ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-14105-2017Partitioning the primary ice formation modes in large eddy simulations of mixed-phase cloudsHandeLuke B.luke.hande@kit.eduHooseCorinnacorinna.hoose@kit.eduhttps://orcid.org/0000-0003-2827-5789Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, GermanyLuke B. Hande (luke.hande@kit.edu) and Corinna Hoose (corinna.hoose@kit.edu)27November20171722141051411826May201731May20179October201713October2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/14105/2017/acp-17-14105-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/14105/2017/acp-17-14105-2017.pdf
State-of-the-art aerosol-dependent parameterisations describing each
heterogeneous ice nucleation mode (contact, immersion, and deposition ice
nucleation), as well as homogeneous nucleation, were incorporated into a
large eddy simulation model. Several cases representing commonly occurring
cloud types were simulated in an effort to understand which ice nucleation
modes contribute the most to total concentrations of ice crystals. The cases
include a completely idealised warm bubble, semi-idealised deep convection,
an orographic cloud, and a stratiform case. Despite clear differences in
thermodynamic conditions between the cases, the results are remarkably
consistent between the different cloud types. In all the investigated cloud
types and under normal aerosol conditions, immersion freezing dominates and
contact freezing also contributes significantly. At colder temperatures,
deposition nucleation plays only a small role, and homogeneous freezing is
important. To some extent, the temporal evolution of the cloud determines the
dominant freezing mechanism and hence the subsequent microphysical
processes. Precipitation is not correlated with any one ice nucleation mode,
instead occurring simultaneously when several nucleation modes are active.
Furthermore, large variations in the aerosol concentration do affect the
dominant ice nucleation mode; however, they have only a minor influence on the
precipitation amount.
Introduction
Ice crystals in the atmosphere can form spontaneously through homogeneous
nucleation, which becomes increasingly probable at temperatures lower than
-35 ∘C . At warmer temperatures an ice
nucleating particle (INP) is required to initiate freezing. Although INPs
represent a small fraction of all atmospheric aerosols
, they have a disproportionately large
influence on mixed-phase cloud microphysics .
Therefore modelling ice microphysical processes accurately is necessary to
correctly model clouds and the myriad subsequent processes influenced by clouds.
Several pathways have been identified through which ice nucleation in the
atmosphere can take place . Deposition nucleation
occurs at cold temperatures, where water vapour is deposited as ice directly
onto an aerosol particle. Immersion and condensation freezing require the
particle to be immersed in super-cooled liquid water, after which freezing
occurs. Contact freezing occurs when an aerosol particle comes into contact
with a super-cooled droplet, which subsequently initiates freezing. A
similar mechanism called inside-out freezing has been identified, where a
immersed particle comes into contact with the water–air interface, which
initiates freezing . Contact freezing and
inside-out freezing have long been hypothesised to be important in areas of
evaporation . Indeed, recent results from a modelling
study support this idea .
present a detailed overview of the latest ice
nucleation research. Ice nucleation can be studied in a wide variety of ways
, including under tightly controlled conditions in
the laboratory. Recent reviews of laboratory experiments
highlight the tendency for much
attention to be directed towards identifying and quantifying the ice
nucleating ability of different aerosols species in each nucleation mode
separately. These laboratory studies do little to elucidate the relative
importance of these modes, so their atmospheric relevance is poorly understood.
provide a review of experimental studies
investigating contact nucleation and go so far as to suggest it could
dominate over immersion freezing for some aerosol species. Given that
laboratory results suggest it is an efficient ice formation mechanism, these
authors specifically pose questions as to whether this also holds true in
simulations. However in more recent experiments,
could not confirm a general enhancement in contact freezing compared to
immersion freezing.
Modelling results from show that immersion freezing
is the dominant pathway through which ice is formed, with contact playing
little to no role. In this study, deposition nucleation was significant in
the early stages of cloud development. used a
model to also show that contact freezing has little impact on heterogeneous
ice nucleation in deep convective clouds. An analysis of trajectories from a
dust dominated region showed air parcels commonly pass through ice-saturated,
but water sub-saturated, regions, where deposition nucleation could occur
. Later, showed that
immersion freezing dominates INP production, and in contrast to the previous
modelling studies, contact freezing played an important role in their
simulations. used a model to show there is
competition between heterogeneous and homogeneous ice nucleation, which is
influenced by thermodynamic and microphysical conditions.
In situ and remote sensing observations have also been employed to study ice
nucleation under atmospheric conditions. observed
altocumulus clouds which almost always had liquid water at cloud top,
suggesting deposition nucleation plays little role. This has been supported
by observations in cases of lee-wave clouds and
stratiform clouds , suggesting
either immersion or contact freezing dominates ice production.
A recent global analysis of satellite observations
indicates there are low cloud glaciation
temperatures in areas of deep convection, not only in the tropics but also
extending to the mid-latitudes. This suggests homogeneous freezing and/or
deposition nucleation are important. The warm ice clouds analysed in their
study, on the other hand, were associated with stratiform cloud systems, and
the authors pose the question of the role that dynamics play in initiating
early cloud glaciation.
Since immersion and contact freezing require the presence of liquid water,
they are thought to be the dominant ice formation pathway in mixed phase
clouds. The above studies
seem to suggest this is the case; however, there is still considerable
uncertainty. In addition, there is little consensus on whether deposition
nucleation or homogeneous freezing contributes significantly to ice production
at cirrus temperatures.
A further complication arises since ice nucleation is clearly influenced by
the ambient environmental conditions, and as such the dominant mode could
depend on the cloud type. This paper aims to help clarify, in a systematic
way, which ice nucleation modes dominate for various cloud types found over
continental regions. The contribution of each mode to precipitation will also
be considered. The cases studied here are a warm bubble, semi-idealised deep
convection, idealised orographic, and a stratiform cloud, and hence cover a
variety of thermodynamic conditions.
