Cloud Droplet Formation Research Articles

CCN Activation and Droplet Growth in Pi Chamber Simulations with Lagrangian Particle–Based Microphysics

Abstract Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment.

Learning about structural errors in models of complex dynamical systems

Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e.g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important. For example, the small scales of cloud dynamics and droplet formation are crucial for controlling climate, yet are unresolvable in global climate models. Semi-empirical closure models for the effects of unresolved degrees of freedom often exist and encode important domain-specific knowledge. Building on such closure models and correcting them through learning the structural errors can be an effective way of fusing data with domain knowledge. Here we describe a general approach, principles, and algorithms for learning about structural errors. Key to our approach is to include structural error models inside the models of complex systems, for example, in closure models for unresolved scales. The structural errors then map, usually nonlinearly, to observable data. As a result, however, mismatches between model output and data are only indirectly informative about structural errors, due to a lack of labeled pairs of inputs and outputs of structural error models. Additionally, derivatives of the model may not exist or be readily available. We discuss how structural error models can be learned from indirect data with derivative-free Kalman inversion algorithms and variants, how sparsity constraints enforce a “do no harm” principle, and various ways of modeling structural errors. We also discuss the merits of using non-local and/or stochastic error models. In addition, we demonstrate how data assimilation techniques can assist the learning about structural errors in non-ergodic systems. The concepts and algorithms are illustrated in two numerical examples based on the Lorenz-96 system and a human glucose-insulin model.

Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model

Abstract. Aerosol particles influence cloud formation and properties. Hence climate models that aim for a physical representation of the climate system include aerosol modules. In order to represent more and more processes and aerosol species, their representation has grown increasingly detailed. However, depending on one's modelling purpose, the increased model complexity may not be beneficial, for example because it hinders understanding of model behaviour. Hence we develop a simplification in the form of a climatology of aerosol concentrations. In one approach, the climatology prescribes properties important for cloud droplet and ice crystal formation, the gateways for aerosols to enter the model cloud microphysics scheme. Another approach prescribes aerosol mass and number concentrations in general. Both climatologies are derived from full ECHAM-HAM simulations and can serve to replace the HAM aerosol module and thus drastically simplify the aerosol treatment. The first simplification reduces computational model time by roughly 65 %. However, the naive mean climatological treatment needs improvement to give results that are satisfyingly close to the full model. We find that mean cloud condensation nuclei (CCN) concentrations yield an underestimation of cloud droplet number concentration (CDNC) in the Southern Ocean, which we can reduce by allowing only CCN at cloud base (which have experienced hygroscopic growth in these conditions) to enter the climatology. This highlights the value of the simplification approach in pointing to unexpected model behaviour and providing a new perspective for its study and model development.

A kinetic partitioning method for simulating the condensation mass flux of organic vapors in a wide volatility range

Organic aerosols are ubiquitous, playing important roles in various atmospheric physicochemical processes such as the formation of cloud droplets and haze. Condensation of organic vapors, as a net effect of association with particles and dissociation from the condensed phase, is a fundamental process that drives the formation of organic aerosols. Kinetic models are often used to simulate the condensation fluxes of low-volatility organic vapors and aerosol growth. However, the widely used kinetic growth models usually calculate the evaporation of a certain species based on previous particulate compositions, without including the co-condensation of other species. Here we present a new kinetic partitioning method for calculating the condensation fluxes of organic vapors in a wide volatility range with low computational cost. In this method, the organic vapors are assumed to be in a quasi-steady state, but never reach real association-dissociation equilibrium during the simultaneous condensation of multiple species. We show a good consistency between the kinetic partitioning method and kinetic models in simulating particle mass fractions and condensation fluxes. Under relevant atmospheric conditions, we reveal that the kinetic partitioning method also reproduce the trend that low-volatility species are almost non-volatile while volatile organic compounds almost reach association-dissociation equilibrium, while there is a transition regime between them. This transition regime varies with atmospheric conditions, such as temperature and vapor concentrations. Compared with previous studies combining kinetic growth methods with equilibrium partitioning theories to simplify the condensation flux calculation, this method helps to improve accuracy without a significant expense of computation cost, and it can be applied in a wider range of atmospheric conditions such as in extremely cold atmospheres and polluted exhaust plumes.

