4.7 Article

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Journal

ATMOSPHERIC CHEMISTRY AND PHYSICS
Volume 22, Issue 9, Pages 6197-6215

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-22-6197-2022

Keywords

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Funding

  1. U.S. Department of Energy's Atmospheric System Research
  2. Office of Science, Office of Biological and Environmental Research program [DESC0016051]

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This study analyzes high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site to build a CCN spectrum product. The study examines the probability distribution shapes of aerosols and CCN metrics, identifies the primary drivers of CCN differences, and explores the dynamics of CCN spectral clusters and concentrations.
When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at similar to 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to similar to 30 %, and time period of 5 years, from 2009-2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1-3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

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