Introduction
Clouds can be classified according to their altitude low, mid-level,
high; see e.g., their temperature (warm, cold) or their
cloud particle phase (liquid, mixed-phase: both liquid and ice, ice).
Especially for intermediate altitudes, these classification criteria overlap:
ice particles may sediment into warm cloud layers, updraughts can transport
liquid water droplets into colder cloud regions and droplet formation may
produce liquid water content in a cold, formerly glaciated cloud
.
To avoid ambiguities, we refer here to all clouds observed at temperatures
between 0 and -38 ∘C (273 to 235 K) as “clouds in the
mixed-phase temperature regime” (mpt clouds). In that temperature regime,
purely liquid (supercooled) clouds can be found as well as mixed-phase clouds
(where liquid water droplets and ice crystals coexist) and also fully
glaciated clouds . Within this temperature
range, important processes take place that transform the cloud's phase or
microphysical characteristics significantly. This phase transition is not
only an important part of precipitation-forming processes like the cold rain
process, it also affects the cloud's radiative properties by influencing the
solar albedo of mpt clouds : with growing
ice fraction, their solar albedo (cooling) effect is reduced
. Thus a correct representation of this cloud
type in global climate models is of importance for an improved certainty of
climate predictions .
Possible paths to glaciation in the mixed-phase temperature regime.
The transformation from a fully liquid to a fully frozen cloud can follow
different, sometimes non-linear paths, as illustrated in
Fig. . After the activation of cloud condensation
nuclei forms small droplets < 50 µm (all-liquid state), initial
freezing can occur in those droplets that contain or touch an ice nucleating
particle (INP) that can be activated in the ambient cloud environment
(resulting in a mixed-phase state: coexistence of ice and water). Different
INPs can induce ice nucleation at different temperatures, depending on their
nature, e.g. whether they are of biological or mineral origin, their morphology
and freezing efficiency. Therefore, the number of droplets containing an INP
needed to heterogeneously form ice is important for its glaciation, and the
temperature of the mpt cloud is also relevant, as the freezing efficiency of
different INPs varies with temperature. The INP properties that favour ice
formation are a major discussion point in cloud and climate research. The
conditions that favour drop freezing are, in a simplified summary, as follows: cold
temperatures, high relative humidities and a “good freezing ability”. For
more details on the specific conditions see e.g. and
references therein. Biological particles are known to induce ice nucleation
in the temperature range between about 0 and -20 ∘C (273 to
253 K), while mineral dust particles initiate ice at temperatures below
about -20 ∘C .
Liquid water content (dashed lines) and ice water content (solid
lines) development with altitude Z (∼ 1/temperature) in mixed-phase
clouds for different vertical velocities adapted from with
modification. Blue lines (updraught 1 m s-1): the cloud
glaciates when RHw falls below 100 %
(Wegener–Bergeron–Findeisen regime); red lines (updraught 2 m s-1):
RHw stays above 100 %, liquid droplets and ice crystals
coexist (coexistence regime).
The persistence of supercooled liquid clouds in case no ice active particles
are present is also reported by . Moreover, the further
development of the glaciation degree of a mpt cloud, in which a few ice crystals
are present, is discussed in this study in relation to the environmental
dynamical conditions. This is illustrated by theoretical considerations
of the partitioning of liquid and ice water content in
rising mixed-phase cloud parcels under different conditions see
Fig. , adapted from. The first scenario
represents an intermediate vertical velocity (1 m s-1; blue lines),
where the Wegener–Bergeron–Findeisen process is
triggered above the altitude marked by the blue line (note that the
temperature decreases with increasing altitude), which leads to full
glaciation of the cloud. At that point, the relative humidity over water
falls below 100 % (RHw < 100 %), as more and more water vapour is
consumed by the many small liquid cloud droplets. As a result, these droplets
evaporate, decreasing the liquid water content. The RH over ice remains above
100 % (RHi > 100 %), allowing the few ice crystals to
grow to large sizes > 50 µm, thus increasing the ice water
content.
In contrast, the red graphs show a scenario for higher vertical velocities
(2 m s-1). Here, due to the high updraught, the supersaturation is
preserved over both water and ice (RHw, RHi>100 %) over the complete altitude range. Subsequently, the liquid and
ice water content increase in coexistence and the cloud continues to be only
partly glaciated (coexistence cloud). These simulations demonstrate that
vertical velocity is a major parameter controlling the occurrence of
different cloud types, because the updraught is the crucial parameter for
possible supersaturations. The supersaturation over water can remain at or
above 100 % only in high updraughts, thus allowing coexistence clouds to
survive down to about -38 ∘C (235 K), where the supercooled
liquid cloud droplets will freeze homogeneously
. Also, secondary ice production
can take place, producing high number concentrations of small ice particles
see overview in. Examples of known processes
are the Hallett–Mossop process also called rime splintering;
see, drop-freezing and ice–ice collisions
. When one of these processes has started, the remaining
liquid fraction of a cloud can glaciate quickly via freezing initiated by ice
crystals colliding with supercooled water droplets, even if the conditions
for the Wegener–Bergeron–Findeisen (WBF) process are not met.
Evaporation of both numerous small liquid droplets and large ice particles
occurs when the environment is subsaturated with respect to both water and
ice (RHi<100, RHw<100 %), as predicted by
for downdraught regions within the cloud. If this state
persists for a sufficiently long time, the cloud will fully evaporate.
Characteristics of the cloud types expected in the mpt regime.
Cloud particle
Particles Dp < 50 µm
Particles Dp > 50 µm
Dominant mass mode
concentration
Mostly liquid
high
Liquid
Drizzle drops/few icecrystals possible
Dp < 50 µm: Type 1a
Coexistence
high
Mostly liquid,some ice crystals
Ice crystals
Dp < 50 µm: Type 1b
Secondary ice
high
Ice crystals
Ice crystals
Dp < 50 µm: Type 1c
Large ice/WBF
low
Ice crystals
Ice crystals
Dp > 50 µm: Type 2
In summary, as illustrated in Fig. and
Table , four types of mpt cloud are expected to
occur: the first type describes clouds with many small (diameter
< 50 µm) liquid droplets that often appear at slightly
supercooled conditions and with lower frequencies as the temperature becomes
colder . This cloud type may additionally contain a low
concentration of large particles (large droplets from coalescence or ice
particles sedimenting from above). The second cloud type is coexistence
clouds with a high concentration of small cloud particles
<50 µm that can be liquid or frozen. The coexistence cloud type
appears at decreasing temperatures in higher updraughts. In case the updraughts
are very strong as in tropical convective clouds, the supercooled liquid
cloud droplets can reach cold temperature regions around -38 ∘C
(235 K) and freeze homogeneously. Furthermore a third type with a high
concentration of small ice particles (diameter <50 µm) might
emerge as a result of secondary ice production, e.g. due to the Hallett–Mossop
process at temperatures between -3 and -8 ∘C (270 to 265 K) or
ice splintering. A fourth cloud type in the case of lower updraughts are fully
glaciated WBF clouds. They contain only very few
or no small liquid droplets (<50 µm), but consist mostly of
large ice crystals and are expected to appear with increasing frequency when
the temperature decreases.
