Optimizing CCN predictions through inferred modal aerosol composition – a boreal forest case study

Abstract. The contribution of natural aerosol particles from boreal forests to total aerosol loadings may increases with anticipated reduction in anthropogenic emissions. It is therefore pertinent to understand the cloud-forming potential of these particles. Observational data on aerosol particle number size distribution and chemical composition is required for predicting cloud condensation nuclei (CCN) concentrations. However, long-term online measurements of chemical composition typically provide data on total sub-micron particulate mass, which only represents the larger end of the number size distribution. To bridge this gap, we employed κ-Köhler theory on a multi-year (2016–2020) dataset from Hyytiälä, southern Finland, to investigate improved closure between observed and predicted CCN concentrations by optimizing the size-resolved chemical composition. This optimization improved the CCN closure primarily at supersaturations above 0.5 % where the Aitken mode makes a substantial contribution to the CCN number. The optimization suggested inorganic enrichment in the accumulation mode compared to organic enrichment in the Aitken mode. The mass fractions of inorganics in the two modes vary with season, the greatest difference taking place in winter (+156 % in the accumulation mode as compared with Aitken mode) and smallest in summer (+52 %). These results reflect the contributions from long range transport and chemical cloud processing as well as the pivotal role of organic vapors in facilitating the growth of newly-formed particles towards CCN-sizes. Our study demonstrates the potential for utilizing CCN measurements for inferring information on the parts of the aerosol size distribution that are beyond the reach of traditional online composition measurements.