Atmos. Chem. Phys. Discuss., 9, 22407-22458, 2009
www.atmos-chem-phys-discuss.net/9/22407/2009/
doi:10.5194/acpd-9-22407-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study
S. M. Gourdji1, A. I. Hirsch2, K. L. Mueller1, A. E. Andrews3, and A. M. Michalak1,4
1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA
2National Renewable Energy Laboratory, Golden, CO 80401, USA
3Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA
4Department of Atmospheric, Oceanic & Space Sciences, University of Michigan, Ann Arbor, MI 48109, USA

Abstract. Using synthetic continuous CO2 measurements from the nine sampling locations operational across North America in 2004, this paper investigates the optimal setup for, and constraint on fluxes achieved by, a regional geostatistical atmospheric CO2 inversion over the continent. The geostatistical framework does not require explicit prior flux estimates, nor any other process-based information, and is therefore particularly well suited for investigating the information content of the atmospheric CO2 measurements from a limited network. The atmospheric data are first used with the Restricted Maximum Likelihood (RML) algorithm to infer the model-data mismatch and a priori spatial covariance parameters applied in the inversion. The implemented RML algorithm is found to infer robust spatial covariance parameters from the atmospheric data, as compared to the "true" solution, for cases where the flux and measurement timescales match, while model-data mismatch variances are inferred correctly across all examined cases. A series of analyses is also performed investigating the impact of the temporal scale of concentration measurements and fluxes on inversion results. Inversions using measurement data at sub-daily resolution are found to yield fluxes with a lower Root Mean Square Error (RMSE) relative to inversions using coarser-scale observations, whereas the flux resolution appears to have a lesser impact on the inversion quality. In addition, night-time data for the tall and marine boundary layer towers are found to help constrain fluxes across the continent, although they can potentially bias near-field fluxes. These general conclusions are likely to also be applicable to inversions using a synthesis Bayesian inversion approach. Overall, despite the relatively sparse and unevenly distributed network of nine towers across the North American continent, a geostatistical inversion using an optimal setup and relying solely on the atmospheric data constraint is found to estimate the North American sink for June 2004 to within approximately 10%.

Citation: Gourdji, S. M., Hirsch, A. I., Mueller, K. L., Andrews, A. E., and Michalak, A. M.: Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study, Atmos. Chem. Phys. Discuss., 9, 22407-22458, doi:10.5194/acpd-9-22407-2009, 2009.
 
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