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Discussion papers
https://doi.org/10.5194/acp-2019-772
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-772
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Sep 2019

Submitted as: research article | 05 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

A Machine Learning Examination of Hydroxyl Radical Differences Among Model Simulations for CCMI-1

Julie M. Nicely1,2, Bryan N. Duncan2, Thomas F. Hanisco2, Glenn M. Wolfe2,3, Ross J. Salawitch1,4,5, Makoto Deushi6, Amund S. Haslerud7, Patrick Jöckel8, Béatrice Josse9, Douglas E. Kinnison10, Andrew Klekociuk11,12, Michael E. Manyin2,13, Virginie Marécal9, Olaf Morgenstern14, Lee T. Murray15, Gunnar Myhre7, Luke D. Oman2, Giovanni Pitari16, Andrea Pozzer17, Ilaria Quaglia16, Laura E. Revell18, Eugene Rozanov19,20, Andrea Stenke19, Kane Stone21,22, Susan Strahan2,23, Simone Tilmes10, Holger Tost24, Daniel M. Westervelt25,26, and Guang Zeng14 Julie M. Nicely et al.
  • 1Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 2NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA
  • 4Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
  • 5Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
  • 6Meteorological Research Institute (MRI), Tsukuba, Japan
  • 7Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway
  • 8Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
  • 9CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
  • 10National Center for Atmospheric Research, Boulder, CO, USA
  • 11Antarctica and the Global System Program, Australian Antarctic Division, Kingston, Australia
  • 12Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
  • 13Science Systems and Applications, Inc., Lanham, MD, USA
  • 14National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
  • 15Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY, USA
  • 16Department of Physical and Chemical Sciences, Universitá dell’Aquila, L’Aquila, Italy
  • 17Max-Planck-Institute for Chemistry, Air Chemistry Department, Mainz, Germany
  • 18School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
  • 19Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
  • 20Physikalisch-Meteorologisches Observatorium Davos – World Radiation Center (PMOD/WRC), Davos, Switzerland
  • 21School of Earth Sciences, University of Melbourne, Melbourne, Australia
  • 22Massachusetts Institute of Technology, Cambridge, MA, USA
  • 23Universities Space Research Association, Columbia, MD, USA
  • 24Institute for Atmospheric Physics, Johannes Gutenberg University, Mainz, Germany
  • 25Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA
  • 26NASA Goddard Institute for Space Studies, New York, NY, USA

Abstract. Hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting CH4 lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among ten models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D) due mostly to clouds, mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx≡NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapor, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in τCH4, while isoprene, CH4, the photolysis frequency of NO2 by visible light (JNO2), overhead O3 column, and temperature account for little-to-no model variation in τCH4. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapor on OH and τCH4, imparting an increasing and decreasing trend of about 0.5 % decade−1, respectively. The responses due to NOx, O3 column, and temperature are also in reasonably good agreement between the two studies, though a discrepancy in the CH4 response highlights a need for further examination of the CH4 feedback on the abundance of OH.

Julie M. Nicely et al.
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Short summary
The short-lived, very reactive species hydroxyl radical (OH) is responsible for oxidizing and removing many pollutants and greenhouse gases like CH4, the second-most important anthropogenic greenhouse gas. However, its reactive nature, spatial heterogeneity, and dependence on so many other chemical, radiative, and physical factors make OH difficult to model and measure. We use a machine learning approach to quantify the key drivers of OH differences between models and OH variations over time.
The short-lived, very reactive species hydroxyl radical (OH) is responsible for oxidizing and...
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