Numerical Simulations of Homogeneous Freezing Processes in the Aerosol Chamber AIDA

ATMOSPHERIC CHEMISTRY AND PHYSICS (Impact Factor: 5.05). 10/2002; 3(2003):195-210. DOI: 10.5194/acp-3-195-2003
Source: DLR


The homogeneous freezing of supercooled H2SO4/H2O aerosols in an aerosol chamber is investigated with a microphysical box model using the activity parameterization of the nucleation rate by Koop et al (2000). The simulations are constrained by measurements of pressure, temperature, total water mixing ratio, and the initial aerosol size distribution, described in a companion paper Möhler et al. (2002). Model results are compared to measurements conducted in the temperature range between 194 and 235 K, with cooling rates in the range between 0.5 and 2.6 K min-1, and at air pressures between 170 and 1000 hPa. The simulations focus on the time history of relative humidity with respect to ice, aerosol size distribution, partitioning of water between gas and particle phase, onset times of freezing, freezing threshold relative humidities, aerosol chemical composition at the onset of freezing, and the number of nucleated ice crystals. The latter three parameters can directly be inferred from the experiments, the former three aid in interpreting the measurements. Sensitivity studies are carried out to address the relative importance of uncertainties of basic quantities such as temperature, H2O mixing ratio, aerosol size spectrum, and deposition coefficient of H2O molecules on ice. The ability of the numerical simulations to provide detailed explanations of the observations greatly increases confidence in attempts to model this process under real atmospheric conditions, for instance with regard to the formation of cirrus clouds or type-II polar stratospheric clouds, provided that accurate temperature and humidity measurements are available.

Download full-text


Available from: Ottmar Möhler, Oct 03, 2015
18 Reads
  • Source
    • "The use of the non - equilibrium water activity is important in situations of large local cooling rates ( O ( 1 m s −1 ) ) at low temperatures ( < 210 K ) . In such cases , large aerosol particles cannot equilibrate with the ambient water vapour on the time - scale over which the ambient relative humidity rises , which delays their freezing ( Haag , et al . , 2003a ) . Homogeneous freezing in liquid aerosol droplets in equilibrium with ambient water vapour commences at critical RHI thresholds in the range 150 – 170% for T > 195 K ( Koop , et al . , 2000 ; Möhler , et al . , 2003 ) , depending on the droplet volume ."
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduce a novel large-eddy model for cirrus clouds with explicit aerosol and ice microphysics, and validate its central components. A combined Eulerian/Lagrangian approach is used to simulate the formation and evolution of cirrus. While gas and size-resolved aerosol phases are treated over a fixed Eulerian grid similar to the dynamical and thermodynamical variables, the ice phase is treated by tracking a large number of simulation ice particles. The macroscopic properties of the ice phase are deduced from statistically analysing large samples of simulation ice particle properties. The new model system covers non-equilibrium growth of liquid supercooled aerosol particles, their homogeneous freezing, heterogeneous ice nucleation in the deposition or immersion mode, growth of ice crystals by deposition of water vapour, sublimation of ice crystals and their gravitational sedimentation, aggregation between ice crystals due to differential sedimentation, the effect of turbulent dispersion on ice particle trajectories, diabatic latent and radiative heating or cooling, and radiative heating or cooling of ice crystals. This suite of explicitly resolved physical processes enables the detailed simulation and analysis of the dynamical–microphysical–radiative feedbacks characteristic of cirrus. We draw special attention to the ice aggregation process which redistributes large ice crystals vertically and changes the ice particle size distributions accordingly. We find that aggregation of ice crystals is the key process to generate precipitation-sized ice crystals in stratiform cirrus. A process-oriented algorithm is developed for ice aggregation based on the trajectories and sedimentation velocities of simulation ice particles for use in the dynamically and microphysically complex, multi-dimensional large-eddy approach. By virtue of an idealized model set-up, designed to isolate the effect of aggregation on the cirrus development, we show that aggregation and its effect on the ice crystal size distribution in the model is consistent with a theoretical scaling relation, which was found to be in good agreement with in situ measurements
    Quarterly Journal of the Royal Meteorological Society 10/2010; 136(2010):2074-2093. DOI:10.1002/qj.689 · 3.25 Impact Factor
  • Source
    • "The postulate α≥1 expresses that fact that large droplets (consisting of large aerosols) will freeze first and vanish from the aerosol pool (see e.g. Haag et al., 2003a, Fig. 8 "
    [Show abstract] [Hide abstract]
    ABSTRACT: A double-moment bulk microphysics scheme for modelling cirrus clouds including explicit impact of aerosols on different types of nucleation mechanism is described. Process rates are formulated in terms of generalised moments of the underlying a priori size distributions in order to allow simple switching between various distribution types. The scheme has been implemented into a simple box model and into the anelastic non-hydrostatic model EULAG. The new microphysics is validated against simulations with detailed microphysics for idealised process studies and for a well documented case of arctic cirrostratus. Additionally, the formation of ice crystals with realistic background aerosol concentration is modelled and the effect of ambient pressure on homogeneous nucleation is investigated in the box model. The model stands all tests and is thus suitable for cloudresolving simulations of cirrus clouds.
    ATMOSPHERIC CHEMISTRY AND PHYSICS 01/2009; 9(2009):685-706. DOI:10.5194/acpd-8-601-2008 · 5.05 Impact Factor
  • Source
    • "which is closely related to the nucleation rate coefficient, J, and the freezing probability , P f . Theoretical studies (e.g., Lin et al., 2002; Khvorostyanov and Curry, 2009) and laboratory experiments (e.g., Tabazadeh et al., 1997a; Koop et al., 2000; Hung et al., 2002; Haag et al., 2003a, b) suggest that J becomes substantially large around some threshold T and s i (Pruppacher and Klett, 1997). Decreasing T (or increasing 15 s i ) beyond this level exponentially increases J so that (unless s i is depleted by water vapor deposition onto growing ice crystals) the probability of freezing, P f , eventually becomes unity (Pruppacher and Klett, 1997; Lin et al., 2002; Khvorostyanov and Curry, 2004; Monier et al., 2006; Barahona and Nenes, 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study presents a comprehensive ice cloud formation parameterization that computes the ice crystal number, size distribution, and maximum supersaturation from precursor aerosol and ice nuclei with any size distribution and chemical composition. The parameterization provides an analytical solution of the cloud parcel model equations and accounts for the competition effects between homogeneous and heterogeneous freezing, and, between heterogeneous freezing in different modes. The diversity of heterogeneous nuclei is described through a nucleation spectrum function which is allowed to follow any form (i.e., derived from classical nucleation theory or from empirical observations). The parameterization reproduced the predictions of a detailed numerical parcel model over a wide range of conditions, and several expressions for the nucleation spectrum. The average error in ice crystal number concentration was −2.0±8.5% for conditions of pure heterogeneous freezing, and, 4.7±21% when both homogeneous and heterogeneous freezing were active. Apart from its rigor, excellent performance and versatility, the formulation is extremely fast and free from requirements of numerical integration.
Show more