Due to the manifold interactions between large-scale and small-scale
dynamics, aerosol particle/INP availability and complex processes of
formation and evolution of supercooled liquid and frozen cloud particles, mpt
clouds are not well understood and therefore poorly represented in global
climate models . As a consequence, the uncertainties
concerning the global mpt cloud cover's radiative impact are large. Of
particular interest is the partitioning of ice and liquid water, i.e. the
glaciation degree. An important step that improves the incomplete understanding
of the phase transition processes is taking reliable observations of the different
types of mpt cloud. However, cloud-particle-phase observations are limited
by technical constraints: passive satellite data mostly provide information
on cloud tops, and ground-based lidars cannot quantify thick layers of liquid
water . Active remote sensing techniques have
been used to derive liquid and ice water paths for the full depth of the
atmosphere reported inp. 580, but are subject to large
errors. In situ measurements may cover the full vertical extent
, but are restricted to the
flight path and have to be analysed carefully .
For in situ data sets in the past, the phase identification often relied on
cloud particle sizes. Small cloud particles <50 µm are usually
regarded as liquid see e.g.. With particle imaging
probes like OAPs (optical array probes), more sophisticated shape recognition
algorithms can be used e.g. , which are
nevertheless limited. Usually, they require a minimum number of pixels
(corresponding to cloud particles with diameters of 70 µm and more)
to recognize round or aspherical particles reliably. Due to these
limitations, the shape identification of small particles has not been
considered in many microphysical cloud studies. In the paper presented here,
we use a new detector that can measure the asphericity of small
(<50 µm) cloud particles together with
a visual shape inspection of particles >50 µm. We thus hope to
provide new insights into the microphysical evolution of clouds in the mpt
regime.
To this end, we use in situ airborne cloud measurements in the cloud particle
size range from 3 to 937 µm to classify the above-described types
of cloud in the mpt regime (see Fig. ): mostly
liquid clouds occur after drop formation, coexistence clouds after initial
freezing, secondary ice clouds are influenced by ice multiplication and
large ice clouds occur after the WBF process. This classification enables us to
revisit a statistical overview published by
, stating at which temperatures purely
liquid or ice-containing clouds were found.
For all except the fourth cloud type, we expect high cloud particle number
concentrations with a peak at cloud particle sizes <50 µm.
Thus, particle size distributions and concentrations allow a
differentiation between glaciated clouds mainly formed via the WBF process
and other cloud types in the mpt regime. To investigate these other types
more closely, they are divided into three groups with differing aspherical
cloud particle fractions, in agreement with the cloud types described above.
The occurrence of the four cloud types is then quantified with regard to
measurement location and temperature by performing a statistical analysis of
the data obtained by the NIXE–CAPS (New Ice
eXpEriment – Cloud and Aerosol Particle Spectrometer) cloud spectrometer with 1 Hz along the
flight path.
The article is structured as follows: in Sect. , the field
campaigns are described as well as the cloud spectrometer NIXE–CAPS and its
data products. In Sect. , the observations are evaluated
with respect to the clouds' size distribution, the correlation of cloud
particle concentrations to expected ice nucleating particle concentrations,
the cloud particle asphericity and the associated vertical velocities.
Section summarises the findings of this study.
Locations of the campaigns presented in this paper.
Methodology
Four airborne field campaigns were performed in Arctic, midlatitude and
tropical regions (see Fig. and
Sect. ). In total, the data set in the mixed-phase
temperature regime between 0 and -38 ∘C (273 to 235 K) covers
38.6 h. Mpt clouds were measured using the cloud spectrometer NIXE–CAPS (see
Sect. ). The data analysis is described in
Sect. .
Flight table for COALESC. Dates are dd/mm/yyyy.
Date
Probed clouds/flight objectives
Cloud T in the mpt regime
Minutes in
mpt clouds
15.02.2011
Warm clouds, mixed clouds, cirrus; test flight
-1.5 to -37.6 ∘C
85.1
23.02.2011
Warm clouds, cirrus clouds
0 to -37.8 ∘C
11.7
24.02.2011
Warm stratocumulus
0 to -0.1 ∘C
0.1
26.02.2011
Stratocumulus
0 to -17.9 ∘C
46.0
01.03.2011
Stratocumulus
0 to -6.4 ∘C
124.7
02.03.2011
Stratocumulus
0 to -3.1 ∘C
92.0
03.03.2011
Stratocumulus
0 to -4.4 ∘C
61.9
05.03.2011
Stratocumulus
0 to -3.3 ∘C
51.4
07.03.2011
No clouds
–
0
08.03.2011
Warm stratocumulus and cirrus clouds
0 to -38.0 ∘C
47.0
11.03.2011
Stratocumulus
0 to -4.9 ∘C
105.9
14.03.2011
Mostly cirrus clouds
-8.9 to -37.9 ∘C
10.6
15.03.2011
Stratocumulus and cirrus
0 to -38.0 ∘C
25.8
16.03.2011
Stratocumulus
0 to -0.3 ∘C
6.7
18.03.2011
No clouds
–
0
19.03.2011
Mostly contrail cirrus
-18.1 to -38.0 ∘C
11.9
Field campaigns
The first campaign, COALESC (Combined Observation of the Atmospheric boundary
Layer to study the Evolution of StratoCumulus), was based in Exeter, UK, in
February and March 2011. The NIXE–CAPS was installed as a wing probe on the
BAe146 aircraft operated by the Facility for Airborne Atmospheric
Measurements (FAAM), UK. All flights took place in the coastal area of
south-eastern England and Wales; the main campaign targets were low stratus and
stratocumulus clouds. The campaign is described in ,
Table provides an overview of the flights. Out of
16 measurement flights, 14 provided observations of mpt clouds, with in total
41042 seconds (11.4 h) of data.
Flight table for VERDI. Dates are dd/mm/yyyy.
Date
Probed clouds/flight objectives
Cloud T in the mpt regime
Minutes in
mpt clouds
25.04.2012
Low mostly liquid stratus; test flight
-3.7 to -9.1 ∘C
47.1
27.04.2012
Stratus (liquid and ice) over sea ice
-8.1 to -16.5 ∘C
73.4
27.04.2012
Low dissipating clouds over sea ice
-9.1 to -17.3 ∘C
47.6
29.04.2012
Stable stratus over sea ice
-8.4 to -12.5 ∘C
77.9
30.04.2012
Extensive cloud with layer structure
-6.3 to -19.1 ∘C
212.8
03.05.2012
Thin low subvisible clouds
-9.4 to -12.1 ∘C
56.15
05.05.2012
Patchy low cloud layer
-8.6 to -16.8 ∘C
77.9
08.05.2012
Mostly supercooled liquid clouds, two layers
-4.9 to -9.7 ∘C
65.8
10.05.2012
Dissolving altostratus layer
-5.5 to -11.2 ∘C
45.1
14.05.2012
Two thin stratus and cumulus
-1.4 to -5.8 ∘C
41.9
15.05.2012
Mostly liquid stratus and a cumulus
-0.7 to -14.1 ∘C
73.2
16.05.2012
Thin, mostly liquid stratus
-1.7 to -5.3 ∘C
95.2
17.05.2012
Mostly liquid stratus with large snow
0 to -6.3 ∘C
54.5
Measurements in Arctic clouds have been conducted during the campaigns VERDI
April and May 2012, study on the Vertical Distribution of Ice in
Arctic Clouds; see also and RACEPAC (April and May 2014,
Radiation–Aerosol–Cloud Experiment in the Arctic Circle). Both campaigns took
place in Inuvik, northern Canada. Research flights were performed with the
Polar-5 and Polar-6 aircraft of the Alfred Wegener Institute, Germany. The 13
flights of both VERDI (see Table ) and RACEPAC
(Table ) covered the region of the Arctic Beaufort
Sea coast with its retreating sea ice in spring. VERDI yielded 59 028 s
(16.4 h) of observations within mpt clouds, RACEPAC contributed 33 354 s
(9.3 h). Although both campaigns took place at the same time of the year,
different synoptic situations lead to different cloud characteristics: VERDI
was dominated by stable anticyclonic periods with weak gradients of
atmospheric parameters that allow the formation of a strong inversion in the
boundary layer associated with persisting stratus, whereas during RACEPAC
frontal systems frequently passed the area of the observations and lead to a
more variable and short-lived cloud situation.
