Cloud Condensation Nuclei Number Research Articles

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.

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.

Derived Profiles of CCN and INP Number Concentrations in the Taklimakan Desert via Combined Polarization Lidar, Sun-Photometer, and Radiosonde Observations

Understanding the vertical structures of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) number concentrations in desert source regions is crucial for examining dust-cloud interactions and other related impacts. To explore the vertical profiles of the CCN and INP number concentrations and their possible atmospheric–dynamic influence factors at the center of the Taklimakan Desert, intensive observations were conducted by employing a ground-based polarization Raman lidar, sounding balloons, and a sun photometer in Tazhong (83.39° E, 38.58° N, 1103 m above sea level) during the summer of 2019. Based on the GRASP algorithm, the extinction-to-volume conversion factor of dust aerosols was 0.85 × 10−12 Mmm3 m−3, and the extinction-to-number conversion factor was predicted to be 0.20 Mm cm−3 on the basis of the sun photometer observations. Thus, the vertical CCN and INP number concentration profiles obtained with different parameterization schemes in the presence of various pollution levels were calculated by combining dust extinction coefficients retrieved by lidar and meteorological data observed by sounding balloon observations. The achieved results indicated that the CCN number concentration varied from 10−2 to 102 cm−3 and decreased from ground level to 12 km with an average value of 36.57 cm−3 at the 10–12 km height range, while the INP number concentration based on parameterization schemes D10 and D15 mainly varied from 10−1 to 102 L−1 and from 1 L−1 to 103 L−1, with average values of 3.50 L−1 and 7.80 L−1, respectively. Moreover, we observed a strong relationship between the INP number concentration of scheme D10 and the wind speed, with an R2 value of 0.72, but a weak relationship between the CCN number concentration and the relative humidity in the boundary layer, with a Spearman’s rank correlation coefficient R2 value of 0.38. The present study provides original and valuable information regarding the CCN and INP number concentrations and their related influencing factors at the center of the Taklimakan Desert and can improve our understanding of the vertical distributions of dust–cloud–atmosphere dynamic interactions, as well as of the roles of dust aerosols in the desert hydrological cycle.

Influence of aerosol physicochemical properties on CCN activation during the Asian winter monsoon at the summit of Mt. Lu, China

To better understand the characteristics of high-altitude aerosol (or condensation nuclei, CN) and cloud condensation nuclei (CCN) in East China during the Asian winter monsoon, a field campaign focused on aerosol and CCN was set up in the area of Mt. Lu during the period from 5 November to 10 December 2019. The features of CCN and CCN formation at Mt. Lu were analyzed. The CCN spectra at Mt. Lu belongs to a polluted continental type, which could be parameterized using N = 2498S0.31, with S denoting supersaturation. The average and standard deviation of the CCN (S = 0.4%) and CN concentrations were 1936 ± 1007 cm−3 and 3041 ± 1646 cm−3, respectively. The CCN activation fraction (AF) values are 0.43, 0.57, 0.64, 0.69, and 0.72 at S = 0.2%, S = 0.4%, S = 0.6%, S = 0.8% and S = 1%, respectively. The aerosol composition would be more important than aerosol size in determining CCN activation at Mt. Lu. The CCN (CN) number concentration had a clear diurnal variation with maximal and minimal values at 13:00 (12:00 and 18:00) and 07:00 (07:00), respectively. Nearly 68.7% of the air mass reaching Mt. Lu came from the northern area. The higher-sulfate-ratio air mass has a relatively high AF efficiency.

Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain

Abstract. To better understand the characteristics of aerosol activation ability and optical properties, a comprehensive airborne campaign was conducted over the North China Plain (NCP) from 8 May to 11 June 2016. Vertical profiles of cloud condensation nuclei (CCN) number concentration (NCCN) and aerosol optical properties were measured simultaneously. Seventy-two-hour air mass back trajectories show that during the campaign, the measurement region was mainly influenced by air masses from the northwest and southeast. Air mass sources, temperature structure, anthropogenic emissions, and terrain distribution are factors influencing NCCN profiles. Cloud condensation nuclei spectra suggest that the ability of aerosol to activate into CCN is stronger in southeasterly air masses than in northwesterly air masses and stronger in the free atmosphere than near the surface. Vertical distributions of the aerosol scattering Ångström exponent (SAE) indicate that aerosols near the surface mainly originate from primary emissions consisting of more fine particles. The long-distance transport decreases SAE and makes it vary more in the free troposphere than near the surface. To parameterize NCCN, the equation NCCN=10β⋅σγ is used to fit the relationship between NCCN and the aerosol scattering coefficient (σ) at 450 nm. The fitting parameters β and γ have linear relationships with the SAE. Empirical estimates of NCCN at 0.7 % water vapor supersaturation (SS) from aerosol optical properties are thus retrieved for the two air masses: NCCN=10-0.22⋅SAE+2.39⋅σ0.30⋅SAE+0.29 for northwesterly air masses and NCCN=10-0.07⋅SAE+2.29⋅σ0.14⋅SAE+0.28 for southeasterly air masses. The estimated NCCN at 0.7 % SS agrees with that measured, although the performance differs between low and high concentrations in the two air masses. The results highlight the important impact of aerosol sources on the empirical estimate of NCCN from aerosol optical properties.

Effect of Sodium Dodecyl Benzene Sulfonate on the Production of Cloud Condensation Nuclei from Breaking Waves.

While sea spray particles are highly soluble by nature, and are thus excellent seeds for nascent cloud droplets, organic compounds such as surfactants have previously been identified within aerosol particles, bulk seawater, and the sea-surface microlayer in various oceans and seas. As the presence of dissolved surfactants within spray particles may limit their ability to act as cloud condensation nuclei (CCN), and since the abundance of CCN available during cloud formation is known to affect cloud albedo, the presence of surfactants in the marine environment can affect the local radiation balance. In this work, we added a model surfactant commonly used in households and industry (sodium dodecyl benzene sulfonate, SDBS) to a control solution of NaCl and observed its effects on the number of CCN produced by artificial breaking waves. We found that the addition of SDBS modified the number of CCN produced by a breaking wave analogue in three main ways: (I) by reducing the hygroscopicity of the resulting particulate; (II) by producing finer particulates than the control NaCl solution; and (III) by reducing the total number of particles produced overall. In addition, measurements of the absorption of ultraviolet light (λ = 224 nm) were used to quantify the concentration of SDBS in bulk water samples and aerosol extracts. We found that SDBS was significantly enriched in aerosol extracts relative to the bulk water even when the concentration of SDBS in the bulk water was below the limit of detection (LOD) of our quantitation methods. Thus, the surfactant studied will influence the production of CCN even when present in minute concentrations.

Impact of urban aerosols on the cloud condensation activity using a clustering model

The indirect effect of aerosols on climate through aerosol-cloud-interactions is still highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is in the number of cloud condensation nuclei (CCN), which itself mainly stems from uncertainty in aerosol sources and how particles evolve to become effective CCN. We analyze particle number size distribution (PNSD) and CCN measurements from an urban site in a two-step method: (1) we use an unsupervised clustering model to classify the main aerosol categories and processes occurring in the urban atmosphere and (2) we explore the influence of the identified aerosol populations on the CCN properties. According to the physical properties of each cluster, its diurnal timing, and additional air quality parameters, the clusters are grouped into five main aerosol categories: nucleation, growth, traffic, aged traffic, and urban background. The results show that, despite aged traffic and urban background categories are those with lower total particle number concentrations (Ntot) these categories are the most efficient sources in terms of contribution to the overall CCN budget with activation fractions (AF) around 0.5 at 0.75 % supersaturation (SS). By contrast, road traffic is an important aerosol source with the highest frequency of occurrence (32 %) and relatively high Ntot, however, its impact in the CCN activity is very limited likely due to lower particle mean diameter and hydrophobic chemical composition. Similarly, nucleation and growth categories, associated to new particle formation (NPF) events, present large Ntot with large frequency of occurrence (22 % and 28 %, respectively) but the CCN concentration for these categories is about half of the CCN concentration observed for the aged traffic category, which is associated with their small size. Overall, our results show that direct influence of traffic emissions on the CCN budget is limited, however, when these particles undergo ageing processes, they have a significant influence on the CCN concentrations and may be an important CCN source. Thus, aged traffic particles could be transported to other environments where clouds form, triggering a plausible indirect effect of traffic emissions on aerosol-cloud interactions and consequently contributing to climate change.

