Preprints
https://doi.org/10.5194/acp-2021-1008
https://doi.org/10.5194/acp-2021-1008

  13 Dec 2021

13 Dec 2021

Review status: this preprint is currently under review for the journal ACP.

Long- and Short-Term Temporal Variability in Cloud Condensation Nuclei Spectra in the Southern Great Plains

Russell J. Perkins1, Peter J. Marinescu1,2, Ezra J. T. Levin1,3, Don R. Collins4, and Sonia M. Kreidenweis1 Russell J. Perkins et al.
  • 1Colorado State University, Fort Collins, CO, 80523, USA
  • 2Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, 80523, USA
  • 3Handix Scientific, Boulder, CO, 80301, USA
  • 4University of California Riverside, Riverside, CA 92521

Abstract. When aerosol particles seed 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 ~45 min resolution, generated using high quality data from eight independent instruments at the US Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 to ~30 %, and time period, 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. Probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. 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 simplified understanding of seasonal and diurnal variations in CCN behaviour. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated and uncertainties of this approach are estimated. Finally, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses, which assist in determining the time scales of changing CCN concentrations at different supersaturations and are important for cloud modelling studies.

Russell J. Perkins et al.

Status: open (until 29 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1008', Anonymous Referee #1, 26 Dec 2021 reply

Russell J. Perkins et al.

Data sets

Southern Great Plains Merged and Extended Cloud Condensation Nuclei Data Russell J. Perkins, Peter J. Marinescu, Ezra J. T. Levin, Don R. Collins, Sonia M. Kreidenweis https://drive.google.com/file/d/1NUFJaKeo5JjhkNOw14iBKonzFIIqH-Mt/view?usp=sharing

Russell J. Perkins et al.

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Short summary
Five years (2009–2013) of aerosol and cloud condensation nuclei (CCN) data from a total of 8 instruments housed at the DOE ARM Southern Great Plains site were merged into a quality-controlled, continuous dataset of CCN spectra at ~45 min resolution. The data cover all seasons, are representative of a rural, agricultural mid-continental site, and are useful for model initialization and validation. Our analysis of this dataset focuses on seasonal and hourly variability.
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