Specific humidity inversions (SHIs) above low-level cloud
layers have been frequently observed in the Arctic. The formation of these
SHIs is usually associated with large-scale advection of humid air
masses. However, the potential coupling of SHIs with cloud layers by turbulent
processes is not fully understood. In this study, we analyze a 3 d
period of a persistent layer of increased specific humidity above a
stratocumulus cloud observed during an Arctic field campaign in June 2017. The
tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG
the lower Atmosphere) recorded vertical profile data of meteorological,
turbulence, and radiation parameters in the atmospheric boundary layer. An
in-depth discussion of the problems associated with humidity measurements in
cloudy environments leads to the conclusion that the observed SHIs do not
result from measurement artifacts. We analyze two different scenarios for the
SHI in relation to the cloud top capped by a temperature inversion: (i) the
SHI coincides with the cloud top, and (ii) the SHI is vertically separated
from the lowered cloud top. In the first case, the SHI and the cloud layer are
coupled by turbulence that extends over the cloud top and connects the two
layers by turbulent mixing. Several profiles reveal downward virtual sensible
and latent heat fluxes at the cloud top, indicating entrainment of humid air
supplied by the SHI into the cloud layer. For the second case, a downward
moisture transport at the base of the SHI and an upward moisture flux at the cloud
top is observed. Therefore, the area between the cloud top and SHI is supplied
with moisture from both sides. Finally, large-eddy simulations (LESs)
complement the observations by modeling a case of the first scenario. The
simulations reproduce the observed downward turbulent fluxes of heat and
moisture at the cloud top. The LES realizations suggest that in the presence
of a SHI, the cloud layer remains thicker and the temperature inversion height
is elevated.
Introduction
The Arctic atmospheric boundary layer (ABL) exhibits numerous particular
features compared to lower latitudes, such as persistent mixed-phase clouds,
multiple cloud layers decoupled from the surface, and ubiquitous temperature
inversions close to the surface. Local ABL and cloud processes are complex and
not completely understood, but they are considered an important component to
explain the rapid warming of the Arctic region . One of
the special features frequently observed in the Arctic are specific humidity
inversions (SHIs), although specific humidity is generally expected to
decrease with height . The relative frequency of
occurrence of low-level SHIs in summer is estimated to be in the range of
70 %–90 % over the Arctic ocean .
Arctic SHIs have been observed during past field campaigns , e.g., the Surface Heat Budget of the Arctic Ocean SHEBA;
in 1997–1998, or the Arctic Summer Cloud Ocean Study
ASCOS; in 2008. Furthermore, a number of studies
on the climatology of SHIs have been published e.g., . SHIs occur most frequently over the Arctic ocean
and are strongest in summer. In the lower troposphere, they often occur in
conjunction with temperature inversions and high relative humidity but are
also linked to the surface energy budget . Formation
processes and interactions of SHIs with clouds have been investigated in large-eddy simulations (LESs). For example, showed that a
specific humidity layer becomes important as a moisture source for the cloud
when moisture supply from the surface is limited. studied
how the SHIs support the mixed-phase clouds to extend into the temperature and
humidity inversion.
Mostly, the formation of the summertime SHIs is attributed to large-scale
advection of humid air masses. In the Arctic, especially over sea ice,
moisture advection is the critical factor for cloud formation and development
. SHIs form when warm, moist air is advected over the
cold sea ice surface and moisture is removed through condensation and
precipitation from the lowest ABL part. This and further simplified formation
processes are discussed by .
SHIs can contribute to the longevity of Arctic mixed-phase clouds
, which dominate the near-surface radiation
heat budget in the Arctic . When a SHI is located closely
above an Arctic stratocumulus, it can provide moisture that may drive the
cloud evolution due to cloud top entrainment. In contrast, in the typical
marine sub-tropical or mid-latitude cloud-topped ABL, dry air from above is
entrained into the cloud . However, SHIs are not well represented in global atmospheric
models, where the SHI strength is typically underestimated ,
or the SHIs are not reproduced .
Previous studies on SHIs have been based on radiosoundings, remote sensing
observations, reanalysis data, or LESs. Most observational studies rely on
profiles of mean thermodynamic parameters from radiosoundings, which might be
influenced by sensor wetting after cloud penetration in the SHI region. A
systematic bias in radiosonde humidity measurements due to sensor wetting or
other error sources is a serious concern when studying SHIs, particularly
under moist and cold conditions. To exclude systematic biases, one aim of this
work is to carefully assess the validity of the SHI observations. Due to the
limited time resolution of radiosondes, those measurements do not allow for
turbulence observations to analyze the exchange processes between the SHI and
cloud top. To date, very few data are available to characterize and
quantify the turbulent and radiative energy fluxes at SHIs. However, in
particular the vertical turbulent exchange of mass and energy is necessary to
understand the importance of SHIs for cloud evolution and lifetime.
To investigate the exchange processes between the cloud layer and the SHI, we
performed tethered balloon-borne high-resolution vertical profile measurements
of turbulence and radiation during a 3 d period in the framework of the
campaign Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and
Aerosol (PASCAL) . The observations are supplemented by
LES for the same period. We focus on a detailed case study with a persistent
SHI above a stratocumulus deck to answer the following research question: how are the
SHI and the cloud top connected by turbulent mixing?
The paper is structured as follows: Sect. describes the
observations. In Sect. , we discuss humidity measurements in
cloudy and cold conditions and potential error sources. For the case study,
Sect. analyzes the vertical ABL structure around the SHI
and the relation of SHI, cloud top, and temperature inversion. In
Sect. , we investigate the turbulent coupling between SHI
and the cloud layer, and the turbulent transport of heat and moisture. We
close with a discussion of the impact of the SHI on the cloud by means of
LES in Sect. .
ObservationsThe PASCAL expedition
The observations analyzed in this study were performed during PASCAL
, which took place in the sea-ice-covered area north of
Svalbard in summer 2017. The RV Polarstern carried
a suite of remote sensing and in situ instrumentation. Additionally, an ice
floe camp was erected in the vicinity of the ship
. describe the synoptic situation during
the operation of the ice floe camp as climatologically warm with prevailing
warm and moist maritime air masses advected from the south and east. The
meteorological conditions were influenced by a high-pressure ridge east of
Svalbard. The present study is based on measurements with instruments carried
by the tethered balloon system BELUGA Balloon-bornE moduLar Utility for
profilinG the lower Atmosphere; . BELUGA was launched from the
sea ice floe at around 82∘ N, 10∘ E in the period of
5–14 June 2017. The balloon measurements are complemented by radiosoundings
launched every 6 h and by ship-based remote
sensing observations from a vertical-pointing, motion-stabilized cloud radar
, a lidar , and a
microwave radiometer of the OCEANET platform , which were
processed with the synergistic instrument algorithm Cloudnet
.
Temporal development of the specific humidity vertical profile observed by radiosondes. The radar-retrieved cloud top height is depicted as a black line; the cloud base height derived from the lidar near-field channel is indicated as a grey line. The red lines represent the BELUGA flight profiles.
Observation period
The observational basis for this study is a persistent layer of increased
specific humidity above a single-layer stratocumulus deck during the period
between 5 and 7 June 2017. Figure illustrates the
temporal development of the vertical specific humidity profile derived from
radiosonde measurements. Cloud top and bottom and the time–height curves of
the corresponding BELUGA flights are added for the investigated period. The
BELUGA flights were conducted around noon on each of the three consecutive
days. A local maximum of specific humidity is observed above the cloud top
throughout almost the entire period, with a slight diurnal cycle peaking at
noon and a maximum specific humidity on 6 June. It is worth noting that the
observations show a well-defined layer of increased specific humidity,
hereafter referred to as the humidity layer, rather than a distinct and sharp SHI
with only a slight decrease above.
The cloud top and base height in Fig. are estimated from
the cloud radar and lidar (near-field channel) data, averaged over 30 s
and with a vertical resolution of 30 m. Throughout the 3 d
period, cloud height and thickness decrease to a minimum at noon of 6 June
and thereafter increase again. The cloud is almost permanently of mixed-phase
type with a maximum liquid water content (LWC) between 0.15 and
0.6 gm-3 and an estimated ice water content (IWC) of about
0.03 gm-3 derived from Cloudnet data (not shown here).
BELUGA flight profiles for 5, 6, and 7 June (red lines) with the radar reflectivity Z and cloud boundaries (black lines, as in Fig. ).
