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We present an analysis of methane (CH4) emissions using atmospheric observations from five sites in California's Central Valley across different seasons (September 2010 to June 2011). CH4 emissions for spatial regions and source sectors are estimated by comparing measured CH4 mixing ratios with transport model (WRF-STILT) predictions based on two 0.1 degree CH4 (seasonally varying "California-specific" (CALGEM) and a static global (EDGAR42)) prior emission models. Region-specific Bayesian analyses indicate that for California's Central Valley the CALGEM- and EDGAR42-based inversions provide consistent annual total CH4 emissions (32.87±2.09 vs. 31.60±2.17 Tg CO2eq yr−1; 68% C.I., assuming uncorrelated errors between regions). Summing across all regions of California, optimized CH4 emissions are only marginally consistent between CALGEM- and EDGAR42-based inversions (48.35±6.47 vs. 64.97±11.85 Tg CO2eq), because emissions from coastal urban regions (where landfill and natural gas emissions are much higher in EDGAR than CALGEM) are not strongly constrained by the measurements. Combining our results with those from a recent study of the South Coast air basin narrows the range of estimates to 43 - 57 Tg CO2eq yr−1 (1.3 - 1.8 times higher than the current state inventory). These results suggest that the combination of rural and urban measurements will be necessary to verify future changes in California's total CH4 emissions.
In this paper, we present an analysis of methane (CH4) emissions using atmospheric observations from five sites in California's Central Valley across different seasons (September 2010 to June 2011). CH4 emissions for spatial regions and source sectors are estimated by comparing measured CH4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on two 0.1° CH4 (seasonally varying ?California-specific? (California Greenhouse Gas Emission Measurements, CALGEM) and a static global (Emission Database for Global Atmospheric Research, release version 42, EDGAR42)) prior emission models. Region-specific Bayesian analyses indicate that for California's Central Valley, the CALGEM- and EDGAR42-based inversions provide consistent annual total CH4 emissions (32.87 ± 2.09 versus 31.60 ± 2.17 Tg CO2eq yr-1; 68% confidence interval (CI), assuming uncorrelated errors between regions). Summing across all regions of California, optimized CH4 emissions are only marginally consistent between CALGEM- and EDGAR42-based inversions (48.35 ± 6.47 versus 64.97 ± 11.85 Tg CO2eq), because emissions from coastal urban regions (where landfill and natural gas emissions are much higher in EDGAR than CALGEM) are not strongly constrained by the measurements. Combining our results with those from a recent study of the South Coast Air Basin narrows the range of estimates to 43–57 Tg CO2eq yr-1 (1.3–1.8 times higher than the current state inventory). Finally, these results suggest that the combination of rural and urban measurements will be necessary to verify future changes in California's total CH4 emissions.
Methane mixing ratios measured at a tall-tower are compared to model predictions to estimate surface emissions of CH4 in Central California for October-December 2007 using an inverse technique. Predicted CH4 mixing ratios are calculated based on spatially resolved a priori CH4 emissions and simulated atmospheric trajectories. The atmospheric trajectories, along with surface footprints, are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. An uncertainty analysis is performed to provide quantitative uncertainties in estimated CH4 emissions. Three inverse model estimates of CH4 emissions are reported. First, linear regressions of modeled and measured CH4 mixing ratios obtain slopes of 0.73 ± 0.11 and 1.09 ± 0.14 using California specific and Edgar 3.2 emission maps respectively, suggesting that actual CH4 emissions were about 37 ± 21% higher than California specific inventory estimates. Second, a Bayesian 'source' analysis suggests that livestock emissions are 63 ± 22% higher than the a priori estimates. Third, a Bayesian 'region' analysis is carried out for CH4 emissions from 13 sub-regions, which shows that inventory CH4 emissions from the Central Valley are underestimated and uncertainties in CH4 emissions are reduced for sub-regions near the tower site, yielding best estimates of flux from those regions consistent with 'source' analysis results. The uncertainty reductions for regions near the tower indicate that a regional network of measurements will be necessary to provide accurate estimates of surface CH4 emissions for multiple regions.
In 2006, California passed the landmark assembly bill AB-32 to reduce California's emissions of greenhouse gases (GHGs) that contribute to global climate change. AB-32 commits California to reduce total GHG emissions to 1990 levels by 2020, a reduction of 25 percent from current levels. To verify that GHG emission reductions are actually taking place, it will be necessary to measure emissions. We describe atmospheric inverse model estimates of GHG emissions obtained from the California Greenhouse Gas Emissions Measurement (CALGEM) project. In collaboration with NOAA, we are measuring the dominant long-lived GHGs at two tall-towers in central California. Here, we present estimates of CH4 emissions obtained by statistical comparison of measured and predicted atmospheric mixing ratios. The predicted mixing ratios are calculated using spatially resolved a priori CH4 emissions and surface footprints, that provide a proportional relationship between the surface emissions and the mixing ratio signal at tower locations. The footprints are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. Integral to the inverse estimates, we perform a quantitative analysis of errors in atmospheric transport and other factors to provide quantitative uncertainties in estimated emissions. Regressions of modeled and measured mixing ratios suggest that total CH4 emissions are within 25% of the inventory estimates. A Bayesian source sector analysis obtains posterior scaling factors for CH4 emissions, indicating that emissions from several of the sources (e.g., landfills, natural gas use, petroleum production, crops, and wetlands) are roughly consistent with inventory estimates, but livestock emissions are significantly higher than the inventory. A Bayesian 'region' analysis is used to identify spatial variations in CH4 emissions from 13 sub-regions within California. Although, only regions near the tower are significantly constrained by the tower measurements, CH4 emissions from the south Central Valley appear to be underestimated in a manner consistent with the under-prediction of livestock emissions. Finally, we describe a pseudo-experiment using predicted CH4 signals to explore the uncertainty reductions that might be obtained if additional measurements were made by a future network of tall-tower stations spread over California. These results show that it should be possible to provide high-accuracy estimates of surface CH4 emissions for multiple regions as a means to verify future emissions reductions.
Methane (CH4) is the primary component of natural gas and has a larger global warming potential than CO2. Some recent top-down studies based on observations showed CH4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R2 = 0.5). The emission estimates for CH4 in the posterior, 46.3 ± 9.2 Mg CH4/h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. Furthermore, we estimate that dairies contributed 5.9 ± 1.7 Mg CH4/h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH4/h in the SoCAB.
Understanding, quantifying, and tracking atmospheric methane and emissions is essential for addressing concerns and informing decisions that affect the climate, economy, and human health and safety. Atmospheric methane is a potent greenhouse gas (GHG) that contributes to global warming. While carbon dioxide is by far the dominant cause of the rise in global average temperatures, methane also plays a significant role because it absorbs more energy per unit mass than carbon dioxide does, giving it a disproportionately large effect on global radiative forcing. In addition to contributing to climate change, methane also affects human health as a precursor to ozone pollution in the lower atmosphere. Improving Characterization of Anthropogenic Methane Emissions in the United States summarizes the current state of understanding of methane emissions sources and the measurement approaches and evaluates opportunities for methodological and inventory development improvements. This report will inform future research agendas of various U.S. agencies, including NOAA, the EPA, the DOE, NASA, the U.S. Department of Agriculture (USDA), and the National Science Foundation (NSF).