In deep convective clouds, heavy rain is often formed involving the ice
phase. Simulations were performed using the 3-D cloud resolving model
COSMO-SPECS with detailed spectral microphysics including parameterizations of
homogeneous and three heterogeneous freezing modes. The initial conditions
were selected to result in a deep convective cloud reaching 14 km of altitude
with strong updrafts up to 40 m s
Deep convective clouds may cover a wide temperature range from
The distribution of liquid and ice water mass is dependent on factors such as altitude, temperature, and in particular aerosol and ice nucleating particle concentrations and composition as well as on the active freezing modes (e.g., Khain et al., 2005; Leroy et al., 2006; Tao et al., 2007; Fan et al., 2013; Hiron and Flossmann, 2015). In deep convective clouds, a large fraction of ice is formed homogeneously; however, heterogeneous freezing at lower altitudes may have important effects on ice formation and thus precipitation (e.g., Gilmore et al., 2004; van den Heever et al., 2006; Ekman et al., 2007; Phillips et al., 2007; Lee et al., 2009).
The present model simulations address the question of how additional heterogeneous freezing will affect ice formation and precipitation although its contribution to total ice formation may be rather low. This situation may create so-called “small trigger effects”; i.e., small perturbations that do not show significant effects on first sight may trigger cloud microphysical responses.
For instance, a small number of ice particles is formed by a small amount of ice nucleating particles. They grow further by the deposition of water vapor, including the effects of the Bergeron–Findeisen process, i.e., at the expense of liquid drops. With increasing sizes of the ice particles collisions with supercooled drops become more likely (Pruppacher and Klett, 2010). The ice particles grow by riming when they collide with smaller supercooled drops that are deposited on the ice surface and subsequently freeze. When small ice particles collide with larger supercooled drops, the latter freeze by contact-induced nucleation of the ice particle. In this way even small amounts of ice particles may efficiently modify the distribution of ice and liquid water in a cloud. Thus, even when homogeneous freezing is dominant in deep convective clouds, additional heterogeneous freezing particularly taking place in lower cloud regions may have an essential impact on ice formation.
Small perturbations may also play a role within heterogeneous freezing processes themselves. Immersion freezing is assumed to represent the most important heterogeneous freezing process (e.g., Phillips et al., 2007). However, even small additional contributions from contact and deposition freezing may alter eventually precipitation. Another situation with small perturbations is the composition of ice nucleating particles (INPs). It was shown that certain aerosol types significantly modify cloud microphysics (e.g., Lohmann and Diehl, 2006; Phillips et al., 2008; Lee et al., 2009; DeMott et al., 2015; Hande et al., 2015). The most important atmospheric INP types are mineral dust and biological particles, but the latter are present in the atmosphere in much lower amounts than mineral dust particles (e.g., Phillips et al., 2009; Paukert and Hoose, 2014). Thus, the low fractions of biological particles may trigger significant effects.
Model simulations dealing with these issues were performed with the state-of-the-art model system COSMO-SPECS, a 3-D cloud model developed by Grützun et al. (2008). A follow-up version that was numerically more effective was provided by Lieber et al. (2012). COSMO-SPECS is well suited for the envisioned investigations as it provides a link between aerosol particles, cloud properties, and precipitation. It contains a detailed description of the cloud microphysical processes achieved by spectral bin microphysics that explicitly solves the microphysical equations. The last versions of COSMO-SPECS included parameterizations of immersion and contact freezing for several particle types such as mineral dust, soot, and biological particles from Diehl et al. (2006). Recently, in Diehl and Mitra (2015) the parameterizations of ice forming processes were extended and improved. They now include deposition nucleation and homogeneous freezing as new ice forming processes and advanced descriptions of immersion and contact freezing. For the present investigations, this new version of the microphysics was implemented in COSMO-SPECS.
