Relationships between Large-Scale Heat and Moisture Budgets and the Occurrence of Arctic Stratus Clouds

Abstract

The occurrence of cloudiness over the Beaufort Sea region of the Arctic Basin during June 1980, is related to the ambient large-scale meteorological conditions. Cloud data are obtained from a hand analysis of the visible and infrared Defense Meteorological Satellite Program (DMSP) images and from the U.S. Air Force Three-Dimensional Nephanalysis (3DNEPH). A comparison of the two cloud cover datasets showed good agreement for mid- and high-level cloudiness, but low-level cloudiness was significantly underestimated by the 3DNEPH. The study therefore uses a composite data set consisting of the 3DNEPH data at mid- and upper-levels, and the DMSP data at lower levels. Atmospheric data are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) objective analysis, and large-scale heat and moisture budgets are constructed. The budgets are used to investigate the processes which contribute to relative humidity changes. The budgets are related to the cloud cover for both the monthly cloudiness values averaged over the entire region, and for the twice-daily grid point values. Large amounts of low cloud cover during June are attributed primarily to the low level advection of moisture and a residual cooling due to radiation and boundary layer turbulence. The occurrence of midlevel cloudiness is associated with the large-scale transport of heat and moisture. Several relative humidity-based parameterizations currently used in GCMs were tested for their ability to diagnose June 1980 conditions in the Arctic using the initialized fields, but their performance was generally poor. The addition of other atmospheric parameters and budget terms in the empirical formulae provided some improvement, although the agreement with observations remained limited. While our results are dependent upon the quality of the atmospheric and cloud data in this region, they provide further examples of the deficiencies of simple diagnostic layered-cloud parameterizations.