Figure depicts the high variability in cloud top and
bottom heights. To illustrate the cloud situation around the BELUGA flights in
more detail, Fig. shows the radar reflectivity and
cloud boundaries for the particular three balloon flights. On 5 June, the
cloud top height is approximately constant, whereas on 6 June the cloud top
fluctuates between 350 and 230 m in the course of the flight. During
the 7 June flight, the cloud layer thins by 110 m starting from the cloud
top.
BELUGA setup
The BELUGA system consists of a 90 m3 helium-filled tethered balloon
with a modular setup of different instrument packages to explore the ABL
between the surface and 1500 m altitude. BELUGA can operate under
cloudy and light icing conditions in the Arctic. Fixed to the balloon tether,
a fast (50 Hz resolution) ultrasonic anemometer, supported by an
inertial navigation system, measures the wind velocity vector in an
Earth-fixed coordinate system together with the sonic temperature. Especially
at low specific humidity, the sonic temperature is close to the virtual
temperature, which will be used in the following. Furthermore, barometric
pressure, relative humidity, and the static air temperature are measured with
lower resolution (1 Hz). Relative humidity (RH) is measured with a
capacitive humidity sensor. The housing of the RH sensor, which has a high
diffusivity for water vapor, also accommodates a temperature sensor for the
sensor-internal temperature. The air temperature is measured with a PT100 for
reference and a thermocouple for temperature fluctuations (at
50 Hz). A second instrument payload is fixed simultaneously to the
tether, measuring broadband terrestrial and solar net irradiances. Technical
details on BELUGA, its instrumentation, and operation during PASCAL as well
as data processing methods are given by .
Specific humidity measurements in a moist environment
A cold and moist environment poses considerable challenges for the measurement
of specific humidity. This can lead to measurement artifacts in the region of
the SHI. Therefore, in this section we discuss the measurement of specific
humidity with BELUGA and radiosondes as well as possible sources of error and
their effects. Specific humidity q is derived from air temperature T and
RH using
q=Rd/Rv⋅es(T)⋅RHp-(1-Rd/Rv)⋅es(T)⋅RH,
with the static pressure p, the ratio of specific gas constants of dry air and water vapor Rd/Rv≈0.622, and the temperature-dependent saturation vapor pressure es(T). In this study, the measurements of RH and T are obtained by regular radiosoundings (Vaisala RS92-SGP) and observations with the BELUGA system. Both methods use capacitive RH sensors, suffering from several limitations .
Error sources for humidity measurements
Several studies address the associated systematic errors of radiosonde RH and
T measurements and identify three main sources, (i) wet-bulbing, (ii) solar
heating, and (iii) time response errors:
Wet-bulbing occurs when a water film develops on the sensor during cloud
penetration, with subsequent evaporative cooling under sub-saturated
conditions above the cloud. This effect leads to an overestimation of RH and
underestimation of T in the sub-saturated environment until the water film
has completely evaporated. show that wet-bulbing is an
issue for the radiosonde type used during PASCAL. However, the error induced
by wet-bulbing is difficult to quantify .
Exposure of an RH sensor to direct sunlight above a cloud causes a
radiation dry bias (measured RH is too low) of up to 5 % in the lower
troposphere . The error is corrected in the
radiosonde data processing algorithm . However, this
correction is intended for cloud-free conditions. Solar heating also
influences temperature measurements , but the effect on
radiosonde temperature is negligible at low altitudes. For BELUGA, the
temperature and RH sensors are shielded against direct solar radiation, but
the sensor surroundings might warm and influence the measurements.
Furthermore, the time response for RH and T measurements is
finite. Compared to the effects (i) and (ii), this part of the sensor behavior
can be quantified by the time constant τ. Assuming a first-order sensor
response, the time dependence of a measured signal xm(t) (RH or
T in our case) is given bydxmdt=1/τxa-xm,with the e-1 time constant τ and the ambient (“true”) signal
xa.
The time-lag-corrected signal isxτ=x̃m(t)-x̃m(t-Δt)⋅e-Δt/τ1-e-Δt/τ,with Δt being the time step between two consecutive measurement points
. Here, we assume that the time-corrected value (index
τ) is equal to the ambient value xa. The tilde in
Eq. () represents the low-pass-filtered, measured time
series.
Although radiosonde data processing routines consider the time response error,
fast humidity changes in cold conditions are still affected . The time constants for the BELUGA RH sensor were estimated in a
laboratory study (see Appendix ) and are
τRH≈50s for RH and τTs≈70s for the internal temperature. The time constant for the
T measurements based on the thermocouple on BELUGA was found to be below
1 s and, thus, has a minor influence on the
vertical temperature profile compared to the humidity observations.
Sensitivity of q to the RH and T profile
We perform sensitivity studies to analyze how the three error sources (cf. Sect. ) for T and RH measurements combine and
influence the derivation of q. The errors are simulated as T and RH
deviations from a synthetic reference case (grey line in Fig. ), which represents a simulated measurement of a
temperature inversion combined with a decrease in RH. The temperature linearly increases by 6 K in the 200 m thick inversion layer, whereas RH linearly decreases from 100 % to 40 % in the same height range, resulting in monotonically decreasing specific humidity without a SHI.
First, we consider the influence of possible measurement errors in the
temperature inversion region for the T and RH sensor separately. That is,
only one sensor will be influenced by an increased or decreased signal while
keeping the other sensor reading at the reference value.
The magnitude of the simulated deviations (Fig. a and b) is
arbitrary, but the qualitative profile of the affected signal is according to
the error sources, as discussed in Sect. .
The effect of the four errors (T or RH too high or too low in the
temperature inversion region) on the specific humidity profile is shown in
Fig. c. An artificial humidity layer above the cloud can
emerge when the RH sensor overestimates the moisture due to wet-bulbing (but
keeping the temperature sensor unaffected), or when the temperature sensor is
heated in the inversion region but the humidity sensor is unaffected. Vice
versa, q shows a deficit compared to the reference when one of the sensors
indicates underestimated values compared to the reference scenario. If a
single phenomenon affects both the temperature and RH sensor (e.g., solar
heating results in underestimated RH and overestimated temperature), the
errors in the determination of q have an opposite effect and, therefore, the
overall error in q is reduced.
Sensitivity of the vertical q profile to a deviation of T and RH compared to a reference case (grey line). Only one parameter (T or RH) experiences a deviation in the inversion region, the other parameter is unchanged. Underestimated temperature (blue) or overestimated RH (green) might result from wet-bulbing. Overestimated temperature (purple) or underestimated RH (orange) might result from solar heating. A slow-response RH sensor overestimates RH on the ascent (green) and underestimates RH on the descent (orange).
As a second step, we simulate the influence of different time constants
τRH and τT for the RH and temperature measurements. If
both time constants have similar values, the resulting q does not change
significantly in magnitude, but the vertical structure shifts upwards or
downwards for an ascent or descent. With a slow-response RH sensor
(τRH≫τT), the measured RH in the SHI region is
overestimated on the ascent and underestimated on the descent with the effects
on q as shown in Fig. c and with an artificial SHI on the
ascent.
As a result of these sensitivity studies, the error in q is reduced when
both the temperature and humidity sensors are affected by the same error
source (e.g., solar heating on both sensors), and when both sensors have
comparable time constants. Under these conditions, a detected SHI can be
considered as most likely real and does not need to be interpreted as an
artifact.
SHIs measured with BELUGA and radiosondes: natural feature or artifact?
A simple and convincing test of the possible influence of the error sources on
the SHI observations is profiling in opposite direction, that is a descent
from the free troposphere through the SHI into the cloud layer. This is
commonly impossible in case of standard radiosoundings, but feasible for the
BELUGA observations. Figure shows vertical
profiles of RH, T, and q as measured by radiosounding and BELUGA on 5 June
2017. Qualitatively, the measurements of both platforms show a similar
vertical structure with a sharp temperature inversion capping the cloud layer.
The cloud top (estimated from the observed downward terrestrial irradiance) is
situated close to the temperature inversion base. However, the cloud top
height derived from radiation observations should be treated with caution due
to the vertical separation of the radiation and thermodynamic sensors by about
20 m, corresponding to a temporal shift between the observations of
about 20 s during profiling. In the course of the measurement period
of almost 2 h, the temperature inversion base and the cloud top remain
at almost constant altitude. The radiosonde observation shows a layer of
increased q between 400 and 550 m altitude just above the
temperature inversion base. The increased specific humidity emerges from RH
remaining high within the temperature inversion, before decreasing to the free
troposphere level well above the inversion base.
