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Title: Subarctic atmospheric aerosol composition: 3. Measured and modeled properties of cloud condensation nuclei

Journal Article · · Journal of Geophysical Research. D. (Atmospheres), 115:Article Number: D04202

Predicting the ability of aerosol particles to act as cloud condensation nuclei (CCN) is still a challenge and not properly incorporated in current climate models. By using field data from measurements at the sub-arctic Stordalen site, approximately 200 km north of the Arctic Circle, a hygroscopicity closure study was performed. Measured CCN number concentrations were compared with predictions that involved size distribution data and hygroscopicity data measured by a HTDMA as a proxy for the chemical composition of the aerosol. The sensitivity of the predictions to simplifying assumptions re-garding mixing state of the particles and the temporal variability of the chemical composition were explored. It was found that involving the full growth factor probability density function (GF-PDF) or the averaged growth factor (GF) or a constant averaged κ-value resulted in reasonable agreement be-tween predicted and measured CCN number concentrations. Probability distribution histograms of the performances of the different closure approaches revealed that involving the full GF-PDF resulted in the narrowest and most symmetric distribution of the predicted-to-measured CCN number concentra-tion ratio around unity. While also involving the averaged GF showed a good agreement, the constant averaged κ-value-approach resulted in most of the cases in an overestimation of CCN number con-centrations by ~15 %. Approaches where a constant estimated hygroscopicity was involved predicted CCN number concentrations in some cases well but largely overestimated (assuming internally mixed ammonium sulphate particles) or underestimated (assuming internally mixed organic aerosol particles with κ = 0.1) CCN number concentrations. It is therefore recommended that at least an averaged measured proxy for the aerosol’s chemical composition be incorporated in future CCN predictions and climate models.

Research Organization:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
978943
Report Number(s):
PNNL-SA-64960; TRN: US201010%%293
Journal Information:
Journal of Geophysical Research. D. (Atmospheres), 115:Article Number: D04202, Vol. 115
Country of Publication:
United States
Language:
English