The model simulations presented here are part of the German Science
Foundation (DFG) research group INUIT (Ice Nuclei Research UnIT),
which was established in 2012 to study heterogeneous ice formation in
laboratory, field, and model studies. As an outcome of the experiments, joint
parameterizations were derived to be fed into cloud models to simulate
mixed-phase cloud microphysics. For more details see the INUIT website:
The COSMO model (Consortium for Small-scale Modeling; Steppeler et al.,
2003; Baldauf et al., 2011) is the regional part of the operational weather
forecast system of the DWD. It is based on the primitive hydro-thermodynamic
equations describing compressible non-hydrostatic flow in a moist atmosphere
(
The original COSMO model works with a Kessler-type cloud microphysics bulk scheme. This includes various states of water, such as cloud and rainwater and several forms of ice, but takes into account mass densities only (Kessler, 1995). Later, two-moment schemes were developed that additionally consider the hydrometeor number concentrations (e.g., Seifert and Beheng, 2006). Those schemes predict the evolution of the mass and number densities of several hydrometeor types. However, they offer only limited potential to include the aerosol particles. In spectral bin schemes the particle mass is discretized so that the hydrometeor spectra are divided into size bins for which number and mass are considered (e.g., Reisin et al., 1996; Khain et al., 2004). In those schemes initial aerosol particle spectra are explicitly included and the particle, drop, and ice particle spectra evolve freely. Thus, spectral microphysical schemes allow for detailed investigations of aerosol–cloud interactions. In particular, when the ice phase is included, explicit information about drop and ice particle sizes and the development of size spectra are given, allowing for conclusions about the correlations of ice formation and precipitation.
Grützun et al. (2008) completely replaced the formerly used microphysical scheme in the COSMO model with the spectral bin microphysics as described in Simmel and Wurzler (2006) and Diehl et al. (2006). The time integration of the coupling scheme between the COSMO model and the bin microphysics is performed with two different time steps because the microphysics operates on much smaller timescales than the concurrent dynamical processes. Within the COSMO model the horizontal and vertical winds as well as temperature and pressure are transported within a time step of 1 to 100 s, leading to dynamically updated values. These are used for the microphysical loop, which consists of time steps of 1 s or smaller during which changes in the hydrometeor spectra due to the included microphysical processes are calculated (Grützun et al., 2008). In the present simulations, the dynamical and the microphysical time steps were 4 and 1 s, respectively; i.e., within one dynamical time step, four microphysical time steps were calculated.
The spectral cloud microphysics describes all microphysical processes during
the development of clouds and the subsequent initiation of precipitation.
The entrainment of aerosol particles, drops, ice particles, temperature, and
humidity is embedded (Simmel et al., 2005). A fixed bin structure is used
in which in a first spectrum wetted aerosol particles and liquid drops are
combined. An initially dry aerosol particle number size distribution is
defined at which the particles are internally mixed with a soluble fraction,
After freezing, the drops are removed from the first liquid spectrum and
shifted into a second spectrum, which is used for mixed-phase particles.
These consist of an ice core and a liquid shell; the liquid water mass may
be zero to describe completely frozen particles. In the mixed-phase spectrum
(with the same bins as the liquid spectrum) particles move by processes such
as growth by water vapor deposition and by riming (i.e., collision with
smaller supercooled droplets), collision and sticking of ice particles, ice
nucleation of supercooled drops by collision with smaller ice particles,
sublimation, and melting. This latter process is modeled by the possible
existence of a liquid water shell. In this study, both spectra are divided
into 66 categories, starting with 0.002
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 the collision and coalescence of drops, impaction scavenging of particles by drops, contact freezing of supercooled drops after collisions with particles, riming of ice particles by collisions with supercooled liquid droplets, nucleation of supercooled drops by collisions with small ice particles, and sticking of ice particles after collision.
In the new version of COSMO-SPECS, drops may freeze homogeneously at
temperatures below
The parameterization of immersion freezing in Diehl and Mitra (2015) is an
updated version related to the insoluble particle mass in drops. It
is based on laboratory data of
Parameterizations of heterogeneous freezing.
The description of contact freezing was modified by Diehl and Mitra (2015)
so that it is also particle size resolved. A particle-type-dependent
parameterization of deposition nucleation was newly added (Diehl and Mitra,
2015). Because of entrainment, inactivated interstitial particles are always
present during the simulations with COSMO-SPECS and may serve as contact and
deposition ice nucleating particles. If during the model simulations
particles collide with supercooled drops the number of frozen drops formed
by contact freezing is calculated according to
Interstitial particles may also serve for deposition nucleation. According
to experimental findings the number of activated particles increases
exponentially with ice supersaturation, which is shown in Fig. 1c and
calculated by Diehl and Mitra (2015) using
New values of contact freezing constants in Eq. (4) for pollen and plant debris based on data from Hiranuma et al. (2015; plant debris) and Hoffmann (2015; pollen).
