A multitower measurement network estimate of California's methane emissions

Jeong, Seongeun; Hsu, Ying-Kuang; Andrews, A. E.; Bianco, L.; Vaca, Patrick; Wilczak, James M.; Fischer, M. L.

doi:10.1002/jgrd.50854

Cited by 85 publications

(266 citation statements)

References 35 publications

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“…Wecht et al (2014b) and Jeong et al (2016) leverage this strategy to estimate CH 4 emissions for California using aircraft and tower-based observations, respectively. Like Zhao et al (2009) and Jeong et al (2013), they also find higher emissions from agriculture relative to EDGAR. Wecht et al (2014a) and Turner et al (2015) further apply this strategy to attribute emissions at continental scales; these studies use Envisat/SCIAMACHY and the GOSAT satellite, respectively, to estimate sector-specific CH 4 emissions across North America.…”

Section: State-and National-scale Inverse Modelingmentioning

confidence: 89%

“…For example, several top-down studies indicate that the California state inventory is likely too low by a factor of 1.2 to 1.9 (Jeong et al, 2013Wecht et al, 2014b), and several top-down studies estimate emissions for oil and gas drilling regions of Utah and Colorado that are up to 3 times bottom-up estimates (e.g., Karion et al, 2013;Pétron et al, 2014). Overall, total US CH 4 emissions are likely ∼50 % larger than estimated by EDGAR or the US EPA Wecht et al, 2014a;Turner et al, 2015).…”

Section: Impact Of Recent Advancesmentioning

confidence: 99%

“….p) is defined at each m s spatial location, at each m t time period, and for each p source sector. In one study, this initial emission estimate is spatially but not temporally resolved (e.g., m t = 1; Zhao et al, 2009), while in another study, it is resolved in both space and time (Jeong et al, 2013). The p unknown scaling factors (β i , where i = 1. .…”

Section: State-and National-scale Inverse Modelingmentioning

confidence: 99%

“…For example, Zhao et al (2009) andJeong et al (2013) use atmospheric measurements from tall towers to estimate emissions from individual source sectors in California. Both studies find higher CH 4 emissions from agriculture relative to the EDGAR emission inventory.…”

Section: State-and National-scale Inverse Modelingmentioning

confidence: 99%

“…If the latter condition does not hold, then the scaling factors may be poorly constrained by the data, resulting in uncertain or unrealistic sector-specific estimates. To account for these challenges, Jeong et al (2013) only report source-specific estimates when they obtain scaling factors that are statistically significantly different from zero.…”

Section: State-and National-scale Inverse Modelingmentioning

confidence: 99%

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Constraining sector-specific CO2 and CH4 emissions in the US

Miller

Michalak

2017

Atmos. Chem. Phys.

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Abstract. This review paper explores recent efforts to estimate state-and national-scale carbon dioxide (CO 2 ) and methane (CH 4 ) emissions from individual anthropogenic source sectors in the US. Nearly all state and national climate change regulations in the US target specific source sectors, and detailed monitoring of individual sectors presents a greater challenge than monitoring total emissions. We particularly focus on opportunities to synthesize disparate types of information on emissions, including emission inventory data and atmospheric greenhouse gas data.We find that inventory estimates of sector-specific CO 2 emissions are sufficiently accurate for policy evaluation at the national scale but that uncertainties increase at state and local levels. CH 4 emission inventories are highly uncertain for all source sectors at all spatial scales, in part because of the complex, spatially variable relationships between economic activity and CH 4 emissions. In contrast to inventory estimates, top-down estimates use measurements of atmospheric mixing ratios to infer emissions at the surface; thus far, these efforts have had some success identifying urban CO 2 emissions and have successfully identified sector-specific CH 4 emissions in several opportunistic cases. We also describe a number of forward-looking opportunities that would aid efforts to estimate sector-specific emissions: fully combine existing top-down datasets, expand intensive aircraft measurement campaigns and measurements of secondary tracers, and improve the economic and demographic data (e.g., activity data) that drive emission inventories. These steps would better synthesize inventory and topdown data to support sector-specific emission reduction policies.

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Section: State-and National-scale Inverse Modelingmentioning

confidence: 89%

Section: Impact Of Recent Advancesmentioning

confidence: 99%

Section: State-and National-scale Inverse Modelingmentioning

confidence: 99%

Section: State-and National-scale Inverse Modelingmentioning

confidence: 99%

Section: State-and National-scale Inverse Modelingmentioning

confidence: 99%

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Constraining sector-specific CO2 and CH4 emissions in the US

Miller

Michalak

2017

Atmos. Chem. Phys.

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U.S. emissions of HFC‐134a derived for 2008–2012 from an extensive flask‐air sampling network

Montzka

Miller

et al. 2015

JGR Atmospheres

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U.S. national and regional emissions of HFC-134a are derived for 2008-2012 based on atmospheric observations from ground and aircraft sites across the U.S. and a newly developed regional inverse model. Synthetic data experiments were first conducted to optimize the model assimilation design and to assess model-data mismatch errors and prior flux error covariances computed using a maximum likelihood estimation technique. The synthetic data experiments also tested the sensitivity of derived national and regional emissions to a range of assumed prior emissions, with the goal of designing a system that was minimally reliant on the prior. We then explored the influence of additional sources of error in inversions with actual observations, such as those associated with background mole fractions and transport uncertainties.

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Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin

et al. 2015

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Methane (CH 4 ) is the primary component of natural gas and has a larger global warming potential than CO 2 . Recent top-down studies based on observations showed CH 4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH 4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH 4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH 4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R 2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R 2 = 0.5). The emission estimates for CH 4 in the posterior, 46.3 ± 9.2 Mg CH 4 /h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH 4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. We estimate that dairies contributed 5.9 ± 1.7 Mg CH 4 /h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH 4 /h in the SoCAB.

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A multitower measurement network estimate of California's methane emissions

Cited by 85 publications

References 35 publications

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

U.S. emissions of HFC‐134a derived for 2008–2012 from an extensive flask‐air sampling network

Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin

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A multitower measurement network estimate of California's methane emissions

Cited by 85 publications

References 35 publications

Constraining sector-specific CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; emissions in the US

Constraining sector-specific CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; emissions in the US

U.S. emissions of HFC‐134a derived for 2008–2012 from an extensive flask‐air sampling network

Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The South Coast Air Basin

Contact Info

Product

Resources

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Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US