Vertical profiles of (a) relative humidity RH, (b) temperature T, and (c) specific humidity q measured by a radiosonde and BELUGA on 5 June 2017 (second profile). RH and q for BELUGA are shown before and after the corrections. The radiosonde was launched at 16:50 UTC; the balloon flew a continuous ascent and descent from 14:15 to 14:40 UTC. The cloud top (from BELUGA radiation data) is shown as horizontal lines. Solid and dashed lines represent the BELUGA ascent and descent, respectively.
Before comparing the q measurements from the radiosonde to BELUGA
observations, we illustrate the effect of the applied RH correction and the
consequences for the q profile. Figure a shows
the uncorrected and time-response-corrected RH for an ascent and descent. The
uncorrected RH ascent profile deviates strongly from the descent in the cloud
top region. While descending through the cloud, the sensor requires a
150 m height difference for rising from 55 % to 95 %
RH. The RH hysteresis around the cloud top is visible as a systematic deviation in
all observed flight data (not shown). A comparison to Fig.
(orange lines) suggests that the major part of the error is due to a slow RH
sensor. Furthermore, the sensor underestimates RH in the cloud on the descent,
which might indicate solar heating. After applying the time lag correction,
the RH profile shows a significantly reduced difference between ascent and
descent. The remaining difference is qualitatively consistent with the
temperature observations as shown in Fig. b. The
temperature profiles show a warming of the cloud top and inversion region
between 300 and 500 m during the descent leading to a reduced RH.
The “uncorrected” specific humidity in Fig. c is
calculated from the uncorrected RH and the temperature measured with the
fast-response thermocouple. The resulting q profiles show a SHI on the
ascent and the descent of the BELUGA flight with a similar structure and
location compared to the radiosonde data. The q profile as observed during
the descent is shifted to lower q values in the region of the hysteresis of
the uncorrected RH.
The corrected q results from the RH and the sensor-internal temperature
Ts after correcting both signals for the time lag error
according to Eq. (). We argue that using
Ts should be preferred instead of the thermocouple readings
because RH and Ts have similar time constants, and RH is
measured at Ts instead of the temperature of the atmospheric
environment. The ambient temperature and Ts differ slightly due
to the thermal inertia of the sensor housing.
After applying the corrections, the maximum value of the SHI, as observed
during the BELUGA ascent, is reduced by about 0.6 gkg-1 compared
to the uncorrected q maximum. After correction, all BELUGA profiles and the
radiosonde data exhibit the SHI with similar structure and amplitude. This
consistency suggests that the observed SHI is a natural feature instead of an
instrumental artifact. We can exclude wet-bulbing as the main reason for the
observed SHIs because the SHI is also present during the descent. The
influence of solar heating and time-lag errors is minimized. Our conclusion
also strengthens the confidence in SHIs as frequently observed by radiosondes.
Vertical profiles of mean ABL parametersComparison of normalized temperature and humidity profiles
Throughout the observation period, we observe a persistent layer of increased
specific humidity above the cloud layer. One of the governing questions of
this analysis is to understand how observed SHIs relate to the general ABL
structure and, in particular, to the temperature
inversion. Figure a and b shows vertical profiles of
potential temperature θ and specific humidity q recorded in the
period of 5–7 June 2017. Both parameters are normalized to their near-surface
values and plotted in relation to the base height of the temperature inversion
zi. The cloud boundaries are shown in
Fig. c for reference.
Balloon-borne vertical profiles of (a) potential temperature θ, (b) specific humidity q, and (c) cloud boundaries for four ascents (solid lines) and descents (dashed lines) on 5, 6, and 7 June 2017. The altitude z is normalized to the temperature inversion base height zi. Potential temperature θ and the specific humidity q are normalized to their near-surface values. The cloud top is derived from the irradiance profile; the cloud base is derived from Cloudnet data.
The profiles are named after the start time (cf. Fig. ).
All measurements show a similar vertical structure of θ within the
ABL. Below the temperature inversion base zi, the stratification
is near-neutral to weakly stable. Above the inversion, the thermodynamic
stability is higher and exhibits more variability compared to below the
inversion. No systematic difference between ascents and descents is
visible. The ABL is thermodynamically coupled to the surface, which makes
normalizing to surface values meaningful.
Boundary layer observations around the cloud top on 5 June 2017, first profile: vertical profiles of (a) potential temperature θ, (b) RH, (c) specific humidity q, (d)
downward terrestrial irradiance Fterr↓,
(e) horizontal wind velocity U, and (f) Richardson number Ri for BELUGA ascent and descent and the radiosonde launched at 11:00 UTC.
The triangles indicate where zi is defined.
The cloud top is shown as horizontal lines (solid for ascents and dashed for descents).
Within the mixed layer below zi, specific humidity
decreases slightly with height but increases when reaching zi. Above zi, the normalized specific humidity exhibits more variability compared to the normalized temperature. The descent of
7 June 09 h shows a temperature inversion with some internal structure in the form of two smaller “steps” in θ. We define zi at the lower step, with the SHI base being located clearly above at the upper step at z≈1.2⋅zi. For this case, a deficit in q is observed below the SHI, which is plausible because between ascent and descent cloud top had decreased to about 0.95⋅zi.
For most profiles, the cloud top coincides with zi, and the
increased humidity is observed above the cloud layer. Only for two profiles
(both descents on 5 June), the lower bound of the SHI is already located below the cloud top. We do not find clouds penetrating into the temperature
inversion, although such situations have been frequently observed in previous
studies e.g., . However, two of the descent profiles (6 June 09 h and
7 June 09 h) show situations where the cloud top had decreased between ascent and descent, and the SHI is vertically separated from the cloud top.
Cloud top variability versus SHI height
The cloud top variability, here defined as the cloud top height difference
between ascent and subsequent descent for each profile, is related to
zi and the lower boundary of the SHI. For all 3 d, a
descending cloud top is observed between the ascent and subsequent descent
with a cloud top height difference of 50 to 100 m. This cloud top
variability is indicated by in situ irradiance and thermodynamic measurements
and also confirmed by radar reflectivity
(cf. Fig. ). In order to illustrate the relation of
cloud top height, SHI, and other ABL parameters,
Figs. – show profiles of mean θ, RH, q, downward
terrestrial irradiance Fterr↓, horizontal wind
velocity U, and Richardson number Ri as measured during ascents and
descents on 5, 6, and 7 June, respectively. We analyze only continuous profile
data without longer breaks at certain heights for the first profile of each
day. The cloud top height is defined by the discontinuity of the
Fterr↓ profile and marked with horizontal lines,
whereas zi is indicated with triangles. The Richardson number is
the ratio between thermodynamic stability and wind shear and, therefore, a
measure for the ability of turbulence generation (Ri≲ 1) or
dissipation (Ri≳ 1).
On 5 June (Fig. ), zi lowers from 430
to 380 m in the course of the BELUGA flight. The temperature
difference across the inversion of Δθ≈9K, which
is also the strongest observed during our flights, stays constant during
ascent and descent. The RH decreases within the temperature inversion,
accompanied by an increase in q above zi of about
0.25 gkg-1 (ascent) and 0.5 gkg-1 (descent). The
radiosonde, launched around 2 h prior to the BELUGA flight, shows a
higher zi but qualitatively a similar vertical structure of
θ, RH, and q. The cloud top agrees well with zi for
the ascent and descent. The horizontal wind velocity U is around
2 ms-1 inside the cloud layer and decreases to
1 ms-1 in the free troposphere, resulting in horizontal wind
shear. During the ascent, the wind shear zone is clearly located below
zi with a sudden increase in Ri to values greater than 1 above
zi and cloud top. During the descent, the strongest wind shear
is observed around zi, and the resulting increase in Ri is
slightly above zi. This vertical shift suggests a slightly
stronger turbulent coupling between cloud top and the SHI above, as compared
to the ascent.
Same as Fig. , but for 6 June 2017 (first profile).