Model description
The non-hydrostatic regional weather forecasting model COSMO (COnsortium for
Small-scale MOdelling) version 5.01 was run
at high resolution of 8.9 × 10-4∘ (≈ 100 m). This
scale is small enough to resolve energy-containing turbulence
. The two-moment cloud microphysics scheme of
was used, which uses the supersaturation to define a
power law, from which cloud condensation nuclei (CCN) concentrations representative of continental
conditions are calculated. The droplet size distribution was calculated from
the model diagnosed cloud liquid water content and droplet number
concentration in every grid box, assuming a modified gamma distribution, with
parameters defined in , for droplets in the size range
1 to 535 µm. Figure shows the spatial and temporal mean
cloud droplet size distribution for each case investigated. These cases are
described in detail in the next section.
Recent work has made significant progress in the development of detailed
parameterisations for deposition nucleation, immersion freezing, and contact
freezing .
These parameterisations were developed either from observations or theory,
and are representative of nucleation on a variety of aerosol species.
In this study, the parameterisation for deposition
nucleation on Arizona test dust (ATD) was used. This parameterisation is a
function of supersaturation with respect to ice, and temperature, and is
active from 226 to 250 K. was employed to describe
immersion freezing, which depends on temperature, and acts between 237 and 261 K.
In these two parameterisations, particle surface area also plays a role
through the use of the ice nucleation active surface site (INAS) densities.
Comparing these two parameterisations to recently developed formulations by
shows good agreement for immersion freezing and lower
deposition nucleation efficiency for desert dust compared to ATD. This provides some
measure of confidence in the reliability of the parameterisations used here.
Prescribed dust aerosol size distribution, and derived mean cloud
droplet size distribution for all cases. Dashed lines indicate dust aerosol
size distribution for sensitivity studies.
The study of was used for contact freezing with generic dust
aerosols. This parameterisation is a function of aerosol and droplet size and
number concentration, relative humidity, temperature, and electrical charges,
and is active between 240 and 268 K. Finally, theoretical expressions of the
homogeneous nucleation rate by were used to
describe homogeneous freezing.
A two-mode log-normal dust aerosol size distribution was used, as shown in
Fig. , covering particle sizes from 0.1 to 100 µm,
which is based on observations from Jungfraujoch research station
(M. Niemand, personal communication, 2015) (mode 1: N= 0.015 × 106 m-3,
μ= 1.355 × 10-6 m, σ= 1.443; mode 2:
N= 0.00001 × 106 m-3, μ= 8.518 × 10-6 m,
σ= 1.358). Aerosol concentrations at sizes larger than about 30 µm are
small enough as to be considered zero. The upper bound in the aerosol size
distribution is only for mathematical convenience. The dust aerosol
concentrations are constant in the vertical dimension throughout the
simulation. Model results suggest that dust aerosols are relatively constant
in the vertical dimension, with only a 25 % decrease in dust aerosol number
concentrations over Germany during summer between the low levels and the
tropopause .
The aerosols are not removed by precipitation or sedimentation in the model.
This simplification is not expected to have a significant effect on the
formation of INPs. The maximum number concentration of aerosols is orders of
magnitude larger than the maximum INP concentrations, as shown later in this
paper. Therefore, any removal of aerosols will make a very small
difference to the total number concentration. Furthermore, in the case of
convectively or orographically forced clouds, entrainment of new aerosols
into the cloud adds a source of aerosols to off-set their removal.
show that the 5th and 95th percentiles of
dust number concentrations are representative of low and high dust
concentrations. These concentrations are often more than an order of
magnitude smaller and larger than the median, depending on the season. The
dust aerosol properties used in this study correspond roughly to the
properties during summer from , during which
concentrations and aerosol sizes are the lowest throughout the year. In order
to investigate the sensitivity of ice nucleation to the aerosol size
distribution, two additional aerosol size distributions are defined in
Fig. , shown as the dashed lines. Here, the total number
concentration of both modes was modified by factors of 10 and 0.1, which
simulate high and low dust aerosol number concentrations. These sensitivity
studies are analysed with a focus on the resulting partitioning into the
different ice nucleation modes, e.g. the role of homogeneous versus
heterogeneous ice nucleation.
The aerosol and droplet distributions were divided into 10 bins, over which
the integration for the parameterisations was performed. The immersion- and
contact-freezing parameterisations are only applied to cloud droplets. Since
rain drops collect many particles through collision–coalescence they may be
important for freezing in the immersion mode, depending on cloud type
. However simple parameterisations for this
process do not exist, limiting applicability of rain freezing through the
immersion mode. Furthermore, show that these
deliquesced aerosol particles can initiate additional contact freezing.
Thermodynamic sounding used to initialise the cases.
(a) Idealised heat bubble (black) and semi-idealised deep convective
(blue). (b) Orographic (black) and stratiform
(blue).
Immersion freezing acts only on the immersed dust aerosols, and contact
freezing acts on the interstitial aerosols. The segregation of immersed and
interstitial aerosols is treated simplistically in this work, where the ratio
of these quantities is pre-defined. In these simulations, 50 % of the total
number of dust aerosols are defined to be interstitial and hence available for
contact freezing, and the remaining 50 % is defined to be immersed and
available for immersion freezing. This is not necessarily a realistic
assumption, but it allows the relative concentrations of immersion and
contact INPs to be compared independent of this assumption, since differences
in INP concentrations will not be due to differences in aerosol
concentrations available for nucleation in a given mode. While some
observations support a roughly equal split of dust particles into
interstitial and immersed aerosol , we expect this assumption
to overestimate the fraction of interstitial dust in conditions where aerosol
processing during long-range transport or high supersaturations increase the
CCN activation of dust particles . Finally,
depletion of immersed aerosols is not taken into account in these
simulations, which has been shown to cause an overestimate of the ice crystal
concentrations by a factor of 2 for an Arctic stratocumulus cloud .
