AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1653-2017Determining stages of cirrus evolution: a cloud classification schemeUrbanekBenediktbenedikt.urbanek@dlr.dehttps://orcid.org/0000-0002-3576-740XGroßSilkeSchäflerAndreashttps://orcid.org/0000-0002-6165-6623WirthMartinhttps://orcid.org/0000-0001-5951-2252Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyBenedikt Urbanek (benedikt.urbanek@dlr.de)3May2017105165316645October201624October20163March20172April2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1653/2017/amt-10-1653-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1653/2017/amt-10-1653-2017.pdf
Cirrus clouds impose high uncertainties on climate prediction, as knowledge
on important processes is still incomplete. For instance it remains unclear
how cloud microphysical and radiative properties change as the cirrus
evolves. Recent studies classify cirrus clouds into categories including in
situ, orographic, convective and liquid origin clouds and investigate their
specific impact. Following this line, we present a novel scheme for the
classification of cirrus clouds that addresses the need to determine specific
stages of cirrus evolution. Our classification scheme is based on airborne
Differential Absorption and High Spectral Resolution Lidar measurements of
atmospheric water vapor, aerosol depolarization, and backscatter, together
with model temperature fields and simplified parameterizations of freezing
onset conditions. It identifies regions of supersaturation with respect to
ice (ice-supersaturated regions, ISSRs), heterogeneous and homogeneous
nucleation, depositional growth, and ice sublimation and sedimentation with
high spatial resolution. Thus, all relevant stages of cirrus evolution can be
classified and characterized. In a case study of a gravity
lee-wave-influenced cirrus cloud, encountered during the ML-CIRRUS flight
campaign, the applicability of our classification is demonstrated. Revealing
the structure of cirrus clouds, this valuable tool might help to examine the
influence of evolution stages on the cloud's net radiative effect and to
investigate the specific variability of optical and microphysical cloud
properties in upcoming research.
Introduction
Cirrus play an important role for weather and climate: besides their
influence on the water vapor budget in the upper troposphere through
condensation and evaporation and dynamics due to latent heat
, they modify the radiation balance of the Earth and
atmosphere. Thin, opaque cirrus clouds transmit most of the incident solar
radiation and absorb long-wave radiation from the Earth's surface. As they
are typically high and cold, they only emit little long-wave radiation into
space, and thus cause a trapping of radiative energy in the Earth–atmosphere
system, which eventually contribute to a rising surface temperature. If the
cloud is thick, reflection of solar radiation back to space can
become greater than the long-wave
absorption, and consequently can cause the surface of the Earth to cool
. This net radiative effect depends on macroscopic cloud
properties like optical thickness, ice water content, and geometric extent as
well as on its microphysical parameters such as ice crystal number, size, and
shape .
Recent studies have investigated factors that affect these properties: the
amount and composition of natural and anthropogenic aerosol particles in the
troposphere and their ability to nucleate ice crystals are determining
details of heterogeneous freezing , but aerosol
distribution and properties have not been well known until today, in particular in
the temperature range of cirrus clouds. Exact freezing conditions and
mechanisms were studied by from in situ measurements taken
during flight campaigns conducted in Central and North America. They found a
dominance of heterogeneous freezing with mineral dust and metallic particles
as the main source of residual particles. Although they managed to span a
range of geographic regions and seasons, it is not clear whether those results
are globally valid.
High water vapor supersaturations inside and outside of cirrus clouds point
to several microphysical processes and often occur together with low ice
crystal numbers . Sublimation of ice crystals was reported
to result in the disappearance of facets and corners, changing the crystal
symmetry . The dependence of freezing on the updraft
velocity during cloud formation is theoretically known ,
but measurements are difficult and rare. In idealized settings, there is also
a good understanding of the relevant processes in cloud formation and break
up. But in nature no two clouds are alike and there exists a confusing
variability of conditions under which they occur. This makes it difficult to
represent cirrus clouds adequately in global circulation models for weather
and climate prediction .
In order to gain more insight into the particular role of different cirrus
clouds, great efforts were made to classify cirrus by the meteorological
contexts in which they occur . Categories
include synoptic, orographic, lee wave and anvil cirrus. Recently a more
general classification was introduced that distinguishes the groups of liquid
origin and in situ clouds that describe whether the cirrus is formed by
existing water droplets freezing at water saturation or by
the freezing of solution droplets or
heterogeneous nucleation at ice supersaturation but below water saturation
. Such classifications of recorded data are a
prerequisite for statistically investigating the specific properties and
influences of different clouds and to extract the governing mechanisms and
parameters from remote sensing and in situ measurements.
