3.2.1 Statistics

About 15 overpasses occur each day over the Antarctic region (Fig. 4a), which we define as the region poleward of 60^∘ S. Following Adhikari et al. (2012) and Mioche et al. (2015), we divide the area into grid boxes of 2^∘ in latitude and 5^∘ in longitude, which correspond approximately to a grid box of 280 km by 220 km at 60^∘ S and of 280 km by 80 km at 82^∘ S (the southernmost latitude observed by the satellites). The grid, on which the overpasses are combined to derive the occurrence frequency of the clouds, appears in the maps of Fig. 4a and c. The shaded areas in Fig. 4d delimit the investigated Antarctic region in this study located between 60 and 82^∘ S and the three different latitudinal bands used to derived latitudinally averaged vertical transects: the Southern Ocean (SO) transect (60–65^∘ S), the coastal transect (65–75^∘ S) and the interior transect (75–82^∘ S).

Figure 4(a) The Cloudsat tracks across the Antarctic on 1 January 2007, and the grid (grid boxes of 2^∘ in latitude and 5^∘ in longitude) used to derive the geographical distribution of cloud occurrences and extending between 60 and 82^∘ S (b) The zonally averaged number of satellite overpasses per grid box as a function of latitude, for the whole winter season each year. (c) Areas of interest used in the study and introduced in Sect. 4.3 to investigate the monthly evolution of cloud occurrences. They are called WSS (in the Weddell Sea sector), ARS (in the Amundsen–Ross sector), WS (in the Weddell Sea), RS (in the Ross Sea), the WAIS (on the West Antarctic Ice Sheet) and AP (on the Antarctic Plateau). Names are recalled in Table 1. (d) The three latitudinal bands used for the average vertical transects presented in Sect. 4.2.

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The sun-synchronous polar orbit of the satellites results in an exponentially increased sampling of the continent as we observe closer to the pole (Fig. 4b). The SO limit at 60^∘ S shows one overpass every 2 days (∼45 per season) per grid box, while the southernmost limit at 82^∘ S shows on average more than 2.5 overpasses per day (∼250 per season) per grid box. The sharp increase in the statistics towards the South Pole is welcome as it is the area where the cloud cover is the lowest (e.g. Bromwich et al., 2012). The measurement statistics hardly change along a given parallel due to the symmetry of the polar orbiters' trajectories in relation to the South Pole (hence the zonal average presented in Fig. 4b). The measurement statistics are very similar from one season and one year to the next. JJA is shown as an example in Fig. 4b. Only DJF 2010 shows a significant reduction (by 40 %) in the number of available DARDAR products. We use the 4 years 2007–2010 as they are the only 4 full years with night-time observations for the CPR, which works only by daylight due to a battery failure from 2011 onwards.

Note that despite the different times of satellite overpasses over the different Antarctic areas, we do not expect any diurnal cycle to bias our observations and conclusions. For instance, the local (UTC) times of the overpasses above the grid box including Rothera are 02:00 (05:00) and 17:00 (20:00), while for Halley they are on average at 01:30 (01:30) and 18:00 (18:00). The morning and evening times correspond to the descending and ascending nodes of the satellite overpasses. Cloud cover varies diurnally as a result of the development of convection, but in and around Antarctica this will be weak at all times of the year. Over the ocean, diurnal variation in the surface temperature is small. Even over the ice sheets, diurnally varying convective boundary layers develop in summer at locations like Dome C, but these layers are very shallow and do not generate convective cloud (King et al., 2006). Moreover, the ceilometer data introduced in Sect. 3.3 and used in Sect. 4.4.1 confirm the low amplitude of the cloud occurrence diurnal cycle (not shown) at Halley (2.5 % absolute variation) and Rothera (6 % absolute variation) compared to the average amplitude of the seasonal cycle (>20 %).

