variables, even though the sea ice evolution is closely linked to the oceanic conditions, both dynamically and thermodynamically. Based on the method of a localized ensemble error subspace transform Kalman filter, satellite‐retrieved sea ice concentration and sea ice thickness are assimilated into an Arctic sea ice‐ocean model. As a new addition, sea surface temperature (SST) data are also assimilated. The additional assimilation of SST improves not only the simulated ocean temperature in the mixed layer of the ocean substantially but also the accuracy of sea ice edge position, sea ice extent, and sea ice thickness in the marginal sea ice zone. The improvement in the simulated potential temperature in the upper 1,000 m can be attributed to the enhanced vertical convection processes in the regions where the assimilated observational SST is colder than the simulated SST without assimilation. The improvements in the sea ice edge position and sea ice thickness simulations are primarily caused by the SST data assimilation reducing biases in the simulated SST and the associated coupled ocean‐sea ice processes. Our investigation suggests that, due to the complex interaction between the sea ice and ocean, assimilating ocean data should be an indispensable component of numerical polar sea ice forecasting systems. Key Points: Sea surface temperature assimilation improves upper ocean temperature, sea ice edge, and marginal sea ice thickness simulationsSimulated upper ocean temperatures improve more where vertical convection processes are more importantSea ice edge and thickness simulations are improved due to the correction of the SST bias [ABSTRACT FROM AUTHOR]

The impact of assimilating weekly CryoSat‐2 sea ice thickness data together with daily SMOS sea ice thickness and daily SSMIS sea ice concentration data on the sea ice fields of a coupled sea ice–ocean model of the Arctic Ocean is investigated. The sea‐ice model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter coded in the Parallel Data Assimilation Framework (PDAF). A period of three months from 1 November 2011 to 30 January 2012 is selected to assess the skill of the assimilation system in the cold season. Compared to the unassimilated solution and a solution where only sea ice concentration is assimilated, the model–data misfits are substantially reduced in areas of both thick and thin ice. The sea ice thickness estimates agree significantly better with in situ observations in the central Arctic Ocean than the sea ice thickness obtained from assimilating SMOS data alone, while the sea ice concentration shows very small improvements. The sea ice fields obtained by the joint assimilation of SMOS and CryoSat‐2 data also have lower errors in thickness and concentration than those obtained from directly assimilating a statistically merged SMOS and CryoSat‐2 sea ice thickness product. These lower errors suggest that model dynamics play a significant role in data blending. [ABSTRACT FROM AUTHOR]

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°C 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. [ABSTRACT FROM AUTHOR]