Explore the vast range of titles available.

This study was part of the international field measurement Campaigns of Air Quality Research in Beijing and Surrounding Region 2006 (CAREBeijing‐2006). We investigated a new particle formation event in a highly polluted air mass at a regional site south of the megacity Beijing and its impact on the abundance and properties of cloud condensation nuclei (CCN). During the 1‐month observation, particle nucleation followed by significant particle growth on a regional scale was observed frequently (∼30%), and we chose 23 August 2006 as a representative case study. Secondary aerosol mass was produced continuously, with sulfate, ammonium, and organics as major components. The aerosol mass growth rate was on average 19 μg m−3 h−1 during the late hours of the day. This growth rate was observed several times during the 1‐month intensive measurements. The nucleation mode grew very quickly into the size range of CCN, and the CCN size distribution was dominated by the growing nucleation mode (up to 80% of the total CCN number concentration) and not as usual by the accumulation mode. At water vapor supersaturations of 0.07–0.86%, the CCN number concentrations reached maximum values of 4000–19,000 cm−3 only 6–14 h after the nucleation event. During particle formation and growth, the effective hygroscopicity parameter κ increased from about 0.1–0.3 to 0.35–0.5 for particles with diameters of 40–90 nm, but it remained nearly constant at ∼0.45 for particles with diameters of ∼190 nm. This result is consistent with aerosol chemical composition data, showing a pronounced increase of sulfate.

Quantifying the aerosol/cloud-mediated radiative effect at a global scale requires simultaneous satellite retrievals of cloud condensation nuclei (CCN) concentrations and cloud base updraft velocities (Wb ). Hitherto, the inability to do so has been a major cause of high uncertainty regarding anthropogenic aerosol/cloud-mediated radiative forcing. This can be addressed by the emerging capability of estimating CCN and Wb of boundary layer convective clouds from an operational polar orbiting weather satellite. Our methodology uses such clouds as an effective analog for CCN chambers. The cloud base supersaturation (S) is determined by Wb and the satellite-retrieved cloud base drop concentrations (Ndb ), which is the same as CCN(S). Validation against ground-based CCN instruments at Oklahoma, at Manaus, and onboard a ship in the northeast Pacific showed a retrieval accuracy of ±25% to ±30% for individual satellite overpasses. The methodology is presently limited to boundary layer not raining convective clouds of at least 1 km depth that are not obscured by upper layer clouds, including semitransparent cirrus. The limitation for small solar backscattering angles of <25° restricts the satellite coverage to ∼25% of the world area in a single day.

We present here the first cloud condensation nuclei (CCN) concentration profiles derived from measurements with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), for different aerosol types at a supersaturation of 0.15%. CCN concentrations, along with the corresponding uncertainties, were inferred for a nighttime CALIPSO overpass on 9 September 2011, with coincident observations with the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft, within the framework of the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign over Thessaloniki, Greece. The CALIPSO aerosol typing is evaluated, based on data from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. Backward trajectories and satellite-based fire counts are used to examine the origin of air masses on that day. Our CCN retrievals are evaluated against particle number concentration retrievals at different height levels, based on the ACEMED airborne measurements and compared against CCN-related retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard Terra and Aqua product over Thessaloniki showing that it is feasible to obtain CCN concentrations from CALIPSO, with an uncertainty of a factor of two to three.

Two years of data on cloud condensation nuclei (CCN) measured at the Liupan Mountain (LPS) Meteorological Station from August 2020 to November 2021 were analyzed in this study. The results show that the mean annual CCN concentration was 851 cm−3 and that the mean concentration of CCN increases with the supersaturation degree. The curves of the diurnal variation in CCN concentration show one peak and one valley, which correspond to the diurnal variation in the mixed-layer height and valley wind. Regarding seasonal variations, the CCN concentration, as well as the degree of internal mixing, is higher in the spring and winter, while the degree of external mixing is higher in the summer and autumn. The transport of CCN is closely related to the wind transport evolution, and the southeast and southwest sides of the LPS station contribute more to the CCN concentration in the spring and winter due to central heating in the wintertime. Though correlations between CCN concentration and pressure are scarce, the CCN concentration and temperature (or humidity) are positively (or negatively) correlated, especially in the spring. Furthermore, the 48-h backward trajectory analysis indicates that the sources in the northwest direction are a major contributor to the CCN concentration. The pollutants mainly came from the northwest and southwest sides, according to the analysis of potential sources using the PSCF and CWT approach. The study of CCN evolution and contribution area is beneficial for further research on the physical properties of cloud droplets, the influence of mountains on CCN changes and the role of CCN in terrain cloud precipitation, which are significant for the improvement of weather modification techniques.

