The surface energy budget of the polar regions controls sea ice growth and snow melt, which in turn exert a profound influence on the global climate system. Arguably the single most important parameter to monitor for energy balance studies is the surface temperature. Because of the relative difficulty in collecting widespread in situ observations, satellite measurements play an increasingly important role in climate reseach. This presents significant challenges, however, due to the inability of thermal satellite sensors to measure the temperature under cloud cover. The problem is further complicated by our inadequate knowledge of how clouds modify the surface energy balance.
In this paper we examine the physical relationship between radiative fluxes, energy exchange, and surface temperature under changing cloud cover using surface observations and a thermodynamic model. The study extends previous work by incorporating solar radiative fluxes and varying cloud properties. Data from the Arctic Leads Experiment (LEADEX), the NOAA/CMDL Barrow Observatory, and SHEBA are used in the analysis. The thermodynamic model includes a sophisticated radiative transfer submodel, a sea ice/snow submodel, and equations for atmospheric turbulence. The data and model results demonstrate that sharp changes in downwelling longwave radiation (LWD), corresponding to transitions between clear and overcast skies, influence surface temperature in a predictable, though not constant, way under all wind conditions. In general, surface temperature increases with increasing cloud cover, even during the summer months when the solar fluxes are large. The maximum correlation between total downwelling irradiance and surface (skin) temperature occurs at a time lag of 0.5 to 1 hour, where the correlation is highest under calm and overcast conditions. These results imply that it may be possible to estimate the cloudy sky surface temperature using thermal satellite data when cloud properties and nearby clear sky temperatures are known.