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Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization

机译:利用气溶胶光学性能估算云凝结核数浓度:粒子数尺寸分布和参数化的作用

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摘要

The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol-cloud interactions within warm clouds. Long-term CCN number concentration (N-CCN) data are scarce; there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating N-CCN from AOP measurements. Such parameterizations have already been made, and in the present work a new parameterization is presented. The relationships between N-CCN, AOPs, and size distributions were investigated based on in situ measurement data from six stations in very different environments around the world. The relationships were used for deriving a parameterization that depends on the scattering Angstrom exponent (SAE), backscatter fraction (BSF), and total scattering coefficient (sigma(sp)) of PM10 particles. The analysis first showed that the dependence of N-CCN on supersaturation (SS) can be described by a logarithmic fit in the range SS < 1 :1 %, without any theoretical reasoning. The relationship between N-CCN and AOPs was parameterized as N-CCN approximate to ((286 +/- 46)SAE ln(SS/(0.093 +/- 0.006))(BSF - BSFmin) + (5.2 +/- 3.3))sigma(sp), where BSFmin is the minimum BSF, in practice the 1st percentile of BSF data at a site to be analyzed. At the lowest supersaturations of each site (SS approximate to 0 :1 %), the average bias, defined as the ratio of the AOP-derived and measured N-CCN, varied from similar to 0.7 to similar to 1.9 at most sites except at a Himalayan site where the bias was > 4. At SS > 0 :4% the average bias ranged from similar to 0.7 to similar to 1.3 at most sites. For the marine-aerosol-dominated site Ascension Island the bias was higher, similar to 1.4-1.9. In other words, at SS > 0:4% N-CCN was estimated with an average uncertainty of approximately 30% by using nephelometer data. The biases were mainly due to the biases in the parameterization related to the scattering Angstrom exponent (SAE). The squared correlation coefficients between the AOP-derived and measured N-CCN varied from similar to 0.5 to similar to 0.8. To study the physical explanation of the relationships between N-CCN and AOPs, lognormal unimodal particle size distributions were generated and N-CCN and AOPs were calculated. The simulation showed that the relationships of N-CCN and AOPs are affected by the geometric mean diameter and width of the size distribution and the activation diameter. The relationships of N-CCN and AOPs were similar to those of the observed ones.
机译:云凝结核(CCN)的浓度是影响温暖云中的气溶胶云相互作用的基本参数。长期CCN号浓度(N-CCN)数据是稀缺的;有关气溶胶光学性质(AOP)的数据有更多的数据。因此,从AOP测量中获得用于估计N-CCN的参数化是有价值的。已经进行了此类参数化,并且在本工作中,呈现了新的参数化。研究了N-CCN,AOP和尺寸分布之间的关系,基于来自世界各地的六个站的原位测量数据。这些关系用于导出参数化,该参数化取决于PM10颗粒的散射埃克斯特罗姆(SAE),反向散射分数(BSF)和总散射系数(Sigma(SP))。该分析首先表明,N-CCN对超饱和度(SS)的依赖性可以通过在SS <1:1%的范围内的对数拟合来描述,而没有任何理论推理。 N-CCN和AOP之间的关系被称为N-CCN近似((286 +/- 46)SAE LN(SS /(0.093 +/- 0.006))(BSF - BSFMIN)+(5.2 +/- 3.3) )SIGMA(SP),其中BSFMIN是最小BSF,实际上是要分析的站点的BSF数据的第一个BSF数据。在每个站点的最低超饱和度(近似为0:1%),定义为AOP导出和测量的N-CCN的比率的平均偏压,不同于在大多数网站上的0.7与1.9相似。除了偏见的喜马拉雅网站> 4.在SS> 0:4%的平均偏差范围与大多数地点的平均偏差相似。对于海洋气溶胶主导地点,偏见偏差较高,类似于1.4-1.9。换句话说,在SS> 0:4%N-CCN估计通过使用浊度计数据,平均不确定性约为30%。偏差主要是由于与散射埃信(SAE)相关的参数化中的偏差。 AOP导出和测量的N-CCN之间的平方相关系数变化为0.5至类似于0.8。为了研究N-CCN和AOP之间的关系的物理说明,产生了Lognormal单峰粒度分布,并计算N-CCN和AOP。模拟表明,N-CCN和AOP的关系受到尺寸分布的几何平均直径和宽度分布的宽度和激活直径的影响。 N-CCN和AOP的关系与观察者的关系类似。

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