Cloud Condensation Nuclei Number Concentration Research Articles

A New Method for Size‐Resolved Aerosol CCN Activity Measurement at Low Supersaturation in Pristine Atmosphere

AbstractUnderstanding aerosol‐cloud interactions under pristine atmospheric conditions is crucial for meaningful global climate change predictions. However, ability of aerosol particles to act as cloud condensation nuclei (CCN) activity of aerosols in pristine atmosphere remains limited, mainly because of the difficulty of measuring it in pristine environments. Given the extremely low CCN number concentration under low supersaturations (SSs) and the complex impact of the kinetic limitation in CCN measurements, size‐resolved CCN activity measurements at low SSs cannot be achieved with traditional methods using differential mobility analyzers and a CCN counter (CCNC) because of the low sample efficiency and the impact of particles with multiple charges. To overcome this difficulty, we propose a new method for measuring size‐resolved CCN activity at low aerosol number concentrations. The new method is based on a combined system comprising an aerodynamic aerosol classifier (AAC) and a CCNC (AAC‐CCNC system). A control program was developed to achieve high‐resolution scans of the SS and particle size within a reasonable time frame. A data inversion scheme, including corrections for the transfer function and kinetic limitation, was developed to obtain accurate size‐resolved CCN activity. In July‐August 2022, the new method was used to measure the size‐resolved CCN activity on the Tibetan Plateau. This new method can advance the size‐resolved CCN measurements, thereby enhancing our understanding of aerosol‐cloud interactions in pristine atmosphere.

Efficient droplet activation of ambient black carbon particles in a suburban environment

Abstract. The cloud condensation nuclei (CCN) activity of black carbon (BC) particles importantly determines their impacts on cloud microphysics and atmospheric lifetime. This process is crucially influenced by the number of hygroscopic coating materials that BC acquire during the ageing process. It remains a challenge for ambient measurements to capture this process and link this with CCN activity of BC. Here, we directly measured the droplet activation diameter (D50) and activation fraction of BC-containing (BCc) particles (Fact,BC) in suburban Beijing using coupled measurements of size-resolved number concentrations of CCN at configured water supersaturation (SS) and BCc particles. The number concentration of BCc particles was found to peak at diameter 180–210 nm after acquiring coatings, larger than that for all particles (50–150 nm). Consequently, the initially smaller BC particles become enlarged and more hygroscopic, thereby exhibiting CCN activities than other particles. The Fact,BC increased from 42 % to 69 % in number and from 67 % to 85 % in mass as SS increased from 0.1 % to 0.2 % but tended to reach a plateau when SS > 0.2 %. Notably, Fact,BC and D50 linearly correlated with equivalent photochemical age, at a rate of +2 % h−1 and −3 nm h−1, respectively. The results suggest BCc particles from anthropogenic sources can readily serve as CCN at a relatively low SS, and more than half of the BC population can be activated within a few hours, indicating that the surface-sourced BC can efficiently incorporate into clouds and potentially exert important indirect radiative impacts.

In-situ observations of cloud microphysics over Arabian Sea during dust transport events

The unique in situ measurements of clouds and precipitation within the shallow and deep cumulus over the north-eastern Arabian Sea region during the Indian monsoon are illustrated in this study with a focus on droplet spectral parameters. The observational period showed a significant incursion of Arabian dust and the presence of giant cloud condensation nuclei (GCCN), modifying the cloud and precipitation spectral properties. Warm rain microphysics supported the mixed-phase development in these clouds and exhibited hydrometeors of snow, graupel and large aggregates as part of ice processes. Cloud base droplet number concentration is about 142 ± 79 cm−3 which is one third of the cloud condensation nuclei (CCN) number concentration at 0.2% supersaturation. A rapid broadening of droplet size distribution (DSD) near to the cloud base was noted in contrast to polluted continental clouds. Relationship between the relative dispersion ( ε; the ratio of DSD spectral width ( σ ) to mean radius ( rm )) and liquid water adiabatic fraction (AF) indicates that the entrainment effect has increased relative dispersion significantly (2–3 times larger) in these clouds. Effective radius ( reff ) is found to be proportional to mean volume radius ( rv ) with a proportionality constant ( β ) that varies between 1.0–1.6, depending on the spectral dispersion parameter. Drop size distributions for the small cloud droplets with size range 2–50 μ m and the large drizzle drops (or ice hydrometeors) with size range 100–6400 μ m are parameterized using the gamma function distributions useful for large-scale cloud models.