Likewise, detailed knowledge of cloud properties at different stages of
evolution is yet to be gained, as a cloud is expected to show different
properties at the time of formation and dissipation. During the evolution of
a cloud ice particle number, effective radius and particle shape evolve as
well. For instance, showed that during dissipation, ice
crystals lose single facets and corners, thus changing their geometry
significantly. Besides such changes of microphysical properties,
macrophysical qualities like the exact location, altitude and extent of cloud
parts in a specific evolution state may also result in a different net radiative
effect. A classification scheme that reveals the spatial distribution of
evolution stages would facilitate the investigation of possible dependencies
on cirrus evolution.
first illustrated and documented the vertical and
dynamical structure of ice-generating cirrus uncinus clouds. This early work
and following in situ measurements indicate that there
is a vertical order of cirrus evolution stages with ice nucleation near cloud
top level, deposition of water vapor onto ice crystals and thus particle
growth in the middle, and sublimation and sedimentation at cloud base level.
A more recent, statistical study by evaluated an
extensive data set of ground-based lidar measurements taken at the ARM
Southern Great Plains site (Oklahoma, USA) over a time period of 1 year.
Vertical profiles of determined relative humidity with respect to ice
(RHi) inside of cirrus clouds were divided into the upper most
25 %, the middle 50 %, and the lower 25 % of total cloud depth.
The frequency distribution of RHi of the upper 25 % shows a
considerable amount of supersaturated regions with high RHi
values up to 160 %, associated with ice nucleation. The distribution of
the lower 25 % is shifted towards subsaturation with a maximum between 70
and 80 % and values down to 10 %, clearly dominated by crystal
sedimentation and sublimation. Therefore they showed that the generally
accepted vertical order of evolution stages dominated the majority of
measured clouds while individual clouds, depending on cloud type, generation
mechanism, cloud age, and atmospheric dynamics, may show strongly differing
distributions . The classification scheme that
we present is based on atmospheric lidar cross sections and therefore
facilitates the detailed investigation of evolution stages, their vertical
and horizontal order, the impact of atmospheric dynamics, and their specific
optical properties.
Such a classification needs information on RHi and temperature, as they are two governing variables in ice
particle formation, growth, and disappearance . In
order for ice to form in the atmosphere, RHi must reach or surpass
100 %. It is well known that tropospheric air masses often show substantial
supersaturations with respect to ice . These so called
ice-supersaturated regions (ISSRs) result mainly from upward motion of air
masses originating in diverse atmospheric dynamics like large-scale synoptic
ascent (e.g., warm conveyor belt), convective systems or mesoscale gravity
waves .
The existence of an ISSR does not automatically imply the existence or
formation of a cirrus cloud. For the homogeneous freezing (HOM) of solution
droplets at cirrus temperatures (typically below 235 K), high
supersaturations of the order of 140 % are necessary .
Additionally, solid aerosol particles can act as ice nuclei and thus lead to
freezing under a much broader range of conditions. Heterogeneous freezing
onset temperatures and saturation ratios depend strongly on aerosol type,
coating of the particles and their size, and are still subject to current
research .
Once ice particles are present and no cooling, for example from updraft motion, takes
place, remaining supersaturation is quickly depleted by deposition of water
vapor onto existing crystals. In updraft regions of ice clouds, however, it
may take a few minutes to a few hours until the air reaches the quasi-steady
supersaturation which is a function of vertical velocity and ice particle
size distribution and higher than 100 % for positive vertical velocities
. Furthermore, effects that hinder phase relaxation are
discussed, stemming from, for example, liquid coating around ice particles
or dynamic processes . Thus,
supersaturation inside of ice clouds can persist for a substantial amount of
time. Likewise, regions of subsaturation with respect to ice can emerge when
heavy ice particles sediment out of the ISSR, or RHi is reduced by
warming. These regions are dominated by sublimation of ice crystals
.
It can be seen that detailed knowledge of humidity and temperature in and
around the cloud, as well as knowledge about freezing onset conditions is
necessary in order to identify different stages of cloud evolution. The
airborne Differential Absorption Lidar WALES (WAter vapour Lidar Experiment
in Space), flown aboard the German research aircraft HALO (High Altitude
and LOng range, model: Gulfstream G550), makes major parts of the needed
data available. It provides an unique data set of collocated, high spatial
resolution measurements of atmospheric backscatter, depolarization, and water
vapor, enabling us to distinguish in-cloud and cloud-free regions, to
identify the relevant aerosol type in the vicinity of the cloud, and to
calculate relative humidity.
