Representation of emissions from European major population centeres in MECO(n) - Lessons learned from EMeRGe-EU

Comprehensive regional chemistry-climate or chemistry transport models are important tools to study the impact of emissions from major population centres (MPC) and/or investigate potential mitigation options for MPC emissions. Before such models can be employed it is important to investigate how well the models represent observed atmospheric conditions. This comparison helps not only in judging the performance of the models, but allows to test our understanding of chemical and physical processes in the atmosphere. A prerequisite for an extensive evaluation of models are the availability of temporally and spatially high resolved observational data. Such a data set was obtained during the EMeRGe-Europe campaign of the HALO research aircraft in July 2017, which targeted the outflow of different MPC in Europe.

We used the data of the EMeRGe-EU campaign together with ground based observations to evaluate the representation of European MPC emissions in the MECO(n) model system. MECO(n) is a global/regional chemistry-climate model which couples the regional chemistry-climate model COSMO-CLM/MESSy on-line (i.e., during runtime) with the global chemistry climate-model EMAC. The dynamics of EMAC is nudged against ERA-Interim reanalysis data. We performed three nesting steps from 300 km on the global scale to 50 km, 12 km and 7 km on the regional scale. In our evaluation we focus on tropospheric ozone (O₃) and related precursors, methane (CH₄) and sulphur dioxide (SO₂). 

Generally, the comparison between the measurements and the model results shows a good representation of European MPC emissions in MECO(n). In detail, however, the measured mixing ratios of carbon monoxide (CO) and reactive nitrogen (NO_y)  are underestimated, while O₃ and SO₂ are overestimated by the model. Potential reasons for these differences are too efficient vertical mixing, and underestimation of MPC emissions. 

To test hypotheses for potential model improvements we performed additional sensitivity studies with different nudging data for EMAC and an alternative anthropogenic emission inventory. The differences of the model results to the observations, however, are only slightly influenced by these changes. Accordingly, further hypotheses for potential model improvements needs to be investigated.  While the simulated mixing ratios differ only slightly between the sensitivity studies, the ozone source apportionment results (using a tagging approach) show much larger differences. This indicates the large uncertainty of  source apportionment analyses caused by uncertainties of emission inventories and model dynamics and requires further analysis in the future.