Seasonality of Methane Emission Estimates for California Dairy Farms by Solar Column Gradient Observations

Abstract:

California dairy farms are estimated to emit close to 50% of the statewide methane (CH₄) emissions, but the magnitude and the inter-annual variability from the dairy CH₄ source is highly uncertain. Quantifying and reducing CH₄ is critical for meeting California’s climate targets and mitigating the climate crisis at the global scale. To help constrain CH₄ emissions from California dairies, we studied variations across four seasons from Spring 2019 to Winter 2020. We measured total atmospheric column gradients (∆X_CH4) from a dense group of dairies in the San Joaquin Valley (SJV) using portable EM27/SUN solar-viewing spectrometers placed upwind and downwind of 30 dairy farms housing >30,500 cows (~20x20 km²). Measurements collected during different seasons provides insight into the meteorological factors that drive the time-varying patterns observed during the year. We performed atmospheric flux inversions to estimate X_CH4 emissions using WRF-STILT, a back-trajectory transport model, and compare observed emissions to WRF-STILT modeled emissions driven by a facility-level emission prior. Inverse modeling is challenging at these spatial scales (~1 km²) with a high sensitivity to errors in turbulence and 3D wind fields. We explore how tools like WRF 4D-Var improves this. Preliminary analysis of the EM27 measurements suggests that CH₄ fluxes were larger in winter than summer. We will discuss these results in the context of bottom-up modeling driven by seasonal variations in farm management and climate. This study will help constrain CH₄ emissions from California dairies, examine how seasonal variability should be handled in these emissions estimates, and demonstrate new modeling approaches for analyzing X_CH4 data at these fine scales.