The French–German Arctic research base AWIPEV (the Alfred Wegener Institute
Helmholtz Centre for Polar and Marine Research – AWI – and the French Polar
Institute Paul Emile Victor – PEV) at Ny-Ålesund, Svalbard, is a unique
station for monitoring cloud-related processes in the Arctic. For the first
time, data from a set of ground-based instruments at the AWIPEV observatory are
analyzed to characterize the vertical structure of clouds. For this study, a
14-month dataset from Cloudnet combining observations from a ceilometer, a
94 GHz cloud radar, and a microwave radiometer is used. A total cloud
occurrence of ∼81 %, with 44.8 % multilayer and 36 %
single-layer clouds, was found. Among single-layer clouds the occurrence of
liquid, ice, and mixed-phase clouds was 6.4 %, 9 %, and 20.6 %,
respectively. It was found that more than 90 % of single-layer liquid and
mixed-phase clouds have liquid water path (LWP) values lower than 100 and
200 g m-2, respectively. Mean values of ice water path (IWP) for ice
and mixed-phase clouds were found to be 273 and 164 g m-2,
respectively. The different types of single-layer clouds are also related to
in-cloud temperature and the relative humidity under which they occur. Statistics
based on observations are compared to ICOsahedral Non-hydrostatic (ICON)
model output. Distinct differences in liquid-phase occurrence in observations
and the model at different environmental temperatures lead to higher
occurrence of pure ice clouds. A lower occurrence of mixed-phase clouds in
the model at temperatures between -20 and -5∘C becomes evident.
The analyzed dataset is useful for satellite validation and model evaluation.
Introduction
Clouds play a crucial role in the energy budget and in the hydrological
cycle. On the one hand, clouds scatter solar radiation back to space, leading
to a shortwave cooling effect at the surface. On the other hand, clouds emit
longwave radiation and therefore warm the surface. The impact
of clouds on the energy budget depends on their macrophysical (cloud
thickness, cloud-top and cloud-base altitudes) and microphysical (phase,
size, and concentration) properties .
One of the most important cloud characteristics defining the radiative
properties is cloud phase composition . In
general, liquid-containing clouds exhibit a stronger cloud radiative effect
than ice clouds . The phase partitioning is especially
essential in the Arctic region, where liquid and mixed-phase clouds can
persist for several days . and
showed that during the polar winter liquid-containing
clouds significantly influence the net cloud radiative effect and lead to an
enhanced warming near the surface. The authors also reported that in
midsummer the cloud-driven shortwave radiation cooling dominates over
longwave warming. This summer SW radiative cooling of the surface was
reported for different Arctic regions except the Summit station in Greenland
where the cloud radiative forcing effect is positive the entire year due to
the high surface albedo of the snow coverage .
The net cloud radiative forcing in the Arctic influences sea ice coverage and
leads to more open water that in turn affects heat exchange between ocean
and atmosphere . Extended periods of open ocean
increase the moisture content in the atmosphere and therefore might enhance
cloud coverage .
Beyond the radiative feedbacks clouds are crucial for precipitation formation
that significantly affects the Arctic climate. Precipitated water forms
rivers and sustains a glacier flow into the sea, thus influencing the
salinity of the Arctic ocean. Being essential for snowmelt ,
sea ice reduction , and affecting the permafrost
stability, Arctic clouds have a significant impact on productivity and
variety in marine and terrestrial environments and thus influence the Arctic
ecosystem .
Formation of Arctic clouds is a complicated process associated with
aerosol–cloud interactions, turbulence, phase transitions, and heat and moisture
exchanges between the surface and the atmosphere . The
interaction of clouds with radiation and aerosols remains the largest
uncertainty in radiative forcing models . Many of
the processes are not well resolved in global climate models
, indicating that the parameterization of cloud
properties still needs improvement .
Better understanding Arctic cloud processes and feedbacks requires
long-term and accurate observations . In
particular, knowledge of the vertical cloud structure and phase is
crucial for an estimation of the cloud radiative impact
. In order to retrieve
information on the vertical distribution of clouds and their properties,
active remote sensing instruments such as lidars and cloud radars have to be
exploited . Using ground- and ship-based remote sensing
measurements, , , and
have provided statistics on cloud phase and cloud macrophysical and
microphysical properties for several Arctic sites and the Beaufort Sea within
the SHEBA (Surface Heat Budget of the Arctic Ocean) program .
Several studies based on satellite observations analyzed Arctic cloud
properties including cloud phase. and
characterized the vertical and seasonal variability of Arctic clouds using
the CloudSat 94 GHz radar and the Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO).
combined active spaceborne and ground-based
measurements to compare annual cycles of the vertical distribution of cloud
properties at the Alaskan site Barrow and the Canadian site Eureka.
combined both sets of observations at Eureka
station in the high Arctic and showed the seasonal variability of the
vertical distribution of clouds and monthly cloud fraction.
Despite high values of the satellite cloud observations, it is often
difficult to observe low-level clouds, which frequently occur in the Arctic
, with spaceborne instruments .
Ground-based remote sensing observations can provide more detailed
information here. However, there are only a few Arctic sites that provide
long-term, continuous information about vertical cloud structure using
the combination of ground-based remote sensing measurements. Such sites are
located, for example, in Barrow Alaska;, in
Atqasuk Alaska;, in Eureka Canada;,
and in Summit Greenland;. , for
instance, compared the occurrence and cloud macrophysical properties of six
observatories in the Arctic.
One of the Arctic cloud observation sites is based in Ny-Ålesund
(78.92∘ N, 11.92∘ E), which is located on the island of
Spitsbergen in Svalbard, Norway, and comprises several international research
stations. Ny-Ålesund is situated at the coastline of Svalbard
close to a fjord, ocean, and mountains, and thus its climate is significantly
influenced by diabatic heating from the warm ocean
and by the surrounding orography
. have already shown a highly
pronounced warming and moistening of the tropospheric column in the Svalbard
region. Analyzing a 22-year dataset (1993–2014) from radiosondes the authors
found that during wintertime there has been a significant increase in
atmospheric temperature (up to 3 K per decade) and mean integrated water
vapor (+0.83±1.22 kg m-2 per decade).
analyzed cloud statistics at Ny-Ålesund based
on data from a micro-pulse lidar only. Recently, have
investigated the relation between cloud fraction and surface longwave and
shortwave radiation fluxes at Ny-Ålesund using data from a
lidar. Nevertheless, the applicability of lidars for cloud profiling is limited
by the strong attenuation of the lidar signal by optically thick clouds,
which often hampers multilayer and mixed-phase cloud observations.
In 2016, the instrumentation of the French–German Arctic research base AWIPEV
at Ny-Ålesund, operated by the Alfred Wegener Institute Helmholtz
Centre for Polar and Marine Research (AWI) and the French Polar Institute
Paul Emile Victor (PEV), was complemented with a cloud radar and now has
state-of-the-art instrumentation for vertically resolved cloud observations.
Within the Transregional Collaborative Research Center (TR 172) project “Arctic
Amplification: Climate Relevant Atmospheric and Surface Processes, and
Feedback Mechanisms (AC)3” , comprehensive
observations of the atmospheric column have been performed at the AWIPEV
station at Ny-Ålesund since June 2016.
Instruments and data used for this study.
InstrumentMeasured quantitiesSettings for Ny-Ålesund andVertical resolutionRetrieved parameterstemporal resolution(range)MWR HATPROBrightness temperatures at 22.24–31.4 GHz (seven frequencies) and at 51.26–58 GHz1–2 sRange-integrated measurementsLWPCeilometer CL51Profiles of attenuated backscatter coefficient12–20 s10 m, min range 10 m, max range 15 kmcloud base, liquid layerFMCW 94 GHz cloud Doppler radar (JOYRAD-94)Profiles of reflectivity (94 GHz) and Doppler velocity (94 GHz), Doppler spectrum width (94 GHz), brightness temperature (89 GHz, passive)2.5 s4 m (100–400 m), 5.3 m (400–1200 m), 6.7 m (1.2–3 km), 17 m (3–10 km)cloud presence, cloud boundariesRadiosonde RS92, RS41Profiles of atmospheric temperature and relative humidityat least one sonde per day, 1 s5–7 mIWV
Cloudnet characteristics for Ny-Ålesund.