For potential contact and deposition INP, minimum sizes are defined for
mineral dust particles of 0.1
With COSMO-SPECS idealized test cases were simulated. A heat bubble over a
flat terrain was initialized by a temperature disturbance of 1.5 K, which
resulted in a deep convective cloud. The complete model domain covered 80
As initial dry aerosol particles, the number size distribution of Kreidenweis
et al. (2003) was selected. As can be seen from Fig. 2b, it is a mono-modal
lognormal size distribution with
Initial conditions of model simulations.
Process studies were performed including various ice forming processes and,
in the case of heterogeneous freezing, different ice nucleating particle types
in various fractions. First, a warm test case without freezing was simulated
to characterize the behavior of the deep convective cloud. Afterwards, a
case with homogeneous freezing only was performed that served as a reference
case. To study the characteristic impacts of the individual heterogeneous
freezing processes, simulations without homogeneous freezing were performed
although these do not represent realistic cases. The next step was to couple
one heterogeneous freezing process with homogeneous freezing, and afterwards two
or all three heterogeneous freezing processes with homogeneous freezing.
This kind of stepwise adding of ice forming processes allows for the study of the
impact of small perturbations of less active freezing processes, such as
contact and deposition freezing. More small perturbations are low numbers of
ice nucleating particles, in particular biological particles.
Therefore, for each type of simulation, the following parameters were
varied:
ice nucleating particle type – biological particles and mineral dust;
and
As examples for the present paper only three types of mineral dust were
selected: feldspar, kaolinite, and Saharan dust. Feldspar represents a very
effective INP type contained in desert dusts and also in illite
samples. Therefore, by scaling down it is representative for dust samples with a dependence on their composition (Atkinson et al., 2013). For example, African and
Asian dust contains around 24 % feldspar, Arizona test dust (ATD)
approximately 20 %, and illite NX 14 %. Kaolinite samples may also include
up to 10 % feldspar, but the CMS kaolinite used for the experiments,
which served as a base for the present parameterization, does not show
detectable amounts (Murray et al., 2011). Therefore, it shows a
significantly lower efficiency than feldspar in immersion and contact
freezing (see Fig. 1a, b) and was used in these modes together with
feldspar to indicate the lowest and highest effects. For deposition
nucleation no parameterization of kaolinite is available, and therefore less
efficient INPs are represented by Saharan dust (see Fig. 1c). Biological
particles are represented by bacteria, plant debris, and pollen.
To reflect atmospheric conditions, the ice-active fractions of mineral dust
were larger than the ones for biological particles.
As a first test case, a warm case was performed in which all freezing processes
were switched off. This study demonstrated the formation of a deep
convective cloud in which the cloud top reached 14 km of altitude with
temperatures of
In a deep convective cloud as presented in Sect. 4.1 the major fraction of
liquid water freezes homogeneously (Phillips et al., 2007). In the present
study, for the reference case only homogeneous freezing was switched on and
occurred at temperatures below
Following the definition of Korolev et al. (2003) the ice water fraction
decides whether a liquid, a mixed-phase, or an ice cloud has been formed. It
is calculated from the integrated ice water content (IWC) and the integrated
liquid water content (LWC) by using
List of simulated cases with single freezing modes resulting in mixed-phase or liquid clouds.
The resulting types of clouds are listed in Table 2. Homogeneous freezing resulted in a mixed-phase cloud and immersion freezing with mineral dust fractions as low as 0.1 % and biological fractions as low as 0.01 % except pollen. With 0.001 % biological fractions bacteria still formed a mixed-phase cloud but not plant debris and pollen. In contact and deposition modes, mixed-phase clouds were found only with 10 and 1 % feldspar and 10 % kaolinite–Saharan dust; in all other cases liquid clouds resulted, even with somewhat higher biological fractions of 0.01 %. Therefore, the biological particles were not included in simulations with contact and deposition freezing. The results in Table 2 indicate that cases representing small perturbations were those with 0.001 % biological material in the immersion mode and those with 1 % mineral dust in contact and deposition modes.
In the following sections, some example results from these simulations are presented for the reference case with homogeneous freezing, for immersion freezing with 1 % feldspar, and for contact and deposition freezing with 10 % feldspar. Figures 4 to 7 show the corresponding results of ice formation: ice water contents (Figs. 4a to 7a), ice particle numbers (Figs. 4b to 7b), and ice particle size spectra (Figs. 4c to 7c). The left columns of panels (b) and (c) indicate results from primary freezing only, i.e., from direct drop freezing (homogeneous, immersion, and contact freezing) or direct particle activation (deposition nucleation). The other columns in panels (b) and (c) give results from complete ice formation including growth by water vapor deposition, riming, collision and sticking of ice particles, and ice nucleation of drops by small ice particles (see Sect. 2.1).
