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. The hydroxyl radical (OH) plays critical roles within thetroposphere, such as determining the lifetime of methane ( CH4 ), yet ischallenging to model due to its fast cycling and dependence on a multitudeof sources and sinks. As a result, the reasons for variations in OH and theresulting methane lifetime ( τ CH 4 ), both between modelsand in time, are difficult to diagnose. We apply aneural network (NN) approach to address this issue within a group of modelsthat participated in the Chemistry-Climate Model Initiative (CCMI). Analysisof the historical specified dynamics simulations performed for CCMIindicates that the primary drivers of τ CH 4 differences among 10 models are the flux of UV light to the troposphere(indicated by the photolysis frequency JO1D ), the mixing ratio oftropospheric ozone ( O3 ), the abundance of nitrogen oxides( NO x ≡ NO + NO 2 ), and details of the various chemical mechanisms that driveOH. Water vapour, carbon monoxide (CO), the ratio of NO:NOx , andformaldehyde (HCHO) explain moderate differences in τ CH 4 ,while isoprene, methane, the photolysis frequency of NO2 by visiblelight ( JNO2 ), overhead ozone column, and temperature account forlittle to no model variation in τ CH 4 . We also applythe NNs to analysis of temporal trends in OH from 1980 to2015. All models that participated in the specified dynamics historicalsimulation for CCMI demonstrate a decline in τ CH 4 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 OHburden. Finally, the identified trends in τ CH 4 are comparedto calculated trends in the tropospheric mean OH concentrationfrom previous work, based on analysis of observations. The comparisonreveals a robust result for the effect of rising water vapour on OH and τ CH 4 , 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 twostudies.

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