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| Titel |
Plant functional type mapping for earth system models |
| VerfasserIn |
B. Poulter, P. Ciais, E. Hodson, H. Lischke, F. Maignan, S. Plummer, N. E. Zimmermann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 4, no. 4 ; Nr. 4, no. 4 (2011-11-16), S.993-1010 |
| Datensatznummer |
250001916
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| Publikation (Nr.) |
 copernicus.org/gmd-4-993-2011.pdf |
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| Zusammenfassung |
| The sensitivity of global carbon and water cycling to climate variability is
coupled directly to land cover and the distribution of vegetation. To
investigate biogeochemistry-climate 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 biogeography-biogeochemistry 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. The 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. |
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