Measurement report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the southern Indian Ocean: observations from the Maïdo Observatory (2150 m a.s.l., Réunion)

Abstract. New particle formation (NPF) in the free troposphere (FT) is thought to be a significant source of particles over the oceans. The entrainment of particles initially formed in the marine FT is further suspected to be a major contributor to cloud condensation nuclei (CCN) number concentrations in the marine boundary layer (BL). Yet, little is known about the process and, more broadly, about the composition of the marine FT, which remains poorly explored due to access difficulties. Here we report measurements performed in April 2018 at the Maïdo Observatory with a nitrate-based chemical ionization atmospheric pressure interface time-of-flight mass spectrometer, which have allowed the first molecular-level characterization of the remote marine FT composition. A number of molecules and clusters were identified and classified into nine groups according to their chemical composition; among the identified species, the groups containing methanesulfonic acid (MSA) and C2 amines show signals that are on average significantly higher when the site is under conditions representative of the marine FT (compared to the BL). The correlation analysis revealed apparent connections between the signals of the identified compounds and several variables concurrently measured at the site (under FT conditions) or related to air mass history, suggesting that oxalic acid, malonic acid, and observed C2 amines could be of terrestrial origin, with, in addition, a possible marine source for oxalic acid and amines, while iodic acid, sulfur species, and maleic acid have a dominant marine origin. Identification of FT conditions at the site was based on the analysis of the standard deviation of the wind direction; this parameter, which can easily be derived from continuous measurements at the site, is shown in the first part of the study to be a relevant tracer when compared to predictions from the Meso-NH atmospheric model. Similar to other high-altitude sites, FT conditions are mainly encountered at night at Maïdo; therefore, the link to NPF could not be established, and further research is needed to assess the composition of precursors to nanoparticle formation in the marine FT.

A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations

Abstract. Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions (ACIs), 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 lookup 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 in lidar measurements. The retrieved optically equivalent particle size distributions and aerosol-type-dependent particle composition are utilized to calculate critical diameters using 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 ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign and is 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 helps to better quantify ACI effects and thus reduce the uncertainty in aerosol climate forcing.

Impacts of the aerosol mixing state and new particle formation on CCN in summer at the summit of Mount Tai (1534m) in Central East China

In this study, the aerosol size distributions, cloud condensation nuclei (CCN) number concentration (NCCN), single-particle chemical composition and meteorological data were collected from May 12 to June 8, 2017, at the summit of Mt. Tai. The effects of new particle formation (NPF) events and aerosol chemical components on CCN at Mt. Tai were analyzed in detail. The results showed that, NPF events significantly enhanced the CCN population, and the enhancement effect increased with increasing supersaturation (SS) value at Mt.Tai. NCCN at SS ranging from 0.1 to 0.9 % on NPF days was 10.9 %, 36.5 %, 44.6 %, 53.5 % and 51.5 % higher than that on non-NPF days from 10:00–13:00 as NPF events progressed. The effect of chemical components on CCN activation under the influence of NPF events was greater than that in the absence of NPF events. The correlation coefficients of EC-Nitrate particles (EC-Sulfate particles) and CCN at all SS levels on NPF days were 1.31–1.59 times (1.17–1.35 times) higher than those on non-NPF days. Nitrate particles promoted CCN activation but sulfate particles inhibited activation at Mt. Tai. There are differences or even opposite effects of the same group of particles on CCN activation under the influence of NPF events in different air masses. EC-Sulfate particles inhibited CCN activation at all SS levels for type I but weakly promoted activation at lower SS ranging from 0.1 to 0.3 % and weakly inhibited it at higher 0.9 % SS for type II. OCEC particles significantly inhibited CCN activation for type II, and this effect decreased with increasing SS. OCEC particles only weakly inhibited activation at SS ranging from 0.5 to 0.7 % for type I. OCEC particles only weakly inhibited this process at 0.1 % SS, while they very weakly promoted activation for SS > 0.1 %. This reveals that the CCN activity is not only related to the chemical composition of the particles, but the mixing state also has an important effect on the CCN activity.

