Observation of CH4 and other Non-CO2 Green House Gas Emissions from California

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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 ... continued below

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Fischer, Marc L.; Zhao, Chuanfeng; Riley, William J. & Andrews, Arlyn C. January 9, 2009.

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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 CH{sub 4} emissions obtained by statistical comparison of measured and predicted atmospheric mixing ratios. The predicted mixing ratios are calculated using spatially resolved a priori CH{sub 4} 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 CH{sub 4} emissions are within 25% of the inventory estimates. A Bayesian source sector analysis obtains posterior scaling factors for CH{sub 4} 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 CH{sub 4} emissions from 13 sub-regions within California. Although, only regions near the tower are significantly constrained by the tower measurements, CH{sub 4} 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 CH{sub 4} 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 CH{sub 4} emissions for multiple regions as a means to verify future emissions reductions.

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  • Report No.: LBNL-2042E
  • Grant Number: DE-AC02-05CH11231
  • DOI: 10.2172/962722 | External Link
  • Office of Scientific & Technical Information Report Number: 962722
  • Archival Resource Key: ark:/67531/metadc926813

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  • January 9, 2009

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Jan. 4, 2017, 6:46 p.m.

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Fischer, Marc L.; Zhao, Chuanfeng; Riley, William J. & Andrews, Arlyn C. Observation of CH4 and other Non-CO2 Green House Gas Emissions from California, report, January 9, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc926813/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.