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

Nicely, Julie M. and Duncan, Bryan N. and Hanisco, Thomas F. and Wolfe, Glenn M. and Salawitch, Ross J. and Deushi, Makoto and Haslerud, Amund S. and Jöckel, Patrick and Josse, Beatrice and Kinnison, Douglas E. and Klekociuk, Andrew R. and Manyin, Michael E. and Marécal, Virginie and Morgenstern, Olaf and Murray, Lee T. and Myhre, Gunnar and Oman, Luke D. and Pitari, Giovanni and Pozzer, Andrea and Quaglia, Ilaria and Revell, Laura E. and Rozanov, Eugene and Stenke, Andrea and Stone, Kane and Strahan, Susan and Tilmes, Simone and Tost, Holger and Westervelt, Daniel M. and Zeng, Guang (2020) A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1. Atmospheric Chemistry and Physics (ACP), 20 (3), pp. 1341-1361. Copernicus Publications. DOI: 10.5194/acp-20-1341-2020 ISSN 1680-7316

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Official URL: https://www.atmos-chem-phys.net/20/1341/2020/

Abstract

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.

Item URL in elib:https://elib.dlr.de/134054/
Document Type:Article
Title:A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Nicely, Julie M.Univ. of Maryland, College Park, MD, USAhttps://orcid.org/0000-0003-4828-0032
Duncan, Bryan N.NASA Goddard, Greenbelt, MD, USAUNSPECIFIED
Hanisco, Thomas F.NASA Goddard, Greenbelt, MD, USAUNSPECIFIED
Wolfe, Glenn M.NASA Goddard, Greenbelt, MD, USAUNSPECIFIED
Salawitch, Ross J.Univ. of Maryland, College Park, MD, USAUNSPECIFIED
Deushi, MakotoMRI, Tsukuba, JapanUNSPECIFIED
Haslerud, Amund S.CICERO, OSLO, Norwegenhttps://orcid.org/0000-0002-3812-3837
Jöckel, PatrickDLR, IPAhttps://orcid.org/0000-0002-8964-1394
Josse, BeatriceCNRM, Toulouse, FrankreichUNSPECIFIED
Kinnison, Douglas E.NCAR, Boulder, CO, USAUNSPECIFIED
Klekociuk, Andrew R.Australian Antarctic Division, Hobart, AustralienUNSPECIFIED
Manyin, Michael E.NASA Goddard, Greenbelt, MD, USAUNSPECIFIED
Marécal, VirginieCNRM, Toulouse, FrankreichUNSPECIFIED
Morgenstern, OlafNIWA, Wellington, New Zealandhttps://orcid.org/0000-0002-9967-9740
Murray, Lee T.Univ. Rochester, NY, USAhttps://orcid.org/0000-0002-3447-3952
Myhre, GunnarCICERO, OSLO, Norwegenhttps://orcid.org/0000-0002-4309-476X
Oman, Luke D.NASA Goddard, Green Belt, MD, USAUNSPECIFIED
Pitari, GiovanniUniv. dell' Aquila, ItalienUNSPECIFIED
Pozzer, AndreaMPI-Chemie, Mainzhttps://orcid.org/0000-0003-2440-6104
Quaglia, IlariaUniv. dell' Aquila, ItalienUNSPECIFIED
Revell, Laura E.Univ. Canterbury, Christchurch, Neuseelandhttps://orcid.org/0000-0002-8974-7703
Rozanov, EugeneETH Zürich und PMOD, Davos, SchweizUNSPECIFIED
Stenke, AndreaETH Zürich, Schweizhttps://orcid.org/0000-0002-5916-4013
Stone, KaneUniv. Melbourne, Australiahttps://orcid.org/0000-0002-2721-8785
Strahan, SusanNASA Goddard, Greenbelt, MD, USAhttps://orcid.org/0000-0002-7511-4577
Tilmes, SimoneNCAR, Boulder, USAUNSPECIFIED
Tost, HolgerUniv. Mainzhttps://orcid.org/0000-0002-3105-4306
Westervelt, Daniel M.Columbia Univ., Palisades, NY, USAhttps://orcid.org/0000-0003-0806-9961
Zeng, GuangNIWA, Wellington, New Zealandhttps://orcid.org/0000-0002-9356-5021
Date:5 February 2020
Journal or Publication Title:Atmospheric Chemistry and Physics (ACP)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:20
DOI :10.5194/acp-20-1341-2020
Page Range:pp. 1341-1361
Publisher:Copernicus Publications
ISSN:1680-7316
Status:Published
Keywords:neural network, machine learning, EMAC, CCMI, MESSy, hydroxyl radical, methane lifetime
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Project Climatic relevance of atmospheric tracer gases, aerosols and clouds, R - Vorhaben Atmosphären- und Klimaforschung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Modelling
Deposited By: Jöckel, Dr. Patrick
Deposited On:11 Feb 2020 12:05
Last Modified:11 Feb 2020 12:05

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