Cloud condensation nuclei concentrations derived from the CAMS reanalysis

Abstract. Determining number concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, and a process key for aerosol–cloud interactions (ACI). However, due to sparse coverage of in situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source as well as temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN numbers is one of the goals for quantifying ACI processes and achieving uncertainty-reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMSRA are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatiotemporal quantification of CCN numbers. The derived CCN dataset captures the general trend and spatial and temporal distribution of total CCN number concentrations and CCN from different aerosol species. A brief evaluation with ground-based in situ measurements demonstrates the improvement of the modelled CCN over the sole use of AOD as a proxy for CCN as the overall correlation coefficient improved from 0.37 to 0.71. However, we find the modelled CCN from CAMSRA to be generally high biased and find a particular erroneous overestimation at one heavily polluted site which emphasises the need for further validation. The CCN dataset (https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1, Block, 2023), which is now freely available to users, features 3-D CCN number concentrations of global coverage for various supersaturations and aerosol species covering the years 2003–2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies.

Cloud Characteristics and Their Effects on Solar Irradiance According to the ICON Model, CLOUDNET and BSRN Observations

We investigated the liquid water path and global solar irradiance (Q) at ground level according to the ICON model; CLOUDNET measurements in Lindenberg, Munich, and Jülich; and BSRN observations in Lindenberg. This research is focused on stratiform non-precipitating clouds. The liquid water path (LWP) is underestimated, while Q is overestimated. The lower LWP is due to liquid water content underestimation practically in all atmosphere layers and a lower frequency of liquid cloud occurrence compared to observations. This is partly associated with the structure of the cloud nucleation scheme of the ICON model and with the default cloud condensation nuclei (CCN) number concentration. An increase in CCNs from 250 cm−3 (typical background value for the region of interest) to 1700 cm−3 (characteristic of polluted continental clouds) leads to an increase in the grid-scale liquid water path by 40% and a decrease in Q by 12% in overcast conditions. However, we also showed that the liquid water path is not a key factor of Q overestimation. The main factor is an inaccurate description of the cloud spatial structure, where the correct prediction of the ratio of direct to global irradiance as a spatial characteristic of clouds plays a more important role than the standard cloud fraction.

A New CCN Number Concentration Prediction Method Based on Multiple Linear Regression and Non‐Negative Matrix Factorization: 1. Development, Validation, and Comparison Using the Measurement Data Over the Korean Peninsula

AbstractTo reduce uncertainty in climate change prediction, a large amount of cloud condensation nuclei number concentration (NCCN) data must be obtained. This study aimed to develop a new NCCN prediction method, hereafter MLRNMF method, that applies multiple linear regression (MLR) and non‐negative matrix factorization (NMF) to aerosol number size distribution data measured in Seoul and over the Yellow Sea. To verify the reliability of the MLRNMF method, a data set separated from the training data set was used, and sufficient time differences of several years were given between the two data sets to make them as independent as possible from each other. The predicted NCCN was in acceptable agreement with the measured NCCN. The coefficient of determination (R2) values between measured and predicted NCCN for the Yellow Sea and Seoul were 0.81 and 0.71, respectively. Mean fractional bias (MFB) and mean fractional error (MFE) also met the performance goals (<±30% and <+50%, respectively). The MLRNMF method had similar accuracy to the backward integration method but showed strength in robustness and the capability to take into account external mixing. The NCCN prediction methods trained using data of a specific season tended to underestimate/overestimate somewhat, but MFB and MFE for all four seasons met the performance goals except for MFB in using June–August (JJA) data, implying that the MLRNMF method trained using only the data of a specific season can predict NCCN for all four seasons to some extent. It is expected that abundant NCCN data can be obtained through the MLRNMF method in future studies.

