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

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...

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Autores principales: Karion, Anna, Lauvaux, Thomas, Lopez Coto, Israel, Sweeney, Colm, Mueller, Kimberly, Gourdji, Sharon, Angevine, Wayne, Barkley, Zachary, Deng, Aijun, Andrews, Arlyn, Stein, Ariel, Whetstone, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6605086/
https://www.ncbi.nlm.nih.gov/pubmed/31275365
http://dx.doi.org/10.5194/acp-19-2561-2019
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author Karion, Anna
Lauvaux, Thomas
Lopez Coto, Israel
Sweeney, Colm
Mueller, Kimberly
Gourdji, Sharon
Angevine, Wayne
Barkley, Zachary
Deng, Aijun
Andrews, Arlyn
Stein, Ariel
Whetstone, James
author_facet Karion, Anna
Lauvaux, Thomas
Lopez Coto, Israel
Sweeney, Colm
Mueller, Kimberly
Gourdji, Sharon
Angevine, Wayne
Barkley, Zachary
Deng, Aijun
Andrews, Arlyn
Stein, Ariel
Whetstone, James
author_sort Karion, Anna
collection PubMed
description 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 down-wind 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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spelling pubmed-66050862019-07-02 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 Deng, Aijun Andrews, Arlyn Stein, Ariel Whetstone, James Atmos Chem Phys Article 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 down-wind 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. 2019 /pmc/articles/PMC6605086/ /pubmed/31275365 http://dx.doi.org/10.5194/acp-19-2561-2019 Text en http://creativecommons.org/licenses/by/4.0/ This work is distributed under the Creative Commons Attribution 4.0 License.
spellingShingle Article
Karion, Anna
Lauvaux, Thomas
Lopez Coto, Israel
Sweeney, Colm
Mueller, Kimberly
Gourdji, Sharon
Angevine, Wayne
Barkley, Zachary
Deng, Aijun
Andrews, Arlyn
Stein, Ariel
Whetstone, James
Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
title Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
title_full Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
title_fullStr Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
title_full_unstemmed Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
title_short Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study
title_sort intercomparison of atmospheric trace gas dispersion models: barnett shale case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6605086/
https://www.ncbi.nlm.nih.gov/pubmed/31275365
http://dx.doi.org/10.5194/acp-19-2561-2019
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