PropertyValueInput parameters (instrument and/or model)LWP (MWR), reflectivity factor and Doppler velocity (94 GHz radar), attenuated backscatter coefficient (ceilometer CL51), hourly model analysis and forecasts (GDAS1 or NWP ICON model)Temporal resolution30 sVertical resolution (range)20 mRetrieved parametersTarget classification, IWC
In this study the vertical structure of clouds at Ny-Ålesund is
characterized for the first time using lidar–radar synergy. In
particular, we used the cloud radar to get information about the cloud
structure through the whole atmospheric column. Instrumentation, data
products, and models used in this study are presented in
Sect. . Thermodynamic conditions at
Ny-Ålesund for the investigated time period are shown in
Sect. . In Sect. the vertical
hydrometeor distribution and occurrence of different cloud types at
Ny-Ålesund are analyzed. For single-layer clouds, which can be
liquid, ice, and mixed phase, liquid and ice water path (LWP and
IWP) are derived and discussed. The cloud occurrence is related to
thermodynamic conditions such as temperature and humidity
(Sect. ). Since environment temperature and humidity are
some of the main parameters affecting cloud formation and development
characterization in models, in Sect. we compare the
observed relations between cloud occurrence and thermodynamic conditions
with those produced by the numerical weather prediction (NWP) model ICON
Icosahedral Non-hydrostatic;. Finally, the discussion
of results and the summary are given in Sect. and the
outlook in Sect. .
Instrumentation and data products
In this study we use various measurements and products continuously running
at the AWIPEV observatory. A set of passive and active remote sensors
provides information about the thermodynamic state and cloud and
precipitation profiling. In the following subsections, we give an overview of
the instruments, data products, and model data, as well as a short description
of the measurement principles and retrieval methods. Table
summarizes the measured quantities and retrieved
parameters of the instruments. Table gives an overview on
the Cloudnet products used for the cloud analysis and provides input
parameters and model data for the Cloudnet algorithm.
Radiosonde observations
Radiosondes have been launched at AWIPEV at least once per day at 11:00 UTC for
more than 2 decades . The radiosondes provide vertical
profiles of temperature, humidity, wind speed, and wind direction. From
21 May 2006 to 2 May 2017 all radiosondes were of the type Vaisala RS92 and have
been processed using the GRUAN version 2 data processing algorithm
. The processing corrects for errors in
temperature and humidity, for instance, temperature uncertainties due to the
heating effect by solar radiation, and for humidity errors due to a radiation
dry bias . reported that after the
GRUAN processing the uncertainties of temperature are 0.25 ∘C and
0.15 ∘C for daytime and nighttime, respectively, and 4 % for relative
humidity at altitudes up to 10 km. Since 2 May 2017 the type of radiosonde
has changed to the new radiosonde type Vaisala RS41. The accuracy of the RS41
radiosonde type reported by the manufacturer is 0.1 ∘C for temperature
and <2 % for humidity.
In the present study we use the radiosonde data to characterize the
thermodynamic state at Ny-Ålesund for the period from June 2016 to
July 2017. In addition, we compare atmospheric parameters of the analyzed
period with a previous 23-year-long homogenized radiosonde dataset
. To relate cloud properties to the
thermodynamic conditions under which they occur, we combined the radiosonde
data with measurements from ground-based instruments operated by AWIPEV.
Microwave radiometer
At AWIPEV, passive microwave observations have been performed with a
humidity and temperature profiler HATPRO; since 2011.
HATPRO is a 14-channel microwave radiometer that measures atmospheric
brightness temperatures (TBs) at K-band (22.24–31.40 GHz) and V-band
(51.26–58 GHz) frequencies with a temporal resolution of 1–2 s. The six
K-band channels (22.24, 23.04, 23.84, 22.44, 26.24,
27.84 GHz) are located close to the water vapor absorption line at 22 GHz.
The 31.4 GHz channel is located in the atmospheric window. The TBs measured
at K-band are used for integrated water vapor (IWV), LWP, and humidity profile
retrievals. The seven V-band channels (from 51.26 to 58 GHz) are located
along the oxygen absorption complex at 60 GHz and are used for vertical
temperature profiling.
A multivariable linear regression algorithm was applied
to the TB observations to derive LWP and IWV as well as temperature and
humidity profiles. In order to determine the site-specific regression
coefficients a dataset of almost 3800 Ny-Ålesund radiosondes was
used in combination with a radiative transfer model (RTM) to simulate the
HATPRO TBs. In addition to GRUAN processing all the radiosonde data were
quality controlled according to . To this end,
radiosondes that did not reach the 30 km height were extended with
climatological profiles.
In order to determine and correct for potential TB offsets, we followed the
method by . The assessment of the TB offsets allows for
the
reduction of systematic errors in TBs originating from instrumental effects as
well as from radiative transfer simulations. For this method, only clear-sky
cases were used. In order to identify clear-sky situations, i.e., cases
without any liquid, HATPRO zenith measurements were checked within 20 min
before and after a radiosonde launch. In particular, we checked standard
deviations of the retrieved LWP every 2 min. If all the standard
deviation values of the LWP within 40 min did not exceed 1.2 g m-2,
these cases were considered as clear sky. The TBs measured by HATPRO were
then compared to the TBs simulated from the radiosonde data, and mean TB
offsets were determined. In this way, a TB offset correction was performed
for each period between two absolute calibrations of the instrument.
For this study, we used the retrieved LWP from HATPRO to get information
about the amount of liquid in the atmospheric column. This LWP information is
also used in the Cloudnet product, which is presented in
Sect. . The typical uncertainty for the LWP retrieved
from HATPRO measurements is 20–25 g m-2. HATPRO
measures continuously during the whole day but cannot provide reliable
information during rain conditions when the radome of the instrument is wet.
In these cases, data have been flagged and excluded from the analysis.
Ceilometer
Since 2011 a Vaisala ceilometer CL51 has been operated at the AWIPEV
observatory . The ceilometer emits pulses at 905 nm
wavelength and measures atmospheric backscatter with a temporal resolution of
about 10 s and a vertical resolution of 10 m. The maximum profiling range is 15 km.
The ceilometer is sensitive to the surface area of the scatterers and is thus
strongly affected by high concentrations of particles like cloud droplets and
aerosols . On the one hand, it is thus well suited to detect
liquid layers and cloud-base heights. On the other hand, the near-infrared
signal is significantly attenuated by liquid layers. Therefore, the
ceilometer often cannot detect cloud particles above the lowest liquid layer
when optical depth exceed a value of around 3.
reported that ceilometers are essential for the reliable
detection of high-level ice clouds. However, a lidar system alone is not
sensitive enough to detect clouds with low ice water content of the order of
less than 10-6 g m-3. In this study, the attenuated
backscatter profiles of the ceilometer CL51 are used in the Cloudnet product
(see Sect. ). The ceilometer is calibrated using the
technique by , which has uncertainties of 10 %.
94 GHz radar
On 10 June 2016 a new 94 GHz cloud radar (JOYRAD-94) of the University
of Cologne was installed at AWIPEV station. JOYRAD-94 is a
frequency-modulated continuous-wave (FMCW) Doppler W-band radar. The active
part of the radar measures at 94 GHz. The radar also has a passive channel at
89 GHz that is well suited for LWP retrievals. showed
the details of the operational principle and signal processing for JOYRAD-94.
At Ny-Ålesund JOYRAD-94 is operated in a high-vertical-resolution
mode . The temporal resolution of the cloud radar is
2.5 s, and the vertical resolution changes with height from 4 to 17 m.
Minimum detection height was 100 m above the ground. Table
shows the main settings and parameters for the high-vertical-resolution mode.
JOYRAD-94 data are available from 10 June 2016 to 26 July 2017, when
it was replaced by a similar instrument MIRAC-A. In this study, we restrict
the analysis to the first year of measurements when JOYRAD-94 was operating.
Profiles of the radar reflectivity factor and the mean Doppler velocity were
used in the Cloudnet algorithm to provide information on cloud boundaries,
cloud phase, and microphysics.
