Abstract

Abstract The Greenland ice sheet (GrIS) has been losing mass at an accelerating rate in recent decades due to warming, and understanding the underlying mechanisms, such as the impacts of clouds, is essential. Using spaceborne data, this study investigates the spatial distribution of ice clouds and liquid-bearing clouds (LBCs) over the GrIS and their surface radiative forcing effects during summer daytime from 2006 to 2017, along with their characteristics during the North Atlantic Oscillation (NAO) events. Due to the perennial high-albedo surface, both ice and LBCs have a less important shortwave radiative cooling effect than in other environments. Based on the spatial variation pattern of clouds with the NAO index, the GrIS can be divided into three regions: the western, central, and eastern GrIS. During the positive NAO, the westerly wind strengthens in the western region, which causes the fraction of both ice clouds and LBCs to increase, and the cloud radiative effect at the surface increases by 2.07 W m−2; the temperature decreases in the central region, the fraction of ice clouds increases, the fraction of LBCs decreases, and the net radiative forcing is −2.05 W m−2; and sinking airflow is generated in the eastern region, both ice cloud and LBCs decrease, and the net cloud radiative effect at the surface is −1.34 W m−2. The spatial and temporal variations in clouds in different phases over the GrIS are closely related to the NAO, and the response of clouds to changes in the atmospheric circulation field during the NAO varies in different regions of the GrIS. Significance Statement This study investigates the associations between the North Atlantic Oscillation and the spatiotemporal variations in clouds, including both cloud fraction and cloud phases, and further examines the impacts of these changes on the surface radiation balance. The findings can help us improve our understanding of cloud variability and the corresponding influence on surface radiation over the GrIS, which are essential for better prediction of ice coverage over this region and for more efficient protection of ecosystems located there.

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