Atmospheric aerosols particles may act as cloud condensation nuclei (CCN) that provide sites for condensation of water vapour for the formation of cloud droplets, called cloud droplet activation. Whether aerosol particles are CCN is determined by their size, composition and the ambient humidity. Cloud macrophysical properties together with the size and number concentration of droplets determine the optical properties of liquid phase clouds. Clouds are an important component in the Earth's radiation balance and aerosol-cloud interactions (ACI) are associated with the largest uncertainty in estimates made of anthropogenic radiative forcing in earth system models.

To constrain ACI and reduce uncertainties, an improvement in our understanding of CCN activation is required. Owing to its complex phase structure and chemical heterogeneity, the organic fraction of atmospheric aerosol introduces significant challenges in developing an exact description of cloud formation. In this thesis, a cloud parcel model is employed to systematically address parametric and process uncertainties in estimates of cloud droplet sizes and number concentrations (CDNC). To do so, the unified framework for organic aerosol (UFO) scheme was developed and embedded into the cloud parcel model, ICPM-UFO. The ICPM-UFO simulates partitioning of organic mass between the gas and aqueous bulk and surface phases, thereby providing means to theoretically diagnose changes in droplet nucleating potential of aerosol particles due to organic aerosol mass transfer processes.

Partitioning of surface active organic aerosol mass from the bulk particle phase to the surface phase results in a lowered, size-dependent surface tension that enhances activation potential of CCN and therefore simulated CDNC. A large fraction of organic aerosol constituents exist partitioned across particle and gas phases and simulation of cloud formation events show this semi-volatile organic mass to condense to the particle phase as humidity increases through the cloud base. This additional particle phase mass may be partially soluble. The more soluble component increases the activation potential by lowering the water activity, while the less soluble but more surface active component also increases the activation potential by further lowering of the surface tension. The compounding effects of the gas-particle and bulk-surface partitioning processes result in significant changes in CCN concentrations and CDNC for simulation on boreal aerosol. These results exhibit a significant over prediction of typical boreal CCN concentrations relative to in-situ measurements, though further sensitivity analysis with respect to the soluble fraction and surface phase description may be advantageous. Based on multivariate statistical approaches applied, resolution of the surface phase in cloud formation parameterisations within climate models is however not currently recommended.

Theoretical description of both partitioning processes require prescription of input parameters that are challenging to measure in-situ. These parameters include: SVOC volatility and enthalpy of vaporisation and organic component surface tension and film thickness. Further work using the inverse modelling framework established herein is recommended to provide estimation of these parameters while simultaneously matching simulated CDNC and/or CCN concentrations with observational data. It is envisaged that such an investigation will also yield insights into structural uncertainties associated with the choice of surface phase model - a point of contention both within this thesis and the wider literature.  

Accommodation of vapor-phase water molecules into ice crystal surfaces is a fundamental process controlling atmospheric ice crystal growth. Experimental studies investigating the accommodation process with various techniques report widely spread values of the water accommodation coefficient on ice, αice, and the results on its potential temperature dependence are inconclusive. We run molecular dynamics simulations of molecules condensing onto the basal plane of ice Ih using the TIP4P/Ice empirical force field and characterize the accommodated state from this molecular perspective, utilizing the interaction energy, the tetrahedrality order parameter, and the distance below the instantaneous interface as criteria. Changes of the order parameter turn out to be a suitable measure to distinguish between the surface and bulk states of a molecule condensing onto the disordered interface. In light of the findings from the molecular dynamics, we discuss and re-analyze a recent experimental data set on αice obtained with an environmental molecular beam (EMB) setup [Kong, X.; J. Phys. Chem. A 2014, 118 (22), 3973−3979] using kinetic molecular flux modeling, aiming at a more comprehensive picture of the accommodation process from a molecular perspective. These results indicate that the experimental observations indeed cannot be explained by evaporation alone. At the same time, our results raise the issue of rapidly growing relaxation times upon decreasing temperature, challenging future experimental efforts to cover relevant time scales. Finally, we discuss the relevance of the water accommodation coefficient on ice in the context of atmospheric cloud particle growth processes. 

