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Volume 17, issue 9
Atmos. Chem. Phys., 17, 5991-6001, 2017
https://doi.org/10.5194/acp-17-5991-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 17, 5991-6001, 2017
https://doi.org/10.5194/acp-17-5991-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 May 2017

Research article | 16 May 2017

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

Antti Arola1, Thomas F. Eck2,3, Harri Kokkola1, Mikko R. A. Pitkänen1,4, and Sami Romakkaniemi1 Antti Arola et al.
  • 1Finnish Meteorological Institute, Kuopio, Finland
  • 2Universities Space Research Association, Columbia, MD, USA
  • 3NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4Department of Applied Physics, University of Eastern Finland, Kuopio, Finland

Abstract. AERONET (AErosol RObotic NETwork), which is a network of ground-based sun photometers, produces a data product called the aerosol spectral deconvolution algorithm (SDA) that utilizes spectral total aerosol optical depth (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, i.e., on those from level 1 (L1) instead of level 2 (L2) in AERONET. The objective of our study was to assess, including all the available AERONET sites, how the fine-mode AOD is enhanced in cloudy conditions, 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 can be due to various cloud-related processes, mainly by the strong hygroscopic growth of particles in the vicinity of clouds and in-cloud processing leading to growth of accumulation mode particles. We estimated these cloud-related enhancements in fine-mode AOD seasonally and found, for instance, that in June–August season the average over all the AERONET sites was 0.011, when total fine-mode AOD from L2 data was 0.154; therefore, the relative enhancement was 7%. The enhancements were largest, both absolutely and relatively, in East Asia; for example, in June–August season the absolute and relative differences in fine-mode AOD, between L1 and L2 measurements, were 0.022 and 10%, respectively. Corresponding values in North America and Europe were about 0.01 and 6–7%. In some highly polluted areas, the enhancement is greater than these regional averages, e.g., in Beijing region and in June–July–August (JJA) season the corresponding absolute values were about 0.1. 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 Ångström exponent (AE) patterns. Moreover, we exploited a cloud parcel model, in order to understand in detail the relative role of different processes. We found that in marine conditions, were aerosol concentration are low and cloud scavenging is efficient, the AE changes in opposite direction than in the more polluted conditions, were hygroscopic growth of particles leads to a negative AE change.

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One of the issues that hinder the measurement-based assessment of aerosol–cloud interactions by remote sensing methods is that typically aerosols and clouds cannot be measured simultaneously by passive remote sensing methods. AERONET includes the SDA product that provides the fine-mode AOD also in mixed cloud–aerosol observations. These measurements have not yet been fully exploited in studies of aerosol–cloud interactions. We applied SDA for this kind of analysis.
One of the issues that hinder the measurement-based assessment of aerosol–cloud interactions by...
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