Crosbie E, Youn J-S, Balch B, Wonaschütz A, Shingler T, Wang Z, Conant W C, Betterton E A, Sorooshian A
Department of Atmospheric Sciences, University of Arizona, Tucson, AZ, USA.
Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
Atmos Chem Phys. 2015 Feb 10;15:6943-6958. doi: 10.5194/acp-15-6943-2015.
A 2-year data set of measured CCN (cloud condensation nuclei) concentrations at 0.2 % supersaturation is combined with aerosol size distribution and aerosol composition data to probe the effects of aerosol number concentrations, size distribution and composition on CCN patterns. Data were collected over a period of 2 years (2012-2014) in central Tucson, Arizona: a significant urban area surrounded by a sparsely populated desert. Average CCN concentrations are typically lowest in spring (233 cm), highest in winter (430 cm) and have a secondary peak during the North American monsoon season (July to September; 372 cm). There is significant variability outside of seasonal patterns, with extreme concentrations (1 and 99 % levels) ranging from 56 to 1945 cm as measured during the winter, the season with highest variability. Modeled CCN concentrations based on fixed chemical composition achieve better closure in winter, with size and number alone able to predict 82% of the variance in CCN concentration. Changes in aerosol chemical composition are typically aligned with changes in size and aerosol number, such that hygroscopicity can be parameterized even though it is still variable. In summer, models based on fixed chemical composition explain at best only 41% (pre-monsoon) and 36% (monsoon) of the variance. This is attributed to the effects of secondary organic aerosol (SOA) production, the competition between new particle formation and condensational growth, the complex interaction of meteorology, regional and local emissions and multi-phase chemistry during the North American monsoon. Chemical composition is found to be an important factor for improving predictability in spring and on longer timescales in winter. Parameterized models typically exhibit improved predictive skill when there are strong relationships between CCN concentrations and the prevailing meteorology and dominant aerosol physicochemical processes, suggesting that similar findings could be possible in other locations with comparable climates and geography.
一个关于0.2%过饱和度下测量的云凝结核（CCN）浓度的两年数据集，与气溶胶粒径分布和气溶胶成分数据相结合，以探究气溶胶数浓度、粒径分布和成分对CCN模式的影响。数据于2012年至2014年期间在亚利桑那州图森市中心收集：这是一个被人口稀少的沙漠环绕的重要城市地区。平均CCN浓度通常在春季最低（233个/cm³），冬季最高（430个/cm³），并在北美季风季节（7月至9月；372个/cm³）出现次高峰。季节模式之外存在显著变化，在冬季（变化最大的季节）测量的极端浓度（1%和99%水平）范围为56至1945个/cm³。基于固定化学成分模拟的CCN浓度在冬季能更好地闭合，仅粒径和数量就能预测CCN浓度方差的82%。气溶胶化学成分的变化通常与粒径和气溶胶数量的变化一致，因此即使吸湿性仍有变化，也可以进行参数化。在夏季，基于固定化学成分的模型最多只能解释方差的41%（季风前）和36%（季风期）。这归因于二次有机气溶胶（SOA）生成的影响、新粒子形成与凝结增长之间的竞争、北美季风期间气象、区域和本地排放以及多相化学的复杂相互作用。发现化学成分是提高春季和冬季较长时间尺度预测能力的重要因素。当CCN浓度与盛行气象和主要气溶胶物理化学过程之间存在强关系时，参数化模型通常表现出更高的预测技能，这表明在气候和地理条件相当的其他地点可能会有类似的发现。
