Geoscientific Model Development (Jul 2022)
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
Abstract
Road traffic emits not only carbon dioxide (CO2) and particulate matter, but also other pollutants such as nitrogen oxides (NOx), volatile organic compounds (VOCs) and carbon monoxide (CO). These chemical species influence the atmospheric chemistry and produce ozone (O3) in the troposphere. Ozone acts as a greenhouse gas and thus contributes to anthropogenic global warming. Technological trends and political decisions can help to reduce the O3 effect of road traffic emissions on climate. In order to assess the O3 response of such mitigation options on climate, we developed a chemistry–climate response model called TransClim (Modelling the effect of surface Transportation on Climate). The current version considers road traffic emissions of NOx, VOC and CO and determines the O3 change and its corresponding stratosphere-adjusted radiative forcing. Using a tagging method, TransClim is further able to quantify the contribution of road traffic emissions to the O3 concentration. Thus, TransClim determines the contribution to O3 as well as the change in total tropospheric O3 of a road traffic emission scenario. Both quantities are essential when assessing mitigation strategies. The response model is based on lookup tables which are generated by a set of emission variation simulations performed with the global chemistry–climate model EMAC (ECHAM5 v5.3.02, MESSy v2.53.0). Evaluating TransClim against independent EMAC simulations reveals low deviations of all considered species (0.01 %–10 %). Hence, TransClim is able to reproduce the results of an EMAC simulation very well. Moreover, TransClim is about 6000 times faster in computing the climate effect of an emission scenario than the complex chemistry–climate model. This makes TransClim a suitable tool to efficiently assess the climate effect of a broad range of mitigation options for road traffic or to analyse uncertainty ranges by employing Monte Carlo simulations.