ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-12741-2015New particle-dependent parameterizations of heterogeneous freezing
processes: sensitivity studies of convective clouds with an air parcel
modelDiehlK.kdiehl@uni-mainz.deMitraS. K.Institute of Atmospheric Physics, Johannes Gutenberg University, Mainz, GermanyK. Diehl (kdiehl@uni-mainz.de)18November20151522127411276323April201517June201527October201529October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/12741/2015/acp-15-12741-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/12741/2015/acp-15-12741-2015.pdf
Based on the outcome of laboratory results, new particle-dependent
parameterizations of heterogeneous freezing were derived and used to improve
and extend a two-dimensional spectral microphysics scheme. They include (1)
a particle-type-dependent parameterization of immersion freezing using the
numbers of active sites per mass, (2) a particle-type and size-resolved
parameterization of contact freezing, and (3) a particle-type-dependent
description of deposition freezing. The modified microphysical scheme was
embedded in an adiabatic air parcel model with entrainment. Sensitivity
studies were performed to simulate convective situations and to investigate
the impact of ice nuclei concentrations and types on ice formation. As a
central diagnostic parameter, the ice water fraction (IWF) was selected, which is
the relation of the ice water content to the total amount of water in
the condensed form. The following parameters were varied: initial aerosol
particle number size distributions, types of ice nucleating particles, final
temperature, and the fractions of potential ice nucleating particles. Single
and coupled freezing processes were investigated. The results show that
immersion freezing seems to be the most efficient process. Contact freezing
is constrained by the collision kernel between supercooled drops and
potential ice nucleating particles. The importance of deposition freezing
lies in secondary ice formation; i.e., small ice particles produced by
deposition nucleation trigger the freezing of supercooled drops by
collisions. Thus, a broader ice particle spectrum is generated than that by
immersion and contact freezing. During coupled immersion–contact and
contact–deposition freezing no competition was observed, and both processes
contribute to cloud ice formation but do not impede each other. As already
suggested in the literature, mineral dust particles seem to be the most
important ice nucleating particles. Biological particles are probably not
involved in significant ice formation. The sensitive parameters affecting
cloud properties are temperature, aerosol particle composition and
concentration, and particle size distribution.
Introduction
The importance of the ice phase in mixed-phase convective clouds is
indisputable. The additional release of the latent heat of freezing enforces
not only the strength of the convection, but also the presence of ice particles in
the cloud substantially modifies the dynamical structure and the amount
of precipitation (e.g., Gilmore et al., 2004). Hence, studying the ice phase
in convective mixed-phase clouds is highly relevant for the understanding of
such clouds and their atmospheric impact. With the convective updraft, water
drops are transported into regions where the temperature is low enough to
allow them to freeze. Homogeneous freezing (i.e., freezing that does not
require the presence of ice nuclei) becomes efficient at temperatures below
-35 ∘C (Pruppacher and Klett, 2010). Thus, at warmer temperatures
in the troposphere heterogeneous freezing (which involves ice nucleating
particles) is the only process of ice initiation, potentially triggering
secondary ice formation. Heterogeneous freezing significantly changes the
availability of liquid water in the upper parts of the clouds, since ice
particles grow at the expense of liquid drops by the deposition of water
vapor (Bergeron–Findeisen process) and by riming (i.e., collection of liquid
water). Thus, the number of ice nucleating particles, as well as their efficiency to
initiate ice formation at temperatures above the level of homogeneous
freezing, determines the nature of convective clouds as it modifies cloud
microphysical processes and cloud development (e.g., van den Heever et al.,
2006; Ekman et al., 2007; Phillips et al., 2007; Lee et al., 2009).
For detailed investigations of cloud microphysical processes adiabatic
parcel models with entrainment are often employed (e.g., Simmel et al.,
2005; Leroy et al., 2006; Diehl et al., 2006; Diehl and Wurzler, 2010; Ervens and
Feingold, 2012). Air parcel models describe a rising bubble of air, whose
volume increases with height. The advantage of parcel models is that they
allow for a detailed description of the cloud microphysical processes, usually
achieved by the use of spectral-bin microphysical models that explicitly
solve the microphysical equations (see Khain et al., 2000, for an overview).
The initiation of the ice phase in numerical models is always parameterized.
As the ability of atmospheric particles to serve as ice nuclei varies over a
wide temperature range for each freezing process (Pruppacher and Klett,
2010), parameterizations (for particular model simulations) are required that
describe the effects of different types of ice nuclei. Model investigations
that compared the effects of different ice nucleation schemes (Fan et al.,
2010; Kulkarni et al., 2012; Ervens and Feingold, 2013) imply that cloud
properties are sensitive to the surface properties of ice nucleating
particles. In those studies particle surface properties were described by
the contact angle O. Other parameterizations were related directly to
different particle types, and model studies showed that certain aerosol types
significantly alter cloud microphysics (Diehl et al., 2006; Lohmann and
Diehl, 2006; Phillips et al., 2008; Hoose et al., 2008; Storelvmo et al.,
2008; Lee et al., 2009). They also allow one to simulate the effects of
particular aerosols such as biomass burning particles (Diehl et al., 2007),
biological particles (Phillips et al., 2009), bacteria (Diehl and Wurzler, 2010),
or mineral dust (DeMott et al., 2015; Hande et al., 2015).
The present investigations are part of the German Science foundation (DFG)
research group INUIT (Ice Nuclei Research UnIT),
which was established to study heterogeneous ice formation in the
atmosphere. In laboratory and field studies, the number concentrations,
chemical composition, surface properties, and sources of atmospherically
relevant ice nuclei are investigated in different freezing modes. As an
outcome of these experiments, joint parameterizations are derived to be fed
into a cloud model to simulate mixed-phase cloud microphysics and to
quantify the contribution of ice nuclei particle types and freezing modes.
For more details see the INUIT website: www.ice-nuclei.de.
One of the models employed during INUIT is an adiabatic air parcel model
with entrainment and a detailed sectional description of the cloud
microphysics. It describes immersion and contact freezing for various ice
nuclei types such as mineral dust, soot, and biological particles (Diehl and
Wurzler, 2004; Diehl et al., 2006). Until now deposition ice nucleation had not been
included as it has been viewed as being of less importance for ice
formation in convective clouds. It might be negligible in mixed-phase clouds
because water saturation is reached in the updraft after a short time (e.g.,
Ansmann et al., 2008, based on lidar observations). However, the formation
of only a comparably small amount of ice (initiated by few very efficient
ice nuclei) might result in secondary ice nucleation with highly complex
interactions and consequences. The present version of the model was improved
in a way that now it contains all heterogeneous freezing processes
(deposition, contact, coupled condensation/immersion). This allows one to
investigate the competition of the freezing modes to understand the
importance of deposition freezing in comparison to immersion and contact
freezing. The effects of various ice nucleating particles were compared to
each other, considering the different freezing modes, to estimate their
importance.
Model description
The impact of ice nucleating particles on mixed-phase convective clouds is
investigated with a microphysical scheme embedded in an air parcel model
with entrainment. The previous version of Diehl et al. (2006) was further
developed in the way that new parameterizations were added or existing ones
were modified or completely replaced.
The model of Diehl et al. (2006) contains a two-dimensional spectral
microphysics scheme, which divides the hydrometeor spectra into size bins
(Simmel and Wurzler, 2006). These describe the number and mass of the drops
or ice particles within the corresponding size range. A fixed bin structure
is used, combining the wetted aerosol particles and the drops in one
spectrum,
where the soluble and total mass of aerosol particles is explicitly
considered in every bin. An initial dry aerosol particle number size
distribution is given where the particles are internally mixed with variable
insoluble and soluble fractions. After starting the rise of the air parcel,
the particles grow into the droplet part of the spectrum by condensation.
The size spectra are allowed to evolve freely; they are not constrained by
an underlying distribution function.
Here two-dimensional means that the microphysics is not a function of only the
drop size (one-dimensional case) but a function of both drop and
aerosol particle size (Simmel and Wurzler, 2006). In the one-dimensional
case, equally sized drops contain only equally sized particles, which means
that drops of the same size freeze at the same temperature (Diehl and
Wurzler, 2004). However, in real clouds equally sized drops contain
differently sized particles, which affect the freezing temperature of the
drops. The soluble parts of the aerosol particles might lead to a freezing
point depression (Koop et al., 2000; Diehl and Wurzler, 2004), while the
insoluble fractions might give higher freezing temperatures via immersion
freezing (see Sect. 2.1). Thus, the two-dimensional description of
microphysics allows for drops of the same size to freeze at different
temperatures, which reflects drop freezing in atmospheric clouds in a more
realistic way. It is divided into 90 categories for the particulate mass and
into 66 categories for the water mass, both starting at 0.002 µm in
diameter, with a mass doubling in every category for the water mass and a
mass doubling in every other category for the particulate mass. This
combination is recommended in Simmel and Wurzler (2006).
The warm microphysical processes include growth of particles and drops by
water vapor deposition, shrinking of particles and drops by evaporation,
collision and coalescence, and impaction scavenging of particles. The
entrainment of aerosol particles, drops, ice particles, temperature, and
humidity is embedded (Simmel et al., 2005). The cold cloud microphysics
describes immersion and contact freezing in parameterized form for various
particle types (Diehl and Wurzler, 2004; Diehl et al., 2006). Condensation
freezing is included implicitly in immersion freezing. Drops that are
nucleated during the ascent of the air parcel by aerosol particles entrained
above the freezing level could freeze immediately by immersion freezing. The
growth of ice particles by water vapor diffusion and by riming (collision
with supercooled droplets) is considered. Ice particles are sampled in a
second spectrum, which shows the same bins as the aerosol particle–liquid
drop spectrum. Once drops are frozen or particles are involved in contact or
deposition freezing (see next sections), they are shifted to the ice particle
spectrum. The saturation ratio during growth and shrinking processes is
iteratively calculated (Simmel and Wurzler, 2006).
Collision processes are described by the linear discrete method (Simmel et
al., 2002) including the collision kernel of Kerkweg et al. (2003). By using
the corresponding densities and terminal velocities, the collision kernel is
appropriate for all collision processes between aerosol particles, drops,
and ice particles, such as growth of drops by collision–coalescence,
impaction scavenging of particles by drops, contact freezing of supercooled
drops, growth of ice particles by riming, and secondary ice formation., i.e.,
freezing of supercooled drops by collision with an ice germ.
Because of the explicit descriptions mentioned above, the microphysical
scheme is a useful tool to study the link between aerosol
particles and the evolution of cloud properties in detail. The incorporation into an
air parcel model has the advantage that all changes in the microphysical
evolution of the cloud can be attributed to microphysical processes. The
model improvements presented in the next sections include the following:
an updated particle-type-dependent description of immersion freezing, which
is now related to the mass of insoluble particles contained in drops;
a modified description of contact freezing, which is dependent on
not only particle type but also particle size-resolved;
a new particle-type-dependent description of deposition freezing.
All parameterizations are directly based on previous and new laboratory
measurements as described in the following sections.
Immersion freezing
The previous description of immersion freezing (Diehl and Wurzler, 2004)
gave the freezing rate of pure water drops containing ice nucleating
particles as a function of the drop volume according to
-dNfdt=NliqBimmVdexp-a1,immTdTdt,
where Nf is the number of frozen drops, Nliq the number of liquid drops,
and the constants a1,imm and Bimm. This parameterization implicitly
reflected the fact that larger drops contain more particles because of
collision and coalescence of drops and impaction scavenging of aerosol
particles. The previous version was replaced by a new one, which is coupled
directly to the mass of insoluble particles in the drops. This is possible
because of the sectional distribution of drops and particles into size
classes (Diehl et al., 2006).
Parameterizations based on laboratory data
The experimental data used as a basis for the parameterizations include the
following ice nucleating particle types: bacteria, pollen, feldspar, illite,
and kaolinite. Murray et al. (2011) investigated kaolinite KGa-1b and Atkinson
et al. (2013) K-feldspar. Illite NX was studied by Broadley et al. (2012)
and recently by Hiranuma et al. (2015). The latter publication summarized
the results from 17 experimental techniques. Wex et al. (2015)
include the results from seven experimental techniques using
Snomax® as a proxy for bacteria; previous data
for pollen (Diehl et al., 2002; v. Blohn et al., 2005) were newly evaluated.
