Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study

Anna Karion, Thomas Lauvaux, Israel Lopez Coto, Colm Sweeney, Kimberly Mueller, Sharon Gourdji, Wayne Angevine, Zachary Robert Barkley, Aijun Deng, Arlyn Andrews, Ariel Stein, James Whetstone

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Original languageEnglish (US)
Pages (from-to)2561-2576
Number of pages16
JournalAtmospheric Chemistry and Physics
Volume19
Issue number4
DOIs
StatePublished - Feb 28 2019

Fingerprint

atmospheric gas
trace gas
shale
methane
tracer
oil shale
ground-based measurement
statistical data
vertical mixing
estimation method
gas production
mass balance
natural gas
greenhouse gas
mitigation

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

Karion, A., Lauvaux, T., Lopez Coto, I., Sweeney, C., Mueller, K., Gourdji, S., ... Whetstone, J. (2019). Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study. Atmospheric Chemistry and Physics, 19(4), 2561-2576. https://doi.org/10.5194/acp-19-2561-2019
Karion, Anna ; Lauvaux, Thomas ; Lopez Coto, Israel ; Sweeney, Colm ; Mueller, Kimberly ; Gourdji, Sharon ; Angevine, Wayne ; Barkley, Zachary Robert ; Deng, Aijun ; Andrews, Arlyn ; Stein, Ariel ; Whetstone, James. / Intercomparison of atmospheric trace gas dispersion models : Barnett Shale case study. In: Atmospheric Chemistry and Physics. 2019 ; Vol. 19, No. 4. pp. 2561-2576.
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Karion, A, Lauvaux, T, Lopez Coto, I, Sweeney, C, Mueller, K, Gourdji, S, Angevine, W, Barkley, ZR, Deng, A, Andrews, A, Stein, A & Whetstone, J 2019, 'Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study', Atmospheric Chemistry and Physics, vol. 19, no. 4, pp. 2561-2576. https://doi.org/10.5194/acp-19-2561-2019

Intercomparison of atmospheric trace gas dispersion models : Barnett Shale case study. / Karion, Anna; Lauvaux, Thomas; Lopez Coto, Israel; Sweeney, Colm; Mueller, Kimberly; Gourdji, Sharon; Angevine, Wayne; Barkley, Zachary Robert; Deng, Aijun; Andrews, Arlyn; Stein, Ariel; Whetstone, James.

In: Atmospheric Chemistry and Physics, Vol. 19, No. 4, 28.02.2019, p. 2561-2576.

Research output: Contribution to journalArticle

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AU - Karion, Anna

AU - Lauvaux, Thomas

AU - Lopez Coto, Israel

AU - Sweeney, Colm

AU - Mueller, Kimberly

AU - Gourdji, Sharon

AU - Angevine, Wayne

AU - Barkley, Zachary Robert

AU - Deng, Aijun

AU - Andrews, Arlyn

AU - Stein, Ariel

AU - Whetstone, James

PY - 2019/2/28

Y1 - 2019/2/28

N2 - Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

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Karion A, Lauvaux T, Lopez Coto I, Sweeney C, Mueller K, Gourdji S et al. Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study. Atmospheric Chemistry and Physics. 2019 Feb 28;19(4):2561-2576. https://doi.org/10.5194/acp-19-2561-2019