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.

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.