A global comparison of GEOS-Chem-predicted and remotely-sensed mineral dust...

Johnson, M. S., N. Meskhidze, and V. P. Kiliyanpilakkil (2012), A global comparison of GEOS-Chem-predicted and remotely-sensed mineral dust aerosol optical depth and extinction profiles, J. Adv. Modeling Earth Syst., doi:10.1029/2011MS000109.

Dust aerosol optical depth (AOD) and vertical distribution of aerosol extinction predicted by a global chemical transport model (GEOS-Chem) are compared to spaceborne data from the Moderate-resolution Imaging Spectroradiometer (MODIS), MultiAngle Imaging SpectroRadiometer (MISR), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) for March 2009 to February 2010. Modelpredicted and remotely-sensed AOD/aerosol extinction profiles are compared over six regions where aerosol abundances are dominated by mineral dust. Calculations indicate that over the regions examined in this study (with the exception of Middle Eastern dust sources) GEOS-Chem predicts higher AOD values compared to MODIS and MISR. The positive bias is particularly pronounced over the Saharan dust source regions, where model-predicted AOD values are a factor of 2 to 3 higher. The comparison with CALIPSO-derived dust aerosol extinction profiles revealed that the model overestimations of dust abundances over the study regions primarily occur below ,4 km, suggesting excessive emissions of mineral dust and/or uncertainties in dust optical properties. The implementation of a new dust size distribution scheme into GEOS-Chem reduced the yearly-mean positive bias in model-predicted AOD values over the study regions. The results were most noticeable over the Saharan dust source regions where the differences between model-predicted and MODIS/MISR retrieved AOD values were reduced from 0.22 and 0.17 to 0.02 and 20.04, respectively. Our results suggest that positive/negative biases between satellite and model-predicted aerosol extinction values at different altitudes can sometimes even out, giving a false impression for the agreement between remotely-sensed and model-predicted column-integrated AOD data.

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