Ground-based cloud microphysical observations at Mount Lu in the East Asian monsoon region from 2015 to 2020

Ground-based cloud microphysical observations were continuously conducted at Mount (Mt.) Lu site from 2015 to 2020, which is located in the East Asian monsoon areas of eastern China. The statistical characteristics of cloud microphysics under different meteorological conditions were analyzed by using visibility, cloud droplet and raindrop size distribution data, and meteorological variables. The mean cloud droplet number concentration (Nc), liquid water content (LWC) and effective diameter (De) are 151 cm−3, 0.08 g m−3 and 11.4 μm, respectively. Nc and LWC observed at Mount Lu are lower than the observation results of cloud from other mountain stations all over the world. Broader cloud droplet spectra are correlated with higher temperatures and lower wind speeds. The clouds are classified into nonprecipitating, lightly-raining and raining clouds. Statistics show that lightly-raining and raining clouds account for 66% of all clouds. Nc and LWC of lightly-raining (drizzling) clouds are highest, while spectral width of raining clouds is largest. Spectral dispersion of raining clouds converges to the narrow range of 0.4–0.7. Notably, medium sized cloud droplets (11–40 μm) promote the formation of large cloud droplets (40–50 μm, pre-drizzle size) and drizzle drops which initiate the auto-conversion from condensation to collision-coalescence growth. This study relying on in-situ continuous cloud observation from mountain station are helpful to understand cloud microphysical structures in East Asian monsoon region, and fitting parameters also provide observational evidence for numerical models.

Modifications on the coastal atmospheric sulfur and cloud condensation nuclei along the Eastern China seas by shipping fuel transition

Marine fuel combustion from shipping releases SO2 and forms sulfate particles, which may alter low cloud characteristics. A series of strategies were implemented to control the sulfur content of ship fuel oil from 2018 to 2020, offering insights into the effects of the ship fuel oil transition on sulfur-related pollutants and the consequent cloud condensation nuclei (CCN) in the atmosphere. Compared to 2018 in the southeast China waters, shipping SO2 emission decreased by 78 % in 2020, resulting in a 76 % reduction in ship-related total sulfur concentration, and a decrease of 54 % in CCN number concentration under supersaturation 0.2 % (CCN0.2) contributed by shipping. The response of CCN0.2 to ship-related sulfate modification is more pronounced in relatively clean environments than polluted environments, highlighting the uneven changes in coastal CCN along the Eastern China Sea induced by the ship fuel policies. CCN can trigger the formation of cloud droplets, 2020 fuel regulation may have and will reduce the cooling radiative forcing effect with strong spatial heterogeneity. The study provides insights into the variations in coastal atmospheric sulfur-related pollutants and CCN in uneven response to changes in ship fuel oil, prompting the need for further comprehensive assessments of the climate effects resulting from potential shifts in ship fuel use in the future.

Glucose Enhances Salinity-Driven Sea Spray Aerosol Production in Eastern Arctic Waters.

Sea spray aerosols (SSA) greatly affect the climate system by scattering solar radiation and acting as seeds for cloud droplet formation. The ecosystems in the Arctic Ocean are rapidly changing due to global warming, and the effects these changes have on the generation of SSA, and thereby clouds and fog formation in this region, are unknown. During the ship-based Arctic Century Expedition, we examined the dependency of forced SSA production on the biogeochemical characteristics of seawater using an on-board temperature-controlled aerosol generation chamber with a plunging jet system. Our results indicate that mainly seawater salinity and organic content influence the production and size distribution of SSA. However, we observed a 2-fold higher SSA production from waters with similar salinity collected north of 81°N compared to samples collected south of this latitude. This variability was not explained by phytoplankton and bacterial abundances or Chlorophyll-a concentration but by the presence of glucose in seawater. The synergic action of sea salt (essential component) and glucose or glucose-rich saccharides (enhancer) accounts for >80% of SSA predictability throughout the cruise. Our results suggest that besides wind speed and salinity, SSA production in Arctic waters is also affected by specific organics released by the microbiota.