Aerosol-cloud interactions constitute the largest source of uncertainty in global radiative forcing estimates, hampering our understanding of climate evolution. Recent empirical evidence suggests surface tension depression by organic aerosol to significantly influence the formation of cloud droplets, and hence cloud optical properties. In climate models, however, surface tension of water is generally assumed when predicting cloud droplet concentrations. Here we show that the sensitivity of cloud microphysics, optical properties and shortwave radiative effects to the surface phase are dictated by an interplay between the aerosol particle size distribution, composition, water availability and atmospheric dynamics. We demonstrate that accounting for the surface phase becomes essential in clean environments in which ultrafine particle sources are present. Through detailed sensitivity analysis, quantitative constraints on the key drivers - aerosol particle number concentrations, organic fraction and fixed updraft velocity - are derived for instances of significant cloud microphysical susceptibilities to the surface phase.

In this study a novel framework for inverse modelling of cloud condensation nuclei (CCN) spectra is developed using Kohler theory. The framework is established by using model-generated synthetic measurements as calibration data for a parametric sensitivity analysis. Assessment of the relative importance of aerosol physicochemical parameters, while accounting for bulk-surface partitioning of surface-active organic species, is carried out over a range of atmospherically relevant supersaturations. By introducing an objective function that provides a scalar metric for diagnosing the deviation of modelled CCN concentrations from synthetic observations, objective function response surfaces are presented as a function of model input parameters. Crucially, for the chosen calibration data, aerosol-CCN spectrum closure is confirmed as a well-posed inverse modelling exercise for a subset of the parameters explored herein. The response surface analysis indicates that the appointment of appropriate calibration data is particularly important. To perform an inverse aerosol-CCN closure analysis and constrain parametric uncertainties, it is shown that a high-resolution CCN spectrum definition of the calibration data is required where single-valued definitions may be expected to fail. Using Kohler theory to model CCN concentrations requires knowledge of many physicochemical parameters, some of which are difficult to measure in situ on the scale of interest and introduce a considerable amount of parametric uncertainty to model predictions. For all partitioning schemes and environments modelled, model output showed significant sensitivity to perturbations in aerosol log-normal parameters describing the accumulation mode, surface tension, organic : inorganic mass ratio, insoluble fraction, and solution ideality. Many response surfaces pertaining to these parameters contain well-defined minima and are therefore good candidates for calibration using a Monte Carlo Markov Chain (MCMC) approach to constraining parametric uncertainties. A complete treatment of bulk-surface partitioning is shown to predict CCN spectra similar to those calculated using classical Kohler theory with the surface tension of a pure water drop, as found in previous studies. In addition, model sensitivity to perturbations in the partitioning parameters was found to be negligible. As a result, this study supports previously held recommendations that complex surfactant effects might be neglected, and the continued use of classical Kohler theory in global climate models (GCMs) is recommended to avoid an additional computational burden. The framework developed is suitable for application to many additional composition-dependent processes that might impact CCN activation potential. However, the focus of this study is to demonstrate the efficacy of the applied sensitivity analysis to identify important parameters in those processes and will be extended to facilitate a global sensitivity analysis and inverse aerosol-CCN closure analysis.

Atmospheric particulate matter is one of the main factors governing the Earth's radiative budget, but its exact effects on the global climate are still uncertain. Knowledge on the molecular-scale surface phenomena as well as interactions between atmospheric organic and inorganic compounds is necessary for understanding the role of airborne nanoparticles in the Earth system. In this work, surface composition of aqueous model systems containing succinic acid and sodium chloride or ammonium sulfate is determined using a novel approach combining X-ray photoelectron spectroscopy, surface tension measurements and thermodynamic modeling. It is shown that succinic acid molecules are accumulated in the surface, yielding a 10-fold surface concentration as compared with the bulk for saturated succinic acid solutions. Inorganic salts further enhance this enrichment due to competition for hydration in the bulk. The surface compositions for various mixtures are parametrized to yield generalizable results and used to explain changes in surface tension. The enhanced surface partitioning implies an increased maximum solubility of organic compounds in atmospheric nanoparticles. The results can explain observations of size-dependent phase-state of atmospheric nanoparticles, suggesting that these particles can display drastically different behavior than predicted by bulk properties only.