As a parameter for immersion freezing, the number of active sites per unit mass
nm was selected as this is derived directly from the particle mass
concentration in the drops during the experiments. Some references give as an
outcome from their measurements the surface density of active sites per unit
particle surface ns; however, this parameter was derived afterwards by
using a specific particle surface.
For kaolinite KGa-1b, K-feldspar, and tree and grass pollen, an exponential
increase of nm with temperature T was found, which is described by
nm=exp(aimm+bimmTs),
where nm is in g-1, aimm and bimm are particle-related constants,
Ts=T0-T, and T0= 0 ∘C, with T in ∘C. The
constants for all particle types are listed in Table 1. Given in Table 1 are
also the parameters Tini and Tlim, representing the onset of
immersion freezing during experiments and the lowest temperature that was
investigated in the experiments, respectively.
Numbers of active sites per unit mass as a function of temperature
calculated by Eq. (2) with constants given in Table 1 for various particle
types in the immersion mode. Based on laboratory data of immersion freezing
experiments (Diehl et al., 2002; v. Blohn et al., 2005, pollen; Murray et
al., 2011, kaolinite KGa-1b; Atkinson et al., 2013, K-feldspar; Hiranuma et
al., 2015, illite NX; Wex et al., 2015, Snomax®.
Based on the best fit of the data of Murray et al. (2011), the constants in
Eq. (2) were derived for kaolinite KGa-1b by using an average specific
particle surface area of 11.8 m2 g-1 as given by Murray et al. (2011); the result is shown in Fig. 1 as an orange line. The solid part of
the line represents the range that is validated by measurements of Murray
et al. (2011) while the dotted part shows an extrapolation towards higher
temperatures. The value of Tini is based on earlier measurements of
Pitter and Pruppacher (1973). The same was performed for K-feldspar by using
the best fit to the experimental data given by Atkinson et al. (2013), which
were changed into Eq. (2) by using the specific particle surface area of 3.2 m2 g-1 from Atkinson et al. (2013). The parameters are listed in
Table 1, the result of Eq. (2) is shown in Fig. 1 as a red line. Regarding
pollen, previous data from Diehl et al. (2002) and v. Blohn et al. (2005)
were evaluated. From the frozen fractions of drops, the drop volume, and the
mass of pollen in the drops, the numbers of active sites nm as functions
of temperature were calculated according to (e.g., Murray et al., 2011)
nm=-ln( 1-fice(T))cpollenVdrop,
where fice(T) is the fraction of frozen drop at temperature T,
cpollen the pollen concentration per drop, and Vdrop the drop
volume. Data for tree and grass pollen were summarized leading to two
parameterizations for tree and grass pollen as given in Eq. (2). The constants
are listed in Table 1 and the results of Eq. (2) are shown in Fig. 1 as
light green lines. The solid parts of the lines represent the ranges, which
are validated by measurements, while the dotted parts show extrapolations
towards lower temperatures.
Values of immersion freezing constants in Eq. (2). Based on data
from 1 Wex et al. (2015), 2 Diehl et al. (2002), 3 v. Blohn et
al. (2005), 4 Atkinson et al. (2013), 5 Hiranuma et al. (2015), and
6 Murray et al. (2011).
Regarding illite NX and Snomax®,
parameterizations were suggested by Broadley et al. (2012), Hiranuma et al. (2015), and Wex et al. (2015). In order to derive analogue descriptions, as in the
cases of the other particle types, these parameterizations were replaced by
new ones following Eq. (2). Regarding illite NX, the parameterization was
derived from the data reported by Hiranuma et al. (2015). The fit as given
by Hiranuma et al. (2015) was changed into an expression for nm by using
the specific particle surface area of 124.4 m2 g-1 of the illite
NX sample (Hiranuma et al., 2015). From the nonlinear curve (solid blue
line in Fig. 2) a linear regression line was derived, which is included in
Fig. 1 (blue solid line). For Snomax®, it was
concluded in Wex et al. (2015) that the average nm values (given in
Fig. 2 as open green symbols) are very well represented by the Hartmann et
al. (2013) parameterization (solid green line in Fig. 2). One can notice a
linear increase in the temperature range down to approximately -9 ∘C and a further progress on a maximum value of 1.4 × 1012 g-1.
Thus, a linear regression curve was derived for the increase of
nm until the maximum value was reached; it is included in Fig. 2 as
a solid green line.
Numbers of active sites per unit mass as a function of
temperature. Parameterizations for illite NX and
Snomax® based on the outcome of laboratory
immersion freezing experiments.
It can be noted from Fig. 1 that bacteria act at the highest temperatures
starting not far below 0 ∘C. Pollen lie in the range of the
mineral dust particles. The differences between the mineral dust types are
significant, with feldspar being the most efficient one and kaolinite the least
efficient one.
Treatment of immersion freezing in the model
The previous Eq. (1), which gives the freezing rate of the supercooled drops,
had to be replaced by a similar expression that couples the number of
active sites and the mass of insoluble particles in the drops. According to
the singular description of heterogeneous freezing (Vali, 1971; Broadley et
al., 2012), the frozen fraction of drops fice is given by
fice(T)=Nf(T)Nliq=1-exp(-nm(T)mpid),
where Nf(T) is the number of frozen drops at temperature T, Nliq the number
of liquid drops, mpid the mass of particles immersed in the drops, and
nm(T) the number of active sites per unit mass at temperature T, which is
related to the cumulative nucleus spectrum K(T) per unit mass per unit
temperature:
nm(T)=∫T0TK(T)dT
when lowering the temperature from T0=0∘C to T. From
Eqs. (4) and (5) an expression for the change of the number of frozen drops
ΔNf per temperature interval ΔT can be derived (Connolly
et al., 2009):
ΔNf=Nliq(1-exp(-K(T)mpidΔT),
where Nliq is the number of supercooled liquid drops and mpid the mass
of particles immersed in the drop. Thus, Eq. (1) can be replaced by
dNfdt=Nliq1-exp(-K(T)mpiddT)dt.
As the aerosol particles are internally mixed, one can assume that only a
fraction of the insoluble mass per drop consists of ice nucleating material.
Thus, the mass mpid was reduced by a factor FINP, so that only this
mass fraction accounts for possible numbers of active sites nm and Eq. (7) was modified to
dNfdt=Nliq1-exp(-K(T)mpidFINPdT)dt.
A similar treatment was applied in Diehl and Wurzler (2010). From Eqs. (2) and
(5) it follows that
K(T)=dnm(T)dT=bimm exp(aimm+bimmTs).
In Eq. (9) the freezing point depression due to the content of soluble
material in the drops is considered; for details see Diehl and Wurzler
(2004). In the model simulations, immersion freezing starts at the
particle-related temperature Tini, and at temperatures below Tlim it
is assumed that the numbers of active sites stay constant. The sizes of
possibly ice nucleating particles are restricted; i.e., dust particles must
be larger than 0.1 µm in diameter and bacteria are limited to their
typical diameters of 0.3 to 2 µm (Matthias-Maser and Jaenicke, 1995).
Pollen are large particles of 10 µm at least (Straka, 1975); however,
for the present simulations a lower limit of 2 µm was selected to
allow for at least some freezing by pollen (see Sect. 3, initial dry particle
number size distributions).
During the model simulations the content of insoluble particles per drop
varies by several orders of magnitude, which is due to the sizes of the
condensation particles, the uptake of particles by the drops via impaction
scavenging, and the drop collisions followed by coalescence. Thus, not all
drops of the same sizes freeze at certain temperatures, which describes the
situation in real clouds (Diehl and Wurzler, 2004).
Contact freezing
In Diehl et al. (2006) contact freezing was described for several particle
types independent of their sizes. However, measurements indicate that the
size of the involved particles affects the contact freezing efficiency in
the way that efficiency increases with increasing particle size (e.g.,
Gorbunov et al., 2001; Hoffmann et al., 2013a; Ladino Moreno et al., 2013).
Therefore, the description of Diehl et al. (2006) was modified in a way
that it is now particle-size dependent.
Parameterizations based on laboratory data
Measurements, which give a direct correlation between particle size and
contact freezing efficiency, are not yet available for a wider range of ice
nucleating particle types. Therefore, for the present parameterization
different size classes were examined. Hoffmann et al. (2013a, b), and
N. Hoffmann and A. Kiselev (personal communication, 2014) performed contact freezing
experiments using an electrodynamical balance with monodisperse particles of
150 to 750 nm diameter. The results showed rather low median freezing
temperatures T50 (the temperature where 50 % of an observed drop
population freeze), e.g., -34 ∘C for illite NX, -32.5 ∘C
for kaolinite Fluka, and less than -25 ∘C for
Snomax®, in comparison to measurements with
polydisperse particle samples.
Those experiments with polydisperse particle samples were performed at the
UCLA vertical wind tunnel by Levin and Yankofsky (1983) and Pitter and
Pruppacher (1973). The latter studied kaolinite and montmorillonite with
particle diameters between 0.1 and 10 µm with a mode between 1 and 2 µm and measured median freezing temperatures of -12 and
-8 ∘C, respectively. Levin and Yankofsky found a median freezing
temperature of -4.5 ∘C for bacteria. Diehl et al. (2012)
investigated polydisperse mineral particles with supercooled drops suspended
in an acoustic levitator. Median freezing temperatures were -11.5 ∘C for illite NX and -8.7 ∘C for montmorillonite K10. The latter
agrees very well with the value found by Pitter and Pruppacher (1973) within
the measurement error (1 K). Extrapolating the data of Hoffmann et al. (2013b) for illite NX towards larger particle sizes as shown in Fig. 3
indicates that a median freezing temperature of -11.5 ∘C could be
affected by dust particles with sizes between 2.3 and 3.0 µm in
diameter (Diehl et al., 2012). Those particles are part of the polydisperse
particle spectrum of illite NX (Hiranuma et al., 2015). Thus, one might
conclude that the median freezing temperatures determined for polydisperse
particle samples are affected by larger particles present in the size
spectrum. This is probably the case also for previous findings measured with
polydisperse particle samples.
Freezing temperatures as a function of particle diameter for
illite NX in the contact mode. Extrapolations to larger particle sizes.
Therefore, as an approximation, data obtained from polydisperse particle
samples were used in the present parameterizations for particles with
diameters mostly larger than 1 µm, and at least larger than 0.7 µm. Data obtained from monodisperse particle samples were taken for ranges
around the respective particle sizes.
The experimental data used as basis for the parameterizations include the
following ice nucleating particle types: bacteria, feldspar,
montmorillonite, illite, and kaolinite. In most cases, a linear correlation
between the frozen fraction of drops and the temperature was found. From the
experimental data, regression lines were calculated, which are shown in
Fig. 4 for various particle types (marked by different colors) and
particle sizes (marked by different line styles).
Frozen fraction of drops by contact freezing as a function of
temperature for various particle types and sizes, marked by different colors
and line styles. Calculated by Eqs. (10) and (11), respectively, with constants
given in Tables 2 and 3 for various particle types, based on the outcome of
laboratory experiments (Pitter and Pruppacher, 1973; Levin and Yankofsky,
1983; Diehl et al., 2012; Hoffmann et al., 2013a, b; N. Hoffmann and A. Kiselev, personal communication, 2014; K. Diehl and S. K. Mitra, personal communication, 2014).
Values of contact freezing constants in Eqs. (10) and (11). Based
on data from a N. Hoffmann and A. Kiselev (personal communication, 2014), who used
Snomax® as a proxy for bacteria and
b Levin and Yankofsky (1983). The latter values were used in Diehl et
al. (2006).
Particle type and sizeaconb1,conb2,conb3,conBacteria 0.3 µm ≤dap< 0.5 µma-1.36581-0.26367-0.01511-2.849110.5 µm ≤dap< 0.7 µma-0.55381-0.10712-0.00616-1.16822aconbcon0.7 µm ≤dap< 2 µmb-0.264-0.742
Values of contact freezing constants in Eq. (10). Based on data from
a Hoffmann et al. (2013b); b Diehl et al. (2012); c Extrapolation; d and f
Pitter and Pruppacher (1973), used in Diehl et al. (2006); d Hoffmann et al. (2013a); g N. Hoffmann and A. Kiselev (personal communication, 2014);
h K. Diehl and S. K. Mitra (personal communication, 2014).
