Area-average representation of land surface covers in large atmospheric models based on remotely sensed land surface cover data

Abstract

The research described in this dissertation is predicted on the hypothesis that remotely sensed information on vegetation cover classes can be used to improve the representation of heterogeneous continental surfaces in global climate models. The problem it addressed was that current understanding of soil-vegetation-atmosphere interactions is considered only to be relevant to small plots of uniform vegetation with dimensions of the order 10-1000 m but, in order to provide realistic simulation of climate, General Circulation Models require description of such interactions for large areas of mixed vegetation with dimensions of the order 100-1000 km. The methods used to investigate this issue was to create and apply a coupled model that provided realistic representation of both surface and atmospheric boundary layer processes, and to use this model to simulate surface-atmosphere interactions with explicit representation of patches of vegetation on the one hand, and with a single, area-average representation of exchanges on the other. These modeling studies were given credibility by initiating and validating the coupled model using appropriate data from the FIFE site in Kansas and the ABRACOS site in Brazil. The results showed that when quite simple aggregation rules are used to derive the effective area-average values of the vegetation-related parameters, these parameters give adequate simulation of surface-atmosphere interactions. These aggregation rules were then applied using remotely sensed maps of land cover to derive parameter values. Significant differences were found in the resulting parameters, and in the surface energy fluxes and modeled climate calculated using those parameters. Thus, it has been shown that remotely sensed data can indeed be used to improve the representation of heterogeneous land surfaces in global climate models using the methods developed in this research, and that using these data significantly alters the simulated global climate.