The Impact of Spatial Variability of Land-Surface Characteristics on Land-Surface Heat Fluxes

Abstract

The impact of subgrid-scale variability of land characteristics on land-surface energy fluxes simulated in atmospheric models (e.g., GCMs) was investigated with Patchy Land-Atmosphere Interactive Dynamics (PLAID), a land-surface scheme developed by Avissar and Pielke that represents the land surface as a mosaic of patches. Eleven different distributions of the five predominant characteristics of land-surface schemes (i.e., stomatal conductance, soil-surface wetness, leaf area index, surface roughness, and albedo) were considered. A total of 5 580 900 steady-state simulations was produced to thoroughly analyze this impact under a broad range of atmospheric conditions. The authors found that the more skewed the distribution within the range of land-surface characteristics that is related nonlinearly to the energy fluxes, the larger the difference between the energy fluxes calculated with the distribution and the corresponding mean. Among the various distributions considered in the study, the lognormal distribution produced the largest such difference, and negatively skewed beta distributions resulted in negligible difference. In general, the latent beat flux was the most sensitive to spatial variability and the radiative flux emitted by the surface was the least sensitive. The results indicate that it is very important to consider the spatial variability of leaf area index, stomatal conductance, and, in bare land, soil-surface wetness. The spatial variability of surface roughness is mostly important under neutral and stable atmospheric conditions. It appears that the relationship between albedo and surface energy fluxes is almost linear, and therefore, using a mean value of this characteristic is appropriate. This analysis emphasizes the need to develop land-surface schemes able to account for spatial variability in atmospheric models, as well as the necessity to provide higher statistical moments when creating datasets of land-surface characteristics.