|Title||Atmospheric Measurement and Inverse Modeling to Improve Greenhouse Gas Emission Estimates|
|LBNL Report Number||LBNL-1006298|
|Year of Publication||2016|
|Authors||Fischer, Marc L., Seongeun Jeong, Sally Newman, and Jingsong Zhang|
California has committed to an ambitious plan to reduce statewide greenhouse gas (GHG) emissions to 1990 levels by 2020 through Assembly Bill 32 (AB-32), which requires accurate accounting of emissions for effective mitigation planning and verification of future emission reductions. Atmospheric GHG measurements from networks of towers can be combined with existing knowledge of emissions in a statistical inverse model — weighing existing knowledge with the new observations — to more accurately quantify GHG emissions. This study quantifies major anthropogenic GHGs including fossil fuel CO2 (ffCO2), methane (CH4) and nitrous oxide (N2O) emissions within California with a Bayesian inverse modeling framework, using atmospheric observations from an expanded GHG measurement network across California over multiple years. We first assess uncertainties in the transport model predictions using a combination of meteorological and carbon monoxide (CO) measurements. Comparison of predicted and measured CO mixing ratios at the four towers during June 2013 – May 2014 yields near-unity slopes (predicted vs. measured) for the majority of sites and seasons, suggesting that the model simulations are sufficient to estimate emissions of CO and likely other GHGs across California to within 10%. The results of this study indicate that ffCO2 emissions from central California are within 6% of the prior estimate (i.e., the estimate based on existing knowledge before measured data are taken into account), and that in the South Coast Air Basin (SoCAB) ffCO2 emissions are within 11% of the prior estimate for that region. Combining results from the two regions (i.e., central California and SoCAB), ffCO2 emissions are consistent to within approximately 10% of the prior estimate.
Summing estimated CH4 emissions across all air basins (i.e., subregions) of California, posterior results (i.e., results after the relevant atmospheric observation is taken into account) suggest that state annual anthropogenic CH4 emissions are higher (1.2 - 1.8 times) than the anthropogenic emission in California Air Resources Board’s (CARB) current GHG inventory. The estimated CH4 emissions drop to 1.0 - 1.6 times the CARB inventory if results are corrected for the median CH4 emissions assuming the 10% model bias in CO is applicable to CH4. The CH4 emissions from the Central Valley and major urban regions (SoCAB and San Francisco Bay Area, SFBA) account for 58% and 26 % of the total posterior emissions, respectively. This study combined with other studies suggests the livestock sector is the major contributor to the state total CH4 emissions, in agreement with CARB’s GHG inventory.
Using N2O measurements from six sites across California, state annual anthropogenic N2O emissions are estimated to be higher (1.5 – 2.5 times) than the current CARB inventory. The estimated N2O emissions drop to 1.3 - 2.3 times the CARB inventory if corrected for the median N2O emissions assuming the 10% model bias in CO is applicable to N2O. This study’s results reinforce the understanding that a large portion of the increase in global atmospheric N2O can be attributed to the use of fertilizer, and agricultural activities are likely a significant source of anthropogenic N2O emissions in California, as currently reflected in CARB’s N2O inventory. The results also indicate that seasonal variations in California’s N2O emissions relative to the annual average are likely smaller than for interior portions (e.g., Midwestern US) of the continental US, consistent with milder climate of California.
In summary, while the ffCO2 emissions, which account for the majority of the total GHG emission in California, are not clearly distinguishable from the state inventory in central and southern California, CH4 and N2O emissions appear to be higher than current inventory estimates.