Particle sizes of bacteria were restricted to their typical sizes with
diameters between 0.3 and 2 µm (Matthias-Maser and Jaenicke, 1995).
Measurements of N. Hoffmann and A. Kiselev (personal communication, 2014) were performed with
monodisperse Snomax® particles of 0.32 and 0.55 µm diameter. These data were used for size ranges from 0.3 to 0.5 µm
and from 0.5 to 0.7 µm. For particles between 0.7 and 2 µm the data of Levin and Yankofsky (1983) were taken. The results are
given in Fig. 4 as green lines.
Mineral dust particles
For mineral dust particles, a lower size limit of 0.1 µm in diameter
was assumed. Illite NX particles were investigated by Hoffmann et al. (2013b) with 0.15, 0.32, 0.55, and 0.75 µm particles. These data were
taken for size ranges from 0.1 to 0.2 µm, 0.2 to 0.4 µm, 0.4
to 0.6 µm, and 0.6 to 0.8 µm. For larger particles, data from
Diehl et al. (2012) were used. The results are shown in Fig. 4 as blue
lines. K-feldspar was investigated by N. Hoffmann and A. Kiselev (personal communication, 2014) with 0.32 and 0.55 µm particles and by K. Diehl and S. K. Mitra
(personal communication, 2014) with polydisperse particles. Here three size ranges were
defined, from 0.1 to 0.4, 0.4 to 0.8 µm, and larger than
0.8 µm. The data are marked in Fig. 4 as red lines. For kaolinite
and montmorillonite, two size ranges were also specified, from 0.1 and 1 µm and larger than 1 µm. The data for the larger particles
sizes were both taken from Pitter and Pruppacher (1973; polydisperse
particle samples). Results from Hoffmann et al. (2013a) were used for the
smaller size range of kaolinite. Under the assumption that the differences
between larger and smaller ice nucleating particles (INP) are similar for kaolinite and montmorillonite
the data for the smaller size range of montmorillonite were obtained by a
parallel shifting analogue to kaolinite. In Fig. 4, results for kaolinite
are given in orange, results for montmorillonite in cyan.
From Fig. 4 it can be noted that particles in the larger size ranges
affect freezing already at temperatures around -10 ∘C, while
smaller particles become active in a temperature range around -25 ∘C. Bacteria act at the highest temperatures, and kaolinite and illite at the
lowest. For all particle types and sizes except bacteria smaller than
0.7 µm,
the frozen fraction of drops increases linearly with temperature
T (given in ∘C) according to Diehl et al. (2006):
NfNliq=aconT+bcon,
where Nf is the number of frozen drops, Nliq the number of liquid drops
colliding with inactivated particles at temperature T, and the constants
acon and bcon. Note that in Eq. (10) the frozen fraction is limited to
values between 0 and 1. In Diehl et al. (2006), the constants were given for
several particle types independent of their sizes, while in the present
parameterization the constants acon and bcon are size resolved. They
are listed in Tables 2 and 3. In the case of small bacteria, the equation to
calculate the frozen fraction of drops with respect to the temperature T (in
∘C) has the form
NfNliq=acon+b1,conT+b2,conT2+b3,conT3.
The size-resolved constants acon, b1,con, b2,con, and
b3,con are given in Table 2.
Treatment of contact freezing in the model
The description of contact freezing includes the following conditions: (1) inactivated particles have to be present, and (2) particles and supercooled
drops have to collide with each other. Furthermore, the sizes of the
particles allowed as activating ice nucleating particles are restricted as
for deposition freezing (i.e., dust particles > 0.1 µm in
diameter, bacteria 0.3 to 2 µm in diameter).
The presence of inactivated particles is always the case during the air
parcel ascent because of entrainment; i.e., new inactivated particles are
continuously mixed in at the edges of the simulated cloud. However, in the
presently employed air parcel model the particles are in equilibrium with
respect to the water vapor in their environment and, thus, they take up some
water due to their size and soluble fraction. As introduced in Diehl et al. (2006) the dryness of a potential ice nucleating particle is defined by the
assumption that the water mass should be smaller than half of the dry
particle mass.
The second condition is considered by a collision kernel K calculated for
supercooled drops and particles (Kerkweg et al., 2003; for more details see
Diehl et al., 2006):
K=EcollV∞,drop-V∞,ap⋅πrdrop-r∞2,
where V∞,drop and V∞,ap are the terminal velocities of
the drop and the particle, respectively, and rdrop and rap the drop and
particle radii, respectively. The collision kernel shows the highest values for
collisions between large drops and particles (Diehl et al., 2006); i.e.,
contact freezing is most efficient when large supercooled drops and
particles are present. If during the model simulations inactivated particles
collide with supercooled drops, the number of frozen drops is calculated. It
is assumed that only a fraction FINP of the aerosol particles is able to
act as ice nucleating particles. Only drops that collide with those INP are
allowed to freeze, which is included in the following modified Eqs. (13) and
(14):
Nf=FINPNliq(aconT+bcon),Nf=FINPNliq(acon+b1,conT+b2,conT2+b3,conT3).
This is under the requirement that these equations are based on measurements
with one drop–particle collision per freezing event so that the fraction
of frozen drops in Eqs. (13) and (14) can be set equal to the freezing
probability (Ladino Moreno et al., 2013). This requirement is fully achieved in the
experiments of Hoffmann et al. (2013a, b), and N. Hoffmann and A. Kiselev
(personal communication, 2014). During the experiments of Pitter and Pruppacher (1973),
Levin and Yankofsky (1983), and Diehl et al. (2012) the number of collisions
per freezing event is not documented. Single supercooled drops were freely
levitated (in a wind tunnel or an acoustic levitator) while one burst of INP
was blown on it. Therefore, as the particles collided almost simultaneously
with the supercooled drop, one could assume that in case the drop froze this
was triggered by the first collision.
Deposition freezingParameterizations based on laboratory data
Activated fraction of particles in the deposition freezing mode as
a function of ice supersaturation for Saharan and Asian dust. Data from
K. Ardon-Dryer and Z. Levin (personal communication, 2012).
The experimental data used as basis include the following ice nucleating
particle types: bacteria, feldspar, illite, and Saharan and Asian dust. The
measurements were performed with INP counters FRIDGE (FRankfurt Immersion and Deposition freezinG Experiment) or with a
continuous flow diffusion chamber (CFDC). Data for Asian and Saharan dust
were taken from measurements with the FRIDGE-TAU (FRankfurt Ice-nuclei Deposition freezinG Experiment, the Tel Aviv University version) (K. Ardon-Dryer and Z.
Levin,
personal communication, 2012), data for illite NX and Snomax® from INUIT FRIDGE experiments (Hiranuma et al., 2015; A. Danielczok and H. Bingemer, personal communication, 2014; Weber, 2014), and data for illite IMt1 and
K-feldspar from CFDC measurements (Yakobi-Hancock et al., 2013). The
Snomax® data were considered as representative
for bacteria in the model simulations.
K. Ardon-Dryer and Z. Levin (personal communication, 2012) measured the activation of Saharan
and Asian dust particles at different ice supersaturations and temperatures
and observed an increase of the activated particles with supersaturation but
no temperature dependence in the observed temperature range between -15 and
-20 ∘C. Figure 5 shows the data of K. Ardon-Dryer and Z. Levin (personal communication, 2012) as
activated fraction of particles as a function of ice supersaturation. From
all data, mean values were calculated and regression lines were derived.
These are included in Fig. 6 as cyan and pink solid lines.
Activated fraction of particles in the deposition freezing mode as
a function of ice supersaturation for various particle types. Regression
lines based on data from K. Ardon-Dryer and Z. Levin (personal communication, 2012; Saharan
and Asian dust), Yakobi-Hancock et al. (2013; K-feldspar and illite IMt1),
A. Danielczok and H. Bingemer (personal communication, 2014; Snomax® and illite NX). Described by Eq. (15) with constants in Table 4.
The INUIT FRIDGE measurements were performed with illite NX and
Snomax® in a temperature range from -10 to
-25 ∘C. A significant temperature dependence was not observed and,
therefore, the activated particle fraction was derived as a function of ice
supersaturation only. As ice saturation is dependent on temperature, an
implicit dependence of deposition freezing on temperature is already
incorporated. Regression lines were calculated based on the average values.
They are given in Fig. 6 as blue (illite NX) and green
(Snomax®) solid lines. It can be noticed that
illite NX is much more efficient than the Saharan and Asian dust particles,
which are characterized by a mixed composition. Asian dust mostly consists
of quartz (Möhler et al., 2006), which acts in the deposition mode at
lower temperatures than illite (Zimmermann et al., 2008). Also Saharan dust
contains a quartz fraction of nearly one-third (Möhler et al., 2006). As
expected the activated fractions of Snomax®
particles are the highest. Measurements with a CFDC were performed at
-40 ∘C with illite IMt1 and K-feldspar particles (Yakobi-Hancock
et al., 2013). Regression lines were derived from the data and are given in
Fig. 6 as blue (illite IMt1) and red (K-feldspar) broken lines,
respectively.
For illite NX and Snomax®, data are available not
only for polydisperse particle samples but also for samples with distinct
particle sizes (A. Danielczok and H. Bingemer, personal communication, 2014; Weber, 2014). In
case of illite, monodisperse particles with diameters of 300 and 500 nm
resulted in higher activated fractions for the larger particle sizes, but in
both cases the activated fractions were higher than in case of the
polydisperse particle samples. In case of Snomax®, monodisperse particles of 100, 200, 300, and 500 nm were investigated
finding again higher activated fractions for the larger particle sizes than
in the polydisperse case. As particle-size-resolved measurements are not
available for all particle types it was decided to treat deposition freezing
size independent in the model. As the activated fractions of the
polydisperse particle samples (which are the basis of the present
parameterizations) are lower than the ones of monodisperse samples, this
would not lead to an overestimation of deposition freezing.
For all particle types, an exponential increase of the activated fraction
with the ice supersaturation was found, which is described by
NactNtotal=exp(adep+bdepsice),
where Nact is the number of activated particles, Ntotal the total
particle number, sice the ice supersaturation given in %, and adep
and bdep are particle-related constants. The values of adep and
bdep are listed in Table 4 for the different particle types. Note that
the activated fraction in Eq. (15) is limited to values between 0 and 1.
Values of deposition freezing constants adep and bdep in
Eq. (15) and values of lower limits of temperature Tini and ice
supersaturation sice,ini. Based on data from a A. Danielczok and H. Bingemer (personal communication, 2014), b Yakobi-Hancock et al. (2013), and c
K. Ardon-Dryer and Z. Levin (personal communication, 2012).
Although data were available only for ice supersaturation ranges below
25 % (bacteria, illite NX, Saharan and Asian dust) and above 20 %
(illite IMt1 and feldspar), respectively, due to the investigated
temperature ranges, it is assumed that Eq. (15) is valid for the complete ice
supersaturation range. However, according to the onset of deposition
freezing in the experiments, lower limits of ice supersaturation and
temperature as measured during the FRIDGE experiments were set in the model.
That means, deposition freezing of illite IMt1 and feldspar start at the
same conditions as illite NX. In the model simulations, the two types of
illite were taken as upper and lower limits and referred to as illite 1 and
illite 2. The values are given in Table 4.
Treatment of deposition freezing in the model
Conditions for deposition freezing are (1) inactivated particles have to be
present as it is required for contact freezing; i.e., the same particles may
affect deposition or contact freezing. Additionally, (2) there are two size
conditions. First, the inactivated particles have to exceed a critical germ
size r∗, depending on temperature and ice supersaturation, which
is according to Pruppacher and Klett (2010):
r∗=2MWσi,vRTρicelnSice,
where MW is the molecular weight of water, σi,v the surface
tension, R the universal gas constant, T the temperature, ρice the
density of ice, and Sice the ice saturation ratio. However, this
condition is actually redundant as it excludes particles smaller than
approximately 0.01 µm and, additionally, size restrictions of the ice
nucleating particles were assumed: dust particles larger than 0.1 µm
in diameter and bacteria between their typical size range of 0.3 and 2 µm in diameter (Matthias-Maser and Jaenicke, 1995).
In each time step it is checked if temperature and ice supersaturation are
above the limit values Tini and sice,ini, if yes it is checked which
available inactivated particles exceed the size limits, and from these the
activated fraction is calculated. It is assumed that only a part FINP of
the available particles is able to act as ice nucleating particles.
