Fog‐ and cloud‐induced aerosol modification observed by the Aerosol Robotic Network (AERONET)
T. F. Eck, B. N. Holben, Jeffrey S. Reid, D. M. Giles, M. A. Rivas, Ramesh P. Singh, Sachchidanand Tripathi, Carol J. Bruegge, Steven Platnick, G. Thomas Arnold, N. A. Krotkov, Simon Carn, A. Sinyuk, Оleg Dubovik, Antti Arola, J. S. Schafer, Paulo Artaxo, A. Smirnov, H. Chen, Philippe Goloub
- 发表年份
- 2012
- 引用次数
- 154
- 访问权限
- 开放获取
摘要
Large fine mode–dominated aerosols (submicron radius) in size distributions retrieved from the Aerosol Robotic Network (AERONET) have been observed after fog or low‐altitude cloud dissipation events. These column‐integrated size distributions have been obtained at several sites in many regions of the world, typically after evaporation of low‐altitude cloud such as stratocumulus or fog. Retrievals with cloud‐processed aerosol are sometimes bimodal in the accumulation mode with the larger‐size mode often ∼0.4–0.5 μ m radius (volume distribution); the smaller mode, typically ∼0.12 to ∼0.20 μ m, may be interstitial aerosol that were not modified by incorporation in droplets and/or aerosol that are less hygroscopic in nature. Bimodal accumulation mode size distributions have often been observed from in situ measurements of aerosols that have interacted with clouds, and AERONET size distribution retrievals made after dissipation of cloud or fog are in good agreement with particle sizes measured by in situ techniques for cloud‐processed aerosols. Aerosols of this type and large size range (in lower concentrations) may also be formed by cloud processing in partly cloudy conditions and may contribute to the “shoulder” of larger‐size particles in the accumulation mode retrievals, especially in regions where sulfate and other soluble aerosol are a significant component of the total aerosol composition. Observed trends of increasing aerosol optical depth (AOD) as fine mode radius increased suggests higher AOD in the near‐cloud environment and higher overall AOD than typically obtained from remote sensing owing to bias toward sampling at low cloud fraction.
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