3.2.2 Cloud fraction mapping

Following Mioche et al. (2015) in their study of Arctic clouds with DARDAR v1 products, we derive a (temporal absolute) cloud fraction (or cloud occurrence frequency) F_cloud. It is the ratio of the number of cloud occurrences N_cloud per grid box over the number of observations (footprints) N_footprints in that grid box: $F_{\mathrm{cloud}}=N_{\mathrm{cloud}}/N_{\mathrm{footprints}}$. A valid cloud occurrence is an occurrence of at least three adjacent vertical pixels flagged with the same condensed phase. We do not distinguish between precipitable and non-precipitable frozen hydrometeors as the ice phase includes both cloud ice and snow in the DARDAR products. We focus on tropospheric clouds, so that stratospheric features are not accounted for in the derived horizontal or vertical distributions of the cloud fraction. We use the tropopause height provided by the CALIOP product and stored in the DARDAR product. The tropopause lies at ∼9 km in the summer and at ∼12 km in the winter. The same method is used to derive the fraction F_X of any given cloud type X (see below). This technique is applied for every month to derive a monthly averaged fraction in every single lat–lon grid box. To obtain the cloud (or any cloud type) seasonal fraction, the number of total occurrences of clouds (or any cloud type) over the 3 months of interest is divided by the total number of footprints in each grid box over these months. The relative fraction (as opposed to the previously defined absolute fraction) can also be computed for the different cloud types, where the number of observations N_footprints is replaced by the number of cloud occurrences N_cloud in the ratio. In DJF (austral summer) of a given year, the month of December is the one from the previous year. For instance, DJF 2007 uses December 2006. Thus, we obtain maps of the geographical distribution of the cloud fractions. The vertical distribution of the cloud or any cloud type fraction is also computed by deriving the ratios as explained above but for each of the 60 m vertical pixels.

The fraction of SLW-containing clouds is called the SLC fraction, F_SLC. Table 1 recalls the acronyms used for the various cloud types (as well as the ones for specific Antarctic regions). The DARDAR-MASK includes a mixed-phase category (“SLW with ice” – first type), and we extend this category by adding the clouds where a pure SLW layer is detected with at least three adjacent vertical pixels containing ice below (second type), following Mioche et al. (2015). In practice, most of the detected mixed-phase clouds are of the first type, but pure SLW layers with an ice phase immediately below are clearly detected. We interpret these as occurrences of a mixed phase since the ice below is immediately in contact with the liquid layer; their microphysics must be interacting. Note that cloud where ice crystals are too small and/or too few to be detected by the radar in the top SLW layer of the cloud is also possible (recall that in the upper atmosphere, for instance, the CPR cannot detect thin cirrus). A cloud top made out of SLW with ice precipitating below is characteristic of boundary layer mixed-phase clouds (e.g. Korolev et al., 2017) and, in practice, cloud layers flagged by DARDAR as actual mixed phase (and not pure SLW) come systematically along with ice below. The mixed-phase clouds (first and second types) are described by the MPC fraction, F_MPC. Supercooled liquid-water-containing clouds (SLCs) that are not part of any mixed-phase clouds as defined above (hence being pure liquid) are categorised as unglaciated supercooled liquid clouds (USLCs), with a fraction of F_USLC. They are liquid clouds for which no glaciation process has occurred (see for example Fig. 3a: the layer appearing in red around 2 km altitude at longitudes between 51^∘ W and 82^∘ W). By “glaciation processes” we designate the processes by which a pure liquid layer becomes a mixed-phase layer. The SLC fraction will refer to any detection of SLW (whether involved in a mixed layer or not). Adding the USLC fraction and the MPC fraction gives the SLC fraction. An all-ice cloud category is defined and accounts for occurrences of the ice phase when no SLW at all is present in the investigated part of the troposphere (the whole of it or the low, middle or high part of it). This is proven to be useful to investigate occurrences of strict ice-only processes in order to put the behaviour of these clouds into perspective with SLCs. Importantly, all-ice and SLC fractions are complementary by definition. We can summarise all the fractions we are interested in and their relationships by writing the following:

Following Mioche et al. (2015), a distinction is made between low-level clouds (at altitudes between 500 m and 3 km above ground level), mid-level clouds (3–6 km above ground level) and high-level clouds (more than 6 km above ground level). When no restriction to a particular altitude level is considered, we will speak about the total cloud fraction or, simply, the cloud fraction. We choose to use ground level and not mean sea level as a reference for altitudes and, similarly, altitude levels rather than pressure levels in order to remain consistent in our description of clouds across the Antarctic region, where ground levels between 0 and 4 km above mean sea level are found. Using a mean sea level reference or pressure levels to discriminate between clouds of different height would artificially lead to an empty low-level cloud category as looking closer to the pole. Thus we do not make use of the International Satellite Cloud Climatology Project (ISCCP, Rossow and Schiffer, 1999) pressure levels (680 and 440 hPa, which approximately correspond to 3 and 6 km above mean sea level) as this was done for studies over the SO only. Our goal is to describe the marine and continental clouds of the Antarctic and their differences rather than comparing our observations to the numerous characterisations made over part of or the whole of the SO using A-Train (and sometimes DARDAR v1) and/or ISCCP observations (e.g. Hu et al., 2010; Haynes et al., 2011; Huang et al., 2012; Mason et al., 2014; Bodas-Salcedo et al., 2014; Huang et al., 2015).

Table 1Acronyms used in the text to designate some cloud phase or cloud types and some Antarctic places or areas.

^a Their fractions are linked by Eq. (2). ^b Geographical areas are defined in Fig. 4c and their names relate to the regions in which they are located.

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3.2.3 Limitations of the products

Finally, two main limitations have to be considered when using space-borne lidar and radar data sets. First, the strong extinction of the lidar meeting a supercooled liquid layer prevents it from detecting any other liquid layer that could exist below this one. The lidar signal can also be extinguished closer to the surface because of optically thick ice clouds above. Figure 3a shows grey-shaded areas flagged “presence of liquid unknown”. This is illustrative of the lidar signal extinction below the detections of supercooled liquid layers (first part of the transect in Fig. 3a). This category is flagged in the mask when the lidar signal is extinguished and when at the same time the radar does not detect any ice. Second, the surface clutter or blind zone of the radar (Tanelli et al., 2008) (surface wave reflection blurring the signal) prevents it from detecting any ice cloud (or identifying the ice part of the MPC) close to the surface. This can be clearly seen when the identification of the ice phase ceases when nearing the ground (at ∼500 m above the surface) in Fig. 3a after the longitude 168^∘ E and in Fig. 3b right at the beginning of the transect.

Practically, the clutter height is not constant, and it is flagged in the CloudSat products used to build the DARDAR-MASK products so that the latter does not take into account the radar signal in areas where the clutter is identified. This will result effectively in a reduced statistics close to the surface. The lidar information, however, is not filtered out. Hence, detection of supercooled liquid layers even in the blind zone of the radar can happen. To derive the geographical distribution of the cloud fraction we consider the atmosphere above 500 m above the surface, ignoring a large part of the radar signal ground contamination in order not to work with the very reduced radar or lidar statistics too close to the surface, following (Mioche et al., 2015). Their statistics was approximately halved (∼60 % loss) between 500 and 1000 m above the surface. In Appendix A we show vertical transects of the occurrence frequency of the lidar extinctions and the radar signal contamination (Fig. A1). Additionally, the monthly time series of the occurrences of radar signal contamination and lidar extinction or attenuation above 500 m above the surface are shown in Fig. A2. In our data set, ∼80 % of the radar observations are still available at an altitude of 500 m above the ground (Fig. A2a), and almost no contamination occurs above 1 km. Importantly, there is almost no seasonality in the radar clutter occurrences. Seasonality in the radar signal reflection can occur because of the changing nature of the surface (caused by more waves at the sea surface at a given time of the year Tanelli et al., 2008). The statistics of lidar observations show a ∼55 % occurrence in the signal extinction at 1 km above the surface and ∼65 % at 500 m above the surface (Fig. A2b). The lower altitude cut-off set at 500 m above the surface to derive the geographical distributions of the cloud fractions does not affect our conclusions, and this is discussed in Appendix B. It is mainly the absolute value of the USLC monthly fraction that is affected by this cut-off but not its relative variations (Fig. B1).

