Evaluation of chemical species in regional climate simulations from FOCI project over Europe

The Work Package 4 of the FOCI project (“Non-CO2 Forcers and their Climate, Weather, Air Quality and Health Impacts”, https://www.project-foci.eu/wp/) is responsible for tuning, testing, and conducting long-term simulations to validate various regional models in downscaling of reanalysis and global climate models for historical period. The long-term simulations are performed by RegCM5-Chem and WRF-Chem, i.e., coupled with chemistry over the European domain. It is important to highlight here the use of RegCM5-Chem for the long-term simulation with full chemistry has been a novelty in the community. Since its previous version (RegCM4-Chem), the tropospheric gas-phase chemistry was introduced using the Carbon Bond Mechanism Z (CBM-Z) chemistry mechanism. This enabled for interactive coupling between the modelled ozone and its radiative effects as well as coupling between the gas-phase chemical processes and aerosol formation (production of nitrates and sulphates). The initial and boundary conditions for the atmospheric variables and chemical composition were created from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and Atmospheric Composition Reanalysis 4 (EAC4) datasets. The emissions of atmospheric pollutants and greenhouse gases are taken from the Copernicus Atmosphere Monitoring Service (CAMS) high-resolution European emission inventory for air quality modelling. The CAMS Regional European air quality reanalyses are used for validating the outputs. Preliminary results for one year simulation show that both models underestimate particulate matter concentrations over the domain in boreal summer. Nitrogen dioxide is mostly overestimated by RegCM-Chem and the opposite occurs in WRF-Chem. Ozone is generally underestimated by both models but overestimated southern 50°N in RegCM-Chem. The long-term evaluation will consider further species, such as methane and sulphur dioxide, and will be represented in terms of seasonal biases regarding the observations.