Flight table for RACEPAC. Dates are dd/mm/yyyy.
Date
Probed clouds/flight objectives
T range/cloud top T
Minutes in
mpt clouds
28.04.2014
Cumulus
-12.9 to -17.8 ∘C
54.1
30.04.2014
Low-level clouds in cold sector of a low
-2.3 to -14.4 ∘C
70.2
01.05.2014
Thin fog layer
-2.0 to -9.6 ∘C
5.0
03.05.2014
Single/double layer liquid-dominated cloud
0 to -2.4 ∘C
27.2
06.05.2014
Single/multilayer clouds
0 to -6.3 ∘C
55.6
08.05.2014
Thick stratus
0 to -3.8 ∘C
22.5
10.05.2014
Two stratus clouds
-3.0 to -9.1 ∘C
49.0
11.05.2014
No clouds
--
0
13.05.2014
No clouds
--
0
14.05.2014
Homogeneous stratus
-1.9 to -10.1 ∘C
25.8
16.05.2014
Mid-level clouds
0 to -10.1 ∘C
75.7
17.05.2014
Liquid and ice clouds on various altitudes
0 to -11.3 ∘C
22.7
20.05.2014
Low-level clouds
-1.5 to -9.5 ∘C
54.2
22.05.2014
Low-level clouds before front
-6.1 to -15.0 ∘C
29.2
22.05.2014
Stratus behind front
-1.5 to -11.8 ∘C
29.6
23.05.2014
Mid-level clouds
-2.3 to -15.1 ∘C
14.3
The tropical measurement campaign ACRIDICON–CHUVA (Aerosol, Cloud,
Precipitation, and Radiation Interactions and Dynamics of Convective Cloud
Systems – Cloud processes of the main precipitation systems in Brazil: A
contribution to cloud resolving modelling and to the GPM – Global
Precipitation Measurement) was carried out in September and October 2014.
The instrument platform was HALO (High Altitude and Long Range Research
Aircraft), a Gulfstream V aircraft operated by DLR (Deutsches Luft- und
Raumfahrtszentrum/German Aerospace Centre). Based in Manaus, Brazil,
ACRIDICON–CHUVA was aimed at convective clouds over tropical rainforest and
deforested areas see Table ; for details,
see. The campaign comprises 14 flights, 11 of which
contained clouds in the mixed-phase temperature regime. Although cloud
profiling at various altitudes and temperatures was a main directive of
ACRIDICON–CHUVA, the total time spent within mpt clouds was only 5368 seconds
(1.5 h). The relatively limited time span was caused by the high flying
speed of HALO (up to 240 m s-1); it results in short penetration times
(in the range of several seconds) of the convective towers. A second reason
is the increasing danger of strong vertical winds and icing in developing
cumulonimbus clouds. From certain cloud development stages on, only the
cloud's anvil and outflow at cold temperatures lower than -38 ∘C
(235 K) could be probed.
Flight table for ACRIDICON–CHUVA. Dates are dd/mm/yyyy.
Date
Probed clouds/flight objectives
Cloud T in the mpt regime
Minutes in
mpt clouds
06.09.2014
Convective cloud and outflow
0 to -32.2 ∘C
13.2
09.09.2014
Convective cloud
0 to -1.2 ∘C
1.1
11.09.2014
Convective cloud and outflow
0 to -38.0 ∘C
8.6
12.09.2014
Cloud tops for satellite comparison
0 to -29.6 ∘C
5.5
16.09.2014
Pyrocumulus and outflow
0 to -38.0 ∘C
18.1
18.09.2014
Shallow convective cloud and outflow
-36.6 to -38.0 ∘C
1.4
19.09.2014
Pyrocumulus, convective outflow
-0.4 to -35.1 ∘C
8.8
21.09.2014
Albedo flight
–
0
23.09.2014
Convective cloud and outflow
0 to -38.0 ∘C
5.5
25.09.2014
Convective cloud anvil/outflow
-29.4 to -38.0 ∘C
13.5
27.09.2014
Warm clouds over forested and deforested areas
–
0
28.09.2014
Convective cloud
0 to -38.0 ∘C
11.1
30.09.2014
Albedo flight
–
0
01.10.2014
Convective cloud and outflow
0 to -5.6 ∘C
2.6
(a) Cross-polarised (S-pol) counts vs. particle size in a
warm liquid cloud observed in the ACRIDICON–CHUVA campaign. The colour code
denotes the relative frequency of particles in this bin (Nbin) to overall
particle count (Ntot). The horizontal line in the bottom of the panels shows
the signal intensity in the S-pol detector which must be exceeded for a
particle to be detected as aspherical. The spherical particles cause a weak
signal in the S-pol detector. Right panel shows the same, but in a cold cloud
(-60 ∘C, 213 K) consisting of ice crystals. Ice crystals can cause
strong signals in the S-pol detector.
The NIXE–CAPS instrument
The observations presented here comprise particle number concentrations, size
distributions and shape information obtained by NIXE–CAPS. Two instruments are
incorporated in NIXE–CAPS : the NIXE–CAS–DPOL (Cloud and Aerosol
Spectrometer with detector for polarization) and the NIXE–CIPg (Cloud Imaging
Probe greyscale). In combination, particles with diameters between 0.61
and 937 µm can be sized and counted. NIXE–CAPS measurements are
thus split into an aerosol data set (particle diameters 0.61 to
3 µm) and cloud particle data set (i.e. hydrometeors with diameters
of 3 to 937 µm). For aircraft speeds between 240 and
80 m s-1, the instruments' sampling volumes limit the particle
concentration measurements to concentrations above 0.02 to 0.05 cm-3
(NIXE–CAS–DPOL) and about 0.0001 to 0.001 cm-3 NIXE-CIPg; the
exact values depend on the particle size; see. The
instrument is mounted below the aircraft wing. A detailed description of the
operating principles, limitations and uncertainties can be found in
and . The overall measurement
uncertainties concerning particle concentrations and sizes are estimated to
be approximately 20 % .
As an improvement over former instrument versions, NIXE–CAPS was modified to
minimise ice crystal shattering on the instrument housing, because those ice
fragments can artificially enlarge the ice particle concentrations
. Therefore, the tube inlet of the
NIXE–CAS–DPOL has been sharpened to a knife edge, and K-tips have been attached
to the NIXE–CIPg's arms .
In the following, we present an overview of the two instrument components
NIXE–CAS–DPOL and NIXE–CIPg as well as the data analysis.
