Abstract
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.
Highlights
We have previously demonstrated the utility of neural networks (NNs) for quantifying differences in OH among a small group of chemical transport models (CTMs), which rely on the specification of meteorological conditions (Nicely et al, 2017)
Tropospheric OH columns are shown for the model and NN alongside the absolute value of the difference between the two
We perform a neural network analysis of the monthly mean output from historical simulations of 10 models that participated in Chemistry-Climate Model Initiative (CCMI) for the purposes of understanding OH and τCH4 differences and temporal trends
Summary
The hydroxyl radical (OH) is a key species of interest for numerous tropospheric chemistry studies over the past several decades (e.g. Prinn et al, 1987, 1992; Spivakovsky et al, 2000; Montzka et al, 2011; Prather et al, 2012; Holmes et al, 2013; Murray et al, 2013; Naik et al, 2013; Voulgarakis et al, 2013; McNorton et al, 2016; Rigby et al, 2017; Turner et al, 2017). Numerous studies have sought to constrain the OH abundance and resulting methane lifetime (τCH4 ) using observations, global atmospheric models, and combinations of the two. Despite the advent of numerous observing systems for species with some bearing on OH chemistry in the last several decades, it is widely acknowledged that current observations are insufficient to unambiguously derive current trends in OH (Prather and Holmes, 2017; Turner et al, 2017, 2019; Nisbet et al, 2019)
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