New particle formation leads to enhanced cloud condensation nuclei concentrations on the Antarctic Peninsula

Abstract. Few studies have investigated the impact of new particle formation (NPF) on cloud condensation nuclei (CCN) in remote Antarctica, and none has elucidated the relationship between NPF and CCN production. To address that knowledge gap, we continuously measured the number size distribution of 2.5–300 nm particles and CCN number concentrations at King Sejong Station on the Antarctic Peninsula from 1 January to 31 December 2018. Ninety-seven NPF events were detected throughout the year. Clear annual and seasonal patterns of NPF were observed: high concentration and frequency of nucleation-mode particles in summer (December–February: 53 NPF cases) and undetected nucleation-mode particles in winter (June–August: no NPF cases). We estimated the spatial scale of NPF by multiplying the time during which a distinct nucleation mode can be observed at the sampling site by the locally measured wind speed. The estimated median spatial scale of NPF around the Antarctic Peninsula was found to be approximately 155 km, indicating the large scale of NPF events. Air back-trajectory analysis revealed that 80 cases of NPF events were associated with air masses originating over the ocean, followed by sea-ice (12 cases), multiple (3 cases), and land (2 cases) regions. We present and discuss three major NPF categories: (1) marine NPF, (2) sea-ice NPF, and (3) multiple NPF. Satellite estimates for sea-surface dimethylsulfoniopropionate (DMSP; a precursor of gaseous dimethyl sulfide) data showed that the production of oceanic biogenic precursors could be a key component in marine NPF events, whereas halogen compounds released from ice-covered areas could contribute to sea-ice NPF events. Terrestrial sources (wildlife colonies, vegetation, and meltwater ponds) from Antarctica could affect aerosol production in multiple air masses. Out of 97 observed NPF events, 83 cases were characterized by the simultaneous increase in the CCN concentration by 2 %–270 % (median 44 %) in the following 1 to 36 h (median 8 h) after NPF events. Overall, Antarctic NPF events were found to be a significant source of particles with different physical characteristics and related to biogenic sources in and around the Antarctic Peninsula, which subsequently grew to cloud condensation nuclei.

Annual cycle of aerosol properties over the central Arctic during MOSAiC 2019–2020 – light-extinction, CCN, and INP levels from the boundary layer to the tropopause

