Changes in trade wind are closely related to global climate. The variation of trade wind can adjust the sea surface temperature (SST) of the tropical Pacific, which in turn affects the global temperature (England et al., 2014;Kosaka & Xie, 2013;Thompson et al., 2015). Trade wind plays an important role in air-sea coupling. Changes in the speed of trade wind can influence the velocity and volume transport of the Kuroshio current (Sawada & Handa, 1998). In addition, thermocline depth responds to changes in trade wind (Venancio et al., 2018). Trade wind-induced ocean heat content increase can lead to the onset of El Niño events (Anderson et al., 2013). As an important part of global atmospheric circulation, trade wind is closely related to Walker circulation (Krishnamur-

This study finds that sea level height in Arctic marginal sea in melting season enters an accelerated rise period since the beginning of the 21st century. It is found that precipitation is the dominant factor affecting the change of sea level height in melting season in 1979-1998. Polar vortex and Arctic Oscillation become dominant factors since the accelerated rise period, especially in Norwegian Sea, Barents Sea and Kara Sea. Main reason for the change of dominant factors may be that a clockwise surface wind anomaly in strong polar vortex year became more significant in these regions during the accelerated rise period. The strong wind anomaly affects distribution of sea water through processes such as surface wind stress. Specifically, a polar vortex-wind-sea level height mechanism is strengthened, thus affecting the change of sea level height. CESM2 future scenario simulation results show that sea level height will rise by 0.4m by 2100.

To understand the Arctic environment, which is closely related to sea ice and to reduce potential risks, reliable sea ice forecasts are indispensable. A practical, lightweight yet effective assimilation scheme of sea ice concentration based on Optimal Interpolation is designed and adopted in an operational global 1/10° surface wave-tide-circulation coupled ocean model (FIO-COM10) forecasting system to improve Arctic sea ice forecasting. Twin numerical experiments with and without data assimilation are designed for the simulation of the year 2019, and 5-day real-time forecasts for 2021 are implemented to study the sea ice forecast ability. The results show that the large biases in the simulation and forecast of sea ice concentration are remarkably reduced due to satellite observation uncertainty levels by data assimilation, indicating the high efficiency of the data assimilation scheme. The most significant improvement occurs in the marginal ice zones. The sea surface temperature bias averaged over the marginal ice zones is also reduced by 0.9 °C. Sea ice concentration assimilation has a profound effect on improving forecasting ability. The Root Mean Square Error and Integrated Ice-Edge Error are reduced to the level of the independent satellite observation at least for 24-h forecast, and sea ice forecast by FIO-COM10 has better performance than the persistence forecasts in summer and autumn.