Ice formation from the reference case with homogeneous freezing.
The maximum ice water content (IWC) reached 10 g kg
In the cases of contact and deposition freezing, after 30 min the cloud regions
with more than 0.1 g kg
Ice formation from immersion freezing with 1 % feldspar.
Figures 4 to 7 (left columns, panels b and c) indicate the altitude
at which primary ice formation proceeded. The maximum in the homogeneous case
was between 10 and 12 km of altitude (
Ice formation from contact freezing with 10 % feldspar.
In homogeneous, immersion, and contact modes most primary frozen drops had
radii around 40
For the homogeneous and immersion case, the ice water contents and the ice particle numbers decreased between 30 and 60 min (Figs. 4a, b and 5a, b), while for the contact and deposition cases the IWC decreased but the ice particle numbers did not. This indicates that primary ice formation still continued at cloud stages after 60 min with contact and deposition freezing. Homogeneous and immersion freezing occurred mainly at altitudes above 9 km at which the numbers of available supercooled drops were reduced after 60 min (compare the warm test case in Sect. 4.1). The inactivated particles required for contact and deposition freezing were always present because of entrainment. Furthermore, for contact freezing taking place at lower altitudes supercooled drops were still available.
Ice formation from deposition nucleation with 10 % feldspar.
With homogeneous and immersion freezing, high numbers of ice particles were
formed (Figs. 4b to 7b), with up to 1
After 60 min in the homogeneous case, small ice particles were still present
at high altitudes that were grown from the very small ones; larger ice
particles moved downwards (Fig. 4c). In contrast, in the
immersion case most of the smaller ice particles grew to larger sizes and
moved downwards (Fig. 5c). With contact freezing, newly
formed smaller ice particles were still present at lower altitudes after 60 min.
In the deposition mode, an important process was the nucleation of larger
drops by collisions with the pristine ice particles. These effects are
visible in particular in Fig. 6c after 30 min when the ice
particle spectrum was separated into two regions. All ice particles larger
than 100
These results from single homogeneous and heterogeneous freezing indicate that there is probably no competition between the different freezing processes because they occur at different altitudes and regions in the cloud. As the primary effects have significantly different magnitudes one may assume that they do not affect each other; e.g., immersion freezing is not restricted by simultaneous contact freezing and vice versa. Because of the fast updraft in the cloud the drop numbers at higher altitudes are hardly reduced by the small effects of contact freezing occurring at lower altitudes.
Total precipitation after 180 min of modeling time for coupled homogeneous and one heterogeneous freezing process. Marked in bold: cases with more than 20 % enhancement of precipitation. Marked in italics: cases with more than 20 % reduction of precipitation.
As a first step model simulations were performed with simultaneous homogeneous freezing and one heterogeneous mode; i.e., homogenous freezing was always switched on plus one heterogeneous mode. The total precipitation after 180 min of modeling time was determined and compared to the value from the reference case with solely homogeneous freezing. Table 3 shows results for feldspar, kaolinite, and Saharan dust, as well as in the immersion mode additionally for bacteria, plant debris, and pollen. The results summarized in Table 3 indicate that in most cases the total precipitation amount was similar to homogeneous freezing, while there were some cases with more than 20 % deviations in both directions. In particular, enhanced precipitation after 180 min was found in the immersion mode for plant debris and pollen and in the deposition mode for 1 % Saharan dust. These cases represent situations in which small perturbations (in this case, small fractions of biological INP or few ice forming effects from deposition nucleation) trigger cloud microphysics so that eventually more precipitation is formed.
Similar observations were made by Hiron and Flossmann (2015), who studied the
role of heterogeneous freezing modes in the framework of a 1.5-D bin-resolved
cloud model. They simulated a convective cloud that reached an altitude of
9.5 km with temperatures near
Figure 8 shows more precipitation details for the cases listed in Table 3. In Fig. 8a and c the development of total precipitation with time is given, and Fig. 8b and d indicate the local distribution of precipitation on a longitudinal line through the model domain after 180 min. In all cases precipitation set in after 45 min (Fig. 8a and c). Deviations were already visible at that time but became more obvious with proceeding time. In some cases precipitation stayed nearly constant during the next hour and increased at later times. This delayed increase in precipitation was noted for the reference case with homogeneous freezing (black solid line), for the cases with contact freezing (purple lines), and for mineral dust cases with immersion freezing except 0.1 % kaolinite (blue lines). In other cases, precipitation increased at early cloud stages, in particular with biological particles in the immersion mode (green lines) and with Saharan dust in the deposition mode (yellow lines).