The general ABL structure observed on 6 June (Fig. )
in terms of the profiles of θ, RH, and q is quite similar to the
5 June observations, showing a decreasing cloud top height during the balloon
operation. Here, zi decreases from 290 m during the
ascent to about 230 m during the descent. The radiosonde, launched
1.5 h after the BELUGA flight, shows a similar zi to the
balloon ascent, indicating that zi and cloud top recover between
BELUGA descent and radiosounding. This is in agreement with the radar
observations in Fig. . The lower bound of the SHI with
Δq≈0.3gkg-1 on the ascent and
0.7 gkg-1 on the descent is coupled to zi in both
cases. On the ascent, zi coincides with the cloud top. During
the descent, the cloud top is almost 20 m below zi,
which could possibly result from cloud top heterogeneity. However, the
temperature gradient is smoother compared to the ascent, which leads to a less
clear determination of zi. The humidity structure above the
cloud layer observed by the radiosonde exhibits a distinct SHI with a lower
bound coupled to the temperature inversion. Peak values of q are comparable
with BELUGA observations made during the descent. The horizontal wind velocity
is about 5 ms-1 and almost height-constant for the entire ascent but increases by about 2 ms-1 inside the cloud layer during the
descent. The radiosonde provides a similar picture to the balloon descent.
For the ascent, the sharp increase in Ri is connected to zi,
whereas for the descent this increase in Ri is – similar to the previous
day – about 20 m above cloud top, allowing for some turbulent
exchange between the cloud and the SHI above.
Same as Fig. , but for 7 June 2017.
On 7 June, a clear SHI develops with a lower boundary at around 580 m,
which is similar in the two BELUGA and the radiosonde profiles
(Fig. ). For the BELUGA ascent and the radiosonde
profile, this boundary agrees well with zi and cloud top (for
the radiosonde data cloud top can be roughly estimated from the RH
profile). The radiosonde profile and BELUGA ascent are shifted in time by
about 70 min and the remarkable match in zi should not
be over-interpreted. For the BELUGA descent, the thermal stratification
changes again (similar to the previous days). The temperature inversion
weakens and zi is shifted downward by about 110 to
480 m, together with the cloud top. Thus, the cloud top and the SHI
base are separated by 110 m on the descent. The terrestrial irradiance
inside the cloud layer fluctuates strongly, especially on the descent, which
suggests a patchy cloud with cloud holes. The horizontal wind velocity agrees
qualitatively for all three profiles. Inside the ABL, a higher wind velocity
of around 6 ms-1 is observed with the BELUGA observations,
showing a local maximum of 8 ms-1 slightly below
zi. Above this maximum, U gradually decreases to
2 ms-1 in the free troposphere. According to the Richardson
number, wind shear limits turbulence above the cloud top for both ascent and
descent.
To resume, we observed mean profiles of several cases where cloud tops
coincide with zi and the SHI base. Although some cloud tops show
more or less strong horizontal wind shear, the stabilizing effect of the
temperature inversion leads to a sudden increase in Ri just above the cloud
layer, which suggests a rather low turbulent exchange with the humidity layers
above. However, for one case a special situation provides a new aspect of this
phenomenon: zi and cloud top height had decreased while the
humidity layer remained at its vertical position, leading to a humidity gap
between cloud top and SHI.
Turbulence at cloud top and around the SHI
Concerning the question of how the humidity and cloud layer interact and to
what extent these layers exchange energy by turbulent transport, we first
describe the interface between the SHI and cloud top by means of observations
at constant altitude (Sect. ). We then analyze the
vertical profiles of basic turbulence parameters (Sect. ) and
turbulent energy fluxes (Sect. ).
Observations at constant altitude in the inversion layer
To get an insight into the transition from cloud top to the humidity layer
above, measurements were taken at a constant height in the temperature
inversion region. Figure shows a 500 s
time series measured on 6 June at a constant altitude around zi≈300m (second last constant altitude segment in
Fig. for 6 June). The local dissipation rate
ε is evaluated in 2 s segments to illustrate the evolving
turbulence intensity.
Constant-altitude time series of (a) virtual potential temperature θv, (b) specific humidity q, (c) vertical wind w, (d) co-variance θv′w′, and (e) dissipation rate ε
for
6 June measured at 300 m altitude around zi.
Within the first third of the record, the virtual potential temperature
θv (as approximately measured by the ultrasonic anemometer)
shows strong variations on a typical timescale of 30–50 s with
amplitudes up to 3 K. Based on the temperature gradient
(Fig. ), the changes in θv would
correspond to a height variation of ∼10m. More likely, parts of
the height-constant measurements (Δz≈1m) are taken in
potentially colder, drier, and more turbulent air masses at the inversion base,
interrupted by measurements in potentially warmer, more humid, and less turbulent
air masses at higher altitudes well within the T inversion. This variability
is also visible in the wind direction (not shown here). Depending on the
relative location of zi to the measurement height, the
co-variance w′θv′ is highly intermittent and no mean flux
is derived from these observations.
The center part of the record is characterized by a comparably low variability
leading to the conclusion that this part of the observations is performed
entirely inside the descending temperature inversion. Finally, observations
are performed well above zi inside the stably stratified T
inversion layer, characterized by values of ε 1 order of
magnitude lower compared to at the inversion base. Here, variations in
θv and q are again correlated and caused by changes in
relative height.
The observations do not allow for drawing quantitative conclusions, such as
time and area-averaged turbulent heat fluxes, from this record. However, these
measurements vividly illustrate the difficulties in estimating turbulent
fluxes based on covariance methods in the vicinity of the temperature
inversion, although the measurement height is kept at a remarkably constant
height level. Therefore, the methods for estimating turbulent fluxes based on
mean vertical gradients and slant profiles are more suitable for this study
and are used below.
Vertical profiles of local dissipation rate ε and TKE for the first ascent and descent of 5, 6, and 7 June 2017. The height is normalized by the temperature inversion base zi. The region of increased specific humidity is marked as blue shading, the cloud layer as grey shading.
Vertical profiles of turbulent energy and dissipation
The vertical distribution of turbulence parameters, such as local dissipation
rate ε and the turbulent kinetic energy TKE, provide an insight
into the coupling between the cloud layer and the SHI. The local
ε values are derived from second-order structure functions by
applying inertial subrange scaling as described by
. Different from that study, here ε is
calculated from non-overlapping, 2 s sub-records yielding a vertical
resolution of about 2 m. Regions without inertial sub-range scaling
are excluded. Turbulent kinetic energy (=0.5⋅ui2) is calculated
in a moving 50 s window. The observed TKE noise level is about
0.005 m2s-2 and is usually reached at z/zi≳ 1.1.
Figure shows ε and TKE for each first
profile of 5, 6, and 7 June as a function of normalized height (the descent of
5 June is excluded due to data issues). The cloud and humidity layers are
shaded for reference. For the presented cases, turbulence is most pronounced
in the upper cloud layer and around cloud top with typical values of
ε∼10-3m2s-3 and
TKE ∼ 0.02 m2s-2. For 5 and 6 June, the turbulence
intensity is rather constant in the cloud. For 7 June, with increased wind
velocity, a maximum of ε is evident just below cloud top.
Figure also illustrates how the SHI and cloud layer
are either separated or overlap, and how they are connected by turbulent
motion. At a certain height level, ε decreases to the
low-turbulence free-troposphere level. The transition is gradual, indicating
turbulent mixing in this region. On 5 June and the ascents of 6 and 7 June,
the SHI and the cloud are directly coupled by turbulent mixing. For the
descents of 6 and 7 June, most of the mixing takes place at the interface of
the cloud top with the humidity gap between cloud and SHI. In this case,
inside the SHI the turbulence intensity is reduced almost to the
free-troposphere level and the SHI seems to be decoupled from the cloud layer
via the humidity gap in between.
We can only speculate about the reason for the development of this humidity
gap, which is most pronounced for the descent of 7 June. One explanation
could be long-range advection of increased moisture in the free troposphere
combined with a temporary collapse of the well-mixed cloud layer leading to a
vertical separation of cloud top and SHI. However, this interesting feature
leads to new research questions that require further observations and a more
detailed LES analysis.
Vertical profiles of turbulent moisture and heat fluxes
The turbulent exchange of moisture can be quantified by the latent heat flux
L=ρ‾⋅Lv⋅w′q′‾,
whereas the virtual sensible heat flux is given by
H=ρ‾⋅cp⋅w′θv′‾,
with an overline describing an average of the sub-record. Here,
ρ‾ is the mean air density, Lv=2.5×106Jkg-1 is the latent heat of evaporation, and
cp=1005Jkg-1K-1 is the specific heat capacity of air.
This direct calculation of H and L requires sufficient long, stationary,
and homogeneous records in a certain height to provide time-averaged estimates
of the covariances with statistical significance
. Our observations focus mainly on vertical
profiling, and only a limited number of height-constant records around the
cloud top and inversion region are available. As shown in
Sect. , the conditions around the temperature inversion are
highly instationary and, thus, we use the vertical profiles to study the
fluxes in this region. We apply two approaches for estimating fluxes from
vertical profiles: (i) describing the flux profile by applying the so-called
“slant profile method” and (ii) relating the turbulent flux to mean
gradients (flux gradient method).