The Cloudnet algorithm corrects the radar reflectivity for attenuation by
atmospheric gases and liquid water. Temperature, humidity, and pressure
profiles from a model are used by Cloudnet for the corrections. The
two-way uncertainty of the gas attenuation estimated by is
about 10 %. The uncertainty of 25 g m-2 in LWP from MWR causes about
±0.2 dB uncertainty in the two-way attenuation at the W-band .
The total radar reflectivity uncertainty consists of the calibration bias,
which is within ±0.5 dB , the random error, and the
gas–liquid attenuation uncertainty. The random error depends on a number of
independent measurements, which for the 30 s Cloudnet sampling varies
from 72 to 108 for the radar settings used (see Table ).
Taking into account the noncoherent averaging of the independent
measurements Eq. 5.193 the standard deviation of the
random error is of the order of 0.5 dB.
Cloudnet products
The Cloudnet algorithm suite combines observations
from a synergy of ground-based instruments. Cloudnet output includes several
products such as a cloud target classification and products with
microphysical properties (e.g., ice water content – IWC, liquid water
content). In order to provide the full vertical information on clouds,
Cloudnet requires measurements from a Doppler cloud radar, a ceilometer–lidar,
a microwave radiometer, and thermodynamic profiles of a NWP model. For
Ny-Ålesund, measurements are taken from the 94 GHz FMCW cloud
radar JOYRAD-94, the ceilometer CL51, and the HATPRO MWR. Model data are
taken from GDAS1 (Global Data Assimilation System) or NWP ICON, which will be
presented in the next subsection. For the first time, data from a FMCW cloud
radar with a varying vertical resolution were implemented in the Cloudnet
algorithm. Within Cloudnet, the measurements are scaled to a common temporal
and vertical grid of 30 s and 20 m, respectively.
Difference in temperature (a) and relative
humidity (b) between GDAS1 and radiosonde data. Radiosondes for the
period from February 2017 to July 2017 at Ny-Ålesund are used. Blue and
red lines show the bias and the standard deviation,
respectively.
For the target classification the lidar backscatter and Doppler radar
parameters are analyzed in combination with thermodynamic profiles of a model
. As an example, measurements from the radar and the
ceilometer and the Cloudnet target classification on 29 September 2016 are
shown in Fig. . The target classification consists of the categories
(1) aerosols and insects, (2) insects, (3) aerosols, (4) melting ice and
cloud droplets, (5) melting ice, (6) ice and supercooled droplets, (7) ice,
(8) drizzle–rain and cloud droplets, (9) drizzle or rain, (10) cloud droplets only,
and (11) clear sky. In this study, the Cloudnet target categorization is used
to differentiate cloud phase (liquid, ice, and mixed phase) and to identify
different cloud types.
For the classification the Cloudnet algorithm identifies the 0 ∘C
isotherm using the wet-bulb temperature calculated from the model data.
Therefore, the model uncertainties (Figs. and )
may lead to liquid–ice misclassification at temperatures
close to 0 ∘C. In the case of precipitating clouds uncertainties of the
model are mitigated by the Cloudnet algorithm using radar Doppler
observations. The algorithm identifies the 0 ∘C isotherm by a
significant gradient in the particle vertical velocity.
Difference in temperature (a) and relative
humidity (b) between the ICON column output over Ny-Ålesund and
radiosonde data for the period from February 2017 to July 2017. Blue and red
lines show the bias and the standard deviation,
respectively.
Radar reflectivity factor (a), lidar backscatter
coefficient, (b) and Cloudnet target classification (c) on
29 September 2016; AWIPEW observatory at Ny-Ålesund.
Based on the target classification, various cloud microphysical retrievals
are applied within Cloudnet. The Cloudnet IWC product, which is used in this
study, is based on a Z–IWC–T relation
where Z is a radar reflectivity factor and T the air temperature. The
Cloudnet IWC has a bias error and typical random error of 0.923 and
1.76 dB, respectively. found that uncertainties of the IWC
retrieval differ for different temperature ranges and are estimated to be
from -50 % to +100 % for temperatures below -40∘C and ranging
from -33 % to 50 % for temperatures above -20∘C. The numbers
here are root mean squared errors given with respect to the reference IWC.
Evaluating the method of
resulted in similar uncertainties, except that there was a positive bias of about 50 %
for temperatures above -30∘C. The authors estimated the uncertainties
from 0 % to +100 % and from -50 % to +100 % at temperatures above
and below -30∘C, respectively. The uncertainty in the radar
reflectivity also influences the IWC retrieval. The total uncertainty of 2 dB
corresponds to a range of about +40 % to -30 % uncertainty in IWC. Part of this uncertainty
is to be included into the uncertainty of the Z–IWC–T relation from
because the relation was found empirically using radar
observations. More detailed information on the Cloudnet products can be found
in .
Cloudnet data availability for Ny-Ålesund for June 2016 to
July 2017. Grey bars correspond to clear-sky profiles, black bars to cloudy
profiles, and white space means no Cloudnet data availability.
For the analyzed period (June 2016–July 2017), the Cloudnet availability is
more than 90 % for most of the months (Fig. ).
Exceptions are June 2016 (installation of the radar on 10 June), July 2016,
and February 2017 (new software installation for cloud radar) with a data
coverage of 64 %, 85 %, and 81 %, respectively. The total number of
analyzed Cloudnet profiles is 1 130 030, which includes 216 860 clear-sky
profiles and 913 170 cloudy profiles.
Model dataGlobal data assimilation system GDAS1
The Global Data Assimilation System GDAS; is
operated by the US National Weather Service's National Centers for
Environmental Prediction (NCEP). This system analyzes different types of
observations and maps the results on a grid used for model initializations.
The GDAS dataset is initialized every 6 h and outputs an analysis
time step followed by forecasts with a temporal resolution of 3 h on 23 pressure levels.
In the present study GDAS1 data (see
https://www.ready.noaa.gov/gdas1.php, last access: 28 February 2019,
for detailed information), which are available on a 1∘ by
1∘ latitude–longitude grid, were used in the Cloudnet algorithm to
provide thermodynamic information for the period from 10 June 2016 to
31 January 2017. The vertical resolution varies from 173 m near the ground to
500 m at a height of 2 km and to ∼2.5 km at a height of 15 km. The
uncertainties of the temperature and relative humidity profiles of GDAS1 are
shown in Fig. . The maximum errors in temperature and
relative humidity do not exceed -1±1.5∘C and -15±24 %, respectively.
NWP ICON model
The ICOsahedral Non-hydrostatic modeling framework for
global NWP and climate modeling is developed by the German Weather Service
and the Max Planck Institute for Meteorology. The grid structure of ICON is
based on an icosahedral (triangular) grid with an average resolution of
13 km. The averaged area of the triangular grid cells is 173 km2. In the
vertical dimension, the model has 90 atmospheric levels up to a maximum
height of 75 km. The vertical resolution ranges from 30 m at the lowest
heights to about 500 m at about 15 km of height. The vertical resolution at
a
2 km height is about 260 m. For this study we used a column output for
Ny-Ålesund taken from the operational global ICON model run. In
particular, vertical profiles of environment temperature and humidity,
specific cloud water content, specific cloud ice content, rain mixing ratio,
and snow mixing ratio were used in this study. The ICON column output for
Ny-Ålesund is available since 1 February 2017 and has been used as
an input for the Cloudnet algorithm since then.
In this study, we use ICON data to exemplarily show how such an observational
dataset of clouds can be used for a model evaluation
(Sect. ). We relate the occurrence of different types
of clouds in the ICON model to temperature and humidity and compare the
results to the observational statistics. ICON model output for
Ny-Ålesund is available twice a day at 00:00 and 12:00 UTC with a
forecast for 7.5 days (180 h) and hourly output intervals. The data only
from the first 12 h after the initialization of the model run were used
in our analysis. The uncertainties of the temperature and relative humidity
profiles of the ICON model are shown in Fig. . The maximum
errors in temperature and relative humidity at an altitude up to 10 km are
-1.5±1.5∘C and -5±20 %, respectively.
Thermodynamic conditions
It is well known that environmental temperature and humidity strongly
influence cloud formation and development. Therefore, we start our analysis
with an insight into the thermodynamic conditions during the study period. In
this section we also check how representative this time period is in terms of
thermodynamic conditions in comparison to the long-term mean.