From Fig. 8b and d one notes that in the cloud center precipitation ranged from 65 (immersion with 1 % kaolinite) to 160 mm (immersion with 0.001 % plant debris) with 75 mm in the reference case. Higher precipitation in the cloud center was observed for the cases with at least 20 % more total precipitation (see Table 3); however, it was also found for cases in which total precipitation was not significantly enhanced but precipitation was increased during early cloud stages, i.e., 10 % Saharan dust in the deposition mode and 0.001 % bacteria and 0.1 % kaolinite in the immersion mode.
To illustrate how ice formation influences the total condensed water in the
deep convective cloud and thus precipitation, results from four example
cases shown in Fig. 8 are considered in more detail. These are immersion
with 1 % feldspar (case 1), immersion with 0.001 % plant debris (case
2), contact with 1 % feldspar (case 3), and deposition with 1 % Saharan
dust (case 4). The amounts of total precipitation were 4.33
Differences between case 1 in which precipitation was mostly delayed and the
other cases are significant. In case 1 more ice was present at middle and high
altitudes after 60 min but less ice was near the ground level after 45 and 60 min
and in mid-levels after 90 min. Thus, only a few melting ice particles
precipitated from the cloud. The ice particle spectrum after 60 min
indicates that there was a high contribution from immersion freezing to ice
particles smaller than 500
In cases 2 to 4, the contributions from heterogeneous freezing to ice
particles smaller than 500
The evaluation of the results indicates that the formation of
precipitation-sized ice particles larger than 500
Total precipitation after 180 min of modeling time for coupled homogeneous and internally mixed immersion freezing. Marked in italics: cases with more than 20 % reduction of precipitation.
Ice formation for different cases with coupled homogeneous and one
heterogeneous freezing mode. Case 1: immersion freezing with 1 %
feldspar;
case 2: immersion freezing with 0.001 % plant debris; case 3: contact
freezing with 1 % feldspar; case 4: deposition nucleation with 1 %
Saharan dust.
A number of cases were modeled with immersion freezing in which the insoluble mass contained in the drops did not consist of pure materials but was internally mixed. These mixtures contained higher fractions of mineral dust and small fractions of biological particles, as they reflect atmospheric conditions. Table 4 lists the compositions of seven cases together with the resulting amount of total precipitation; Fig. 10 shows the precipitation results.
In all mixed cases precipitation was lower than in the reference case with homogenous freezing. However, as can be seen from Fig. 10, the temporal development and the local distribution of precipitation were modified by the particle composition. In cases 1 and 2 with 5 % mineral dust fractions and in cases 3, 4, and 5 with 1 % mineral dust fractions, the development of precipitation was delayed below the reference line (homogeneous freezing) during early cloud stages (Fig. 10a). The lower the fraction of efficient dust INP, the earlier the increase in precipitation above the reference line. This was distinctly visible in case 7 with the lowest dust fractions. The 75 mm precipitation in the cloud center from homogeneous freezing was enhanced up to 100 to 120 mm in those cases. However, in comparison to the pure mineral dust cases no enhancement effects resulted from additional biological INP fractions. This indicates that the major fraction of composed INP decides ice formation and hence the development of precipitation.
Total precipitation after 180 min of modeling time for coupled homogeneous, immersion, contact, and deposition freezing in various combinations. Marked in bold: cases with more than 20 % enhancement of precipitation. Marked in italics: cases with more than 20 % reduction of precipitation.
Finally, model simulations were performed in which contact and/or deposition
freezing were switched on in addition to homogeneous and immersion
freezing. In a first series of model simulations the
Total precipitation after 180 min of modeling time for mixed cases with coupled homogeneous, immersion, contact, and deposition freezing. Marked in italics: cases with more than 20 % reduction of precipitation.
