The Arctic region is known to be very sensitive to climate change. Clouds
and in particular mixed-phase clouds (MPCs) remain one of the greatest
sources of uncertainties in the modelling of the Arctic response to climate
change due to an inaccurate representation of their variability and their
quantification. In this study, we present a characterisation of the
vertical, spatial and seasonal variability of Arctic clouds and MPCs over
the entire Arctic region based on satellite active remote sensing
observations. MPC properties in the region of the Svalbard archipelago
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It is now well established that the Arctic region is more sensitive to climate change than any other regions of the world (Sanderson et al., 2011; Serreze et al., 2009; Solomon et al., 2007). Solar elevation or surface albedo, for instance, are responsible for strong feedback mechanisms leading to the so-called Arctic amplification. Clouds are one of the main components driving the Arctic climate system as they play a key role in the radiation budget (Curry, 1995; Curry et al., 1996; Garrett et al., 2009). They interact with shortwave and longwave radiations, cooling or warming the surface and the atmosphere depending on their macrophysical and microphysical properties. The low sun elevation in summer and the lack of solar radiation during the winter polar night are responsible for the predominant longwave radiative effect in the Arctic (Lubin and Vogelmann, 2006), tending to a regional net warming effect.
However, our sparse knowledge of cloud-radiation interactions and cloud properties at high latitudes remains one of the main source of uncertainties in predicting future climate by numerical simulations (Solomon et al., 2007; Stephens, 2005). In particular, the determination of the cloud thermodynamic phase is crucial for assessing the cloud radiative impact and hence, its influence on the radiation budget and climate feedbacks (Choi et al., 2014; Komurcu et al., 2014).
In addition, the Arctic is known for the frequent occurrence of mixed-phase clouds (hereafter referred to as MPCs) especially near the surface, wherein liquid droplets and ice crystals coexist (Curry et al., 2000; Korolev and Isaac, 2003; Shupe, 2011). In particular, these clouds exert a large influence on the surface radiation budget Shupe and Intrieri, 2004). However, the inter-annual spatial variability of Arctic MPC properties has been poorly quantified at a large scale. An assessment of the annual variability of MPCs was performed during the Surface Heat Budget of the Arctic Ocean experiment (SHEBA, Uttal et al., 2002), showing that MPC and liquid-containing clouds are prevalent over the Western Arctic Ocean. The SHEBA experiment highlighted that liquid-containing clouds (i.e. including supercooled and warm liquid clouds) over the Beaufort sea represent about 40 % of the clouds during winter and reach more than 90 % in summer (Intrieri et al., 2002). Additionally, Shupe et al. (2006) showed that nearly 60 % of the clouds in the Arctic are MPCs.
Even though numerous observations, as well as numerical modelling focused on MPCs for several years (Verlinde et al., 2007; de Boer et al., 2009; Gayet et al., 2009; Jourdan et al., 2010; McFarquhar et al., 2011, among others), results still suffer from large uncertainties and important discrepancies are observed between observations and simulations (Chernokulsky and Mokhov, 2012; Klein et al., 2009; Morrison et al., 2009; Thomas et al., 2004).
This implies that our understanding of all steps of the MPC life cycle needs to be improved. Indeed, formation, development, persistence and dissipation of MPCs are governed by a combination of local and large-scale processes (Morrison et al., 2012). At the small scale, the Wegener–Bergeron–Findeisen (WBF) process is one of the main mechanisms responsible for ice crystals growth at the expense of supercooled water droplets (Bergeron, 1935; Findeisen, 1938; Wegener, 1911). Such a mechanism leads to a rapid glaciation of the MPCs. On the other hand, dynamical processes, such as turbulence or entrainment may facilitate the formation of new supercooled water droplets. For example, resupply of water vapour from the surface or from entrainment of moisture above the clouds may contribute to the continuous formation of liquid droplets within MPC. The coupling of such various processes is, thus, necessary to maintain the unstable equilibrium between liquid droplets and ice crystals within MPCs. This may explain the longevity of MPCs, which can last up to several days or weeks as has been frequently observed (Shupe, 2011; Verlinde et al., 2007; Morrison et al., 2012). Previous studies from Korolev et al. (2003), Korolev and Isaac, (2003) and Korolev (2007) also point out that the lifetime of MPCs could not be simply reduced to the WBF process, but also depends on numerous parameters such as local thermodynamical conditions or is linked to cloud dynamics. Local and long-range dynamic processes are also involved in aerosol, heat and moisture transport, which have a significant impact on Arctic MPC formation and properties (Cesana et al., 2012; Morrison et al., 2012; Shupe and Intrieri, 2004). It remains, therefore, difficult to fully understand the complexity of interactions between all these processes and to assert which of them play a key role in the MPC evolution (Harrington et al., 1999; Morrison et al., 2012).
The characterisation of the Arctic cloud mixed-phase state at both global and local scales is not yet accurately described, since the remote and extreme conditions encountered in this region of the world remain particularly challenging. For these reasons observations remain very sparse. Ice crystals and liquid droplets exhibit significantly different microphysical and optical properties (number, size, shape), leading to different interactions with radiation. The impact of mixed-phase clouds on the energy budget and climate is thus strongly dependent on the ice/liquid partitioning. Moreover, the determination of separate properties of ice crystals and liquid droplets within the same sample volume remains challenging due to instrument limitations in both remote sensing and in situ measurements. Moreover, the definition of the “mixed-phase” state depends strongly on the observation scale (Baumgardner et al., 2012).
Multi and single-layer clouds are the two main MPC types frequently observed
in the Arctic. Multi-layer MPCs consist of several supercooled liquid layers
embedded in ice clouds at different altitude levels. Ice crystals
precipitate from upper layers, feeding underneath layers or evaporating
during sedimentation, leading to very complex interactions between ice
crystals and droplets (Hobbs and Rangno, 1998; Luo et
al., 2008). On the other hand, the typical single-layer MPC structure is
characterised by the presence of a single supercooled liquid layer at cloud
top and ice crystals below, precipitating down to the surface
(Curry et al., 1997; Shupe et al., 2005; Verlinde et al., 2007; Gayet et al., 2009). These particular clouds have been
frequently observed in situ in the Arctic at a smaller scale for several years
in previous airborne experiments, such as:
in 1994, the Beaufort and Arctic Storms Experiment (BASE, Curry et
al. (1997); in 1998, the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment Arctic Clouds Experiment (FIRE-ACE, Curry et al., 2000); in 2004, the Mixed-Phase Arctic Cloud Experiment (M-PACE, Verlinde et al., 2007); in 2004 and 2007, the Arctic Study of Tropospheric cloud, Aerosol and Radiation (ASTAR, Gayet et al., 2009; Jourdan et al., 2010); in 2008, the Polar Study using Aircraft, Remote Sensing Surface Measurements and Models of Climate, Chemistry, Aerosols and Transport (POLARCAT, Delanoë et al. (2013)
and the Indirect and Semi-Direct Aerosol Campaign (ISDAC, McFarquhar et al., 2011); in 2010, the Solar Radiation and Phase Discrimination of Arctic Clouds experiment (SORPIC, Bierwirth et al., 2013); in 2012, the study on the Vertical Distribution of Ice in Arctic clouds (VERDI, Klingebiel et al., 2015); in 2014, the Radiation-Aerosol-Cloud Experiment in the Arctic Circle (RACEPAC).
However, in situ measurements are by essence localised in time and space. It is therefore of great importance to know in which regional framework such observations are performed. So, a description of Arctic MPCs at regional scale is of utmost importance.
Although taking cloud measurements still remains challenging in this region,
important progresses have been made over several years. In particular,
several ground-based observation sites are now well equipped for cloud
observations, such as Barrow (71
Observations based on passive remote sensing measurements performed by instruments such as AVHRR, ISCCP, MODIS (Frey et al., 2008; Key and Barry, 1989; Rossow and Garder, 1993) have well-known limitations in the Arctic region. Previous studies, based on comparisons with ground-based observations or spaceborne active remote sensing measurements, highlighted that passive remote sensing observations might drive to an underestimation of the cloud fraction by up to 35 % Schweiger and Key, 1992), in particular at low-level altitudes (Chan and Comiso, 2013). These limitations are mostly due to the low contrast between clouds and the underlying surface.
Spaceborne active remote sensing is a recent alternative measurement
technique that aims to reduce these limitations. This is the case for
the new generation of spaceborne instrumentation onboard CALIPSO
(Winker et al., 2003) and CLOUDSAT
(Stephens et al., 2002) satellites. Since 2006, they
perform lidar (532 nm and 1064 nm) and radar (94 GHz) measurements, and
provide an unprecedented data set of the vertical structure of clouds,
continuously in time and space (up to latitude 82
The aim of this study is to characterise the vertical, spatial and seasonal
variability of Arctic MPCs at a regional scale from space remote sensing. A
focus on MPC properties in the area around the Svalbard archipelago
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This work is based on the lidar–radar spaceborne measurements synergy of CALIPSO/CLOUDSAT observations. Cloud thermodynamic phase and distribution are determined from DARDAR retrieval products (Delanoë and Hogan, 2008, 2010, and recently Ceccaldi et al., 2013). Then properties over the Svalbard region are investigated and compared to those at the regional scale. Section 2 describes the DARDAR retrieval algorithm and presents the data set used and the method applied to detect MPC and determine cloud occurrences. In this section, uncertainties and limitations of DARDAR retrievals are analysed. In Sect. 3, total cloud occurrence determined from DARDAR products is first compared to previous works in order to validate the methodology. Then Sect. 4 focuses on mixed-phase clouds variability at regional scale and over the region of Svalbard. In Sect. 5, regional and local MPC properties are compared and differences observed between the whole Arctic and the Svalbard region are discussed.
Cloud thermodynamic phase distributions over the Arctic region are studied using CALIPSO and CLOUDSAT measurements. Active remote sensing observations are expected to provide a detailed characterisation of the cloud vertical structure in the Arctic. Indeed, compared to passive instruments, active remote sensing techniques are less disturbed by the important Arctic ice cover throughout the year or the lack of sunlight during the polar night (Chan and Comiso, 2013; Liu et al., 2010; Wang and Key, 2005).
DARDAR classes used in V1 version.
The present study is based on the operational DARDAR (for raDAR/liDAR)
retrieval products. The DARDAR algorithm (Delanoë
and Hogan, 2008, 2010) exploits the synergy of CALIPSO and CLOUDSAT
observations to retrieve vertical profiles of cloud thermodynamic phase
(DARDAR-MASK product) and ice cloud properties (DARDAR-CLOUD product). These
products are now widely used for cloud studies
(Delanoë et al., 2013; Huang et
al., 2012; Jouan et al., 2012, 2014). DARDAR-MASK algorithm uses Level 1B
CALIPSO 532 nm lidar backscatter coefficient profiles, “2B-GEOPROF”
CLOUDSAT 94 GHz radar reflectivity, as well as thermodynamic variables
(pressure, temperature, humidity) from “ECMWF-AUX” products. It presents
the great advantage of being able to merge all of these data on the same resolution grid and
to allow the cloud phase discrimination. Pixel size along the track is 60 m
vertically and 1.7 km horizontally. The combination of both lidar and radar
cloud masks is used to classify the cloud hydrometeors phase. The main steps of
the algorithm for cloud thermodynamic phase retrieval are summarised in Fig. 1 and all DARDAR retrieval classes are summarised in Table 1. This includes
surface and clear sky detection (class
Representation of the DARDAR algorithm retrieval steps for cloud thermodynamic phase.
A complete description of DARDAR algorithm is presented in Delanoë and Hogan (2008, 2010) and in Ceccaldi et al. (2013). Version 1 of DARDAR-MASK (v. 1.1.4), distributed by the French data centre ICARE, is used in this study and provides a categorisation of pixels according to the classes summarised in Table 1.
In this study, mixed-phase clouds are defined based on DARDAR
classification. All DARDAR pixels satisfying one of the following two
conditions are assumed to belong to MPCs:
they are classified as a mixing of ice and supercooled water (class 2, see Table 1); they are classified as supercooled only (class 4, see Table 1) associated with the
presence of ice or ice and supercooled water mixing in the three vertical adjacent pixels.
Note that “isolated” supercooled water pixels are not classified as mixed-phase clouds.
The annual and seasonal variability of the total cloud fraction and MPC is
investigated using CALIPSO/CLOUDSAT measurements performed continuously from
2007 to 2010. In this study the Arctic region is defined by all latitudes
between 60
The spatial frequency of occurrence and vertical distributions are
determined for both total cloud and MPC. The spatial distribution of clouds
is studied for altitudes spanning from 500 m up to 12 km (according to the
study of Liu et al., 2012) as well as for low-level altitudes
ranging between 500 m and 3 km (according to Zygmuntowska et al., 2012). A
minimum altitude of 500 m is chosen to avoid surface contamination in the
CLOUDSAT data (Marchand et al., 2008) and also to avoid
false water detection (due to the lidar high backscattered signal from the
ground) in the DARDAR algorithm. These instrument shortcomings will be
discussed further in Sect. 2.3. The seasonal occurrence is calculated for
March-April-May (MAM for spring), June-July-August (JJA for summer),
September-October-November (SON for autumn) and December-January-February
(DJF for winter). The horizontal spatial resolution of cloud occurrence is
5
For each season (or month), cloud and MPC spatial occurrences (hereafter
Before deriving a climatology of the MPC occurrence, we first have to keep in mind that our analysis of CALIPSO and CLOUDSAT observations and therefore DARDAR retrieval products suffer from uncertainties due to both measurement technique and the retrieval method. In the following we will discuss these limitations and their potential impact on the results presented in this study.
Space remote sensing has inherent and well-known shortcomings near the ground. For example, if the radar clutter is not properly removed, CLOUDSAT radar ground echoes lead to an overestimation of low-level cloud amount (Marchand et al., 2008). On the other hand, the probability of total or partial attenuation of lidar laser beam by upper cloud layer increases when approaching ground level and could induce an underestimation of the low-level cloud amount assessed by the lidar. The difficulty for the DARDAR algorithm to distinguish ground lidar attenuated backscattering signal from an overlaying liquid layer is another shortcoming in the retrieval method, which can lead to a cloud misclassification. The final impact (overestimation or underestimation) of these different issues on low-level cloud occurrence remains uncertain.
As described in Sect. 2.2., a minimum altitude threshold of 500 m is chosen to compute cloud occurrence. However, ground contamination could also affect CLOUDSAT cloud detection measurements above an altitude of 500 m.
We recall that the DARDAR MASK algorithm is based on the CLOUDSAT cloud mask (and also the CALIPSO lidar mask) to classify pixels as clouds (Delanoë and Hogan, 2010). Regarding CLOUDSAT measurements, a pixel is considered as cloud if its radar mask reports a value of 30 or greater (indicating a high confidence in the cloud detection, according to Mace, 2007). Values smaller than this threshold are considered inaccurate and possibly contaminated by ground echoes. Therefore, DARDAR MASK algorithm excludes data contaminated by ground. This implies that the data set is reduced between 500 m and 1000 m. The fraction of reliable data (clear sky or atmospheric features) has been determined and represents about 40 % of the total data in this altitude range. The remaining 60 % correspond to data assigned either to ground contamination on radar data (70 %) or to lidar total attenuation (30 %). This result gives an insight on how ground contamination affects the observations and reduces the data set in the 500 m–1 km altitude range.
Moreover, in previous studies, Kay and Gettelman (2009) used
a detection altitude limit of 720 m to prevent false detection. On the other
hand, Huang et al. (2012) showed that DARDAR data between 700 m and 1000 m were reliable for cloud occurrence determination. In the present study, a
short sensitivity test is made by computing cloud occurrences with a
threshold of 1000 m in order to check differences with the occurrences
obtained with a 500 m altitude threshold. As expected, the use of a 1000 m
threshold leads to a decrease of the cloud occurrences compared to a 500 m
threshold. Differences lie between 4 % and 18 % depending on the cloud
type and the altitude domain considered. These results are consistent with
similar tests made on CALIPSO/CLOUDSAT standard NASA products by
Zygmuntowska et al. (2012), showing that
The unique way to accurately assess and quantify the limitations of DARDAR
products in the low-level altitude domain is to compare them with collocated
ground-based measurements. Such a work has been recently done by
Blanchard et al. (2014). They directly compared ground-based
observations at the Eureka Arctic station, located in the Canadian
archipelago (80.2
To complement the study of Blanchard et al. (2014) a short comparison is made between the occurrence of low-level clouds using the DARDAR product and the one derived from ground-based locations available in previous studies.
Vertical profiles of cloud occurrences over the Ny-Ålesund station during March and April 2007. The black line refers to ground-based lidar observations at 1 km vertical resolution (Hoffmann et al., 2009) and the coloured lines refer to a DARDAR product, according to different configurations and settings. DARDAR retrievals have a vertical resolution of 60 m on the left panel and 1 km on the right panel.
Figure 2 shows the comparison of the vertical profile of cloud occurrence from the DARDAR product (with various configurations and different settings, cf. legend of the figure) with that from Ny-Ålesund ground-based observations (Micro Pulse Lidar (MPL) measurements during March and April 2007, see Hoffmann et al., 2009). Figure 2a displays DARDAR profiles at the original vertical resolution (60 m). In Fig. 2b the DARDAR profiles are shown with 1 km vertical resolution, to be consistent with the measurements performed by Hoffmann et al. (2009). Above approximately 5 km, DARDAR detects more cloud than the ground-based lidar. This is due to the attenuation of the ground-based lidar signal at high altitude levels. A proportion of high clouds, probably the optically thin ones, are also missed when considering radar measurements alone. These results are in agreement with those presented in Blanchard et al. (2014). Between 2 and 5 km, space and ground-based observations agree quite well, as also shown in Blanchard et al. (2014). Below 2 km, cloud occurrences from space observations are larger than from ground based by about 20 % between 1 km and 2 km, and by about 10 % below 1 km. One can also note that if ground clutter is not removed (red curve), cloud fraction may be greatly overestimated by up to 40 %.
Seasonal occurrence of MPCs over the Eureka station from June 2006 to December 2007 from ground-based observations (blue line from De Boer et al., 2009) and from DARDAR product (green and red lines).
Figure 3 displays ground-based observations of the single-layer MPC seasonal occurrence over the Eureka station (from de Boer et al. (2009), blue line) as well as the one derived from DARDAR products (red and green lines). The results show that DARDAR and ground-based observations are in good agreement, in particular regarding the annual variability. The maximum discrepancies (5 %) occur in autumn even though no systematic bias can be identified. One can also note that the choice of a 500 m or 0 m minimum altitude threshold on DARDAR data does not significantly impact the cloud occurrence.
These results show that the variability of
The uncertainties of DARDAR products in the low-level altitude domain remain thus difficult to accurately assess, but some conclusions can be drawn regarding these results. First, the DARDAR product is reliable above 2 km. Secondly, below 2 km, uncertainties in cloud or MPC occurrence is up to 20 % between 500 m and 2 km, and up to 25 % below 500 m when considering ground-based observations as a reference. This information will be considered in the discussion regarding cloud and MPC occurrences results below.
One more ambiguity concerning MPCs is the ability of remote sensing to detect several liquid layers. Indeed, the lidar suffers from a strong attenuation when liquid is present and therefore it is quite difficult to detect more than one supercooled liquid layer. Therefore, the detection of multi-layer MPCs from remote sensing observations may be biased, inducing an inaccurate classification of multi-layer MPCs in single-layer MPCs, and thus a possible overestimation of the ratio of single-layer MPCs to multi-layer ones. Note that so far none of the remote sensing techniques (ground or satellite) can address this. The only solution remains to fly an aircraft between two layers.
An additional possible source of uncertainty concerns the DARDAR retrieval
algorithm. DARDAR detects supercooled liquid (and warm liquid) layers from a
strong lidar backscatter signal (see Fig. 1). But some additional pixels
with a strong backscatter signal located above and below the liquid layer
can also be misclassified as liquid. So, these additional supercooled pixels
may lead to an overestimation of supercooled liquid water occurrence and
supercooled liquid layers thickness. The present study uses version 1 of the
DARDAR products, but a new algorithm (Ceccaldi et al., 2013)
is expected to remove these additional pixels, which may be currently
included in version 1 of DARDAR MASK. The detailed study by
Ceccaldi et al. (2013) showed that ice fraction will increase by a factor
of about 15 % in the future version 2, associated to a decrease of the
supercooled water fraction (up to 25 %), as compared to version 1.
Therefore, version 1 may overestimate supercooled liquid layers thickness,
and thus also the occurrence of mixed-phase clouds. However, the DARDAR V2
product is currently not available for the entire CALIPSO/CLOUDSAT data set,
and the results from Ceccaldi et al. (2013) are based on one
case study and 3 months of data only. So, in order to quantify how these
differences can affect the results of the present study, comparisons are
performed between the two product versions over 1 month of data (April
2007). Figure 4a shows differences between the two versions in terms of
At last, as shown in Fig. 1, supercooled and mixed-phase pixels detection from the DARDAR algorithm is dependent on the a priori threshold considered for variables such as temperature or layer thickness. In particular, temperature is determined based on ECMWF reanalysis, and its accuracy may be a source of error in supercooled liquid retrieval algorithm. However, the ECMWF temperature uncertainty is estimated to 0.6 K (Benedetti, 2005), which is considered to be acceptable by Delanoë and Hogan (2010).
The Arctic region is considered northward 60
In this section, the total cloud occurrence (
Monthly total cloud occurrence in the Arctic from ground and
spaceborne remote sensing observations:
Figure 5a shows the monthly total cloud occurrence
Stereographic projections of the seasonal occurrence of:
Ground-based observations are expected to capture more accurately low-level
clouds below 500 m Blanchard et al. (2014) and discussion in
Sect. 2.3). This could explain the differences between space borne and
ground-based measurements observed in Fig. 5a. However, the variability
within the ground-based measurements still remains significant due to the
discrepancies of the measurement techniques or the localisation of the
observatories. Furthermore, comparing the annual or seasonal variability
over Western Arctic regions (Fig. 5a) and over the Svalbard region (Fig. 5b), no noticeable trend is observed: in Fig. 5b, Ny-Ålesund ground visual
and MPL observations show a maximum occurrence in summer and early autumn
(
The standard NASA retrieval products from CALIPSO/CLOUDSAT in 2007 and 2008
(2B-GEOPROF-LIDAR products, Zygmuntowska et al., 2012) as
well as the DARDAR products are also reported in Fig. 5a. Both products show
an annual variability with two maxima in spring and autumn. As expected,
NASA and DARDAR products are consistent, justifying the methodology based on
DARDAR products proposed for this study. The small differences observed
between the two products could result from the data processing methodology
(differences linked to the geographical domain of the studied area and the
surface type taken into account as well as the difference in the vertical
resolution between NASA and DARDAR products). Figure 5b shows that the
annual variability of
Finally, Figs. 5a and b highlight that, except during summer, the Svalbard region exhibits a year-round total cloud occurrence exceeding the mean Arctic occurrence by at least 5 %. This is especially true in winter and spring, where the cloud occurrence over Svalbard is larger than the Arctic average by about 20 %.
The arctic stereographic projections of the mean seasonal occurrences of
clouds (
Monthly total MPC (
Figure 8a presents the vertical profiles of the mean seasonal vertical
distribution (
Monthly-averaged ratio of low-level MPC-IB over low-level MPC over the Arctic region (top) and over the Svalbard region (bottom).
Finally, Fig. 8b displays the mean seasonal
At last, we determine the occurrence of a specific type of Arctic MPCs: single-layer mixed-phase clouds characterised by a single supercooled liquid layer at the cloud top and ice below, precipitating down to the surface. This type of cloud will be noted thereafter MPC-IB (for mixed-phase cloud with ice below). In DARDAR products, pixels identified as mixed-phase and presenting at least three ice pixels below are classified as MPC-IB type. Clouds characterised by ice pixels above a liquid layer (i.e. corresponding to an embedded mixed-phase) are rejected.
First, we estimate the proportion of MPC-IB among MPCs. Figure 9 displays the ratio of MPC-IB over MPC for the low-altitude domain (500 m–3 km), both for the entire Arctic and the Svalbard region. The variability of this ratio is rather small throughout the year. The proportion of MPC-IB among MPC varies between 55 and 70 % over the whole Arctic and between 65 and 80 % over the Svalbard region.
As was done for MPCs, the main characteristics of the MPC-IB occurrences are studied. The results are not shown here because the vertical distribution and seasonal variability of MPC-IB clouds are quite similar to those of MPCs: they are present all along the year, more frequently above open water than land or sea ice, and more frequently in the Svalbard region, particularly in spring.
However, MPC-IB statistics must be used with caution as the measurement uncertainties linked to the discrimination of multi-layer MPCs from single-layer MPCs based on CALIPSO/CLOUDSAT observations remains difficult to assess, as described in Sect. 2.3.2. For the purpose of comparison, only a few previous studies tried to quantify the multi-layer cloud occurrence. For example, Verlinde et al. (2013) and Intrieri et al. (2002) describe that multi-layer clouds may be as frequent as single-layer clouds.
In this study, the spatial and vertical occurrence of Arctic MPCs at the regional scale has been assessed. The results highlight that the Arctic, and particularly the Svalbard area, is a very favourable region for the formation and the evolution of MPCs and MPC-IB. However, all the processes controlling the MPC life cycle remain difficult to understand. Indeed, these processes are numerous and occur at different spatial and temporal scales, leading to complex interactions between them (Morrison et al., 2012). Additionally, the Arctic region benefits from peculiar meteorological conditions (Orbaek et al., 1999) and large-scale atmospheric circulation patterns.
The larger MPC and MPC-IB occurrences observed over the Svalbard compared to the Arctic average may be linked to the contribution of the humid air and warmer water transported from the North Atlantic Ocean. This specific synoptic regime brings to the Svalbard region (and region of Greenland sea) more moisture as compared to the rest of the Arctic (Serreze and Barry, 2005). Thus cloud formation and its development can be amplified, since the supply of moisture by the North Atlantic Ocean is favourable for the nucleation of liquid droplets. The initiation of new supercooled liquid droplets will balance the loss due to the WBF process within the MPCs, and therefore participate to maintain the equilibrium between ice and supercooled droplets. However, moisture is not sufficient by itself to maintain the ice–liquid equilibrium. As shown in Korolev et al. (2003), Korolev (2007) and Morrison et al. (2012), it has to be coupled with dynamics processes, such as updraft from cloud base, or turbulence and entrainment at cloud top. Moreover, winter and transition seasons are characterised most of the time by stable atmospheric conditions (Orbaek et al., 1999), coupled with a temperature range favourable for mixed-phase conditions. These conditions associated with temperature and eventually humidity inversions at the cloud top, which frequently occur in these seasons (Nygård et al., 2014; Sedlar et al., 2012), can contribute to the containment of Arctic clouds and MPCs at low altitudes by limiting their vertical extension. The summer and autumn seasons exhibit large occurrence values for MPCs in the Western Arctic (Chukchi and Canadian seas, Fig. 3a). During these seasons, the large open seas enable the warm water to be transported across the Arctic, resulting in warm and moist air advection in the Western Arctic. Open water facilitates the vertical transfer of moisture, responsible for an increase of cloud formation as compared to sea ice, which could explain the prevalence of MPCs during this period in the Western Arctic.
In an attempt to strengthen this assumption and to bring perspective to this
work, a short analysis of the variability of sea ice extent is done and its
potential link with humidity, temperature and cloud occurrence is
investigated. Sea ice concentration data included in CALIOP Level 1 data
from the National Snow and Ice Data Center (NSIDC) are used. Two specific
areas are selected:
over the Greenland Sea, between 70 over the Western Arctic (Chukchi and Beaufort seas), between 70
Figure 10a displays the annual variability of the specific humidity (top
panel) and the 2 m temperature (bottom panel) in solid lines between 2007 and
2010 for the whole Arctic region (black), over the Greenland Sea (blue) and
over the Western Arctic (orange). On both figures, dashed lines represent
the sea ice concentration from NSIDC (in %). Humidity and temperature are
from ECMWF and are interpolated onto the DARDAR grid. Specific humidity is
averaged over the 0–500 m altitude range. Note that the accuracy of these
ECMWF retrievals is not discussed here since we use them only to demonstrate
their variability.
From this figure, one can see that sea ice variability is very pronounced in the Western Arctic with a large decrease from late spring to late autumn (values larger than 90 % in April decrease to less than 10 % in September). Over the Greenland Sea, sea ice variability is rather small, with values between 5 and 20 %. A small decrease is still observed from spring to autumn. This figure highlights clearly that sea ice concentration is inversely correlated with humidity and temperature. Moreover, a delay is observed between the sea ice concentration minimum (September) and the temperature and humidity maxima (July).
Finally, Fig. 10b compares directly the total cloud and MPC occurrences
with sea ice concentration. Cloud and MPC occurrences clearly decrease when
sea ice concentration increases. This trend is observed both for all clouds
and MPCs (slope around
Therefore, the results of the present study seem to indicate that sea ice melting and large-scale circulation can have an important impact on MPC annual variability in the Arctic, by preventing warm water and moisture advection to the entire Arctic during winter and early spring, and favouring it the rest of the year (which is consistent with Kay and Gettelman (2009) and Palm et al., 2010). The decrease of low-level clouds and MPCs in summer could be explained by an increase in air temperature coupled with less stable atmospheric conditions than during the rest of the year, causing the formation of MPC at higher altitudes (as shown on vertical profiles in Fig. 8a).
At last, it is obvious that numerous other regional and local processes are involved in the MPC life cycle and exert an influence on their spatial and temporal variability. Further studies are needed to investigate these different processes and establish a link between them. For example, efforts are currently focused on the study of local sources or long-range transported aerosols in the Arctic region and their role to act as cloud condensation nuclei (CCN) or ice nuclei (IN). The link between the variations of aerosol concentrations or compositions and the MPC characteristics should be investigated as it might partially explain some of the variations in the cloud and MPC occurrences. It would improve our understanding in the aerosol-cloud interactions in the Arctic, like the studies performed by Avramov et al., 2011; Jackson et al., 2012 or Tjernström et al., 2014. Additionally, Orellana et al., 2011 and Tjernström et al., 2014 highlighted that sea ice melting during summer produces biogenic aerosol particles, which could potentially act as CCN or IN for cloud and MPC formation. The influence of the surface, the turbulence and dynamics effect at small and large temporal and spatial scales on MPC occurrence also needs to be assessed more accurately. The most difficult task remains to couple all these processes and quantify their respective importance in the Arctic MPC evolution.
This work presents a study of cloud and mixed-phase cloud occurrences over
the Arctic region with a focus on the Svalbard region. The methodology used
is based on satellite active remote sensing measurements. The
CALIPSO/CLOUDSAT observations are processed with the DARDAR retrieval
algorithm to perform a cloud thermodynamic phase classification. The
uncertainties and limitations of this method are first evaluated from
comparison with ground-based observations. The spatial frequencies of
occurrence and vertical distributions are then determined for total cloud
and MPCs, over the entire Arctic region and over the Svalbard region. The
cloud occurrences are also investigated and discriminated according to the
surface type (open water, sea ice and land). The main results are summarised
below:
Based on comparisons with ground-based observations, uncertainties in the determination of
cloud and MPC occurrence from the DARDAR product below 2 km of altitude are estimated to up to
20 % between 500 m and 2 km, and around 25 % below 500 m. Clouds are present in the Arctic throughout the year, with occurrences between 50 % and
nearly 100 %, depending on the season and the location. Moreover, clouds are more frequent in
the Svalbard region than in the rest of the Arctic by around 5–10 %. Over the whole Arctic region, MPCs represent around 30 % of the clouds during winter and
around 50 % of the clouds the rest of the year. Over the Svalbard region, MPC occurrence is almost
constant year-round, with values around 55 %. MPCs are mainly present at low-altitudes as
70 to 90 % of the MPCs are located below 3 km (both for the entire Arctic and the Svalbard region),
especially in winter, spring and autumn. In summer, the MPC occurrence becomes significant at mid-level altitudes
(3–6 km). During spring, MPCs are located mainly over the Greenland, Barents and Norwegian seas. In autumn, large
occurrences are observed in the Western Arctic (over the Chukchi and the Canadian seas). Moreover, MPCs are
generally more frequently encountered over open sea than over land or sea ice. The particular Arctic MPCs composed of a single supercooled liquid layer at the cloud top and ice
in the lower part of the cloud (MPC-IB) represent between 55 and 70 % of the MPCs over the
whole Arctic and between 65 and 80 % of the MPC over the Svalbard region. Spatial, seasonal
and vertical variability of this cloud type have strong similarities with MPCs. Differences between MPC seasonality over the Svalbard region and over the entire Arctic region
can be partly explained by the vicinity of the North Atlantic Ocean and the particular atmospheric
conditions encountered around the Svalbard archipelago. The mixture of cold air and warm water from
the North Atlantic Ocean seems to be responsible for the large MPC amount observed during spring over
the Svalbard. In the Western Arctic, the MPC maximum frequency occur later during summer and autumn
when heat and moisture are released due to the melting of sea ice. A short analysis based on comparisons
of sea ice concentration with temperature, humidity and clouds and MPC occurrences seems to establish a link
between sea ice and cloud and MPC occurrences. Evidently, a more detailed study is needed here, as MPC
variability is also dependent on the regional scale characteristics such as the oceanic circulation, and
on more local conditions (such as the proportion of open water/sea ice which exhibits a seasonal variability).
Space remote sensing observations of Arctic MPCs at the regional scale highlighted large occurrence frequencies, in particular concerning low-level MPCs. Thus, the present study contributes to understand in which regional frame airborne campaigns and ground-based observations have been performed. However, space remote sensing observations present well-known uncertainties near the surface, which may have an important impact on low-level cloud amount determination. Therefore, airborne campaigns will provide a more thorough characterisation of MPC properties at a small scale. In particular, in situ measurements will help to understand the microphysical processes involved in MPCs.
As an example, in situ airborne measurements performed during the ASTAR 2007 and POLARCAT 2008 campaigns, in spring, over the open water around the Svalbard region and the Greenland sea, provided data from several boundary layer mixed-phase clouds with a typical MPC-IB structure (Gayet et al., 2009). Several ascents and descents in all of these clouds have been performed between the cloud top and the sea level. Despite the small amount of data collected and the fact that they are very localised in time and space, these in situ measurements can be considered a very useful data set for studying MPC at small scale.
In particular, the quantification of ice and liquid particle microphysical and optical properties, and the vertical profiles of water droplet and ice crystal microphysical properties, will give an insight into the microphysical processes responsible for particles growth within MPCs. Accurate profiles of relevant cloud parameters (for example asymmetry parameter, optical depth, ice/liquid water mass fraction, ice crystal morphology, size and concentration, among others) may also be provided to contribute to an improvement of cloud representation in global and meso-scale models and to improve airborne and spatial remote sensing retrieval algorithms, such as those of CALIPSO/CLOUDSAT, or in the near future, EarthCARE.
Finally, it would be of great interest to associate the present work with further studies at various scales, especially at a small scale. The synergy of in situ and remote sensing observations will allow the multi-scale study of MPCs and it will particularly help to resolve some actual unanswered questions concerning the definition of mixed-phase clouds according to the scale of observation. Such a multi-scale approach will, thus, improve the parameterisation of MPCs in remote sensing retrieval algorithms and their representation in models at different resolutions.
This work is part of the French scientific community EECLAT project (Expecting EarthCare, Learning from A-Train) and is supported by the French Centre National des Etudes Spatiales (CNES). This research used resources from the ICARE centre for space remote sensing data processing. This work was also supported by the French ANR CLIMSLIP project (Climate Impacts of Short-Lived Pollutants and Methane in the Arctic). We thank the anonymous reviewers who made important comments that helped to strengthen the manuscript. Edited by: M. Krämer