In this paper, we present a detailed classification scheme for the evolution
of cirrus clouds. Besides WALES measurements we use complementary model
temperature fields from the European Centre for Medium-range Weather
Forecasts (ECMWF). Provided with high-resolution, two-dimensional lidar cross
sections of the atmosphere, we are able to study the structure of clouds and
the spatial distribution of classified evolution stages. By setting in situ
and remote sensing data in perspective to cirrus evolution, it facilitates
the study of the specific optical, microphysical, and radiative properties of
evolution stages. We apply our scheme in a first case study of a lee-wave-influenced cirrus cloud over France,
encountered during the ML-CIRRUS 2014 campaign , demonstrate
its applicability, and investigate the impact of both mesoscale and
large-scale dynamics on the cloud structure. We end with a summary of the
classification scheme and a brief outline of its potential
in cirrus cloud research.
Water vapor remote sensing during ML-CIRRUS
In spring 2014, the Mid Latitude Cirrus Experiment ML-CIRRUS was conducted. It was designed to investigate natural cirrus and
anthropogenic contrail cirrus with regard to their nucleation, life cycle,
and climate impact. In this campaign, the German research aircraft HALO,
equipped with a combined in situ and remote sensing payload, performed
16 measurement flights above Europe. The on-board cloud probes, WALES lidar
and novel ice residual, aerosol, trace gas, and radiation instruments probed
midlatitude cirrus clouds originating from, for example, air traffic, warm
conveyor belts, jet streams, or mountain waves .
WALES is an airborne Differential Absorption Lidar that measures the
tropospheric water vapor concentration below the research aircraft by
simultaneously emitting laser pulses at three online and one offline
wavelength in the water vapor absorption band around 935 nm
. The averaged pulse energy is 35 mJ with a repetition rate
of 200 Hz. The partly overlapping contributions from the three online wavelengths
provide the needed sensitivity to compose a complete water vapor profile that ranges from just below the aircraft down to ground level. Additionally, WALES is
equipped with one channel at 1064 nm and one high spectral resolution
channel at 532 nm using an iodine filter. Both receiver channels are
designed to detect the depolarization of the backscattered light
.
WALES is capable of providing collocated measurements of humidity in the form
of water vapor volume mixing ratio rw, backscatter ratio (BSR),
and aerosol depolarization ratio (ADEP). Those measurements form a two
dimensional curtain along the flight track of the research aircraft
intersecting the atmosphere below. The lidar data we use in this paper have a
vertical resolution of 15 m. Raw data are sampled at a rate of 5 Hz. At HALO's
typical ground speed of 210 m s-1 and after averaging for a better
signal-to-noise ratio, horizontal resolution is 2.5 km for humidity and 210 m
for BSR and ADEP.
We use ECMWF analysis temperature data (available every 6 h), with a
horizontal resolution of 0.25∘ and 91 vertical levels, which we interpolate
linearly in time and bilinearly in space onto the lidar measurement
cross section. Then we calculate relative humidity with respect to ice from
this temperature information and the measured absolute humidity:
RHi=rw×nair×T×kBesat,i(T),
with temperature T, volume number density of air nair, and
Boltzmann constant kB. We use the parameterization for water
vapor saturation pressure over ice esat,i by
.
The accuracy of calculated relative humidity relies strongly on the quality
of absolute humidity and temperature data. WALES humidity measurements
exhibit a mean statistical uncertainty of 5 %. The applicability of ECMWF
temperature in this calculation was investigated by . They
showed that during ascent and descent of a similar research flight in 2010,
the mean temperature difference between ECMWF and on-board temperature
sensors was 0.8 K and estimated a resulting maximum relative uncertainty of
10 to 15 % as an upper boundary for the calculated RHi at typical
cirrus temperatures. In cases where collocated radiosonde or dropsonde
measurements are available, their temperature profiles can be used to
calibrate ECMWF temperature fields, eliminating possible offsets. As modern
sondes feature measurement uncertainties of down to 0.2 K, the total relative
uncertainty of RHi can therefore potentially be reduced to values as low
as 6 %.
Cirrus evolution classification scheme
With atmospheric lidar cross sections at hand, we are able to identify
in-cloud and cloud-free regions by applying a threshold for the backscatter
ratio (see Fig. 1). As there is no sharp boundary between a cloud and its
surrounding, this threshold value holds a certain arbitrarity. In the case
study, we use a value of 2, but in cases where, for example, thick aerosol layers are
present, this threshold might need to be increased to avoid classifying parts
of the aerosol layer as in-cloud regions.
Looking at cloud-free parts of the cross section, regions that might possibly
lead to cirrus cloud formation can be identified by searching for data points
exhibiting ice supersaturation (RHi>100 %). Moderately
supersaturated cloud-free parts are classified as ISSRout.
With higher supersaturations, the chances for the imminent nucleation of ice
particles become increasingly higher. Therefore we introduce the classes
HETout and HOMout in our classification. They
represent regions outside of the cloud where onset conditions for
heterogeneous and homogeneous freezing, respectively, are surpassed. Their
classification is implemented via temperature-dependent humidity thresholds.
