Abstract
Abstract. In this work we propose an approach to solving a source estimation problem based on representation of carbon dioxide surface emissions as a linear combination of a finite number of pre-computed empirical orthogonal functions (EOFs). We used National Institute for Environmental Studies (NIES) transport model for computing response functions and Kalman filter for estimating carbon dioxide emissions. Our approach produces results similar to these of other models participating in the TransCom3 experiment. Using the EOFs we can estimate surface fluxes at higher spatial resolution, while keeping the dimensionality of the problem comparable with that in the regions approach. This also allows us to avoid potentially artificial sharp gradients in the fluxes in between pre-defined regions. EOF results generally match observations more closely given the same error structure as the traditional method. Additionally, the proposed approach does not require additional effort of defining independent self-contained emission regions.
Highlights
It is well known that greenhouse gases and, in particular, greenhouse gases of anthropogenic origin, influence the Earth climate to a great extend
We propose representing geographic distribution of surface emissions of carbon dioxide as a linear combination of a number of pre-computed empirical orthogonal functions
We presented an alternative inversion method to the traditional approach that uses discrete geographical spatial regions
Summary
It is well known that greenhouse gases and, in particular, greenhouse gases of anthropogenic origin, influence the Earth climate to a great extend. The problem was underdetermined and a solution was found by gathering a priori information on surface fluxes In this case, influence from each grid box could be estimated, but it could be computationally expensive due to increasing the number of unknowns, and creating adjoint versions of forward models is not straightforward. We propose representing geographic distribution of surface emissions of carbon dioxide as a linear combination of a number of pre-computed empirical orthogonal functions This combination contains information about climatological spatial variability of the emissions as well as statistical correlations between different grid-points. This approach would yield smooth surface fluxes on a global scale and it does not require additional research for defining independent self-contained emission regions. Practical applications of the derived EOFs can be envisioned in a framework of the geostatistical inverse modeling (Michalak et al, 2004), which requires a set of the global flux patterns to approximate optimal flux field
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