A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems

V. Yadav, A.M. Michalak, J. Ray and Y.P. Shiga

Atmospheric observations provide a large-scale constraint on emissions of carbon dioxide from human activity and from the natural components of the global carbon cycle. In many cases, it is important to understand the relative contributions of natural and anthropogenic fluxes to the atmospheric signal, however, both to develop understanding of carbon-climate feedbacks and to quantify anthropogenic emissions. Here, we develop a statistical framework for isolating fossil fuel CO2 emissions within the framework of an atmospheric inverse problem. Atmospheric CO2 inversions couple observations of CO2 concentrations with an atmospheric transport model to characterize surface emissions and uptake. We show here that the unique spatial signatures of these two types of fluxes can be used to isolate their contributions to the overall signal.

Figure: Estimates of fossil fuel, biospheric, and total fluxes with one standard deviation (first hash mark) and two standard deviation uncertainty bounds for the Northeast, Southeast, Midwest, and South Central regions of the United States. These estimates are based on a synthetic data inversions, with the true fluxes designated as squares.


Independent verification and quantification of fossil fuel (FF) emissions constitutes a considerable scientific challenge. By coupling atmospheric observations of CO2 with models of atmospheric transport, inverse models offer the possibility of overcoming this challenge. However, disaggregating the biospheric and FF flux components of terrestrial fluxes from CO2 concentration measurements has proven to be difficult, due to observational and modeling limitations. In this study, we propose a statistical inverse modeling scheme for disaggregating winter time fluxes on the basis of their unique error covariances and covariates, where these covariances and covariates are representative of the underlying processes affecting FF and biospheric fluxes. The application of the method is demonstrated with one synthetic and two real data prototypical inversions by using in situ CO2 measurements over North America. Inversions are performed only for the month of January, as predominance of biospheric CO2 signal relative to FF CO2 signal and observational limitations preclude disaggregation of the fluxes in other months. The quality of disaggregation is assessed primarily through examination of a posteriori covariance between disaggregated FF and biospheric fluxes at regional scales. Findings indicate that the proposed method is able to robustly disaggregate fluxes regionally at monthly temporal resolution with a posteriori cross covariance lower than 0.15 µmol m−2 s−1 between FF and biospheric fluxes. Error covariance models and covariates based on temporally varying FF inventory data provide a more robust disaggregation over static proxies (e.g., nightlight intensity and population density). However, the synthetic data case study shows that disaggregation is possible even in absence of detailed temporally varying FF inventory data.

Yadav, V., A.M. Michalak, J. Ray, Y.P. Shiga (2016), "A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems", Journal of Geophysical Research: Atmospheres, 121 (20), 12490-12504, doi:10.1002/2016JD025642.