It should be noted that ice forms at the latest point, as soon as conditions for homogeneous freezing are reached, as
there is always a sufficient amount of solution droplets in the atmosphere
. Therefore, a cloud classification should not feature
considerable regions of HOMout. This fact should be kept in mind
when choosing a BSR threshold value for the cloud border detection, making
sure that HOM regions lie inside the cloud. However, this might not always be
completely achievable without misclassifying aerosol layers (see above).
found that the homogeneous freezing temperatures of numerous
aqueous solution droplets (1–10 µm) would fall on a single
solute-independent curve, when plotted in terms of water activity
aw. They suggested that this curve could be constructed by
shifting the melting point curve by Δaw. From experiments
on homogeneous freezing, they determined a shift by Δaw=0.305. For atmospheric applications, water activity is equal to relative
humidity, when the droplet is in equilibrium with the water vapor pressure of
the surrounding air . We use these findings to extract a
parameterization of the temperature-dependent onset humidity for HOM (see
Table 1, RHi,HOM(T)).
Cirrus evolution classification scheme based on WALES backscatter
ratio (BSR) and relative humidity (RHi) derived from WALES
humidity and ECMWF temperature field (description see text).
To determine a humidity threshold for HET, detailed information of the
involved aerosol type, its coating, and size distribution would be required.
Then results from laboratory experiments on onset freezing temperatures and
saturations for this kind of aerosol could be used. As heterogeneous freezing
conditions are still subject to current research and as
comprehensive aerosol information is difficult to acquire solely from remote
sensing, we make only a coarse distinction between two important aerosol
types: mineral dust (MD) and coated soot (CS).
Together with a synergistic analysis, WALES lidar data can be used to
identify the relevant aerosol type in the measurement area. To this end, we
apply an aerosol classification suggested by . It uses the
fact that aerosol types can be distinguished by three intensive optical
properties, aerosol lidar ratio, aerosol linear depolarization ratio, and
color ratio. Mineral dust shows linear depolarization values of more than
20 % and coated soot less than 20 %. Then we employ simplified onset
parameterizations RHi,HETMD(T) and
RHi,HETCS(T) (see Table 1 and their
Fig. 4). Until more detailed parameterizations are available,
this imposes an uncertainty for the determination of the exact border of
heterogeneous freezing regions. In contrast to HOMout,
HETout regions may exist in cases with no sufficient amount
of aerosol ice nuclei or due to simplifications in the utilized
RHi,HET(T) threshold. The classes ISSRout and
HETout represent pre-stages of cirrus formation and indicate
regions where a cirrus cloud is likely to develop.
Inside of a cloud (BSR > 2), we proceed in the same manner. When the
RHi,HOM(T) threshold is surpassed we classify
this region as HOMin. The region
shows active ice nucleation. Together with HETin, which we
classify analogously, it represents the youngest evolution stage of a cirrus
cloud. HETin is also expected to show active nucleation as long
as ice nuclei are present in the freezing region. However, due to the
limitations mentioned above, the border of HET towards lesser supersaturated
areas must be interpreted with caution. Also, ice crystals found in HET must
not necessarily be formed by heterogeneous freezing, as sedimentation from
higher levels featuring different nucleation conditions may take place.
Still, heterogeneous freezing is an important freezing mechanism in
midlatitudes and the class HETin adds more
information to the classification, leading to a more complete
characterization of cirrus clouds.
When relative humidity inside the cloud is lower than the freezing
thresholds, we classify as DEP, as the remaining supersaturation is depleted
by deposition of water vapor onto the existing ice particles. This
intermediate evolution stage is dominated by depositional growth of ice
crystals. The final evolution stage of a cloud sets in, when relative
humidity falls below 100 %. In such an environment ice inevitably must
sublimate. We classify this region as SUB.
Meteorological situation on 29 March 2014 over western Europe:
(a) ECMWF analysis data (12:00 UTC) at 300 hPa of geopotential
(blue isolines) and horizontal wind speed (shaded green). (b) SEVIRI
false-color image taken at 14:30 UTC (see text). High ice clouds have a blue
color, lower liquid clouds are yellow, cloud-free ground has green color. The
path of the research flight is plotted in red and the flight leg used in the
case study (14:16–14:58 UTC) is marked white.
During the life cycle of a cloud, nucleation, growth and sublimation events
may occur more than once, e.g., when atmospheric dynamics cause renewed
updrafts and a second freezing event on top of pre-existing ice takes place.
As described, our method is able to identify nucleation, growth, sublimation
regions and pre-stages of cloud formation. However on its own, it does not
yield any information about earlier developments of those regions. Its very
strength is to reveal the actual atmospheric state with regards to cirrus
evolution at the time of measurement. This is done on a high spatial
resolution that exceeds typical resolutions of GCMs, enabling the detailed
study of individual cloud parts.