Case study description
Ice nucleation is influenced by ambient environmental conditions; therefore,
in order to systematically study the relative contribution of each mode, a
distinction between cloud types must be made. In this section, the model
configurations for two cases of convection, an idealised orographic cloud
and a stratiform cloud are described.
Since deep convective clouds span temperature ranges relevant for warm and
cold cloud microphysics, including into the homogeneous nucleation regime,
two cases will be investigated here: a fully idealised warm bubble case, and
a semi-idealised cloud. Starting with the former, the thermodynamic profile
described in was used to initialise the
simulation, shown in the left panel of Fig. as the
black lines. A 3-D temperature disturbance of 1.5 K, with radius of 10 km, was
placed in the centre of the domain at a height of 1.4 km. In total, 100 vertical
levels, with 600 × 600 grid cells horizontally, were used, and the
time step was 1 s for the duration of the 4 h simulation.
The semi-idealised deep convective cloud represents a more realistic
simulation of convection, and provides an interesting comparison with the
previous idealised heat bubble. A detailed description of the model
configuration for this case appears in and is
summarised here. A real sounding with a convective available potential energy (CAPE)
of 1889 J kg-1 was used to initialise the simulation, and
realistic topography was specified at each grid point, as shown in Fig. 6
of . The topography represents the region near
Jülich, in western Germany, with mountains reaching up to 560 m in the
southwest of the domain. A total of 100 vertical levels were used, and 600 × 600
grid cells horizontally, with a time step of 2 s for the duration of the 9 h simulation.
To initialise the orographic mixed-phase cloud case, an idealised
bell-shaped hill was used along with a real sounding, shown in the right
panel of Fig. as the black lines. The hill has a
maximum height of 800 m and a half-width of 15 km. In the longitudinal
direction, 1441 grid points were used, and 271 in the latitudinal direction,
with 100 vertical levels. A time step of 1 s was used for the duration
of the 4 h simulation.
The final case to be investigated is a stratiform cloud, which was
initialised from a real sounding from central Germany during winter, shown in
the right panel of Fig. as the blue lines. A smaller
domain with 400 × 400 horizontal grid points was used, again with
100 vertical levels. In this case, the horizontal wind speed was artificially
increased by a factor of 1.5 in the lowest 5.5 km, in order to increase the
dynamical forcing enough to activate cloud droplets through shear-driven
turbulence in the boundary layer. Due to the higher wind speeds in this
simulation, a shorter time step of 0.5 s was used for the 9 h
simulation. All investigated cases employed fully periodic boundary conditions.
Domain mean horizontal cross section of INP number concentrations in
each mode (a) and cloud droplet properties (b) for the heat
bubble convective cloud at 0.5 h into the simulation for normal dust
concentrations. Dashed horizontal lines represent the temperature limits of
the parameterisations. Contours represent the sign of the vertical velocity
(solid: positive; dashed: negative).
Domain mean horizontal cross section of INP number concentrations in
each mode (a) and cloud droplet properties (b) for the
semi-idealised deep convective cloud at 4 h into the simulation for normal
dust concentrations. Dashed horizontal lines represent the temperature limits
of the parameterisations. Contours represent the sign of the vertical
velocity (solid: positive; dashed: negative).
Spatial distribution of INPs
In this section the spatial distribution of INPs in each mode will be
analysed, along with the cloud droplet properties. Contact-freezing INPs are
parameterised in terms of a rate, so the number concentrations are obtained by multiplying by the time step of the
simulation. All diagrams in this
section are domain mean horizontal cross sections taken at a particular time
step indicated in the figure captions, where the mean is taken over all
latitudes. As described in the Sect. 2, cloud droplet size was calculated
from cloud liquid water content and number concentration, assuming a gamma
distribution at each grid point. The mode in the cloud droplet radius
distribution which is shown in the following diagrams is simply the radius at
which the maximum in the cloud droplet size distribution occurred, and the
variance and skewness of the distributions are not represented.
Starting with the idealised heat bubble, Fig.
shows the concentrations of INPs (left panels), along with the cloud droplet
properties (right panels) at 0.5 h into the simulation. Immersion and
contact freezing both contribute significantly at warmer temperatures, and
homogeneous nucleation is a major contributor at colder temperatures.
Deposition nucleation, however, is limited to low concentrations occurring
over a narrow temperature range.
Looking closer at immersion freezing, there is a trend of higher INP
concentrations at colder temperatures. This should be expected since,
according to this parameterisation, there is an inverse exponential
relationship between INAS density and temperature.
Contact freezing, on the other hand, shows the opposite trend. Although the
contact-freezing efficiency also increases exponentially with decreasing
temperature, droplet properties have a larger influence on INP
concentrations, as discussed in . The highest
concentrations in the contact mode occur at around 6 km, co-located with the
maximum in cloud droplet size. At colder temperatures above this height, the
size and number concentration of cloud droplets is lower, reducing the
effectiveness of contact freezing since the contact-freezing collection
kernel strongly favours large aerosol–large droplet interactions.
The final panel in Fig. shows the in-cloud
relative humidity with respect to liquid water. On both sides of the central updraught, indicated by the
solid contours, there are regions of downdraughts, shown by the dashed
contours. This results in lower relative humidity, which acts to suppress the
formation of INPs.
