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On the competition among aerosol number, size and composition in predicting CCN variability: a multi-annual field study in an urbanized desert

机译:关于气溶胶数,规模和组成的竞争预测CCN变异性:城市化沙漠中的多年田野研究

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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?3), highest in winter (430 cm?3) and have a secondary peak during the North American monsoon season (July to September; 372 cm?3). There is significant variability outside of seasonal patterns, with extreme concentrations (1 and 99 % levels) ranging from 56 to 1945 cm?3 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.
机译:在0.2%的过饱和测量CCN(云凝结核)的浓度的2年数据集与气雾剂的粒度分布和气溶胶组合物数据来探测烟雾浓度数,尺寸分布和组成上CCN图案的效果相结合。收集资料,历时2年(2012-2014)的图森市中心,亚利桑那州:一个显著市区由一个人烟稀少的沙漠所包围。平均CCN浓度通常春季最低(232立方厘米?),最高的是冬季(430毫升?),并有在北美季风季节的第二峰(七月至九月372立方厘米?)。有季节模式的显著变化以外,与极端浓度(1级99%的水平)范围从56到冬天,最高变异季节期间测得1945厘米?3。基于固定的化学组成建模CCN浓度达到在冬季更好闭合,具有尺寸和单独能够数目来预测CCN浓度方差的82%。在气雾剂的化学成分的变化典型地与在大小的变化和气溶胶数,使得吸湿性能,即使它仍然是可变的参数化对准。在夏季,基于固定的化学组成的模型解释充其量只有41%的方差的(预季风)和36%(季风)。这归因于二次有机气溶胶(SOA)的产生的效果,北美季风期间新颗粒的形成和生长凝结,气象的复杂的相互作用,地区和地方排放和多相化学之间的竞争。化学成分被发现是对于改善春季预测性和在冬天更长时间尺度的一个重要因素。参数化模型通常表现出改善的预测技能的时候有CCN浓度和当时的气象和主导气溶胶的物理化学过程之间牢固的关系,这表明类似的结果可能与可比的气候和地理其他位置是可能的。

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