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.