4.7 Article

Measured and modelled cloud condensation nuclei number concentration at the high alpine site Jungfraujoch

期刊

ATMOSPHERIC CHEMISTRY AND PHYSICS
卷 10, 期 16, 页码 7891-7906

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-10-7891-2010

关键词

-

资金

  1. MeteoSwiss
  2. Swiss National Science Foundation
  3. EU
  4. US-NSF [0701013]

向作者/读者索取更多资源

Atmospheric aerosol particles are able to act as cloud condensation nuclei (CCN) and are therefore important for the climate and the hydrological cycle, but their properties are not fully understood. Total CCN number concentrations at 10 different supersaturations in the range of SS=0.12-1.18% were measured in May 2008 at the remote high alpine research station, Jungfraujoch, Switzerland (3580 m a.s.l.). In this paper, we present a closure study between measured and predicted CCN number concentrations. CCN predictions were done using dry number size distribution (scanning particle mobility sizer, SMPS) and bulk chemical composition data (aerosol mass spectrometer, AMS, and multi-angle absorption photometer, MAAP) in a simplified Kohler theory. The predicted and the measured CCN number concentrations agree very well and are highly correlated. A sensitivity study showed that the temporal variability of the chemical composition at the Jungfraujoch can be neglected for a reliable CCN prediction, whereas it is important to know the mean chemical composition. The exact bias introduced by using a too low or too high hygroscopicity parameter for CCN prediction was further quantified and shown to be substantial for the lowest supersaturation. Despite the high average organic mass fraction (similar to 45%) in the fine mode, there was no indication that the surface tension was substantially reduced at the point of CCN activation. A comparison between hygroscopicity tandem differential mobility analyzer (HTDMA), AMS/MAAP, and CCN derived kappa values showed that HTDMA measurements can be used to determine particle hygroscopicity required for CCN predictions if no suitable chemical composition data are available.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据