Journal article

A new step-wise Carbon Cycle Data Assimilation System using multiple data streams to constrain the simulated land surface carbon cycle

P Peylin, C Bacour, N MacBean, S Leonard, PJ Rayner, S Kuppel, EN Koffi, A Kane, F Maignan, F Chevallier, P Ciais, P Prunet

Copernicus GmbH


Abstract. Large uncertainties in Land surface models (LSMs) simulations still arise from inaccurate forcing, incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model-data integration techniques, in order to reduce the simulated carbon fluxes and stocks uncertainties. In this study we present a Carbon Cycle Data Assimilation System (CCDAS) that assimilates three major data streams, namely MODIS-NDVI observations of vegetation activity, net ecosy..

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University of Melbourne Researchers

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