The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates
Authors: Worden John R. , Cusworth Daniel H. , Qu Zhen , Yin Yi , Zhang Yuzhong , Bloom A. Anthony , Ma Shuang , Byrne Brendan K. , Scarpelli Tia , Maasakkers Joannes D. , Crisp David , Duren Riley , Jacob Daniel J. 
Published: May 2022

Abstract. We use optimal estimation (OE) to quantify methane fluxes
based on total column CH4 data from the Greenhouse Gases Observing
Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We
then project these fluxes to emissions by sector at 1∘ resolution and
then to each country using a new Bayesian algorithm that accounts for prior
and posterior uncertainties in the methane emissions. These estimates are
intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here
and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are
∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions
are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are
180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous
inversions based on GOSAT data and the GEOS-Chem model. In addition,
anthropogenic fossil estimates are consistent with those reported to the
United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian
approach (also known as prior swapping) to determine their impact on
posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on
isotopic evidence) and find little impact on our posterior fluxes. This
indicates that these alternative inventories are inconsistent with our
remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the
prior inventories. We find that total emissions for approximately 57
countries can be resolved with this observing system based on the
degrees-of-freedom for signal metric (DOFS > 1.0) that can be
calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of
prior inventories. The top five emitting countries (Brazil, China, India,
Russia, USA) emit about half of the global anthropogenic budget, similar to
our choice of prior emissions but with the posterior emissions shifted
towards the agricultural sector and less towards fossil emissions,
consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different
(although within uncertainty) than bottom-up inventories, isotopic evidence,
or estimates based on sparse in situ data, indicating a need for further
studies reconciling these different approaches for quantifying the methane
budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and
those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon
Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and
sectoral attribution of subsequent methane emissions estimates.