Same as Fig. , but for the virtual sensible heat flux H (eddy covariance method) and the latent heat flux L (flux gradient method).
The slant profile method is based on the assumption that for a certain height
range the profile data are considered as a homogeneous record and
Eq. () can be applied. For this method, instantaneous
values of H are estimated for a defined height range, defining also the
length scales contributing to the flux. For our observations, this method
provides only results for H due to the lack of fast-response humidity
measurements. Alternatively, L can be estimated with the flux gradient
method. This method is based on the relation between the covariances and the
mean gradients of θv and q:
w′θv′‾=-KH⋅∂θ‾v∂z,
and
w′q′‾=-KQ⋅∂q‾∂z,
with KH and KQ being the turbulent exchange coefficients for
sensible and latent heat, respectively. The coefficients are defined as
positive, which means that the flux is directed against the mean
gradient. Values of K can be derived from parameterizations based on
turbulence observations such as proposed by or by directly
applying Eq. () with the measured H, yielding
KH. With KQ≈KH for a wide range of
stratification and the mean humidity gradient ∂q‾/∂z, we estimate L by combining Eqs. () and
().
Before estimating H from the slant profiles by applying
Eq. (), the turbulent fluctuations must be
determined. This is done by applying a high-pass filter of Bessel type with a
filter window of 10 s, corresponding to a horizontal length scale of
about 10 to 70 m (depending on the horizontal wind velocity) and a
vertical length scale of about 10 m. After filtering, the fluxes are
averaged over a moving 50 s window by applying
Eq. (). The filter and averaging windows are similar to
the values proposed by and , who
estimated turbulent fluxes from aircraft-based slant profiles.
Figure shows five selected cases
(cf. Fig. ) with profiles of H based on the slant
profile method and L based on the flux gradient method. The upper part of
the cloud layer is mainly characterized by an upward-oriented heat flux (H>0), most pronounced for the last two profiles with a local maximum between
0.8<z/zi<1. Only for the first ascent of 5 June is the H
flux almost height-constant with much lower values compared to the other
days. For this day, θv exhibits larger variability around
and slightly above zi, which differs from the typical structure
of a turbulent flow. This variability mainly causes the positive values of H
around zi, which, therefore, should not be misinterpreted. This
is a similar effect to that discussed in Sect. . A negative peak
of H around or slightly above zi is visible for the descent of
6 June and both profiles of 7 June. On 7 June, a secondary, weaker negative
peak in H is located at the lower part of the SHI.
Although it is known that in general K=K(z), we estimate a constant
KH for each ascent and descent in the lower region of the SHI, which is
the focus area of our study. In that region, we observe negative H fluxes
and positive θv gradients. Applying
Eq. () leads to mean values of KH between 0.001 and
0.004 m2s-1 for the five profiles. The KH(=KQ) values
for each profile are used for calculating the L profile based on the flux
gradient method.
A negative peak in L is observed for all days in the lower SHI region. The
downward energy flux at cloud top is common for the entrainment region, where
potentially warmer and usually drier air from the free troposphere is mixed
downward into the (cloudy) ABL. However, for our observations, this downward
flux in the lower SHI region means a downward transport of potentially warmer
but more humid air into the region below. The situation is different for the
descent profile of 7 June, with the vertical humidity gap between cloud top
and SHI. Here, the negative peak in L at the lower SHI is accompanied by a
positive L at cloud top. This profile does not suggest a significant
transport of humidity into the cloud top. Instead, for the special case where
the cloud and the SHI are separated, the gap in between receives moisture from
both the SHI above and from the cloud layer below.
LES results (with and without an initial SHI) and BELUGA observations for 7 June 2017: vertical profiles of (a) virtual potential temperature θv, (b) specific humidity q, (c) liquid (LWC) and ice water content (IWC), (d) virtual sensible heat flux H, and (e) latent heat flux L. The light blue area is the cloud extent for the observations (cloud top is derived from BELUGA irradiance measurements, cloud base from lidar data).
Possible influence of the humidity layer on ABL and cloud structure: an LES study
The observational data discussed so far provide insight into the turbulent
structure of cloudy ABLs that are capped by humidity layers. What remains
unclear is how the presence of such humidity layers might have impacted the
general ABL and clouds as observed on this day. For this purpose numerical
experiments at cloud- and turbulence-resolving resolutions can be used to good effect, providing virtual datasets for detailed process studies and allowing sensitivity tests for hypothesis testing . In this section idealized Lagrangian large-eddy simulations (LESs) are discussed that were generated to match the observed vertical structure of the ABL as closely as possible. For a detailed technical description of the experimental design of these realizations, we refer to Appendix . Two simulations are discussed, one based on an initial profile without a SHI, the other with a SHI superimposed. The LES simulations are Lagrangian, following an air mass from a location 12 h upstream of the RV Polarstern. This allows for proper model spinup and also gives the SHI ample time to impact the turbulence and clouds below. The simulations are sampled when the air mass arrives at RV Polarstern on 7 June 2017 at 10:48 UTC. The LES output considered includes the mean thermodynamic and cloudy state, as well as the turbulent fluxes of heat H and moisture L, calculated as the covariance between vertical velocity and perturbations in static energy and humidity, respectively.
Figure shows vertical profiles of the LES output (with
and without an initial SHI) and the BELUGA ascent, where cloud top,
zi, and SHI base coincide. The LES profiles represent averages
over the horizontal domain over a 900 s period. The temperature
differences across the inversion as well as the lapse rates above are
reasonably well reproduced by the LES (Fig. a). The
experiment including an initial SHI features a temperature inversion base
zi, and similarly a mixed-layer depth, that agrees well with the
observations. Without the initial humidity layer, zi is
approximately 40 m lower. The vertical profile of specific humidity
shows a similar vertical structure and a distinct increase in q above the
cloud layer in both the model and the observations
(Fig. b). The strength of the SHI of Δq=1.1gkg-1 in the LES is close to the radiosonde SHI strength of
Δq=0.9gkg-1, but larger than the SHI observed with
BELUGA of Δq=0.6gkg-1. In the LES without initial SHI,
specific humidity decreases by Δq≈0.2gkg-1
within the temperature inversion height range. Within the mixed layer, both
experiments slightly underestimate θv and q compared to
the BELUGA soundings. This is probably explained by the calibration of these
experiments to the radiosonde soundings, which show a similar offset compared
to BELUGA (cf. Fig. ).
Compared to the balloon measurements, a thinner liquid cloud layer forms in
the LES, as indicated in the LWC profiles in
Fig. c. While the observed mixed-phase cloud is around
500 m thick, the simulations result in a liquid cloud of about
300 m vertical extent. Note that significant ice water is present
below the liquid cloud base in the model, for which lidar readings are
sensitive . For this reason, the model bias in cloud base
height could be artificial. Without a humidity layer, the liquid cloud is
thinner, extending only 260 m. The cloud top is simulated at around
600 m altitude for the scenario with SHI and at 560 m altitude
for the scenario without SHI, respectively. In the SHI case, the higher cloud
top reflects the larger mixed-layer depth compared to the case without SHI.
The LES provides a positive (i.e., upward-directed) virtual sensible heat flux
inside the cloud layer (Fig. d). The negative virtual
heat flux at cloud top is seen with and without initial SHI. The LES, with or
without an initial SHI, shows a positive moisture flux L between surface and
cloud top (Fig. e). In the presence of an initial SHI,
the cloud top region exhibits a negative moisture flux. This negative
moisture flux coincides with the negative virtual sensible heat flux and
indicates that downward humidity transport takes place between the humidity
layer and the underlying mixed layer. Lacking the initial SHI, the total
moisture flux is close to zero near the inversion. This means that in this
case dry air, rather than humidity, is entrained into the mixed layer from
above. The direction of fluxes is in agreement with the flux estimates in
Sect. for 7 June, where a SHI is present above cloud top
on the ascent.
More research is necessary to further investigate how the additional entrained
moisture of the humidity layer is processed in the cloud (e.g., through phase
transition) and how exactly the humidity layer contributes to the cloud
evolution (e.g., the role of clouds penetrating into the inversion or
thermodynamically decoupled clouds).
