05 Oct 2022
05 Oct 2022
Status: this preprint is currently under review for the journal AMT.

Use of Lidar Aerosol Extinction and Backscatter Coefficients to Estimate Cloud Condensation Nuclei (CCN) Concentrations in the Southeast Atlantic

Emily D. Lenhardt1, Lan Gao1, Jens Redemann1, Feng Xu1, Sharon P. Burton2, Brian Cairns3, Ian Chang1, Richard A. Ferrare2, Chris A. Hostetler2, Pablo E. Saide4,5, Calvin Howes4, Yohei Shinozuka6, Snorre Stamnes2, Mary Kacarab7, Amie Dobracki8, Jenny Wong9, Steffen Freitag10, and Athanasios Nenes11,12 Emily D. Lenhardt et al.
  • 1School of Meteorology, University of Oklahoma, Norman, OK, 73072, United States
  • 2NASA Langley Research Center, Hampton, VA, 23666, United States
  • 3NASA Goddard Institute for Space Studies, New York, NY, 10025, United States
  • 4Department of Atmospheric and Oceanic Sciences, University of California – Los Angeles, Los Angeles, CA, 90095, United States
  • 5Insitute of the Environment and Sustainability, University of California – Los Angeles, Los Angeles, CA, 90095, United States
  • 6Bay Area Environmental Research Institute, Moffett Field, CA, 94035, United States
  • 7School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, United States
  • 8Department of Atmospheric Sciences, University of Miami, Miami, FL, 33146, United States
  • 9Department of Chemistry and Biochemistry, Mount Allison University, Sackville, New Brunswick, E4L 1E2, Canada
  • 10State Agency for Nature, Environment and Consumer Protection North Rhine-Westphalia, Recklinghausen, 45659, Germany
  • 11Institute for Chemical Engineering Sciences, Foundation for Research and Technology, Hellas, Patras, GR-26504, Greece
  • 12School of Architecture, Civil & Environmental Engineering, Ecole Polytechnique fédérale de Lausanne, CH-1015, Lausanne, Switzerland

Abstract. Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol-cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically-resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES) project, which took place over the Southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018.

Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN – HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN – backscatter and CCN – extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from WRF-CAM5. These results demonstrate the utility of deriving vertically-resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations.

Emily D. Lenhardt et al.

Status: open (until 01 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on amt-2022-262', Goutam Choudhury, 10 Nov 2022 reply

Emily D. Lenhardt et al.

Data sets

ORACLES Jens Redemann, Steve Howell, Athanasios Nenes, Chris Hostetler

Emily D. Lenhardt et al.


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Short summary
Small atmospheric particles, such as smoke from wildfires or pollutants from human activities, impact cloud properties, and clouds have a strong influence on climate change. To better understand the distributions of these particles, we develop relationships to derive their concentrations from remote sensing measurements from an instrument called a lidar. Our method is reliable for smoke particles, and similar steps can be taken to develop similar relationships for other particle types.