Therefore, the total number of particles in Eq. (15) is reduced:
Nact=FINPNtotalexp(adep+bdepsice).
The activated particles are moved to the ice particle spectrum and grow
further by water vapor deposition and they may serve as germs for secondary
ice formation; i.e., they may initiate freezing of supercooled drops by
collision (Diehl et al., 2006).
Model initiation and sensitivity studies
During the present sensitivity studies, convective clouds were simulated. In
those clouds liquid drops are transferred into higher regions in the
atmosphere and supercooled. Once frozen the ice particles grow further by
riming (i.e., collision with other supercooled droplets) and by the
deposition of water vapor (i.e., at the expense of liquid drops, the
Bergeron–Findeisen process, which is typically much faster than
condensation). Due to the vertical velocity, large precipitation-sized ice
particles can form in convective clouds and fall out as graupel, hailstones,
or (when they fall through the melting layer) as large raindrops. The
present model simulations were initialized with a convective vertical
profile where temperatures in higher altitudes were low enough to assure ice
formation. It has an average lapse rate of approximately 0.6 K per 100 m
without any maxima or minima (B. Langmann, personal communication, 2004); see Fig. 7.
Development of temperature and dew point with altitude during
simulations with the air parcel model (B. Langmann, personal communication, 2004).
The ascent of the air parcel is driven by a temperature difference between
the air bubble and its environment. Depending on the temperature difference,
the updraft of the air parcel proceeds at various speeds and reaches various
heights with corresponding temperatures (Diehl et al., 2006). For the
present simulations, the final temperatures were -24.5, -29, and -40 ∘C with corresponding maximum altitudes
of 9, 9.5, and 11 km; the maximum vertical velocities during the
ascent of the parcel were 15, 16.5, and 19 m s-1.
Two different dry aerosol particle number size distributions were used to
compare the effects on ice formation. One was an average continental
distribution (OPAC database; Hess et al., 1998), which is a rather broad
spectrum and characterized by a large number of small particles (see black
line in Fig. 8); its parameters are N=7000 cm-3, d=42.4 nm,
and σ=2.24 (with N the particle number, d the diameter, and
σ the standard deviation). The other one was a regional haze
distribution (Reid et al., 1998), which is a rather narrow spectrum and
characterized by larger particle sizes. The parameters are N=6000 cm-3, d=0.1 µm, and σ=1.65; see red line in
Fig. 8. Both rather simple mono-modal distributions were selected to avoid
a mixture of effects due to ice physics and activation of aerosol particles.
Thus, the effects of ice physics should be emphasized. The soluble fraction
ε of the aerosol particles was set to 0.5, which is a typical
value of atmospheric particles (Busch et al., 2002).
Initial dry aerosol particle number size distributions: Number
concentrations per cm3 and µm as a function of particle
diameter.
Regarding the conditions on the sizes of the ice nucleating particles as
given in Sect. 2 (mineral dust particles larger than 0.1 µm,
bacteria larger than 0.3 µm, pollen larger than 2 µm), one can
conclude from Fig. 8 that – in particular in the average continental case
– the majority of the continental particles is too small to affect ice
formation. The dry particle spectrum influences the drop spectrum, which is
important for immersion and contact modes. Figure 9 shows the number
concentrations as a function of diameter for two different altitudes and
corresponding temperatures of zero and -29 ∘C for the two cases:
average continental and regional haze particles. Because the liquid water
content is the same during both model simulations, fewer but larger drops
develop with the regional haze distribution and numerous but smaller drops
with average continental distribution. On the other hand, as the continental
spectrum is broader, collision and coalescence processes are more effective
and, thus, larger drops evolve. Also given in Fig. 9 are the number
concentrations of the interstitial aerosol particles. Note that during the
updraft inactivated aerosol particles are available for ice formation in
deposition contact modes.
Results from model runs without freezing. Development of liquid
drop (solid lines) and interstitial particle numbers (dashed lines) with
altitude for two initial aerosol particle number size distributions: average
continental (black lines), regional haze (red lines), with ΔT= 2 K.
Number concentrations per cm3 and µm as a function of
particle diameter.
With these initial conditions, a multitude of sensitivity studies was
performed to demonstrate the impact of ice nuclei concentrations and types
on ice formation in convective mixed-phase clouds. First, single freezing
processes were studied while the following parameters were varied:
dry aerosol particle number size distribution: average continental and
regional haze;
ice nucleating particle type – biological particles and mineral dust;
temperature difference: 3, 2, and 1.5 K, leading to final temperatures of
-40, -29, and -24.5 ∘C, respectively;
fraction of potential ice nucleating particles FINP – variation between
0.001 and 10 %.
Afterwards, coupled freezing processes were investigated to study the
competition between the different freezing processes. These were undertaken
only with those parameters that resulted in higher ice formation.
Results and discussionIce water fractions and single freezing processes
To evaluate the efficiency of the different freezing processes and ice
nucleating particle types, as a central diagnostic parameter the ice water
fraction IWF was selected, which is calculated from the ice water content IWC and
the liquid water content LWC:
IWF=IWCLWC+IWC.
According to Korolev et al. (2003) an ice cloud is defined by IWF > 0.9,
a liquid cloud by IWF < 0.1, and mixed-phase clouds by 0.1 ≤ IWF ≤ 0.9. Note that in an air parcel model, the IWF is only influenced by
in situ ice formation processes and not by sedimentation of ice into or out
of the considered parcel. In the following Tables 5 to 7, the ice water
fractions as results from the sensitivity studies are listed for immersion,
contact, and deposition freezing. Ice and mixed-phase clouds are marked by
bold face type.
Ice water fractions from sensitivity studies with immersion
freezing. Liquid clouds: IWF < 0.1, mixed-phase clouds: 0.1 ≤ IWF ≤ 0.9, and ice clouds: IWF > 0.9. Cases referring to mixed-phase clouds and ice clouds are written in bold.
Ice water fractions from sensitivity studies with contact freezing.
Liquid clouds: IWF < 0.1, mixed-phase clouds: 0.1 ≤ IWF ≤ 0.9, and ice clouds: IWF > 0.9. Cases referring to mixed-phase clouds and ice clouds are written in bold.
Ice water fractions from sensitivity studies with deposition
freezing. Liquid clouds: IWF < 0.1, mixed-phase clouds: 0.1 ≤ IWF ≤ 0.9, and ice clouds: IWF > 0.9. Cases referring to mixed-phase clouds and ice clouds are written in bold.
Tables 5–7 show that mixed-phase or ice clouds mainly
evolved from the continental particle distribution with bacteria, feldspar,
or illite INP and with larger ΔT, i.e., final temperatures reaching
below -25 ∘C. The different points are discussed in detail in the
following.
Particle number size distributions
With the regional haze particles (larger particles but smaller numbers),
less ice was formed in most cases than with the average continental
particles (smaller particles but larger number). Less but mostly large drops
develop with the regional haze distribution and many but smaller drops with
average continental distribution (see Fig. 9). On the other hand, a few
still larger drops evolved from the tail of the continental spectrum. The
presence of large drops favors immersion and contact freezing (immersion
mode: the drops contain more insoluble material; contact mode: the collision
kernel between large drops and particles is enhanced); see Tables 5 and 6.
This was suggested already by the findings of Diehl et al. (2006) and
confirmed by, e.g., Lance et al. (2011), who concluded from their
observations that the drop size distribution modulates ice processes in
mixed-phase clouds.
In the deposition mode (Table 7) not the drops but the interstitial particles are relevant. More inactivated particles are present in the
regional haze case than in the continental case (see Fig. 9), so that
because of the higher competition between the many particles, less ice
particles develop by the deposition of water vapor.
Freezing modes
Immersion freezing affects the most mixed-phase clouds with ice water
fractions of more than 0.5 and even ice clouds with all kinds of
investigated INP (Table 5). Pollen are, of course, disadvantaged because of
their large size, kaolinite particles are the less efficient mineral dust
particles while bacteria and feldspar are the most efficient INP. With
bacteria, freezing occurred already with potential fractions of INP
FINP as low as 0.001 %. With mineral dust, potential fractions of INP
FINP of 0.01 % (feldspar, illite) or 0.1 % (kaolinite) were
necessary. Ice formation was sensitive to the type of mineral dust as well
as to the potential fraction of INP. For instance, under the same conditions
an ice cloud could form with feldspar but a mixed-phase cloud with only a
small ice water fraction with kaolinite.
In the contact mode, there is very little ice formation (Table 6). Only
liquid clouds formed with the regional haze particle distribution, but in
case of the average continental distribution at least some mixed-phase
clouds formed with ice water fractions between 0.1 and 0.3. High potential
fractions of INP FINP of 10 % were required. Bacteria did not affect
ice formation, this probably results from the restricted particle sizes. The
type of mineral dust decides whether mixed-phase clouds are formed
(feldspar, montmorillonite) or not (illite, kaolinite).
Effective deposition INP are bacteria, feldspar, and illite while the mixed
particle samples Saharan and Asian dust form liquid clouds only. Mixed-phase
clouds formed with high potential INP fractions FINP between 1 and
10 %. The results for the different mineral dust types are rather similar,
but mixed-phase clouds were formed with pure minerals only but not with
mixtures (Saharan and Asian dust) that are dominated by quartz (see Sect. 2.3.1).
Ervens et al. (2011), who investigated the impact of immersion and deposition
freezing modes on ice formation in mixed-phase clouds, stated that immersion
freezing formed less ice than deposition freezing because of lower onset
temperatures in the immersion mode. However, this could not be confirmed in
the present study. Newer laboratory measurements, which were used as basis of
the parameterizations, showed in contrary higher initial temperatures in the
immersion mode than in the deposition mode (see Tables 1 and 4 with
references therein) and, thus, immersion freezing produced more ice than
deposition freezing. This agrees, on the other hand, with the findings of
de Boer et al. (2011) and Lance et al. (2011) that liquid-dependent ice
nucleation modes are dominant.
Temperature difference ΔT and final
temperature
In all freezing modes, cases with the lowest temperature difference (ΔT=1.5 K) and the corresponding highest final temperature of
-24.5 ∘C showed hardly any ice formation. Exceptions are only cases
with bacteria and feldspar in the immersion mode (Table 5). Contact ice
formation did not occur with lower ΔT as the temperatures reached
during the ascent of the air parcel were not low enough to give the more
effective smaller particles the chance to act (Table 6). In the deposition
mode, obviously temperatures below -25 ∘C are required to develop
mixed-phase clouds (Table 7) as at higher temperatures the ice
supersaturation is still too low. On the other hand, the largest ΔT
of 3 K also hindered ice formation in contact and deposition modes, which
might be affected by the presence of less interstitial particles.
Comparison to measured INP numbers
In this section there is a discussion on how realistic the assumed
concentrations of INP are that lead to partial or complete cloud
glaciation.
Immersion freezing
Regarding the cases of immersion freezing listed in Table 5, one has to look
at the composition of cloud residuals that was investigated in several
field campaigns. For example, Kamphus et al. (2010) measured 8 % minerals in
cloud droplet residuals and Hiranuma et al. (2013) found 3 % mineral dust
particles in cloud droplet residuals for all particle sizes and
particle-size-resolved measurements indicated enhanced fractions for larger
particles up to 17 %. In the model simulations mixed-phase clouds were
formed already with potential INP fractions between 0.01 and 1 %. Thus,
the dominant role of immersion freezing with mineral dust seems to be
validated. It is confirmed by the fact that during INUIT field campaigns
(Worringen et al., 2015; Schmidt et al., 2015) fractions of mineral dust up
to 40 % in ice residuals were observed. Similar numbers between 30 and
40 % are reported by Kamphus et al. (2010) in ice particle residuals.
