Abstract
Abstract. We estimate the amount of methane (CH4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic 13∕12CH4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution. Our top-down CH4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s−1 are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH4 emissions of 2.2 to 3.5 ppt s−1 at Chino.
Highlights
Atmospheric methane (CH4) concentration has increased by 150 % since the pre-industrial era, contributing to a global average change in radiative forcing of 0.5 W m−2 (Forster et al, 2007; Myhre et al, 2013; IPCC, 2013)
The XCH4 and XCO2 variabilities captured by the instruments are due to changes in wind speed and direction, i.e., with high XCH4 signals when the wind blows from the dairies to the instruments
The EM27/SUN are clearly able to detect variability of greenhouses gases at local scales indicating that these mobile column measurements have the potential to provide estimates of local source emissions
Summary
Atmospheric methane (CH4) concentration has increased by 150 % since the pre-industrial era, contributing to a global average change in radiative forcing of 0.5 W m−2 (Forster et al, 2007; Myhre et al, 2013; IPCC, 2013). Methane is naturally emitted by wetlands, but anthropogenic emissions contribute to more than half of its total budget (Ciais et al, 2013), ranking it the second most important anthropogenic greenhouses gas after carbon dioxide (CO2). The United Nations Framework Convention on Climate Change (UNFCCC, http://newsroom.unfccc.int/) aims to reduce CH4 emissions by reaching global agreements and collective action plans. In the United States (USA), the federal government aims to reduce CH4 emissions by at least 17 % below 2005 levels by 2020 by targeting numerous key sources such as (in order of importance) agriculture, energy sectors (including oil, natural gas, and coal mines), and landfills (Climate Action Plan, March 2014).
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