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

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dc.contributor.authorNicely JM
dc.contributor.authorDuncan BN
dc.contributor.authorHanisco TF
dc.contributor.authorWolfe GM
dc.contributor.authorSalawitch RJ
dc.contributor.authorDeushi M
dc.contributor.authorHaslerud AS
dc.contributor.authorJöckel P
dc.contributor.authorJosse B
dc.contributor.authorKinnison DE
dc.contributor.authorKlekociuk A
dc.contributor.authorManyin ME
dc.contributor.authorMarécal V
dc.contributor.authorMorgenstern O
dc.contributor.authorMurray LT
dc.contributor.authorMyhre G
dc.contributor.authorOman LD
dc.contributor.authorPitari G
dc.contributor.authorPozzer A
dc.contributor.authorQuaglia I
dc.contributor.authorRozanov E
dc.contributor.authorStenke A
dc.contributor.authorStone K
dc.contributor.authorStrahan S
dc.contributor.authorTilmes S
dc.contributor.authorTost H
dc.contributor.authorWestervelt DM
dc.contributor.authorZeng G
dc.contributor.authorRevell, Laura
dc.date.accessioned2021-06-25T01:38:12Z
dc.date.available2021-06-25T01:38:12Z
dc.date.issued2020en
dc.date.updated2021-03-29T21:42:20Z
dc.description.abstractThe hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH ), 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 (τCH ), 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 τCH 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 (NO = NO C NO ), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO V NO , and formaldehyde (HCHO) explain moderate differences in τCH , while isoprene, methane, the photolysis frequency of NO by visible light (JNO ), overhead ozone column, and temperature account for little to no model variation in τCH . 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 τCH during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NO , and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH 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 τCH , imparting an increasing and decreasing trend of about 0.5 % decade-1, respectively. The responses due to NO , ozone column, and temperature are also in reasonably good agreement between the two studies. 4 4 4 x 2 x 4 2 2 4 4 x 4 4 xen
dc.identifier.citationNicely JM, Duncan BN, Hanisco TF, Wolfe GM, Salawitch RJ, Deushi M, Haslerud AS, Jöckel P, Josse B, Kinnison DE, Klekociuk A, Manyin ME, Marécal V, Morgenstern O, Murray LT, Myhre G, Oman LD, Pitari G, Pozzer A, Quaglia I, Revell LE, Rozanov E, Stenke A, Stone K, Strahan S, Tilmes S, Tost H, Westervelt DM, Zeng G (2020). A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1. Atmospheric Chemistry and Physics. 20(3). 1341-1361.en
dc.identifier.doihttp://doi.org/10.5194/acp-20-1341-2020
dc.identifier.issn1680-7316
dc.identifier.issn1680-7324
dc.identifier.urihttps://hdl.handle.net/10092/102107
dc.languageen
dc.language.isoen
dc.publisherCopernicus GmbHen
dc.rightsAll rights reserved unless otherwise stateden
dc.rights.urihttp://hdl.handle.net/10092/17651en
dc.subject.anzsrc0201 Astronomical and Space Sciencesen
dc.subject.anzsrc0401 Atmospheric Sciencesen
dc.subject.anzsrcFields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370104 - Atmospheric composition, chemistry and processesen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4611 - Machine learning::461104 - Neural networksen
dc.subject.anzsrcFields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370109 - Tropospheric and stratospheric physicsen
dc.subject.anzsrcFields of Research::37 - Earth sciences::3702 - Climate change science::370202 - Climatologyen
dc.titleA machine learning examination of hydroxyl radical differences among model simulations for CCMI-1en
dc.typeJournal Articleen
uc.collegeFaculty of Science
uc.departmentSchool of Physical & Chemical Sciences
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