Bacteria need to be present as potential INP only as low as 0.001 to
0.01 % to affect mixed-phase clouds, pollen with 1 % potential INP. For
pollen these numbers probably do not represent realistic cases; however, the
pollen cases were anyway rather artificial as such large particles
(> 10 µm) were actually not part of the aerosol particle
spectrum. Bacteria concentrations in cloud water are given as average values
of, e.g., 1.5 × 109 m-3 (Sattler et al., 2001), 2 × 1010 m-3 (Bauer et al., 2002), and 7 × 104 m-3 (Amato et al., 2005; see also the review paper of Delort et al.,
2010). Unfortunately, field measurements that give the number fractions of
bacteria or primary biological aerosol particles (PBAP) related to the total
concentrations of in-droplet particles are not available so far. An
estimation was undertaken based on the results of Bauer et al. (2002). From
field measurements in a continental background site, they determined the
numbers of bacterial and fungi cells in cloud water and calculated the
corresponding amount that would contribute to the amount of organic carbon
(OC). For bacteria this contribution was estimated as 0.01 %. Analyses of
cloud droplet residuals show, e.g., fractions of 3 % (Twohy and Anderson,
2008) and 9 % (Hiranuma et al., 2013) organic carbon. Based on these
numbers, one could assume that 0.0003 to 0.0009 % of the material contained
in cloud drops consists of bacteria. This comes near the fraction of
potential ice nucleating particles FINP of 0.001 %, which affects
mixed-phase clouds via immersion freezing (see Table 5). Joly et al. (2014)
investigated the ice nucleation efficiency of cloud water samples and
distinguished total and biological INP. They estimated that – assuming that
all biological INP were bacteria – in the temperature range between -8 and
-12 ∘C 0.6 to 3.1 % of the bacterial cells present in the cloud
water samples could have acted as INP. Taking into account these values, a
fraction FINP of 0.001 % is probably still an overestimation of
realistic bacterial ice nucleating particles in cloud drops.
Thus, one may conclude that the atmospheric immersion freezing with mineral
dust particles will play a dominant role, while ice formation via immersion
freezing of bacteria might take place in some extreme cases only.
Contact and deposition freezing
According to the cases listed in Tables 6 and 7, the formation of
mixed-phase clouds via contact or deposition freezing requires that 10 %
of background aerosols consist of potential ice nucleating particles.
Measurements during an INUIT field campaign on the high Alpine site
Jungfraujoch (Schmidt et al., 2015) indicate a fraction of 1 % minerals of
the background aerosol; however, this value might be enhanced for
continental situations in lower altitudes. A closer look was taken on the
total number concentrations of ice nucleating particles acting as contact
and deposition freezing particles during the model simulations. As shown in
Fig. 9 for two different altitudes and corresponding temperatures,
interstitial aerosol particles were always present during the ascent of the
air parcel. Among these, only particles larger than 0.1 µm in
diameter were allowed to act as ice nucleating particles. The total number
concentrations of those during the model simulations reached values up to 4 × 105 m-3 for the average continental and up to 2 × 106 m-3 for the regional haze distribution. Thus,
fractions of potential ice nucleating particles FINP of 10 % resulted
in particle concentrations available for deposition and contact nucleation
of at the most 4 × 104 and 2 × 105 m-3, respectively.
In Saharan dust events, Bangert et al. (2012) measured particle number
concentrations up to 5 × 107 m-3. In Hande et al. (2015),
simulated mineral dust number concentrations are 4 × 105 m-3
on average up to extreme values of 5.8 × 106 m-3. Thus, the numbers of potential INP used in the model simulations
do not exceed realistic particle concentrations of mineral dust.
Near-surface concentrations of bacteria range between 1 × 103
and 5 × 105 m-3 depending on ecotypes (Burrows
et al., 2009), but these values are certainly not reached in upper cloud
regions. On the other hand, DeLeon-Rodriguez et al. (2013) reported from
field measurements in low- and high-altitude air masses that bacterial cells
represented nearly 20 % of the total particles in the diameter range
between 0.25 and 1 µm. This is approximately the size range of
potential ice nucleating particles in the present model simulations. For
even larger particles in the coarse-mode high fractions of PBAP (primary
biological particles) are also reported by Manninen et al. (2014). However,
the numbers of potential INP used in the model simulations probably
overestimate real bacteria concentrations.
Considering these factors one may conclude that the conditions for
atmospheric deposition and contact freezing could be sufficient in some
cases to form mixed-phase clouds from primary ice formation by mineral dust
particles. In some extreme cases, the formation of mixed-phase clouds might
be possible via deposition nucleation on bacteria. On the other hand, the
initial particle spectra used for the present model simulations contain
small amounts of particles larger than 1 µm (see Fig. 8), which
would be able to act as contact ice nucleating particles much more
efficiently (see Sect. 2.2.1). Thus, in cases where larger INP are present
in or around atmospheric clouds contact freezing might be significantly
enhanced.
Ice particle spectra, single and coupled freezing processes
In this section only simulations with the most efficient ice formation are
treated, i.e., using the average continental particle distribution and a
medium ΔT=2 K leading to final temperatures of -29 ∘C.
The potential fractions FINP were set to 10 % for feldspar, illite,
montmorillonite, and kaolinite. Bacteria and pollen were not considered here
as those high values of FINP are not realistic (see discussion in
Sect. 4.2). Figure 10 shows the ice particle number size distributions at
different altitudes and corresponding temperatures for single deposition,
contact, and immersion freezing processes that are discussed in the next
paragraphs.
Development of ice particle numbers with altitude and
corresponding temperature, marked by different line styles, for deposition,
contact and immersion freezing. Types of INP marked by colors. Model
simulations with average continental number size distribution and with
ΔT=2 K. Number concentrations per L and µm as a function
of particle diameter.
The ice particle spectra affected by immersion freezing are rather narrow
starting with a particle diameter of 1 µm due to the smallest drop
size. However, larger drops around 30 µm in diameter were frozen
first as their content of insoluble particles is higher. With decreasing
temperature, also smaller drops can freeze while ice particles grow by the
deposition of water vapor and by riming; i.e., the ice particle spectra
broaden in both directions. Finally, they are still smaller than the ones
formed by deposition freezing. The differences between the dust types are
much more evident than in the other modes and the final ice particle numbers
are higher, in particular for feldspar.
In the contact mode, the simulated ice particle spectrum is even somewhat
narrower. At temperatures around -20 ∘C, the maximum of the drop
number concentration lies at 30 µm. This indicates that large
supercooled drops froze by collisions with larger particles as in this
temperature range smaller particles are hardly efficient as contact INP.
Lowering the temperature down to -29 ∘C extends the ice particle
spectra towards larger sizes; i.e., the ice particles grow by the deposition
of water vapor and by riming but still only small amounts of small drops
are freezing. Thus, contact freezing is strongly controlled by the collision
kernel. The number concentrations of ice particles formed on feldspar and
montmorillonite are rather similar at -30 ∘C but strongly
different at -21 ∘C. This is due to the fact that smaller feldspar
particles are active at higher temperatures as smaller montmorillonite
particles (see Fig. 4).
The ice particle spectra due to deposition freezing start with small sizes
and develop towards larger sizes during the ascent of the cloud. This
indicates that first, as primary ice formation, small ice particles are
formed due to the sizes of the involved particles (0.1 µm at least up
to 3 µm maximum, see the initial particle spectra in Fig. 8).
Afterwards, these pristine ice particles serve as nuclei for secondary ice
formation, i.e., by collisions with supercooled liquid drops; this process
produces ice particles of larger sizes; see maximum around 40 µm in
Fig. 10. Furthermore, all ice particles grow by the deposition of water
vapor and by riming leading to ice particles larger than 100 µm.
Thus, a broad spectrum of ice particles evolved from deposition freezing.
The number concentrations vary by 1 order of magnitude from illite2 to
feldspar. The oscillations of the spectra on the left hand side are an
artifact effect of the size classes of the particles acting as INP starting
with 0.1 µm diameter; they vanish if the lower size of the INP is
limited by the critical radius only (Eq. 16).
Coupled cases were simulated to investigate the competition between contact
and immersion modes as both freeze supercooled drops, and the competition
between contact and deposition freezing as both interact with inactivated
particles. The following combinations were studied: (1) feldspar and (2) illite1 as for these particle types parameterizations for all freezing modes
are available based on INUIT measurements. Additionally, some mixed cases
were investigated, i.e., one INP type for one freezing mode, another INP type
for the other freezing mode; these were for coupled contact and immersion
freezing: (3) montmorillonite + kaolinite and (4) feldspar + kaolinite. These
cases were selected to combine weaker immersion freezing INP (kaolinite)
with stronger contact freezing INP (feldspar and montmorillonite); see
Fig. 12a. For coupled deposition and contact freezing the mixed cases were
(3) illite2 + montmorillonite and (4) Saharan dust + feldspar. As can be
noted from Tables 5 to 7, the ice water fractions evolved from feldspar INP
are similar for deposition and contact freezing but significantly higher for
immersion freezing. The effects from illite1 INP differ between the freezing
modes (immersion highest, contact lowest). The combination illite2 +
montmorillonite in deposition and contact modes was selected because the
resulted ice water fractions were similar, which is also the case for the
combination montmorillonite + kaolinite in contact and immersion modes.
Development of ice particle numbers with altitude and
corresponding temperature, marked by different line styles, for coupled
freezing processes. Types of INP marked by colors. Model simulations with
average continental number size distribution, and with ΔT=2 K.
Number concentrations per L and µm as a function of particle
diameter.
In contrast, the fourth combinations Saharan dust + feldspar (deposition
and contact) and feldspar + kaolinite (contact and immersion) were chosen
as the ice water fractions evolved by contact freezing were higher. Figure 11 shows the ice particle number size distributions at different altitudes
and corresponding temperatures for coupled deposition and contact, and
coupled contact and immersion freezing processes.
One can note from Fig. 11 that deposition and contact freezing are hardly
in competition. The narrow ice particle spectrum due to contact freezing
alone is enhanced towards smaller sizes due to deposition freezing. Small
ice particles are formed by deposition freezing due to the sizes of the
involved particles (0.1 to 3 µm), large ice particles are formed by
contact freezing due to the sizes of involved liquid drops (30 to 50 µm, see Fig. 10). When the first ice formation is observed via contact and
deposition freezing at temperatures around -20 ∘C, in the contact
mode only dust particles larger than 0.4 µm (feldspar), 0.8 µm
(illite), and 1 µm (kaolinite) are active (see Fig. 4); thus, all
smaller particles and fractions of the larger particles are available for
deposition freezing. Even at lower temperatures, still small particles
remain for deposition freezing as these are less efficient to collide with
drops. The common ice particle spectra of coupled deposition and contact
freezing are as broad as the ones from single deposition freezing, but the
number concentrations in the larger size range are enhanced in cases with
efficient contact freezing, i.e., with feldspar and montmorillonite INP.
Regarding coupled contact and immersion freezing, here the latter is the
dominant process. Using the same particle types there is no effect visible
from contact freezing (feldspar, illite1). However, in cases where the
contact INP are more efficient than the immersion INP (feldspar or
montmorillonite in contrast to kaolinite) the number concentrations are
slightly enhanced in the size range larger than 90 µm (see Figs. 10
and 11).
Total numbers of liquid drops and ice particles as a function of
temperature for single and coupled freezing processes that are marked by
different line styles. Types of INP marked by colors. Model simulations with
average continental number size distribution and with ΔT=2 K.
Numbers concentrations per cm3 as a function of temperature.
The total numbers of liquid drops and ice particles in cm-3 as a
function of temperature are given in Fig. 12. Results from single freezing
processes are shown in Fig. 12a. In the upper part of the figure, liquid
drop numbers are shown as solid lines. The black line represents liquid drop numbers on the order of 8 × 102 cm-3, which applies for
all cases except immersion freezing with feldspar. Below, in all remaining
plot lines ice particle numbers are given in Fig. 12a for the three single
freezing processes as colored lines (the colors describe the types of INP)
with different line styles: solid lines for immersion freezing (feldspar,
illite1, and kaolinite), dotted lines for contact freezing (feldspar,
montmorillonite, and illite), and broken lines for deposition freezing
(feldspar, illite1, illite2, and Saharan dust).
From the ice particle numbers at the lowest temperature of -29 ∘C,
one can clearly distinguish between cloud types: ice particle numbers around
1 cm-3 represent an ice cloud (immersion freezing with feldspar), ice
particle numbers around 1 × 10-8 cm-3 represent a
liquid cloud (i.e., the ice water fraction is smaller than 0.1; contact
freezing with illite), and ice particle numbers between 1 × 10-4 cm-3 and 1 × 10-2 cm-3 represent
mixed-phase clouds (all other cases). In the only case of ice cloud
formation (immersion with feldspar), the liquid drop number is affected as
well: it is reduced to 5 × 101 cm-3 (i.e., the ice water
fraction is larger than 0.9); see red solid line in the upper part of the
figure.
