Spatial Variability of Surface-Level Meteorological Variables over Arctic Sea Ice (Invited Presentation)

Numerical models are divided into horizontal grid cells that can range in size from a few kilometers to hundreds of kilometers. In these models, surface-level variables are often assumed to be uniform over any given grid cell. Using a year of data from the experiment to study the Surface Heat Budget of the Arctic Ocean (SHEBA), I investigate the accuracy of this assumption of grid cell uniformity for surface-level variables over sea ice. The variables that I study are pressure, air temperature, wind speed, humidity, surface temperature, incoming longwave radiation, and the turbulent surface fluxes of momentum and sensible heat. I base this analysis on three statistics: the spatial correlation function, the spatial bias, and a normalized bias. For the five SHEBA sites, which had a maximum separation of 12 km, the analysis supports the assumption of grid cell uniformity in pressure, air temperature, wind speed, humidity, and momentum flux in all seasons. Because of site selection, the surface temperature also appears spatially uniform in the analysis but is known to depend on ice thickness, at least in winter. Variables that depend strongly on surface temperature, such as the emitted longwave radiation and the surface fluxes of sensible and latent heat, may therefore not be spatially uniform. I also suggest that incoming longwave radiation may not be uniform over a grid cell in winter, when the incidence of fractional cloudiness is largest. In other seasons, the bimodal distribution in cloud cover—either clear skies or total cloud cover—tends to homogenize the incoming radiation.