Abstract

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 and to 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 inference 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 inhomogeneity in the number of observations make it less useful than GOSAT for quantifying emissions at that scale. 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.

Highlights

  • Methane (CH4) is the second most important anthropogenic greenhouse gas in the atmosphere after CO2

  • The regional bias for Gases Observing Satellite (GOSAT) is below the “breakthrough requirement” of 5 ppbv set by Buchwitz et al (2015) as needing to be achieved for regional or global inversions of satellite observations, and the regional bias for TROPOspheric Monitoring Instrument (TROPOMI) is below their “threshold requirement” of 10 ppbv

  • By analytical solution to the inverse problem, we were able to quantitatively compare the information content from the two satellite data sets. This includes averaging kernel sensitivities and degrees of freedom for signal (DOFS) that quantify the number of independent pieces of information on the distribution of methane emissions

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Summary

Introduction

Methane (CH4) is the second most important anthropogenic greenhouse gas in the atmosphere after CO2. We present a global inverse analysis of one year (2019) of these early TROPOMI observations to evaluate their capability for quantifying methane emissions, comparing to an inversion for that same year using the sparser but more mature observations from GOSAT. We present global analytical inversions of TROPOMI and GOSAT data for 2019 at 2◦ × 2.5◦ resolution to infer methane sources and sinks and to attribute emissions to different sectors. This involves evaluation and intercomparison of the TROPOMI and GOSAT retrievals prior to the inversion, as any biases in the observations will propagate to the inversion results. This enables us to assess the consistency and complementarity of the two data sets

Methane observations
Inversion method
GEOS-Chem simulations and prior estimates
Analytical inversion
Results and discussion
Information content from the inversions
Cross-fit to TROPOMI and GOSAT observations
Global distribution
Major source regions
Conclusions
Full Text
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