Plant functional type mapping for earth system models

Abstract

Abstract. The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistryclimate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10-20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (ß) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeographybiogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30 % (20 %) uncertainty in the sensitivity of GPP (transpiration) to precipitation. TheCorrespondence to: B. Poulter (benjamin.poulter@lsce.ipsl.fr) availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.

Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO<sub>2</sub> airborne fraction

“…This uncertainty in PFT distributions results from both incomplete bioclimatic information to define the fundamental niche, and to the complexity of modeling species competitive interactions that define the realized niche. To better represent present-day managed and natural land cover, four plant functional type datasets were created using a uniform methodology that combined Köppen-Geiger climate zones (delineated with climate data from the Global Historical Climatological Network v2.0 (Peel et al, 2007)) with physiognomy and phenology-type, and managed or natural grasslands, from land-cover data provided by MODIS (V004 and V005), GLC2000, and GlobCover (Table 2; Poulter et al, 2011). The satellite derived PFT fractions were prescribed directly to the maximum annual FPAR variable in LPJ, which defines the fraction of photosynthetic active radiation (FPAR) absorbed by each PFT and is equal to that PFT's fractional coverage.…”

“…The uncertainty of NEE in northern regions is mainly controlled by RH uncertainty (implying greater temperature sensitivity of soil respiration), whereas in topical and arid regions, NPP and RH uncertainty are equally important. differences in how the classification systems handled forest cover thresholds (Poulter et al, 2011;Fritz and See, 2008). Due to these classification differences, the GLC2000 and GlobCover datasets tended to have 4-5 % higher woody vegetation in warm regions, which MODIS categorized as C4 grasslands.…”

The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use

“…Evolutions of other productions and other cropland areas are forced by external scenarios. Areas of permanent pastures are taken from Ramankutty et al (2008) and forests areas from Poulter et al (2011). On grid points where the sum of forest, pasture and cropland fractions exceed 100 %, forest fractions were reduced to match 100 %.…”

“…Main input data for each region of the model at the base year 2001. Cropland and pasture areas are from Ramankutty et al (2008) and forests areas from Poulter et al (2011), other data are from Agribiom (Dorin, 2011). Population is in millions.…”

The Nexus Land-Use Model, An Approach Articulating Biophysical Potentials and Economic Dynamics to Model Competition for Land-Uses

“…Areas of permanent pastures are taken from and forests areas from Poulter et al [2011]. The forest category includes managed and unmanaged forests.…”