Summary and conclusion
A persistent layer of increased specific humidity above a stratocumulus deck
has been observed by tethered-balloon-borne instrumentation in the Fram Strait
northwest of Svalbard (82∘ N, 10∘ E) in the period from 5
to 7 June 2017. Vertical profiles of thermodynamic parameters, wind velocity,
and terrestrial irradiance were sampled in situ. An in-depth discussion of the
problems associated with humidity measurements in cloudy and cold environments
led to the conclusion that the observed SHIs are a natural feature and not a
result of measurement artifacts. The high resolution of the measurements
allows for estimating local turbulence parameters such as local energy
dissipation rates. Based on slant profiles, the turbulent virtual sensible
heat flux was estimated by applying the eddy covariance method. The vertical
profile of the latent heat flux was calculated by applying the flux gradient
method. The observations allow for the first time detailed analyses of the
relative position of the SHI, cloud top, and the temperature inversion height
zi and give a first qualitative indication of how these
different layers are coupled by turbulent transport.
We observed two different scenarios: (i) the base of the SHI qualitatively
coincides with zi and the cloud top height and (ii) cloud top
height and zi had decreased with the SHI base remaining at a
constant height, leading to a “humidity gap” between cloud top and SHI base.
Turbulence, as described by local ε, decreases gradually above
zi suggesting that turbulent energy exchange is possible in that
region. Vertical profiles of latent heat fluxes qualitatively show a downward
moisture transport at the base of the SHIs for all profiles. When the SHI
coincides with the cloud top as in the first scenario (i), this suggests the
cloud is being supplied with moisture from the overlying SHI. For the second
scenario (ii), the sign of the latent heat fluxes suggests upward humidity
transport from the cloud together with downward humidity transport from the
SHI base, both feeding the vertical gap between the SHI base and the cloud top
with moisture.
For one case study of the first type of scenario, LESs were performed. The
simulations support the observational findings by showing a negative moisture
flux at the SHI base towards the cloud region below. Further, the LESs show
that the moisture supply does directly influence the dynamics of the cloudy
ABL by increasing zi and the cloud layer thickness.
For more general conclusions beyond case studies, further observations over a
larger measurement period are necessary. An improvement for future
measurements would be a fast-response humidity sensor that operates reliably
under cold and cloudy conditions. Those observations would allow for
quantifying the vertical moisture transport by applying the eddy covariance
method instead of relying on estimating the exchange coefficient and mean
humidity gradients.
Furthermore, we suggest a thorough LES study driven by our observations. These
studies are capable of investigating the consequences of the two observed
scenarios on ABL dynamics and cloud lifetime and will help to answer the
question of how important the SHIs are for the Arctic cloudy ABL.
Estimating the time constants of the BELUGA humidity sensor
Time response of the humidity sensor to a step function experiment: (a) sensor-internal temperature Ts and (b) RH at 8.6 ms-1 with fitted time constants τ. Panel (c) shows the time constants depending on the flow speed. A root fit function is added to the values.
We determine the time constants for the BELUGA humidity sensor in laboratory
experiments by analyzing the sensor response to a step-like change of the
surrounding thermodynamical parameters. The sensor is brought from a calm and
saturated environment into a sub-saturated airstream with constant T and RH.
The flow speed of the sub-saturated air is varied between 2 and
9 ms-1. In addition to RH, the sensor provides a measure for the
internal sensor temperature Ts, which is determined by a PT-1000.
Figure a and b show an example for the time response of the
humidity sensor on BELUGA. The time constants τRH and
τTs are obtained from an exponential fit to the response
function at a constant flow speed of
8.6 ms-1. Figure c summarizes the
resulting time constants for different flow speeds. The time constant of a
temperature and RH sensor is influenced by the heat and moisture transfer,
which scale with the flow speed ∝1/Ue.g.,for heat
transfer. Based on this relationship, a least-square fit to the
observations yields the τ values depending on the flow speed. For flow
speeds typical for atmospheric observations, we estimate time constants of
τTs≈70s and τRH≈50s. Similar to , we multiply the estimated
time constant with a factor of 0.8 before the time series reconstruction to
avoid potential over-correction.
For the reconstruction of the time series, τ is evaluated for each
measurement point with the measured wind velocity by applying
Eq. (). Low-pass filtering in
Eq. () is realized by a Savitzky–Golay filter with a
window length of τ. This low-pass filtering is necessary to avoid
amplification of gradients caused by signal noise or digitization steps
. The time-response correction is applied to the RH
and the internal temperature data.
LES model configuration
In this study the LES configuration as designed by for the
PASCAL observation period 5–7 June 2017 is adopted. For the full details of
this method, we refer to this publication, the essence of which can be
summarized as follows. The Dutch Atmospheric Large-Eddy Simulation model
DALES, is used, being equipped with a well-established
double-moment mixed-phase microphysics scheme . A
Lagrangian framework is adopted, following cloudy mixed layers as embedded in
warm air masses moving towards the RV Polarstern. The large-scale
forcings along the 950 hPa back trajectory are derived from an
amalgamation of analysis and short-range forecast data of the European Centre
for Medium-range Weather Forecasts (ECMWF), using the method as described by
. The initial profiles are obtained by sampling the ECMWF
data at a specified location and time point upstream of the ship and are
further adjusted in a reverse engineering approach to yield a good agreement
with the RV Polarstern radiosonde in terms of mixed-layer depth and
thermodynamic state. The surface temperature along the trajectory is
prescribed, while the surface fluxes are interactive, resulting in weakly
coupled cloudy mixed layers. In this setup, the low-level turbulence and
clouds are free to evolve.
thoroughly evaluated these LES simulations against PASCAL
measurements, reporting satisfactory agreement concerning the thermodynamic
state, clouds, and surface radiative fluxes. The observed SHIs were less well
reproduced, with their strength and depth somewhat underestimated. To improve
on this underestimation, and to cater to the specific needs of this study, two
new simulations were conducted for 7 June 2017, adopting a configuration that
slightly differs from the setup described above at the following points:
Instead of 48 h the model initializes only 12 h before the arrival of the simulated air mass at RV Polarstern. A shorter lead time facilitates the adjustment of the initial profile for obtaining a good agreement with the observed sounding in terms of temperature and inversion height. On the other hand, a period of 12 h is still long enough to allow complete spinup of the mixed-phase clouds and turbulence.
The simulated doubly periodic and homogeneously forced domain has dimensions of 2.56×2.56×1.28km3 discretized at a spatial resolution of 20×20×10m3, adopting flexible time-stepping to ensure numerical stability.
The initial state derived from the ECMWF data is adjusted by lowering the thermal inversion height, following the method of . A second initial profile is then obtained by superimposing a humidity layer of 200 m depth and 0.5 gkg-1 strength on this initial profile, placed immediately above the new temperature inversion. These values reflect the structure of the observed SHIs.
The surface sensible and latent heat fluxes are switched off, in effect decoupling the cloud layer from the surface. Imposing a surface decoupling has proven to be an effective way to maintain humidity inversions . It should be noted that no measurements were made of the surface heat fluxes along the upstream trajectory, preventing us from assessing the validity of this modification.
These modifications yield two cases, one with and one without an initial SHI. These cases are idealized but include one realization in which the strength and depth of the humidity layer agree well with the observations. In combination, the SHI and no-SHI experiments provide insight into the impact of this feature on the observed evolution and behavior of turbulence and clouds on this day.
Data availability
Observational data related to the present article are available with open access through PANGAEA – Data Publisher for Earth & Environmental Science: 10.1594/PANGAEA.899803. The full LES case configuration, as well as a selection of standard output, are available online at https://doi.pangaea.de/10.1594/PANGAEA.919946.
Author contributions
UE and MG performed the measurements and analyzed the observational data. HS was responsible for the overall balloon system. HS, MW, and AE contributed to the data analysis. RN performed the LES and analyzed the results. HG provided the remote sensing data and advice on the data. UE drafted the paper with contributions from all co-authors.
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/ESSD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We gratefully acknowledge the funding by 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 sub-project A02. We greatly appreciate the participation in RV Polarstern cruise PS 106.1 (expedition grant number AWI-PS106-00). We thank ECMWF for providing access to the large-scale model analyses and forecast fields used to force the LES. We gratefully acknowledge the Regional Computing Centre of the University of Cologne (RRZK) for granting us access to the CHEOPS cluster. The Gauss Centre for Supercomputing e.V. (http://www.gauss-centre.eu, last access: 26 April 2021) is acknowledged for providing computing time on the GCS Supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under project no. HKU28.
Financial support
This research has been supported by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) (grant
no. Projektnummer 268020496 – TRR 172).
The publication of this article was funded by the Open Access Fund of the Leibniz Association.
