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Volume 15, issue 18
Atmos. Chem. Phys., 15, 10597–10618, 2015
https://doi.org/10.5194/acp-15-10597-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 15, 10597–10618, 2015
https://doi.org/10.5194/acp-15-10597-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Sep 2015

Research article | 25 Sep 2015

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

M. J. M. Penning de Vries1, S. Beirle1, C. Hörmann1, J. W. Kaiser1, P. Stammes2, L. G. Tilstra2, O. N. E. Tuinder2, and T. Wagner1 M. J. M. Penning de Vries et al.
  • 1Max Planck Institute for Chemistry, Mainz, Germany
  • 2Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands

Abstract. 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 Ångström 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 (Ångström 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 5° × 5° 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.

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