HR: 09:30h
AN: B31E-07    [Abstracts]
TI: Global Variability of Light Use Efficiency: a Model Data Integration Approach
AU: * Tomelleri, E
EM: etomell@bgc-jena.mpg.de
AF: Max Planck Institut für Biogeochemie, Hans Knoell Str. 10, Jena, 07745, Germany
AU: Reichstein, M
EM: markus.reichstein@bgc-jena.mpg.de
AF: Max Planck Institut für Biogeochemie, Hans Knoell Str. 10, Jena, 07745, Germany
AU: Papale, D
EM: darpap@unitus.it
AF: Universitá della Tuscia - Laboratorio di Ecologia Forestale, Via S. Camillo de Lellis, Viterbo, 01100, Italy
AU: Beer, C
EM: cbeer@bgc-jena.mpg.de
AF: Max Planck Institut für Biogeochemie, Hans Knoell Str. 10, Jena, 07745, Germany
AB: Estimating the present global gross primary production (GPP) and giving reliable future predictions is one of the major challenges of carbon cycle research. Light-use efficiency (LUE) algorithms are a potentially effective approach for monitoring global primary production (GPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). Their advantage is that all the drivers can be easily derived from remote sensed data or from existing climate observation networks offering a number of opportunities for up- scaling approaches. These algorithms are applied at relatively wide geographical areas, which may subsume significant heterogeneity in vegetation LUE_max and, hence, introduce error. However, data on the variability of the LUE_max coefficient, are scarce, often derived from leaf-level or NPP measurements and sometimes conflicting. A solution to this problem is to estimate the this physiological parameter inverting the algorithm against eddy covariance derived measurements of GPP, that are now globally available in a consistent format. Hence, the objective of this study is to fill this gap by means of quantifying and compare the variability of LUE_max and its sensitivity to environmental conditions among plant vegetation types. For this purpose, we optimized the MOD17 algorithm against the GPP time series from the FLUXNET measurement network. In doing this, we made a Bayesian data model synthesis by means of the Metropolis-Hastings algorithm. The LUE_max values from the BPLUT lookup table in the MOD17 user's manual were used as prior. The uncertainties in flux data were characterized specifically site by site. The parameter estimation considerably increased LUE_max for vegetation types with a short leaf-life while for evergreen vegetation types a posteriori parameter values were lower than the a priori ones. These optimized a posteriori values open different research questions regarding the uses of this model at wide geographical scale. In particular, while a classification in plant functional types showed a biome dependent LUE_max variability, intra-vegetation variability is still hampering upscaling approaches. This issue has to be addressed in our future research. Furthermore multiple- constraint approaches considering water fluxes could contribute significantly to the reduction of uncertainties in model estimates.
DE: 0428 Carbon cycling (4806)
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0466 Modeling
SC: Biogeosciences [B]
MN: 2007 Fall Meeting