Review statement
This paper was edited by Radovan Krejci and reviewed by two anonymous referees.
ReferencesAlbrecht, B. A., Penc, R. S., and Schubert, W. H.: An Observational Study of Cloud-Topped Mixed Layers, J. Atmos. Sci., 42, 800–822, 10.1175/1520-0469(1985)042<0800:AOSOCT>2.0.CO;2, 1985.Brooks, I. M., Tjernström, M., Persson, P. O. G., Shupe, M. D., Atkinson, R. A., Canut, G., Birch, C. E., Mauritsen, T., Sedlar, J., and Brooks, B. J.: The Turbulent Structure of the Arctic Summer Boundary Layer During The Arctic Summer Cloud-Ocean Study, J. Geophys. Res.-Atmos., 122, 9685–9704, 10.1002/2017JD027234, 2017.Brunke, M. A., Stegall, S. T., and Zeng, X.: A climatology of tropospheric humidity inversions in five reanalyses, Atmos. Res., 153, 165–187, 10.1016/j.atmosres.2014.08.005, 2015.
Bruun, H. H.: Hot-Wire Anemometry, Oxford University Press, Oxford, UK, 1995.Bühl, J., Ansmann, A., Seifert, P., Baars, H., and Engelmann, R.: Toward a quantitative characterization of heterogeneous ice formation with lidar/radar: Comparison of CALIPSO/CloudSat with ground-based observations, Geophys. Res. Lett., 40, 4404–4408, 10.1002/grl.50792, 2013.Devasthale, A., Sedlar, J., and Tjernström, M.: Characteristics of water-vapour inversions observed over the Arctic by Atmospheric Infrared Sounder (AIRS) and radiosondes, Atmos. Chem. Phys., 11, 9813–9823, 10.5194/acp-11-9813-2011, 2011.Dirksen, R. J., Sommer, M., Immler, F. J., Hurst, D. F., Kivi, R., and Vömel, H.: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde, Atmos. Meas. Tech., 7, 4463–4490, 10.5194/amt-7-4463-2014, 2014.Dyer, A. J.: The turbulent transport of heat and water vapour in an unstable atmosphere, Q. J. Roy. Meteor. Soc., 93, 501–508, 10.1002/qj.49709339809, 1967.Edwards, D., Anderson, G., Oakley, T., and Gault, P.: Met Office Intercomparison of Vaisala RS92 and RS41 Radiosondes, available at: https://www.vaisala.com/sites/default/files/documents/Met_Office_Intercomparison_of_Vaisala_RS41_and_RS92_Radiosondes.pdf
(last access: 22 April 2021), 2014.Egerer, U., Gottschalk, M., Siebert, H., Ehrlich, A., and Wendisch, M.: The
new BELUGA setup for collocated turbulence and radiation measurements using a
tethered balloon: first applications in the cloudy Arctic boundary layer,
Atmos. Meas. Tech., 12, 4019–4038, 10.5194/amt-12-4019-2019,
2019a.Egerer, U., Gottschalk, M., Siebert, H., Wendisch, M., and Ehrlich, A.: Tethered balloon-borne measurements of turbulence and radiation
during the Arctic field campaign PASCAL in June 2017 (supplement to: Egerer, U., Gottschalk, M., Siebert, H., Ehrlich, A., and Wendisch, M.: The new BELUGA setup for collocated turbulence and radiation measurements using a tethered balloon: first applications in the cloudy Arctic boundary layer, Atmos. Meas. Tech., 12, 4019–4038, 10.5194/amt-12-4019-2019, 2019), Leibniz-Institut für Troposphärenforschung e.V., Leipzig, PANGEA, 10.1594/PANGAEA.899803, 2019b.Griesche, H., Seifert, P., Engelmann, R., Radenz, M., and Bühl, J.:
Cloudnet target categorization during PS106, Leibniz-Institut für Troposphärenforschung e.V., Leipzig, PANGEA, 10.1594/PANGAEA.919344, 2020a.Griesche, H., Seifert, P., Engelmann, R., Radenz, M., and Bühl, J.: OCEANET-ATMOSPHERE low level stratus clouds during PS106, PANGEA, 10.1594/PANGAEA.920246, 2020b.Griesche, H., Seifert, P., Engelmann, R., Radenz, M., and Bühl, J.: OCEANET-ATMOSPHERE Cloud radar Mira-35 during PS106, PANGEA, 10.1594/PANGAEA.919556, 2020c.Griesche, H. J., Seifert, P., Ansmann, A., Baars, H., Barrientos Velasco, C., Bühl, J., Engelmann, R., Radenz, M., Zhenping, Y., and Macke, A.: Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106, Atmos. Meas. Tech., 13, 5335–5358, 10.5194/amt-13-5335-2020, 2020d.Hanna, S. R.: A Method of Estimating Vertical Eddy Transport in the Planetary Boundary Layer Using Characteristics of the Vertical Velocity Spectrum, J. Atmos. Sci., 25, 1026–1033, 10.1175/1520-0469(1968)025<1026:AMOEVE>2.0.CO;2, 1968.Heus, T., van Heerwaarden, C. C., Jonker, H. J. J., Pier Siebesma, A., Axelsen, S., van den Dries, K., Geoffroy, O., Moene, A. F., Pino, D., de Roode, S. R., and Vilà-Guerau de Arellano, J.: Formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES) and overview of its applications, Geosci. Model Dev., 3, 415–444, 10.5194/gmd-3-415-2010, 2010.Intrieri, J. M., Fairall, C. W., Shupe, M. D., Persson, P. O. G., Andreas, E. L., Guest, P. S., and Moritz, R. E.: An annual cycle of Arctic surface cloud forcing at SHEBA, J. Geophys. Res.-Oceans, 107, SHE 13-1–SHE 13-14, 10.1029/2000JC000439, 2002.Jensen, M. P., Holdridge, D. J., Survo, P., Lehtinen, R., Baxter, S., Toto, T., and Johnson, K. L.: Comparison of Vaisala radiosondes RS41 and RS92 at the ARM Southern Great Plains site, Atmos. Meas. Tech., 9, 3115–3129, 10.5194/amt-9-3115-2016, 2016.Katzwinkel, J., Siebert, H., and Shaw, R. A.: Observation of a Self-Limiting, Shear-Induced Turbulent Inversion Layer Above Marine Stratocumulus, Bound.-Lay. Meteorol., 145, 131–143, 10.1007/s10546-011-9683-4, 2012.Knudsen, E. M., Heinold, B., Dahlke, S., Bozem, H., Crewell, S., Gorodetskaya, I. V., Heygster, G., Kunkel, D., Maturilli, M., Mech, M., Viceto, C., Rinke, A., Schmithüsen, H., Ehrlich, A., Macke, A., Lüpkes, C., and Wendisch, M.: Meteorological conditions during the ACLOUD/PASCAL field campaign near Svalbard in early summer 2017, Atmos. Chem. Phys., 18, 17995–18022, 10.5194/acp-18-17995-2018, 2018.Knust, R.: Polar research and supply vessel POLARSTERN operated by the
Alfred-Wegener-Institute., Journal of large-scale research facilities, 3, , 10.17815/jlsrf-3-163, 2017.Lenschow, D. H., Li, X. S., Zhu, C. J., and Stankov, B. B.: The stably stratified boundary layer over the great plains: I. Mean and Turbulence Structure, Bound.-Lay. Meteorol., 42, 95–121, 10.1007/BF00119877, 1988.Lenschow, D. H., Mann, J., and Kristensen, L.: How Long Is Long Enough When Measuring Fluxes and Other Turbulence Statistics?, J. Atmos. Ocean. Tech., 11, 661–673, 10.1175/1520-0426(1994)011<0661:HLILEW>2.0.CO;2, 1994.Macke, A. and Flores, H.: The expeditions PS106/1 and 2 of the research vessel POLARSTERN to the Arctic ocean in 2017, Reports on polar and marine research, Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, 719, 10.2312/BzPM_0719_2018, 2018.Miloshevich, L. M., Paukkunen, A., Vömel, H., and Oltmans, S. J.: Development and Validation of a Time-Lag Correction for Vaisala Radiosonde Humidity Measurements, J. Atmos. Ocean. Tech., 21, 1305–1327, 10.1175/1520-0426(2004)021<1305:DAVOAT>2.0.CO;2, 2004.Miloshevich, L. M., Vömel, H., Whiteman, D. N., and Leblanc, T.: Accuracy assessment and correction of Vaisala RS92 radiosonde water vapor measurements, J. Geophys. Res.-Atmos., 114, D11305, 10.1029/2008JD011565, 2009.Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, 10.1038/ngeo1332, 2012.Naakka, T., Nygård, T., and Vihma, T.: Arctic Humidity Inversions: Climatology and Processes, J. Climate, 31, 3765–3787, 10.1175/JCLI-D-17-0497.1, 2018.Neggers, R.: LES results to accompany measurements at the POLARSTERN Research Vessel during the PASCAL field campaign on 7 June 2017, PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.919946, 2020.Neggers, R. A. J., Chylik, J., Egerer, U., Griesche, H., Schemann, V.,
