| Summary: | To improve the sea ice initial condition in the Arctic, we assimilate the satellite-derived sea ice concentrations within the Data Assimilation Research Testbed (DART) system, based on the ensemble Kalman filter (EnKF), coupled with the sea ice model in the Community Earth System Model (CESM). The EnKF-based assimilation results show that the Arctic sea ice initial condition is significantly improved by assimilating the satellite-derived sea ice concentration data. However, during the Arctic sea ice freezing season, the assimilation impact tends to be degraded due to the reduction of the ensemble spread within the EnKF-based assimilation system. To counteract the ensemble spread reduction, we apply the randomized-dormant ensemble Kalman filter (RD-EnKF) method in which the model backgrounds are more perturbed by leaving the dormant ensemble members out of the total ensemble members from the analysis update, inflating the ensemble spread. Compared with the assimilation results using the EnKF method, the additional analysis benefits are obtained due to the increment of the ensemble spread derived by applying the RD-EnKF method, in particular, during the Arctic sea ice freezing season.
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