The results for the semi-idealised deep convective case, shown in
Fig. , are remarkably consistent with the previous case:
immersion and contact freezing both dominate, and homogeneous nucleation
contributes the most at cold temperatures. Furthermore, the trend in
immersion and contact INPs is the same as the idealised heat bubble.
The added complexity in this case highlights an interesting feature of the
contact parameterisation employed in this study. Looking at the relative
humidity, between about 16 and 26 km in the horizontal direction, the relative
humidity is less than approximately 80 %. Despite this, the concentrations of
contact INPs are as high as 105 m-3. That INPs can still form in
this environment is a consequence of the phoretic forces
increasing the collision efficiency between aerosols
and cloud droplets in lower-humidity regions. The lifetime of droplets can be
calculated using Eq. (3.14) from , ignoring
curvature effects and assuming pure spherical droplets. A 10 µm droplet
exposed to relative humidity of 80 % at 260 K should completely evaporate in
5.7 s, decreasing to 2.8 s at relative humidity of 60 %.
Furthermore, show that in a deep convective cloud,
droplets warmer than about 260 K can have number concentrations up to
108 m-3. These two points indicate there should be high numbers of
droplets available for collisions within a few seconds before evaporating.
Finally, another interesting feature of the deep convective case is the high
levels of variability in INP concentrations along isotherms. This variability
is attributable to the large influence of relative humidity and droplet
properties on the contact-freezing rate.
Domain mean horizontal cross section of INP number concentrations in
each mode (a) and cloud droplet properties (b) for the
orographic cloud at 2 h into the simulation for normal dust concentrations.
Dashed horizontal lines represent the temperature limits of the
parameterisations. Contours represent the sign of the vertical velocity
(solid: positive; dashed: negative).
Domain mean horizontal cross section of INP number concentrations in
each mode (a) and cloud droplet properties (b) for the
stratiform cloud at 3 h into the simulation for normal dust concentrations.
Dashed horizontal lines represent the temperature limits of the
parameterisations. Contours represent the sign of the vertical velocity
(solid: positive; dashed: negative).
The orographic cloud case is shown in Fig. . Here,
homogeneous freezing and deposition nucleation play no role in ice formation,
since the cloud top does not reach sufficiently cold temperatures, and
immersion INP concentrations are significantly higher than contact INP concentrations. Immersion
INP concentrations are more or less homogeneously distributed throughout
the cloud, and the highest concentrations in the contact mode are co-located
with high concentrations of large cloud droplets. In the lee of the hill
there is a downdraught, indicated by the dashed contours. As was seen in the
first case, this reduces the relative humidity and suppresses ice formation.
Given the different dynamical environment of the stratiform case, the
resulting INP concentrations, shown in Fig. , are
quite low and the cloud is only sparsely populated with INPs, particularly in
the immersion mode. Although the relative humidity in the mid-troposphere is
high (around 60–70 %) compared to the other profiles shown in
Fig. , homogeneous freezing and deposition nucleation do not
contribute to ice formation. Immersion INP concentrations are several orders
of magnitude larger than contact INP concentrations.
The sounding used to initialise this case, shown in Fig. ,
has a strong decrease in moisture at 5.5 km (T= 248 K,
p= 475 hPa), which inhibits INP formation at colder temperatures. The
maximum in the cloud droplet number concentration and size is between 1 and 2 km,
which is outside the temperature range of the contact nucleation parameterisation.
Therefore, the concentration of contact INPs is reduced due to the lower
concentration of smaller cloud droplets in the region of contact freezing.
Temporal evolution of INPs
The temporal development of the ice phase influences a host of cloud
properties, including cloud lifetime, radiative properties, and precipitation
amount. Figure shows the evolution of each INP mode
over the duration of the idealised heat bubble simulation, where the domain
mean concentrations over all latitudes and longitudes are taken. INPs in the
contact mode appear in low concentrations after 15 min. The cloud develops
rapidly, producing high concentrations of INPs in the immersion- and contact-freezing modes, as well as through homogeneous freezing. Deposition
nucleation also plays a role early in the simulation. As the simulation
progresses, the initial convective cell dissipates, and after about 2 h
the simulation enters somewhat of a steady state as secondary convection is
initiated throughout the domain. Immersion freezing plays less of a role in
later stages of the simulation, and all other modes persist with roughly
constant concentrations.
The bottom panel shows the domain mean accumulated precipitation for the
duration of the simulation. Precipitation is initiated after about 1 h,
and there is a break in precipitation coinciding with the dissipation of the
main convective cell, with steady precipitation resuming after 2 h.
Interestingly, both cases with higher and lower dust aerosol concentrations
result in higher precipitation. By the end of the simulation, there is a
maximum difference of about 20 % in the total precipitation. Correlation
coefficients for the domain mean integrated INP concentrations in each mode
and the domain mean total precipitation were calculated, and the correlation
coefficients were not significant to any sufficiently high level of
confidence. The CCN are not influenced by the dust aerosol distribution used
in the INP parameterisations.
As in the previous section, the results in the two convective cases are
similar, with the temporal evolution of the INPs in the semi-idealised deep
convective case closely mirroring the evolution in the idealised heat bubble
case, as shown in Fig. . In the semi-idealised
convective case the evolution of the cloud is notably slower, reaching
maximum INP concentrations after 4 h, at which time immersion freezing
dominates. Towards the end of the simulation contact freezing becomes more
significant. INPs produced at cold temperatures of less than about
-35 ∘C reach their maximum late in the simulation, with the greatest
contribution from homogeneous freezing.
Precipitation is initiated during the peak in ice formation, between 3.5 and
5.5 h into the simulation. This time period is when the immersion
and contact INP concentrations reach their maximum, and when homogeneous and
deposition nucleation begin to play a role. Perturbations to the dust aerosol
concentrations give the opposite effect compared to the previous simulation.