Case study ML-CIRRUS 2014-03-29
We demonstrate the applicability of our classification scheme in a cirrus
case that was obtained during the ML-CIRRUS field campaign on 29 March 2014.
The meteorological situation over western Europe and the Iberian Peninsula on
the flight day is dominated by a trough extending from the west of Ireland to the
Iberian Peninsula and further to the western part of northern Africa (Fig. 2a).
At 300 hPa, high southerly winds with wind speeds up to 35 m s-1 are
observed on the leading edge over southern France and Spain. Two days before
the research flight, model forecasts indicated the existence of cirrus
forming from high updrafts over the Pyrenees, as well as cirrus influenced by
lee waves north of the mountain ridge. Additionally, highly dust-loaded air
masses over southern France, originating from Saharan dust events over
Algeria were expected.
In this meteorological setting the research flight was performed with the aim
of sampling all stages of cirrus evolution that resulted from an overflow of
the Pyrenees with high wind speeds and consequent gravity wave excitations in
the lee of the mountain ridge. Therefore, the flight path in the relevant
measurement region was chosen to run along the main wind direction, sampling
the clouds along their path of advection.
The flight (Fig. 2, red flight path) started in Oberpfaffenhofen, Germany at
12:37 UTC and first went westward towards Paris, followed by a southward
flight leg towards Spain at an altitude of 11 200 m. The investigated cirrus
cloud was encountered over southern France during this leg, which runs
with a bearing of 190∘ (white flight leg). Inside cirrus clouds, over the
Pyrenees mountains, three legs at different lower altitudes followed. From
the Mediterranean coast the aircraft turned eastward and probed cirrus at
several altitudes near the Balearic Islands before it went northward towards
Oberpfaffenhofen (landing at 19:50 UTC).
Cirrus leg overview
The following discussion of the classification scheme focuses on the
southward flight leg stretching about 400 km to the north and 200 km to the
south of the Pyrenees (Fig. 2, white flight path). Figure 2b shows a false-color image of the Pyrenees area derived from SEVIRI (Spinning Enhanced
Visible and Infrared Imager) data at 14:30 UTC. The red, green, and blue
color channels of the image take SEVIRI's 635 nm, 850 nm, and inverted
10.8 µm channel data. This way, the high and therefore cool cirrus clouds
stand out with a bluish color. Low clouds are depicted in yellow and the
surface of the Earth has a green tone. Coming from the north, the flight path
intersects an ice cloud that is part of a larger cloud regime expanding from
southern France towards the Algerian coast. This cloud is followed by a
completely cloud-free area north of the Pyrenees. Over the mountain ridge
some localized high clouds are crossed.
Backscatter ratio (BSR) at 532 nm measured along the flight path
(white line in Fig. 2). Hatched areas indicate data that were excluded due to
detector saturation or low signal-to-noise ratio and the terrain profile is
shown in dark gray. The black rectangle marks the cirrus region that is
studied further (see Figs. 4 and 5). The arrow shows the main wind direction
at cirrus level.
Earlier satellite pictures and ECMWF data (not shown) indicate that the
cirrus cloud regime formed under the influence of a highly humid air mass
stemming from southern Spain that also shows the tendency to produce lower
liquid clouds. As the air mass is transported to the north, it is impacted by
gravity waves generated when crossing the Pyrenees. The analysis of
preliminary temperature data (not shown) provided by the on-board, passive
Microwave Temperature Profiler shows that
temperature oscillations, caused by gravity waves, continue to exist inside
the cirrus cloud intersected by the flight path.
Humidity data of cirrus marked in Fig. 3: (a) water vapor
mixing ratio rw as measured with WALES and ECMWF temperature
contour lines. (b) Relative humidity with respect to ice
RHi derived from WALES data and ECMWF temperature field. The
cirrus is outlined by a black contour line (BSR = 2) and invalid data are
marked by black hatching.
Classified evolution regions of cirrus cloud. In-cloud and
cloud-free regions can be distinguished by the black contour line of
BSR = 2. Regions of ice nucleation (HOMin/out,
HETin/out), ice supersaturation outside (ISSRout),
and depositional growth (DEP) and subsaturation (SUB) inside the cloud are
visualized. Subsaturated regions outside the cloud are left blank and invalid
data are marked by black hatching. The blue line shows a trajectory used for
simulation of adiabatic cooling (see Sect. 4.3 and Fig. 7).
