Land transport is an important emission source of nitrogen oxides, carbon monoxide, and volatile organic compounds. The emissions of nitrogen oxides affect air quality directly. Further, all of these emissions serve as a precursor for the formation of tropospheric ozone, thus leading to an indirect influence on air quality. In addition, ozone is radiatively active and its increase leads to a positive radiative forcing. Due to the strong non-linearity of the ozone chemistry, the contribution of emission sources to ozone cannot be calculated or measured directly. Instead, atmospheric chemistry models equipped with specific source attribution methods (e.g. tagging methods) are required. In this study we investigate the contribution of land transport emissions to ozone and ozone precursors using the MECO(n) model system (MESSy-fied ECHAM and COSMO models nested n times). This model system couples a global and a regional chemistry climate model and is equipped with a tagging diagnostic. We investigate the combined effect of long-range-transported ozone and ozone which is produced by European emissions by applying the tagging diagnostic simultaneously and consistently on the global and regional scale. We performed two simulations each covering 3 years with different anthropogenic emission inventories for Europe. We applied two regional refinements, i.e. one refinement covering Europe (50&thinsp;<span classCombining double low line"inline-formula">km</span> resolution) and one covering Germany (12&thinsp;<span classCombining double low line"inline-formula">km</span> resolution). The diagnosed absolute contributions of land transport emissions to reactive nitrogen (<span classCombining double low line"inline-formula">NOy</span>) near ground level are in the range of 5 to 10&thinsp;<span classCombining double low line"inline-formula">nmol mol-1</span>. This corresponds to relative contributions of 50&thinsp;% to 70&thinsp;%. The largest absolute contributions appear around Paris, southern England, Moscow, the Po Valley, and western Germany. The absolute contributions to carbon monoxide range from 30&thinsp;<span classCombining double low line"inline-formula">nmol mol-1</span> to more than 75&thinsp;<span classCombining double low line"inline-formula">nmol mol-1</span> near emission hot-spots such as Paris or Moscow. The ozone which is attributed to land transport emissions shows a strong seasonal cycle with absolute contributions of 3&thinsp;<span classCombining double low line"inline-formula">nmol mol-1</span> during winter and 5 to 10&thinsp;<span classCombining double low line"inline-formula">nmol mol-1</span> during summer. This corresponds to relative contributions of 8&thinsp;% to 10&thinsp;% during winter and up to 16&thinsp;% during summer. The largest values during summer are confined to the Po Valley, while the contributions in western Europe range from 12&thinsp;% to 14&thinsp;%. Only during summer are the ozone contributions slightly influenced by the anthropogenic emission inventory, but these differences are smaller than the range of the seasonal cycle of the contribution to land transport emissions. This cycle is caused by a complex interplay of seasonal cycles of other emissions (e.g. biogenic) and seasonal variations of the ozone regimes. In addition, our results suggest that during events with large ozone values the ozone contributions of land transport and biogenic emissions increase strongly. Here, the contribution of land transport emissions peaks up to 28&thinsp;%. Hence, our model results suggest that land transport emissions are an important contributor during periods with large ozone values.

Anthropogenic and natural emissions influence the tropospheric ozone budget, thereby affecting air quality and climate. To study the influence of different emission sources on the ozone budget, often source apportionment studies with a tagged tracer approach are performed. Studies investigating air quality issues usually rely on regional models with a fine spatial resolution, while studies focusing on climaterelated questions often use coarsely resolved global models. It is well known that simulated ozone mixing ratios depend on the resolution of the model and the resolution of the emission inventory. Whether the contributions simulated using source apportionment approaches also depend on the model resolution, however, is still unclear. Therefore, this study attempts for the first time to analyse the impact of the model, the model resolution, and the emission inventory resolution on simulated ozone contributions using a diagnostic tagging method. The differences in the ozone contributions caused by these factors are compared with differences that arise from the usage of different emission inventories. To do so, we apply the MECO(n) (MESSy-fied ECHAM and COSMO models nested n times) model system which couples online a global chemistry-climate model with a regional chemistry-climate model equipped with a tagging scheme for source apportionment. The results of the global model (at 300 km horizontal resolution) are compared with the results of the regional model at 50 km (Europe) and 12 km (Germany) resolutions. Besides model-specific differences and biases that are discussed in detail, our results have important implications for other modelling studies and modellers applying source apportionment methods. First, contributions from anthropogenic emissions averaged over the continental scale are quite robust with respect to the model, model resolution, and emission inventory resolution. Second, differences on the regional scale caused by different models and model resolutions can be quite large, and regional models are indispensable for source apportionment studies on the subcontinental scale. Third, contributions from stratospheric ozone transported to the surface differ strongly between the models, mainly caused by differences in the efficiency of the vertical mixing. As stratospheric ozone plays an important role for ground level ozone, but the models show large differences in the amount of downward transported ozone, source apportionment methods should account for this source explicitly to better understand inter-model differences.