That is, both cases of lower and higher dust concentrations give slightly
less domain mean accumulated precipitation throughout the simulation.
The temporal evolution of the orographic case, shown in Fig. ,
indicates INP production begins in the contact mode soon
after initialisation, followed 15 min later by the immersion mode. As the
simulation progresses, the cloud gets a few hundred metres deeper, immersion
INP concentrations get gradually higher and contact INP concentrations get
gradually lower.
(a–d)
Temporal evolution of INP number concentrations in each mode for the
heat bubble convective cloud for normal dust concentrations.
Panel (e) shows total precipitation. The dashed (dotted) line is for the high
(low) aerosol simulation.
(a–d) Temporal evolution of INP number concentrations in each mode for the
semi-idealised deep convective cloud for normal dust concentrations.
Panel (e) shows total precipitation. The dashed (dotted) line is for the high
(low) aerosol simulation.
(a–d) Temporal evolution of INP number concentrations in each mode for the
orographic cloud for normal dust concentrations. Panel (e) shows total
precipitation. The dashed (dotted) line is for the high (low) aerosol
simulation.
The total precipitation in the orographic case is much lower than the
previous two cases. Here, precipitation begins after 0.5 h and is light
and steady for the duration of the simulation. In contrast to the previous
cases, the changes in the aerosol concentrations give a systematic change in
accumulated precipitation, where higher aerosol concentrations result in
higher precipitation, and vice versa. The difference in accumulated
precipitation at the end of the simulation is around ±10 %.
The initial development of the stratiform cloud is similar to that of the
other cases, where contact INPs are produced first, followed by immersion
mode INPs, as shown in Fig. . The contact mode develops
slowly over the whole simulation, and is limited to low concentrations.
Immersion INPs are produced later, but with higher average concentrations,
and the cloud is stable for the duration of the simulation.
For the stratiform case, the precipitation is the lowest amongst all the
cases. The simulation with higher dust concentrations shows about 25 % more
precipitation, despite minimal changes in droplet size and number
concentration. The simulation with lower dust concentrations has a negligible impact.
Domain mean INPs
The results thus far are strikingly consistent: immersion and contact
freezing dominate at varying times in the simulations, and in the convective
cases, homogeneous freezing dominates in the cirrus regime. To quantify this
further, Table shows the spatial and temporal mean INP
concentrations in each mode, including homogeneous freezing, along with the
relative contribution to the total INP concentrations. The aerosol
sensitivity simulations for each case are also shown, with -(+) indicating
lower (higher) dust aerosol concentrations. Furthermore, the contribution of
each mode until the onset of precipitation (> 0.05 kg m-2) is shown. The
concentrations quoted here are domain-wide averages, meaning non-cloudy grid
points are included, in order to not bias the results towards short-lived,
high INP concentrations.
This confirms that immersion freezing is clearly the dominant INP production
mechanism in all cases. Contact freezing plays a significant role in most
simulations, accounting for up to one-third of the total INP concentration in the
simulation with normal aerosol concentrations. In the convective cases,
homogeneous freezing contributes most at cirrus temperatures, and deposition
nucleation plays little role.
Leading up to the onset of precipitation, contact ice nucleation plays a dominant role in
the semi-idealised convective case and the orographic cloud case. This is
since contact nucleation is often the first ice formation mechanism
activated, and in these two simulations it contributes significantly at early
stages of cloud formation. In the other two cases, immersion freezing
contributes only slightly more than the simulations with normal aerosol concentrations.
Discussion
The INAS density for immersion freezing depends inverse exponentially on
temperature. At temperatures of around 248 K, in the middle of the
temperature range for the parameterisation, the
INAS density approaches 1010 m-2. This should give an activated
fraction of around 0.1 (0.95) for dust aerosols with radius 1 (5) µm.
Given that most dust aerosols are much larger than 1 µm, immersion
freezing is efficient in these simulations.
(a–d) Temporal evolution of INP number concentrations in each mode for the
stratiform cloud for normal dust concentrations. Panel (e) shows total
precipitation. The dashed (dotted) line is for the high (low) aerosol
simulation.
Temporal and spatial mean INP concentrations (m-3) for each
case. +(-) indicates higher (lower) dust aerosol concentrations, as shown
in Fig. . The relative contribution (%) of each mode to
the total INP concentrations is shown in parentheses.
According to , the contact-freezing parameterisation
depends primarily on aerosol and droplet sizes. These authors show that the
highest contact-freezing rates are obtained when large aerosol particles
(≳ 0.3 µm) interact with large cloud droplets
(≳ 30 µm). Only at the very largest sizes is the frozen fraction 1. In these
simulations, droplets are mostly smaller than 20 µm, resulting in a
contact nucleation rate orders of magnitudes smaller than the maximum possible.
Deposition nucleation as parameterised by depends
inverse exponentially on temperature and exponentially on ice
supersaturation. However, it is tightly constrained by observations, such that
it is only active at ice supersaturated conditions within a 24 K temperature
window. This strongly limits the number of deposition INPs produced in the
simulations. The homogeneous freezing parameterisation, on the other hand, is
not as tightly constrained and therefore dominates INP production at cold temperatures.
Some studies do suggest that, in the presence of large aerosol
concentrations, homogeneous freezing could be inhibited by heterogeneous INP
formation . The results presented here show that
in the cirrus regime deposition nucleation contributes very little to ice
formation, despite the high number concentration of aerosols in this region.
The difference between the concentration of homogeneously formed ice and
deposition nucleation INP is several orders of magnitude. This indicates that
deposition nucleation is not suppressing homogeneous freezing in the simulated cases.
