Explicit simulation of chemical composition, size distribution and cloud condensation nuclei of secondary organic aerosol from α-pinene ozonolysis

Abstract. Secondary organic aerosols (SOA) contribute significantly to cloud condensation nuclei (CCN), which depend on size distribution, chemical composition and hygroscopicity parameter (κ). However, how well current understanding of SOA formation can reproduce CCN concentrations and the influence these factors on modelled CCN uncertainties are still unclear. In chemical transport models, it is difficult to address the issue due to model complexity and oversimplified representation of chemical mechanisms, particle size and κ. Here, we explicitly simulated CCN concentrations of SOA from α-pinene ozonolysis, a bench-mark system for SOA studies using a box model (PyCHAM). Using state-of-the-art treatment of chemical mechanisms, aerosol size and κ, we assessed how CCN as well as chemical composition, aerosol size and κ can be modelled against measurement and evaluated the influence of these factors on CCN simulation. The model well simulated SOA mass concentration but overestimated O:C and H:C ratios, suggesting lack of particle-phase chemistry. Highly oxygenated molecules contributed substantially to SOA mass and thus CCN. Modeled κ closely aligned with measurements at moderate supersaturation (0.37 %) but overestimate κ (by 19 %) at low supersaturation (~0.19 %) and underestimate κ (by 21 %) at high supersaturation (0.73 %). The model well reproduced particle growth, but exhibited wider and flatter size distribution compared with measurement. The simulated CCN concentrations agreed well with measurement at moderate to high SS (0.37–0.73 %) but had a significant bias at low SS. Sensitivity analysis highlights the importance of accurate representation of both size distribution and κ for CCN prediction especially at lower SS (<0.4 %).