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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 23
Atmos. Chem. Phys., 13, 11643–11660, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 13, 11643–11660, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Dec 2013

Research article | 03 Dec 2013

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation

A. Chatterjee*,2,1 and A. M. Michalak2 A. Chatterjee and A. M. Michalak
  • 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, including variational and the ensemble Kalman filter methods, 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 DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection–diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are 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) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical 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. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source–sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.

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