Review of the global models used within the Chemistry-Climate Model Initiative (CCMI)

We present an overview of state-of-the-art chemistry-climate and -transport models that are used within the Chemistry Climate Model Initiative (CCMI). CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models’ projections of the stratospheric ozone layer, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI recommendations for simulations have been followed is necessary to understand model response to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI simulations with the aim to inform CCMI data users

Review of the global models used within the Chemistry-Climate Model Initiative (CCMI)

We present an overview of state-of-the-art chemistry-climate and -transport models that are used within the Chemistry Climate Model Initiative (CCMI). CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models’ projections of the stratospheric ozone layer, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI recommendations for simulations have been followed is necessary to understand model response to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI simulations with the aim to inform CCMI data users

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

The 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