Abstract. The MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition was the largest Arctic field campaign ever conducted. MOSAiC offered the unique opportunity to monitor and characterize aerosols and clouds with high vertical resolution up to 30 km height at latitudes from 80 to 90∘ N over an entire year (October 2019 to September 2020). Without a clear knowledge of the complex aerosol layering, vertical structures, and dominant aerosol types and their impact on cloud formation, a full understanding of the meteorological processes in the Arctic, and thus advanced climate change research, is impossible. Widespread ground-based in situ observations in the Arctic are insufficient to provide these required aerosol and cloud data. In this article, a summary of our MOSAiC observations of tropospheric aerosol profiles with a state-of-the-art multiwavelength polarization Raman lidar aboard the icebreaker Polarstern is presented. Particle optical properties, i.e., light-extinction profiles and aerosol optical thickness (AOT), and estimates of cloud-relevant aerosol properties such as the number concentration of cloud condensation nuclei (CCN) and ice-nucleating particles (INPs) are discussed, separately for the lowest part of the troposphere (atmospheric boundary layer, ABL), within the lower free troposphere (around 2000 m height), and at the cirrus level close to the tropopause. In situ observations of the particle number concentration and INPs aboard Polarstern are included in the study. A strong decrease in the aerosol amount with height in winter and moderate vertical variations in summer were observed in terms of the particle extinction coefficient. The 532 nm light-extinction values dropped from >50 Mm−1 close to the surface to <5 Mm−1 at 4–6 km height in the winter months. Lofted, aged wildfire smoke layers caused a re-increase in the aerosol concentration towards the tropopause. In summer (June to August 2020), much lower particle extinction coefficients, frequently as low as 1–5 Mm−1, were observed in the ABL. Aerosol removal, controlled by in-cloud and below-cloud scavenging processes (widely suppressed in winter and very efficient in summer) in the lowermost 1–2 km of the atmosphere, seems to be the main reason for the strong differences between winter and summer aerosol conditions. A complete annual cycle of the AOT in the central Arctic could be measured. This is a valuable addition to the summertime observations with the sun photometers of the Arctic Aerosol Robotic Network (AERONET). In line with the pronounced annual cycle in the aerosol optical properties, typical CCN number concentrations (0.2 % supersaturation level) ranged from 50–500 cm−3 in winter to 10–100 cm−3 in summer in the ABL. In the lower free troposphere (at 2000 m), however, the CCN level was roughly constant throughout the year, with values mostly from 30 to 100 cm−3. A strong contrast between winter and summer was also given in terms of ABL INPs which control ice production in low-level clouds. While soil dust (from surrounding continents) is probably the main INP type during the autumn, winter, and spring months, local sea spray aerosol (with a biogenic aerosol component) seems to dominate the ice nucleation in the ABL during the summer months (June–August). The strong winter vs. summer contrast in the INP number concentration by roughly 2–3 orders of magnitude in the lower troposphere is, however, mainly caused by the strong cloud temperature contrast. A unique event of the MOSAiC expedition was the occurrence of a long-lasting wildfire smoke layer in the upper troposphere and lower stratosphere. Our observations suggest that the smoke particles frequently triggered cirrus formation close to the tropopause from October 2019 to May 2020.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modeling: II. Simulation of diurnal cycle

The accurate forecast of the diurnal cycle of the number concentration of trace gases is vital due to their influence on precipitation processes by controlling the number concentration of cloud condensation nuclei (CCN). 1-D hybrid Monte Carlo-Gear solver developed to retrieve vertical profiles of the number concentration of CCNs for microphysics modeling has been tested for representation of the diurnal cycle in the present paper. The retrieved profiles of CH4 and SO2 have been tested with the Copernicus Atmosphere Monitoring Service (CAMS) model at 3-hour time intervals for four megacities: Delhi, Kolkata, Chennai, and Mumbai for rainy and non-rainy days. The retrieved profiles have shown diurnal variation up to 18 UTC at all pressure levels with lead or lag with that of the CAMS model. After 18 UTC there was a furious increase in the number concentrations. During non-rainy days, the 1-D model slightly overestimated (underestimated) the maximum (minimum) number concentrations of CH4 over Delhi whereas concentrations are overestimated over Kolkata, Chennai, and Mumbai. Forecasted CH4 has a good (weak) correlation over Chennai (Mumbai) respectively. The 1-D model overestimated (overestimated) the maximum (minimum) number concentrations of SO2 over Delhi but the maximum (minimum) concentrations are underestimated (overestimated) over Kolkata, Chennai, and Mumbai. The number concentrations of SO2 have shown a good correlation for all megacities except Delhi. CH4 number concentration is overestimated during rainy days. Delhi and Kolkata show a good correlation of CH4 during rainy days. SO2 during rainy days is underestimated except over Chennai and both models show a good correlation except over Mumbai. Overall, it can be stated that the 1-D hybrid solver is successful in simulating the monthly mean diurnal variation of vertical profiles of CH4 and SO2, and its implementation in the global model may estimate the number concentrations with better accuracies.