NIXE–CAS–DPOL – particle asphericity detection
The NIXE–CAS–DPOL (hereafter referred to as the CAS) covers the small
particle size range between 0.61 and 50 µm. As particles pass
through the spectrometer's laser beam, the forward-scattered light intensity
is used for particle sizing . As a new feature, the
CAS records the change of polarisation in the backward-scattered light, thus
giving information about the particle asphericity .
Light scattered by spherical particles in the near-backward direction
(168—176∘) will retain the same angle of polarisation as the
incident light. In contrast, depending on the amount of asphericity, light
scattered by non-spherical particles will have some components that are not
at the same incident light polarisation. The CAS uses a linearly polarised
laser and two detectors that measure the backscattered light. One detector is
configured to only detect scattered light with polarisation that is
perpendicular (cross-polarised) to the incident light. This signal is
referred to as S-pol. In Fig. , we show that the
intensity of the S-pol signal generates characteristic values for both
spherical and aspherical particles. The signature of spherical particles is
measured in warm cloud sections (T>0 ∘C/273 K), if possible
during each measurement campaign. Figure shows an
example obtained during the ACRIDICON–CHUVA campaign: measurements of the
cross-polarised light as a function of cloud particle size are shown for both
a liquid and a glaciated cloud. The liquid spherical particles cause only a
very weak S-pol signal. From this measurement, we derive an asphericity
threshold (see black line in Fig. ), providing a
method to distinguish between spherical and aspherical particles. This
asphericity threshold is verified, if possible, during each of the airborne
campaigns by analysing a flight segment in clouds warmer than 0 ∘C
(273 K). The S-pol signal caused by ice particles is shown in
Fig. a for a cirrus cloud (at
-60 ∘C/213 K). Clearly, the ice crystals cause strong S-Pol
signals above the asphericity threshold. It can also be seen that the signal
strength depends on the size of the crystals. In particular, the instrument
sensitivity with regard to particle asphericity decreases for particles
smaller than 20 µm (note that the particles with diameters smaller
than 3 µm are aerosol particles). This was found during the
experiments described by , who compared several
asphericity detection methods, including the CAS. also
show that ice crystals can be near spherical. The low signal caused in the
CAS polarisation detector by this type of crystal can lead to an
underestimation of the glaciation degree of a mixed-phase cloud if it is
derived from aspherical cloud particle fractions see
also. In addition, there are variations in the S-pol signals
that are caused by the orientation of the crystal with respect to the laser
beam .
Taking into account these uncertainties, we find that it is possible to use
the S-pol signal for a classification of mpt clouds. Firstly, we perform the
asphericity analysis only for particle sizes between 20 and 50 µm,
the range with the strongest S-pol signal. For this size range, we derive
aspherical fractions (AFs) as the percentage of aspherical particles per
second, which means that particle bulk properties are analysed, not single
particle signatures alone. Secondly, we do not interpret each aspherical
fraction measurement alone, but divide the AFs into three groups:
(i) AF is 0 % (zero), (ii) AF is 0–50 % (low) and
(iii) AF is 50–100 % (high).
Upper panel: size distributions along time during flight 08 of the
VERDI campaign (colour code: dN / dlogDp). Two types of cloud can be distinguished; one is dominated by the
large particle mode (Type 2, example in lower a), the second by
small particles (Type 1, example in lower b). The two cloud types
are also associated with strongly differing particle number concentration
ranges; see Fig. .
NIXE–CIPg
The NIXE–CIPg (called CIP from here on) is an optical array probe (OAP) that
nominally records particles between 7.5 and 960 µm. Shadow image
pixels are defined by shadow intensities of 100–65, 65–35 and 35–0 %
of the incident light. Particle sizes and concentrations are derived by using
the SODA2 programme (Software for OAP Data Analysis, provided by A. Bansemer,
National Center for Atmospheric Research NCAR/University Corporation for
Atmospheric Research UCAR, 2013). For a detailed description of SODA2, see
for example . Pixels with shadow intensities of 35 % and
higher were used for the image analysis. In the observations presented here,
only the number concentrations for particles with diameters >22 µm are taken from the CIP data set. The smaller particle
fraction is covered by the CAS measurements. The shadow images can be
analysed for particle asphericity using various algorithms
; in this study, however, the occurrence of
irregular (i.e. ice) particles was verified manually.
Data analysis
NIXE–CAPS records four individual data sets: histogram and particle by
particle (PBP) data for each of the CIP and the CAS instruments. All data sets
are evaluated using the NIXElib library .
In the 1 Hz histogram data sets, particles are sorted into size bins
according to predefined forward-scattering cross sections (CAS) or maximum
shadow diameters (CIP). These histograms are created for every second.
The PBP data set recorded by the CIP consists of a time stamp and the shadow
image of each individual particle. The shadow images can be analysed with
regard to maximum diameter, equivalent size, area ratio and shape. The CAS
PBP data are limited to 300 particles per second. For these particles,
detailed information is stored: the forward-, backward P-pol- and backward
S-pol-scattering intensities, a time stamp and the particle interarrival
time.
Apart from the asphericity analysis, this data set also allows a diagnosis of
ice crystal shattering following and
. Thus, an interarrival time (IAT) correction was
applied additionally to the instrument modifications
described above. This correction rejects particles if their IATs are
significantly shorter than those of majority of ice crystals, as these short
IATs might result from shattering. IAT histograms compiled during the data
analysis showed only very few measurements with short IATs, during which a
maximum of about 5 % of the cloud particle population might result from
shattering.
Results and discussion
Mpt cloud classification based on particle number size distributions
Four cloud types are expected in the mpt regime (see
Table ). As mentioned in the introduction, however,
only two typical particle number size distributions (PSDs) are found
frequently in mpt clouds. Figure shows NIXE–CAPS PSDs
measured during VERDI flight 08, where both types alternate: some cloud
regions show very high particle concentrations of small particles with a mode
diameter <50 µm (see example of PSD in the lower-right corner).
Alternatively, the clouds consist mostly of large ice crystals >50 µm with either no small particles or concentrations below the
NIXE–CAS detection limit (see example of PSD in the lower-left corner).
As a first step of the mpt cloud classification, we sort all clouds according
to their particle size distribution type and address these types separately.
To this end, we calculate two cloud particle number concentrations: one for
particles with diameters between 3 and 50 µm (Nsmall)
and one for all larger particles (Nlarge). For the classification
of the first cloud type (Type 1), Nsmall must exceed
1 cm-3, while Nlarge can be zero or larger. The mode of the
cloud particle mass distribution is at particle diameters <50 µm. We assume that this type matches the young clouds after
droplet condensational growth in Fig. . In the
second cloud type (Type 2) we classify those clouds with Nsmall
below 1 cm-3 and Nlarge present. The mode of the cloud
particle mass distribution is here at particle diameters >100 µm. This type matches fully glaciated clouds, e.g. as a result
of the WBF process (see Fig. ).
Histogram of cloud particle concentrations
(Dp 3 to 937 µm) of Type 1
and Type 2 clouds in the mixed-phase temperature regime between 0 and
-38 ∘C (273 to 235 K). For cloud type definitions see
Sect. . The 6 % between the two clear modes were
classified as “Type 1” in this study. Nsmall is particles with
diameters between 3 and 50 µm. Nlarge is particles with
diameters > 50 µm. Ncloud is all particles with
diameters of 3 µm and larger.
In Fig. , a histogram is provided that shows the
occurrence of cloud particle concentrations throughout our data set. The
spectrum of observed concentrations is continuous, but the two modes
associated with the Type 1 and Type 2 clouds (as described above) are clearly
visible. The area between the two modes (a total of 6 % of all
observations) might result from clouds in a transition state to glaciation.