The results in Table 5 indicate that in none of the investigated cases did additional contact and/or deposition freezing as small perturbations cause an increase in total precipitation. However, modifications of the temporal development and the local distribution of precipitation are visible and demonstrated in Fig. 11. In the case of feldspar, precipitation was delayed during early cloud stages with all combinations of freezing modes. The local distribution of precipitation was not modified by additional contact freezing but significantly changed by additional deposition freezing. Precipitation was reduced in the cloud center from 75 to 35 mm and was spread over a wider area with a 12 km diameter (instead of 4 km). This indicates the influence of deposition freezing on precipitation at the cloud edges. In contrast, for the kaolinite–Saharan dust cases, delayed precipitation from solely immersion freezing was slightly increased during later cloud stages by additional contact freezing and even more enhanced by additional contact and deposition freezing. Similarly, precipitation in the cloud center was increased from 70 mm with solely immersion freezing to 80 mm with additional contact freezing, to 95 mm with additional deposition freezing, and to 115 mm with both additional freezing modes. For the latter case Fig. 12 illustrates how additional contact and deposition modes altered the ice particle spectra. Although their impact on ice formation was rather low they modified the ice particle spectra so that higher numbers of larger ice particles were formed.
Ice particle size spectra in the center cell of the cloud after
120 and 150 min for cases with 1 % kaolinite–Saharan dust. Number
concentrations per m
For the cases listed in Table 6 with lower internally mixed immersion INP fractions and higher contact and deposition INP fractions, an enhancement of total precipitation was not found, but again the development of precipitation and the local distribution were modified. In Fig. 11c and d results from solely immersion freezing are also shown. The 0.1 % feldspar in the immersion mode (dashed blue line) effected a delay in precipitation, while 1 % additional contact and deposition INP (solid blue lines, cases 1 and 2) as well as additional biological INP (solid green line, case 3) led to an increase during early cloud stages (Fig. 11c). However, with biological INP (case 3) this increase was less significant. In contrast, the enhancement of precipitation affected by 0.1 % kaolinite in the immersion mode (dashed orange line) was reduced by additional contact and deposition freezing (orange solid line, case 4) and by additional biological INP (solid light green line, case 5). Here the reduction was less significant with biological INP. Figure 11d again indicates that more precipitation during early cloud stages was coupled with a significant increase in precipitation in the cloud center although the total precipitation was not higher than in the reference case.
Thus, when contact and deposition freezing equally contribute to ice formation as immersion freezing (with higher INP fractions) they may work in both directions. They may enhance precipitation during early cloud stages, thereby counterbalancing the delaying effect of immersion freezing. On the other hand, when immersion freezing itself shows some “small trigger effects” these may be suppressed by contact and deposition freezing during early cloud stages. Additional biological particles counteract these tendencies.
The results presented in this section indicate that immersion freezing as the major ice forming process inhibits an increase in total precipitation above the amount from the reference case with homogeneous freezing. However, additional deposition nucleation and contact freezing as small perturbations have the chance to modify precipitation. With a dependence on the active INP types, possible changes are an increase in precipitation during early cloud stages coupled with more precipitation in the cloud center or no effect on the temporal development but a spread of precipitation over a wider area beyond the cloud. When contact and deposition freezing nearly equally contribute to ice formation as immersion freezing the effects do not show a clear trend. Their interactions may favor or suppress the formation of larger ice particles and thus an enhancement or a delay of precipitation. In comparison to pure dust cases no significant differences caused by additional biological particles are visible.
Growth processes in mixed-phase clouds are determined by the collision efficiencies of the involved drops and ice particles, which in turn are dependent on the sizes of the collision partners (Pruppacher and Klett, 2010; Diehl et al., 2006). Thus, the development of drop and ice particle spectra during cloud evolution determines the effectivity of growth processes and eventually the formation of precipitation-sized hydrometeors.
In this paper an improved version of the 3-D cloud modeling system
COSMO-SPECS (Grützun et al., 2008) is presented, which allows for the
study of
the impact of aerosol particle types and three heterogeneous freezing modes
on ice formation and precipitation. A deep convective cloud with a wide
temperature range from
The following conclusions were drawn.
The different freezing processes hardly compete with and affect each
other. Homogeneous, immersion, and contact freezing, which require
supercooled drops, occur at different altitudes in the cloud. Deposition
nucleation dominating at the highest altitudes is not in competition with
homogeneous freezing because it is not coupled to supercooled drops. Also,
contact and deposition freezing are not in competition; they are both
coupled to inactivated particles but are dominant at different altitudes.