The effect of perturbations in the dust aerosol concentrations is complex
and depends on the cloud type. In the convective cases, increasing aerosol
concentrations increases the relative contribution of immersion freezing by
an almost equivalent amount. The other freezing modes then compensate,
resulting in a decrease in their relative contribution. The opposite is also
true. Decreasing concentrations of dust aerosol decreases the contribution of
immersion freezing, while increasing the relative contribution of the other
modes. Indeed, in the idealised heat bubble case, contact freezing becomes
the dominant mode in low aerosol conditions. There are, however, two
exceptions where complex non-monotonic responses are evident: in deposition
nucleation in the deep convective simulation, and in contact freezing in the
stratiform case.
A natural question arises as to the sensitivity to the thermodynamic profile
used to initialise the simulations, and hence how generalisable the results
are. Given that the two convective cases, which had vastly different
thermodynamic profiles, produced very similar results, this suggests the
relative contribution of the ice nucleation modes is more or less
insensitive to the initial conditions in these cases. Notice that the droplet
properties of both convective cases, shown in Figs. ,
, and , are very similar.
, however, show that thermodynamics contributes
significantly to cloud microphysical processes for orographic mixed-phase
clouds. This suggests the sensitivity for non-convectively forced clouds
could be larger.
The stratiform case study represents the only cloud type in this study which
is weakly forced. Despite high levels of moisture above the main inversion,
the conditions for homogeneous freezing or deposition nucleation were not met
in this simulation. There is a fundamental difference between cirrus produced
in different dynamical environments. In the convective cases, liquid water is
lifted from the mixed-phase regime to colder temperatures, where it freezes.
Since the stratiform case is weakly forced, the origin of the moisture is
from higher altitudes. These two categories are known as either “liquid
origin cirrus” or “in situ cirrus” . Since the stratiform cloud
investigated here has no cirrus, the dominant ice-forming mechanism for this
so-called “in situ cirrus” remains an open question.
A few of the assumptions built into the simulations may influence the results
presented. The even separation of immersed and interstitial aerosols will
most likely cause an overestimate of contact freezing, in particular in the
updraught where the supersaturation is the highest, and immersion freezing
could be more dominant. Unprocessed dust has low CCN activity, whereas aged
dust is more likely to be immersed . The effect
of this uncertainty is, however, expected to be small compared to the orders-of-magnitude difference in INP number concentrations between the different
nucleation modes. Also, neglecting contact freezing and aerosol-dependent immersion freezing of rain droplets should
not have a large influence on the dominant freezing mode in these
simulations; however, it could affect the precipitation formation .
A final consideration concerning the aerosol species needs to be made.
Aerosol composition has a large influence on nucleation ability in different
temperature and supersaturation regimes. show
that biological aerosols have a high onset temperature in the immersion mode,
and given that certain biological aerosols can have large INAS densities at
these warm temperatures , this could represent an
important contributor to ice nucleation. A similar distinction between
different dust species could also be made, since soil dust, for example, is
more ice active in the immersion mode . Whether this
has a significant impact on the dominant ice nucleation mode remains to be investigated.
Conclusions
A number of high-resolution modelling case studies are presented in order to
systematically investigate which ice nucleation modes dominate for a number
of typical cloud types. The results indicate that immersion freezing
dominates in all cases. Contact nucleation plays a significant role in most
simulations, accounting for between about 2 and 33 % of total INP
concentrations under the reference aerosol conditions. Deposition nucleation
only contributes a fraction of a percent in the convective cases, and
homogeneous freezing accounts for up to 6 % of total ice crystal
concentrations. However, in the non-convective cases, no INPs were produced
in the cirrus regime.
In the later stages of the convective clouds, homogeneous freezing became
more important, and contact freezing dominated at warm temperatures. INP
formation in the orographic and stratiform case reached a steady state soon
after the formation of the cloud. The occurrence of precipitation is not
correlated with any one ice nucleation mode, instead occurring at the same time
as multiple ice nucleation modes, including homogeneous nucleation.
Since the results from the two convective cases were quite similar, this
suggests ice nucleation could be insensitive to thermodynamical conditions in
these cases. The main consequence of the much higher CAPE in the heat bubble
case, compared to the semi-idealised deep convective case, was faster cloud
development.
For the convective cases, perturbation in aerosol concentrations produced
proportional changes in the relative contribution of immersion-freezing INPs.
The relative contribution of the other modes decreased for increased dust
concentrations. In particular, homogeneous freezing is nearly entirely
suppressed. In contrast, for the orographic case, the relative contribution
of contact ice nucleation increased under higher aerosol concentrations, and
immersion freezing decreased. In the stratiform case, all aerosol
perturbations produced relatively more immersion-freezing INPs, and fewer
contact INPs. This indicates aerosol conditions have a complex influence on
the dominant ice nucleation mode.
The response of the precipitation to perturbations in aerosol concentrations
is also complex, and each case exhibits a different response. For the heat
bubble, increasing and decreasing aerosol concentrations lead to an increase
in precipitation. The opposite is true for the semi-idealised deep
convective cloud, where both aerosol perturbations result in a decrease in
precipitation. The orographic case shows proportional changes in
precipitation in response to changing the aerosol concentrations, and in the
stratiform case the higher aerosol concentrations produce more precipitation,
with lower concentrations having no impact. This indicates that, although
aerosol concentration plays a role in modifying precipitation, it is not the
sole contributor. There could also be complex feedbacks present, where
changes in dust aerosol concentrations change the amount of ice produce, which
in turn changes the latent heat release. This would have an impact on both
the amount of liquid condensate and also the dominant ice nucleation mechanism.
The data are available from the corresponding authors 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.