Seifert, P., Siebert, H., and Macke, A.: Local and remote controls on Arctic
mixed-layer evolution, J. Adv. Model. Earth Sy., 11, 2214–2237, 10.1029/2019MS001671, 2019.Nicholls, S. and Leighton, J.: An observational study of the structure of stratiform cloud sheets: Part I. Structure, Q. J. Roy. Meteor. Soc., 112, 431–460, 10.1002/qj.49711247209, 1986.Pleavin, T. D.: Large eddy simulations of Arctic stratus clouds, PhD thesis, University of Leeds, available at: http://etheses.whiterose.ac.uk/4934/ (last access: 22 April 2021), 2013.Schmithüsen, H.: Upper air soundings during POLARSTERN cruise PS106.1 (ARK-XXXI/1.1), PANGEA, 10.1594/PANGAEA.882736, 2017.Sedlar, J. and Shupe, M. D.: Characteristic nature of vertical motions observed in Arctic mixed-phase stratocumulus, Atmos. Chem. Phys., 14, 3461–3478, 10.5194/acp-14-3461-2014, 2014.Sedlar, J. and Tjernström, M.: Stratiform Cloud – Inversion Characterization During the Arctic Melt Season, Bound.-Lay. Meteorol., 132, 455–474, 10.1007/s10546-009-9407-1, 2009.Sedlar, J., Shupe, M. D., Tjernström, M., Sedlar, J., Shupe, M. D., and Tjernström, M.: On the Relationship between Thermodynamic Structure and Cloud Top, and Its Climate Significance in the Arctic, J. Climate, 25, 2374–2393, 10.1175/JCLI-D-11-00186.1, 2012.Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, 10.1007/s00703-005-0112-4, 2006.Shupe, M. D., Persson, P. O. G., Brooks, I. M., Tjernström, M., Sedlar, J., Mauritsen, T., Sjogren, S., and Leck, C.: Cloud and boundary layer interactions over the Arctic sea ice in late summer, Atmos. Chem. Phys., 13, 9379–9399, 10.5194/acp-13-9379-2013, 2013.Smit, H., Kivi, R., Vömel, H., and Paukkunen, A.: Thin Film Capacitive
Sensors, in: Monitoring Atmospheric Water Vapour, vol. 10 of ISSI Scientific Report Series, edited by: Kämpfer N., Springer, New York, NY, 11–38, 10.1007/978-1-4614-3909-7_2, 2013.Solomon, A., Shupe, M. D., Persson, O., Morrison, H., Yamaguchi, T., Caldwell, P. M., and Boer, G. D.: The Sensitivity of Springtime Arctic Mixed-Phase Stratocumulus Clouds to Surface-Layer and Cloud-Top Inversion-Layer Moisture Sources, J. Atmos. Sci., 71, 574–595, 10.1175/JAS-D-13-0179.1, 2014.Sotiropoulou, G., Sedlar, J., Forbes, R., and Tjernström, M.: Summer Arctic clouds in the ECMWF forecast model: an evaluation of cloud parametrization schemes, Q. J. Roy. Meteor. Soc., 142, 387–400, 10.1002/qj.2658, 2016.Sotiropoulou, G., Tjernström, M., Savre, J., Ekman, A. M. L., Hartung, K., and Sedlar, J.: Large-eddy simulation of a warm-air advection episode in the summer Arctic, Q. J. Roy. Meteor. Soc., 144, 2449–2462, 10.1002/qj.3316, 2018.
Stull, R. B.: An introduction to boundary layer meteorology, Kluwer Academic Publishers, Dordrecht, the Netherlands, 1988.Sun, B., Reale, A., Schroeder, S., Seidel, D. J., and Ballish, B.: Toward improved corrections for radiation-induced biases in radiosonde temperature observations, J. Geophys. Res.-Atmos., 118, 4231–4243, 10.1002/jgrd.50369, 2013.Tjernström, M.: Turbulence Length Scales in Stably Stratified Free Shear Flow Analyzed from Slant Aircraft Profiles, J. Appl. Meteorol., 32, 948–963, 10.1175/1520-0450(1993)032<0948:TLSISS>2.0.CO;2, 1993.Tjernström, M., Leck, C., Birch, C. E., Bottenheim, J. W., Brooks, B. J., Brooks, I. M., Bäcklin, L., Chang, R. Y.-W., de Leeuw, G., Di Liberto, L., de la Rosa, S., Granath, E., Graus, M., Hansel, A., Heintzenberg, J., Held, A., Hind, A., Johnston, P., Knulst, J., Martin, M., Matrai, P. A., Mauritsen, T., Müller, M., Norris, S. J., Orellana, M. V., Orsini, D. A., Paatero, J., Persson, P. O. G., Gao, Q., Rauschenberg, C., Ristovski, Z., Sedlar, J., Shupe, M. D., Sierau, B., Sirevaag, A., Sjogren, S., Stetzer, O., Swietlicki, E., Szczodrak, M., Vaattovaara, P., Wahlberg, N., Westberg, M., and Wheeler, C. R.: The Arctic Summer Cloud Ocean Study (ASCOS): overview and experimental design, Atmos. Chem. Phys., 14, 2823–2869, 10.5194/acp-14-2823-2014, 2014.
Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D. K., Moritz, R. E., Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R., Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A., Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe, M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W. S., Stanton, T. P., and Grenfeld, T. C.: Surface Heat Budget of the Arctic Ocean, B. Am. Meteorol. Soc., 83, 255–276, 10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2, 2002.Van Laar, T. W., Schemann, V., and Neggers, R. A. J.: Investigating the diurnal evolution of the cloud size distribution of continental cumulus convection using multi-day LES, J. Atmos. Sci., 76, 729–747, 10.1175/JAS-D-18-0084.1, 2019.Wang, J., Zhang, L., Dai, A., Immler, F., Sommer, M., and Vömel, H.: Radiation Dry Bias Correction of Vaisala RS92 Humidity Data and Its Impacts on Historical Radiosonde Data, J. Atmos. Ocean. Tech., 30, 197–214, 10.1175/JTECH-D-12-00113.1, 2013.Wendisch, M. and Brenguier, J.-L. (Eds.): Airborne measurements for environmental research, Wiley-VCH Verlag GmbH and Co. KGaA, Weinheim, Germany, 10.1002/9783527653218, 2013.Wendisch, M., Macke, A., Ehrlich, A., Lüpkes, C., Mech, M., Chechin, D., Dethloff, K., Velasco, C. B., Bozem, H., Brückner, M., Clemen, H.-C., Crewell, S., Donth, T., Dupuy, R., Ebell, K., Egerer, U., Engelmann, R., Engler, C., Eppers, O., Gehrmann, M., Gong, X., Gottschalk, M., Gourbeyre, C., Griesche, H., Hartmann, J., Hartmann, M., Heinold, B., Herber, A., Herrmann, H., Heygster, G., Hoor, P., Jafariserajehlou, S., Jäkel, E., Järvinen, E., Jourdan, O., Kästner, U., Kecorius, S., Knudsen, E. M., Köllner, F., Kretzschmar, J., Lelli, L., Leroy, D., Maturilli, M., Mei, L., Mertes, S., Mioche, G., Neuber, R., Nicolaus, M., Nomokonova, T., Notholt, J., Palm, M., van Pinxteren, M., Quaas, J., Richter, P., Ruiz-Donoso, E., Schäfer, M., Schmieder, K., Schnaiter, M., Schneider, J., Schwarzenböck, A., Seifert, P., Shupe, M. D., Siebert, H., Spreen, G., Stapf, J., Stratmann, F., Vogl, T., Welti, A., Wex, H., Wiedensohler, A., Zanatta, M., and Zeppenfeld, S.: The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, B. Am. Meteorol. Soc., 100, 841–871, 10.1175/BAMS-D-18-0072.1, 2019.Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423, 10.1175/MWR-D-11-00121.1, 2012.