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Homogeneous and heterogeneous ice nucleation at LACIS: Operating principle and theoretical studies

by S. Hartmann, D. Niedermeier, J. Voigtl??nder, T. Clauss, R. A. Shaw, H. Wex, A. Kiselev, F. Stratmann
Atmospheric Chemistry and Physics ()
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At the Leipzig Aerosol Cloud Interaction Simulator (LACIS) experiments investigating homogeneous and heterogeneous nucleation of ice (particularly immersion freezing in the latter case) have been carried out. Here both the physical LACIS setup and the numerical model developed to design experiments at LACIS and interpret their results are presented in detail. Combining results from the numerical model with experimental data, it was found that for the experimental parameter space considered, classical homogeneous ice nucleation theory is able to predict the freezing behavior of highly diluted ammonium sulfate solution droplets, while classical heterogeneous ice nucleation theory, together with the assumption of a constant contact angle, fails to predict the immersion freezing behavior of surrogate mineral dust particles (Arizona Test Dust, ATD). The main reason for this failure is the compared to experimental data apparently overly strong temperature dependence of the nucleation rate coefficient. Assuming, in the numerical model, Classical Nucleation Theory (CNT) for homogeneous ice nucleation and a CNT-based parameterization for the nucleation rate coefficient in the immersion freezing mode, recently published by our group, it was found that even for a relatively effective ice nucleating agent such as pure ATD, there is a temperature range where homogeneous ice nucleation is dominant. The main explanation is the apparently different temperature dependencies of the two freezing mechanisms. Finally, reviewing the assumptions made during the derivation of the CNT-based parameterization for immersion freezing, it was found that the assumption of constant temperature during ice nucleation and the chosen ice nucleation time were justified, underlining the applicability of the method to determine the fitting coefficients in the parameterization equation.

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