Observations of atmospheric new particle formation impacts on cloud condensation nuclei in summer at Mt.Tian

As one of the important sources of aerosols, new particle formation events can affect the spatiotemporal distribution of CCN. To analyze the characteristics of new particle formation (NPF) events and their effects on cloud condensation nuclei (CCN) during the summer at Mt.Tian, the data of aerosol size distribution and CCN number concentrations were measured using an aerosol wide-range particle size spectrometer and a cloud condensation nuclei counter from August 4 to 25, 2019, and combined with meteorological data and polycyclic aromatic hydrocarbons (PAHs) data. NPF events at Mt.Tian occurred seven days during the observation period, accounting for 32% of the observation days, and usually occurred at 13:00 and ended at 17:00 (Beijing Time, same below). NPF events particularly influenced the diurnal variations of aerosol number concentrations in different modes. The number concentrations of nucleation and Aitken modes increased rapidly while the accumulation mode increased slowly during 13:00 and 17:00 on NPF days. The peak particle number concentrations in the nucleation, Aitken and accumulation modes on NPF days were 5360.1 cm−3, 7185.6 cm−3 and 839.3 cm−3, respectively, which were 3.3, 1.8 and 1.7 times higher than those on non-NPF days during 13:00 and 17:00. The meteorological conditions of temperature higher than 20 °C, air pressure lower than 806 hPa, relative humidity (RH) lower than 40% and wind speed higher than 2.2 m s−1 are favorable for the occurrence of NPF events. The NPF events at Mt.Tian can impact the mass concentration of PAHs. More Fluoride (FL) and Phenanthrene (PHE) were generated on NPF days. The mass concentrations of FL and PHE on NPF days were 0.002 ± 0.003 ng m−3 and 0.020 ± 0.010 ng m−3, which were 243.54% and 28.44% higher than those on non-NPF days, respectively. NPF events were conducive to increased CCN concentration at Mt.Tian. After the NPF events, the mean number concentrations of CCN at each supersaturation (SS) on NPF days was about 20%–50% higher than on non-NPF days. The contribution of NPF events to the CCN number concentrations was 35.75%, 28.56%, 44.15%, 42.11% and 29.12% at 0.1%, 0.2%, 0.4%, 0.6% and 0.8% SS, respectively.

Aerosol Properties and Their Influences on Marine Boundary Layer Cloud Condensation Nuclei over the Southern Ocean

Five overcast marine stratocumulus cases during the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES) aircraft field campaign were selected to examine aerosol and cloud condensation nuclei (CCN) properties with cloud influence. The Aitken- and accumulation-mode aerosols contributed approximately 70% and 30% of the total aerosols, respectively. The aerosol properties before and after periods of drizzle were investigated using in situ measurements during one case. Sub-cloud drizzle processes impacted accumulation-mode aerosols and CCN distribution. There was a nearly linear increase in CCN number concentration (NCCN) with supersaturation (S) during the ‘before drizzle’ period, but this was not true for the ‘after drizzle’ period, particularly when S > 0.4%. Using the hygroscopicity parameter (κ) to quantitatively investigate the chemical cloud-processing mechanisms, we found that higher κ values (>0.4) represent cloud-processing aerosols, while lower κ values (<0.1) represent newly formed aerosols. When the supersaturation is less than the Hoppel minimum (0.22%), cloud processing is dominant, whereas sea-spray aerosols are dominant contributors to CCN activation when S exceeds 0.22% but is less than 0.32%, the effective supersaturation threshold. Sea salt is considered a non-cloud-processing aerosol and is large and hygroscopic enough to form cloud droplets.

Rapid growth and high cloud-forming potential of anthropogenic sulfate aerosol in a thermal power plant plume during COVID lockdown in India

The COVID lockdown presented an interesting opportunity to study the anthropogenic emissions from different sectors under relatively cleaner conditions in India. The complex interplays of power production, industry, and transport could be dissected due to the significantly reduced influence of the latter two emission sources. Here, based on measurements of cloud condensation nuclei (CCN) activity and chemical composition of atmospheric aerosols during the lockdown, we report an episodic event resulting from distinct meteorological conditions. This event was marked by rapid growth and high hygroscopicity of new aerosol particles formed in the SO2 plume from a large coal-fired power plant in Southern India. These sulfate-rich particles had high CCN activity and number concentration, indicating high cloud-forming potential. Examining the sensitivity of CCN properties under relatively clean conditions provides important new clues to delineate the contributions of different anthropogenic emission sectors and further to understand their perturbations of past and future climate forcing.

