气候变化领域集成服务门户(CCPortal): A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

CCPortal

DOI	10.5194/acp-20-1341-2020
	A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
	Nicely J.M.; Duncan B.N.; Hanisco T.F.; Wolfe G.M.; Salawitch R.J.; Deushi M.; Haslerud A.S.; Jöckel P.; Josse B.; Kinnison D.E.; Klekociuk A.; Manyin M.E.; Marécal V.; Morgenstern O.; Murray L.T.; Myhre G.; Oman L.D.; Pitari G.; Pozzer A.; Quaglia I.; Revell L.E.; Rozanov E.; Stenke A.; Stone K.; Strahan S.; Tilmes S.; Tost H.; Westervelt D.M.; Zeng G.
发表日期	2020
ISSN	1680-7316
起始页码	1341
结束页码	1361
卷号	20 期号:3
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 (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 C NO2), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO V 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. © 2020 Wiley-Blackwell. All rights reserved.
语种	英语
scopus关键词	annual variation; artificial neural network; computer simulation; hydroxyl radical; machine learning; methane; troposphere; water vapor
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141569
作者单位	Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States; NASA Goddard Space Flight Center, Greenbelt, MD, United States; Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, United States; Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States; Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, United States; Meteorological Research Institute (MRI), Tsukuba, Japan; Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway; Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft-und Raumfahrt (DLR), Oberpfaffenhofen, Germany; CNRM UMR 3589, Météo-France/CNRS, Toulouse, France; National Center for Atmospheric Research, Boulder, CO, United States; Antarctica and the Global System Program, Australian Antarctic Division, Kingston, Australia; Antarctic Climate and Ecosystems Cooperative Research Centre, ...
推荐引用方式 GB/T 7714	Nicely J.M.,Duncan B.N.,Hanisco T.F.,et al. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1[J],2020,20(3).
APA	Nicely J.M..,Duncan B.N..,Hanisco T.F..,Wolfe G.M..,Salawitch R.J..,...&Zeng G..(2020).A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1.Atmospheric Chemistry and Physics,20(3).
MLA	Nicely J.M.,et al."A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1".Atmospheric Chemistry and Physics 20.3(2020).