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|Title:||Carbon Cycle Data Assimilation with a Generic Phenology Model|
|Authors:||KNORR Wolfgang; KAMINSKI Thomas; SCHOLZE Marko; GOBRON Nadine; PINTY Bernard; GIERING Ralf; MATHIEU Pierre Philippe|
|Citation:||JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES vol. 115 no. G04017 p. 1-16|
|Publisher:||AMER GEOPHYSICAL UNION|
|Type:||Articles in periodicals and books|
|Abstract:||Photosynthesis by terrestrial plants is the main driver of the global carbon cycle, and the presence of actively photosynthesizing vegetation can now be observed from space. However, challenges remain when translating remotely sensed data into carbon fluxes. One reason is that the Fraction of Absorbed Photsynthetically Active Radiation (FAPAR), which documents the presence of photsynthetically active vegetation, relates more directly to leaf development and leaf phenology than to photosynthetic rates. Here, we present a new approach for linking FAPAR and vegetation-to-atmosphere carbon fluxes through variational data assimilation. The scheme extends the Carbon Cycle Data Assimilation System (CCDAS) by a newly developed, globally applicable and generic leaf phenology model, which includes both temperature and water-driven leaf development. CCDAS is run for seven sites, six of them included in the FLUXNET network. Optimization is carried out simultaneously for all sites against 20 months of daily FAPAR from the Medium Resolution Imaging Spectrometer (MERIS) on-board the European Space Agency¿s ENVISAT platform. 14 parameters related to phenology and 24 related to photosynthesis are optimized simultaneously and their posterior uncertainties computed. We find that with one parameter set for all sites, the model is able to reproduce the observed FAPAR spanning boreal, temperate, humid-tropical and semi-arid climates. Assimilation of FAPAR has led to reduced uncertainty (>10%) of 10 of the 38 parameters, including 1 parameter related to photosynthesis, and a moderate reduction in NPP uncertainty. The approach can easily be extended to regional or global studies and to the assimilation of further remotely sensed data sources.|
|JRC Institute:||Sustainable Resources|
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