Articles | Volume 20, issue 3
https://doi.org/10.5194/acp-20-1341-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-1341-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2020
Research article |
| 05 Feb 2020
| 05 Feb 2020
A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Bryan N. Duncan
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Thomas F. Hanisco
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Glenn M. Wolfe
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, Baltimore, MD, USA
Ross J. Salawitch
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Department of Chemistry and Biochemistry, University of Maryland,
College Park, MD, USA
Makoto Deushi
Meteorological Research Institute (MRI), Tsukuba, Japan
Amund S. Haslerud
Center for International Climate and Environmental Research-Oslo
(CICERO), Oslo, Norway
Patrick Jöckel
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
Béatrice Josse
CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
Douglas E. Kinnison
National Center for Atmospheric Research, Boulder, CO, USA
Andrew Klekociuk
Antarctica and the Global System Program, Australian Antarctic
Division, Kingston, Australia
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Michael E. Manyin
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Science Systems and Applications, Inc., Lanham, MD, USA
Virginie Marécal
CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
Olaf Morgenstern
National Institute of Water and Atmospheric Research (NIWA),
Wellington, New Zealand
Lee T. Murray
Department of Earth and Environmental Sciences, University of
Rochester, Rochester, NY, USA
Gunnar Myhre
Center for International Climate and Environmental Research-Oslo
(CICERO), Oslo, Norway
Luke D. Oman
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Giovanni Pitari
Department of Physical and Chemical Sciences, Universitá
dell'Aquila, L'Aquila, Italy
Andrea Pozzer
Max-Planck-Institute for Chemistry, Air Chemistry Department, Mainz, Germany
Ilaria Quaglia
Department of Physical and Chemical Sciences, Universitá
dell'Aquila, L'Aquila, Italy
Laura E. Revell
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
Eugene Rozanov
Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
Physikalisch-Meteorologisches Observatorium Davos – World Radiation Center (PMOD/WRC), Davos, Switzerland
Andrea Stenke
Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
Kane Stone
School of Earth Sciences, University of Melbourne, Melbourne,
Australia
Department of Earth, Atmospheric, and Planetary Science, Massachusetts Institute of Technology, Cambridge, MA, USA
Susan Strahan
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Universities Space Research Association, Columbia, MD, USA
Simone Tilmes
National Center for Atmospheric Research, Boulder, CO, USA
Holger Tost
Institute for Atmospheric Physics, Johannes Gutenberg University,
Mainz, Germany
Daniel M. Westervelt
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Guang Zeng
National Institute of Water and Atmospheric Research (NIWA),
Wellington, New Zealand
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- Australian Fire Emissions of Carbon Monoxide Estimated by Global Biomass Burning Inventories: Variability and Observational Constraints M. Desservettaz et al. 10.1029/2021JD035925
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22 citations as recorded by crossref.
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- Development of the global atmospheric chemistry general circulation model BCC-GEOS-Chem v1.0: model description and evaluation X. Lu et al. 10.5194/gmd-13-3817-2020
- Exploring the drivers of tropospheric hydroxyl radical trends in the Geophysical Fluid Dynamics Laboratory AM4.1 atmospheric chemistry–climate model G. Chua et al. 10.5194/acp-23-4955-2023
- Hydroxyl Radical (OH) Response to Meteorological Forcing and Implication for the Methane Budget J. He et al. 10.1029/2021GL094140
- On the role of trend and variability in the hydroxyl radical (OH) in the global methane budget Y. Zhao et al. 10.5194/acp-20-13011-2020
- Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al. 10.5194/acp-23-6319-2023
- 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 D. Anderson et al. 10.5194/acp-21-6481-2021
- Atmospheric methane and nitrous oxide: challenges alongthe path to Net Zero E. Nisbet et al. 10.1098/rsta.2020.0457
- Reconciling the bottom-up and top-down estimates of the methane chemical sink using multiple observations Y. Zhao et al. 10.5194/acp-23-789-2023
- Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
- Measurements and Modeling of the Interhemispheric Differences of Atmospheric Chlorinated Very Short‐Lived Substances B. Roozitalab et al. 10.1029/2023JD039518
- The impact of internal climate variability on OH trends between 2005 and 2014 Q. Zhu et al. 10.1088/1748-9326/ad4b47
- A machine learning methodology for the generation of a parameterization of the hydroxyl radical D. Anderson et al. 10.5194/gmd-15-6341-2022
- Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets Y. Zhao et al. 10.5194/acp-20-9525-2020
- Methyl Chloroform Continues to Constrain the Hydroxyl (OH) Variability in the Troposphere P. Patra et al. 10.1029/2020JD033862
- COSMOGENIC14CO FOR ASSESSING THE OH-BASED SELF-CLEANING CAPACITY OF THE TROPOSPHERE C. Brenninkmeijer et al. 10.1017/RDC.2021.101
- Environmental effects of stratospheric ozone depletion, UV radiation, and interactions with climate change: UNEP Environmental Effects Assessment Panel, Update 2020 R. Neale et al. 10.1007/s43630-020-00001-x
- An observation-based, reduced-form model for oxidation in the remote marine troposphere C. Baublitz et al. 10.1073/pnas.2209735120
- Simulating tropospheric BrO in the Arctic using an artificial neural network I. Bougoudis et al. 10.1016/j.atmosenv.2022.119032
- Australian Fire Emissions of Carbon Monoxide Estimated by Global Biomass Burning Inventories: Variability and Observational Constraints M. Desservettaz et al. 10.1029/2021JD035925
- Trends in global tropospheric hydroxyl radical and methane lifetime since 1850 from AerChemMIP D. Stevenson et al. 10.5194/acp-20-12905-2020
- Atmospheric removal of methane by enhancing the natural hydroxyl radical sink Y. Wang et al. 10.1002/ghg.2191
Latest update: 29 Jun 2024
Short summary
Differences in methane lifetime among global models are large and poorly understood. We use a neural network method and simulations from the Chemistry Climate Model Initiative to quantify the factors influencing methane lifetime spread among models and variations over time. UV photolysis, tropospheric ozone, and nitrogen oxides drive large model differences, while the same factors plus specific humidity contribute to a decreasing trend in methane lifetime between 1980 and 2015.
Differences in methane lifetime among global models are large and poorly understood. We use a...
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