Using Novel Molecular-Level Chemical Composition Observations of High Arctic Organic Aerosol for Predictions of Cloud Condensation Nuclei.

Predictions of cloud droplet activation in the late summertime (September) central Arctic Ocean are made using κ-Köhler theory with novel observations of the aerosol chemical composition from a high-resolution time-of-flight chemical ionization mass spectrometer with a filter inlet for gases and aerosols (FIGAERO-CIMS) and an aerosol mass spectrometer (AMS), deployed during the Arctic Ocean 2018 expedition onboard the Swedish icebreaker Oden. We find that the hygroscopicity parameter κ of the total aerosol is 0.39 ± 0.19 (mean ± std). The predicted activation diameter of ∼25 to 130 nm particles is overestimated by 5%, leading to an underestimation of the cloud condensation nuclei (CCN) number concentration by 4–8%. From this, we conclude that the aerosol in the High Arctic late summer is acidic and therefore highly cloud active, with a substantial CCN contribution from Aitken mode particles. Variability in the predicted activation diameter is addressed mainly as a result of uncertainties in the aerosol size distribution measurements. The organic κ was on average 0.13, close to the commonly assumed κ of 0.1, and therefore did not significantly influence the predictions. These conclusions are supported by laboratory experiments of the activation potential of seven organic compounds selected as representative of the measured aerosol.

Prediction of CCN spectra parameters in the North China Plain using a random forest model

Cloud condensation nuclei (CCN) spectra, that indicate the dependence of CCN number concentrations (NCCN) on supersaturation, are important factors in aerosol–cloud interactions. Owing to the lack of direct measurements of NCCN, calculating the CCN spectra based on conventional observational data of aerosols is important to obtain NCCN; however, this is challenging owing to the complex relationship between aerosol properties and CCN activity. Machine learning techniques have recently been applied to estimate NCCN and found to be a promising method for CCN prediction. In this study, the random forest (RF) model was applied to predict CCN spectral parameters using the observation data measured in four campaigns in the North China Plain, and the effects of chemical and optical properties of aerosol on the estimation of CCN spectra were investigated. The results show that the RF model trained with the data of one campaign can be used to estimate CCN spectral parameters in another campaign, with the coefficient of determination between the estimated and measured CCN spectral parameters being approximately 0.5. The deviations of the estimation by the RF model may result from the difference in both the aerosol properties and measurement uncertainties among different campaigns, whose influence on the deviations can be further magnified by overfitting the RF model. Further analysis revealed that the major aerosol properties among the input variables of the RF model were the mass concentration of black carbon and aerosol hemispheric backscattering fraction at a wavelength of 450 nm. In addition, the roles of chemical compositions of aerosol in estimating CCN spectra parameters are different among different campaigns because the poor correlation between aerosol chemical compositions and CCN spectra parameter can affect the performance of the RF model. For estimating CCN spectra, using aerosol optical properties, including the hemispheric backscattering fraction and absorption coefficient, as model inputs is more recommended than using aerosol chemical properties.

Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia

Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

Circum-Antarctic abundance and properties of CCN and INPs

Abstract. Aerosol particles acting as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs) play a major role in the formation and glaciation of clouds. Thereby they exert a strong impact on the radiation budget of the Earth. Data on abundance and properties of both types of particles are sparse, especially for remote areas of the world, such as the Southern Ocean (SO). In this work, we present unique results from ship-borne aerosol-particle-related in situ measurements and filter sampling in the SO region, carried out during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016–2017. An overview of CCN and INP concentrations over the Southern Ocean is provided and, using additional quantities, insights regarding possible CCN and INP sources and origins are presented. CCN number concentrations spanned 2 orders of magnitude, e.g. for a supersaturation of 0.3 % values ranged roughly from 3 to 590 cm−3. CCN showed variable contributions of organic and inorganic material (inter-quartile range of hygroscopicity parameter κ from 0.2 to 0.9). No distinct size dependence of κ was apparent, indicating homogeneous composition across sizes (critical dry diameter on average between 30 and 110 nm). The contribution of sea spray aerosol (SSA) to the CCN number concentration was on average small. Ambient INP number concentrations were measured in the temperature range from −5 to −27 ∘C using an immersion freezing method. Concentrations spanned up to 3 orders of magnitude, e.g. at −16 ∘C from 0.2 to 100 m−3. Elevated values (above 10 m−3 at −16 ∘C) were measured when the research vessel was in the vicinity of land (excluding Antarctica), with lower and more constant concentrations when at sea. This, along with results of backward-trajectory analyses, hints towards terrestrial and/or coastal INP sources being dominant close to ice-free (non-Antarctic) land. In pristine marine areas INPs may originate from both oceanic sources and/or long-range transport. Sampled aerosol particles (PM10) were analysed for sodium and methanesulfonic acid (MSA). Resulting mass concentrations were used as tracers for primary marine and secondary aerosol particles, respectively. Sodium, with an average mass concentration around 2.8 µg m−3, was found to dominate the sampled, identified particle mass. MSA was highly variable over the SO, with mass concentrations up to 0.5 µg m−3 near the sea ice edge. A correlation analysis yielded strong correlations between sodium mass concentration and particle number concentration in the coarse mode, unsurprisingly indicating a significant contribution of SSA to that mode. CCN number concentration was highly correlated with the number concentration of Aitken and accumulation mode particles. This, together with a lack of correlation between sodium mass and Aitken and accumulation mode number concentrations, underlines the important contribution of non-SSA, probably secondarily formed particles, to the CCN population. INP number concentrations did not significantly correlate with any other measured aerosol physico-chemical parameter.

Measurement report: Distinct size dependence and diurnal variation in organic aerosol hygroscopicity, volatility, and cloud condensation nuclei activity at a rural site in the Pearl River Delta (PRD) region, China

Abstract. Organic aerosol (OA) has a significant contribution to cloud formation and hence climate change. However, high uncertainties still exist in its impact on global climate, owing to the varying physical properties affected by the complex formation and aging processes. In this study, the hygroscopicity, volatility, cloud condensation nuclei (CCN) activity, and chemical composition of particles were measured using a series of online instruments at a rural site in the Pearl River Delta (PRD) region of China in fall 2019. During the campaign, the average hygroscopicity of OA (κOA) increased from 0.058 at 30 nm to 0.09 at 200 nm, suggesting a higher oxidation state of OA at larger particle sizes, supported by a higher fraction of extremely low volatility OA (ELVOA) for larger size particles. Significantly different diurnal patterns of κOA were observed between Aitken mode particles and accumulation mode particles. For Aitken mode particles (30–100 nm), the κOA values showed daily minima (0.02–0.07) during daytime, while the accumulation mode exhibited a daytime peak (∼ 0.09). Coincidently, a daytime peak was observed for both aged biomass burning organic aerosol (aBBOA) and less oxygenated organic aerosol (LOOA) based on source apportionment, which was attributed to the aging processes and gas–particle partitioning through photochemical reactions. In addition, the fraction of semi-volatile OA (SVOA) was higher at all measured sizes during daytime than during nighttime. These results indicate that the formation of secondary OA (SOA) through gas–particle partitioning can generally occur at all diameters, while the aging processes of pre-existing particles are more dominated in the accumulation mode. Furthermore, we found that applying a fixed κOA value (0.1) could lead to an overestimation of the CCN number concentration (NCCN) up to 12 %–19 % at 0.1 %–0.7 % supersaturation (SS), which was more obvious at higher SS during daytime. Better prediction of NCCN could be achieved by using size-resolved diurnal κOA, which indicates that the size dependence and diurnal variations in κOA can strongly affect the NCCN at different SS values. Our results highlight the need for accurately evaluating the atmospheric evolution of OA at different size ranges and their impact on the physicochemical properties and hence climate effects.