In this study, these measurements were assigned to Type 1 clouds. In addition
to the two modes, a small peak at very low cloud particle concentrations
(about 10-4 cm-3) indicates slightly elevated concentrations
around the detection limit of the CIP (a total of 5 % of all
observations). We assume that these are measurements in precipitation,
especially in snow that occurred frequently in the Arctic campaigns and in
sedimenting aggregates of ice crystals from tropical convective clouds (see
Sect. ).
Type 1 clouds: example of CIP images (background picture). The
stripes represent a series of CIP shadow images, depicting the particles that
have passed subsequently through the detector. Foreground: average particle
size distributions (PSDs) in 5 K intervals, all campaigns. The thin vertical
line at 3 µm marks the boundary between aerosol and cloud
particles. The line at 20 µm marks the transition from the
NIXE–CAS–DPOL to the NIXE–CIPg instrument. The thick blue line divides the
cloud particle population into particles smaller and larger than
50 µm.
In the following, we discuss the cloud types described above in more detail.
Type 1 cloud characteristics measured during all campaigns are shown in
Fig. . These clouds have a clear mode between 3 and
50 µm and are very dense, while cloud particle number concentrations
reach average values of dozens to more than 200 cm-3.
Table shows average cloud particle concentrations for
the Type 1 clouds at 5 K intervals. Low number concentrations of large ice
particles >50 µm are sometimes found, but all clouds of this
type are dominated by Nsmall, which may consist of liquid
droplets, frozen droplets or small ice from ice multiplication processes.
With regard to the concentrations of Nsmall in the different
temperature intervals (Fig. and
Table ), it can be clearly seen that they decrease with
decreasing temperature. When a cloud consists of liquid droplets, they grow
by condensation when lifted to higher altitudes – and thus colder
temperatures – followed by an increasing coalescence of the droplets, which
consequently causes a higher number of Nlarge while depleting the
concentration of small droplets. This is also visible in
Fig. . Note, however, that Nlarge also
decreases with increasing temperature, reaches a minimum around 260 K and
then rises again, possibly reflecting the increasing occurrence of
sedimenting particles. Visual inspection of the CIP images indicates that in
the Nlarge cloud mode ice crystals can be found in addition to
the drizzle drops. Three of the cloud types of the mpt regime are expected to
show Type 1 cloud characteristics: liquid, coexistence and
secondary ice clouds (Types 1a, 1b and 1c).
Same as in Fig. but for Type 2 clouds.
The second set of PSDs (Type 2: Fig. ) is not strongly
dominated by Nsmall. Here, Nlarge form a distinct
mode. Both mode concentration and maximum values decrease with decreasing
temperatures. Clouds of this PSD type have low number concentrations of, on
average, less than 0.1 cm-3 in the size range 3 to 50 µm
(see Table ). For the sizes >50 µm, the
CIP images show ice crystals or aggregates. This is the typical appearance of
a fully glaciated cloud, formed either via the WBF process during which the
small liquid droplets evaporate or at lower altitudes (higher temperatures),
due to sedimentation, when aggregates precipitate from higher levels. Again,
the two temperature groups are seen as for the Type 1 clouds
(Fig. ), with a clear accumulation of mass at larger
particle sizes for temperatures below 247.5 K. An explanation could be that
Type 2 clouds most probably develop from Type 1: once the environment becomes
subsaturated (RHw < 100, RHi > 100 %),
all liquid droplets evaporate, leaving only the ice crystals that have already
formed, e.g. via immersion freezing or ice seeding. Therefore,
Nlarge of Type 2 is only a fraction of those of Type 1, which
might reflect the number of active INPs in the respective temperature
interval in the case that no ice multiplication takes place (see Sect. ).
Thus, the larger differences between the two temperature groups – as seen
for Type 1 clouds – more or less balance out. Indeed, an increase in average
ice crystal numbers can be seen (Table , bottom,
Nlarge), which might be interpreted as an increasing fraction of
activated INPs with decreasing temperature. Note that Nsmall is
still larger than Nlarge. Since shattering artefacts are unlikely
(see Sect. ), this means that in Type 2 clouds, a
significant number of small particles also persist over the whole temperature
range.
In addition to these two types, thin clouds with only low concentrations
(less than 1 cm-3) of small particles (<50 µm) and no
large particles are sometimes found, which are most likely evaporating
clouds. They are not considered a separate cloud type, since they do not
appear frequently and cannot be regarded as a distinct type, they are
remnants of one of the two cloud types defined above. Further, the respective
measurements stem from the CAS instrument alone and are close to its
detectable concentration limit, thus suffering from an enhanced uncertainty.
Comparison of cloud particle with ice nucleating particle numbers
A comparison of the measured cloud particle number concentrations to INP
concentrations (NINP) can indicate whether the ice particles
may result from primary ice nucleation. No direct INP measurements are
available for our data set, so we estimated NINP using the
formula provided by , for which aerosol number concentrations
of particles between 0.5 and 3 µm are related to INP
concentrations. NIXE–CAPS records particles larger than 0.6 µm; the
fraction from 0.6 to 3 µm is used as the aerosol fraction. Due to
the slightly smaller range of our aerosol measurements, the NINP
might be underestimated. However, we believe that this uncertainty is small
in comparison to that of the parameterisation by itself,
since (i) the difference at the lower sizes is only 0.1 micrometre and
(ii) aerosol particles larger than 3 micrometres contribute only very little
to the concentration of particles larger than 0.5 micrometre see
e.g.. The purpose of using NINP derived in the
described way is to show the differences found between the measurement campaigns
and temperature ranges.
Average cloud particle concentrations for the two cloud types
defined in Sect. (see also Fig. ), for
both small (Dp<50 µm) and large
(Dp>50 µm) cloud particles.
Type 1
Nsmall (cm-3)
Nlarge (cm-3)
235 K
2.207
0.162
240 K
2.632
0.177
245 K
19.894
0.134
250 K
24.902
0.166
255 K
109.944
0.035
260 K
109.798
0.022
265 K
269.979
0.032
270 K
166.362
0.047
275 K
67.788
0.098
Type 2
Nsmall (cm-3)
Nlarge (cm-3)
235 K
0.057
0.023
240 K
0.08
0.025
245 K
0.069
0.017
250 K
0.062
0.01
255 K
0.064
0.004
260 K
0.14
0.003
265 K
0.07
0.003
270 K
0.116
0.005
275 K
0.117
0.017
Frequencies of ice nucleating particle number concentrations
(NINP) vs. temperature for all measurement campaigns, colour
coded by their frequency of occurrence. NINP is estimated from
NIXE–CAPS measurements of aerosol concentrations (Dp
0.6–3 µm) following . The black lines indicate
INP concentrations for constant aerosol concentrations of 0.01 standard cm-3 (leftmost
line), 0.1 standard cm-3, 1 standard cm-3 (thick line), 10 standard cm-3 and
100 standard cm-3 (rightmost line).
Same as Fig. , but frequencies of cloud
particle number concentrations for Nsmall (a, c) and
Nlarge (b, d). Top row: Type 1 clouds, bottom row:
Type 2 clouds.
