Abstract

Abstract. Ice nucleation in clouds is often observed at temperatures >235 K, pointing to heterogeneous freezing as a predominant mechanism. Many models deterministically predict the number concentration of ice particles as a function of temperature and/or supersaturation. Several laboratory experiments, at constant temperature and/or supersaturation, report heterogeneous freezing as a stochastic, time-dependent process that follows classical nucleation theory; this might appear to contradict deterministic models that predict singular freezing behavior. We explore the extent to which the choice of nucleation scheme (deterministic/stochastic, single/multiple contact angles θ) affects the prediction of the fraction of frozen ice nuclei (IN) and cloud evolution for a predetermined maximum IN concentration. A box model with constant temperature and supersaturation is used to mimic published laboratory experiments of immersion freezing of monodisperse (800 nm) kaolinite particles (~243 K), and the fitness of different nucleation schemes. Sensitivity studies show that agreement of all five schemes is restricted to the narrow parameter range (time, temperature, IN diameter) in the original laboratory studies, and that model results diverge for a wider range of conditions. The schemes are implemented in an adiabatic parcel model that includes feedbacks of the formation and growth of drops and ice particles on supersaturation during ascent. Model results for the monodisperse IN population (800 nm) show that these feedbacks limit ice nucleation events, often leading to smaller differences in number concentration of ice particles and ice water content (IWC) between stochastic and deterministic approaches than expected from the box model studies. However, because the different parameterizations of θ distributions and time-dependencies are highly sensitive to IN size, simulations using polydisperse IN result in great differences in predicted ice number concentrations and IWC between the different schemes. The differences in IWC are mostly due to the different temperatures of the onset of freezing in the nucleation schemes that affect the temporal evolution of the ice number concentration. The growth rates of ice particles are not affected by the choice of the nucleation scheme, which leads to very similar particle sizes. Finally, since the choice of nucleation scheme determines the temperature range over which ice nucleation occurs, at habit-prone temperatures (~253 K), there is the potential for variability in the initial inherent growth ratio of ice particles, which can cause amplification or reduction in differences in predicted IWC.

Highlights

  • The interactions of aerosol particles and clouds represent the largest uncertainty in current estimates of radiative forcing (Solomon et al, 2007)

  • Empirical expressions have been developed that deterministically predict number concentration of ice particles (Nice) (Fletcher, 1969; Cotton et al, 1986; Meyers et al, 1992). Such parameterizations often do not include any constraint on the total Nice, i.e., they are not limited by the number of potential ice nuclei (IN) that exist in an aerosol population as opposed to laboratory studies where this number is constrained by the sample size in the ice chamber

  • In a first set of parcel model simulations, it is assumed that only a fraction of a single cloud condensation nuclei (CCN) size class (DCCN = 800 nm) act as ice nuclei with a concentration of number concentration of potential ice nuclei (NIN) = 4 l−1; freezing of droplets formed on other particle sizes and ice nucleation by contact or deposition freezing are not considered

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Summary

Introduction

The interactions of aerosol particles and clouds represent the largest uncertainty in current estimates of radiative forcing (Solomon et al, 2007). Parameterizations of the number of frozen particles as a function of cooling rates have been developed in order to describe the freezing behavior of biological material or other solutes immersed in water drops (Bigg, 1953; Vonnegut and Baldwin, 1984; Diehl et al, 2002) Such experimental results are in agreement with classical nucleation theory (CNT) that describes ice nucleation as a stochastic, time-dependent process (Fukuta and Schaller, 1982; Khvorostyanov and Curry, 2000; Shaw et al, 2005; Curry and Khvorostyanov, 2012).

Ice nucleation schemes
Deterministic scheme
Description of the box model
Description of the adiabatic parcel model
Agreement of nucleation schemes with laboratory studies
Impact of IN diameter DIN on frozen fraction Ffr
Variation of updraft velocity
Initiation of the Bergeron-Findeisen process
Parcel model studies: polydisperse IN
Findings
Size distribution of ice particles
Full Text
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