Acknowledgements
This work was funded by the Deutsche Forschungsgemeinschaft through the
research unit INUIT-2 (FOR 1525, HO4612/1-2). The authors would like to
gratefully acknowledge Christian Barthlott (KIT) and Lulin Xue (NCAR) for
assistance with the model configuration.
The article processing charges for this open-access publication
were covered by a Research Centre of the Helmholtz
Association.
Edited by: Allan Bertram
Reviewed by: two anonymous referees
ReferencesAnsmann, A., Tesche, M., Seifert, P., Althausen, D., Engelmann, R., Fruntke,
J., Wandinger, U., Mattis, I., and Müller, D.: Evolution of the ice phase
in tropical altocumulus: SAMUM lidar observations over Cape Verde, J.
Geophys. Res.-Atmos., 114, D17208, 10.1029/2008JD011659, 2009.Barthlott, C. and Hoose, C.: Spatial and temporal variability of clouds and
precipitation over Germany: multiscale simulations across the “gray zone”,
Atmos. Chem. Phys., 15, 12361–12384, 10.5194/acp-15-12361-2015, 2015.Carro-Calvo, L., Hoose, C., Stengel, M., and Salcedo-Sanz, S.: Cloud Glaciation
Temperature Estimation from Passive Remote Sensing Data with Evolutionary Computing,
J. Geophys. Res.-Atmos., 121, 13591–13608, 10.1002/2016JD025552, 2016.Cui, Z., Carslaw, K. S., Yin, Y., and Davies, S.: A numerical study of aerosol
effects on the dynamics and microphysics of a deep convective cloud in a
continental environment, J. Geophys. Res.-Atmos., 111, D05201, 10.1029/2005JD005981, 2006.Cziczo, D. J., Ladino, L., Boose, Y., Kanji, Z. A., Kupiszewski, P., Lance,
S., Mertes, S., and Wex, H.: Chapter 8: Measurements of Ice Nucleating Particles
and Ice Residuals, Meteorol. Monogr., 58, 8.1–8.13, 10.1175/AMSMONOGRAPHS-D-16-0008.1, 2016.De Boer, G., Morrison, H., Shupe, M., 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.
DeMott, P. H., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy,
C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting global
atmospheric ice nuclei distributions and their impacts on climate, P. Natl.
Acad. Sci. USA, 107, 11217–11222, 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 Mitra, S. K.: New particle-dependent parameterizations of
heterogeneous freezing processes: sensitivity studies of convective clouds with
an air parcel model, Atmos. Chem. Phys., 15, 12741–12763, 10.5194/acp-15-12741-2015, 2015.Durant, A. J. and Shaw, R. A.: Evaporation freezing by contact nucleation
inside-out, Geophys. Res. Lett., 32, L20814, 10.1029/2005GL024175, 2005.Fan, J., Leung, L. R., Rosenfeld, D., and DeMott, P. J.: Effects of cloud
condensation nuclei and ice nucleating particles on precipitation processes and
supercooled liquid in mixed-phase orographic clouds, Atmos. Chem. Phys., 17,
1017–1035, 10.5194/acp-17-1017-2017, 2017.
Field, P., Heymsfield, A., Shipway, B., DeMott, P., Pratt, K., Rogers, D.,
Stith, J., and Prather, K.: Ice in clouds experiment-layer clouds. Part II:
Testing characteristics of heterogeneous ice formation in lee wave clouds,
J. Atmos. Sci., 69, 1066–1079, 2012.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.Hande, L. B., Hoose, C., and Barthlott, C.: Aerosol and droplet dependent
contact freezing: Parameterisation development and case study, J. Atmos. Sci.,
74, 2229–2245, 10.1175/JAS-D-16-0313.1, 2017.Hiranuma, N., Paukert, M., Steinke, I., Zhang, K., Kulkarni, G., Hoose, C.,
Schnaiter, M., Saathoff, H., and Möhler, O.: A comprehensive parameterization
of heterogeneous ice nucleation of dust surrogate: laboratory study with hematite
particles and its application to atmospheric models, Atmos. Chem. Phys., 14,
13145–13158, 10.5194/acp-14-13145-2014, 2014.Hoose, C. and Möhler, O.: Heterogeneous ice nucleation on atmospheric
aerosols: a review of results from laboratory experiments, Atmos. Chem. Phys.,
12, 9817–9854, 10.5194/acp-12-9817-2012, 2012.
Hoose, C., Kristjánsson, J. E., Chen, J.-P., and Hazra, A.: A
classical-theory-based parameterization of heterogeneous ice nucleation by
mineral dust, soot, and biological particles in a global climate model, J.
Atmos. Sci., 67, 2483–2503, 2010.
Houze, R. A.: Cloud dynamics, in: International Geophysics, Vol. 104, Academic
Press/Elsevier, Oxford, UK, 2014.
Jeffery, C. and Austin, P.: Homogeneous nucleation of supercooled water: Results
from a new equation of state, J. Geophys. Res., 102, 25–269, 1997.Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D.
J., and Krämer, M.: Chapter 1: Overview of Ice Nucleating Particles,
Meteorol. Monogr., 58, 1.1–1.33, 10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017.Koop, T. and Murray, B. J.: A physically constrained classical description of
the homogeneous nucleation of ice in water, J. Chem. Phys., 145, 211915,
10.1063/1.4962355, 2016.Krämer, M., Rolf, C., Luebke, A., Afchine, A., Spelten, N., Costa, A., Meyer,
J., Zöger, M., Smith, J., Herman, R. L., Buchholz, B., Ebert, V., Baumgardner,
D., Borrmann, S., Klingebiel, M., and Avallone, L.: A microphysics guide to
cirrus clouds – Part 1: Cirrus types, Atmos. Chem. Phys., 16, 3463–3483,
10.5194/acp-16-3463-2016, 2016.Kumar, P., Sokolik, I. N., and Nenes, A.: Measurements of cloud condensation
nuclei activity and droplet activation kinetics of fresh unprocessed regional
dust samples and minerals, Atmos. Chem. Phys., 11, 3527–3541, 10.5194/acp-11-3527-2011, 2011.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.
