Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley

Abstract

We quantify methane (CH4) emissions in California's San Joaquin Valley (SJV) by using 4 days of aircraft measurements from a field campaign during May–June 2010 together with a Bayesian inversion method and a mass balance approach. For the inversion estimates, we use the FLEXible PARTicle dispersion model (FLEXPART) to establish the source‐receptor relationship between sampled atmospheric concentrations and surface fluxes. Our prior CH4 emission estimates are from the California Greenhouse Gas Emissions Measurements (CALGEM) inventory. We use three meteorological configurations to drive FLEXPART and subsequently construct three inversions to analyze the final optimized estimates and their uncertainty (one standard deviation). We conduct May and June inversions independently and derive similar total CH4 emission estimates for the SJV: 135 ± 28 Mg/h in May and 135 ± 19 Mg/h in June. The inversion result is 1.7 times higher than the prior estimate from CALGEM. We also use an independent mass balance approach to estimate CH4 emissions in the northern SJV for one flight when meteorological conditions allowed. The mass balance estimate provides a confirmation of our inversion results, and these two independent estimates of the total CH4 emissions in the SJV are consistent with previous studies. In this study, we provide optimized CH4 emissions estimates at 0.1° horizontal resolution. Using independent spatial information on major CH4 sources, we estimate that livestock contribute 75–77% and oil/gas production contributes 15–18% of the total CH4 emissions in the SJV. Livestock explain most of the discrepancies between the prior and the optimized emissions from our inversion.

Short-term methane emissions from 2 dairy farms in California estimated by different measurement techniques and US Environmental Protection Agency inventory methodology: A case study

“…Similarly, long-term measurements by Jeong et al (2016) estimated that CH 4 emissions from dairy livestock in the San Joaquin Valley are 2 times greater than the California Air Resource Board (CARB) estimates. Short-term measurements by Trousdell et al (2016) and Cui et al (2017) estimated that CH 4 emissions from dairy livestock in the San Joaquin Valley are up to 2.4 times greater than CARB estimates. Other regional short-term TD measurements have suggested that dairy livestock emissions are similar to CARB estimates (Peischl et al, 2013;Wecht et al, 2014b).…”

“…Regional top-down (TD) measurements, which used inverse modeling to attribute CH 4 emissions to different emission sources, have suggested that the national and California inventories underestimate CH 4 emissions from livestock (Miller et al, 2013;Wecht et al, 2014a;Bergamaschi et al, 2015;Jeong et al, 2016;Cui et al, 2017). Based on long-term measurements, Miller et al (2013) estimated that national CH 4 emissions from ruminants and manure management are up to 2 times greater than the US Environmental Protection Agency (EPA) inventory estimates.…”

Inversion Estimates of Lognormally Distributed Methane Emission Rates From the Haynesville‐Bossier Oil and Gas Production Region Using Airborne Measurements

“…Journal of Geophysical Research: Atmospheres estimate CH 4 emissions in California using aircraft measurements (Cui et al, 2015(Cui et al, , 2017. We assume that concentration deviations above background values and emissions follow lognormal distributions associated with the errors (the model-observation mismatch and flux error) to be of multiplicative nature (Bocquet et al, 2010;Tian et al, 2013).…”

Estimating Methane Emissions From Underground Coal and Natural Gas Production in Southwestern Pennsylvania

“…As CH 4 emanates from numerous reported and unreported sources (Turner et al, 2017), atmospheric studies have played a significant role in evaluating CH 4 emissions (Cui et al, 2017;Ganesan et al, 2017; 10.1029/2019GL082131 Ren et al, 2018;Schwietzke et al, 2016). In comparison to site-level data collected from various anthropogenic sources, atmospheric measurements integrate emissions across large areas, from both natural and man-made sources, allowing for detection and quantification of sources that may be missed or underrepresented in bottom-up inventories (Levin et al, 2010).…”