03 Nov 2022
 | 03 Nov 2022
Status: this preprint is currently under review for the journal ACP.

Resolving Vertical Profile of Cloud Condensation Nuclei Concentrations from Spaceborne Lidar Measurements

Piyushkumar Patel, Jonathan Jiang, Ritesh Gautam, Harish Gadhavi, Olga Kalashnikova, Michael Garay, Lan Gao, Feng Xu, and Ali Omar

Abstract. Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions (ACI), contributing to the largest uncertainties in the understandings of global climate change. We present a novel remote sensing-based algorithm that quantifies the vertically-resolved CCN number concentrations (NCCN) using aerosol optical properties measured by a multiwavelength lidar. The algorithm considers five distinct aerosol subtypes with bimodal size distributions. The inversion used the look-up tables developed in this study, based on the observations from the Aerosol Robotic Network to efficiently retrieve optimal particle size distributions from lidar measurements. The method derives dry aerosol optical properties by implementing hygroscopic enhancement factors to lidar measurements. The retrieved optically equivalent particle size distributions and aerosol type dependent particle composition are utilized to calculate critical diameter using the κ-Köhler theory and NCCN at six supersaturations ranging from 0.07 % to 1.0 %. Sensitivity analyses indicate that uncertainties in extinction coefficients and relative humidity greatly influence the retrieval error in NCCN. The potential of this algorithm is further evaluated by retrieving NCCN using airborne lidar from the NASA ORACLES campaign and validated against simultaneous measurements from the CCN counter. The independent validation with robust correlation demonstrates promising results. Furthermore, the NCCN has been retrieved for the first time using a proposed algorithm from spaceborne lidar - Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) - measurements. The application of this new capability demonstrates the potential for constructing a 3D CCN climatology at a global scale, which help to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

Piyushkumar Patel et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-547', Anonymous Referee #2, 05 Dec 2022
  • RC2: 'Comment on acp-2022-547', Anonymous Referee #3, 09 Dec 2022

Piyushkumar Patel et al.

Piyushkumar Patel et al.


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Short summary
The lack of global measurements of cloud condensation nuclei (CCN) prevents detailed testing of aerosol-cloud effects and predicting climate change. Therefore, we developed a novel remote sensing-based algorithm for retrieving vertically resolved CCN number concentrations from the spaceborne lidar system. This new capability provides global distribution of CCN number concentrations from space that will be beneficial for evaluating models and accurately quantifying aerosol climate forcing.