In Fig. 3 we plot a cross section showing backscatter ratio at a wavelength
of 532 nm along the chosen part of the flight path. Here atmospheric features
apparent in Fig. 2 can be studied in greater detail. On the lee side, north
of the Pyrenees (14:19–14:34 UTC), a high cirrus cloud is observed that
extends from a height of 7 to 10.4 km. The southern and middle parts are
dominated by high BSR values from 50 up to 200, whereas the northern section
exhibits lower values. Aerosol linear depolarization ratios of more than
30 % inside the cloud (not shown) and temperatures below 240 K clearly
indicate a pure ice cloud. Over the Pyrenees (14:42–14:53 UTC) a lower cirrus
cloud is located at an altitude of about 6 km. Its spatially restricted
occurrence over the mountain ridge indicates a formation due to forced
updrafts stemming from the southerly cross-mountain flow. Even lower, at a
height of 4 km a thick aerosol layer is discernible. An analysis of ADEP
shows values between 20 and 30 %, typical for Saharan mineral dust
which is consistent with the origin of the air masses in
northern Africa, as forecasted by dust models.
Furthermore, in the region between the two clouds (14:34–14:43 UTC) gravity
lee waves are discernible at an altitude of about 9500 m and also in the
lower aerosol layer. These waves are expected to influence at least parts of
the northern cirrus cloud. We will investigate them in more detail in Sect. .
Classifying evolution stages
In the following we will apply our classification scheme to the high cirrus
cloud north of the Pyrenees. Figures 4 and 5 give a close-up view of the
selected data marked with a black rectangle in Fig. 3. Water vapor volume
mixing ratio rw measured by WALES is plotted in Fig. 4a,
together with red isolines of ECMWF temperature. A black contour line (BSR = 2) marks the cloud border. Being an absolute humidity measure,
rw generally decreases with increasing altitude, as temperature
is decreasing and almost all sources of water vapor are located at the
Earth's surface. Contrastingly, a humid layer, surrounded by dryer air at a
height of approximately 9000 m, can be found upstream of the cirrus cloud
(14:34–14:40 UTC). In this region, the water vapor data exhibit the same
oscillations as previously seen in the BSR data.
Relative humidity with respect to ice (Fig. 4b) is calculated from this data
using the ECMWF model temperature field. As expected, supersaturated regions
(blue) are found mostly inside of the cirrus. There are also major
subsaturated regions (red) in the northern part of the cloud. South of the
cirrus, high supersaturations exist in cloud-free air, mostly in the crests of
the gravity waves (14:34–14:36 UTC). The highest supersaturations are
measured in the most southern part of the cloud (14:33–14:34 UTC). They
indicate a nucleation region.
To investigate individual parts of the cloud in more detail, we apply our
classification and visualize the result in Fig. 5. Data pixels are classified
(Sect. 3.2) and marked in color accordingly, and in-cloud and cloud-free
regions can be distinguished by the black contour line for a BSR value of 2.
Heterogeneous freezing is identified using the HET threshold for coated soot
(RHi,HETCS(T)). We base this decision on 48 h
backward trajectory calculations of air masses at 8.5 to 9.5 km (not
shown) that indicate no influence from lower Saharan dust-loaded levels.
Subsaturated regions outside of the cloud are left blank and areas where no
valid data are available are indicated by black hatching.
The abovementioned humid layer, discernible in Fig. 4, reaches ice
supersaturation (ISSR) in the two crests of the gravity lee wave to the south
of the cloud (14:34–14:36 UTC). At the cloud edge, the HET and HOM
freezing thresholds are also surpassed (14:33–14:34 UTC). As no relevant isolated
HET region exists and HOM freezing also sets in at the cloud edge, we assume
that homogeneous freezing might be the dominant freezing mechanism here. This
upper, southern section of the cirrus is dominated by ice nucleation and
represents the youngest part of the cloud.
The top level of the cloud and the ISSR south of it climbs from about 9.3
to 10.4 km at 14:31:50 UTC. Considering the speed of the aircraft
(200 m s-1) and the wind speed (about 30 m s-1 in opposite
direction), and assuming that the found pattern is stationary (more in Sect. 4.3), this corresponds to a vertical velocity of about 50 cm s-1. In
the temperature range between 200 and 220 K, such high vertical velocities
typically lead to high ice crystal number densities with small ice particles
(r0<10µm; ). As the updraft region is
observed between 14:32 and 14:36 UTC, we assume that freezing takes place
at least between the cloud edge and the peak of the cloud top at
14:31:50 UTC, although no valid data are available for parts of the upper
cloud due to detector saturation.
In the middle (14:26–14:32 UTC), a section of moderate supersaturation (DEP)
is located. This is an already well developed part of the cirrus that is
dominated by depositional growth of ice crystals. After the initial ascent
(14:32–14:34 UTC), the cloud top slopes from over 10 km down to under 9 km at
the northern edge, corresponding to a vertical velocity of about
-30 cm s-1. Sedimentation may contribute to this, but sub-10 µm
particles are rather associated with fall speeds of under 2 cm s-1their Fig. 1. This large-scale descent, also apparent
in ECMWF model data (not shown), reduces supersaturation and evokes the
intermediate DEP region as well as large connected regions of subsaturation
(SUB) in the northern part of the cloud (14:19–14:26 UTC). Here the cloud is
starting to break up, as ice particles are sublimating.
