Refined SOA hygroscopicity reduces climate-relevant uncertainties in aerosol liquid water and CCN predictions

Large uncertainties in atmospheric organic aerosol (OA) hygroscopicity substantially limit the accurate evaluation of aerosol physicochemical evolution, aqueous-phase processing, and aerosol-cloud climatic effects. Traditional retrieval methods generally adopt simplified homogeneous mixing assumptions or oxidation-dependent parameterizations, which largely neglect ambient mixing-state heterogeneity and may cause systematic biases in secondary organic aerosol hygroscopicity ( κ SOA ) estimation. Herein, based on wintertime field observations in Beijing, we develop a mixing state-resolved approach to retrieve size-resolved κ SOA by accounting for the partitioning of aerosol chemical components between hydrophobic and hygroscopic modes. Approximately 93% of the retrieved κ SOA values exceed 0.20, with a campaign average of 0.47 ± 0.20, whereas conventional schemes severely underestimate ambient κ SOA by approximately 50%. Such biases are particularly pronounced for larger particles (>80 nm) with complex mixing transitions. Chemical and mechanistic validations confirm that elevated κ SOA is dominated by highly hygroscopic low-molecular-weight organic acids formed via aqueous acid-catalyzed reactions. Benefiting from the refined κ SOA constraint, our approach substantially improves aerosol liquid water closure ( R 2 = 0.96), reduces low-humidity water-uptake underestimation, and enhances cloud condensation nuclei number concentration prediction accuracy by 3–10%. This study highlights that explicitly resolving aerosol mixing state is critical for narrowing OA hygroscopicity uncertainties and reliably simulating aerosol environmental and climatic effects.