Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6341-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Aug 2022
Model description paper |
| 17 Aug 2022
| 17 Aug 2022
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Daniel C. Anderson
CORRESPONDING AUTHOR
GESTAR II, University of Maryland, Baltimore County, Baltimore, MD,
USA
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
Melanie B. Follette-Cook
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
GESTAR II, Morgan State University, Baltimore, MD, USA
now at: Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sarah A. Strode
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
GESTAR II, Morgan State University, Baltimore, MD, USA
Julie M. Nicely
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Junhua Liu
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
GESTAR II, Morgan State University, Baltimore, MD, USA
Peter D. Ivatt
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Bryan N. Duncan
Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA
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Sarah A. Strode, Ghassan Taha, Luke D. Oman, Robert Damadeo, David Flittner, Mark Schoeberl, Christopher E. Sioris, and Ryan Stauffer
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Kelvin H. Bates, Daniel J. Jacob, Ke Li, Peter D. Ivatt, Mat J. Evans, Yingying Yan, and Jintai Lin
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Simple aromatic compounds (benzene, toluene, xylene) have complex gas-phase chemistry that is inconsistently represented in atmospheric models. We compile recent experimental and theoretical insights to develop a new mechanism for gas-phase aromatic oxidation that is sufficiently compact for use in multiscale models. We compare our new mechanism to chamber experiments and other mechanisms, and implement it in a global model to quantify the impacts of aromatic oxidation on tropospheric chemistry.
Dandan Wei, Hariprasad D. Alwe, Dylan B. Millet, Brandon Bottorff, Michelle Lew, Philip S. Stevens, Joshua D. Shutter, Joshua L. Cox, Frank N. Keutsch, Qianwen Shi, Sarah C. Kavassalis, Jennifer G. Murphy, Krystal T. Vasquez, Hannah M. Allen, Eric Praske, John D. Crounse, Paul O. Wennberg, Paul B. Shepson, Alexander A. T. Bui, Henry W. Wallace, Robert J. Griffin, Nathaniel W. May, Megan Connor, Jonathan H. Slade, Kerri A. Pratt, Ezra C. Wood, Mathew Rollings, Benjamin L. Deming, Daniel C. Anderson, and Allison L. Steiner
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Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven Wofsy
Atmos. Chem. Phys., 21, 13729–13746, https://doi.org/10.5194/acp-21-13729-2021, https://doi.org/10.5194/acp-21-13729-2021, 2021
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hottest20 % of parcels.
Daniel C. Anderson, Bryan N. Duncan, Arlene M. Fiore, Colleen B. Baublitz, Melanie B. Follette-Cook, Julie M. Nicely, and Glenn M. Wolfe
Atmos. Chem. Phys., 21, 6481–6508, https://doi.org/10.5194/acp-21-6481-2021, https://doi.org/10.5194/acp-21-6481-2021, 2021
Short summary
Short summary
We demonstrate that large-scale climate features are the primary driver of year-to-year variability in simulated values of the hydroxyl radical, the primary atmospheric oxidant, over 1980–2018. The El Niño–Southern Oscillation is the dominant mode of hydroxyl variability, resulting in large-scale global decreases in OH during El Niño events. Other climate modes, such as the Australian monsoon and the North Atlantic Oscillation, have impacts of similar magnitude but on on more localized scales.
Yuanhong Zhao, Marielle Saunois, Philippe Bousquet, Xin Lin, Antoine Berchet, Michaela I. Hegglin, Josep G. Canadell, Robert B. Jackson, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Ole Kirner, Sarah Strode, Simone Tilmes, Edward J. Dlugokencky, and Bo Zheng
Atmos. Chem. Phys., 20, 13011–13022, https://doi.org/10.5194/acp-20-13011-2020, https://doi.org/10.5194/acp-20-13011-2020, 2020
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Decadal trends and variations in OH are critical for understanding atmospheric CH4 evolution. We quantify the impacts of OH trends and variations on the CH4 budget by conducting CH4 inversions on a decadal scale with an ensemble of OH fields. We find the negative OH anomalies due to enhanced fires can reduce the optimized CH4 emissions by up to 10 Tg yr−1 during El Niño years and the positive OH trend from 1986 to 2010 results in a ∼ 23 Tg yr−1 additional increase in optimized CH4 emissions.
Cited articles
Anderson, D. C., Duncan, B. N., Fiore, A. M., Baublitz, C. B., Follette-Cook, M. B., Nicely, J. M., and Wolfe, G. M.: Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers, Atmos. Chem. Phys., 21, 6481–6508, https://doi.org/10.5194/acp-21-6481-2021, 2021.
Anderson, D. C., Follette-Cook, M. B., Strode, S. A., Nicely, J. M., Liu, J., Ivatt, P. D., and Duncan, B. N.: Code for the development of a
parameterization of OH for CCMs, Zenodo [code],
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Anderson, D. C., Follette-Cook, M. B., Strode, S. A., Nicely, J .M., Liu, J., Ivatt, P. D., and Duncan, B. N.: Sample ECCOH OH Parameterization
(1.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.6604130, 2022b.
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Short summary
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain...
