Aerosol modelling for regional climate studies: application to anthropogenic particles and evaluation over a European/African domain

A simplified anthropogenic aerosol model for use in climate Studies is developed and implemented within the regional climate model RegCM. The model includes sulphur dioxide, sulphate, hydrophobic and hydrophilic black carbon (BC) and organic carbon (OC) and is run for the winter and summer seasons of 2000 over a large domain extending from northern Europe to South tropical Africa. An evaluation of the model performance is carried out in terms of surface concentrations and aerosol optical depths (AODs). For sulphur dioxide and sulphate concentration, comparison of simulated fields and experimental data collected over the EMEP European network shows that the model generally reproduces the observed spatial patterns of near-surface sulphate. Sulphate concentrations are within a factor of 2 of observations in 34% (JJA) to 57% (DJF) of cases. For OC and BC, simulated concentrations are compared to different datasets. The simulated and observed values agree within a factor of 2 in 56% (DJF) to 62% (JJA) of cases for BC and 33% (JJA) to 64% (DJF) for OC. Simulated AODs are compared with ground-based (AERONET) and satellite (MODIS, MISR, TOMS) AOD datasets. Simulated AODs are in the range of AERONET and MISR data over northern Europe, and AOD spatial patterns show consistency with MODIS and TOMS retrievals both over Europe and Africa. The main model deficiencies we find are: (i) an underestimation of surface concentrations of sulphate and OC during the summer and especially over the Mediterranean region and (ii) a general underestimation of AOD, most pronounced over the Mediterranean basin. The primary factors we identify as contributing to these biases are the lack of natural aerosols (in particular, desert dust, secondary biogenic aerosols and nitrates), uncertainties in the emission inventories and aerosol cycling by moist convection. Also, in view of the availability of better observing datasets (e.g. as pail of the AMMA project), we are currently working on improving these aspects of the model before applying it to climate studies. Despite the deficiencies identified above, we assess that our model shows a performance in line with that other coupled climate/aerosol models and can presently provide it useful tool for sensitivity and process studies.