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Abstract

Sea ice exists between the atmosphere and the ocean and plays a fundamental role in the global energy budget. Skillfully dynamic sea ice prediction requires the ocean and atmosphere forcings and the concerned response processes to be adequate precision. Clearly, we still need to use the data assimilation method compared with the different observations to optimize the model forecast quantitatively. As a flow-dependent data assimilation method of Ensemble Kalman Filter (EnKF), multiple types of observations from ocean and sea ice have been assimilated into a strongly coupled ocean and sea-ice forecast system-TOPAZ. In this study, the two parallel assimilation runs were done in the TOPAZ system with and without the assimilation of SIT during 2014-2017. Except for the observations for SIT, both of the two runs assimilate other rest types of observations from ocean and sea ice as well. Firstly, we investigate the autocorrelations of SIC and SIT in the two runs respectively. The result of the SIT variability shows a much longer timescale (100 days). The differences incurred from the additional assimilation of the SIT observations have been investigated as well. The further analysis highlights the dynamics between the SIT and the key variables from the atmospheric forcing through the singular value decomposition (SVD), which improves understanding of the coupling responses between sea ice and the atmosphere.