Vertical profiles of monthly mean atmospheric
temperature (a) and absolute (b) and relative
humidity (c) from radiosonde observations at Ny-Ålesund from
June 2016 to July 2017.
Figure a shows the monthly mean atmospheric temperature based on the
radiosonde data for the period from June 2016 to July 2017. The atmospheric
temperature follows an annual cycle typical for the Northern Hemisphere with
higher temperatures during summer and autumn and lower temperatures in winter
and spring. The lowest values of the monthly mean temperature in the lowest
50 m were observed in March and January 2017 (-11 and
-10∘C, respectively). The highest observed monthly mean temperature
was +7∘C in July 2016. Looking at the monthly mean temperature at
5 km of height, minimum and maximum values of -41 and
-17∘C were found in January and July, respectively. Despite the fact
that Ny-Ålesund is located at the coastline where the climate is
supposed to be less variable due to the impact of the ocean, the monthly mean
temperature changes by 19 and 24 ∘C in the lowest 50 m and
5 km of height, respectively. This large amplitude of the temperature change at
Ny-Ålesund can be explained by the regular occurrence of polar day
and polar night. When a polar night begins in the beginning of October,
atmospheric temperature dramatically decreases and it starts to increase
again in late March (Fig. a). Moreover, the smaller temperature
variance at lower altitudes might be related to processes between the surface
and the atmosphere and the conserved energy near the ground.
Figure b provides information on monthly mean absolute humidity from
June 2016 to July 2017. In summer the water vapor is mostly concentrated in
the lowest 1.5 km with the highest monthly mean values of up to 6 g m-3
in July 2016 and July 2017. The water vapor in this altitude range is thus
the main contributor to the integrated water vapor (IWV). In winter, the
monthly mean absolute humidity is much lower with a minimum value of
∼1.5 g m-3 (January) in the lowest 1.5 km.
In terms of relative humidity with respect to water (RHw,
Fig. c), it can be seen that the monthly mean RHw is highest
in the lowest 2 km of the atmosphere. This is in agreement with
. There is no strong seasonal variability of the monthly
mean RHw at altitudes higher than 4 km. In the lower troposphere,
the monthly mean RHw, following the temperature cycle, is higher in
summer and autumn (ranging from 60 % to 94 % in the lowest 2 km) and lower
in winter and spring (ranging from 52 % to 81 % in the lowest 2 km), except
for March 2017. In March 2017, the coldest month in the period of this study,
the monthly mean absolute humidity was relatively low (1.7 g m-3) in the
lowest 1.5 km, while monthly mean RHw was up to 85 %
(Fig. c). In summer and autumn months high values of monthly
mean RHw occur from the surface to 1.7 km. In winter and spring, the
atmospheric layer near the surface is drier and high values of RHw
appear from 0.3 to 1.5 km.
In order to determine if and in which way the thermodynamic properties were
special for the study period, monthly mean tropospheric temperature anomalies
are presented in Fig. a. These anomalies have been calculated with
respect to the previous 23 years (1993–2015). Figure a shows that,
compared to the long-term mean, temperatures are higher for some particular
summer months. For example, July 2016 and June 2017 were warmer throughout
the whole troposphere with maximum temperature anomalies of up to 2 and
4 ∘C, respectively. Winter months were slightly warmer, too: the
difference in atmospheric temperature was up to 2 ∘C in December 2016
and February 2017. January 2017 was much colder with a temperature difference
of down to -5∘C. In comparison to the previous 23 years, atmospheric
temperatures in March 2017 were higher in the upper troposphere (up to
2 ∘C) and lower (-2∘C) in the lowest 1.5 km. The largest
positive temperature difference was found for autumn 2016, especially for
October 2016 with maximum temperature differences of up to +8∘C.
have already shown that moisture intrusions from the
North Atlantic can cause significant local warming in some regions of the
Arctic that can reach up to 8 ∘C. In addition,
analyzed the variability of the near-surface air temperature (at 925 mb
level) in the Arctic for the period from October 2016 to September 2017. The
authors reported that there was an extreme temperature anomaly exceeding
5 ∘C in the autumn 2016 that is in agreement with our results.
Moreover, the authors showed that this extremely high temperature anomaly was
associated with a persistent and unusual pattern in the geopotential height
field that separated the polar vortex in the central Arctic into two parts.
This situation led to southerly winds that transported warm air into the
Arctic from the midlatitude Pacific and Atlantic oceans .
Anomalies of monthly mean atmospheric temperature (a) and
absolute humidity (b) from radiosonde observations at Ny-Ålesund
from June 2016 to July 2017. Anomalies are calculated with respect to the
monthly mean values of the previous 23 years (1993–2015). The blue line
corresponds to the IWV anomaly for the same time period.
The observed anomalies in the monthly mean absolute humidity and IWV
(Fig. b) in principle follow the sign of the discussed temperature
anomalies. Figure b shows a correlation between the temperature and IWV
increase. For instance, months that have a positive temperature difference
also have an increase in the absolute humidity and IWV. Negative temperature
differences correspond to decreases in the absolute humidity. For example,
January 2017 was particularly colder and drier with anomalies in absolute
humidity and IWV of ∼-0.5 g m-3 and ∼-0.8 kg m-2, respectively.
Higher IWV values in comparison with the previous years were observed in
June 2016, autumn 2016, December 2016, and July 2017. The differences in IWV varied
from 1 to 5 kg m-2 with the largest contributions from the lowest 3 km. In
October 2016, the absolute humidity anomaly was highest (∼2 g m-3)
in the lowest 3 km. This led to a positive change in IWV of more than
5 kg m-2 in comparison with previous years.
Thus, it turns out that the period of our study had specific features
especially for some months. have shown that in general
a significant warming of the atmospheric column at Ny-Ålesund is
observed in January and February. The authors reported that this warming in
winter is related to the higher frequency of large-scale flow from
south-southeast and less from the north. However, in our study January 2017
was much colder in comparison to the previous years. In January 2017, and
also in the other winter months, the wind direction occurred more frequently
from south-southwest (not shown) in comparison with the earlier period
from 1993 to 2014 with wind direction dominating from the southeast. However, it is not
clear yet what exactly caused the relatively cold January 2017.
Frequency of occurrence of profiles with ice, liquid, and any kind of
hydrometeor. The frequency is given in % and normalized to the total number
of Cloudnet profiles for each month.
ResultsHydrometeor occurrence
From June 2016 to July 2017, cloudy profiles occur around 80 % of the time
(Fig. ). The frequency of cloud occurrence is largest in October 2016
and June 2016 (∼92 %) and lowest in April 2017 (68 %). In order to
have a closer look at which types of hydrometeors occur in the atmospheric
column, Fig. also gives separate overviews of the frequency of occurrence
of liquid and ice hydrometeors.
For this statistics we check all the range bins in Cloudnet profiles for
hydrometeor types. If a Cloudnet bin contains cloud droplets, rain, or drizzle
we count it as liquid. If ice particles have been detected in a range bin,
then we define it as ice. Note that Cloudnet does not distinguish between
snow and cloud ice. Mixed-phase range bins are considered as both liquid and
ice. Then profiles that contain at least one “liquid” (“ice”) bin are counted
as liquid (ice) containing. Profiles containing liquid and ice phases are
counted in both classes.
Liquid hydrometeors (dashed black line in Fig. ) have the highest
frequency of occurrence during summer and autumn (70 %–80 %) and the lowest
in winter (∼36 %). A pronounced seasonal variability is thus visible.
Ice (densely dashed black line in Fig. ) occurs more often in autumn,
winter, and early spring with the frequency of occurrence varying from 72 % to
88 %. In summer ice occurs typically around 58 %–78 % of the time. The
frequency of ice occurrence does not show a clear seasonal variability like the liquid phase.
Figure shows vertical distributions of hydrometeors. For these
statistics we used the abovementioned bin classification. The frequency of
occurrence at a certain altitude was normalized to the total number of
Cloudnet profiles in a corresponding month. The highest frequency of
occurrence was 60 % and 70 % in March 2017 and October 2016, respectively
(Fig. a, left). The lowest frequency of occurrence was in
July 2016 (<30 %), while for the other months in summer 2016 the frequency of
occurrence of all hydrometeors was around 60 %. In January 2017 the
occurrence of clouds above 3 km was less than 10 %, which correlates with
low RHw (Fig. c) at these altitudes and the lowest value of
IWV (Fig. b).