The results for NINP are shown in Fig. as
a function of temperature. Generally, NINP increases with
decreasing temperature, as already mentioned in the last section. The most
frequent NINP range is between the lowest calculated value of
10-4 (0.1 L-1) and ∼ 10-3 cm-3
(1 L-1), while the maximum reaches up to 0.3 cm-3
(∼ 300 L-1). In comparison to a compilation of INP measurements
presented recently by , the estimated range of
NINP is shifted to somewhat smaller concentrations.
In Fig. , Nsmall and Nlarge for
both Type 1 and Type 2 clouds are now presented in the same way
as before NINP. In Type 1 clouds, especially for
Nsmall (upper left panel), we find concentrations between
2 cm-3 and more than 200 cm-3 down to temperatures of
-20 ∘C (253 K), well exceeding all INP estimations in this
temperature range. However, also for Nlarge (upper right panel), the
cloud particle concentrations exceed the expected NINP by several
orders of magnitude. For colder temperatures, where the measured cloud
particle number concentrations are lower, the estimated NINP are
also mostly lower than the cloud particle concentrations. In general, we can
exclude primary ice nucleation as a main contributor for cloud particles in
the Type 1 clouds.
The Nlarge of Type 2 clouds (lower-right panel) agree quite well
with NINP for a wide range of temperatures. However, in warm
areas, the cloud particle concentrations can be higher – they might
represent large ice crystals sedimenting from upper layers, as mentioned in
Sect. 3.1. For the colder regions, the agreement is consistent with the
assumption that the Type 2 clouds we observed were formed by the WBF process
(see Sect. ) and that the formation of the initial ice
crystals has been likely initiated by INPs immersed in the cloud droplets.
Nsmall is slightly increased in comparison with NINP.
Again, it is possible that this is an effect of the CAS limited detectable
concentration range, as discussed in Sect. . Detailed
microphysical cloud simulations might help to further investigate this
concentration range.
Mpt cloud classification based on particle asphericity
Size distributions, cloud particle number concentrations and comparisons with
expected INP number concentrations provide little information on the cloud
particle phase (see Sects. , ). For further
insights on the nature of the observed clouds, information on cloud particle
asphericity is used.
As described in Sect. , for Nsmall we define three
groups with regard to AFs (1 Hz data of aspherical fractions) to help to
classify the mpt clouds: (i) AF is 0 % (zero), (ii) AF is 0 to
50 % (low) and (iii) AF is 50 to 100 % (high). AFs found in group (i)
zero AF are classified as liquid, while AF observations in group
(ii) low AF are regarded as mixed-phase clouds (liquid and ice).
Group (iii) high AF is most likely fully frozen. Particles >100 µm are mostly irregular (i.e. ice) in group (ii) and (iii). In
group (i), large ice particles can occasionally be found. In the size range
between 50 and 100 µm, the distinction between drizzle drops and
ice particles is not possible, because the shadow images do not contain
enough pixels to differentiate between spherical and irregular particles (see
Sect. ).
Figure shows the aspherical fractions of Type 1 and 2 cloud
particles vs. temperature; the data points are colour coded by the respective
field campaigns. The horizontal lines show the 0 (liquid) and
-38 ∘C (ice) temperature thresholds (273 and 235 K,
respectively). Looking at the data points in pure ice clouds below
-38 ∘C (235 K) it can be seen that most of the measurements are
found in group (iii) high AF range. These AFs can therefore be associated
with fully glaciated clouds. Note that Type 2 clouds show AFs comparable to
those of cirrus clouds. The small particles found in relatively large number
concentrations in this cloud type (see Sect. and
Table ) must therefore be small ice crystals.
Due to the ambiguities of the polarisation measurement discussed in
Sect. 2.2.1, AF covers a broad range, most often between 70 and 80 %. Note
that even in the cirrus clouds the AF never reaches 100 %. A possible
reason for this deviation can be columnar ice crystals: these are not well
recognised by the CAS sensor see. Alternatively,
frozen droplets might have maintained their compact, quasi-spherical shape.
All aspherical fractions derived from CAS measurements must therefore be seen
as minimum aspherical fractions.
Cloud type detection in the mpt regime
The different cloud types that can be expected in the mpt regime
(Table ) can be identified by the combination of
information about Nsmall, Nlarge and the respective
aspherical fractions (AFs) in each size range. Following this line, we
developed algorithms to sort the mpt clouds – second by second – into the
four cloud types, using the following criteria:
Mostly liquid Type 1a clouds are classified where Nsmall is >1 cm-3 and AF is zero (liquid).
Coexistence Type 1b clouds are classified where Nsmall is >1 cm-3 and AF is low (<50 %, liquid and ice) and large ice crystals Nlarge are present.
Secondary ice Type 1c clouds are classified where Nsmall is >1 cm-3, AF is high (ice) and large ice crystals Nlarge are present.
WBF and large ice Type 2 clouds are classified where Nsmall is <1 cm-3, AF is high (>50 %, ice) and large ice crystals Nlarge are present.
Mpt cloud classification: results
The mpt clouds observed in this study were probed under a wide range of
meteorological conditions (see Sect. ). We can therefore
assume that these clouds have formed and evolved in different environments
with regard to INP properties and updraughts, which are shown in the previous
section to be the major parameters influencing the mpt cloud glaciation
process.
Aspherical fractions (AFs) for Dp=20 to 50 µm. Type 1 clouds show a
variety of AF. Type 2 shows AFs comparable to cirrus clouds – which is
illustrated by observations from the ML-Cirrus campaign – throughout the
temperature range.
For a comprehensive interpretation of the observed clouds, we divided the
clouds into Arctic, midlatitude and tropical clouds, analysed the vertical
velocities from the aircraft's meteorological data for the different cloud
types (Fig. ), estimated INP numbers
(Fig. ) and finally established distributions of the
four mpt cloud categories (see Sect. 3.4) as a function of temperature (note
that the temperatures are related to different altitudes depending on the
geographical region, Fig. ). The results are presented
in Fig. .
Arctic clouds
The cloud types found during the field campaigns VERDI and RACEPAC are shown
in Fig. a. For the probed temperature ranges (253 to
273 K – note that the temperature values in the figure indicate midbins),
50 to 80 % of the mpt clouds belong to the Type 1a/mostly
liquid (pink) category. Further, we find a low number of Type 1b/coexistence
clouds (brown) and a small percentage of glaciated Type 2/WBF clouds
(dark blue). As the estimated INP concentration based on aerosol measurements
do not show clear conditions in the Arctic (see Fig. ),
a possible explanation for the large number of Type 1a/mostly liquid
clouds could be a lack of biological INP at the time and location of our
Arctic measurements as predicted in a model study by
, so those clouds might not freeze at low
temperatures . This might explain the
lack of ice crystals, even though – possibly due to the low altitude of
those warm layers (see Fig. ) – the overall aerosol
concentration is comparable to the midlatitudes.
The INP estimations for the Arctic (see Fig. a) have to
be used with caution, because the “out of cloud” probed altitude range only
covers warm temperatures, for which the INP estimation is not very sensitive to
the measured aerosol concentrations.
Frequency of occurrence for vertical velocities (w) within mpt
clouds during the campaigns VERDI (Arctic), COALESC (midlatitudes) and
ACRIDICON–CHUVA (tropics) for the different cloud types (T).
