Preprints
https://doi.org/10.5194/acp-2021-309
https://doi.org/10.5194/acp-2021-309

  23 Apr 2021

23 Apr 2021

Review status: this preprint is currently under review for the journal ACP.

Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments

Zhen Qu1, Daniel J. Jacob1, Lu Shen1, Xiao Lu1, Yuzhong Zhang1,2, Tia R. Scarpelli1, Hannah O. Nesser1, Melissa P. Sulprizio1, Joannes D. Maasakkers3, A. Anthony Bloom4, John R. Worden4, Robert J. Parker5,6, and Alba L. Delgado3 Zhen Qu et al.
  • 1School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA
  • 2Key Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou, Zhejiang, China
  • 3SRON Netherlands Institute for Space Research, Utrecht, the Netherlands
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 5National Centre for Earth Observation, University of Leicester, Leicester, UK
  • 6Earth Observation Science, School of Physics and Astronomy, University of Leicester, UK

Abstract. We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of methane sources for 2019 at 2° × 2.5° horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals, as well as a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT, and regional biases of 6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding 20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses an analytical solution to the Bayesian optimization of methane sources, thus providing an explicit characterization of error statistics and information content together with the solution. TROPOMI has ~100 times more observations than GOSAT but error correlation on the 2° × 2.5° scale of the inversion and large spatial variations of the number of observations make it less useful than GOSAT for quantifying emissions at that resolution. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI and GOSAT inversions show consistent downward adjustments of global oil/gas emissions relative to a prior estimate based on national inventory reports to the United Nations Framework Convention on Climate Change, but consistent increases in the south-central US and in Venezuela. Global emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We find large artifacts in the TROPOMI inversion over Southeast China, where seasonal rice emissions are particularly high but in phase with extensive cloudiness, and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the global scale.

Zhen Qu et al.

Status: open (until 18 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Zhen Qu et al.

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Short summary
The recent launch of TROPOMI offers unprecedented opportunity to more accurately quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate the global methane budget and find consistent adjustments. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse resolution inversion due to the larger error correlations and spatial variations of the number of observations.
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