Contribution of the Orbiting Carbon Observatory to the estimation of CO2 sources and sinks: Theoretical study in a variational data assimilation framework

Authors

  • Frédéric Chevallier,

    1. Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, Commissariat àl'Energie Atomique, Centre National de la Recherche Scientifique, Universitéde Versailles Saint-Quentin-en-Yvelines, Gif-sur-Yvette, France
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  • François-Marie Bréon,

    1. Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, Commissariat àl'Energie Atomique, Centre National de la Recherche Scientifique, Universitéde Versailles Saint-Quentin-en-Yvelines, Gif-sur-Yvette, France
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  • Peter J. Rayner

    1. Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, Commissariat àl'Energie Atomique, Centre National de la Recherche Scientifique, Universitéde Versailles Saint-Quentin-en-Yvelines, Gif-sur-Yvette, France
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Abstract

[1] NASA's Orbiting Carbon Observatory will monitor the atmospheric concentrations of carbon dioxide (CO2) along the satellite subtrack over the sunlit hemisphere of the Earth for more than 2 years, starting in late 2008. This paper demonstrates the application of a variational Bayesian formalism to retrieve fluxes at high spatial and temporal resolution from the satellite retrievals. We use a randomization approach to estimate the posterior error statistics of the calculated fluxes. Given our prior information about the fluxes (with error standard deviations about 0.4 g C m−2 d−1 over ocean and 4 g C m−2 d−1 over vegetated areas) and our observation characteristics (with error standard deviations about 2 ppm), we show error reductions of up to about 40% at weekly scale for a grid point of the transport model. We simulate the impact of undetected biases by perturbing the observations and show that regional biases of a few tenths of a part per million in column-averaged CO2 can bias the inverted yearly subcontinental fluxes by a few tenths of a gigaton of carbon, which is larger than the uncertainty on the anthropogenic carbon fluxes but smaller than that of natural fluxes over most vegetated areas.

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