Cloud response to co-condensation of water and organic vapors over the boreal forest

Abstract. Accounting for the condensation of organic vapors along with water vapor (co-condensation) has been shown in adiabatic cloud parcel model (CPM) simulations to enhance the number of aerosol particles that activate to form cloud droplets. The boreal forest is an important source of biogenic organic vapors, but the role of these vapors in co-condensation has not been systematically investigated. In this work, the environmental conditions under which strong co-condensation-driven cloud droplet number enhancements would be expected over the boreal biome are identified. Recent measurement technology, specifically the Filter Inlet for Gases and AEROsols (FIGAERO) coupled to an iodide-adduct chemical ionization mass spectrometer (I-CIMS), is utilized to construct volatility distributions of the boreal atmospheric organics. Then, a suite of CPM simulations initialized with a comprehensive set of concurrent aerosol observations collected in the boreal forest of Finland during spring 2014 is performed. The degree to which co-condensation impacts droplet formation in the model is shown to be dependent on the initialization of temperature, relative humidity, updraft velocity, aerosol size distribution, organic vapor concentration, and the volatility distribution. The predicted median enhancements in cloud droplet number concentration (CDNC) due to accounting for the co-condensation of water and organics fall on average between 16 % and 22 %. This corresponds to activating particles 10–16 nm smaller in dry diameter that would otherwise remain as interstitial aerosol. The highest CDNC enhancements (ΔCDNC) are predicted in the presence of a nascent ultrafine aerosol mode with a geometric mean diameter of ∼ 40 nm and no clear Hoppel minimum, indicative of pristine environments with a source of ultrafine particles (e.g., via new particle formation processes). Such aerosol size distributions are observed 30 %–40 % of the time in the studied boreal forest environment in spring and fall when new particle formation frequency is the highest. To evaluate the frequencies with which such distributions are experienced by an Earth system model over the whole boreal biome, 5 years of UK Earth System Model (UKESM1) simulations are further used. The frequencies are substantially lower than those observed at the boreal forest measurement site (< 6 % of the time), and the positive values, peaking in spring, are modeled only over Fennoscandia and the western parts of Siberia. Overall, the similarities in the size distributions between observed and modeled (UKESM1) are limited, which would limit the ability of this model, or any model with a similar aerosol representation, to project the climate relevance of co-condensation over the boreal forest. For the critical aerosol size distribution regime, ΔCDNC is shown to be sensitive to the concentrations of semi-volatile and some intermediate-volatility organic compounds (SVOCs and IVOCs), especially when the overall particle surface area is low. The magnitudes of ΔCDNC remain less affected by the more volatile vapors such as formic acid and extremely low- and low-volatility organic compounds (ELVOCs and LVOCs). The reasons for this are that most volatile organic vapors condense inefficiently due to their high volatility below the cloud base, and the concentrations of LVOCs and ELVOCs are too low to gain significant concentrations of soluble mass to reduce the critical supersaturations enough for droplet activation to occur. A reduction in the critical supersaturation caused by organic condensation emerges as the main driver of the modeled ΔCDNC. The results highlight the potential significance of co-condensation in pristine boreal environments close to sources of fresh ultrafine particles. For accurate predictions of co-condensation effects on CDNC, also in larger-scale models, an accurate representation of the aerosol size distribution is critical. Further studies targeted at finding observational evidence and constraints for co-condensation in the field are encouraged.

Effects of South Asian outflow on aerosol hygroscopicity and cloud droplet activation over the northern Indian Ocean

As a part of the Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB-2018), shipborne measurements of aerosol properties were carried out to study the influence of South Asian outflow on cloud condensation nuclei (CCN) characteristics over the South East Arabian Sea (SEAS) and equatorial Indian Ocean (EIO) during winter, 2018. The poor association between CCN concentration and mass concentrations of the major aerosol species (organic matter, sulfate, and elemental carbon) is observed over SEAS, where airmass is mostly continental. In comparison, the CCN is well-correlated with aerosol mass concentrations over remote EIO. The dependence of CCN activation (at lower supersaturations) on the ultrafine particle fraction differs between SEAS and EIO depending on the mass concentration of organic aerosols. The coexistence of small and hydrophobic aerosols (organics) decreased the CCN activation ratio during ultrafine particle events. The hygroscopicity estimated using the chemical composition of aerosols is comparable to that estimated from CCN measurements over the Indian Ocean, except for the SEAS, where organic aerosols dominate. The effect of outflow aerosols on cloud droplet number concentration (CDNC) is simulated using a parcel model, which showed a good association (R ∼0.96) with CCN measurements over the EIO; however, the estimated CDNC did not follow the CCN variation over the SEAS region. This study highlights the importance of aerosol chemical composition, especially organics, in CCN activation and cloud droplet formation in the South Asian outflow over the Indian Ocean.