It can be seen that the development of ice particle numbers with decreasing
temperature via immersion freezing (colored solid lines) is similar to the
development of the numbers of active sites immersed in the drops with
decreasing temperature (shown in Fig. 1). In contrast, contact freezing
(dotted lines) is not ruled by the temperature alone but also by the
collision efficiencies between potential INP and supercooled drops (see
Sect. 2.2), while deposition freezing (broken lines) is ruled by the ice
supersaturation (see Sect. 2.3), which, of course, increases with
decreasing temperature.
Comparing the liquid drop numbers to the ice particle numbers, one notes that
the differences are approximately between 4 and 6 orders of magnitude in
cases of mixed-phase clouds and still more than 1 order of magnitude in
the case of the ice cloud. This indicates that the glaciation of the clouds
proceeds mainly by the growth of ice particles at the expense of liquid
drops (Bergeron–Findeisen process or riming). Here, some effects are
presumably overestimated as from the air parcel no cloud particle
sedimentation is possible.
Figure 12b shows results for the coupled freezing processes. Again liquid
drop numbers are given as solid lines in the upper part of the figure. The
black line represents liquid drop numbers, which applies for all cases except
immersion + contact freezing with feldspar. Lower plot lines in Fig. 12b
show ice particle numbers for the two coupled freezing processes as colored
lines (the colors describe the types of INP) with different line styles:
solid lines for immersion + contact freezing (feldspar, illite1, kaolinite
+ feldspar, kaolinite + montmorillonite), and broken lines for
deposition + contact freezing (feldspar, illite1, illite2 +
montmorillonite, Saharan dust + feldspar).
Ice clouds and mixed-phase clouds differ in the ice particle numbers at the
lowest temperature of -29 ∘C: ice particle numbers around 1 cm-3 represent an ice cloud (immersion + contact freezing with
feldspar), and ice particle numbers between 5 × 10-4 and 1 × 10-2 cm-3 represent mixed-phase clouds (all
other cases). In the only case of ice cloud formation the liquid drop number
is affected as well; see red solid line in the upper part of the figure.
It is obvious that in those cases where one process is inferior the ice
particle numbers are completely determined by the dominant process. This is
the case in coupled immersion and contact freezing with feldspar and illite1
but not with kaolinite + feldspar and kaolinite + montmorillonite, where
the effects of contact freezing are visible at lower temperatures (<-20 ∘C). Coupled deposition and contact freezing shows the same
results as for deposition freezing alone in the case of illite. In the other
cases, feldspar, illite2 + montmorillonite, and Saharan dust + feldspar,
there are at least some small enhancements visible at temperatures lower
than -20 ∘C when contact freezing becomes more efficient.
Comparison of presently simulated ice particle numbers to previous
literature data
In this section results from the model simulations are compared to data from
previous model simulations and atmospheric measurements. As comparison
parameter the number of ice particles formed at the end of the air parcel
ascent was used as shown in Fig. 12. These simulations were performed with
the continental particle distribution, dT=2 K, and FINP=10 %;
i.e.,
for FINP values lower than 10 % less ice particle numbers are formed.
Eidhammer et al. (2009) used an air parcel model to perform intercomparisons
of three heterogeneous ice nucleation parameterizations, which linked aerosol
types and numbers to ice particle number concentrations. One of these was
the previous immersion freezing parameterization of Diehl and Wurzler (2004)
that has been replaced by a new one in this study. This description was
related to the volume of liquid similar as the well-known Bigg
parameterization (Bigg, 1953). The criticisms was that some constraints are
required that limit the number of potential ice nucleating particles. The
present parameterization is related to the insoluble particle mass contained
in the drops and the constraint is given by the factor FINP in Eq. (8).
Ice particle numbers found by Eidhammer et al. (2009) with the Diehl and
Wurzler (2004) description were in the range of 5 × 10-2 to 5 × 10-1 cm-3, while results from the current simulations
are on the order of 1 × 10-3 cm-3 for mixed-phase
clouds (see Fig. 12; immersion freezing).
In Ervens and Feingold (2012), the effects of five ice nucleation schemes for
immersion freezing with particles showing surface characteristics like
kaolinite particles on the properties of mixed-phase clouds were compared in
air parcel studies. The present parameterization of immersion freezing is
similar to the so-called deterministic scheme of Ervens and Feingold (2012),
which includes a cumulative activation spectrum as a function of
temperature. Using this scheme in their parcel model with polydisperse INP,
they found ice particle numbers from 2 × 10-4 to 1 × 10-3 cm-3. In spite of differences between the present and
previous air parcel studies (e.g., height of parcel elevation) the present
results of immersion freezing with kaolinite particles, i.e., 3.5 × 10-4 cm-3 (see Fig. 12a), agree very well.
Fan et al. (2010) simulated two deep convective clouds from field campaigns,
one cloud formed under clean conditions, the other under polluted conditions
with biomass burning. They used a three-dimensional cloud-resolving model
with different immersion–condensation–deposition and homogeneous ice
nucleation parameterizations. The averaged cloud properties for their clean
case showed ice particle numbers between 1.3 × 10-2 and 1.5 × 10-2 cm-3, depending on the ice nucleation
schemes. For the comparison coupled immersion and deposition freezing were
simulated with kaolinite acting in the immersion mode, Saharan dust in the
deposition mode (not shown in Fig. 12b). These two particle types might
represent the behavior of “mean” atmospheric INP. During the calculations
dT was varied by 2, 2.5, and 3 K. The final ice particle numbers were 4.8 × 10-4, 2.2 × 10-2, and
6.4 × 10-1 cm-3, respectively, and, thus, best
agreement with a deep convective cloud was obtained with dT=2.5 K.
Kulkarni et al. (2012) used Arizona test dust (ATD) and kaolinite to
experimentally investigate and model deposition freezing. However, their
measured activated fractions of particles as a function of ice
supersaturation were at least 1 to 2 orders of magnitude larger than the
ones used as basis for the parameterizations in the present study.
Therefore, not surprisingly, the modeled ice particle numbers found by
Kulkarni et al. (2012) were between 2 × 10-3 and 1 × 10-2 cm-3 and up to 2 orders of magnitude larger
than the ones found in the present simulations (1.4 × 10-4 cm-3 for Saharan dust, 7.2 × 10-4 cm-3 for
illite2, 1.8 × 10-3 cm-3 for illite1, and 4.5 × 10-3 cm-3 for feldspar; see Fig. 12a).
Observations of atmospheric clouds reported ice particle number
concentrations of 1 × 10-4 to 10 cm-3 in
convective clouds (Hobbs et al., 1980), 1 × 10-5 to 5 × 10-2 cm-3 in mixed-phase clouds (McFarquhar et
al.,
2007; modeled by Fridlind et al., 2007), and 1 × 10-1 to 1 cm-3 in ice clouds (Krämer et al., 2009). Thus, the ice
particle numbers formed during the present model simulations; i.e., 1 × 10-4 to 1 × 10-2 cm-3 for mixed-phase
clouds and 9 × 10-1 cm-3 for ice clouds (see Fig. 12),
represent the status of real atmospheric clouds.
Summary and conclusions
In this paper improvements and modifications of the spectral-bin
microphysics embedded in an adiabatic air parcel model with entrainment as
described in Diehl et al. (2006) are presented. They include (1) a
particle-type-dependent parameterization of immersion freezing, (2) a
particle-type and size-resolved parameterization of contact freezing, and
(3) a particle-type-dependent description of deposition freezing.
Sensitivity studies with the modified version of the microphysics package
demonstrated the impact of ice nuclei concentrations and types on ice
formation in convective mixed-phase clouds. Single and coupled freezing
processes were studied and the following parameters were varied: initial
aerosol particle number size distributions, final temperature, types of INP,
and the fractions of potential INP.
The majority of mixed-phase and ice clouds were formed at temperatures below
-25 ∘C, with the average continental particle number size
distribution and with immersion freezing, even with smaller values of
FINP (fractions of potential INP).
First, larger drops freeze in the immersion mode because they contain more
particulate material. During the updraft of the cloud and corresponding
lower temperatures, the ice particle spectra develop towards smaller sizes.
Contact freezing is limited by the collision kernel between supercooled
drops and potential ice nucleating particles. Large particles collide more often with large drops. Because they are more efficient con-tact INP than smaller particles this leads at first to the formation of larger ice particles.. Smaller particles that represent in general the majority of the
particle spectrum become effective at lower temperatures and, thus, contact
freezing becomes relevant below -25 ∘C. During coupled immersion
and contact freezing immersion freezing is the dominant process in cases
with the same INP types (feldspar, illite). In mixed cases where contact INP
are more efficient than immersion INP (feldspar or montmorillonite with
kaolinite), contact freezing contributes similarly as immersion freezing to
ice formation. In such cases sufficient liquid drops are available for
contact freezing.
The importance of deposition freezing lies in secondary ice formation: at
first, small pristine ice particles are formed due to the sizes of the
involved particles, which trigger the freezing of supercooled drops by
collisions. Thus, a broader ice particle spectrum is generated than by
immersion and contact freezing. Regarding coupled contact and deposition
freezing, there is hardly competition because both start moderately at
higher temperatures so that inactivated particles are present for both
freezing modes.
The most effective ice nucleating particles are bacteria, feldspar, and
illite; on the contrary, of minor importance are pollen, montmorillonite, and kaolinite. Ice
formation by immersion freezing is very sensitive to the different mineral
dust types leading to ice particle numbers varying by 3 orders of
magnitude.
From the model results it can be concluded that the formation of mixed-phase
and ice clouds in convective situations is promoted by (1) the immersion
freezing mode; (2) broad drop size spectra containing small as well as large
drops; (3) insoluble particles composed by bacteria, feldspar, and illite;
and (4) temperatures below -25 ∘C.
The dominance of the immersion mode confirms the findings of de Boer et al. (2011) and Lance et al. (2011) about the importance of liquid-dependent ice
nucleation modes. The role of contact freezing remained still unclear. In
Diehl et al. (2006) it was estimated to be the most efficient process;
however, this was because of assuming high freezing efficiencies for all
particle sizes. Thus, the effects in the present model simulations with the
modified contact freezing description should be closer to the real situation
in the atmosphere. Contact freezing might be enhanced in atmospheric
situations where particles larger than 1 µm are present in higher
amounts. The dependence of ice formation on the properties of the liquid
drop size spectrum was also observed by Lance et al. (2011). This should be
investigated in more detail but was not the main focus of the present
simulations, which concentrated more on the impact of ice nucleating particle
types.
Bacteria as primary biological particles were found to affect mixed-phase clouds or
even ice clouds; however, the critical factor is the availability in
atmospheric environment and clouds. Together with estimations of their
atmospheric occurrence the present model simulations indicate that they are
probably not involved in significant ice formation. This has been suggested
already by Phillips et al. (2009), Diehl and Wurzler (2010), and Paukert and
Hoose (2014). Thus, mineral dust particles seem to be the most important
INP. The model results indicate that ice formation by immersion freezing is
similarly sensitive to the mineral dust types as to the potential fractions
of INP. Therefore, the composition of dust particles decides their impact on
ice nucleation in clouds; essential components are feldspar and illite. In
particular, the investigation of typical atmospheric mixtures of mineral
dust is relevant.
The microphysical package presented here was included into an air parcel
model, which has the advantage that all changes in the microphysical
evolution of the cloud can be attributed to microphysical processes. On the
other hand, some compromises are required concerning the cloud dynamics
including some well-known weaknesses as precipitation-sized cloud particles
do not sediment but stay inside the parcel. As they are not removed from the
parcel they could grow to unrealistic sizes. This can happen notably in the
ice phase and lead to an overestimation of cloud glaciation. Some more
limitations that should be considered are related to immersion and contact
freezing. During the cloud model runs each drop contains insoluble material
serving as immersion ice nucleus because the particles in the model are
internally mixed. In a real cloud there might be drops that do not contain
any potential ice nucleating material. Furthermore, the values of
FINP used in the model represent maximum estimations as not all dust
particles are able to affect freezing at atmospheric temperatures. For
contact freezing, the amount of interstitial inactivated particles is
limited in an air parcel model while in a real cloud there might be more
inactivated particles at the edges or beneath the cloud.