4.2.1 Cloud vertical distribution

The highest vertical cloud fractions (70 %) occur at low altitudes in the SO transects (Fig. 7Aa, d, g and j). The maximum of the summer cloud fraction occurs across the boundary between the Weddell Sea sector and the Indian sector (20^∘ E) and in autumn in the Indian sector and the Amundsen–Ross sector (Fig. 7Aa and d). In spring, the latter has the highest occurrences of low-level clouds. To the east of the Antarctic Peninsula (∼65^∘ W, hereafter called the peninsula), north of and in the Weddell Sea (60–25^∘ W), the cloud fraction is halved at each altitude level between 0.5 and 2 km a.s.l. in winter (20 %–30 % Fig. 7Ag and h) compared to summer (40 %–60 % Fig. 7Aa and b). This reduction is less pronounced above 2 km a.s.l. To the west of the peninsula, the cloud fraction is hardly changed at similar altitudes. Hence, this drop in the cloud fraction induces a dramatic difference in the winter and spring longitudinal distributions of low-level clouds across the peninsula, between the west (45 %–55 %) and the east (20 %–30 %) of the mountain chain. Additionally, and whatever the season (Fig. 7b, e, h and k), there is a ∼20 % absolute difference in the mid-level cloud fractions between both sides of the peninsula, i.e. well above the highest peak of the peninsula (∼2.5 km). East of the peninsula, the lowest low-level cloud fractions in winter and spring coincide with the largest sea ice fractions (Fig. 4k and l).

In EA, east of the AIS (around 90–100^∘ E, Fig. 7Ah) a local increase in the vertical extension of coastal cloud fraction occurs in winter, at altitudes up to 6 km a.s.l. This feature in the vertical distribution of clouds occurs while the climatic low-pressure system located off the coast is strengthened (Fig. 4c). This low-pressure system is the weakest in summer (Fig. 4a), and the cloud fraction is also at its lowest (∼25 %) (Fig. 7Ab). This seasonal variation is seen during each year taken separately. South of the AIS (∼70^∘ E), the cloud fraction is lower than immediately to the east and to the west of the AIS (Fig. 7Ae, h and k). This is the effect of the land depression there, preventing the orographically induced cloud formation from occurring.

Generally, the cloud vertical extension follows the air mass interactions with the coastal topography. This is also clearly visible in the interior transects (Fig. 7Ac, f, i and l) around 100^∘ W, on the WAIS. There, the ASL brings moisture from lower latitudes, triggering cloud formation through adiabatic cooling on the steep coasts. In winter the vertical extension of clouds lead to values of 45 % and 30 % at 2 km and 6 km a.s.l. against 40 % and 10 % in summer. Hence, higher clouds occur at higher altitudes in winter and this is consistent with the deeper ASL and the contraction of the westerly circulation towards the coast (Fig. 4c). In EA, the area of lowest cloud fraction (∼5 %–10 %) is visible around 140^∘ E consistently with observations made by Verlinden et al. (2011) over 2007–2009. It is the area of largest subsidence and also immediately west of the Transantarctic Mountains, which prevents moisture or cloudiness from WA entering EA. The land depression extending poleward from the AIS is the area of maximum cloud fraction in the interior in winter (∼70^∘ E, Fig. 7Ac, f, i).

4.2.2 Supercooled liquid-water vertical distribution

The largest SLC fractions are consistently found in the lowest (warmest) atmospheric layers, below 2 km altitude in the Southern Ocean transect (Fig. 7Ba, d, g, and j) and the coastal regions (Fig. 7Bb, e, h, and k). The largest SLC fractions over the largest oceanic area are found in autumn across the Weddell Sea sector and the Indian sector (0–70^∘ E) at 1–1.5 km altitude (Fig. 7Bd). This corresponds to an area of preferred MPC formation compared to USLCs (compare transects Fig. 8Ad and Bd). In the coastal transect the Weddell Sea is an area of enhanced SLC formation (60–25^∘ W, Fig. 7Bb). This maximum is principally due to the increase in the USLC fraction there (Fig. 8Bb) rather than to the MPC fraction (Fig. 8Ab). This already appeared in the USLC fraction geographical distribution (Fig. 6i). This suggests that the Weddell Sea is an area more prone to maintain layers of supercooled liquid with no significant glaciation process. There is a clear cut in the SO zonal distribution of the SLC fractions at the northern tip of the peninsula (∼60^∘ W), causing an asymmetry in this distribution. Lower altitudes (a.s.l.) are reached by SLCs to the east of the peninsula compared to the west. This is particularly visible outside summer months (Fig. 7Bd, g, and j) and can be explained by the lower surface temperatures on the eastern side of the peninsula, which is well documented in the literature (e.g. Morris and Vaughan, 2003). Also note the changes in the isotherms, which have lower altitudes to the east of the peninsula (Fig. 8Aa, d, g and j).