The total vertical distribution (Fig. , right panels, solid black
line) shows that hydrometeors occur predominantly in the lowest 2 km with a
maximum frequency of occurrence of ∼53 % at a height of 660 m.
Above 2 km, the frequency of occurrence is less than 30 % and
monotonically decreases with height. In terms of seasons, the vertical
frequency of occurrence of all hydrometeors reveals variations in the maximum
within ±10 % with the highest values of frequency of occurrence in
autumn 2016 of more than 60 % (∼1 km of height). In summer 2016, the
hydrometeor frequency of occurrence is in general higher than in summer 2017,
indicating a pronounced year-to-year variability that will be analyzed in
the future when multiyear datasets become available.
Liquid hydrometeors (Fig. b) occur most of the time in the lowest
2 km. Above 2 km, the frequency of occurrence of liquid is less than 5 %
and above 3 km almost no liquid particles are observed. The frequency of
occurrence of liquid has a maximum at around 0.7–0.9 km of height. Largest
values of liquid-phase occurrence vary from 40 % to 50 % in summer and
autumn 2016. The maximum frequency of occurrence in the winter months does
not exceed 15 %. A strong seasonal variability of liquid, with high values
in summer (32 %) and lowest values in winter (12 %), can be seen.
Monthly, seasonal, and total (for the whole time period) frequency of
occurrence of all hydrometeors (a), liquid (b), and
ice (c) as a function of height for the period from June 2016 to
August 2017. Frequency of occurrence is given in % and normalized to the
total number of Cloudnet profiles for each month.
The vertical occurrence of ice hydrometeors is shown in Fig. c. Ice is
mostly present at altitudes below 2 km. On average the frequency of
occurrence peaks at around 700 m with values of 40 %. In contrast to the
ice occurrence anywhere in a column (Fig. ), which does not show a
strong seasonal variability, the vertical distribution of the ice phase shows a
pronounced seasonal cycle, in particular in the lowest 2 km. For higher
altitudes, the seasonal variability is less pronounced. Above 2 km, the
frequency of occurrence of ice decreases from ∼30 % to less than 10 % at 8 km.
Similar to liquid hydrometeors, the frequency of occurrence of ice is highest
in the lowest 2 km with values of 60 % and 70 % in October 2016 and
March 2017, respectively (Fig. c, left). The lowest ice frequency of
occurrence is found for the summer months. In July 2016, which is the warmest
month during the observation period, the freezing level often reached
altitudes up to 2 km and therefore almost no ice was observed below this
height. In January 2017 ice rarely occurred at heights above 4 km,
which was probably caused by the presence of dry air. On the right side of
Fig. c it can be seen that the highest frequency of occurrence of the ice
phase is in the lowest 2 km and around 52 % in autumn, winter, and spring.
Statistics on different types of clouds
In addition to the occurrence of hydrometeor types, a classification of
clouds into single-layer and multilayer was also made. Single-layer clouds
were furthermore separated into liquid, ice, and mixed phase.
For the classification every Cloudnet profile was checked from the top to the
bottom for cloud layers. A cloud is defined here as a layer of at least three
consecutive cloudy height bins. Based on a number of identified cloud layers
we classified single-layer and multilayer clouds. We considered cases as
multilayer if two or more cloud layers were separated by one or more
clear-sky height bins. Figure gives an overview of the cloud type
occurrence at Ny-Ålesund for the whole period of this study. The
total occurrence for the whole period (rightmost bar) shows 44.8 %
(506 253 profiles) multilayer and 36 % (406 810 profiles)
single-layer clouds. Among single-layer clouds the most frequent type was
mixed phase, followed by ice and liquid single-layer clouds with cloud
occurrence of 20.6 %, 9.0 %, and 6.4 %, respectively. Note that clouds were
considered mixed phase if ice and liquid phases were both present in the
same cloud boundaries regardless of whether liquid and ice were in the same
range bin or not. This implies that mixed-phase clouds include not only cases
with liquid cloud top and ice below, but also cases when both phases (ice and
liquid) are present anywhere within the detected cloud layer.
Monthly frequency of occurrence of different types of single-layer
clouds (liquid, ice, and mixed phase), multilayer clouds, and clear-sky
profiles for the period from June 2016 to July 2017. The last right column
shows the total frequency of occurrence.
Figure also shows the monthly occurrence of different cloud types.
The monthly cloud occurrence, i.e., the sum of all different cloud types,
corresponds to the frequency of occurrence of all hydrometeors shown by a
solid black line in Fig. . As seen for liquid and ice hydrometeors
(Fig. ), the occurrence of single-layer liquid and ice clouds also
has a seasonal and monthly variability. About 15 % of single-layer liquid
clouds were detected in summer but less than 2 % in other seasons. The
occurrence of single-layer ice clouds was 15 %–20 % in winter and spring and
less than 5 % in other months. Single-layer mixed-phase clouds and
multilayer clouds were present most of the time with typical values of
frequency of occurrence of around 20 % and 45 %, respectively. Thus,
most of the time cloud systems had a complicated structure and/or
consisted of both phases, liquid and ice, indicating that they are related to
complex microphysical processes. In turn, the observational capabilities of
these types of clouds are limited. In situations with multiple liquid layers,
whether warm or mixed phase, partitioning the observed LWP from HATPRO among
these different layers is particularly challenging and results in larger
uncertainties . A multilayer cloud classification requires
a reliable profiling of liquid layers, which is limited by significant
attenuation of lidar signals in the first liquid layer. Radar signals have
better propagation through the whole vertical cloud structure in comparison
with a lidar. However, the radar reflectivity is often dominated by
scattering from relatively large particles, which masks the presence of small
particles, like liquid droplets, present in the same volume. In the
case of multilayer mixed-phase clouds, the liquid phase thus cannot be reliably
detected based on radar reflectivity alone.
Single-layer clouds and their relation to thermodynamic conditions
Taking into account the abovementioned limitations of multilayer cloud
observations, our further analysis is concentrated only on single-layer cases.
For the following analysis of single-layer clouds we also used LWP from
HATPRO and the Cloudnet IWC product. We excluded cases for which this information
was not available. In particular, profiles with the presence of liquid
precipitation and flagged data due to wet HATPRO radome were excluded. The
resulting dataset (Fig. , lines with circles, stars, and diamonds) was
thus reduced to 149 960 profiles (37 % of all single-layer profiles) with
65 299 profiles (16 %) for single-layer mixed-phase clouds,
59 364 profiles (15 %) for single-layer ice clouds, and 25 297 profiles (6 %)
for single-layer liquid clouds only. Thus, all results are relevant for
single-layer clouds without liquid precipitation. Nevertheless, with this
subset of single-layer clouds we can still capture the monthly variability
and thus assume that it is still representative for all single-layer cloud cases.
Frequency of occurrence of ice-only, liquid-only, and mixed-phase
single-layer clouds based on Cloudnet categorization data (for lines with
circles, diamond and star profiles with liquid precipitation are not
included). The frequency is given in % and normalized to the total number of
Cloudnet profiles in each month.
A comparison of Figs. and shows that the occurrence
variability of liquid and ice single-layer clouds is similar. The occurrence of
mixed-phase clouds differs because of the exclusion of liquid precipitation
clouds, which often contain an ice-phase and melting layer, and are thus considered
mixed phase. This is in agreement with , who reported
that most liquid precipitation is formed including the ice phase. The
maximum and minimum occurrences of single-layer mixed-phase clouds (25 %
and 4 %) were observed in May 2017 and June 2016, respectively. The
annual-averaged top height of single-layer mixed-phase clouds was 2 km (not
shown). Our findings are in good agreement with spaceborne radar–lidar
observations of clouds in the Svalbard region in the period from 2007 to 2010
. It has been shown that single-layer mixed-phase clouds
in the Svalbard region mostly occur in May.
Frequency of occurrence of cloud thickness for single-layer
clouds (a), LWP for single-layer liquid and mixed-phase
clouds (b), and IWP for single-layer ice and mixed-phase
clouds (c) for the period from June 2016 to July 2017. The y axis
is shown in logarithmic scale. In the x axes Δ shows the bin width.