Role of gas–molecular cluster–aerosol dynamics in atmospheric new-particle formation

New-particle formation from vapors through molecular cluster formation is a central process affecting atmospheric aerosol and cloud condensation nuclei numbers, and a significant source of uncertainty in assessments of aerosol radiative forcing. While advances in experimental and computational methods provide improved assessments of particle formation rates from different species, the standard approach to implement these data in aerosol models rests on highly simplifying assumptions concerning gas–cluster–aerosol dynamics. To quantify the effects of the simplifications, we develop an open-source tool for explicitly simulating the dynamics of the complete particle size spectrum from vapor molecules and molecular clusters to larger aerosols for multi-compound new-particle formation. We demonstrate that the simplified treatment is a reasonable approximation for particle formation from weakly clustering chemical compounds, but results in overprediction of particle numbers and of the contribution of new-particle formation to cloud condensation nuclei for strongly clustering, low-concentration trace gases. The new explicit approach circumvents these issues, thus enabling robust model–measurement comparisons, improved assessment of the importance of different particle formation agents, and construction of optimal simplifications for large-scale models.

Combined effects of soil moisture and microphysical perturbations on convective clouds and precipitation for a locally forced case over Central Europe

AbstractTo study the combined impact of soil moisture and microphysical perturbations on convective clouds and precipitation over Central Europe, an ensemble of five dozen real‐world weather prediction forecasts was conducted with the COnsortium for Small‐scale MOdeling (COSMO) model at convection‐permitting resolution for a case with weak large‐scale forcing (6 June 2016). We find a large sensitivity of precipitation, ranging from +10% to 23% in 12‐hr precipitation totals. While the homogeneous soil‐moisture bias of 25% primarily controls the timing of convection initiation and the amount of surface rainfall, the number of cloud condensation nuclei and width of the cloud droplet size distribution mainly control the number, size, and lifetime of convective clouds. In moisture‐limited conditions, mainly positive couplings are acting. Drier soils, cleaner air, and a broader cloud droplet size distribution result in less rainfall. Wetter soils and more polluted conditions lead to fewer, but larger, cloud clusters. Since microphysical process rates depend systematically on the sign of the perturbations, but rainfall does not, there are compensating effects at work that buffer microphysical perturbations directly and impact the cloud condensate amount and the rainfall at the ground.

Unprecedented levels of ultrafine particles, major sources, and the hydrological cycle

Ultrafine particles (UFP) acting as cloud condensation nuclei (CCN) are the driving force behind changing rainfall patterns. Recently observed weather extremes like floods and drought might be due to changing anthropogenic UFP emissions. However, the sources and budgets of anthropogenic primary and secondary particles are not well known. Based on airborne measurements we identified as a major contribution modern fossil fuel flue gas cleaning techniques to cause a doubling of global primary UFP number emissions. The subsequent enhancement of CCN numbers has several side effects. It’s changing the size of the cloud droplets and delays raindrop formation, suppressing certain types of rainfall and increasing the residence time of water vapour in the atmosphere. This additional latent energy reservoir is directly available for invigoration of rainfall extremes. Additionally it’s a further contribution to the column density of water vapour as a greenhouse gas and important for the infrared radiation budget. The localized but ubiquitous fossil fuel related UFP emissions and their role in the hydrological cycle, may thus contribute to regional or continental climate trends, such as increasing drought and flooding, observed within recent decades.

Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification

Abstract. The Cape Verde Atmospheric Observatory (CVAO), which is influenced by both marine and desert dust air masses, has been used for long-term measurements of different properties of the atmospheric aerosol from 2008 to 2017. These properties include particle number size distributions (PNSD), light-absorbing carbon (LAC) and concentrations of cloud condensation nuclei (CCN) together with their hygroscopicity. Here we summarize the results obtained for these properties and use an unsupervised machine learning algorithm for the classification of aerosol types. Five types of aerosols, i.e., marine, freshly formed, mixture, moderate dust and heavy dust, were classified. Air masses during marine periods are from the Atlantic Ocean and during dust periods are from the Sahara Desert. Heavy dust was more frequently present during wintertime, whereas the clean marine periods were more frequently present during springtime. It was observed that during the dust periods CCN number concentrations at a supersaturation of 0.30 % were roughly 2.5 times higher than during marine periods, but the hygroscopicity (κ) of particles in the size range from ∼ 30 to ∼ 175 nm during marine and dust periods were comparable. The long-term data presented here, together with the aerosol classification, can be used as a basis to improve our understanding of annual cycles of the atmospheric aerosol in the eastern tropical Atlantic Ocean and on aerosol-cloud interactions and it can be used as a basis for driving, evaluating and constraining atmospheric model simulations.

On the evolution of sub- and super-saturated water uptake of secondary organic aerosol in chamber experiments from mixed precursors

Abstract. To better understand the chemical controls of sub- and super-saturated aerosol water uptake, we designed and conducted a series of chamber experiments to investigate the evolution of secondary organic aerosol (SOA) particle physicochemical properties during photo-oxidation of single and mixed biogenic (α-pinene, isoprene) and anthropogenic (o-cresol) volatile organic compounds (VOCs) in the presence of ammonium sulfate seeds. During the 6 h experiments, the cloud condensation nuclei (CCN) activity at super-saturation of water (0.1 %–0.5 %), hygroscopic growth factor at 90 % relative humidity (RH), and non-refractory PM1 chemical composition were recorded concurrently. Attempts to use the hygroscopicity parameter κ to reconcile water uptake ability below and above water saturation from various VOC precursor systems were made, aiming to predict the CCN activity from the sub-saturated hygroscopicity. The thermodynamic model AIOMFAC (aerosol inorganic-organic mixtures functional groups activity coefficients) was used to simulate κ values of model compound mixtures to compare with the observation and to isolate the controlling factors of water uptake at different RHs. The sub- and super-saturated water uptake (in terms of both κHTDMA and κCCN) were mainly controlled by the SOA mass fraction, which depended on the SOA production rate of the precursors, and the SOA composition played a second-order role. For the reconciliation of κHTDMA and κCCN, the κHTDMA/κCCN ratio increased with the SOA mass fraction and this was observed in all investigated single and mixed VOC systems, independent of initial VOC concentrations and sources. For all VOC systems, the mean κHTDMA of aerosol particles was ∼25 % lower than the κCCN at the beginning of the experiments with inorganic seeds. With the increase of condensed SOA on inorganic seed particles throughout the experiments, the discrepancy of κHTDMA and κCCN became weaker (down to ∼0 %) and finally the mean κHTDMA was ∼60 % higher than κCCN on average when the SOA mass fraction approached ∼0.8. As indicated by AIOMFAC model simulations, non-ideality alone cannot fully explain the κ discrepancy at high SOA mass fraction (0.8). A good agreement in κCCN between model and observation was achieved by doubling the molecular weight of the model compounds or by reducing the dry particle size in the CCN counter. This indicates that the evaporation of semi-volatile organics in the CCN counter together with non-ideality could have led to the observed κ discrepancy. As a result, the predicted CCN number concentrations from the κHTDMA and particle number size distribution were ∼10 % lower than CCN counter measurement on average at the beginning, and further even turned to an overestimation of ∼20 % on average when the SOA mass fraction was ∼0.8. This chemical composition-dependent performances of the κ-Köhler approach on CCN prediction can introduce a variable uncertainty in predicting cloud droplet numbers from the sub-saturated water uptake, the influence of which on models still needs to be investigated.