Frequencies of occurrence of INP concentrations (NINP)
vs. temperature during VERDI (Arctic), COALESC (midlatitudes) and
ACRIDICON–CHUVA (tropics). INP number concentrations are estimated via
aerosol concentrations for particles > 0.6 µm following
. The black lines indicate INP concentrations for constant
aerosol concentrations of 0.01 (leftmost line), 0.1, 1, 10 and 100 cm-3
(rightmost line).
Temperature vs. altitude for the field campaigns VERDI and RACEPAC
(Arctic), COALESC (midlatitudes) and ACRIDICON–CHUVA (tropics). The profile
differs due to the varying latitudes.
However, an inspection of the vertical velocities measured during the Arctic
campaigns in Fig. a indicates that 60 % of the Type
1a/mostly liquid (pink) clouds are found in areas with very low updraughts,
of 0.1 m s-1 and lower while mostly fluctuating around zero, and
40 % are found in weak updraughts/downdraughts. Comparably weak
updraughts are also frequently found in the Type 2/WBF (dark blue) clouds.
This is to be expected, because the WBF regime develops in weak updraughts,
implying that the trigger to transform a cloud from liquid to ice is the
available INP concentration. The coexistence (brown) and secondary
ice clouds were observed with low frequency (<1 %) in the Arctic
and show a slightly wider spread in updraught velocities. In particular, higher
updraughts occurred more often (∼ 30 %) in these clouds, which is
consistent with the theoretical considerations shown in
Fig. for the coexistence regime. Note that, due to
the uncertainties in the vertical velocity measurements, the statistical
differences found between the cloud types should be regarded as an incentive
for future investigations. While single data points might thus contain
measurement errors, the distribution of observed vertical velocities is
smooth and centred near zero, which is expected for the meteorological
situations discussed in Sect. 2.1. Due to this, and because our data set
consists of a large number of observations, we would like to point out the
systematic differences found between cloud types and campaigns
Midlatitude clouds
At midlatitudes (COALESC field campaign), the largest cloud fractions are the
fully glaciated Type 2/WBF clouds (dark blue in Fig. ,
middle panel). This is consistent with the assumption that at midlatitudes,
the WBF process is the dominant process for cloud evolution .
More INP seem to be available that are ice active below -10 ∘C
(263 K). At temperatures warmer than -20 ∘C (253 K), the
fraction of this cloud type is slowly reduced, while more and more Type
1a/mostly liquid clouds (pink in Fig. ) and Type
1b/coexistence clouds (brown in Fig. ) are found for
higher temperatures. The WBF process depends on the presence of INPs (or seed
ice from higher cloud layers), which are likely available in higher
quantities at midlatitudes than in the Arctic (compare Sect. 3.5.1
and Fig. ). The varying occurrence of different cloud
types with temperature – i.e. Type 1a clouds at higher temperatures (lower
altitudes) and an increasing part of Type 2/WBF clouds with decreasing
temperature (increasing altitude) – might correspond to different INP
regimes. At temperatures below about -20 ∘C (253 K), for example,
efficient mineral dust INP might initiate the freezing process, while at
warmer temperatures less frequently occurring biological particles most
likely act as INP . In addition, the
increasing fraction of Type 2/WBF clouds with decreasing temperature
reflects the fact that the colder the environment, the higher the
probability that the RHw falls below 100 %: with
decreasing temperature, more and more droplets freeze and exploit the gas
phase water when they grow. As a consequence, less gas-phase water is
available the colder the temperature is. In the transition range between
predominantly Type 1a/mostly liquid and only Type 2/WBF clouds
(temperatures between -20 and -10 ∘C or 253 and 263 K), Type
1b/coexistence clouds appear, which we interpret as clouds in which the
freezing process has started, but in which the RHw is still above
100 % (blue curve slightly below RHw = 100 % in
Fig. ).
Type 1c/secondary ice clouds appear in midlatitude clouds more often
than in the Arctic, which might reflect the increased availability of initial
ice. It is unlikely that these small particles are shattering artefacts,
because they often occur in clouds with no or few large ice particles –
these large particles, however, are those that usually shatter
. In addition, as discussed in Sect. 2.3, based on
IAT analysis, shattering could be almost excluded in the measurements. In
contrast, the majority of those clouds occur at temperatures between -5 and
-13 ∘C (268 to 258 K), which is an indication of an efficient
Hallett–Mossop process having altered the cloud at slightly warmer
temperatures. Note that the classification is aimed at the result of cloud
transforming processes, not the cloud transformation itself. Which process
precisely took place before the cloud section was probed cannot be proven
with this 1 Hz data set.
At midlatitudes, Type 1a/mostly liquid, Type 1b/coexistence and
Type 2/WBF clouds show the same vertical velocity distributions
(Fig. ). The peak updraughts are slightly higher and the
widths slightly narrower in comparison to the Arctic clouds. This is another
hint that underscores the above-discussed dependence of the cloud categories
on RHw: within the same vertical velocity range, the relative
humidity can vary strongly depending on the available amount of water and the
cloud development stage (cloud particle nucleation, sedimentation,
evaporation). The Type 1c/secondary ice clouds show a different updraught
distribution with faster vertical velocities, which might indicate that these
clouds occurred in more turbulent environments, which is consistent with the idea that
the cloud particles need to collide during the rime-splintering process.
Tropical clouds
During the tropical field campaign ACRIDICON–CHUVA in convective towers,
stronger updraughts and downdraughts were observed more frequently than during
the other campaigns (Fig. , right panel). The records
include extreme vertical velocities up to -10 and +15 m s-1 (not
shown here). However, these events were rarely observed, because due to
flight safety, these cloud sections were mostly avoided. Velocities of 0.5 to
1.0 m s-1 were observed in more than 10 % of all data points. The
wider distribution of vertical velocities shows that the cloud dynamics are
much stronger in the tropical clouds than at midlatitudes and in the Arctic.
In comparison to the other regions, fewer Type 1a/mostly liquid clouds are
found in the tropics, also for warmer temperatures. This might be a
consequence of sedimenting ice, or it might indicate a higher concentration
of INPs that are already ice active at comparably high temperatures, pointing
to biological INPs. This seems to be plausible for tropical regions, but is
only partially confirmed by the INP estimate (see
Fig. , right panel). The probed clouds occurred in both
very clean air with fewer INPs than in the midlatitudes and in heavily
polluted areas over fire clearance regions. A more detailed study on how the
aerosol concentration affects the cloud-type distribution during
ACRIDICON–CHUVA was done by , based also on NIXE–CAPS
aspherical fractions. The study shows that clouds in polluted environments
contained more and smaller liquid water droplets and less ice, while clouds
in clean conditions held more ice crystals and few liquid water droplets.
As a consequence of the higher vertical velocities in the convective towers,
more Type 1b/coexistence clouds are observed than at midlatitudes or in
the Arctic. A small number of the liquid droplets <50 µm
survived down to the homogeneous drop freezing temperature
(∼ -38 ∘C, 235 K) in cases where the vertical velocity was
high enough (see also Fig. , red).
Occurrence of the cloud types defined in
Sect. : Type 1a/mostly liquid clouds are dominated by
small, exclusively spherical particles (for details on the particle size
distributions (PSDs), see Sect. ). They have high overall
number concentrations. Type 1b/coexistence clouds are dense too, but do
contain some small aspherical particles, indicating that a glaciation process
has begun. The Type 1c/secondary ice cloud type is again very dense – the
particle numbers exceed the INP concentration estimations by far (see
Sect. ). Here, most of the small particles in the size range
between 20 and 50 µm are aspherical; the cloud must therefore
consist of ice. In contrast, clouds in the category Type 2/WBF and large ice
show low overall number concentrations. These clouds are dominated by large
ice particles which may resume from the Wegener–Bergeron–Findeisen process
or, especially in the tropics, be large, sedimenting ice aggregates from
cumulonimbus anvils.
