Estimating Dairy Methane Emissions from California's San Joaquin Valley Using Multi-species Observations

Abstract:

California has committed to reduce methane emissions from individual source sectors by 40% below the 2013 levels by 2030. Because dairy agriculture in the San Joaquin Valley (SJV) is a large yet uncertain source of methane, quantifying the regional scale emissions is important for meeting California’s climate change goals. We estimate total methane emissions from SJV in an inverse model driven by atmospheric observations of methane from coastal marine background sites together with four ground sites in the Central Valley. We also attribute the fraction of the total emission to biological versus oil and gas sources using volatile organic compounds (VOCs) measured at two of the valley sites in a one-year period from March 2019 to February 2020. To improve the separation of dairy methane emissions from oil and gas production and other sources, we develop spatial inventories for the dairy and oil & gas sectors with higher spatial accuracy than previous studies using updated activity data. A hierarchical Bayesian inversion method is applied to estimate optimized methane emissions by comparing the multi-species observations with predicted enhancements simulated using transport model predictions of surface influence (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) together with the updated California-specific prior emissions. Although work is in progress, we expect to report dairy methane emissions from SJV, reducing uncertainties for this important source sector.