A ten year global record of absorbing aerosols above clouds from OMI's near UV observations
Aerosol cloud interaction continues to be one of the leading uncertain components of climate models, primarily due to the lack of an adequate knowledge of the complex microphysical and radiative processes associated with the aerosolcloud system. The situations when aerosols and clouds are found in the same atmospheric column, for instance, when light absorbing aerosols such as biomass burning generated carbonaceous particles or wind blown dust overlay low level cloud decks, are commonly found over several regional of the world. Contrary to the cloud free scenario over dark surface, for which aerosols are known to produce a net cooling effect (negative radiative forcing) on climate, the overlapping situation of absorbing aerosols over cloud can potentially exert a significant level of atmospheric absorption and produces a positive radiative forcing at top of atmosphere. The magnitude of direct radiative effects of aerosols above cloud depends directly on the aerosol loading, microphysical optical properties of the aerosol layer and the underlying cloud deck, and geometric cloud fraction. We help in addressing this problem by introducing a novel product of optical depth of absorbing aerosols above clouds retrieved from near UV observations made by the Ozone Monitoring Instrument (OMI) on board NASA's Aura platform. The presence of absorbing aerosols above cloud reduces the upwelling radiation reflected by cloud and produces a strong 'color ratio' effect in the near UV region, which can be unambiguously detected in the OMI measurements. Physically based on this effect, the OMACA algorithm retrieves the optical depths of aerosols and clouds simultaneously under a prescribed state of atmosphere. The algorithm architecture and results from a ten year global record including global climatology of frequency of occurrence and above cloud aerosol optical depth, and a discussion on related future field campaigns are presented.
SPIE Asia Pacific Remote Sensing