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Detecting the optical properties of aerosols usingpassive satellite-borne measurements alone isa difficult task due to the broadband effect ofaerosols on the measured spectra and the influences ofsurface and cloud reflection. We present anotherapproach to determine aerosol type, namely by studyingthe relationship of aerosol optical depth (AOD) withtrace gas abundance, aerosol absorption, and meanaerosol size.  Our new Global Aerosol ClassificationAlgorithm, GACA, examines relationships between aerosolproperties (AOD and extinction Ångström exponent fromthe Moderate Resolution Imaging Spectroradiometer(MODIS), UV Aerosol Index from the second Global OzoneMonitoring Experiment, GOME-2) and trace gas columndensities (NO<sub>2</sub>, HCHO, SO<sub>2</sub> fromGOME-2, and CO from MOPITT, the Measurements ofPollution in the Troposphere instrument) on a monthlymean basis. First, aerosol types are separated based onsize (Ångström exponent) and absorption (UV AerosolIndex), then the dominating sources are identifiedbased on mean trace gas columns and their correlationwith AOD.  In this way, global maps of dominant aerosoltype and main source type are constructed for eachseason and compared with maps of aerosol compositionfrom the global MACC (Monitoring AtmosphericComposition and Climate) model. Although GACA cannotcorrectly characterize transported or mixed aerosols,GACA and MACC show good agreement regarding the globalseasonal cycle, particularly for urban/industrialaerosols. The seasonal cycles of both aerosol type andsource are also studied in more detail for selected5° × 5° regions. Again, goodagreement between GACA and MACC is found for allregions, but some systematic differences becomeapparent: the variability of aerosol composition(yearly and/or seasonal) is often not well captured byMACC, the amount of mineral dust outside of the dustbelt appears to be overestimated, and the abundance ofsecondary organic aerosols is underestimated incomparison with GACA.  Whereas the presented study isof exploratory nature, we show that the developedalgorithm is well suited to evaluate climate andatmospheric composition models by including aerosoltype and source obtained from measurements into thecomparison, instead of focusing on a single parameter,e.g., AOD. The approach could be adapted to constrainthe mix of aerosol types during the process ofa combined data assimilation of aerosol and trace gasobservations

A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances