A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

J. M. Nicely; J. M. Nicely; B. N. Duncan; T. F. Hanisco; G. M. Wolfe; G. M. Wolfe; R. J. Salawitch; R. J. Salawitch; R. J. Salawitch; M. Deushi; A. S. Haslerud; P. Jöckel; B. Josse; D. E. Kinnison; A. Klekociuk; A. Klekociuk; M. E. Manyin; 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; E. Rozanov; A. Stenke; K. Stone; K. Stone; S. Strahan; S. Strahan; S. Tilmes; H. Tost; D. M. Westervelt; D. M. Westervelt; G. Zeng

doi:10.5194/acp-20-1341-2020

Atmospheric Chemistry and Physics (Feb 2020)

A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

J. M. Nicely,
J. M. Nicely,
B. N. Duncan,
T. F. Hanisco,
G. M. Wolfe,
G. M. Wolfe,
R. J. Salawitch,
R. J. Salawitch,
R. J. Salawitch,
M. Deushi,
A. S. Haslerud,
P. Jöckel,
B. Josse,
D. E. Kinnison,
A. Klekociuk,
A. Klekociuk,
M. E. Manyin,
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,
E. Rozanov,
A. Stenke,
K. Stone,
K. Stone,
S. Strahan,
S. Strahan,
S. Tilmes,
H. Tost,
D. M. Westervelt,
D. M. Westervelt,
G. Zeng

Affiliations

J. M. Nicely: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
J. M. Nicely: NASA Goddard Space Flight Center, Greenbelt, MD, USA
B. N. Duncan: NASA Goddard Space Flight Center, Greenbelt, MD, USA
T. F. Hanisco: NASA Goddard Space Flight Center, Greenbelt, MD, USA
G. M. Wolfe: NASA Goddard Space Flight Center, Greenbelt, MD, USA
G. M. Wolfe: Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA
R. J. Salawitch: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
R. J. Salawitch: Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
R. J. Salawitch: Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
M. Deushi: Meteorological Research Institute (MRI), Tsukuba, Japan
A. S. Haslerud: Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway
P. Jöckel: Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
B. Josse: CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
D. E. Kinnison: National Center for Atmospheric Research, Boulder, CO, USA
A. Klekociuk: Antarctica and the Global System Program, Australian Antarctic Division, Kingston, Australia
A. Klekociuk: Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
M. E. Manyin: NASA Goddard Space Flight Center, Greenbelt, MD, USA
M. E. Manyin: Science Systems and Applications, Inc., Lanham, MD, USA
V. Marécal: CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
O. Morgenstern: National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
L. T. Murray: Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY, USA
G. Myhre: Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway
L. D. Oman: NASA Goddard Space Flight Center, Greenbelt, MD, USA
G. Pitari: Department of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, Italy
A. Pozzer: Max-Planck-Institute for Chemistry, Air Chemistry Department, Mainz, Germany
I. Quaglia: Department of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, Italy
L. E. Revell: School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
E. Rozanov: Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
E. Rozanov: Physikalisch-Meteorologisches Observatorium Davos – World Radiation Center (PMOD/WRC), Davos, Switzerland
A. Stenke: Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
K. Stone: School of Earth Sciences, University of Melbourne, Melbourne, Australia
K. Stone: Department of Earth, Atmospheric, and Planetary Science, Massachusetts Institute of Technology, Cambridge, MA, USA
S. Strahan: NASA Goddard Space Flight Center, Greenbelt, MD, USA
S. Strahan: Universities Space Research Association, Columbia, MD, USA
S. Tilmes: National Center for Atmospheric Research, Boulder, CO, USA
H. Tost: Institute for Atmospheric Physics, Johannes Gutenberg University, Mainz, Germany
D. M. Westervelt: Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
D. M. Westervelt: NASA Goddard Institute for Space Studies, New York, NY, USA
G. Zeng: National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand

DOI: https://doi.org/10.5194/acp-20-1341-2020
Journal volume & issue: Vol. 20
pp. 1341 – 1361

Abstract

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The 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 methane 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 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D), the 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 vapour, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in τCH4, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone 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 vapour on OH and τCH4, imparting an increasing and decreasing trend of about 0.5 % decade−1, respectively. The responses due to NOx, ozone column, and temperature are also in reasonably good agreement between the two studies.

Published in Atmospheric Chemistry and Physics

ISSN: 1680-7316 (Print); 1680-7324 (Online)
Publisher: Copernicus Publications
Country of publisher: Germany
LCC subjects: Science: Physics; Science: Chemistry
Website: http://www.atmospheric-chemistry-and-physics.net/

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