Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction ngstrom exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (ngstrom exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5A degrees x 5A degrees regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (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 underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

The ash cloud of the Eyjafjallajökull (also referred to as: Eyjafjalla (e.g. Schumann et al., 2011), Eyjafjöll or Eyjafjoll (e.g. Ansmann et al., 2010)) volcano on Iceland caused closure of large parts of European airspace in April and May 2010. For the validation and improvement of the European volcanic ash forecast models several research flights were performed. Also the CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) flying laboratory, which routinely measures at cruise altitude (≈11 km) performed three dedicated measurements flights through sections of the ash plume. Although the focus of these flights was on the detection and quantification of the volcanic ash, we report here on sulphur dioxide (SO2) and bromine monoxide (BrO) measurements with the CARIBIC DOAS (Differential Optical Absorption Spectroscopy) instrument during the second of these special flights on 16 May 2010. As the BrO and the SO2 observations coincide, we assume the BrO to have been formed inside the volcanic plume. Average SO2 and BrO mixing ratios of ≈40 ppb and ≈5 ppt respectively are retrieved inside the plume. The BrO to SO2 ratio retrieved from the CARIBIC observation is ≈1.3×10−4. Both SO2 and BrO observations agree well with simultaneous satellite (GOME-2) observations. SO2 column densities retrieved from satellite observations are often used as an indicator for volcanic ash. As the CARIBIC O4 column densities changed rapidly during the plume observation, we conclude that the aerosol and the SO2 plume are collocated. For SO2 some additional information on the local distribution can be derived from a comparison of forward and back scan GOME-2 data. More details on the local plume size and position are retrieved by combining CARIBIC and GOME-2 data.