Over water, the largest USLC fractions generally occur between 0.5 km and 1 km a.s.l., and the largest vertical extent of the largest USLC fractions occurs in the Weddell Sea (Fig. 8Bb). The maximum MPC fractions are located between 1 km and 1.5 km a.s.l. with no MPCs detected below 500 m a.s.l. The isotherms indicate the average temperatures at which MPCs and USLCs form. In the SO transect and in the coastal transect, the largest MPC fractions occur between −15 and −5 ^∘C and more particularly between −15 and −10 ^∘C. In the SO transect, USLCs occur at temperatures above −5 ^∘C in summer (Fig. 8Ba) and between −10 and −5 ^∘C in other seasons (Fig. 8Bd, g and j). The high USLC fractions in the Weddell Sea in summer between 0.5 km and 1 km a.s.l. occur at temperatures between −10 and −5 ^∘C (Fig. 8Bb).

In the interior transects, the SLC fraction is the largest above the WAIS (∼100^∘ W) and the RIS (170^∘ E–150^∘ W) throughout the year (Fig. 7Bc, f, i, and l). In EA, on the plateau, SLCs occur almost exclusively in summer at temperatures down to −35 ^∘C. The SLC fraction maximises in summer at 3 km a.s.l. over the WAIS and below 500 m a.s.l. above the RIS (Fig. 7Bc), mainly in the form of USLCs (compare Fig. 8Bc and Ac). Over the WAIS, this maximum occurs at average temperatures between −23 ^∘C and −20 ^∘C (Fig. 8Bc) and around −25 ^∘C in other seasons. It is reminiscent of quasi-steady-state mountain-wave orographic clouds displaying supercooled droplets down to −33 ^∘C with no ice (Heymsfield and Miloshevich, 1993). However, satellite observations do not allow a statement to be made on the lifetime of such a feature. Note that low-level SLCs that are categorised as USLCs in the radar blind zone above the RIS (below 500 m a.s.l.) could actually be MPCs. Silber et al. (2018), who investigated liquid-bearing clouds with ground-based measurements at McMurdo Station (167^∘ E) at the edge of the RIS throughout the year 2016 did not differentiate between pure and mixed SLC layers. Thus, we cannot determine the preferred formation of USLCs or MPCs at very low altitudes there. In EA, the presence of SLCs is evidenced in summer (Fig. 7Bc), while no SLCs are detected over the plateau in winter, except where the depression of the land south of the AIS is (Fig. 7Bi). There, poleward intrusion of moisture and cloudiness from the coastal areas would cause enhanced SLC fractions (Fig. 7Ai).

Unlike for the cloud fraction, no discontinuity occurs in the SLC fraction vertical distribution close to the surface, especially over seas (Fig. 7B). This suggests that the statistics of the SLC fraction vertical distribution close to the surface is not much affected by the reduced statistics due to the lidar extinctions (∼80 % near the surface, Fig. A1d). Above land, some spurious SLC fraction enhancements appear at the surface on very rare occasions, though (e.g. Fig. 7Bc, at ∼50^∘ E). It is also interesting to note that the maximum in the vertical MPC fraction occurs above 1 km height above ground level in the transects (Fig. 8A), suggesting that the decrease in MPC occurrences below 1 km is rather real and not an artefact of the 40 % reduced statistics (at 500 m a.s.l.) caused by the radar blind zone. The consequence of this is that the picture given by the DARDAR-MASK products of the MPC fraction and the USLC fraction is representative of their actual averaged distribution down to 500 m a.s.l. and possibly down to the surface for the SLC fraction at least (which does not rely on the radar signal). Hence, using a 500 m lower altitude cut-off for deriving the distributions of MPC and USLC fractions seems legitimate despite the reduced statistics.