The frequency of occurrence is normalized by the total number of
corresponding cloud types.
The geometrical thickness of single-layer clouds is shown in
Fig. a. The geometrical thickness of a cloud is calculated as the
distance between the upper border of the uppermost cloud range bin and the
lower border of the lowermost cloud range bin. The thickness of single-layer
liquid clouds varies between 60 and 2200 m with mean and median values of
280 and 240 m, respectively. Less than 1 % of observed single-layer liquid
clouds have a thickness larger than 800 m. In contrast, single-layer
mixed-phase clouds typically have a larger geometrical cloud thickness, which
varies from 100 to 8500 m with median and mean values of 1100 and 1500 m,
respectively. In comparison with mixed-phase single-layer clouds, the
geometrical cloud thickness distribution for single-layer ice clouds is
broader, ranging from 60 to 9500 m. The median and mean values of the
geometrical cloud thickness for single-layer ice clouds are 1500 and 2100 m,
respectively. The mode of the thickness distribution of single-layer ice
clouds corresponds to 800 m. Less than 1 % of single-layer mixed-phase and
ice clouds have a geometrical cloud thickness larger than 3 and 4.2 km, respectively.
The frequency of LWP occurrence for liquid and mixed-phase clouds is shown in
Fig. b. Both types of clouds are characterized by relatively low
values of LWP. The median values of LWP for single-layer liquid and
mixed-phase clouds are 17 and 37 g m-2, and mean values are 30 and
66 g m-2, respectively. More than 90 % of single-layer liquid and
mixed-phase clouds have LWP values lower than 100 and 200 g m-2,
respectively. It has to be noted that particularly in these LWP ranges, the
relative uncertainty in the retrieved LWP is quite large (see
Sects. and ). Larger LWP values in
mixed-phase clouds might be related to their larger geometrical thickness (Fig. a).
Median values of IWP for single-layer ice and mixed-phase clouds are 14.6 and
21.4 g m-2, and mean values are 273 and 164 g m-2, respectively
(Fig. c). IWP values exceeding 400 g m-2 are more frequent in
single-layer ice clouds than in single-layer mixed-phase clouds. However, for
both cloud types the occurrence of IWP values higher than 125 g m-2 is
less than 3 %.
A number of studies comparing observed and modeled LWP and IWP values for
Arctic regions have revealed the challenge for NWP models to accurately
simulate LWP and IWP. evaluated six regional models
that were set to a common domain over the western Arctic and found that one-half
of the models showed nearly 0 bias in LWP, while the other half
underestimated LWP by ∼20 g m-2. The authors reported that some of
the models showed -30 to 30 g m-2 biases in IWP. In addition, a low
correlation between the observations and modeled IWP and LWP was found. Most
of the models showed too-low variability of IWP.
compared nine global climate models in the Arctic region. The authors showed
that means and standard deviations of modeled IWP and LWP can vary by a factor
of 2. concluded that such discrepancies may be related to
an insufficient representation of microphysical processes. The authors note that
some of the major challenges are phase partitioning and a parameterization of
cloud particle formation and development.
The relative probabilities of different ranges of the liquid
fraction LWP / TWP given at various cloud-top temperatures of single-layer
mixed-phase clouds. The probability is normalized by the total number of
profiles for each cloud-top temperature range. Numbers at the top of plot
show the number of cases included in the temperature range. The total number
of profiles is 3824.
compared 26 models with airborne and ground-based
observations over north Alaska (Barrow and Oliktok Point). The authors
found that although many models showed an LWP exceeding IWP (as observed),
simulated LWP values were significantly underestimated. Since climate and NWP
models typically parameterize cloud phase as a function of temperature, we
thus analyzed relations between temperature and phase partitioning for
mixed-phase clouds at Ny-Ålesund. Figure shows the
probability of liquid fraction, i.e., (LWP / (LWP + IWP)), in mixed-phase clouds
for different cloud-top temperature ranges based on the Ny-Ålesund
radiosonde observations. In general, the liquid fraction increases with cloud-top temperature. Thus, high liquid fraction values in single-layer
mixed-phase clouds are found at cloud-top temperatures ranging from
-15 to 0 ∘C. The occurrence of the liquid fraction of 0.4–0.6,
implying that both phases are roughly equally present, is relatively
high for cloud temperature ranges between -25 and -15∘C
but is rare for cloud-top temperatures below -25∘C. Almost no liquid
was observed at cloud-top temperatures below -40∘C. A nonzero
liquid fraction below -40∘C is mostly associated with thick clouds
having high cloud tops with liquid layers detected at lower altitudes.
In-cloud atmospheric temperature and humidity are important for NWP models as
these parameters determine cloud particle formation and development.
For instance, laboratory studies show that the shapes of ice crystals are defined
by the environment temperature and humidity .
There is also some evidence that similar effects happen in the real
atmosphere . Aggregation efficiency
and deposition growth rate are also temperature and humidity dependent
. Therefore, in this study we also
relate different cloud types to the environmental conditions under which they
occur. The frequency of occurrence of the different hydrometeors in
single-layer clouds as a function of in-cloud temperature and relative
humidity observed at Ny-Ålesund is shown in Fig. a–d.
Here, temperature and relative humidity were determined for each cloud bin
between cloud boundaries. For this analysis we only used single-layer cloud
profiles observed 1 h before and after a radiosonde launch. We assumed
that the atmospheric conditions did not change too much within this time
period. For temperatures lower than 0 ∘C the relative humidity with
respect to ice (RHi) was used. Values of RHw were used at temperatures
exceeding 0 ∘C. For the cloud classification we used the method
specified in Sect. .
Two-dimensional histograms of in-cloud atmospheric temperature and
relative humidity for all clouds (a, e), ice clouds (b, f),
and mixed-phase clouds (c, g). For (a, c, e, g) ice and/or
liquid phases are present. For (b) and (f) only the ice phase
is present. The liquid phase of liquid-containing clouds is shown in (d)
and (h). Only cases of single-layer clouds are included and shown
for observations (a–d) and for column output of the NWP model ICON over
Ny-Ålesund (e–h). Frequency of occurrence is normalized by the
total number of bins of the correspondent single-layer clouds detected
between the period of 1 h before and after radiosonde
launch.
All single-layer clouds were observed in the temperature range from
-60 to +10∘C (Fig. a). In some cases single-layer
clouds appeared at low RHi and RHw (Fig. a–d) that might be
associated with hydrometeors falling from saturated to subsaturated
atmospheric layers. Another reason could be that the radiosondes, which
drift, do not provide representative information for the sampling volume of
the zenith-pointing ground-based instruments. However, cases with very low
relative humidity values occurred in less than 1 % of the analyzed
observations. According to the uncertainties in
temperature due to radiosonde drifts in the Northern Hemisphere do not
exceed 0.4 ∘C up to 10 km of altitude. Uncertainties in relative
humidity are about 3 %.
Figure b shows that ice clouds mostly occur in the temperature range
from -45 to -5∘C, including the temperature range (<-38∘C)
of homogeneous nucleation. The highest occurrence of ice was
observed in the temperature range from -25 to -20∘C and
under conditions that are subsaturated with respect to water but saturated
with respect to ice (Fig. b). Observed ice particles mostly occur at
RHis between 100 % and 125 %. The presence of ice at positive temperatures might
be related to cases of cloud type misclassification, for example when a
cloud was identified as ice instead of mixed phase. These cases might also be
associated with uncertainties in the model temperature profile used in the
classification algorithm.
Mixed-phase clouds were observed at supersaturation with respect to ice
(Fig. c). Most of the cases were located at the water saturation
line. Frequently, the mixed phase occurs at temperatures from -25 to
+5∘C with two maxima in the range of -15 to 0 ∘C.
The temperatures -15 and -5∘C correspond to the highest
efficiency of the deposition growth of ice crystals at water saturation levels .
The liquid phase mostly occurs near water saturation at temperatures from
-15 to +5∘C (Fig. d). Supercooled liquid was
observed at temperatures down to -40∘C. The lowest temperature limit
for liquid clouds only was -30∘C (not shown).
