Articles | Volume 18, issue 4
Atmos. Chem. Phys., 18, 3027–3045, 2018

Special issue: Data assimilation in carbon/biogeochemical cycles: consistent...

Atmos. Chem. Phys., 18, 3027–3045, 2018

Technical note 02 Mar 2018

Technical note | 02 Mar 2018

Technical Note: Atmospheric CO2 inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation

Panagiotis Kountouris1, Christoph Gerbig1, Christian Rödenbeck1, Ute Karstens1,a, Thomas Frank Koch2, and Martin Heimann1 Panagiotis Kountouris et al.
  • 1Department of Biogeochemical Systems, Max Planck Institute for Biogeochemistry, Jena, Germany
  • 2Meteorological Observatory Hohenpeissenberg, Deutscher Wetterdienst, Germany
  • anow at: ICOS Carbon Portal, Lund University, Lund, Sweden

Abstract. Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model–data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model–model mismatch. The synthetic study showed a quite good agreement between posterior and true fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the known truth by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

Final-revised paper