The further goal is to implement the new microphysical scheme into to a more
complex state-of-the-art model system. For this purpose presently the
three-dimensional
cloud model COSMO-SPECS (Grützun et al., 2008) has been employed. This model
contains the microphysical scheme as used in Diehl et al. (2006) within the
adiabatic parcel model, which is replaced by the microphysical scheme
presented here. Such an improvement will allow for more complex model
simulations including the formation of precipitation, which will elucidate
the role of ice nucleating particles in atmospheric clouds.
Acknowledgements
This work is part of the research group INUIT (Ice Nuclei research UnIT)
FOR1525 and was supported by the Deutsche Forschungsgemeinschaft under grant
DI 1539/1-1. We appreciate the INUIT laboratory and field groups for
providing their experimental data as basis of parameterizations and for
helpful discussions. Thanks to Karin Ardon-Dryer for providing unpublished
material. We would like to thank Corinna Hoose for fruitful discussions and
helpful comments and suggestions.Edited by: B. Ervens
References
Amato, P., Ménager, M., Sancelme, M., Laj, P., Mailhot, G., and Delort,
A.-M.: Microbial population in cloud water at the Puy de Dôme:
implications for the chemistry of clouds, Atmos. Environ. 39, 4143–4153,
2005.Ansmann, A., Althausen, D., Müller, D., Seifert, P., Freudenthaler, V.,
Heese, B., Wiegner, M., Pisani, G., Knippertz, P., and Dubovik, O.:
Influence of Saharan dust on cloud glaciation in southern Morocco during
Saharan mineral dust experiment, J. Geophys. Res., 113, D04210,
10.1029/2007JD008785, 2008.Atkinson, J. D., Murray, B. J., Woodhouse, M. T., Whale, T. F., Baustian, K. J.,
Carslaw, K. S., Dobbie, S., O'Sullivan, D., and Malkin, T. L.: The importance
of feldspar for ice nucleation by mineral dust in mixed-phase clouds,
Nature, 498, 355–358, 10.1038/nature12278, 2013.Bangert, M., Nenes, A., Vogel, B., Vogel, H., Barahona, D., Karydis, V. A.,
Kumar, P., Kottmeier, C., and Blahak, U.: Saharan dust event impacts on cloud
formation and radiation over Western Europe, Atmos. Chem. Phys., 12,
4045–4063, 10.5194/acp-12-4045-2012, 2012.Bauer, H., Kasper-Giebl, A., Löflund, M., Giebl, H., Hitzenberger, R.,
Zibuschka, F., and Puxbaum, H.: The contribution of bacteria and fungal
spores to the organics content of cloud water, precipitation and aerosols,
Atmos. Res., 64, 109–119, 2002.Bigg, E. K.: The formation of atmospheric ice crystals by the freezing of
droplets, Q. J. Roy. Meteorol. Soc., 79, 510–519, 1953.Broadley, S. L., Murray, B. J., Herbert, R. J., Atkinson, J. D., Dobbie, S.,
Malkin, T. L., Condliffe, E., and Neve, L.: Immersion mode heterogeneous ice
nucleation by an illite rich powder representative of atmospheric mineral
dust, Atmos. Chem. Phys., 12, 287–307, 10.5194/acp-12-287-2012, 2012.Burrows, S. M., Elbert, W., Lawrence, M. G., and Pöschl, U.: Bacteria in
the global atmosphere – Part 1: Review and synthesis of literature data for
different ecosystems, Atmos. Chem. Phys., 9, 9263–9280,
10.5194/acp-9-9263-2009, 2009.Busch, B., Kandler, K., Schütz, L., and Neusüß, C.: Hygroscopic
properties and water soluble volume fraction of atmospheric particles in the
diameter range from 50 nm to 3.8 µm during LACE 98, J. Geophys.
Res., 107 D, LAC 2-1–LAC 2-11, 2002.Connolly, P. J., Möhler, O., Field, P. R., Saathoff, H., Burgess, R.,
Choularton, T., and Gallagher, M.: Studies of heterogeneous freezing by three
different desert dust samples, Atmos. Chem. Phys., 9, 2805–2824,
10.5194/acp-9-2805-2009, 2009.de Boer, G., Morrison, H., Shupe, M. D., and Hildner, R.: Evidence of liquid
dependent ice nucleation in high-latitude stratiform clouds from surface
remote sensors. Geophys. Res. Lett., 38, L01803, 10.1029/2010GL046016,
2011.DeLeon-Rodriguez, N., Lathem, T. L., Rodriguez-R, L. M., Barazesh, J. M.,
Anderson, B. E., Beyersdorf, A. J., Ziemba, L. D., Bergin, M., Nenes, A., and
Konstantinidis, K. T.: Microbiome of the upper troposphere: Species
composition and prevalence, effects of tropical storms, and atmospheric
implications, P. Natl. Acad. Sci., 110, 2575–2580,
10.1073/pnas.1212089110, 2013.Delort, A.-M., Vaïtilingom, M., Amato, P., Sancelme, M., Parazols, M.,
Mailhot, G., Laj, P., and Deguillaume, L.: A short overview of the microbial
population in clouds: Potential roles in atmospheric chemistry and nucleation
processes, Atmos. Res., 98, 249–260, 2010.DeMott, P. J., Prenni, A. J., McMeeking, G. R., Sullivan, R. C., Petters, M.
D., Tobo, Y., Niemand, M., Möhler, O., Snider, J. R., Wang, Z., and
Kreidenweis, S. M.: Integrating laboratory and field data to quantify the
immersion freezing ice nucleation activity of mineral dust particles, Atmos.
Chem. Phys., 15, 393–409, 10.5194/acp-15-393-2015, 2015.Diehl, K. and Wurzler, S.: Heterogeneous drop freezing in the immersion
mode: Model calculations considering soluble and insoluble particles in the
drops, J. Atmos. Sci., 61, 2063–2072, 2004.Diehl, K. and Wurzler, S.: Air parcel model simulations of a convective
cloud: Bacteria acting as immersion ice nuclei, Atmos. Environ., 44,
4622–4628, 2010.Diehl, K., Matthias-Maser, S., Mitra, S. K., and Jaenicke, R.: The ice
nucleating ability of pollen. Part II: Laboratory studies in immersion and
contact freezing modes, Atmos. Res., 61, 125–133, 2002.Diehl, K., Simmel, M., and Wurzler, S.: Numerical simulations of the impact
of aerosol properties and drop freezing modes on the glaciation,
microphysics, and dynamics of clouds, J. Geophys. Res., 111, D07202,
10.1029/2005JD005884, 2006.Diehl, K., Simmel, M., and Wurzler, S.: Effects of drop freezing on
microphysics of an ascending cloud parcel under biomass burning conditions,
Atmos. Environ., 41, 303–314, 2007.Diehl, K., Schmithüsen, H., Debertshäuser, M., Borrmann, S., and
Mitra, S. K.: Laboratory investigations of contact and immersion freezing of
mineral dust using an acoustic levitator, Proceedings European Aerosol
Conference, Granada, Spain, 2012.Eidhammer, T., DeMott, P. J., and Kreidenweis, S. M.: A comparison of
heterogeneous ice nucleation parameterizations using a parcel model
framework. J. Geophys. Res., 114, D06202, 10.1029/2008JD011095, 2009.Ekman, A. M. L., Engström, A., and Wang, C.: The effect of aerosol
composition and concentration on the development and anvil properties of a
continental deep convective cloud, Q. J. Roy. Meteorol. Soc., 133,
1439–1452, 2007.Ervens, B. and Feingold, G.: On the representation of immersion and
condensation freezing in cloud models using different nucleation schemes,
Atmos. Chem. Phys., 12, 5807–5826, 10.5194/acp-12-5807-2012, 2012.Ervens, B. and Feingold, G.: Sensitivities of immersion freezing:
Reconciling classical nucleation theory and deterministic expressions, J.
Geophys. Res. Lett., 40, 3320–3324, 10.1002/grl.50580, 2013.Ervens, B., Feingold, G., Sulia, K., and Harrington, J.: The impact of
microphysical parameters, ice nucleation mode, and habit growth on the
ice/liquid partitioning in mixed-phase Arctic clouds, J. Geophys. Res., 116,
D17205, 10.1029/2011JD015729, 2011.Fan, J., Comstock, J. M., Ovchinnikov, M., McFarlane, S. A., McFarquhar, G.,
and Allen, G.: Tropical anvil characteristics and water vapour of the
tropical tropopause layer: Impact of heterogeneous and homogeneous freezing
parameterizations, J. Geopys. Res., 115, D12201, 10.1029/2009JD012696,
2010.Fridlind, A. M., Ackerman, A. S., McFarquhar, G., Zhang, G., Poellot, M. R.,
DeMott, P. J., Prenni, A. J., and Heymsfield, A. J.: Ice properties of
single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2.
Model results, J. Geophys. Res., 112, D24202, 10.1029/2007JD008646, 2007.Gilmore, M. S., Straka, J. M., and Rasmussen, E. N.: Precipitation and
Evolution Sensitivity in Simulated Deep Convective Storms: Comparisons
between liquid-only and simple ice and liquid phase microphysics, Mon.
Weather Rev., 132, 1897–1916, 2004.Gorbunov, B., Baklanov, A., Kakutkina, N., Windsor, H. L., and Toumi, R.: Ice
nucleation on soot particles, J. Aerosol Sci., 32, 199–215, 2001.Grützun, V., Knoth, O., and Simmel, M.: Simulation of the influence of
aerosol particle characteristics on clouds and precipitation with LM-SPECS:
Model description and first results, Atmos. Res., 90, 233–242, 2008.Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Seasonal variability of
Saharan desert dust and ice nucleating particles over Europe, Atmos. Chem.
Phys., 15, 4389–4397, 10.5194/acp-15-4389-2015, 2015.Hartmann, S., Augustin, S., Clauss, T., Wex, H., Šantl-Temkiv, T., Voigtländer, J., Niedermeier, D., and Stratmann, F.:
Immersion freezing of ice nucleation active protein complexes, Atmos. Chem. Phys., 13, 5751–5766, 10.5194/acp-13-5751-2013, 2013.Hess, M., Koepke, P., and Schult, I.: Optical properties of aerosols and
clouds: The software package OPAC, B. Am. Meteor. Soc., 79, 831–844, 1998.Hiranuma, N., Brooks, S. D., Moffet, R. C., Glen, A., Laskin, A., Gilles,
M. K., Liu, P., Macdonald, A. M., Strapp, J. W., and McFarquar, G. M.:
Chemical characterization of individual particles and residuals of cloud
droplets and ice crystals collected on board research aircraft in the ISDAC
2008 study, J. Geophys. Res., 118, 6564–6579, 10.1002/jgrd.50484, 2013.Hiranuma, N., Augustin-Bauditz, S., Bingemer, H., Budke, C., Curtius, J.,
Danielczok, A., Diehl, K., Dreischmeier, K., Ebert, M., Frank, F., Hoffmann,
N., Kandler, K., Kiselev, A., Koop, T., Leisner, T., Möhler, O., Nillius, B.,
Peckhaus, A., Rose, D., Weinbruch, S., Wex, H., Boose, Y., DeMott, P. J.,
Hader, J. D., Hill, T. C. J., Kanji, Z. A., Kulkarni, G., Levin, E. J. T.,
McCluskey, C. S., Murakami, M., Murray, B. J., Niedermeier, D., Petters, M.
D., O'Sullivan, D., Saito, A., Schill, G. P., Tajiri, T., Tolbert, M. A.,
Welti, A., Whale, T. F., Wright, T. P., and Yamashita, K.: A comprehensive
laboratory study on the immersion freezing behavior of illite NX particles: a
comparison of 17 ice nucleation measurement techniques, Atmos. Chem. Phys.,
15, 2489-2518, 10.5194/acp-15-2489-2015, 2015.Hobbs, P. V., Politovich, M. K., and Radke, L. F.: The structure of summer
convective clouds in Eastern Montana. I. Natural clouds, J. Appl. Meteorol.,
19, 645–663, 1980.Hoffmann, N., Kiselev, A., Rzesanke, D., Duft, D., and Leisner, T.:
Experimental quantification of contact freezing in an electrodynamic balance,
Atmos. Meas. Tech., 6, 2373–2382, 10.5194/amt-6-2373-2013, 2013a.Hoffmann, N., Duft, D., Kiselev, A., and Leisner, T.: Contact freezing
efficiency of mineral dust aerosols studied in an electrodynamic balance:
Quantitative size and temperature dependence for illite particles, Faraday
Discuss., 165, 383–390, 10.1039/C3FD00033H, 2013b.Hoose, C., Lohmann, U., Erdin, R., and Tegen, I.: The global influence of
dust mineralogical composition on heterogeneous ice nucleation in mixed-phase
clouds, Environ. Res. Lett., 3, 025003–025017, 2008.Joly, M., Amato, P., Deguillaume, L., Monier, M., Hoose, C., and Delort,
A.-M.: Quantification of ice nuclei active at near 0 ∘C temperatures
in low-altitude clouds at the Puy de Dôme atmospheric station, Atmos. Chem.
