2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane

Abstract

Abstract. We use 2010–2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5∘×0.625∘ resolution in concentrated source regions. The analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTs model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a−1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a−1). The main discrepancy is for the oil and gas production sectors, where we find higher emissions than the GHGI by 35 % and 22 %, respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production, and we find that these are lower than our estimate by a factor of 2. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a−1, on the low end of the prior WetCHARTs inventory uncertainty range (14.2 (3.3–32.4) Tg a−1), which calls for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a−1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil and gas production in the eastern US. We also find that oil and gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

Reply on RC2

“…and 30 Tg a -1 (GOSAT) over CONUS, close to the posterior estimates of 31 Tg a -1 from the 0.5 o´0 .625 o inversion over 2010-2015 [Maasakkers et al, 2021]. The joint inversion adjusts emissions slightly upwards to 34 Tg a -1 due to the larger sensitivity…”

“…The largest reductions of emissions in Europe are from coal and oil/gas emissions. In CONUS, the large upward adjustment in the south-central region reflects the well-known underestimate of oil/gas emissions by the US EPA inventory in that region [Kort et al, 2014;Smith et al, 2017;Peischl et al, 2018;Alvarez et al, 2018;Maasakkers et al, 2021;Gorchov Negron et al, 2020;Lyon et al, 2021]. The posterior estimates from both TROPOMI and GOSAT adjust national methane emissions slightly downwards from 31 Tg a -1 to 27 Tg a -1 (TROPOMI)…”

“…Once the Jacobian matrix has been constructed, it can be applied to conduct ensembles of inversions at no added cost exploring the dependence of the solution on inversion parameters or observational data selection. Analytically-based inversions of GOSAT satellite data have been used to pinpoint areas where the inversion results are most informed by the observations [Turner et al, 2015;Maasakkers et al, 2019], to diagnose the ability of the inversion to separate contributions from different source sectors [Maasakkers et al, 2021; and from sources and sinks [Y. Zhang et al, 2018;Maasakkers et al, 2019], and to compare the information content from satellite and suborbital observations [Lu et al, 2021;Baray et al, 2021].…”

Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions

“…The Zhang et al. (2021) global flux inversion analytically solves the inverse problem assuming Gaussian errors with a Bayesian optimal estimation (OE) approach (Maasakkers et al., 2019, 2021). A feature of this inversion approach is that it provides an averaging kernel matrix ( A ) and a posterior error covariance matrix () for the posterior estimates () of wetland emissions.…”

Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH<sub>4</sub> ObsPack) and satellite (GOSAT) observations

“…We acknowledge all of the data providers and laboratories (https://search.datacite.org/works/10.25925/20190108, last access: 20 March 2021) that contributed to the GLOBALVIEWplus CH 4 ObsPack v1.0 data product compiled by NOAA Global Monitoring Laboratory. We acknowledge methane observations collected from the CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) project (Machida et al, 2019). Data collected at WLEF Park Falls towers were supported by the NSF DEB-0845166 and DOE AmeriFlux Management Project.…”