4.3.1 Total cloud and phase fractions

We now spatially average the geographical distribution of the total cloud fractions presented in Sect. 4.1 over distinct areas defined in Fig. 4c. In doing so, we increase the statistics compared to a single grid box, while we pin down the monthly evolution in these regions. The geographical areas investigated are called WSS (in the Weddell Sea sector), ARS (in the Amundsen–Ross sector), WS (in the Weddell Sea), RS (in the Ross Sea), AP (the Antarctic Plateau) and the WAIS (Fig. 4c and Table 1). We also show the monthly time series for the whole Antarctic region (60–82^∘ S). Note that ARS and WSS are of similar sizes, as WS and RS. Figure 9 shows the monthly evolution of several total fractions: cloud (a), SLC (b), all-ice (c), MPC (d) and USLC (e). The shaded areas indicate the 4-year maximum and minimum monthly average values as an indication of the amplitude of interannual variability.

Figure 9Four-year (2007–2010) average monthly time series of the total cloud fraction (a), SLC fraction (b), all-ice fraction (c), MPC fraction (d), and USLC fraction (e). See Sect. 3.2 for the definition of the cloud categories. The different colours correspond to the different investigated regions (see map in Fig. 4c): WSS (in the Weddell Sea sector), ARS (for the Amundsen–Ross sector), WS (in the Weddell Sea), RS (in the Ross Sea), the WAIS (on the West Antarctic Ice Sheet) and AP (Antarctic Plateau). The shaded areas indicate the amplitude between the monthly minimum and maximum encountered over the years from 2007 to 2010.

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A striking feature is the constant average cloud fraction throughout the year for the whole Antarctic region, around 68 % (black lines in Fig. 9) (Fig. 9a). When considering specific regions, different patterns appear. Generally, the maximum cloud fraction over continental regions occurs in winter and the minimum in summer, while the opposite occurs over oceanic regions. The cloud fraction derived for ARS shows the lowest amplitude of variation. It decreases from 90 % to 92 % in mid-autumn and throughout the winter and reaches a minimum of 78 % by the end of it. It increases again, reaching a second maximum around 90 % in late spring. A similar pattern appears for WSS, with a stronger decrease throughout winter, down to 65 %. This is consistent with the larger sea ice fractions observed in that area in JJA (Fig. 2o) and SON (Fig. 2p) and can be related to the likely reduced moisture flux into the atmosphere. WS and RS show the same pattern of a decreasing cloud fraction, starting from a maximum in summer. However, the cloud fractions are on average lower in winter over RS (∼60 %) than over WS (∼70 %). On the continent, the WAIS shows a slight increase in cloud fraction from summer (60 %) to winter (75 %) before decreasing abruptly from September to October. A much clearer trend emerges over the AP with a steady increase in cloud fraction from summer to winter and a maximum in July. It is the area where the seasonal cycle has the largest amplitude of variation (as already noted by Verlinden et al., 2011, using vertical transects). The same abrupt decrease in the cloud fraction as over the WAIS is noticeable between September and October.

The monthly evolution of the SLC fraction (Fig. 9b) is a general decrease from summer to winter with a minimum reached in August, before increasing again. This seasonal cycle is not biased by one of the lidar signal extinctions, which has occurrences that are equal to or lower than the SLC occurrences and follow the same pattern (Appendix A, Fig. A2). As a lidar signal extinction will happen below a SLC detection, this is expected. Some of the SLCs may be detected just above the 500 m lower altitude cut-off, so that the SLC occurrence is then counted, but the extinguished area below is missed in the statistics. Extinction or attenuation of the lidar signal can also happen because of optically thick ice clouds, and this is why the occurrences of extinctions and attenuations are almost as important in the winter as they are in the summer over the WAIS (Fig. A2). Overall, the seasonal cycle of the SLC fraction above 500 m above the surface is not biased by the lidar extinctions. The largest SLC fractions occur in ARS and WSS (both 75 %) in summer against 40 % and 30 % in winter. The lowest values of the SLC fraction are observed above the continental areas. The SLC fraction in the WAIS is 40 % in summer and 10 % in winter. The plateau has few but non-negligible SLC occurrences, with ∼10 % in January, and none by the end of winter and early spring. The relatively simple SLC fraction seasonal cycle points to the temperature seasonality as being one of the main drivers everywhere in the Antarctic (colder temperatures favour more glaciation in clouds).

Contrary to the cloud fraction evolution, the all-ice cloud fraction shows the same evolution over each area, increasing from the end of the summer to the winter (Fig. 9c). ARS and WSS show similar values ranging from 15 % to 35 %, while the WAIS reaches the largest 4-year average of 60 %. The AP has the second largest values of all-ice fractions in winter (50 %). This fraction is lower than in the WAIS and can be explained by the ASL located off the WAIS coast, contributing to a direct inflow of moisture (and cloudiness). Note the almost identical evolution for the cloud and all-ice fractions over the AP, showing the almost exclusive presence of ice clouds there. These fractions only differ during the summer months, when SLC fractions are not negligible (∼10 %, Fig. 9b).

A striking difference appears between the MPC and the USLC fractions (Fig. 9d and e) when considering the transition from beginning of summer to autumn. All regions – with the exception of the AP – show a local maximum of the MPC fraction in late summer or early autumn before a decrease in the following months, with a minimum reached around August. Conversely, the USLC fraction shows a steep decrease over the same period, which starts in January. This difference suggests that the glaciation process converting the supercooled liquid to a mixed phase follows a distinctly different cycle from the one describing the mere occurrence of supercooled liquid (although the former is obviously related to the latter). These differing behaviours are readily observed by comparing the Antarctic averages (solid black lines) in Fig. 9d and e. The differences in MPC fractions between marine areas (ARS, WSS, WS and RS) are larger than the differences in the USLC fraction between the same areas. For instance the USLC fractions are within a 5 % range of values except during winter (8 %), while the MPC fractions can differ by more than 15 %. This points to larger regional differences in the glaciation process (and occurrences of MPC) than in the mere occurrences of USLCs.

4.3.2 Cloud and phase fractions at low, middle and high levels

To look further into the details of the monthly evolution of the different cloud fractions, we divide them into low-level, mid-level and high-level fractions, as defined in Sect. 3.2. Figure 10 shows the all-ice fractions (a–c), the SLC fractions (d–f) and the MPC fraction (g–i). Since the addition of the all-ice and the SLC fractions gives the cloud fraction, it is easy to infer what the dominant component of the cloud fraction is and we do not show the cloud fraction here, although we still refer to it.

Over the continent, the monthly variability of the cloud fraction is primarily driven by the mid-level and high-level all-ice clouds (Fig. 10b and c). The monthly variability is within a 30 % range and 35 % range for mid- and high-level all-ice fractions on the WAIS, and 30 % and 15 % over the AP. Regarding the AP, the mid-level clouds can virtually be considered high clouds (in comparison to the oceanic regions) since the average altitude of the plateau is 3 km a.s.l. The evolution of the continental mid-level and high-level all-ice cloud fractions appears to be the same in both WAIS and AP, changing from a minimum in summer to a maximum in winter. This is consistent with the increases in cyclogenesis and depressions offshore in that season (e.g. King and Turner, 1997), leading to more intrusions of weather systems over the continent. The monthly evolution of continental clouds is essentially driven by the all-ice clouds. The mid- and high-level clouds detected over the WAIS and the AP are almost exclusively of the all-ice type given the much smaller mid-level SLC fractions (0 % and ≤10 % for AP and WAIS) compared to the mid-level all-ice fractions (20 %–50 % and 10 %–40 % for AP and WAIS) on one side and the almost null high-level SLC fraction (except over ARS) compared to the all-ice fractions on the other side.