Impact of acidity and surface-modulated acid dissociation on cloud response to organic aerosol

Abstract. Acid dissociation of the organic aerosol fraction has the potential to impact cloud-activating properties by altering aqueous-phase H+ concentrations and water activity but is currently overlooked in most atmospheric aerosol models. We implemented a simple representation of organic acid dissociation in the aerosol–chemistry–climate box model ECHAM6.3–HAM2.3 and investigated the impact on aerosol-forming aqueous sulfur chemistry, cloud droplet number concentrations, and the shortwave radiative effect. Many atmospheric organic acids are also surface-active and may be strongly adsorbed at the surface of small aqueous droplets. The degree of dissociation has recently been observed for several atmospheric surface-active organics with Brönsted acid character to be significantly shifted in the surface, compared to the bulk aqueous solution. In addition to the well-known bulk acidity, we therefore introduced an empirical account of this surface-modulated dissociation to further explore the potential impact on aerosol climate effects. Malonic acid and decanoic acid were used as proxies for atmospheric organic aerosols of different surface-active and acid strengths. Both acids were found to yield sufficient hydrogen ion concentrations from dissociation in an aqueous droplet population to strongly influence aqueous aerosol sulfur chemistry, leading to enhanced cloud droplet number concentrations and a cooling shortwave radiative effect. Further considering the surface modulation of organic acid dissociation, the impact on cloud microphysics was smaller than according to the well-known bulk solution acidity but still significant. Our results show that organic aerosol acid dissociation can significantly influence predictions of aerosol and cloud droplet formation and aerosol–cloud–climate effects and that, even for a well-known bulk solution phenomenon such as acidity, it may be important to also consider the specific influence of surface effects when surface-active acids comprise a significant fraction of the total organic aerosol mass.

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.

Droplet Growth or Evaporation Does Not Buffer the Variability in Supersaturation in Clean Clouds

Abstract Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to , i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth. Significance Statement The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed.

Maximum Supersaturation in the Marine Boundary Layer Clouds Over the North Atlantic

AbstractThe maximum supersaturation (Sx) in clouds is a key parameter affecting the cloud's microphysical and radiative properties. We investigate the Sx of the marine boundary layer clouds by combining airborne and surface observations in the Eastern North Atlantic. The cloud droplet number concentration (Nc) in the least diluted cloud cores agrees well with the number concentration of particles larger than the Hoppel Minimum (HM) (N>HM) below clouds, indicating that the HM represents the average size threshold above which particles are activated to form cloud droplets. The Sx values derived from surface observations vary from 0.10% to 0.50% from June 2017 to June 2018, with a clear seasonal variation exhibiting higher values during winter. Most of the Sx variance (∼60%) can be explained by the cloud condensation nuclei (CCN) concentration and updraft velocity (w), with the CCN concentration playing a more important role than w in explaining the variation of Sx. The influence of CCN concentration on Sx leads to a buffered response of Nc to aerosol perturbations. The response of Nc to low aerosol concentration during winter is further buffered by the high w. The global Community Earth System Model (CESM) simulated Sx values in the Azores have a positive bias compared to measured Sx, likely due to overestimated w and underestimated CCN concentration. The CESM simulated Sx exhibits higher values further north over the North Atlantic Ocean, which is attributed to stronger w. The suppression of Sx by aerosol is also evident in regions with high CCN concentrations.

Aerosol and dynamical contributions to cloud droplet formation in Arctic low-level clouds

Abstract. The Arctic is one of the most rapidly warming regions of the globe. Low-level clouds and fog modify the energy transfer from and to space and play a key role in the observed strong Arctic surface warming, a phenomenon commonly termed “Arctic amplification”. The response of low-level clouds to changing aerosol characteristics throughout the year is therefore an important driver of Arctic change that currently lacks sufficient constraints. As such, during the NASCENT campaign (Ny-Ålesund AeroSol Cloud ExperimeNT) extending over a full year from October 2019 to October 2020, microphysical properties of aerosols and clouds were studied at the Zeppelin station (475 m a.s.l.), Ny-Ålesund, Svalbard, Norway. Particle number size distributions obtained from differential mobility particle sizers as well as chemical composition derived from filter samples and an aerosol chemical speciation monitor were analyzed together with meteorological data, in particular vertical wind velocity. The results were used as input to a state-of-the-art cloud droplet formation parameterization to investigate the particle sizes that can activate to cloud droplets, the levels of supersaturation that can develop, the droplet susceptibility to aerosol and the role of vertical velocity. We evaluate the parameterization and the droplet numbers calculated through a droplet closure with in-cloud in situ measurements taken during nine flights over 4 d. A remarkable finding is that, for the clouds sampled in situ, closure is successful in mixed-phase cloud conditions regardless of the cloud glaciation fraction. This suggests that ice production through ice–ice collisions or droplet shattering may have explained the high ice fraction, as opposed to rime splintering that would have significantly reduced the cloud droplet number below levels predicted by warm-cloud activation theory. We also show that pristine-like conditions during fall led to clouds that formed over an aerosol-limited regime, with high levels of supersaturation (generally around 1 %, although highly variable) that activate particles smaller than 20 nm in diameter. Clouds formed in the same regime in late spring and summer, but aerosol activation diameters were much larger due to lower cloud supersaturations (ca. 0.5 %) that develop because of higher aerosol concentrations and lower vertical velocities. The contribution of new particle formation to cloud formation was therefore strongly limited, at least until these newly formed particles started growing. However, clouds forming during the Arctic haze period (winter and early spring) can be limited by updraft velocity, although rarely, with supersaturation levels dropping below 0.1 % and generally activating larger particles (20 to 200 nm), including pollution transported over a long range. The relationship between updraft velocity and the limiting cloud droplet number agrees with previous observations of various types of clouds worldwide, which supports the universality of this relationship.

