2010–2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors

Abstract

Abstract. We use 7 years (2010–2016) of methane column observations from the Greenhouse Gases Observing Satellite (GOSAT) to examine trends in atmospheric methane concentrations over North America and infer trends in emissions. Local methane enhancements above background are diagnosed in the GOSAT data on a 0.5∘×0.5∘ grid by estimating the local background as the low (10th–25th) percentiles of the deseasonalized frequency distributions of the data for individual years. Trends in methane enhancements on the 0.5∘×0.5∘ grid are then aggregated nationally and for individual source sectors, using information from state-of-science bottom-up inventories. We find that US methane emissions increased by 2.5±1.4 % a−1 (mean ± 1 standard deviation) over the 7-year period, with contributions from both oil–gas systems (possibly unconventional oil–gas production) and from livestock in the Midwest (possibly swine manure management). Mexican emissions show a decrease that can be attributed to a decreasing cattle population. Canadian emissions show year-to-year variability driven by wetland emissions and correlated with wetland areal extent. The US emission trends inferred from the GOSAT data account for about 20 % of the observed increase in global methane over the 2010–2016 period.

Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement

“…However, when used in the context of other inputs and chemical transport modeling, satellite results can be helpful in targeting reduction measures. Specific local enhancements can be detected using GOSAT observations, as shown by Sheng, Jacob, Turner, et al (), who used satellite results, together with a gridded inventory of emissions (Maasakkers et al, ), to infer trends in North American emissions from different source types (oil and gas, livestock, and wetlands). In an influential study, Kort et al () coupled satellite observation with ground‐based validation to identify the largest satellite‐detected anomaly in the United States within the Four Corners region in the Southwest United States (particularly coalbed extraction), which they showed to have emissions up to 10% of the total U.S. methane emission inventory.…”

“…However, when used in the context of other inputs and chemical transport modeling, satellite results can be helpful in targeting reduction measures. Specific local enhancements can be detected using GOSAT observations, as shown by Sheng, Jacob, Turner, et al (2018), who used satellite results, together with a gridded Figure 5. Example of drone-carried Tedlar bag sampling for methane mole fraction and δ 13 C CH4 Greatwood et al, 2017). inventory of emissions , to infer trends in North American emissions from different source types (oil and gas, livestock, and wetlands).…”

Quantifying methane emissions in the Uintah Basin during wintertime stagnation episodes

“…Global methane (CH 4 ) levels have increased over the past several decades, with numerous studies attributing the rise at least partially to increased anthropogenic emissions (Bruhwiler et al 2017;Miller et al 2013;Nisbet et al 2016;Saunois et al 2016Saunois et al , 2017Sheng et al 2018;Thompson et al 2018;Turner et al 2016Turner et al , 2017Turner et al , 2019. One key anthropogenic CH 4 source is leakage of gases during oil and natural gas (ONG) extraction and processing activities.…”

Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories

“…The national reports submitted to the United Nations Framework Convention on Climate Change (UNFCCC) by different countries might use different methods to estimate emissions, which could be different from global inventory datasets produced by the scientific community, such as the Global Carbon Project (GCP) [16], and the Emissions Database for Global Atmospheric Research (EDGAR) [17][18][19]. Complementary to these bottom-up emission estimations, top-down estimations by inverse models combined with atmospheric measurements have been widely used and have proven worthy for emission inventory evaluation (e.g., [20][21][22][23][24][25][26]). Houweling et al [27] reviewed the development of global inverse modeling of methane, and Jacob et al [28] also supported their research by presenting the advantages of increased observational data from surface stations, satellites and aircraft, and state-of-the-art computational facilities.…”