Examining Cloud Condensation Nuclei
ARM multi-wavelength lidar data is used to evaluate a new method for estimating cloud condensation nuclei concentrations.
All cloud droplets in the atmosphere form around atmospheric aerosol particles. However, due to their differing composition, not all aerosol particles are equally suitable to be one of these cloud condensation nuclei (CCN). Knowledge of the spatial and vertical distribution of aerosols and their capability to serve as CCN is fundamental to understanding cloud formation and aerosol impacts on cloud properties. In particular, it is important to understand aerosol properties at the altitudes at which clouds form. However, except for relatively rare aircraft measurements, most CCN measurements are made at the surface.
In this study, scientists develop an algorithm for estimating CCN concentration from vertically resolved multi-wavelength lidar measurements. By using the lidar measurements, the scientists can estimate CCN concentrations at cloud base, which is important for understanding aerosol-cloud interactions. The algorithm is evaluated using measurements from the ARM Southern Great Plains (SGP) site. The study demonstrates the potential of using multi-wavelength Raman lidar measurements to profile aerosol and CCN properties. This type of height-dependent information of aerosols and CCN can be useful for investigating the aerosol indirect effect and evaluating model simulations.
The vertical distribution of aerosols and their capability of serving as cloud condensation nuclei (CCN) are important for improving our understanding of aerosol indirect effects. Although ground-based and airborne CCN measurements have been made, they are generally scarce, especially at cloud base where it is needed most. We have developed an algorithm for profiling CCN number concentrations using backscatter coefficients at 355, 532, and 1,064 nm and extinction coefficients at 355 and 532 nm from multi-wavelength lidar systems. The algorithm considers three distinct types of aerosols (urban industrial, biomass burning, and dust) with bimodal size distributions. The algorithm uses look-up tables, which were developed based on the ranges of aerosol size distributions obtained from the Aerosol Robotic Network, to efficiently find optimal solutions. CCN number concentrations at five supersaturations (0.07-0.80%) are determined from the retrieved particle size distributions. Retrieval simulations were performed with different combinations of systematic and random errors in lidar-derived extinction and backscatter coefficients: systematic errors range from -20% to 20% and random errors are up to 15%, which fall within the typical error ranges for most current lidar systems. The potential of this algorithm to retrieve CCN concentrations is further evaluated through comparisons with surface-based CCN measurements with near-surface lidar retrievals. This retrieval algorithm would be valuable for aerosol-cloud interaction studies as it provides more information about CCN at cloud base altitudes.
University of Maryland
Data were obtained from the ARM Climate Research Facility, a U.S. Department of Energy Office of Science User Facility sponsored by the Office of Biological and Environmental Research. This work was supported by the Key R&D Program of China (2017YFC1501702), the National Natural Science Foundation of China (91544217), and the National Science Foundation of the United States (AGS-1337599 and AGS1534670).
Lv, M., Z. Wang, Z. Li, T. Luo, R. Ferrare, D. Liu, et al. “Retrieval of cloud condensation nuclei number concentration profiles from lidar extinction and backscatter data.” Journal of Geophysical Research: Atmospheres 123(11), 6082-6098 (2018). DOI:10.1029/2017JD028102