The auto-conversion from cloud droplet to raindrop is a process whereby rain drops formed by collision-coalescence of cloud droplets. As an essential link connecting aerosol-cloud interaction, it significantly influences the changes in cloud morphology and precipitation. In order to explore the sensitivity of auto-conversion schemes to cloud condensation nuclei (CCN) concentration, using the auto-conversion scheme in the Thompson scheme (TH-AU) and Milbrandt-Yau scheme (MY-AU), we set four groups of CCN concentrations to simulate a strong convection process in Ningxia region of China. The results show that: The sensitivity of different auto-conversion schemes to changes in CCN concentrations varies significantly, and the aerosol-induced changes in precipitation and convection strongly depend on the auto-conversion scheme. With the increase of CCN concentration, the mixing ratio of cloud droplets increases, and the particle size decreases, resulting in a decrease in the auto-conversion intensity for the two schemes, which makes more supercooled water participate in the ice phase process. Compared with the TH-AU, the MY-AU has lower auto-conversion intensity at the same CCN concentration, the proportion of supercooled cloud droplets participating in the ice phase process is higher than that in the TH-AU, which leads to the raindrop mixing ratio of 4000–6000 m in MY-AU is lower than that in TH-AU at the same CCN concentration, and the mixing ratio of ice phase particles in MY-AU scheme is higher in the convective mature stage, especially snow and graupel particles, and the graupel particle generation height of MY-AU is lower than that of TH-AU. In terms of dynamic structure, with the increase of CCN concentration, more cloud droplets are activated and frozen which makes the enhancement of updraft mainly occur in the upper layer in both schemes, but the stronger gravitational drag caused by graupel particles in MY-AU may enhance the downdraft in the middle and lower layers, which makes the convection of MY-AU decay early at higher CCN concentration. In addition, changes in microphysical processes also lead to differences in cumulative precipitation and accumulated ground graupel-fall of the two schemes. The cumulative precipitation and the accumulated ground graupel-fall of the MY-AU decrease strongly with the increase of CCN concentration because the warm rain process of MY-AU is strongly inhibited. Compared with MY-AU, the warm rain process of TH-AU is not significantly inhibited, which leads to the cumulative precipitation and the accumulated ground graupel-fall of the TH-AU scheme increases when the CCN concentration is 50–200 cm −3 and slightly decreases when the CCN concentration is 200–10000 cm −3 .

By use of a three-dimensional compressible non-hydrostatic convective cloud model with detailed microphysics featuring spectral bins of cloud condensation nuclei （CCN）, liquid droplets, ice crystals, snow and graupel particles, the spatial and temporal distributions of hydrometeors in a supercell observed by the （Severe Thunderstorm Electrification and Precipitation Study） STEPS triple-radar network are simulated and analyzed. The bin model is also employed to study the effect of CCN concentration on the evolution characteristics of the supercell. It is found that the CCN concentration not only affects the concentration and spectral distribution of water droplets, but also influences the characteristics of ice crystals and graupel particles. With a larger number of CCN, more water droplets and ice crystals are produced and the growth of graupel is restrained. With a small quantity of CCN the production of large size water droplets are promoted by initially small concentrations of water droplets and ice crystals, leading to earlier formation of small size graupel and restraining the recycling growth of graupel, and thus inhibiting the formation of large size graupel （or small size hail）. It can be concluded that both the macroscopic airflow and microphysical processes influence the formation and growth of large size graupel （or small size hail）. In regions with heavy pollution, a high concentration of CCN may restrain the formation of graupel and hail, and in extremely clean regions, excessively low concentrations of CCN may also limit the formation of large size graupel （hail）.

The susceptibility of cloud droplet number concentration Nd$\\left({N}_{\\mathrm{d}}\\right)$to aerosols (β)$(\\beta )$remains challenging to constrain in satellite observations. This difficulty arises from limitations in representing cloud condensation nuclei, which depend on aerosol size and composition. To address this, we combine aerosol‐type‐specific retrievals of dry extinction coefficient and number concentration from Cloud–Aerosol LiDAR and Infrared Pathfinder Satellite Observation with co‐located Nd${N}_{\\mathrm{d}}$from CloudSat and Moderate Resolution Imaging Spectroradiometer. We find that β$\\beta $associated with mineral dust is consistently near zero across all aerosol‐Nd${N}_{\\mathrm{d}}$combinations. Furthermore, β$\\beta $decreases nonlinearly as the dust fraction increases, with a pronounced reduction occurring only when dust exceeds approximately 70%. Accordingly, excluding dust from the analysis increases the globally aggregated β$\\beta $from 0.24–0.26 to 0.30–0.37. These findings highlight the importance of considering aerosol composition when constraining aerosol–cloud interactions and their associated radiative forcing in satellite observations.