From these results all cirrus evolution stages can be identified: from
cloud-free ISSR and ice nucleation (HET, HOM) aided by vertical displacements
in a gravity lee wave, to crystal growth by deposition of water vapor in a
moderately supersaturated region DEP, to the dissolving of the cloud in a
subsaturated region (SUB), caused by a large-scale descent.
The spatial distribution of these major stages of cirrus evolution feature a
horizontal component with ice nucleation in the south and dissipation in the
north. Also, we find SUB regions at the cloud top level, in part, located
above DEP regions in the north of the cloud (Fig. 5). Our findings fit well
into the perspective of model simulations, investigating the influence of
dynamics on the evolution of a cirrus cloud that also
found more complex horizontal distributions deviating from a simplistic
cirrus evolution pattern comprising ice nucleation at cloud top level, a
crystal growth in the middle and sublimation at the bottom. Thus our
classification illustrates how the large-scale meteorological context, wind,
and gravity wave fields can affect the structure of individual clouds.
Unfortunately, no dropsonde temperature measurements were conducted in this
cirrus case. Thus we must assume a relative uncertainty of RHi between
10 and 15 % at cirrus level (see Sect. 2). This directly translates into
an uncertainty in locating the exact border between evolution stages.
However, we found that the horizontal distribution order and the existence of
SUB regions at the cloud top continue to exist even after offsetting the
temperature field by ±0.8 K. Therefore we are confident that ECMWF
temperature data are suitable for this kind of analysis.
Investigating the influence of lee waves
A special feature of this case study is the presence of lee wave patterns in
the cloud region. Figure 6 gives a close-up view of BSR data in the cloud-free
area south of the cirrus cloud. A layer of slightly higher BSR (> 1.2) is
located above an altitude of 9500 m. It shows clear oscillations at its
boundary to a lower, cleaner layer of air. One period extends over about
66 s in measurement time, which corresponds to an apparent wavelength of
14 km with vertical displacements of up to 190 m.
ECMWF model data, available for this cirrus case, features a horizontal grid
spacing of about 16 km and thus ECMWF is not able to resolve the mesoscale
lee waves in its temperature gradients (see Fig. 4a). As a result,
temperatures in the crests might be even lower and RHi values are therefore
underestimated.
Close-up view of BSR data in the lee wave region south to the cirrus
cloud. Dotted line: contour line (BSR = 1.2); solid line: trajectory used for
simulation (contour line shifted by 200 m).
To investigate this possible deviation and its influence on our
classification results, we simulate adiabatic cooling of an air parcel along
a hypothesized trajectory (Figs. 5 and 6) in front of the cirrus cloud.
The trajectory runs 200 m under and parallel to the contour line of BSR = 1.2
(Fig. 6), that separates the two distinct layers of air. The wind direction
in this region differs by less than 10∘ from the flight path. This makes us
confident that the simulated trajectory resembles a real trajectory
reasonably well, under the assumption of a stationary air flow.
SEVIRI images from 14:00 and 15:00 UTC (not shown) indicate that the
northern and southern edges of the cloud are only moving about 20 km to the
north along the flight path. That corresponds to a cloud velocity of under
6 m s-1, compared to wind speeds of up to 35 m s-1 at cirrus
altitude. Similar to real lee wave clouds , air is flowing
through the region, becoming part of the cloud in the south and leaving the
cloud in the north. In the confined area and time frame of our simulation, we
consider the underlying wind and wave fields to be quasi-stationary. However
this might certainly not be true for the duration of the whole flight leg
(14:18–14:41 UTC). Also, our simulation is not intended to provide corrected
temperature data but to illustrate the general influence of gravity waves on
cloud formation and of unresolved temperature fluctuations on our
classification.
Along this trajectory we calculated peak vertical velocities of up to
120 cm s-1, more than twice as high as the average vertical velocity
of 50 cm s-1 in the initial updraft region, estimated in Sect. 4.2.
During nucleation, such peaks may lead to even smaller ice crystals and
higher ice crystal number densities of up to 50 cm-1.
In Fig. 7, ECMWF temperature and relative humidity calculated with ECMWF
temperature along the trajectory are plotted (blue) as a function of
measurement time. As the trajectory follows the vertical displacements of the
gravity wave along the wind direction, i.e. from right to left in Figs. 3 to
7, relative humidity and ECMWF temperature show oscillations. They stem from
the undisturbed temperature gradient, as ECMWF does not resolve the mesoscale
lee waves. Besides clear oscillations, a development towards higher RHi
and lower temperature, approaching the cloud, is discernible. From
14:36:20 UTC on, RHi shows supersaturation and reaches values of 120
and 130 % in the following two crests, surpassing the HET threshold.
