Many land models classify vegetation by biomes. These biomes set vegetation characteristics such as albedo, roughness length, rooting depth, and stomatal physiology. The use of biomes is reasonable in a top-down modeling approach based on bulk parameterizations of surface energy, water, and momentum fluxes required by atmospheric models. However, land models are expanding beyond their traditional biogeophysical roots to include biogeochemistry, especially photosynthesis and the carbon cycle. Inclusion of photosynthesis and the carbon cycle poses a problem with the biome-based land classification. How does one obtain the necessary leaf physiological and whole-plant allocation parameters for a biome? For monocultures, this is relatively easy. Ecological parameters for a needleleaf evergreen tree are used for a needleleaf evergreen forest biome. For mixed life-form biomes, this is more difficult. What is the photosynthetic light response curve of a savanna, which consists of physiologically distinct grasses and trees? Rooting depths also vary greatly between these life-forms. In grasslands, photosynthesis and stomatal conductance differ between C3 and C4 plants. Mixed forests are particularly difficult; trees differ not only in physiology but also phenology. One solution is to recognize that biomes consist of individual species or plant functional types (PFTs) that do have measurable leaf physiology and carbon allocation. Plant functional types reduce the complexity of species diversity in ecological function to a few key plant types. Ecologists developing physiologically-based ecosystem and dynamic global vegetation models have wrestled with the same problem imposed by mixed life-form vegetation. Representing the landscape as patches of PFTs is a common theme that can link climate and ecosystem models. It provides direct linkage to leaf-level ecophysiological measurements and ecological theory.
In the Community Land Model, vegetation is not represented as biomes (e.g., savanna) but rather as patches of plant functional types (e.g., grasses, trees). The PFT determines plant physiology while community composition (i.e., the PFTs and their areal extent) and vegetation structure (e.g., height, leaf area index) is direct input to each grid cell for each PFT. This also allows the model to interface with models of ecosystem processes and vegetation dynamics.
PFTs are inferred from 1-km satellite data. Oleson and Bonan (2000) describe this methodology for a region of the boreal forest. Bonan et al. (2002) describe the global implementation.
As described by Bonan et al. (2002), 0.5 degree maps of the abundance of 7 primary PFTs (needleleaf evergreen or deciduous tree, broadleaf evergreen or deciduous tree, shrub, grass, crop) were derived from the 1-km IGBP DISCover dataset and the 1-km University of Maryland tree cover dataset. Temperature and precipitation climatologies were used to distinguish arctic, boreal, temperate, and tropical plants, C3 and C4 grasses, and evergreen and deciduous shrubs (see PFT list). Two types of crops are allowed in the dataset to distinguish, for example, corn (a C4 crop) from wheat (a C3 crop). However, the land cover datasets used to derive the PFTs did not distinguish varieties of crops and only one crop type is currently active. Monthly leaf area index for each PFT in each 0.5 degree grid cell was obtained from 1-km Advanced Very High Resolution Radiometer (AVHRR) red and near infrared reflectances for April 1992 to March 1993. Stem area index, canopy top height, and canopy bottom height were based on the NCAR LSM values prescribed for each PFT. This figure shows the geography of PFTs in terms of the percent of the grid cell covered. This figure shows the leaf area index.
Each PFT is defined by a variety of optical, morphological, and physiological parameters.