Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-20-1341-2020 |
A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 | |
Nicely, Julie M.1,2; Duncan, Bryan N.2; Hanisco, Thomas F.2; Wolfe, Glenn M.2,3; Salawitch, Ross J.1,4,5; Deushi, Makoto6; Haslerud, Amund S.7; Joeckel, Patrick8; Josse, Beatrice9; Kinnison, Douglas E.10; Klekociuk, Andrew11,12; Manyin, Michael E.2,13; Marecal, Virginie9; Morgenstern, Olaf14; Murray, Lee T.15; Myhre, Gunnar7; Oman, Luke D.2; Pitari, Giovanni16; Pozzer, Andrea17; Quaglia, Ilaria16; Revell, Laura E.18; Rozanov, Eugene19,20; Stenke, Andrea19; Stone, Kane21,22; Strahan, Susan2,23; Tames, Simone10; Tost, Holger24; Westervelt, Daniel M.25,26; Zeng, Guang14 | |
2020-02-05 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2020 |
卷号 | 20期号:3页码:1341-1361 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Japan; Norway; Germany; France; Australia; New Zealand; Italy; Switzerland |
英文摘要 | 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 (tau CH4), 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 tau CH4 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 (O-3), the abundance of nitrogen oxides (NOx equivalent to 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 tau CH4, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO(2)), overhead ozone column, and temperature account for little to no model variation in tau CH4. 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 tau CH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O-3, JO(1)D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in tau CH4 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 tau CH4, 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. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000512999100002 |
WOS关键词 | CLIMATE MODEL ; ATMOSPHERIC METHANE ; TROPOSPHERIC OZONE ; METHYL CHLOROFORM ; STRATOSPHERIC OZONE ; CARBON-MONOXIDE ; OH VARIABILITY ; GLOBAL AVERAGE ; CHEMISTRY ; TRANSPORT |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/278600 |
专题 | 地球科学 |
作者单位 | 1.Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA; 2.NASA, Goddard Space Flight Ctr, Code 916, Greenbelt, MD 20771 USA; 3.Univ Maryland Baltimore Cty, Joint Ctr Earth Syst Technol, Baltimore, MD 21228 USA; 4.Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA; 5.Univ Maryland, Dept Chem & Biochem, College Pk, MD 20742 USA; 6.Meteorol Res Inst, Tsukuba, Ibaraki, Japan; 7.CICERO, Oslo, Norway; 8.Deutsch Zentrum Luft & Raumfahrt DLR, Inst Phys Atmosphare, Oberpfaffenhofen, Germany; 9.CNRS, Meteo France, CNRM UMR 3589, Toulouse, France; 10.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA; 11.Australian Antarctic Div, Antarctica & Global Syst Program, Kingston, NF, Australia; 12.Antarctic Climate & Ecosyst Cooperat Res Ctr, Hobart, Tas, Australia; 13.Sci Syst & Applicat Inc, Lanham, MD USA; 14.Natl Inst Water & Atmospher Res NIWA, Wellington, New Zealand; 15.Univ Rochester, Dept Earth & Environm Sci, Rochester, NY USA; 16.Univ Aquila, Dept Phys & Chem Sci, Laquila, Italy; 17.Max Planck Inst Chem, Air Chem Dept, Mainz, Germany; 18.Univ Canterbury, Sch Phys & Chem Sci, Christchurch, New Zealand; 19.ETH Zurich ETHZ, Inst Atmospher & Climate Sci, Zurich, Switzerland; 20.Phys Meteorol Observ Davos, World Radiat Ctr PMOD WRC, Davos, Switzerland; 21.Univ Melbourne, Sch Earth Sci, Melbourne, Vic, Australia; 22.MIT, Dept Earth Atmospher & Planetary Sci, Cambridge, MA USA; 23.Univ Space Res Assoc, Columbia, MD USA; 24.Johannes Gutenberg Univ Mainz, Inst Atmospher Phys, Mainz, Germany; 25.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY USA; 26.NASA, Goddard Inst Space Studies, New York, NY 10025 USA |
推荐引用方式 GB/T 7714 | Nicely, Julie M.,Duncan, Bryan N.,Hanisco, Thomas F.,et al. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2020,20(3):1341-1361. |
APA | Nicely, Julie M..,Duncan, Bryan N..,Hanisco, Thomas F..,Wolfe, Glenn M..,Salawitch, Ross J..,...&Zeng, Guang.(2020).A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1.ATMOSPHERIC CHEMISTRY AND PHYSICS,20(3),1341-1361. |
MLA | Nicely, Julie M.,et al."A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1".ATMOSPHERIC CHEMISTRY AND PHYSICS 20.3(2020):1341-1361. |
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