Sulfuric Acid Nucleation Potential Model Applied to Complex Reacting Systems in the Atmosphere

AbstractAtmospheric aerosol particles impact Earth's radiation balance by acting as seeds for cloud droplet formation. Over half of global cloud seed particles are formed by nucleation, a process where gas‐phase compounds react to form stable particles. Reactions of sulfuric acid (SA) with a wide variety of atmospheric compounds have been previously shown to drive nucleation in the lower troposphere. However, global climate models poorly predict particle nucleation rates since current nucleation models do not describe nucleation for systems containing tens to hundreds of precursor compounds. The nucleation potential model (NPM) was recently developed to model SA nucleation of complex mixtures by measuring an effective base concentration using a 1‐nm condensation particle counter. This technique for estimating particle nucleation rates can be deployed at a much higher spatial and temporal resolution than current methods which require detailed knowledge of all nucleation reactions and measurements, typically using a mass spectrometer, of all nucleation precursor gases. This work expands NPM by showing that this model can capture enhancement and suppression of SA nucleation rates within a complex mixture of organic and inorganic acids, ambient air, and across a range of atmospherically relevant relative humidities. In addition, an expression for calculating atmospheric nucleation rates was also derived from the NPM. Ultimately, NPM provides a simple way to measure and model the extent compounds in a complex mixture enhance SA nucleation rates using a condensation particle counter.

Technical note: In situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a quartz crystal microbalance with dissipation monitoring (QCM-D)

Abstract. Aerosols and films are found in indoor and outdoor environments. How they interact with pollutants, such as ozone, has a direct impact on our environment via cloud droplet formation and the chemical persistence of toxic aerosol constituents. The chemical reactivity of aerosol emissions is typically measured spectroscopically or by techniques such as mass spectrometry, directly monitoring the amount of material during a chemical reaction. We present a study which indirectly measures oxidation kinetics in a common cooking aerosol proxy using a low-cost quartz crystal microbalance with dissipation monitoring (QCM-D). We validated this approach by comparison with kinetics measured both spectroscopically and with high-intensity synchrotron radiation. Using microscopy, we found that the film morphology changed and film rigidity increased during oxidation. There was evidence of surface crust formation on oxidised particles, though this was not consistent for all experiments. Crucially, our kinetic modelling of these experimental data confirmed that the oleic acid decay rate is in line with previous literature determinations, which demonstrates that performing such experiments on a QCM-D does not alter the underlying mechanism. There is clear potential to take this robust and low-cost but sensitive method to the field for in situ monitoring of reactions outdoors and indoors.

Molecular Self-Organization in Surfactant Atmospheric Aerosol Proxies.

