We estimate seasonal variations in methane (CH_{4}) emissions from central California from December 2007 through November 2008 by comparing CH_{4} mixing ratios measured at a tall tower with transport model predictions based on a global 1° a priori CH_{4}emissions map (EDGAR32) and a 10 km seasonally varying California-specific map, calibrated to statewide by CH_{4}emission 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. CH_{4}signals 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 CH_{4}emissions 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 CH_{4} 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.

We estimate nitrous oxide (N_{2}O) emissions from Central California for the period of December 2007 through November 2009 by comparing N_{2}O mixing ratios measured at a tall tower (Walnut Grove, WGC) with transport model predictions based on two global *a priori* N_{2}O emission models (EDGAR32 and EDGAR42). Atmospheric particle trajectories and surface footprints are computed using the Weather Research and Forecasting (WRF) and Stochastic Time-Inverted Lagrangian Transport (STILT) models. Regression analyses show that the slopes of predicted on measured N_{2}O from both emission models are low, suggesting that actual N_{2}O emissions are significantly higher than the EDGAR inventories for all seasons. Bayesian inverse analyses of regional N_{2}O emissions show that posterior annual N_{2}O emissions are larger than both EDGAR inventories by factors of 2.0 ± 0.4 (EDGAR32) and 2.1 ± 0.4 (EDGAR42) with seasonal variation ranging from 1.6 ± 0.3 to 2.5 ± 0.4 for an influence region of Central California within approximately 150 km of the tower. These results suggest that if the spatial distribution of N_{2}O emissions in California follows the EDGAR emission models, then actual emissions are 2.7 ± 0.5 times greater than the current California emission inventory, and total N_{2}O emissions account for 8.1 ± 1.4% of total greenhouse gas emissions from California.

[1] Methane mixing ratios measured at a tall tower are compared to model predictions to estimate surface emissions of CH_{4} in Central California for October–December 2007 using an inverse technique. Predicted CH_{4} mixing ratios are calculated based on spatially resolved a priori CH_{4} 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 CH_{4} emissions. Three inverse model estimates of CH_{4} emissions are reported. First, linear regressions of modeled and measured CH_{4} 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 CH_{4} 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 CH_{4} emissions from 13 subregions, which shows that inventory CH_{4} emissions from the Central Valley are underestimated and uncertainties in CH_{4} emissions are reduced for subregions 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 CH_{4} emissions for multiple regions.