On the use of Ecosystem Functional Types to represent lower boundary conditions in the WRF/Noah Model
Alternatively to traditional land-cover types, ecosystems can be grouped into Ecosystem Functional Types (EFTs) in the same manner as plant species can be grouped into plant functional types. In ecology, classifications into functional units aim to reduce the diversity of biological entities (for instance genes, species or ecosystems) on the basis of processes, and allows for the identification of homogeneous groups that show a specific and coordinated response to the environmental factors. EFTs are groups of ecosystems that share functional characteristics in relation to the amount and timing of the exchanges of matter and energy between the biota and the physical environment. In other words, EFTs are homogeneous patches of the land surface that exchange mass and energy with the atmosphere in a common way.
In this work, we have derived EFTs from satellite products, and in a first stage estimated their biophysical properties by relating them to those associated with traditional land covers. Since EFTs can be defined on an annual basis, the year-to-year variability of the surface conditions can thus be identified. Model simulations were then carried out for austral spring in South America to evaluate the sensitivity of climate to the changing surface conditions as estimated from the EFTs. A dry year and a wet year, respectively having low and high Net Primary Production in the Ecosystems, were chosen to investigate the interannual variability of land-atmosphere interactions and their effect on regional climate. The simulations suggest that the range of surface and atmospheric responses to the given EFT changes was larger in a dry year for 2-m temperature and 10-m wind, while for a wet year it was larger for the mixing ratio, convective available potential energy, vertically-integrated moisture fluxes and precipitation. These preliminary results suggest that the use of Ecosystem Functional Types may be a promising way of representing surface conditions that include interannual variability.