HR: 08:00h
AN: A11F-01 INVITED     [Abstracts]
TI: A Global Carbon Cycle Data Assimilation System (CCDAS) to Infer Atmosphere-Biosphere CO2 Exchanges and Their Uncertainties
AU: * Scholze, M
EM: marko.scholze@bris.ac.uk
AF: QUEST, University of Bristol, Department of Earth Sciences Queen's Road, Bristol, BS8 1RJ United Kingdom
AU: Rayner, P
EM: peter.rayner@csiro.au
AF: CSIRO Atmospheric Research, PMB 1, Aspendale, 3195 Australia
AU: Knorr, W
EM: wknorr@bgc-jena.mpg.de
AF: Max-Planck-Institute for Biogeochemistry, Postfach 100164, Jena, 07701 Germany
AU: Widmann, H
EM: heiner.widmann@dkrz.de
AF: Max-Planck-Institute for Biogeochemistry, Postfach 100164, Jena, 07701 Germany
AU: Kaminski, T
EM: thomas.kaminski@fastopt.de
AF: FastOpt, Schanzenstrasse 36, Hamburg, 20357 Germany
AU: Giering, R
EM: ralf.giering@fastopt.de
AF: FastOpt, Schanzenstrasse 36, Hamburg, 20357 Germany
AB: Atmospheric inversion studies have become an important tool for identifying sources and sinks of CO2 at the interannual time scale. For determining detailed patterns they suffer from the inverse problem being seriously under-constrained. Such methods are usually contrasted with process-based models of the terrestrial or oceanic carbon cycle. These models, however, cannot take into account information gained from CO2 measurements such as the extensive flask-sampling network. Here, we present results of a two-stage assimilation study of satellite radiances (identifying vegetation activity) and atmospheric CO2 concentration data using the terrestrial biosphere model BETHY. The controlling parameters for the second stage in this model are inferred by nonlinear optimization based on the model's adjoint. Uncertainties in these parameters are calculated from observational and model uncertainties via the model's Hessian and then mapped forward on predicted quantities such as net CO2 fluxes to the atmosphere via the model's Jacobian. The adjoint, Hessian, and Jacobian are generated by automatic differentiation of the model's source code. The model is able to fit the observations moderately well on a seasonal time scale and very well on an interannual time scale. It appears that the requirement to fit both the seasonal and interannual dynamics in the CO2 record is a strong constraint on model formulation. We will report on progress in model development and also on new experiments such as including ocean basis function in the optimization procedure.
DE: 4806 Carbon cycling
DE: 4842 Modeling
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting