This study aims to characterize the microphysical and optical
properties of ice crystals and supercooled liquid droplets within low-level
Arctic mixed-phase clouds (MPCs). We compiled and analyzed cloud in situ
measurements from four airborne spring campaigns (representing 18 flights and
71 vertical profiles in MPCs) over the Greenland and Norwegian seas mainly
in the vicinity of the Svalbard archipelago. Cloud phase discrimination and
representative vertical profiles of the number, size, mass and shape of ice
crystals and liquid droplets are established. The results show that the
liquid phase dominates the upper part of the MPCs. High concentrations (120 cm
The Arctic region is more sensitive to climate change than any other region of the Earth (Solomon et al., 2007). Clouds and particularly low-level mixed-phase clouds related processes have a major impact on the Arctic surface energy budget (Curry, 1995; Curry et al., 1996; Morrison et al., 2012). Observations suggest that boundary layer mixed-phase clouds (MPCs, mixture of liquid droplets and ice) are ubiquitous in the Arctic and persist for several days under a variety of meteorological conditions (Mioche et al., 2015; Morrison et al., 2012; Shupe et al., 2011; Shupe and Intrieri, 2004). They occur as single or multiple stratiform layers of supercooled droplets near the cloud top in which ice crystals can form and precipitate. These clouds have a large impact on the surface radiative fluxes and Arctic climate feedbacks (Kay et al., 2012; Kay and Gettelman, 2009). The strong impact of MPCs on the energy budget stems from their persistence and microphysical properties which result from a complex web of interactions between numerous local and larger-scale processes that greatly complicate their understanding and modeling (Klein et al., 2009; Morrison et al., 2012).
However, major uncertainties limit our understanding of the interactions and feedbacks between the physical processes involved in their life cycle. This complexity translates into the large discrepancies that can be found in numerical models to represent the cloud processes, which in turn impacts their capability to forecast cloud properties in the Arctic. For instance, global climate models (GCMs) tend to underestimate the amount of liquid water in MPCs (Komurcu et al., 2014). Therefore, the representation of ice formation and growth processes and their interactions with the liquid phase (e.g., liquid/ice partitioning, Wegener–Bergeron–Findeisen process) has to be improved, as already shown in previous modeling studies (Prenni et al., 2007 or Klein et al., 2009). Among the various cloud properties which need to be described more accurately, the cloud thermodynamic phase is a parameter of primary importance since it governs the cloud optical and therefore radiative properties as well as its life cycle (longevity and precipitation formation).
However, measuring the spatial phase distribution in low-level Arctic
mixed-phase clouds, in order to relate it to environmental conditions
(height, temperature, surface conditions, air mass origins, etc.) to
parameterize and model it, remains a challenge. The parameterizations of
liquid and ice partitioning in numerical simulations vary from one model to
another. A study carried out by
Klein et al. (2009) compared
outputs from 26 different numerical models. They found that using different
schemes of temperature-dependent partitioning yields liquid water content
ranging from 12 to 83 % for the same cloud top temperature of
Beyond the experimental limitations related to the accurate measurement of the phase partitioning (discussed hereafter), the cloud phase quantification is also hampered by difficulties to translate observational characterization into realistic representations for cloud models with a wide range of scales. The definition of a mixed-phase system is actually controversial. A mixed-phase cloud can be regarded as a complete cloud system that contains both liquid and ice involved in mixed microphysical processes but does not necessarily imply that all volumes in the system contain both phases (Shupe et al., 2008). Additionally, the definition of a mixed-phase cloud or volume could be based either on a threshold value for its optical properties or for the ratio between supercooled liquid droplets and ice crystal mass or number (Cober et al., 2001). The threshold values are questionable. The standard assumption in climate models is that liquid and ice are uniformly mixed throughout each entire model grid box (with a typical horizontal resolution of 100 and 1 km in the vertical; Tan and Storelvmo, 2016). However, some field measurements (see, among others, Rangno and Hobbs, 2001 or Korolev and Isaac, 2003) suggest that different pockets of solely water or ice in mixed-phase regions coexist with typical scale of tens of meters. This has consequences on how processes like the Wegener–Bergeron–Findeisen process (WBF, Bergeron, 1935; Findeisen, 1938; Wegener, 1911) should be parameterized in large-scale models.
A better assessment of the ice/liquid partitioning will improve our
understanding of the life cycle and more precisely the persistence of MPCs
since modeling studies show that this persistence is governed by a delicate
balance between dynamical, radiative and microphysical processes occurring
mainly in the boundary layer (Savre and Ekman, 2015).
This understanding is still limited by the description of the microphysical
processes related to the initiation and the maintenance of the ice phase.
The cloud processes responsible for the production of ice crystals in the
upper part of the cloud seem to be mostly driven by the cloud top
temperature and the entrainment rates (Savre and Ekman, 2015). In
particular, the assessment of ice nuclei (IN) concentration and its time evolution is of
primary importance but relies on a very limited set of in situ observations and needs to
be improved (Ovchinnikov et al., 2014). The ice
crystal number concentrations usually exceed the number of IN particles.
These discrepancies could be explained by the limitations of in situ instruments
and especially the overestimation of the ice crystal number due to the
shattering of large ice crystals on the probe inlets or the inability of
instruments measuring IN particles to detect all the activation modes
(Baumgardner et al., 2012; Korolev et al., 2011). Secondary ice formation processes or
the recycling of IN particles through subcloud sublimation
(Lawson et al., 2001; Rangno and Hobbs, 2001; Solomon et al., 2015) may also play an important role and
explain such discrepancies. Given the temperatures observed in MPCs,
heterogeneous ice nucleation mechanisms are preferentially involved. The
concentration of large ice crystals (
The recent developments of ground-based stations (Barrow, EUREKA, Ny-Ålesund, among others) and spaceborne remote sensing observations (as lidar and radar observations from the CALIPSO and CloudSat platforms, respectively) allow reliable studies today of Arctic cloud phase variability from a few kilometers to the pan-Arctic region (Dong et al., 2010; Kay and Gettelman, 2009; Liu et al., 2012; Shupe et al., 2011). Moreover, remote sensing observations from space performed by active instruments onboard CALIPSO (Winker et al., 2003) and CloudSat (Stephens et al., 2002) satellites as a part of the A-Train constellation provide a unique way of characterizing Arctic cloud vertical properties. However, the cloud phase distribution and characterization are highly dependent on the measurement principle of the instruments.
The aforementioned techniques provide cloud properties typically averaged
over 1 km, which may be insufficient to study cloud processes at a
microphysical scale (i.e., measurements of microphysical cloud properties,
spatial resolution less or equal to 100 m). In situ observations are based on
direct measurement techniques at a higher spatial resolution (generally
In Mioche et al. (2015), the spatial, seasonal and surface conditions' variability of MPC properties using CloudSat and CALIPSO spaceborne observations has been investigated. The study showed a large occurrence of MPCs all year long both over the whole Arctic and the Svalbard regions. It was clearly evidenced that the Svalbard region, due to its specific location near the Atlantic Ocean, presents a larger occurrence of low-level MPCs compared to the averaged Arctic. Then, it appears important to investigate the microphysical properties of MPCs in the Svalbard and Greenland Sea regions from a statistical point of view to provide representative profiles that can be compared to previous works focused on the western Arctic region.
This work provides statistical analysis of liquid and ice properties of low-level Arctic MPCs from in situ data collected in single-layer MPCs during several airborne campaigns in the region of Norwegian and Greenland seas carried out between 2004 and 2010. We compiled observations of microphysical composition of Arctic mixed-phase clouds (cloud phase, hydrometeor number, mass and shape) to present vertical profiles of liquid and ice properties. The main objective is a step to a better understanding of the processes involved in the Arctic low-level MPC life cycle at the microphysical scale. We aimed to relate these properties to environmental conditions in order to improve the cloud parameterizations used in models and remote sensing algorithms. The results will also complement previous works concerning Arctic cloud characterizations performed in the western Arctic.
This paper is organized in four sections. The description of the field experiments, instrumentation and datasets will be made in Sect. 2. Section 3 will present and discuss the vertical profiles of microphysical properties of the low-level MPCs. Finally, key parameterizations useful for modeling or remote sensing will be proposed in Sect. 4.
This study is based on in situ data collected in single-layer MPCs during
the following four international airborne campaigns organized in the
“European” Arctic region:
The Arctic Study of Tropospheric Aerosols, clouds and Radiation
experiments (ASTAR; Herber et al., 2004;
Jourdan et al., 2010;
Ehrlich et al., 2009;
Gayet et al., 2009; Lampert et al., 2009) took place in the vicinity
of Svalbard (Longyearbyen, Norway, 78 The Polar Study using Aircraft, Remote Sensing, Surface Measurements
and Models, of Climate, Chemistry, Aerosols, and Transport (POLARCAT-France;
Delanoë et al., 2013; Law et al., 2008; Quennehen et al., 2011) was carried out in northern
Sweden (Kiruna, 68 The Solar Radiation and Phase Discrimination of Arctic Clouds
experiment (SORPIC; Bierwirth et al., 2013) was performed in the
Svalbard region in May 2010 with the AWI Polar-5 aircraft.
All the clouds sampled during these four campaigns were located over the
Arctic Greenland and Norwegian seas as displayed in Fig. 1. The scientific
flights during ASTAR and SORPIC covered latitudes ranging from 75 to 79
Location of the MPC measurements during the ASTAR, POLARCAT and SORPIC campaigns.
For this study, we restricted the measurements to continuous ascent and
descent flight sequences into single-layer MPCs at the aircraft speed
(between 80 and 100 m s
Summary of in situ observations of Arctic single-layer MPCs.
A similar in situ instrumentation was loaded on the three aircraft: the German
Polar-2 and Polar-5 and the French ATR-42. The same data processing
procedure was used in order to derive the cloud microphysical parameters (at
the same scale: i.e.,
The suite of in situ instruments used to measure the MPC microphysical and optical
properties consists of the following:
The cloud particle imager (CPI; Lawson et al., 2001)
captures cloud particle images on a 1024 The forward scattering spectrometer probe
(FSSP-100;
Baumgardner et al., 2002; Knollenberg, 1981)
provides the droplet size distribution from 3 to 45 The polar nephelometer (PN; Gayet et al., 1997)
measures the angular scattering intensities (non-normalized scattering phase
function) of an ensemble of cloud particles (either droplets, ice crystals
or a mix), from a few micrometers to about 800 The Nevzorov probe (Korolev et al., 1998) uses
the hot-wire technique to retrieve the liquid water content and the total
water content. Note that the Nevzorov data are only used to determine liquid
water content during ASTAR 2004 because the FSSP-100 was not used during
this campaign. The retrieval method used to determine the liquid water
content is described in Appendix A.
All these cloud probes were heated in order to avoid icing during the
flights. The combination of these probes provides the microphysical
properties of cloud particles from a few micrometers (typically 3
Uncertainties of cloud properties derived from CPI, FSSP, PN and Nevzorov instruments.
The three research aircraft also measured basic meteorological parameters
along the flight track (see Gayet et al., 2009). We recall that the static air temperature is calculated with an
accuracy better than
Table 3 summarizes, for the 71 selected profiles, the statistics of
altitudes for the MPCs' top and base, as well as the thickness of the cloud layer
containing liquid water. The mean cloud top altitude is located around 1200
Statistics of cloud base and cloud top altitudes along with cloud layer thickness obtained from the 71 profiles sampled in MPCs.
To obtain representative statistical results, the cloud layers have been stratified in 10 levels with intervals of 0.2 of normalized altitude, each containing around 2000 observations (i.e., about 10 % of the dataset). The vertical profiles of MPC microphysical properties presented hereafter are obtained by averaging the in situ measurements over each normalized altitude layer. The profiles are computed for the whole dataset and for each main meteorology situation separately (see Sect. 2.5) for a better analysis and discussions of the results.
As stated above, the asymmetry parameter (
Figure 2 displays the mean PN scattering phase function (Fig. 2a) according
to the normalized MPC altitude levels as well as the vertical profile of the
corresponding
The liquid droplet properties are determined from the FSSP or Nevzorov
probe measurements when
Summary of the method for the assessment of the cloud thermodynamical phase and liquid droplet and ice crystal properties from the combination of PN, CPI, FSSP and Nevzorov probes.
All the selected situations correspond to low-level single-layer mixed-phase
clouds in the boundary layer during spring. If these criteria ensure the
homogeneity of the dataset, weather conditions still vary significantly from
one campaign to another or even within a campaign. In order to provide a
comprehensive dataset to improve model parameterization, it is of great
importance to discriminate and classify the observations depending on
environmental conditions. The most trivial classification is the temperature
regime. Savre and Ekman (2015), showed that it is one of the major factors
(with cloud top entrainment) controlling the production of new ice crystals
and the maintenance of MPCs. In the present study, two temperature regimes
have been selected based on the mean cloud top temperature of each
situation: the “cold” situations (
Classification of the MPC situations according to temperature regimes and air mass origins.
The mean vertical profiles of temperature of these three regimes are displayed
in Fig. 3. The results show a well-pronounced temperature inversion
(
Vertical profiles (normalized altitude) of the mean temperature for each regime. Shaded spreads represent the standard deviation. The mean cloud base and top altitudes and their standard deviation for each regime are indicated.
The purpose of this section is to provide a quantitative assessment of the average microphysical and optical properties of the MPC cloud layers at a spatial scale of approximately 100 m. The vertical profiles presented in this study come from aircraft in situ measurements and are obtained from several distinctive clouds. It should be emphasized that these profiles cannot be strictly regarded as vertical and instantaneous profiles (each ascending or descending flight sequence is generally made in 5 to 10 min). It differs from the remote sensing measurements that usually provide snapshots of the same cloud.
Figure 4 displays the average vertical profiles expressed with the
normalized altitude reference for the liquid phase properties: the
extinction coefficient, the droplet number concentration, the liquid water
content and the effective diameter (Fig. 4a to d). These profiles are
obtained using FSSP-100 or Nevzorov probe measurements and constrained by PN
Vertical profiles (expressed in normalized altitude) of
liquid droplet properties from FSSP or Nevzorov probe measurements (3–45
The MPC properties are characterized by increasing values of LWC with
altitude. LWC mean values range between 0.1 g m
The main features of the vertical distribution for the liquid phase
properties are in agreement with previous observations (e.g.,
Lawson et al., 2001; McFarquhar et al., 2007 or Jackson et al., 2012). These
studies focused on MPCs in the western Arctic region under meteorological
situations that can be connected to the ones presented in our work.
Lawson et al. (2001) studied a boundary layer
MPC in spring over the Beaufort Sea during the First International Satellite
Cloud Climatology Regional Experiment Arctic Cloud Experiment (FIRE-ACE).
The temperature range lied between
McFarquhar et al. (2007) merged four MPC
situations (corresponding to 53 cloud profiles) in autumn over Barrow and
Oliktok Point, Alaska, during the Mixed-Phase Arctic Cloud Experiment
(M-PACE). The MPCs were associated with a low-level northeasterly flow over
the ice pack resulting in persistent roll clouds at low-level altitude.
Cloud top temperatures lied between
Vertical profiles (expressed in normalized altitudes) of
ice crystal properties from CPI measurements (15
The ice crystal properties derived from the CPI measurements when the PN
Typical IWC and particle concentration (for crystals with size larger than
125
Deeper in the precipitation layer, closer to the sea level (
Vertical profiles of particle shapes (from CPI
measurements and for particles larger than 100
The particle shape vertical distribution was also investigated based on the
CPI images. It can provide insight on the main microphysical growth
processes occurring in such MPCs. Figure 6 displays the particle shape
distributions relative to number and mass concentration with
Our results clearly show that rimed and irregular ice crystals are dominant
within MPCs (up to 80 % of the total). In particular, irregular ice
particles are encountered at all altitudes and temperatures. They account for
30 to 50 % of the total number concentration (and between 20
and 30 % of mass concentration) depending on the altitude or temperature
of the MPC layer. Rimed particles are predominant inside the liquid-containing
cloud layer (
An interesting feature is the significant occurrence (around 40 %) of ice
crystals with a predominant
Below the cloud (
Overall, these results agree with the ones presented in
McFarquhar et al. (2007) based on in situ
observations of MPCs during the M-PACE experiment. McFarquhar et al. (2007) also stated that small supercooled water droplets dominated the
upper layer of the cloud while larger ice particles were present in the
lower part and below the cloud (including irregular, aggregate or
rimed-branched crystals). But our results differ since they observed a
fraction of needles and columns particles a lot larger than in our study
(respectively, up to 50 % below the cloud versus less than 10 %). On the
contrary, our results are not in agreement with the observations described
in Korolev et al. (1999); this is because they
observed even less regular ice crystals: irregular-shaped ice crystals
accounted for up to 98 % of the total number of ice particles. This
disagreement could be explained by two reasons. First,
Korolev et al. (1999) considered a wide variety of
clouds sampled in the Canadian and US Arctic (stratocumulus and cirrus at
temperatures ranging from 0 to
The quantitative estimates of the separate properties of droplets and ice crystals may provide insight on the microphysical processes occurring in MPCs. These processes are involved in the MPC life cycle, in particular to maintain the coexistence of liquid droplets and ice crystals, leading to its persistence (Morrison et al., 2012). More specifically, the increase with height of droplet size and LWC observed in the vertical profiles is consistent with a condensational growth process. The slight decrease of LWC and number concentration observed at the very top of the cloud may be due to turbulent mixing (Korolev et al., 2015) and entrainment of dry air. Additionally, the data collected in this part of the cloud may also lead to a slight underestimation of the LWC since a mixing of cloudy and cloud-free patches could be averaged together given the sampling resolution (i.e., 100 m). The analysis of the vertical profiles of ice properties and ice crystal shapes (see Fig. 6) shows that the presence of pristine particles, mainly plates and stellars, could be linked to a very fast ice crystal growth by vapor deposition due to the WBF process in which ice crystals grow at the expense of liquid droplets. The large contribution of rimed particles confirmed that riming process shall be significant in a mixed-phase cloud. The prevalence of irregular particles is in agreement with the previous studies from Korolev et al. (1999) and McFarquhar et al. (2007) and suggests that aggregation growth processes or a combination of several growth mechanisms are involved. This also indicates that turbulence or mixing into the cloud may have an important influence by redistributing the precipitating ice crystals in the upper cloud levels. Measurements of the vertical wind speed (which are not available for these campaigns) would be helpful to confirm this hypothesis.
Theoretical adiabatic LWC has also been determined assuming a non-entraining parcel of moist air rising and reaching saturation. It is calculated from the pressure and temperature measurements from cloud base to cloud top. These theoretical values are then compared to the observed LWC values to evaluate the influence of turbulence or mixing effects on LWC as well as the efficiency of ice growth by WBF process or riming processes. The profiles of the adiabatic ratio (the ratio of the adiabatic LWC to the observed LWC) are displayed in Fig. 7. Subadiabatic values are found for all meteorological regimes. This means that processes responsible for a decrease of LWC compared to the adiabatic prediction are prevalent. In particular, this strengthens the assumption that a turbulent entrainment of dry air, resulting in the evaporation of liquid droplets, may occur at cloud top. Moreover, this confirms that the WBF and riming processes are efficient and responsible for the decrease of LWC compared to adiabatic values. These statements are in agreement with the study from Jackson et al. (2012), who showed for several boundary layer MPCs over Barrow, Alaska, during the ISDAC campaign that the subadiabatic profile of LWC and the decreasing droplet concentration at cloud top may be associated with the ice crystal growth processes involving the liquid phase (riming and WBF) and/or the entrainment of dry air from above.
Vertical profiles of the ratio of measured LWC over theoretical adiabatic LWC for the three regimes.
However, Figs. 4 and 5 also showed significant differences in cloud vertical
profiles from one regime to another. The COLD situations exhibit the largest
values for ice properties (IWC up to 0.075 g m
The adiabatic ratio, shown in Fig. 7, confirms this assumption where larger values are encountered for the WARM_SO situations. Indeed, a large adiabatic ratio denotes that processes responsible for the depletion of liquid droplets (mainly riming or WBF) are relatively less efficient.
The ice crystal properties relative to the WARM_NO situations
are similar to the WARM_SO cases, except for the effective
diameter where values are similar to the COLD regime (
The meteorological classification used in our study is also based on the air
mass origin since it shall impact the cloud microphysical properties, as
shown in Gultepe and Isaac (2002). In particular,
COLD and WARM_NO situations characterized by a northern air
mass origin should be associated with more pristine conditions and drier air
compared to the WARM_SO situations. Airborne in situ aerosol
measurements were only available during the POLARCAT 2008 campaign (with
particle counters). However, nearly continuous aerosol measurements (with
particle counters and sizers) but ground based were performed at the
Mount Zeppelin station (Ny-Ålesund, Svalbard, 475 m above sea level,
79
The prevalence of the ice phase for the COLD regime is thus consistent both with the cold temperature and the pristine conditions associated with northern air masses. Despite similar air mass origins, the WARM_NO cases exhibit a smaller concentration of ice crystals than the COLD situations. This suggests that the influence of the cloud top temperature prevails to promote the growth or production of ice crystals. The WARM_SO cases which combine warm temperatures and continental air masses clearly show that the ice crystal growth or production is reduced, as well as the precipitation efficiency, and that the liquid phase dominates the cloud structure.
Additionally, the comparison of the vertical profiles of MPC properties of the present work to the previous studies concerning the western Arctic in Sect. 3.2 showed that the cloud properties for the COLD and WARM_NO situations agree with that of M-PACE (REF), in particular in terms of ice concentration and IWC. The WARM_SO cases agree more with the ISDAC situations, in particular the low ice concentration. Jackson et al. (2012) explained the very low ice concentration observed during ISDAC as a consequence of more polluted situations encountered (compared to M-PACE) that might reduce the secondary ice crystal production efficiency (thermodynamic indirect effect). This conclusion is thus in accordance with our assumption that the air mass coming from the south may be more impacted by pollution and may reduce the ice growth efficiency.
These analyses show that microphysical properties of Arctic MPCs over the Greenland and Norwegian seas are closely linked to the cloud top temperature regime and the environmental conditions such as the air mass origin. Similar conclusions have already been made for MPCs in the western Arctic regions by Gultepe and Isaac (2002) who demonstrated the impact of the air mass origin (Pacific Ocean or Arctic Ocean) on the MPC microphysical properties.
However, a more thorough analysis involving collocated in situ aerosol measurements is obviously needed to confirm these findings. For instance, our results are somehow consistent with Lance et al. (2011) or Rangno and Hobbs (2001) who showed that “polluted” MPCs exhibit higher droplet concentrations and smaller ice-precipitating particles compared to “clean” MPCs. A large number of droplets are expected to reduce the riming process and thus contribute to the large observed values of LWC (as liquid droplets are not consumed by the ice crystals).
To go further into the analysis of our microphysical dataset, additional measurements of key parameters are necessary. In particular, quantifying the mechanisms responsible for the formation and growth of droplets and ice crystals within MPCs by measuring the numbers of IN and cloud condensation nuclei (CCN) is needed. It would enable us to perform an accurate ice closure and to quantify, for example, the possible impact of secondary ice production processes). A better characterization of the dynamical processes at cloud scale, with accurate high spatial resolution measurements of vertical wind velocities into and around the MPCs would also be necessary. For instance, upward air motion and turbulent entrainment of air from above the cloud are critical to maintain liquid water in MPCs. Accurate humidity measurements would also be needed to better identify condensational growth of ice crystals (WBF process or direct condensation of water vapor on ice, as described by Korolev, 2007) and resolve the issue of turbulence and mixing at cloud edges and into clouds. All these parameters, along with radiative flux measurements, are of primary importance to constrain our assumptions on the microphysical processes.
At last, coupling our results (and further observations with new parameters and improved instrumentation) with modeling is of course the best way to quantify the relative impact of each process on the MPC lifetime. However, such work remains beyond the scope of the present study.
In Sect. 3, we have shown that in situ data provide a detailed characterization of the microphysical and optical properties of MPCs. These measurements can also be used to develop cloud parameterizations and to evaluate remote sensing retrieval products or modeling outputs. This section focuses on the key properties and hence parameters which must be better quantified (Morrison and Pinto, 2006), namely (i) IWC (and LWC) – extinction coefficient relationships, (ii) the variability of the ice and liquid water paths, (iii) the temperature-dependent ice crystal concentration and (iv) the liquid water fraction (ratio of LWC over total water content) as a function of the cloud level or temperature.
Linking cloud microphysical and optical properties is an important step in order to model the cloud radiative properties or to constrain/develop remote sensing retrieval methods. In particular, accurate IWC–extinction relationships and integrated properties such as ice and liquid water paths are needed to improve the remote sensing retrieval products and cloud modeling (Heymsfield et al., 2005; Waliser et al., 2009). In this section, we provide such relationships and parameters based on in situ measurements.
Figure 8a and b display the IWC and the LWC measurements as a function of
the ice and droplet extinction coefficients, respectively, with the
temperature superimposed in color. The averaged values of IWC and LWC over
intervals of 0.1 and 2 km
It should also be noted that including the temperature as an additional
parameter for the linear fitting did not improve the accuracy of the
parameterizations, contrary to previous studies of
Heymsfield et al. (2005),
Hogan et al. (2006), or Protat et al. (2007, 2016). However, these previous studies
concerned tropical and midlatitude clouds and cover a much broader range of
temperatures (from
Integrated properties such as liquid water path (LWP) and ice water path (IWP) are common modeling outputs which
suffer from large discrepancies depending on the model specifications
(Waliser et al., 2009). Moreover, only a very limited number of studies were devoted to retrieving these properties from in situ
measurements in this region of the Arctic. Since the
flight legs selected in our study target ascending and descending sequences
into single-layer MPCs, in situ measurements can be used to determine IWP and LWP
according to the following equation:
We recall that these integrated properties should be considered quasi-instantaneous, as ascending and descending flight sequences are obviously not fully vertical and need about 5–10 min to be performed (compared to the snapshots performed by remote sensing measurements).
Figure 8c displays the ice (green) and liquid (blue) water paths as a
function of the cloud top temperature (1
The accurate knowledge of the ice crystal concentration is of primary importance to correctly parameterize the initiation and evolution of the ice phase in models and reduce the significant uncertainties in the modeling of the ice/liquid partitioning within MPCs.
Maximum ice crystal concentration as a function of cloud top temperature. The colored circles represent the values for each profile (with fitting in the black solid line and mean absolute error in dotted lines). The Meyers et al. (1992), Cooper (1986) and Young et al. (2017) parameterizations are also displayed in purple, orange and brown dashed lines, respectively.
Figure 9 shows the maximum number concentration of ice crystals with size
greater than 100
The parameterizations of Cooper (1986) and Young et al. (2017) do not match with the present parameterization since the ice crystal concentrations predicted are around 1 order of magnitude lower than the ones in the present study. This difference can be explained by the different seasons, cloud types and locations of the observations used for the parameterization of Cooper (1986) and the fact that the range of their measured concentrations lies within a factor of 10 as they noted.
In contrast, the sampling conditions for the determination of the
Young et al. (2017) parameterization are more similar to the
present work; they used measurements in Arctic MPCs over the Greenland Sea.
The dataset was collected during spring and summer, above open sea, ice
sheet and transitions. This variability in the seasons and surface
conditions may explain the differences observed compared to the present
work. Above all, Young et al. (2017) displayed an averaged
concentration, whereas the maximum ice number is presented here. However, even by
taking the averaged ice concentrations in the present work, the
parameterization does not match with that of Young et al. (2017)
(not shown here). Finally, the detailed time series displayed in the
Young et al. (2016) and Lloyd et al. (2015) works which present the cases used for the determination of the
parameterization of Young et al. (2017) showed that the maximum ice
number concentrations frequently displayed values between 1
and 5 L
Our results could not be compared to more sophisticated parameterizations accounting for supersaturation and aerosol properties (such as (DeMott et al., 2011) since additional data are needed (aerosol and CCN/IN measurements, humidity). These additional data are also necessary to discuss the processes such as the secondary ice production processes which could explain the higher crystal numbers observed in the present study compared to the other works presented in this section.
The MPC liquid fraction can be determined based on the separate liquid and ice properties presented in Sect. 3. The liquid water fraction (hereafter LWF) is defined as the ratio of LWC over the TWC (IWC plus LWC) at each altitude level.
To our knowledge, very few previous studies have assessed the liquid water fraction in MPCs. Most of them were concerned with MPCs only in western Arctic regions (de Boer et al., 2009; McFarquhar et al., 2007; Shupe et al., 2006).
Liquid water fraction according to
Figure 10a displays the liquid fraction according to the normalized altitude
They used in situ measurements from 53 profiles in single-layer MPCs sampled over
Alaska with temperatures ranging from
Figure 10b shows the liquid fraction according to cloud top temperature.
Each point represents the mean value of the liquid fraction determined for
each profile. The error bars corresponding to the standard deviation display
large values around 80 %, which is indicative of a large variability. Nevertheless,
Fig. 10b shows that LWF is well correlated with the cloud top temperature
(Eq. 8). The decrease in LWF associated with a decrease of
temperature is consistent with Fig. 9 which shows that ice number
concentration increases for colder temperatures.
However, large shifts are observed from one regime to another, especially when comparing the COLD regime to the WARM_NO and the WARM_SO. This shift is clearly linked to the temperature profiles (see Fig. 3). However, one can note that the results for the WARM_NO regime are the ones in the closest agreement with the MF07 parameterization.
In order to compare our results to those of Shupe et
al. (2006), we also determined the total liquid water fraction
(LWF
In this study, a characterization of Arctic boundary-layer mixed-phase clouds microphysical properties has been performed. In situ data from four airborne campaigns over the Greenland Sea and the Svalbard region are compiled and analyzed. The dataset represents in total 18 flights and 71 vertical profiles in MPCs (more than 350 min of cloud in situ observations). Cloud phase discrimination is achieved and vertical profiles of the number, size, mass and shape of ice crystals and liquid droplets within MPCs are determined.
The main conclusions of the present work are summarized as follows:
Liquid phase is mainly present in the upper part of the MPCs with high
concentration of small droplets ( The vertical profiles of the microphysical properties and the shape
distribution can also be used to provide insight on the microphysical
processes occurring in MPCs. It is likely that adiabatic lifting
(condensation) is the main process for liquid droplet initiation and
growth, and that evaporation at cloud top due to entrainment of dry air
seems to occur. In the cloud layer, where liquid droplets and ice crystals
coexist, the Wegener–Bergeron–Findeisen and riming processes are the main
mechanisms involved in the ice crystal growth. The large occurrence of
irregular particles highlights the fact that the ice crystals undergo a
variety of growth processes, and the turbulence in the MPC life cycle is
efficient for mixing the cloud.
The analysis of the scattering phase function showed a very high correlation
between optical properties and liquid to ice fraction within the MPC layers. Statistical analysis exhibits significant differences in the vertical
profiles of MPC properties depending if the cloud top is cold or warm and if
the air mass originates from higher or lower latitudes. The largest
droplet concentration and LWC values observed (200 cm The main results of the present work were compared to the previous studies
which concern mainly MPCs in the western Arctic region. The main findings
showed that the properties of the COLD and WARM_NO situations
(large values of ice properties) of the present work are consistent with the
rather clean situations of previous western Arctic studies such as M-PACE.
On the contrary, the MPC properties of the WARM_SO cases
(prevalence of liquid phase and very low values of ice properties) are more
in agreement with the more polluted situations in the western Arctic, such as
ISDAC. These findings confirm that the MPC properties are strongly linked to
the environmental conditions such as temperature and air mass origin. Several parameterizations for remote sensing or modeling are proposed.
This concerns the determination of IWC (and LWC) – extinction relationships, ice
and liquid integrated water paths, the ice concentration and liquid water
fraction. Comparisons with the few previous works available in the
literature showed, in general, a good agreement. Obviously, the application
range of the established relationships is only for Arctic MPCs and
temperature range between 0 and
This study provided, for the first time, a statistical analysis of Arctic MPC
in situ data from four airborne campaigns located in the eastern Arctic region. An
accurate characterization of the vertical variability of liquid droplet and
ice crystal properties has been made, allowing the development of
parameterizations.
Further studies should involve new measurement techniques to provide accurate
characterization of cloud phase and microphysical properties, in particular
for the small particles. This will allow to complete and validate the
present results. For example, instruments like the small ice detector
(SID-3; Ulanowski et al., 2014;
Vochezer et al., 2016) or the cloud particle spectrometer with polarization
detection (CPSPD; Baumgardner et al., 2014)
should provide valuable measurements to differentiate droplets from ice
crystals even at sizes lower than 50
All the information to access to the cloud in situ data is on the Arctic data portal website:
The methodology developed by Lawson and Baker (2006) to derive
the IWC from 2-D particle images recorded by the CPI
instruments is applied (Eq. A1 below).
The extinction coefficient (
The LWC derived from the Nevzorov probe measurements is calculated according
to Korolev et al. (1998):
The epsilon terms refer to the collection efficiencies of liquid droplets
(l index) or ice crystals (i index) on the LWC and TWC sensors. These
efficiencies are set as follows:
The uncertainties associated with the microphysical and optical properties
derived from FSSP-100, PN, Nevzorov and CPI measurements are detailed in
Baumgardner and Spowart (1990), Gayet et al. (2002), Korolev et al. (1998), and Mioche (2010), respectively,
and are summarized in Table 2.
Comparison of extinction from PN and FSSP plus CPI
measurements. Grey bars represent the 25 % uncertainties on the PN
extinction. The red dotted line is the linear fitting (slope of 0.98,
Techniques and methods exist now to avoid or estimate this shattering effect, such as newly designed inlets or measurements of the particles' interarrival time (Field et al., 2003), but none of these were available for this study. However, in order to assess the accuracy of the present dataset and highlight a possible impact of the shattering effect, a brief intercomparison of the extinction coefficient from the three datasets was conducted. Indeed, the extinction coefficient is the only parameter which can be derived by the measurements of the three probes. Moreover, it is not determined with the same method, since it is calculated from the PSD for the CPI and the FSSP and from the scattering phase function for the PN. One more important point is that CPI, FSSP and PN all have different size inlets (23, 40 and 10 mm diameters, respectively). So, from this information, we could assume that, if a shattering effect is present on ice particles, its magnitude (i.e., the number of smaller new artifact particles) would differ from one instrument to another. Thus, the comparison of the extinction coefficient from CPI, FSSP and PN measurements would highlight such discrepancies. Figure B1 displays the comparison of the extinction coefficient derived from the PN and from the combination of the CPI and FSSP for all the in situ data available for this study. Note that the combination of CPI and FSSP data covers the same size range of the PN. Figure B1 clearly shows that the extinction coefficient measurements derived from the combination of the CPI and FSSP and the PN are very well correlated (with a coefficient of 0.87) and no significant bias is observed (regression coefficient of 0.98). Thus, since the design of the instruments and data processing are different for each dataset, these results highlight that the shattering effect is probably smaller than the measurement uncertainties (25, 35 and 55 % for PN, FSSP and CPI, respectively; see Table 2).
The authors declare that they have no conflict of interest.
This research was funded by the Centre National de la Recherche Scientifique
– Institut National des Sciences de l'Univers (CNRS-INSU) and the Expecting
EarthCare Learning from A-Train (EECLAT) project. We thank the Alfred
Wegener Institute (AWI) and the Service des Avions Français
Instrumentés pour la Recherche en Environnement (SAFIRE) for the
organization of the campaigns and for providing research aircrafts. The
authors acknowledge the NOAA Air Resources Laboratory (ARL) for the
provision of the HYSPLIT transport and dispersion model and READY website
(