Phys., 14, 8185–8195, 10.5194/acp-14-8185-2014, 2014.Kamphus, M., Ettner-Mahl, M., Klimach, T., Drewnick, F., Keller, L., Cziczo,
D. J., Mertes, S., Borrmann, S., and Curtius, J.: Chemical composition of
ambient aerosol, ice residues and cloud droplet residues in mixed-phase
clouds: single particle analysis during the Cloud and Aerosol
Characterization Experiment (CLACE 6), Atmos. Chem. Phys., 10, 8077–8095,
10.5194/acp-10-8077-2010, 2010.Kerkweg, A., Wurzler, S., Reisin, T., and Bott, A.: On the cloud processing
of aerosol particles: An entraining air parcel model with two-dimensional
spectral cloud microphysics and a new formulation of the collection kernel,
Q. J. Roy. Meteorol. Soc., 129, 1–18, 2003.Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.:
Notes on the state-of-the-art numerical modeling of cloud microphysics,
Atmos. Res., 55, 159–224, 2000.Koop, T., Beiping, L., Tsias, A., and Peter, T.: Water activity as the
determinant for homogeneous ice nucleation in aqueous solutions, Nature, 406,
611–614, 2000.Korolev, A. V., Isaac, G. A., Cober, S. G., Strapp, J. W., and Hallett, J.:
Microphysical characterization of mixed-phase clouds, Q. J. Roy. Meteorol.
Soc., 129, 39–65, 2003.Krämer, M., Schiller, C., Afchine, A., Bauer, R., Gensch, I., Mangold, A.,
Schlicht, S., Spelten, N., Sitnikov, N., Borrmann, S., de Reus, M., and
Spichtinger, P.: Ice supersaturations and cirrus cloud crystal numbers,
Atmos. Chem. Phys., 9, 3505–3522, 10.5194/acp-9-3505-2009, 2009.Kulkarni, G., Fan, J., Comstock, J. M., Liu, X., and Ovchinnikov, M.:
Laboratory measurements and model sensitivity studies of dust deposition ice
nucleation, Atmos. Chem. Phys., 12, 7295–7308, 10.5194/acp-12-7295-2012,
2012.Ladino Moreno, L. A., Stetzer, O., and Lohmann, U.: Contact freezing: a
review of experimental studies, Atmos. Chem. Phys., 13, 9745–9769,
10.5194/acp-13-9745-2013, 2013.Lance, S., Shupe, M. D., Feingold, G., Brock, C. A., Cozic, J., Holloway, J.
S., Moore, R. H., Nenes, A., Schwarz, J. P., Spackman, J. R., Froyd, K. D.,
Murphy, D. M., Brioude, J., Cooper, O. R., Stohl, A., and Burkhart, J. F.:
Cloud condensation nuclei as a modulator of ice processes in Arctic
mixed-phase clouds, Atmos. Chem. Phys., 11, 8003–8015,
10.5194/acp-11-8003-2011, 2011.Lee, S. S., Donner, L. J., and Phillips, V. T. J.: Impacts of aerosol chemical
composition on microphysics and precipitation in deep convection, Atmos.
Res., 94, 220–237, 2009.Leroy, D., Monier, M., Wobrock, W., and Flossmann, A. I.: A numerical study
of the effects of the aerosol particle spectrum on the development of the ice
phase and precipitation formation. Atmos. Res., 80, 15–45,
10.1016/j.atmosres.2005.06.007, 2006.Levin, Z. and Yankofsky, S. A.: Contact versus immersion freezing of freely
suspended droplets by bacterial ice nuclei, J. Clim. Appl. Meteorol., 22,
1964–1966, 1983.Lohmann, U. and Diehl, K.: Sensitivity studies of the importance of dust
nuclei for the indirect aerosol effect on stratiform mixed-phase clouds, J.
Atmos. Sci., 63, 968–982, 2006.Manninen, H .E., Bäck, J., Sihto-Nissilä, S.-L., Huffman, J. A.,
Pessi, A.-M., Hiltunen, V., Aalto, P. P., Hidalgo, P .J., Hari, P., Saarto,
A., Kulmala, M., and Petäjä, T.: Patterns in airborne pollen and
other primary biological aerosol particles (PBAP), and their contribution to
aerosol mass and number in a boreal forest, Boreal Environ. Res., 19 (suppl.
B), 383–405, 2014.Matthias-Maser, S. and Jaenicke, R.: Size distribution of primary
biological aerosol particles with radii ≥ 0.2 µm, J. Atmos.
Res., 39, 279–286, 1995.McFarquhar, G., Zhang, G., Poellot, M. R., Kok, G. L., MaCoy, R., Tooman, T.,
Fridlind, A., and Heymsfield, A. J.: Ice properties of single-layer
stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2.
Observations. J. Geophys. Res., 112, D24201, 10.1029/2007JD008633, 2007.Möhler, O., Field, P. R., Connolly, P., Benz, S., Saathoff, H., Schnaiter,
M., Wagner, R., Cotton, R., Krämer, M., Mangold, A., and Heymsfield, A. J.:
Efficiency of the deposition mode ice nucleation on mineral dust particles,
Atmos. Chem. Phys., 6, 3007–3021, 10.5194/acp-6-3007-2006, 2006.Murray, B. J., Broadley, S. L., Wilson, T. W., Atkinson, J. D., and Wills, R.
H.: Heterogeneous freezing of water droplets containing kaolinite particles,
Atmos. Chem. Phys., 11, 4191–4207, 10.5194/acp-11-4191-2011, 2011.Paukert, M. and Hoose, C.: Modeling immersion freezing with
aerosol-dependent prognostic ice nuclei in Arctic mixed-phase clouds. J.
Geophys. Res. Atmos., 119, 9073–9092, 10.1002/2014JD021917, 2014.Phillips, V. T. J., Donner, L. J., and Garner, S. T.: Nucleation processes in
deep convection simulated by a cloud-system-resolving model with
double-moment bulk microphysics, J. Atmos. Sci., 64, 738–761, 2007.Phillips, V. T. J., DeMott, P. J., and Andronache, C.: An empirical
parameterization of heterogeneous ice nucleation for multiple chemical
species of aerosol, J. Atmos. Sci., 65, 2757–2783, 2008.Phillips, V. T. J., Andronache, C., Christner, B., Morris, C. E., Sands, D.
C., Bansemer, A., Lauer, A., McNaughton, C., and Seman, C.: Potential impacts
from biological aerosols on ensembles of continental clouds simulated
numerically, Biogeosciences, 6, 987–1014, 10.5194/bg-6-987-2009, 2009.Pitter, R. L. and Pruppacher, H. R.: A wind tunnel investigation of freezing
of small water drops falling at terminal velocity in air, Q. J. Roy.
Meteorol. Soc., 99, 540–550, 1973.Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and
Precipitation, 2nd rev. exp. Edn., Atmospheric and Oceanographic Sciences
Library, 18, Springer Science & Business Media, 2010.Reid, J. S., Hobbs, P. V., Ferek, R. J., Blake, D. R., Martins, J. V., Dunlap,
M. R., and Liousse, C.: Physical, chemical, and optical properties of
regional hazes dominated by smoke in Brazilm J. Geophys. Res., 103,
32059–32080, 1998.Sattler, B., Puxbaum, H., and Psenner, R.: Bacterial growth in supercooled
cloud droplets, Geophys. Res. Lett., 28, 239–242, 2001.Schmidt, S., Schneider, J., Klimach, T., Mertes, S., Schenk, L. P., Curtius,
J., Kupiszewski, P., Hammer, E., Vochezer, P., Lloyd, G., Ebert, M., Kandler,
K., Weinbruch, S., and Borrmann, S.: In-situ single submicron particle
composition analysis of ice residuals from mountain-top mixed-phase clouds in
Central Europe, Atmos. Chem. Phys. Discuss., 15, 4677–4724,
10.5194/acpd-15-4677-2015, 2015.Simmel, M. and Wurzler, S.: Condensation and nucleation in sectional cloud
microphysical models based on the linear discrete method, Atmos. Res., 80,
218–236, 2006.Simmel, M., Trautmann, T., and Tetzlaff, G.: Numerical solution of the
stochastic collection equation – Comparison of the linear discrete method
with other methods, Atmos. Res., 61, 135–148, 2002.Simmel, M., Diehl, K., and Wurzler, S.: Numerical simulation of the
microphysics of an orographic cloud: Comparison with measurements and
sensitivity studies, Atmos. Environ., 39, 4365–4373, 2005.Storelvmo, T., Kristjánsson, J. E., and Lohmann, U.: Aerosol influence
on mixed-phase clouds in CAM-Oslo, J. Atmos. Sci., 65, 3214–3230, 2008.Straka, H.: Pollen- und Sporenkunde, Fischer Verlag, Stuttgart, 1975.Twohy, C. H. and Anderson, J. R.: Droplet nuclei in non-precipitating clouds:
composition and size matter, Environ. Res. Lett., 3, 045002,
10.1088/1748-9326/3/4/045002, 2008.
Vali, G.: Quantitative evaluation of experimental results on the heterogeneous freezing nucleation of supercooled liquids, J. Atmos. Sci., 28, 402–409, 1971.van den Heever, S. C., Carrió, G. G., Cotton, W. R., DeMott, P. J., and
Prenni, A. J.: Impacts of Nucleating Aerosol on Florida Storms. Part I:
Mesoscale Simulations, J. Atmos. Sci., 63, 1752–1775, 2006.v. Blohn, N., Mitra, S. K., Diehl, K., and Borrmann, S.: The ice nucleating
ability of pollen. Part III: New laboratory studies in immersion and contact
freezing modes including more pollen types, Atmos. Res., 78, 182–189, 2005.
Weber, D.: Eisnukleation von Aerosolen: Laborexperimente und Messung von
Schiffsemissionen, MSc-thesis, Goethe University, Frankfurt/M., 2014.Wex, H., Augustin-Bauditz, S., Boose, Y., Budke, C., Curtius, J., Diehl, K.,
Dreyer, A., Frank, F., Hartmann, S., Hiranuma, N., Jantsch, E., Kanji, Z. A.,
Kiselev, A., Koop, T., Möhler, O., Niedermeier, D., Nillius, B., Rösch,
M., Rose, D., Schmidt, C., Steinke, I., and Stratmann, F.: Intercomparing
different devices for the investigation of ice nucleating particles using
Snomax® as test substance, Atmos. Chem.
Phys., 15, 1463–1485, 10.5194/acp-15-1463-2015, 2015.Worringen, A., Kandler, K., Benker, N., Dirsch, T., Mertes, S., Schenk, L.,
Kästner, U., Frank, F., Nillius, B., Bundke, U., Rose, D., Curtius, J.,
Kupiszewski, P., Weingartner, E., Vochezer, P., Schneider, J., Schmidt, S.,
Weinbruch, S., and Ebert, M.: Single-particle characterization of
ice-nucleating particles and ice particle residuals sampled by three
different techniques, Atmos. Chem. Phys., 15, 4161–4178,
10.5194/acp-15-4161-2015, 2015.Yakobi-Hancock, J. D., Ladino, L. A., and Abbatt, J. P. D.: Feldspar minerals
as efficient deposition ice nuclei, Atmos. Chem. Phys., 13, 11175–11185,
10.5194/acp-13-11175-2013, 2013.Zimmermann, F., Weinbruch, S., Schütz, L., Hofmann, H., Ebert, M.,
Kandler, K., and Worringen, A.: Ice nucleating properties of the most
abundant mineral dust phases, J. Geophys. Res., 113, D23204,
10.1029/2008JD010655, 2008.