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

Journal Article · · Journal of Geophysical Research: Atmospheres
DOI:https://doi.org/10.1002/2016JD025642· OSTI ID:1338339
 [1];  [2];  [3];  [4]
  1. 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
  2. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory
  3. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  4. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory; Stanford Univ., CA (United States). Department of Civil and Environmental Engineering
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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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; AC02-05CH11231; FG02-06ER64315
OSTI ID:
1338339
Report Number(s):
SAND-2014-16474J; 534419
Journal Information:
Journal of Geophysical Research: Atmospheres, Vol. 121, Issue 20; ISSN 2169-897X
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 11 works
Citation information provided by
Web of Science

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Adaptive wavelet compression of large additive manufacturing experimental and simulation datasets journal July 2018
Constraining sector-specific CO 2 and CH 4 emissions in the US journal January 2017
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