Development of an inorganic and organic aerosol model (CHIMERE 2017β v1.0): seasonal and spatial evaluation over Europe
Abstract. A new aerosol module was developed and integrated in the air quality model CHIMERE. Developments include the use of the Model of Emissions and Gases and Aerosols from Nature (MEGAN) 2.1 for biogenic emissions, the implementation of the inorganic thermodynamic model ISORROPIA 2.1, revision of wet deposition processes and of the algorithms of condensation/evaporation and coagulation and the implementation of the secondary organic aerosol (SOA) mechanism H2O and the thermodynamic model SOAP.
Concentrations of particles over Europe were simulated by the model for the year 2013. Model concentrations were compared to the European Monitoring and Evaluation Programme (EMEP) observations and other observations available in the EBAS database to evaluate the performance of the model. Performances were determined for several components of particles (sea salt, sulfate, ammonium, nitrate, organic aerosol) with a seasonal and regional analysis of results.
The model gives satisfactory performance in general. For sea salt, the model succeeds in reproducing the seasonal evolution of concentrations for western and central Europe. For sulfate, except for an overestimation of sulfate in northern Europe, modeled concentrations are close to observations and the model succeeds in reproducing the seasonal evolution of concentrations. For organic aerosol, the model reproduces with satisfactory results concentrations for stations with strong modeled biogenic SOA concentrations.
However, the model strongly overestimates ammonium nitrate concentrations during late autumn (possibly due to problems in the temporal evolution of emissions) and strongly underestimates summer organic aerosol concentrations over most of the stations (especially in the northern half of Europe). This underestimation could be due to a lack of anthropogenic SOA or biogenic emissions in northern Europe.
A list of recommended tests and developments to improve the model is also given.
