Atmos. Chem. Phys. Discuss., 13, 12825-12865, 2013
www.atmos-chem-phys-discuss.net/13/12825/2013/
doi:10.5194/acpd-13-12825-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
Review Status
This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation
A. Chatterjee1,2,* and A. M. Michalak2
1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA
2Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
*now at: Data Assimilation Research Section, The National Center for Atmospheric Research, Boulder, CO, USA

Abstract. Data assimilation (DA) approaches, such as the variational and the ensemble Kalman filter, provide a computationally efficient framework for solving the CO2 source-sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of alternative DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA methods (an ensemble square root filter and a variational technique) using a simple 1-dimensional advection-diffusion inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are specifically designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) in order to isolate the degradation in the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA methods to keep the problem setup analogous to a real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints imposed to make the DA algorithms practically feasible. The overall advantages/disadvantages of the two examined DA approaches are complementary and highlight that, specifically for CO2 applications, selection of one method over the other should be dictated by the carbon science questions being asked, and the inversion conditions under which the approaches are being applied.

Citation: Chatterjee, A. and Michalak, A. M.: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation, Atmos. Chem. Phys. Discuss., 13, 12825-12865, doi:10.5194/acpd-13-12825-2013, 2013.
 
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