Li, W., Li, P., Sun, G., Zhou, S., Yuan, Q., and Wang, W.: Cloud residues and
interstitial aerosols from non-precipitating clouds over an industrial and
urban area in northern China, Atmos. Environ., 45, 2488–2495, 2011.Luebke, A. E., Afchine, A., Costa, A., Grooß, J.-U., Meyer, J., Rolf, C.,
Spelten, N., Avallone, L. M., Baumgardner, D., and Krämer, M.: The origin
of midlatitude ice clouds and the resulting influence on their microphysical
properties, Atmos. Chem. Phys., 16, 5793–5809, 10.5194/acp-16-5793-2016, 2016.
Murray, B., O'Sullivan, D., Atkinson, J., and Webb, M.: Ice nucleation by
particles immersed in supercooled cloud droplets, Chem. Soc. Rev., 41, 6519–6554, 2012.Nagare, B., Marcolli, C., Welti, A., Stetzer, O., and Lohmann, U.: Comparing
contact and immersion freezing from continuous flow diffusion chambers, Atmos.
Chem. Phys., 16, 8899–8914, 10.5194/acp-16-8899-2016, 2016.
Niehaus, J. and Cantrell, W.: Contact freezing of water by salts, J. Phys.
Chem. Lett., 6, 3490–3495, 2015.
Niemand, M., Möhler, O., Vogel, B., Vogel, H., Hoose, C., Connolly, P.,
Klein, H., Bingemer, H., DeMott, P., Skrotzki, J., and Leisner, T.: A
particle-surface-area-based parameterization of immersion freezing on desert
dust particles, J. Atmos. Sci., 69, 3077–3092, 2012.
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, 2014.Paukert, M., Hoose, C., and Simmel, M.: Redistribution of ice nuclei between
cloud and rain droplets: Parameterization and application to deep convective
clouds, J. Adv. Model. Earth Syst., 9, 514–535, 10.1002/2016MS000841, 2017.
Phillips, V. T., 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.Rogers, D. C., DeMott, P. J., Kreidenweis, S. M., and Chen, Y.: Measurements
of ice nucleating aerosols during SUCCESS, Geophys. Res. Lett., 25, 1383–1386, 1998.
Schättler, U., Doms, G., and Schraff, C.: A description of the nonhydrostatic
regional COSMO-model. Part VII: user's guide, Deutscher Wetterdienst Rep. COSMO-Model 4,
Deutscher Wetterdienst, Offenbach, Germany, 221 pp., 2008.
Seifert, A. and Beheng, K.: A two-moment cloud microphysics parameterization
for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys.,
92, 45–66, 2006.Spichtinger, P. and Cziczo, D. J.: Impact of heterogeneous ice nuclei on
homogeneous freezing events in cirrus clouds, J. Geophys. Res.-Atmos., 115,
D14208, 10.1029/2009JD012168, 2010.Steinke, I., Hoose, C., Möhler, O., Connolly, P., and Leisner, T.: A new
temperature- and humidity-dependent surface site density approach for deposition
ice nucleation, Atmos. Chem. Phys., 15, 3703–3717, 10.5194/acp-15-3703-2015, 2015.Steinke, I., Funk, R., Busse, J., Iturri, A., Kirchen, S., Leue, M., Möhler,
O., Schwartz, T., Schnaiter, M., Sierau, B., Toprak, E., Ullrich, R., Ulrich,
A., Hoose, C., and Leisner, T.: Ice nucleation activity of agricultural soil
dust aerosols from Mongolia, Argentina, and Germany, J. Geophys. Res.-Atmos.,
121, 13559–13576, 10.1002/2016JD025160, 2016.Tobo, Y., Prenni, A. J., DeMott, P. J., Huffman, J. A., McCluskey, C. S., Tian,
G., Pöhlker, C., Pöschl, U., and Kreidenweis, S. M.: Biological aerosol
particles as a key determinant of ice nuclei populations in a forest ecosystem,
J. Geophys. Res.-Atmos., 118, 10100–10110, 10.1002/jgrd.50801, 2013.
Ullrich, R., Hoose, C., Möhler, O., Niemand, M., Wagner, R., Höhler, K.,
Hiranuma, N., Saathoff, H., and Leisner, T.: A new ice nucleation active site
parameterization for desert dust and soot, J. Atmos. Sci., 74, 699–717, 2017.Vali, G., DeMott, P. J., Möhler, O., and Whale, T. F.: Technical Note: A
proposal for ice nucleation terminology, Atmos. Chem. Phys., 15, 10263–10270,
10.5194/acp-15-10263-2015, 2015.
Wang, P., Grover, S., and Pruppacher, H.: On the effect of electric charges on
the scavenging of aerosol particles by clouds and small raindrops, J. Atmos.
Sci., 35, 1735–1743, 1978.
Weisman, M. L. and Klemp, J. B.: The dependence of numerically simulated convective
storms on vertical wind shear and buoyancy, Mon. Weather Rev., 110, 504–520, 1982.Westbrook, C. D. and Illingworth, A. J.: Evidence that ice forms primarily in
supercooled liquid clouds at temperatures >-27 ∘C, Geophys. Res.
Lett., 38, L14808, 10.1029/2011GL048021, 2011.Wiacek, A. and Peter, T.: On the availability of uncoated mineral dust ice
nuclei in cold cloud regions, Geophys. Res. Lett., 36, L17801, 10.1029/2009GL039429, 2009.