The theory of ice nucleation by heterogeneous freezing of deliquescent mixed CCN. Part II: Parcel model simulation

被引:50
作者
Khvorostyanov, VI
Curry, JA [1 ]
机构
[1] Georgia Inst Technol, Sch Earth & Atmospher Sci, Atlanta, GA 30332 USA
[2] Cent Aerol Observ, Moscow, Russia
关键词
D O I
10.1175/JAS-3367.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The new theory of ice nucleation by heterogeneous freezing Of deliquescent mixed cloud condensation nuclei (CCN) presented in Part I is incorporated into a parcel model with explicit water and ice bill microphysics to simulate the process of ice nucleation under transient thermodynamic conditions. Simulations are conducted over the temperature range -4degrees to -60degreesC, with vertical velocities varying from 1 to 100 cm s(-1), for varying initial relative humidities and aerosol characteristics. These simulations show that the same CCN that are responsible for the drop nucleation may initiate crystal nucleation and can he identified as ice nuclei (IN) when crystals form. The simulated nucleation rates and concentrations of nucleated crystals depend oil temperature and supersaturation simultaneously, showing good agreement with observations but with noticeable differences when compared with classical temperature-only and supersaturation-only parameterizations. The kinetics of heterogeneous ice nucleation exhibits a negative feedback via water supersaturation, whereby ice nucleation depends on the water supersaturation that is diminished by ice crystal diffusional growth. This feedback is stronger than the corresponding feedback for drop nucleation. and may explain discrepancies between observed ice nuclei concentrations and ice crystal concentrations, the very small fraction of CCN that may serve as IN, and the Much smaller crystal concentrations as compared to drop concentrations. The relative importance of heterogeneous versus homogeneous nucleation is examined for a variety of cloud conditions. Based on these calculations, a simple parameterization for ice crystal concentration is suggested for use in cloud models and large-scale models.
引用
收藏
页码:261 / 285
页数:25
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