Impact of surfactants on cloud condensation nuclei activity: from microscale to global scale
Surfactants, surface-active organic compounds found in aerosols, influence cloud condensation nuclei (CCN) activity by forming films at the gas-liquid interface and reducing particle surface tension, thereby altering cloud formation dynamics. Traditional κ-Köhler theory, commonly used for estimating CCN activity of organic/inorganic aerosol mixtures, typically neglects surfactant-induced surface tension reduction. In this study, we integrate an effective surface tension model into the particle-resolved aerosol model PartMC-MOSAIC, enabling precise tracking of surface tension changes for individual particles as they uptake water. This approach avoids assumptions about aerosol mixing state that traditional modal or sectional models require so that we can have a better understanding of surfactants influence on surface tension at a per-particle level, thereby gaining insights into its impact on CCN activity. We further extend our approach to the regional scale using the WRF-PartMC framework to evaluate surfactant impacts on CCN concentrations and examine interactions between effective surface tension treatments and composition averaging methods over California. Initial results indicate significant underestimations of CCN concentrations when surfactant effects are neglected, averaging approximately 25% across the modeled domain, with particularly pronounced deviations for particles in the 40-70 nm size range. Comparative error analysis also highlights the necessity of applying effective surface tension calculations alongside composition averaging processes for accurate global CCN estimations. At last, considering computational limitations at global scales, we propose developing a machine learning emulator trained on extensive datasets generated by particle-resolved simulation outputs. This emulator will efficiently predict effective surface tension at critical supersaturations, facilitating its incorporation into global aerosol models to further calculate CCN. Our aim is to bridge detailed particle-level processes with global-scale climate modeling, thereby enhancing the accuracy of climate predictions related to aerosol-cloud interactions.