A Bayesian Framework for Verifying Methane Inventories and Trends With Atmospheric Methane Data

Abstract

The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”. Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25 to 1.3 Tg CH4/ yr / yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Brazilian arc of deforestation; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

Authors 12

1. John R. Worden corresponding

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory / California Institute for Technology

2. Sudhanshu Pandey

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory / California Institute for Technology

3. Yuzhong Zhang corresponding

   Harvard University · Westlake University

   Affiliation as printed

   Harvard University

   Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China University of Edinburgh

4. Daniel Cusworth

   University of Arizona

   Affiliation as printed

   University of Arizona / Carbon Monitor

5. Zhen Qu corresponding

   Harvard University

   Affiliation as printed

   Harvard University

6. A. Anthony Bloom

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory / California Institute for Technology

7. Shuang Ma corresponding

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory / California Institute for Technology

8. Bram Maasakkers corresponding Aachen SRON Netherlands Institute for Space Research

   Space Research Organisation Netherlands

   Affiliation as printed

   SRON Netherlands Institute for Space Research

9. Brendan Keith Aidan Byrne corresponding

   Jet Propulsion Laboratory

   Affiliation as printed

   Jet Propulsion Laboratory / California Institute for Technology

10. Riley Duren

    University of Arizona

    Affiliation as printed

    University of Arizona / Carbon Monitor

11. David Crisp corresponding

    Jet Propulsion Laboratory

    Affiliation as printed

    Jet Propulsion Laboratory / California Institute for Technology

12. Daniel J. Jacob

    Harvard University

    Affiliation as printed

    Harvard University