Some deposition nucleation is possible at lower heights at the cloud edges,
while contact freezing concentrates rather towards the cloud center where
more drops are available. Regarding the vertical velocity in the cloud, because of the release of
latent heat during freezing the fields of highest vertical updraft were
vertically extended in comparison to the warm case (see Fig. 3) by the
additional release of latent heat during freezing. However, cases with
different ice formation resulted in small local changes only (results not
shown here). Precipitation is modified by the formation of larger ice particles. This is
suppressed in regions where homogeneous freezing is dominant because
high numbers of small ice particles compete for growth via water vapor or
drop deposition. It may also be suppressed in median regions when the impact
of immersion freezing is high, i.e., with higher fractions of efficient INP.
In such cases even small drops contain sufficient insoluble material
to affect freezing. In lower regions where contact freezing is active growth
processes are hindered because of shorter times until the ice particles
reach the melting level. In contrast, the formation of larger ice particles by growth processes is
supported in median regions of the cloud when only small fractions of
immersion INPs are active. Then larger drops freeze because higher masses of
insoluble material in the drops are required. Additionally, supercooled
drops are present for riming and the ice particles need some time to reach
the melting level. Deposition nucleation primarily affects the formation of
very small pristine ice particles; however, afterwards the formation of
large ice particles by collision with supercooled drops is supported. In
this way, deposition nucleation indirectly promotes the formation of large
ice particles. The results indicate so-called “small trigger effects” of heterogeneous
freezing in comparison to homogeneous freezing and “small trigger
effects” of deposition and contact freezing in comparison to immersion
freezing. Therefore, although homogeneous freezing is dominant in a deep
convective cloud, heterogeneous freezing processes should not be neglected.
Aside from immersion freezing, contact and deposition freezing are also
important. This finding is in contrast to the conclusion of Hiron and
Flossmann (2015). From the fact that contact and deposition freezing
contributed only low amounts of ice particles in comparison to the other
freezing modes they concluded that these could be neglected in cloud models
with less complexity. “Small trigger effects” of biological particles in comparison to mineral
dust particles were not found, and thus the role of biological particles in
atmospheric clouds still remains unclear as was concluded earlier by
Hiron and Flossmann (2015). They studied a case with solely bacterial INP
(acting in a nonspecific ice nucleation mode), which resulted in
significantly higher amounts of precipitation than in all other cases. However,
when bacteria were added in a simulation in which all other INPs were also
forming ice their influence became negligible. In comparison to the reference case with homogeneous freezing, these small
perturbations may affect an enhancement of total precipitation but mostly
the effects are limited to modifications of the temporal development of
precipitation, i.e., a gradual increase at early cloud stages instead
of a strong increase at later cloud stages. The effects are coupled with
changes in the local distribution of precipitation, i.e., approximately
50 % more precipitation in the cloud center. The modifications depend on
the active freezing modes, the fractions of active INP, and the composition
of the internal mixtures in the drops. In general, precipitation from the simulated deep convective cloud did not
show significant variations in the total precipitation amount. Changes in
the local distribution of precipitation were more remarkable. Because of the
strong vertical updraft in the present case precipitation is highly
influenced by cloud dynamics but cloud microphysics still has an important
impact.
Further simulations will switch to cloud situations with weaker cloud
dynamics, i.e., reduced vertical velocity, slower ascent of the air, and
reduced cloud top height. Thus, microphysical changes in the cloud
may have more time to develop. Homogeneous freezing will have a smaller
impact on ice formation, while heterogeneous freezing will show a higher
impact.
The data are available from the corresponding author upon request.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Results from the ice nucleation research unit (INUIT) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
This work is part of the research group INUIT (Ice Nuclei research UnIT) FOR1525 and was supported by the Deutsche Forschungsgemeinschaft under grant DI 1539/1-2. We appreciate the INUIT community for providing experimental data as a base for parameterizations and for helpful discussions. We would like to thank Ralf Wolke and Jens Stoll from TROPOS Leipzig for providing the COSMO-SPECS code and for their support during restarting the model and solving initial problems. Thanks to Martin Simmel from TROPOS Leipzig for fruitful discussions and support. Karoline Diehl would like to thank Daniel Kunkel from IPA Mainz for his aid in installing, starting, and performing COSMO-SPECS model simulations on the high-performance cluster MOGON at the University of Mainz. Edited by: Barbara Ervens Reviewed by: two anonymous referees