- 1Department of Environmental Science (ACES), Stockholm University, 10691, Stockholm, Sweden
- 2Bolin Centre for Climate Research, Stockholm University, 10691, Stockholm, Sweden
- 3Department of Meteorology, Stockholm University, Stockholm, Sweden
- 4Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, United Kingdom
- 5Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, 00014, Helsinki, Finland
- 6Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
Boreal forests are a significant natural source of aerosol particles. As anthropogenic emissions are expected to decline in the future, the relative contribution of boreal forest aerosols to cloud formation is likely to grow. Understanding the cloud-forming potential of these particles and accurately representing their effects in climate models is essential for assessing aerosol-cloud interactions. Previous research has highlighted the importance of aerosol particle number size distribution in predicting cloud condensation nuclei (CCN) concentrations, often outweighing uncertainties in overall aerosol composition. However, online measurement techniques typically provide data on total sub-micron particulate mass, without resolving chemical composition by size—a limitation that affects the accuracy of CCN predictions.
To address this limitation, we applied k-Köhler theory to evaluate how well observed and predicted CCN concentrations align, while simultaneously estimating size-resolved chemical composition. This approach leveraged an extensive dataset from the Hyytiälä research station in southern Finland, encompassing aerosol size distribution, CCN concentrations, and sub-micron aerosol composition derived from the Aerosol Chemical Speciation Monitor (ACSM) and an aethalometer. By exploring combinations of Aitken and accumulation mode compositions—expressed as mass fractions of organics, ammonium sulfate, and black carbon—we identified the composition that minimized prediction errors, achieving what we term "inverse CCN closure."
Our analysis of five years of data revealed distinct patterns in aerosol composition: inorganic compounds were enriched in the accumulation mode, while organics dominated the Aitken mode. This finding underscores the critical role of low-volatility organics in enabling the growth of newly-formed particles to CCN-relevant sizes, alongside the influence of aged aerosols from distant industrial sources and cloud-processed sulfate in the accumulation mode. Moreover, Aitken-mode particles were shown to contribute, sometimes substantially, to CCN concentrations in this boreal forest environment. These results highlight the necessity of investigating compositional differences between Aitken and accumulation mode particles to refine CCN predictions further. The uncertainty in the estimated modal aerosol chemical composition, stemming from measurement errors, will be quantified and presented.
This work was supported by the European Union’s Horizon 2020 research and innovation programme through the project FORCeS (grant agreement No. 821205) and the INTEGRATE project, funded by the European Research Council Consolidator Grant (No. 865799). Göran Gustafsson foundation is also gratefully acknowledged for financial support. Additional support for the SMEAR II station was provided by the University of Helsinki through ACTRIS-HY.
How to cite: Ranjan, R., Heikkinen, L., Dewey, M., Ekman, A. M. L., Partridge, D., Ahonen, L., Petäjä, T., Aalto, P. P., Luoma, K., and Riipinen, I.: Predicting Modal Aerosol Chemical Composition for Improved CCN Closure: A Boreal Forest Case Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15271, https://doi.org/10.5194/egusphere-egu25-15271, 2025.