Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

Abstract

Abstract. The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol–cloud interactions within warm clouds. Long-term CCN number concentration (NCCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between NCCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (σsp) of PM10 particles. The analysis first showed that the dependence of NCCN on supersaturation (SS) can be described by a logarithmic fit in the range SS <1.1 %, without any theoretical reasoning. The relationship between NCCN and AOPs was parameterized as NCCN≈((286±46)SAE ln(SS/(0.093±0.006))(BSF − BSFmin) + (5.2±3.3))σsp, where BSFmin is the minimum BSF, in practice the 1st percentile of BSF data at a site to be analyzed. At the lowest supersaturations of each site (SS ≈0.1 %), the average bias, defined as the ratio of the AOP-derived and measured NCCN, varied from ∼0.7 to ∼1.9 at most sites except at a Himalayan site where the bias was >4. At SS >0.4 % the average bias ranged from ∼0.7 to ∼1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, ∼1.4–1.9. In other words, at SS >0.4 % NCCN was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Ångström exponent (SAE). The squared correlation coefficients between the AOP-derived and measured NCCN varied from ∼0.5 to ∼0.8. To study the physical explanation of the relationships between NCCN and AOPs, lognormal unimodal particle size distributions were generated and NCCN and AOPs were calculated. The simulation showed that the relationships of NCCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of NCCN and AOPs were similar to those of the observed ones.

Constraining the Twomey effect from satellite observations: issues and perspectives

“…for monthly averages over regions, AOD from ground-based remote-sensing retrievals (AERONET; Holben et al, 2001) correlates well with CCN surface measurements (Andreae, 2009;Shen et al, 2019). Similar results were also reported for aircraft measurements (Clarke and Kapustin, 2010;Shinozuka et al, 2015). However, at shorter timescales or less spatial aggregation, there are significant deviations from a perfect correlation (Liu and Li, 2014).…”

“…In situ observations suggest that AOD may even be anticorrelated with CCN at cloud base (Kacarab et al, 2020). A way forward is the use of spaceborne vertically resolved observations such as lidar measurements (Shinozuka et al, 2015;Stier, 2016). The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; Winker et al, 2009) lidar retrieves aerosol backscatter profiles and thus is capable of identifying aerosol layers (Costantino and Bréon, 2010).…”

“…AOD is thus heavily influenced by the variability of relative humidity. The light extinction caused by dry particles (at relative humidities below 30 %) is much better correlated to CCN concentrations than the extinction of particles at ambient relative humidity (Shinozuka et al, 2015). Liu and Li (2018) showed that using total AOD compared to dry AOD as a CCN proxy when estimating β from measurements at different Atmospheric Radiation Measurements (ARM) sites resulted in a 23 % underestimate.…”

Constraining the Twomey effect from satellite observations: Issues and perspectives

“…At aggregate scales, i.e. for monthly averages over regions, AOD from ground-based remote sensing retrievals (AERONET; Holben et al, 2001) correlates well with CCN surface measurements (Andreae, 2009;Shen et al, 2019). Similar results were also reported for aircraft measurements (Clarke and Kapustin, 2010;Shinozuka et al, 2015).…”

Cluster Analysis of Submicron Particle Number Size Distributions at the SORPES Station in the Yangtze River Delta of East China

“…Both the climate and human health effects of atmospheric aerosols depend on particle sizes (Kerminen et al., 2012; Salma et al., 2015). Particles larger than ∼50–100 nm not only can serve as cloud condensation nuclei but can also scatter solar radiation more effectively, which influences the Earth’s radiative balance (Schmale et al., 2018; Shen et al., 2019). Recent evidence has shown that ultrafine particles smaller than 50 nm can also be activated in deep convective clouds (Fan et al., 2018).…”