Atmospheric aerosols often contain surface‐active organics, which reduce surface tension and enhance cloud droplets activation. This effect is often neglected in the application of Köhler theory where a constant surface tension equivalent to pure water is assumed. Using a cloud parcel model, we evaluated the impact of four representative surface‐active organics, humic‐like substances (HULIS), sodium dodecyl sulfate (SDS), cis‐pinonic acid, and dicarboxylic acids, on cloud condensation nuclei (CCN) activation under varied atmospheric conditions. Our results indicate that HULIS significantly enhance CCN activation, particularly at high aerosol concentrations, low updraft velocities, and small particle sizes. SDS, cis‐pinonic acid, and dicarboxylic acids also increase activation but to a lesser degree. The surface activity of HULIS has a stronger influence on CCN activation than its hygroscopicity, with particle size being the most sensitive parameter. This study emphasizes the need to incorporate surface‐active organics into climate models to improve the prediction of aerosol‐cloud interactions.

Aerosol measurements during the DOE ARM Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign were used to quantify the differences between clean and smoky cloud condensation nuclei (CCN) budgets. Accumulation‐mode particles accounted for ∼70% of CCN at supersaturations <0.3% in clean and smoky conditions. Aitken‐mode particles contributed <20% and sea‐spray‐mode particles <10% at supersaturations <0.3%, but at supersaturations >0.3% Aitken particles contributions increased to 30%–40% of clean CCN. For clean conditions, the Hoppel minimum diameter was correlated to the accumulation‐mode number concentration, indicating aerosol‐correlated cloud activation was controlling the lower diameter cutoff for which particles serve as CCN. For smoky conditions, the contributions of Aitken particles increase and the correlation of cloud activation to accumulation‐mode particles is masked by the lower‐hygroscopicity smoke. These results provide the first multi‐month in situ quantitative constraints on the role of aerosol number size distributions in controlling cloud activation in the tropical Atlantic boundary layer. Plain Language Summary Tiny airborne particles provide the “seeds” on which cloud droplets form, and clouds are in turn important for regulating climate around the world. The small number of measurements characterizing these particles in conditions that are not affected by man‐made emissions make it difficult to represent these cloud processes in computer models that compare current climate to pre‐industrial conditions. Aerosol measurements collected for 17 months on an isolated island in the tropical Atlantic Ocean show how the size and number of particles affect cloud characteristics. The long timescale and wide range from very clean to very smoky aerosol conditions revealed not only differences in the particles that activate in clouds but also in the mechanisms that control that droplet formation process. In clean air, the size required to form a cloud droplet is influenced by the number of particles, as well as how quickly particles take up water during growth in cloud. However, in smoky air, the larger number and size of particles mean that cloud activation processes are less affected by the number of particles that take up water. Key Points Clean cloud condensation nuclei (CCN) at <0.3% supersaturation were ∼70% accumulation, <10% sea spray, and <20% Aitken mode particles Hoppel minimum diameters correlated to accumulation‐mode particles showing aerosol‐correlated activation for clean conditions (<400 cm−3) Smoky accumulation‐mode particles were 30 nm larger and had 15%–30% more CCN, which dampened correlations to cloud activation

It is commonly assumed that models are more prone to errors in predicted cloud condensation nuclei (CCN) concentrations when the aerosol populations are externally mixed. In this work we investigate this assumption by using the mixing state index (χ) proposed by Riemer and West (2013) to quantify the degree of external and internal mixing of aerosol populations. We combine this metric with particle-resolved model simulations to quantify error in CCN predictions when mixing state information is neglected, exploring a range of scenarios that cover different conditions of aerosol aging. We show that mixing state information does indeed become unimportant for more internally mixed populations, more precisely for populations with χ larger than 75 %. For more externally mixed populations (χ below 20 %) the relationship of χ and the error in CCN predictions is not unique and ranges from lower than −40 % to about 150 %, depending on the underlying aerosol population and the environmental supersaturation. We explain the reasons for this behavior with detailed process analyses.