A Bayesian framework for deriving sector-based methane emissions from top-down fluxes

Daniel H Cusworth,Kevin Bowman,Joannes D Maasakkers,Tia R Scarpelli,Shuang Ma,John R Worden,Daniel J Jacob,A Anthony Bloom,Zhen Qu,Charles E Miller,Yuzhong Zhang,Yi Yin

doi:10.1038/s43247-021-00312-6

Abstract

Atmospheric methane observations are used to test methane emission inventories as the sum of emissions should correspond to observed methane concentrations. Typically, concentrations are inversely projected to a net flux through an atmospheric chemistry-transport model. Current methods to partition net fluxes to underlying sector-based emissions often scale fluxes based on the relative weight of sectors in a prior inventory. However, this approach imposes correlation between emission sectors which may not exist. Here we present a Bayesian optimal estimation method that projects inverse methane fluxes directly to emission sectors while accounting uncertainty structure and spatial resolution of prior fluxes and emissions. We apply this method to satellite-derived fluxes over the U.S. and at higher resolution over the Permian Basin to demonstrate that we can characterize a sector-based emission budget. This approach provides more robust comparisons between different top-down estimates, critical for assessing the efficacy of policies intended to reduce emissions.

Highlights

Atmospheric methane observations are used to test methane emission inventories as the sum of emissions should correspond to observed methane concentrations
We compare with the partitioned results from the 2010–2015 GOSAT inversion to show how projection to a common prior can be used to assess regional emission trends since uncertainties from different priors and different spatial resolutions of the flux inversions are removed
ΔCH4 = 0.00/−0.06 TgCH4 a−1), the small CH4 flux enhancement observed in Fig. 1a is partitioned entirely to the oil sector, but produces emissions lower than the prior, which contrasts with the increasing production reported in the basin[24]

Summary

Introduction

Atmospheric methane observations are used to test methane emission inventories as the sum of emissions should correspond to observed methane concentrations. We present a Bayesian optimal estimation method that projects inverse methane fluxes directly to emission sectors while accounting uncertainty structure and spatial resolution of prior fluxes and emissions We apply this method to satellite-derived fluxes over the U.S and at higher resolution over the Permian Basin to demonstrate that we can characterize a sectorbased emission budget. Referred to as “inversions” or top-down inventories, these methods estimate CH4 fluxes by assimilating tower, aircraft, or satellite-based CH4 measurements[8,9,10,11,12,13,14] These top-down methods only estimate total fluxes (i.e., sum of all emission sector contributions) explicitly, and may rely on using prior ratios or relative weights (RWs) between source categories to partition fluxes to specific source sectors.

Methods

Results

Conclusion