ConspectusAerosols are ubiquitous in the atmosphere. Outdoors, they take part in the climate system via cloud droplet formation, and they contribute to indoor and outdoor air pollution, impacting human health and man-made environmental change. In the indoor environment, aerosols are formed by common activities such as cooking and cleaning. People can spend up to ca. 90% of their time indoors, especially in the western world. Therefore, there is a need to understand how indoor aerosols are processed in addition to outdoor aerosols.Surfactants make significant contributions to aerosol emissions, with sources ranging from cooking to sea spray. These molecules alter the cloud droplet formation potential by changing the surface tension of aqueous droplets and thus increasing their ability to grow. They can also coat solid surfaces such as windows ("window grime") and dust particles. Such surface films are more important indoors due to the higher surface-to-volume ratio compared to the outdoor environment, increasing the likelihood of surface film-pollutant interactions.A common cooking and marine emission, oleic acid, is known to self-organize into a range of 3-D nanostructures. These nanostructures are highly viscous and as such can impact the kinetics of aerosol and film aging (i.e., water uptake and oxidation). There is still a discrepancy between the longer atmospheric lifetime of oleic acid compared with laboratory experiment-based predictions.We have created a body of experimental and modeling work focusing on the novel proposition of surfactant self-organization in the atmosphere. Self-organized proxies were studied as nanometer-to-micrometer films, levitated droplets, and bulk mixtures. This access to a wide range of geometries and scales has resulted in the following main conclusions: (i) an atmospherically abundant surfactant can self-organize into a range of viscous nanostructures in the presence of other compounds commonly encountered in atmospheric aerosols; (ii) surfactant self-organization significantly reduces the reactivity of the organic phase, increasing the chemical lifetime of these surfactant molecules and other particle constituents; (iii) while self-assembly was found over a wide range of conditions and compositions, the specific, observed nanostructure is highly sensitive to mixture composition; and (iv) a "crust" of product material forms on the surface of reacting particles and films, limiting the diffusion of reactive gases to the particle or film bulk and subsequent reactivity. These findings suggest that hazardous, reactive materials may be protected in aerosol matrixes underneath a highly viscous shell, thus extending the atmospheric residence times of otherwise short-lived species.

Enhancements in Cloud Condensation Nuclei Activity From Turbulent Fluctuations in Supersaturation

AbstractThe effect of aerosols on the properties of clouds is a large source of uncertainty in predictions of weather and climate. These aerosol‐cloud interactions depend critically on the ability of aerosol particles to form cloud droplets. A challenge in modeling aerosol‐cloud interactions is the representation of interactions between turbulence and cloud microphysics. Turbulent mixing leads to small‐scale fluctuations in water vapor and temperature that are unresolved in large‐scale atmospheric models. To quantify the impact of turbulent fluctuations on cloud condensation nuclei (CCN) activation, we used a high‐resolution Large Eddy Simulation of a convective cloud chamber to drive particle‐based cloud microphysics simulations. We show small‐scale fluctuations strongly impact CCN activity. Once activated, the relatively long timescales of evaporation compared to fluctuations causes droplets to persist in subsaturated regions, which further increases droplet concentrations.

Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)

Abstract. Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics, which are a well-known source of uncertainty in weather forecasts. Via algorithmic differentiation, which computes the sensitivities of prognostic variables to changes in model parameters, these uncertainties can be quantified. In this article, we present visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along strongly ascending trajectories, so-called warm conveyor belt (WCB) trajectories. We propose a visual interface that enables us to (a) compare the values of multiple sensitivities at a single time step on multiple trajectories, (b) assess the spatiotemporal relationships between sensitivities and the trajectories' shapes and locations, and (c) find similarities in the temporal development of sensitivities along multiple trajectories. We demonstrate how our approach enables atmospheric scientists to interactively analyze the uncertainty in the microphysical parameterizations and along the trajectories with respect to the selected prognostic variable. We apply our approach to the analysis of WCB trajectories within extratropical Cyclone Vladiana, which occurred between 22–25 September 2016 over the North Atlantic. Peaks of sensitivities that occur at different times relative to a trajectory's fastest ascent reveal that trajectories with their fastest ascent in the north are more susceptible to rain sedimentation from above than trajectories that ascend further south. In contrast, large sensitivities to cloud condensation nuclei (CCN) activation and cloud droplet collision in the south indicate a local rain droplet formation. These large sensitivities reveal considerable uncertainty in the shape of clouds and subsequent rainfall. Sensitivities to cloud droplets' formation and subsequent conversion to rain droplets are also more pronounced along convective ascending trajectories than for slantwise ascents. The slantwise ascending trajectories are characterized by periods of slower ascent and even descent, during which the sensitivities to the formation of cloud droplets and rain droplets alternate. This alternating pattern leads to large-scale precipitation patterns, whereas convective ascending trajectories do not exhibit this pattern. Thus the primary source for uncertainty in large-scale precipitation patterns stems from slantwise ascents. The strong ascent of convective trajectories leads to large sensitivities of rain mass density to riming and freezing parameters at high altitudes, which are barely present in slantwise ascending trajectories. These sensitivities correspond to uncertainties concerning graupel and hail formation in convective ascents.

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.