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

Yadav, Vineet; Michalak, Anna M.; Ray, Jaideep; Shiga, Yoichi P.

doi:10.1002/2016JD025642

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

Journal Article · Thu Oct 27 00:00:00 EDT 2016 · Journal of Geophysical Research: Atmospheres

DOI:https://doi.org/10.1002/2016JD025642· OSTI ID:1338339

Yadav, Vineet ^[1]; Michalak, Anna M. ^[2]; Ray, Jaideep ^[3]; Shiga, Yoichi P. ^[4]

California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory; Carnegie Institution for Science, Stanford, California (United States). Previously at Department of Global Ecology
California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory; Stanford Univ., CA (United States). Department of Civil and Environmental Engineering

We study independent verification and quantification of fossil fuel (FF) emissions that 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. Also, 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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC02-05CH11231; AC04-94AL85000; FG02-06ER64315

OSTI ID:: 1338339

Report Number(s):: SAND--2014-16474J; 534419

Journal Information:: Journal of Geophysical Research: Atmospheres, Journal Name: Journal of Geophysical Research: Atmospheres Journal Issue: 20 Vol. 121; ISSN 2169-897X

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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