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
Data sets
Full Results Accompanying A Machine Learning Examination of Hydroxyl Radical Differences Among Model Simulations for CCMI-1 J. M. Nicely, B. N. Duncan, T. F. Hanisco, G. M. Wolfe, R. J. Salawitch, M. Deushi, A. S. Haslerud, P. Jöckel, B. Josse, D. E. Kinnison, A. Klekociuk, M. E. Manyin, V. Marécal, O. Morgenstern, L. T. Murray, G. Myhre, L. D. Oman, G. Pitari, A. Pozzer, I. Quaglia, L. E. Revell, E. Rozanov, A. Stenke, K. Stone, S. Strahan, S. Tilmes, H. Tost, D. M. Westervelt, and G. Zeng https://doi.org/10.13016/vvbp-p6o8
CCMI-1 Data Archive CEDA Archive http://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/output
Climate Data at the National Center for Atmospheric Research Climate Data Gateway at NCAR https://www.earthsystemgrid.org/
BADC Data Access IGAC/SPARC http://blogs.reading.ac.uk/ccmi/badc-data-access/
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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