The effects of remotely sensed plant functional type and leaf area index on simulations of boreal forest surface fluxes by the NCAR land surface model

The land surface models used with atmospheric models typically characterize landscapes in terms of generalized biome types. However, the advent of high-spatial resolution satellite-derived data products such as land cover and leaf area index (LAI) allow for more accurate specification of landscape patterns. In this paper, the authors report on the use of 1-km land-cover [converted to plant functional type (PFT)] and LAI datasets developed from the Boreal Ecosystem-Atmosphere Study (BOREAS) to develop and to test a methodology for incorporating satellite data into the National Center for Atmospheric Research (NCAR) land surface model. In this approach, the landscape is composed of patches of PFTs, each with its own LAI, rather than as biomes. Large differences in PFT fractional cover between the remotely sensed and standard model representations were found for the BOREAS region. Changes in the needleleaf evergreen PFT fraction were the most extensive both in terms of spatial distribution and magnitude (up to ±40%). Large differences in LAI were also found (up to ±3 m² m^-2). Although the response of the model to these differences was somewhat small in terms of regionally averaged changes in surface fluxes, the spatial variability of the model response was substantial. The PFT and LAI data were generally of equal importance in modifying the surface fluxes and were most useful for improving the description of spatial variability due to mixtures of recently burned, regrowth, and mature-growth areas.