Now we start a parcel at the beginning of the trajectory (measurement time:
14:40 UTC) initialized with the ECMWF temperature at this point. As it
follows the trajectory, the temperature is calculated from its vertical
displacement using the dry adiabatic temperature gradient. The simulated
temperature and relative humidity is plotted in green. Compared to ECMWF, the
temperature in the last two crests south of the cloud edge is more than 0.5 K
lower and values of RHi are higher by 10 %. The deviations result from
the nonadiabatic temperature gradient in the ECMWF data.
Simulation of temperature T and relative humidity RHi.
Green lines are simulated values along a derived trajectory (Fig. 5) assuming
adiabatic cooling and heating within the gravity wave, respectively. In blue
are ECMWF values interpolated to the trajectory location.
These results emphasize the role of lee waves in the most southern part of
the studied cirrus cloud. Comparably cooler temperatures due to adiabatic
cooling in the wave crests facilitate the early nucleation of ice crystals.
We find that our classification, using original ECMWF data with relatively
coarse spatial resolution (horizontal grid spacing: 16 km), is able to
reveal the relevant classification features within the gravity wave region.
The classification quality will be even better in cases where data from the
latest ECMWF model implementation, with an improved grid
spacing of 9 km, or output from regional models are available.
Overall our classification proved to be applicable in a meteorological
context that comprises both mesoscale and large-scale dynamics. It identifies
all relevant stages of cirrus evolution and their detailed spatial
distribution and thus also reveals the influences of gravity waves and
large-scale atmospheric motion on the studied cirrus cloud.
Summary and conclusions
We presented a novel cirrus
classification scheme capable of identifying all relevant stages of cirrus
cloud evolution. It is based on airborne lidar measurements with high spatial
resolution of water vapor, backscatter, and aerosol depolarization. This data
are used together with ECMWF model temperature fields and knowledge and
assumptions about onset conditions for homogeneous and heterogeneous
freezing, to retrieve a cross section of the cloud, revealing the detailed
distribution of evolution stages.
In cloud-free air (BSR < 2), ice supersaturated regions
(ISSRout) as well as regions of homogeneous
(HOMout) and heterogeneous freezing (HETout)
are determined. These indicate favorable areas for cirrus cloud formation.
Inside of a cloud, ice nucleation (HETin,
HOMin), depositional growth (DEP), and sublimation regions
(SUB) are distinguished. They represent the formation, growing, and breakup
phases of a cirrus cloud, respectively.
We demonstrated the applicability of our classification in a first case study
of a cirrus cloud that was observed in a complex meteorological situation
comprising a thick aerosol layer, large-scale dynamics, and mesoscale gravity
lee waves. Here it revealed a nonstandard horizontal order of the
aforementioned evolution stages and helped to identify the influence of
underlying wind and gravity wave conditions as well as large-scale dynamics
on individual parts of the cloud.
With this valuable tool at hand, in our ongoing research we are investigating
the large airborne lidar data set obtained during the ML-CIRRUS campaign.
This classification scheme facilitates the study of the spatial distribution
of evolution stages and can be used to set in situ and other remote sensing
data, obtained during the campaign, in relation to cirrus evolution. By
bringing together those data sources, the specific optical and microphysical
properties of different cirrus stages can now be explored. The possibility of
combining our classification with trajectory-based methods promises to reveal
details of the temporal succession of evolution stages. Thus, we aim to
achieve more detailed insights in radiative properties of cirrus clouds under
various formation and life cycle conditions.
The data used in this work are accessible on request via
the HALO database with dataset IDs: #4992, #4993 and #4994 (DLR, 2017a, b,
c).
The authors declare that they have no conflict of
interest.
Acknowledgements
ML-CIRRUS campaign was mainly funded by Deutsches Zentrum für Luft- und
Raumfahrt (DLR) and Deutsche Forschungsgemeinschaft (DFG). This work has been
funded by the DLR VO-R young investigator group. The authors would like to thank the
staff members of the HALO aircraft from DLR Flight Experiments for preparing
and performing the measurement flights, Christiane Voigt, Andreas Minikin and everybody contributing to the successful planning and
execution of ML-CIRRUS, the European Centre for Medium-Range Weather
Forecasts (ECMWF) for providing model data, and Mareike Kenntner for
providing preliminary MTP temperature data. Our special thanks go to Florian Ewald for providing SEVIRI satellite image data and Klaus Gierens
and Benedikt Ehard for fruitful discussions and helpful suggestions that
contributed to the quality of this work.
The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Edited by: D. Baumgardner
Reviewed by: three anonymous referees
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