The impact of particulate pollution control on aerosol hygroscopicity and CCN activity in North China

Air quality has greatly improved in China owing to the strict control policy enforced during the last decade. This study investigated the impact of particulate pollution control on aerosol hygroscopicity and cloud condensation nuclei (CCN) activity in North China based on several data sources. The mass concentration of particles with an aerodynamic diameter smaller than 2.5 µm (PM2.5) decreased by one third from the summer of 2014 to the summer of 2020 in Xinzhou (XZ). The mass fractions of aerosol chemical components in PM2.5 also clearly changed, showing an increase in hydrophilic inorganics and a decrease in hydrophobic organics and black carbon from 2014 to 2020. Measurements of the particle number size distribution in XZ indicate that the occurrence frequency of new particle formation (NPF) events decreased significantly from 2014 to 2020, leading to a reduction in the generation of daytime ultrafine particles. The weakened NPF and increasing influence of morning and evening peak emissions modified the diurnal variations of the number concentration of condensation nuclei (N CN) and CCN (N CCN). The aerosol activation ratio was always higher in the summer of 2014 than in the summer of 2020. These results demonstrate that particulate pollution control can decrease N CN and N CCN but enhance aerosol hygroscopicity and activation ability.

Cloud-scale dynamical and microphysical properties in contrasting monsoon environments from observations and simulations

Three distinct environmental regimes (moist-rainy monsoon, dry-intermittent-rainy monsoon and post-monsoon) based on atmospheric moist static energy and surface rainfall measurements over the rain shadow region of the Indian peninsula are investigated with large eddy simulation (LES) model and in situ cloud microphysics observations. Cloud condensation nuclei (CCN) number concentration measurement showed increased values for dry-intermittent-rainy monsoon and post-monsoon seasons and aerosol activation characteristics were distinct.The model simulation showed general features as deeper clouds with higher fractional cloud coverage and rain water formation for the moist-rainy monsoon environment compared to the dry-intermittent-rainy and post-monsoon environments. The highest ice water content in the mixed-phase region was noted for the moist-rainy monsoon case and conversion to rain water occurred through the melting of ice. Dry-intermittent-rainy and post-monsoon clouds have no rainwater in the model due to smaller droplet size and lower cloud depth. Cloud core buoyancy, latent heating and updraft velocity are relatively higher (except at the cloud-top level) for the dry-intermittent-rainy monsoon conditions compared to the moist-rainy monsoon conditions. A higher negative buoyancy (associated with latent cooling) at the cloud top curtails cloud growth in dry conditions. Convective mass flux and lateral entrainment rate have decreased with drier environmental conditions. This indicates that clouds are less diluted and mixed with the environmental air in the dry-intermittent-rainy conditions and thereby maintain higher liquid water content in the cloud core region.

Retrieval and validation of cloud condensation nuclei from satellite and airborne measurements over the Indian Monsoon region