Percentage of clouds containing no ice (left y axis) or ice (right
axis); modified after . Black/grey lines:
measurements reported in . The coloured
lines refer to the left axis and represent measurements from this study.
Blue: Arctic (VERDI/RACEPAC), green: midlatitude (COALESC), red: tropics
(ACRIDICON–CHUVA).
However, the Type 2/WBF and large ice clouds (Fig. ,
right panel) are the most frequent at all temperatures. Those large cloud
particles might stem from sedimentation out of the cloud anvils, which
usually consist of mostly large aggregates, or might be transported downwards
in the strong downdraughts within the convective clouds
compare.
It is, nevertheless, important to note again that due to security
restrictions, the in situ measurements were mostly restricted to cloud
regions with small updraught velocities (see Fig. ), i.e. to
young developing clouds or edges of convective towers. Due to this flight
pattern, we most probably have probed conditions that favour the WBF process
(consistent with Fig. , dark blue), even if those
conditions might not be representative for tropical convective clouds in
general. This part of the analysis should therefore be seen as an incentive
for further studies and not be used as a basis for cloud type statistics in
tropical dry seasonal convection.
In the tropical data set, the cloud type 1c/secondary ice is scarce at the
lower levels – as at midlatitudes – but prevalent at cold temperatures,
i.e. at high levels. The high concentrations of small aspherical particles
might indicate a population of frozen droplets that quickly develop complex
shapes in supersaturation. Alternatively, other ice multiplication processes
e.g. ice splintering or plasma-induced particle shattering due to
lightning; see might take place. Again, as discussed in
Sect. 3.5.2, shattering artefacts can be almost excluded as the reason for
the high number of small aspherical particles: large ice crystals appear at
all temperatures up to 0 ∘C (273 K); the secondary ice cloud
type 1c is, however, only observed at temperatures between -38 and
-20 ∘C (235 and 253 K). Additionally, an analysis of
interarrival times of the secondary ice cloud sections did not show
shorter interarrival times than in other parts of the data set.
Summary and conclusions
The study presented here gives an overview of typical cloud properties
observed between 0 and -38 ∘C (273 to 235 K, mixed-phase
temperature regime) and links the clouds at differing stages of glaciation
to ice formation and evolution mechanisms. It gives hints to the relevance of
cloud processes at different geographical locations and altitudes.
To this end, the cloud spectrometer NIXE–CAPS was deployed in four airborne
field campaigns to conduct measurements of cloud particle sizes, number
concentrations and, as an additional parameter, the cloud particles'
asphericity. Based on the observations, which consist of 38.6 h within
clouds, we developed algorithms based on the measurements of particle size
distributions and aspherical fractions to identify four cloud types:
Type 1a/mostly liquid refers to dense clouds consisting of mostly small droplets. All particles in the size range from 20 to 50 µm are spherical.
The few large cloud particles >50 µm might occasionally
include ice crystals.
Type 1b/coexistence is dense clouds consisting of mostly small particles with a low percentage (<50 %) of small aspherical ice particles. Ice
crystals >50 µm are present. The coexistence of liquid
droplets and ice crystals is most probably due to supersaturation over both
water and ice caused by higher vertical velocities.
Type 1c/secondary ice refers to dense clouds consisting of mostly small particles between 3 and 50 µm with a high percentage (>50 %) of
aspherical ice particles. The aspherical fractions found are comparable to
those of cirrus clouds; we thus conclude that these clouds are completely
glaciated. The large cloud particles >50 µm are also frozen.
The ice crystal numbers exceed the expected ice nuclei concentrations by
several orders of magnitude, which suggests that the small crystals result
from secondary ice production. Small ice crystal production by shattering can
be almost excluded from IAT analysis of the specific situations.
Type 2/WBF and large ice refers thin clouds with low number concentrations and a mass distribution dominated by large cloud particles >50 µm.
The aspherical fractions of the small particles are high and the large
particles are frozen: these clouds are fully glaciated. The reduced number of
small particles in comparison to the mostly liquid clouds can be
explained by the WBF process. However, from the asphericity detection it is
obvious that small ice crystals are present in WBF clouds with higher
concentrations than large ice crystals. Alternatively, these clouds might
consist of sedimenting aggregates. The cloud particle number concentrations
agree reasonably well with the estimated ice nuclei concentrations.
We quantified the occurrence of these cloud types for Arctic, midlatitude
and tropical regions.
For the Arctic, we observed mpt clouds for temperatures higher than
-20 ∘C (253 K). The most common were Type 1a/mostly liquid
clouds, with a small percentage of Type 1b/coexistence and Type 2/WBF and large ice clouds. We hypothesise that this cloud type distribution is a
result of low concentrations of ice active INPs, particularly biological INPs,
during our field campaign in the Arctic. This hypothesis is in agreement with
the low INP concentrations found for this region in a modelling study by
, which is based on field measurements.
At midlatitudes, mpt clouds down to -40 ∘C (233 K) were probed,
mostly in frontal systems with moderate updraughts between 0 and
0.5 m s-1. Here, the glaciated Type 2/WBF and large ice clouds
dominate most of the temperature range, pointing to a sufficient availability
of INPs. Only at temperatures warmer than -20 ∘C (253 K) was an
increasing fraction of Type 1b/coexistence clouds and Type 1c/secondary ice clouds found. The temperature range for the
secondary ice clouds is consistent with the preconditions for the
Hallett–Mossop process.
In the tropics, mostly moderate but also very strong vertical velocities
were recorded. Correspondingly, the glaciated Type 2/WBF and large ice
clouds dominate the measurements over all temperature ranges, but
Type 1b/coexistence clouds are also observed down to -40 ∘C
(233 K). The supercooled liquid droplets freeze homogeneously when
transported to higher altitudes. Type 1c/secondary ice clouds are
observed at colder temperatures (higher altitudes) than at midlatitudes,
indicating that ice-splintering processes other than the Hallett–Mossop
process might be active here.
summarise several studies (see
Fig. ) that tracked the percentage of clouds containing
no ice crystals (left y axis) or the percentage of clouds containing ice
crystals (right y axis) as a function of temperature. Their findings agree
well with our observations at midlatitudes (green line in
Fig. ). It is noteworthy, however, that our observations
in the Arctic (blue line) show higher liquid fractions, while in the tropical
observations (red line) more ice clouds are found.
In general, the analysis of small cloud particle aspherical fractions advises
against the assumption that all cloud particles smaller than 50 µm
are liquid. In contrast to previous assumptions, small particles were frequently
found to be aspherical. The aspherical particle fractions are an important
parameter for the identification of the four cloud types investigated here.
Observations that contain this information e.g. can be
used to extend the cloud statistics presented here. In case no small particle
shapes are available, particle size distributions can be used to differ
between the Type 1 cloud group (mostly liquid/coexistence/secondary ice
clouds) and the Type 2 clouds (WBF and large ice clouds). A sufficiently large
database would, for example, allow the quantification of the efficiency of the WBF
process with regard to temperature and location. Along these lines, this
study might serve as a starting point for a growing cloud type database in
the mpt regime.