The Dependence of Arctic Cloud on the Synoptic Conditions in the Beaufort and Chukchi Seasonal Ice Zone

How atmosphere and sea ice interact depends on the prevailing weather. Synoptic activities transport energy and moisture into the Arctic and modify the structure of the atmosphere, cloud conditions, and the surface energy budget over sea ice. The structure of the atmosphere, such as temperature inversions and specific humidity inversions are critical for the life cycle of Arctic clouds. Cloud radiative heating is an important component of the Arctic surface energy budget. The differences in the structure of the atmosphere, clouds, and the radiation balance at the surface under different synoptic conditions will determine which processes will govern the interaction between the atmosphere and clouds.

In this study, four dominant synoptic states over the Beaufort and Chukchi seasonal ice zone (BCSIZ) are identified using the ERA-Interim reanalysis and a k-mean clustering synoptic classification algorithm. The synoptic classification algorithm categorizes individual weather events in the atmospheric reanalysis into synoptic states with distinct signatures in baroclinicity and temperature advection. Using the CloudSat/Calipso joint cloud mask, the cloud conditions of the four synoptic states in the BCSIZ are examined. The cloud fraction of the four states are significantly different at different levels, which are associated with differences in the lower tropospheric static stability and the abundance of moisture, especially in the middle and lower atmosphere. These differences are also captured by the ERA-Interim although the ERA-Interim greatly overestimates the low-level cloud fraction and underestimates cloud fraction higher up, which is partly due to the limitation of the joint CloudSat/Calipso cloud mask below 1 km. In addition, the seasonal cycle and horizontal distribution of cloud fraction and cloud radiative heating at surface are examined for the four synoptic states using the joint CloudSat/Calipso retrievals. Two versions of independent retrievals, such as GEOPROF-LIDAR and CCCM, are used to evaluated the uncertainty associated with retrieval algorithms.