Application for model evaluation
This observational cloud dataset can provide useful information for a model
evaluation. As an example, this section presents a comparison of the NWP
model ICON with the observations at Ny-Ålesund. Note that the
intention here is not to perform a thorough model evaluation but to show the
potential of such a dataset to test, for example, if the dependence of the
occurrence of clouds on the thermodynamic conditions can be represented by the model.
The statistics on different types of clouds, their phases, and the relation to
atmospheric conditions provide a useful dataset for comparison with similar
statistics based on the model output.
Based on a 10-7 kg kg-1 threshold in specific cloud water content,
specific cloud ice content, rain mixing ratio, and snow mixing ratio, we
identify clouds in the model. We classify the clouds using the same procedure
as for the observations (see Sect. ). The value of the
threshold in the hydrometeor contents was found empirically: the usage of a
lower threshold leads to the higher occurrence of ice clouds in the ICON
model that were not identified in observations. For a higher threshold fewer
ice clouds were present in the ICON model than in observations. According to
the Z–IWC–T relation from , the chosen threshold in the ice
mixing ratio corresponds to the radar reflectivity factor ranging from -55 to
-32 dBZ at temperatures from -60 to -5∘C. In general,
these values are close to the radar sensitivity, although at high altitudes
the radar sensitivity is about -40 dBZ . Nevertheless,
most of the observed hydrometeors are located within 2 km of the surface
(see Sect. ), and therefore the lack of sensitivity at high
altitudes does not significantly affect the results. For a more detailed
analysis of the uncertainties due to differences between the instrument and
the model, sensitivity tests can be done using observation simulators
e.g.,. Such an analysis is out of the scope of the current study.
The right panels in Fig. show the frequency of occurrence of different
hydrometeors in single-layer clouds as a function of in-cloud temperature and
RHw based on the ICON model data. Figure e shows that modeled
single-layer clouds occur within the temperature range similar to the
temperatures observed in clouds at Ny-Ålesund (Fig. a).
Figure f indicates that ice clouds in the ICON model typically exist
at temperatures from -65 to -5∘C. Both the ICON model and
observations reveal that ice particles typically occur at relative
humidities higher than the saturation over ice but lower than the saturation
over water. High occurrence of the ice phase in ICON is found at RHi up to
110 %, while the observations reveal RHi of up to 125 %
(Fig. b). The presence of ice particles at lower supersaturation
over ice in the ICON model in comparison with observations may be associated
with ice nuclei (IN) parameterization in the ICON model, which is known to still be
a challenge . We speculate that a higher concentration of
IN and thus ice particles leads to faster deposition of water vapor onto
the ice particle surface. Therefore, a more efficient vapor-to-ice
transition in the model could lead to lower relative humidity. Similarly, the
parameterization of deposition growth rate and secondary ice processes may
also have an impact on the in-cloud relative humidity.
Mixed-phase clouds in the ICON model appear near the water saturation
(Fig. g) that is consistent with the observations (Fig. c).
The model mostly produces mixed-phase clouds within the temperature range
from -10 to +5∘C, which is narrower in comparison to the observations.
Modeled liquid phase occurs near water saturation at temperatures from
-15 to +5∘C (Fig. h), which is in good agreement
with observations. In the ICON model the occurrence of the liquid phase at
temperatures below -5∘C is only 6 %, while in the observations
this occurrence is more than 30 %.
Distribution of in-cloud atmospheric temperature for different types
of single-layer clouds, liquid, and ice phase for observations (a)
and the NWP ICON model over Ny-Ålesund (b).
Figure summarizes the temperature dependencies of hydrometeor
occurrences in the ICON model and in observations. The temperature
distributions of single-layer liquid clouds (solid red lines, Fig. )
are narrow (-10 to +5∘C) for both the model and observations,
although the observed distribution has larger values of occurrence. The
total distributions of the liquid phase (dashed red lines, Fig. )
are different. The observed distribution is larger and occupies a wider
temperature range (-25 to +10∘C). In the model, most of
the liquid phase is concentrated in the temperature range from -10 to
+5∘C. This difference leads to a divergence between mixed-phase
cloud occurrences (solid green lines, Fig. ): the observed frequency
distribution for mixed-phase clouds shows a broader temperature range than
the model. showed a similar difference between observed
and modeled single-layer mixed-phase clouds. For the modeling, the polar
version of the nonhydrostatic mesoscale model from the National Center for
Atmospheric Research was used. The authors found that for temperatures below
-18∘ the liquid fraction in single-layer mixed clouds was completely
absent in simulations.
Ice cloud observations (solid blue line in Fig. a) show a broad temperature range
from -60 to +5∘C. In comparison to the observations, the
model (solid blue line in Fig. b) shows a broader temperature range for
single-layer ice clouds (-70 to +5∘C). Due to the low
occurrence of the liquid phase at temperatures below -5∘C in the
model, most of the clouds at lower temperatures are classified as pure ice.
Therefore, the model shows a significantly higher occurrence of ice clouds at
temperatures warmer than -20∘C. Also, this explains similarities
between modeled ice phase in pure ice and ice-containing clouds (dashed blue
line). In addition, the occurrence of simulated ice clouds is higher at
temperatures below -40∘C, which corresponds to the homogeneous ice
nucleation regime.
Summary and discussion of results
This study provided, for the first time, a statistical analysis of clouds at
Ny-Ålesund, Svalbard, and their relation to the thermodynamic
conditions under which they occur. We analyzed an almost 14-month-long
measurement period at Ny-Ålesund and presented statistics on
vertically resolved cloud properties, hydrometeors, and thermodynamic
conditions. The Cloudnet classification scheme, based on observations from a
set of ground-based remote sensing instruments (active and passive), was
applied in order to provide vertical profiles of clouds, their macrophysical
and microphysical properties, and their phase. In total, 1 130 030 Cloudnet profiles are
available for the period from June 2016 to July 2017.
The statistics on cloud properties and atmospheric thermodynamic conditions
are essential for a better understanding of cloud processes and can also be
used for model evaluation. In this study, the relation between cloud
properties and thermodynamic conditions from observations was compared to
results from the NWP ICON model.
The thermodynamic conditions were derived from radiosonde data for the period
from June 2016 to July 2017 and were compared with the previous 23 years. This
comparison revealed that the period of our study differs from the previous
years. January 2017 was significantly colder with temperature differences
down to -5∘C, while October 2016 was extremely warm with temperature
anomalies of more than +5∘C. Also, in comparison to the previous
23 years, IWV was lower in January 2017 by 1 kg m-2 and more than
5 kg m-2 higher in October 2016.
The main findings and related discussions are listed below.
The total occurrence of clouds is ∼81 %. The highest frequency of
occurrence is in October 2016 (92 %). Similar results of high cloud
occurrence in summer and autumn at Ny-Ålesund based on micro-pulse
lidar measurements were previously found by . Nevertheless,
the observed total occurrence of clouds at Ny-Ålesund for the
investigated period is higher than the one from . The
authors showed that the total annual cloud fraction at Ny-Ålesund
for the period from March 2002 to May 2009 was 61 %. On the one hand, we
analyzed a different time period. On the other hand, the occurrence of clouds
in might be underestimated when only a lidar is used
. However, our results are in good agreement with a
previous study by . The authors used spaceborne
observations over the Svalbard region for the period from 2007 to 2010. They
applied the DARDAR algorithm that utilizes
measurements from CALIPSO and CLOUDSAT. They showed that cloud occurrence
over the Svalbard region was in the range from 70 % to 90 % with
peaks in spring and autumn. found the lowest cloud
occurrence in July, while the statistics in the present study reveal high
cloud occurrence (∼80 %) in this month. Also here, this difference
might be related to the different periods investigated. Another reason might
be that the observed clouds in July are predominantly located at heights
below 1.5 km. These low-level clouds are difficult to capture by CloudSat due
to its “blind zone” in the lowest 1.2 km .
