This study quantitatively estimates the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Results show that current inventories from the US Environmental Protection Agency (EPA) and the Emissions Database for Global Atmospheric Research underestimate methane emissions nationally by a factor of ∼1.5 and ∼1.7, respectively. Our study indicates that emissions due to ruminants and manure are up to twice the magnitude of existing inventories. In addition, the discrepancy in methane source estimates is particularly pronounced in the south-central United States, where we find total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. The spatial patterns of our emission fluxes and observed methane-propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13%) in the south-central United States. This result suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global methane inventory. These results cast doubt on the US EPA's recent decision to downscale its estimate of national natural gas emissions by 25-30%. Overall, we conclude that methane emissions associated with both the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.climate change policy | geostatistical inverse modeling

International agreements to limit greenhouse gas emissions require verification to ensure that they are effective and fair. Verification based on direct observation of atmospheric greenhouse gas concentrations will be necessary to demonstrate that estimated emission reductions have been actualized in the atmosphere. Here we assess the capability of ground-based observations and a highresolution (1.3 km) mesoscale atmospheric transport model to determine a change in greenhouse gas emissions over time from a metropolitan region. We test the method with observations from a network of CO 2 surface monitors in Salt Lake City. Many features of the CO 2 data were simulated with excellent fidelity, although data-model mismatches occurred on hourly timescales due to inadequate simulation of shallow circulations and the precise timing of boundary-layer stratification and destratification. Using two optimization procedures, monthly regional fluxes were constrained to sufficient precision to detect an increase or decrease in emissions of approximately 15% at the 95% confidence level. We argue that integrated column measurements of the urban dome of CO 2 from the ground and/or space are less sensitive than surface point measurements to the redistribution of emitted CO 2 by small-scale processes and thus may allow for more precise trend detection of emissions from urban regions.atmospheric inversion | cities | climate change policy A greements to limit anthropogenic greenhouse gas (GHG) emissions will have major economic and political consequences. Compliance will be demonstrated primarily with selfreported emission inventories derived from activity data and generalized conversion factors (1, 2), but associated uncertainties may exceed the magnitude of emission reduction targets (2-5). Therefore, measurement, reporting, and verification (MRV) will be critical elements of any international climate treaty, as emphasized by a recent National Research Council (NRC) report (2), a related study by the JASON scientific advisory group (6), and by the Intergovernmental Panel on Climate Change (1). Verification procedures based on direct atmospheric observations can provide independent constraints on reported emissions and are necessary to ensure that emission reductions are actualized in the atmosphere.The NRC report on MRV (2) highlighted the potential utility of atmospheric observations and models for detecting trends in emissions from strong localized source regions, such as urban areas, where enhancements in GHG concentration are readily detectable in the atmosphere. A large fraction of a country's emissions likely emanate from such regions and results from several representative cities over time could provide strong tests of claimed emission reductions at national or regional scales. But the NRC (2) estimated that current uncertainties in this approach exceed 100%, far too large to detect emission changes mandated by treaties or national policies. This imprecision is attributable to a dearth of research on the concept and the com...

We present top‐down emission constraints for two non‐CO2 greenhouse gases in large areas of the U.S. and southern Canada during early summer. Collocated airborne measurements of methane and nitrous oxide acquired during the COBRA‐NA campaign in May–June 2003, analyzed using a receptor‐oriented Lagrangian particle dispersion model, provide robust validation of independent bottom‐up emission estimates from the EDGAR and GEIA inventories. We find that the EDGAR CH4 emission rates are slightly low by a factor of 1.08 ± 0.15 (2σ), while both EDGAR and GEIA N2O emissions are significantly too low, by factors of 2.62 ± 0.50 and 3.05 ± 0.61, respectively, for this region. Potential footprint bias may expand the statistically retrieved uncertainties. Seasonality of agricultural N2O emissions may help explain the discrepancy. Total anthropogenic U.S. and Canadian emissions would be 49 Tg CH4 and 4.3 Tg N2O annually, if these inventory scaling factors applied to all of North America.

Abstract. Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° × 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous sub-continental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr−1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts.

This paper describes the coupling between a mesoscale numerical weather prediction model, the