Assessment of cloud related fine mode AOD enhancements based on AERONET SDA product

Tuesday, 16 December 2014
Antti T Arola1, Thomas F Eck2, Harri Kokkola1, Tomi Laaksoviita1, Anders V. Lindfors1, Mikko Riku Aleksi Pitkänen1 and Sami Romakkaniemi1, (1)Atmospheric Research Centre of Eastern Finland, Kuopio, Finland, (2)Nasa Goddard SFC, Greenbelt, MD, United States
AERONET (AErosol RObotic NETwork), which is a network of ground-based sun photometers, includes also so-called Aerosol Spectral Deconvolution Algorithm (SDA) that utilizes spectral total extinction AOD data to infer the component fine and coarse mode optical depths at 500nm. Based on its assumptions, SDA identifies cloud optical depth as the coarse mode AOD component and therefore effectively computes the fine mode AOD also in mixed cloud-aerosol observations. Therefore, it can be argued that the more representative AOD for fine mode fraction should be based on all direct sun measurements and not only on those cloud-screened for clear-sky conditions, in other words on those from Level 1 (L1) instead of Level 2 (L2). The objective of our study was to assess, including all the available AERONET sites, the magnitude of this cloud enhancement in fine mode AOD, in other words contrasting SDA L1 and L2 in our analysis. Assuming that the cloud-screening correctly separates the cloudy and clear-sky conditions, then the increases in fine mode AOD in can be due to various cloud-related processes, mainly by in-cloud processing and hygroscopic growth. We estimated these cloud-related enhancements in fine mode AOD seasonally and found, for instance, than in June-July season the average over all the AERONET sites was 0.034, when total fine mode AOD from L2 data was 0.192, therefore the relative enhancement was 18%. It is difficult to separate the fine mode AOD enhancements due to in-cloud processing and hygroscopic growth, but we attempted to get some understanding by conducting a similar analysis for SDA-based fine mode Angstrom Exponent patterns. Moreover, we included OMI NO2 and Glyoxal data, to infer whether the regional patterns of fine mode AOD enhancements contain similar features than these two data products that could serve as a proxy of the strength of in-cloud processing.