Anthropogenic emissions of CO2 into the atmosphere are a primary concern in global change research. Current studies suggest that increasing levels of CO2 in the atmosphere will lead to a warming of the atmosphere in the next century. Even a slight increase in global temperature could have a significant effect on Earth systems (IPCC, 1990). However, there is a disparity between current estimates of anthropogenic emissions and measurements of accumulated CO2 in the atmosphere, indicating a terrestrial and/or oceanic sink (IPCC, 1990; Schimel, 1995). An understanding of this discrepancy is critical to our ability to monitor and manage CO2 concentrations in the future. Improving our understanding of the feedbacks between land surface CO2 exchange with the atmosphere through the coupling of land surface and atmospheric models will help us to understand the significance of land surface-atmosphere interactions in both regional and global carbon balance. If we can clarify the processes by which CO2 is being sequestered in terrestrial ecosystems today we will be better able to predict how such sinks might operate in the future.

Most modeling efforts of land-atmosphere interactions at regional and global scales rely to some extent on remotely sensed inputs, either to represent surface processes or to provide state variables. This is especially true of SiB2 (see Sellers, et al., 1996a,b). The advantage of using remote sensing in environmental modeling is its ability to provide parameter fields not easily measured, either temporally or spatially, at the ground surface. However, as spatial scales increase from local to regional to global, the modeled interactions between the land surface and the atmosphere may vary because of changing landscape heterogeneity and contrasting surface properties. It is therefore important to understand more fully the contribution of remotely sensed data to modeled results if we are to have confidence in them and use them appropriately. Currently, the relationships between ground measurements and remotely sensed data, and the sensitivity of land surface and atmospheric models to remotely sensed inputs, are not well resolved. The work in this proposal addresses these issues.