Abstract:

The distribution and variation of cloud attributes in spring play important roles in preconditioning September Arctic sea ice change. However, given the background of increased warming both globally and in the Arctic, the characteristics of potential connections between springtime clouds and September sea ice in different areas of the Arctic Sea should be updated. In this paper, we analyze the impact of springtime clouds on September Arctic sea ice using ERA5 radiation data, MODIS cloud fraction (CF) and cloud water path data, and sea ice concentration (SIC) data from the National Snow and Ice Data Center (USA). First, the climatological spatial distribution characteristics of cloud microphysics properties (i.e., CF and total water path (TWP)) and cloud radiation properties (i.e., longwave cloud radiation effects (LWCRE) and shortwave cloud radiation effects (SWCRE)) in spring (2000–2017) in the Arctic region are presented. Then, the correlation between cloud macroscopic properties and cloud radiation is presented, and the response of sea ice to springtime cloud properties in different areas of interest is discussed. Results show that the CF distribution decreases as the SIC increases, and that the TWP distribution increases as the latitude increases. The distribution of LWCRE is discontinuous over the Arctic Ocean and no significant regularity is observed. In areas other than the Barents and eastern Greenland seas and the Arctic Ocean to the north, the difference in SWCRE is small. Additionally, Arctic CF and TWP are correlated positively (negatively) with LWCRE (SWCRE). The correlation (both positive and negative) between TWP and cloud radiation effects is not as notable as that with CF in terms of significance and range. Over areas with a higher proportion of the marginal ice zone, i.e., the Laptev and Kara seas and the Arctic Ocean to the north (ROI1), and the Beaufort, Chukchi, and eastern Siberia seas and the Arctic Ocean to the north (ROI4), the warming effect of spring LWCRE tends to enhance sea ice melting in September, whereas the cooling effect of SWCRE exhibits the opposite effect on September sea ice with a time lag of approximately 4 months. The coefficient of determination in the multiple regression model, which can be used to characterize the degree of explanation of the independent variable to the dependent variable, indicates that CF variability of ROI1 significantly explains 18.53% of the cause of sea ice loss. In ROI4, no significant connection is found between springtime CF (and TWP) and sea ice loss in September.

Key words: Arctic, cloud properties, cloud radiation effects, sea ice