|Title||Seasonal variation of CH4 emissions from central California|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Seongeun Jeong, Chuanfeng Zhao, Arlyn E Andrews, Laura Bianco, James M Wilczak, Marc L Fischer|
|Journal||Journal of Geophysical Research - Atmospheres|
|Keywords||atmospheric transport, emission inventory, greenhouse gas, inverse model, methane|
We estimate seasonal variations in methane (CH4) emissions from central California from December 2007 through November 2008 by comparing CH4 mixing ratios measured at a tall tower with transport model predictions based on a global 1° a priori CH4emissions map (EDGAR32) and a 10 km seasonally varying California-specific map, calibrated to statewide by CH4emission totals. Atmospheric particle trajectories and surface footprints are computed using the Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport models. Uncertainties due to wind velocity and boundary layer mixing depth are evaluated using measurements from radar wind profilers. CH4signals calculated using the EDGAR32 emission model are larger than those based on the California-specific model and in better agreement with measurements. However, Bayesian inverse analyses using the California-specific and EDGAR32 maps yield comparable annually averaged posterior CH4emissions totaling 1.55 ± 0.24 times and 1.84 ± 0.27 times larger than the California-specific prior emissions, respectively, for a region of central California within approximately 150 km of the tower. If these results are applicable across California, state total CH4 emissions would account for approximately 9% of state total greenhouse gas emissions. Spatial resolution of emissions within the region near the tower reveal seasonality expected from several biogenic sources, but correlations in the posterior errors on emissions from both prior models indicate that the tower footprints do not resolve spatial structure of emissions. This suggests that including additional towers in a measurement network will improve the regional specificity of the posterior estimates.