Systematic aerosol characterization by combining UV aerosol indices with trace gas concentrations

Knowledge of aerosol type or source is of importance for the calculation of aerosol radiative effects, for the development and monitoring of mitigation strategies, and for the construction of climatologies of aerosol optical properties (needed for, e.g., aerosol retrieval). Our Global Aerosol Classification Algorithm, GACA, examines aerosol properties (MODIS Aerosol Optical Thickness (AOT) and extinction Ångström exponent, GOME-2 UV Aerosol Indices) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT) measured from space in order to classify different aerosol types and dominating source types. Using GACA, global maps of dominant aerosol type and main source type were constructed for each season. The seasonal cycles of both aerosol type and source were also studied in more detail for several 5° x 5° regions. The agreement with aerosol types from ECMWF’s MACC model is generally good for urban/industrial (SO4) aerosols and biomass burning smoke, but the variability (yearly and/or seasonal) is often not well captured by MACC. The amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is severely underestimated in comparison with GACA. The presented study is of exploratory nature, but we show that our method is well suited to evaluate climate models by comparing measured and modeled aerosol type and source instead of focusing on a single parameter, e.g. AOT.