Characterizing methane emissions from point sources to the global budget

Emission reductions for methane (CH₄) have strong promise for mitigating climate change on a decadal time scale. With a global warming potential 85 times that of carbon dioxide over 20 years, methane also affects tropospheric ozone and related air quality issues and modulates the atmosphere’s self-cleansing capacity. The atmospheric methane growth rate has been accelerating over the past decade, with causes that are unclear. This dissertation explores key knowledge gaps for the atmospheric methane budget from point-source to global scales. Analyses focus in particular on identifying missing methane sources, improving model predictions of their underlying drivers, and evaluating how uncertainties in atmospheric oxidation rates impact methane source estimates.

First, we employed airborne measurements over multiple seasons to quantify methane emissions from major animal feeding operations and sugar plants in Minnesota. Top-down emission estimates (derived via mass balance) are consistent with bottom-up estimates for enteric fermentation, but not for manure management. Top-down estimates also reveal that sugar waste emissions are 80% lower than the facility-reported magnitudes. These findings indicate large uncertainties in extrapolating point-source quantifications to larger scales, especially in the US where agriculture and waste account for over half of the anthropogenic methane source.

We then combined aircraft measurements with inverse modeling to quantify the methane budget for the US Upper Midwest. This region is crucial to the national methane budget with its extensive wetlands and livestock operations. The inversions indicate that wetlands (20 Gg d^-1, 32% of the total) and livestock (15 Gg d^-1, 25% of the total) are the two major emission contributors. Wetland methane emissions are underestimated in the Prairie Pothole region, where hydrology is highly variable due to temporal variability in groundwater and rainfall, but are overestimated in the Great Lakes coastal regions. The seasonal onset of wetland emission occurs too early in the employed bottom-up inventory, due to insufficient representation of the spring warming lag between land surface and soil. Major errors in livestock emission estimates arise from inadequate representation of manure management. Furthermore, this work uncovers substantial mitigation opportunities in the Upper Midwest: methane emissions could potentially be reduced by 4.5 Tg y^-1 through widespread deployment of anaerobic digestion, an amount nearly double the total oil + gas emissions of the entire Permian basin.

We next evaluated the capabilities of current-generation satellite sensors for high-resolution mapping of methane sources through Observing System Simulation Experiments. Current inventories have inconsistent emission distributions, and the dense data coverage provided by new satellite instruments offers unique promise to address this issue. We show that inversions using synthetic TROPOspheric Monitoring Instrument (TROPOMI) observations over North America can successfully improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors. However, performance is severely degraded when both of these errors are present—as is commonly the case. We further developed new mathematical formalisms to help mitigate these issues for improved identification of missing methane sources from space. 

Finally, we combined 4D-Var inverse analysis with a novel spatial downscaling approach to map global methane sources at 0.1°×0.1° resolution based on TROPOMI satellite data for 2018–2020. The inversions converge on distinct solutions when the global methane sink is optimized versus when it is held constant. Results show that simultaneous optimization of methane sources and sinks remains an ill-posed problem, even with the dense observations provided by TROPOMI. Based on the model fidelity in simulating atmospheric hydroxyl radical and carbon monoxide, a methane budget with 588 Tg y^-1 global emissions and 571 Tg y^-1 global removal is found to be most robust. Inversions reveal emission underestimates associated with the global expansion of agriculture, including in Amazonia and Southeast Asia, and with rapidly developing fossil fuel activities in the Middle East, the United States, and Venezuela. Furthermore, the Asian monsoon brings unexpected emissions from wetlands, rice, and waste systems, potentially due to the increased water levels associated with seasonal rainfall. 

Overall, the above findings advance current understanding of the global methane budget, thus helping to support greenhouse gas mitigation planning.