Subarctic atmospheric aerosol composition: 3. Measured and modeled properties of cloud condensation nuclei
Publikation/Tidskrift/Serie: Journal of Geophysical Research
Dokumenttyp: Artikel i tidskrift
Förlag: American Geophysical Union
Aerosol particles can modify cloud properties by acting as cloud condensation nuclei (CCN). Predicting CCN properties is still a challenge and not properly incorporated in current climate models. Atmospheric particle number size distributions, hygroscopic growth factors, and polydisperse CCN number concentrations were measured at the remote subarctic Stordalen mire, 200 km north of the Arctic Circle in northern Sweden. The CCN number concentration was highly variable, largely driven by variations in the total number of sufficiently large particles, though the variability of chemical composition was increasingly important for decreasing supersaturation. The hygroscopicity of particles measured by a hygroscopicity tandem differential mobility analyzer (HTDMA) was in agreement with large critical diameters observed for CCN activation (kappa approximate to 0.07-0.21 for D = 50-200 nm). Size distribution and time- and size-resolved HTDMA data were used to predict CCN number concentrations. Agreement of predictions with measured CCN within +/- 11% was achieved using parameterized Kohler theory and assuming a surface tension of pure water. The sensitivity of CCN predictions to various simplifying assumptions was further explored: We found that (1) ignoring particle mixing state did not affect CCN predictions, (2) averaging the HTDMA data in time with retaining the size dependence did not introduce a substantial bias, while individual predictions became more uncertain, and (3) predictions involving the hygroscopicity parameter recommended in literature for continental sites (kappa approximate to 0.3 +/- 0.1) resulted in a significant prediction bias. Future modeling studies should therefore at least aim at using averaged, size-resolved, site-specific hygroscopicity or chemical composition data for predictions of CCN number concentrations.
- Physical Geography
- ISSN: 2156-2202