The estimation of cloud condensation nuclei (CCN) concentration at the cloud base is an essential constraint to aerosol-cloud interaction (ACI) studies. The long-term CCN measurements are sparse and difficult to obtain, which often led to the use of aerosol optical properties as proxies of CCN in ACI studies and remains the most significant uncertainty in numerical models and observational studies. In the present study, we estimate the height-resolved CCN from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) using Optical Modelling of CALIPSO Aerosol Microphysics (OMCAM) algorithm and validated the results with airborne in-situ CCN measurements collected during the Cloud Aerosol Interaction and Precipitation Enhanced Experiment (CAIPEEX) at six locations over the Indian Summer Monsoon (ISM) region. Out of 40 CAIPEEX flight measurements collected in the year of 2009, 15 profiles were selected for the validation of CALIPSO-derived CCN number concentrations (NCCN). We found a reasonable agreement between CALIPSO and the in-situ measurements with a correlation coefficient of 0.72. However, the overall comparison suggests that the CALIPSO overestimates the NCCN by 54 ± 33%. We further analyzed CALIPSO-derived NCCN for the in-situ stations. A strong seasonal variation of NCCN at SS 0.4% is observed in all six stations. The mean NCCN is maximum (minimum) during the post-monsoon (pre-monsoon) for Pathankot 2592 ± 1026 cm−3 (1636 ± 809 cm−3), Bareilly 3618 ± 1326 cm−3 (2078 ± 792 cm−3) stations whereas maximum (minimum) during winter (monsoon) for Guwahati 3696 ± 1400 cm−3 (2661 ± 1096 cm−3), Pune 3422 ± 1628 cm−3 (2241 ± 1086 cm−3), Hyderabad 3765 ± 1366 cm−3 (2498 ± 1084 cm−3) and Bangalore 2956 ± 1274 cm−3 (2401 ± 1071 cm−3).

Model-based insights into aerosol perturbation on pristine continental convective precipitation

Abstract. The Tibetan Plateau (TP) is of great importance for weather and climate due to its role as a heat and water resource. Relatively clean aerosol conditions over the Plateau make the study on the aerosol–cloud–precipitation interactions in this pristine continental region distinctive. In order to investigate the impacts of aerosols on small-scale convection processes over the TP, a convective event with precipitation observed on 24 July 2014 in Naqu was selected to explore the influence of aerosols on the onset and intensity of precipitation. We use the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis to derive the cloud condensation nuclei (CCN) number concentration, which can be regarded as the real-time background. These values are adopted to initialize the regional Weather Research Forecast (WRF) 4.0 meteorological model and to simulate the onset of convective events and the formation of precipitation. Four sets of experiments, named clean (1/10 CCN), control (default setting), Tibetan Plateau (CCN calculated from MERRA-2 reanalysis), and polluted (10 times CCN), were adopted for our simulations. A detailed analysis of the microphysical processes shows that the conversion of cloud water into rain is enhanced by small increases in aerosol concentration, while it is suppressed by larger increases in concentration. However, the transformation of cloud water to graupel and the development of convective clouds are favored under a polluted situation. As a result, the onset of the precipitation is delayed and cold-rain intensity increases.

The density of ambient black carbon retrieved by a new method: implications for cloud condensation nuclei prediction

Abstract. The effective density of black carbon (BC) is a crucial factor that is relevant to its aging degree and adds uncertainty in evaluating its climate effect. Here, we have developed a new method to retrieve the effective density of internally mixed BC in the atmosphere, combining field observations conducted from 15 November to 14 December 2016 in urban Beijing with the Köhler theory. The uncertainty in the retrieval method was evaluated to be within ±30 %, which was primarily caused by assumptions on both the hygroscopic parameter of organics and the proportional distribution of primary organic aerosols in different hygroscopic modes. Using the method, we find that the ambient internally mixed BC, accounting for 80 % ± 20 % of total BC aerosol particles, was retrieved with a campaign mean density of 1.1 ± 0.6 g cm−3 during the observed periods. The retrieved result was comparable with that reported in the literature. By applying a lower (0.14 g cm−3) and upper (2.1 g cm−3) limit of the retrieved BC density in the cloud condensation nuclei (CCN) number concentration (NCCN) estimation, we derived that the neglect of such variations in the BC density would lead to an uncertainty of −28 %–11 % in predicting NCCN at supersaturations of 0.23 % and 0.40 %. We also find that the NCCN was more sensitive to the variations in BC density when it was <1.0 g cm−3. This illustrates the necessity of accounting for the effect of BC density on CCN activity closer to source regions where the BC particles are mostly freshly emitted. The CCN closure was achieved when introducing the retrieved real-time BC density and mixing state. This study provides a unique way of utilizing field measurements to infer ambient BC density and highlights the importance of applying variable BC density values in models when predicting CCN and assessing its relevant climate effect.