, for example, showed that the Ny-Ålesund
ground-based measurements revealed the highest cloud occurrence in summer
(between 60 % and 80 %), while satellite observations showed the
minimum in that season. The lowest cloud occurrence in the study by
is around 50 % in March. In our study, the lowest cloud
occurrence (∼65 %) was also observed in spring. This might be
associated with a relatively low atmospheric temperature and less moisture
being available in the atmosphere. The increase in cloudiness in summer and
autumn is probably due to higher values of relative humidity at the site in
comparison with other seasons. Also, sea ice coverage might impact the cloud
occurrence. As during summer and autumn sea ice coverage is the lowest, areas
of open water are larger and can therefore lead to enhanced evaporation
and latent heat exchange with the Arctic atmosphere.
We found that multilayer and single-layer clouds occur 44.8 % and 36 % of the time,
respectively. The most common type of single-layer clouds is mixed phase with
a frequency of occurrence of 20.6 %. The total occurrences of single-layer
ice and liquid clouds are 9 % and 6.4 %, respectively. The cloud
occurrence of single-layer liquid and ice clouds has a pronounced
month-to-month and seasonal variability.
The analysis of cloud phase shows that liquid is mostly present in the lowest
2 km with the highest occurrence in summer and autumn (especially in
October 2016) and the lowest in winter. However, in winter the occurrence of liquid
hydrometeors is still significant and reaches 12 % at a height of 1 km.
The occurrence of the ice phase within the first 2 km is lowest in summer
(22 %) and highest in October 2016 and March 2017 with 60 % and
70 %, respectively. The largest frequency of occurrence of ice and liquid
in October 2016 (>50 %) is related to strong temperature and humidity
anomalies in this month. According to , the anomalies
were associated with warm air transported into the Arctic from midlatitudes
and
the Pacific and Atlantic oceans.
We analyzed 149 960 Cloudnet profiles with single-layer clouds only.
Single-layer liquid and mixed-phase clouds typically have very low values of
LWP with median values of 17 and 37 g m-2 and mean values of 30 and
66 g m-2, respectively. It has to be noted that these low values of LWP
may significantly affect shortwave and longwave radiation .
These clouds with LWP values between 30 and 60 gm-2 have the largest
radiative contribution to the surface energy budget . The
LWP of single-layer mixed-phase clouds is larger than for single-layer liquid
clouds. This result is in agreement with a study by . The
authors reported that the LWP for mixed-phase single-layer clouds is larger
than for pure liquid clouds due to thicker liquid layers in mixed-phase clouds.
showed that in Barrow the occurrence of single-layer
mixed-phase clouds is lower than that of single-layer liquid-only cloud at
LWP values exceeding 120 g m-2. At LWP values below 120 g m-2
liquid-only clouds become dominant over mixed-phase clouds. We found a
similar behavior at Ny-Ålesund but with the transition at 50 g m-2.
The IWP statistics show that in general single-layer ice clouds contain more
ice than single-layer mixed-phase clouds with corresponding mean values of
273 and 164 g m-2, respectively. The median values of IWP for
single-layer ice and mixed-phase clouds are 14.6 and 21.4 g m-2,
respectively. This difference might be related to the cloud geometrical
thickness. On average single-layer ice clouds are thicker than mixed-phase
clouds. Single-layer mixed-phase clouds have a higher occurrence than ice
clouds for IWP values ranging from 25 to 400 g m-2. For IWP values
exceeding 400 g m-2 ice clouds were more frequent than mixed phase.
Since phase partitioning in NWP models depends on atmospheric conditions, we
analyzed relations between cloud-top temperature and liquid fraction for
mixed-phase clouds. It was found that liquid is present at temperatures down
to -40∘C. The highest occurrence of the liquid phase is at cloud-top
temperatures ranging from -15 to 0 ∘C.
We analyzed the occurrence of different cloud types at
Ny-Ålesund as a function of environment conditions. In addition to
observations we also used the ICON model output for these analyses. We found
that the temperature distribution of single-layer liquid clouds is narrow
with temperatures typically ranging from -10 to +5∘C.
Similar results are also found for the ICON model. However, the distribution
of the liquid phase for mixed-phase clouds is one of the major differences
between the model and observations. The observed distribution ranges from
-25 to +10∘C, while in the ICON model the liquid phase is
concentrated in the temperature range from -10 to +5∘C.
This difference results in a significant divergence between observed and
modeled single-layer ice and mixed-phase clouds. The observed single-layer
mixed-phase clouds occur in a much wider temperature range (from
-25 to +5∘C) than in the ICON model (from -15 to
+0∘C). Such differences have been previously reported by
. The authors showed that models can completely miss
single-layer mixed-phase clouds below -18∘C. Observed ice clouds
occur at temperatures from -60 to +5∘C, while the model
simulates ice clouds down to -70∘C. The occurrence of modeled ice
clouds is significantly larger than observed at temperatures warmer than
-20∘C. Due to the lower occurrence of the liquid phase in the model at
temperatures below -5∘C, modeled clouds are often classified as pure
ice. Also, the model shows a higher occurrence of ice clouds at temperatures
below -40∘C where homogeneous ice nucleation takes place.
Outlook
In order to have more robust statistics and also to account for year-to-year
variability, long-term observations at Ny-Ålesund are needed.
Therefore, measurements of cloud and thermodynamic profiles are still
ongoing at Ny-Ålesund within the (AC)3 project. The aim of
this study is to present the results from the first year of observations and
to show their potential to provide vertically resolved cloud information.
The statistics on LWP and IWP for single-layer clouds, provided in this
study, show that most of the time single-layer clouds at Ny-Ålesund have
very low LWP, which is within the uncertainty range (<30 g m-2). In
the future, retrievals of LWP can be improved by using the infrared and
higher frequencies of the MWR . Information from
the 89 GHz passive channel of the FMCW radar and 183, 233,
and 340 GHz frequencies of LHUMPRO (low humidity profiler) of the University
of Cologne, which are currently measuring at Ny-Ålesund, can be
used to reduce the uncertainty of LWP.
The next step will be to derive cloud microphysical properties such as LWC,
IWC, and effective radius for different types of clouds using methods by
, , and . This information is
essential for cloud–radiation interaction studies. Therefore, the derived
profiles of single-layer clouds and their microphysical properties will be
used in combination with a radiative transfer model to calculate the cloud
radiative forcing at Ny-Ålesund. In addition, to show the
representativeness of derived cloud properties at Ny-Ålesund among
other Arctic sites with similar ground-based instrumentation, our results
will be compared with other locations in the Arctic. In order to make such a
comparison consistent, similar methods have to be used to derive cloud
microphysical properties and the same time period has to be analyzed.
Information on cloud microphysical properties can be used to test the
representation of clouds and their dependency on temperature and humidity in
models and therefore for an evaluation of high-resolution models.
Data availability
The radiosonde data were taken from the information system
PANGAEA: https://doi.org/10.1594/PANGAEA.879767,
https://doi.org/10.1594/PANGAEA.879820,
https://doi.org/10.1594/PANGAEA.879822, and
https://doi.org/10.1594/PANGAEA.879823. The Cloudnet
data are available at the Cloudnet website (http://devcloudnet.fmi.fi/, ).
Author contributions
TN applied the statistical algorithm, performed the
analysis, and
prepared and wrote the paper. KE, UL, and MM contributed with research
supervision, discussions of the results, and paper review. UL helped to
apply retrievals for HATPRO. MM provided the long-term radiosonde dataset.
CR provided instrumentation data for this study. EO applied the Cloudnet
algorithm for Ny-Ålesund.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Arctic mixed-phase
clouds as studied during the ACLOUD/PASCAL campaigns in the framework of (AC)3
(ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We gratefully acknowledge funding from the Deutsche Forschungsgemeinschaft
(DFG, German Research Foundation; project number 268020496, TRR 172)
within the Transregional Collaborative Research Center – ArctiC
Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and
Feedback Mechanisms (AC)3 – in subproject E02. We acknowledge the staff
of the AWIPEV research base in Ny-Ålesund for helping us in
operating the cloud radar, launching radiosondes, and providing the MWR and
ceilometer data. We gratefully acknowledge the DWD service for providing the data
of the global NWP ICON model for Ny-Ålesund. We also thank Patric Seifert
for providing GDAS1 data for Ny-Ålesund and helpful discussions. We thank
two reviewers and the coeditor for the constructive suggestions that improved
the paper.
Edited by: Jost Heintzenberg
Reviewed by: two anonymous referees
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