This paper evaluates the ability of 35 models from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, Bering Sea and Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the 21st century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with Sea Ice Simulator (SIS) sea-ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Sea. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multi-year linear trend and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature (SAT) over 1979-2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than observed (1.37×106 km2 K-1).
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-
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