Interestingly, over water (WS, RS, WSS and ARS regions) the mid-level cloud fraction shows almost no monthly variability compared to the low-level cloud fraction and the high-level cloud fraction (not shown). Mid-level cloud fractions are always within a 10 % range of values in ARS, WSS, RS and WS. We can understand the absence of monthly variation for mid-level cloud fractions over marine areas, since the ∼13 % increase in mid-level all-ice fraction (Fig. 10b) is almost compensated by a similar decrease in the SLC fraction (∼10 % decrease, Fig. 10e). This may be explained by the mid-level liquid phase being more often converted or replaced by ice in the winter season. Over water, the low-level cloud fractions are within a ∼40 % range of values in WS, ∼30 % in RS, ∼35 % in WSS and ∼20 % in ARS and this variability is caused by the SLC fraction (Fig. 10d). High-level cloud fractions are driven by the all-ice fraction and are within a ∼25 % range of values in WS, ∼15 % in ARS and WSS, and ∼10 % in RS. This demonstrates that the variability of the cloud fractions over water is firstly due to the low-level liquid-bearing clouds, which dominate the cloud fraction, and secondly to the high-level all-ice clouds, while mid-level clouds have little influence. Over marine regions (WS, RS, WSS, ARS), the monthly variability of the all-ice fraction (Fig. 10a–c) is largely driven by the mid- and high-level all-ice clouds (Fig. 9b and c), pointing to the increased cyclonic activity and number of frontal systems in winter (as for the general cloud fraction).

Figure 10(a–c) Four-year (2007–2010) average monthly time series of the low (a), middle (b) and high-level (c) all-ice fraction for the different Antarctic regions defined in Fig. 4c. (d–f) Same as top row but for the SLC fraction. (g–i) Same as top row but for the MPC fraction.

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The monthly evolution of the low-level MPC fraction clearly differs from the low-level all-ice fraction, but also from the low-level SLC fraction. Over marine areas, little monthly variation in the low-level all-ice fraction occurs throughout the year in comparison to the low-level MPC fraction, suggesting that different factors affect their respective formation and evolution. More particularly, the monthly variation observed for the low-level all-ice fraction in WS, WSS and ARS is in a range of values of 5 % (10 % for RS), while the monthly variations in low-level MPC fractions are within a larger range of values, i.e. 20 % for WSS, 15 % for WS and RS, and 10 % for ARS. The largest part of the total USLC fraction is driven by the low-level USLCs (not shown), which does not show a local maximum at the end of summer or beginning of autumn, explaining the different patterns between the low-level SLC (Fig. 10d) and MPC (Fig. 10g) seasonal cycles. Finally, Fig. 10g demonstrates that the singular evolution of the MPC fraction from summer to autumn (Fig. 9d) is due to the low-level MPC. The mid-level MPC fraction does not display any similar local maximum in autumn. The particular monthly variation in the low-level MPC fractions points to a seasonal cycle of the glaciation process, involving interactions with the surface and/or the lowest layers of the troposphere. Note that, given the absence of seasonality in the radar clutter occurrences (Fig. A2), identifying ice in SLCs to assess the existence of a mixed-phase cloud seasonality is not biased.

Figure 11(a–b) Monthly time series of the in-cloud temperature (Ttop) (a), and of the in-cloud water vapour mixing ratio (Qtop) (b), at the top of the low-level MPC and USLC layers, for the different regions of interest. (c–d) Monthly time series of near-surface (2 m) temperature (c) and the water vapour mass mixing ratio (d) below these layers. The shaded areas indicate the interannual variability for the MPC layers. For readability, the ones for the USLC layers are not shown, but they are of similar amplitude.

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Figure 11 shows the monthly time series of the in-cloud temperature and water vapour mass mixing ratio at the top (Ttop and Qtop) of the low-level MPCs and USLCs, as well as the ones at the surface (T2m and Q2m) below where these clouds occur. The seasonal cycles of Ttop and T2m show a similar pattern to that of the SLC fraction suggesting the temperature as being the main driver of the SLC fraction evolution. The decrease in Qtop and Q2m is a direct consequence of the formation of sea ice and the reduction in moisture coming from the sea surface. Ttop of USLCs are larger than those of the MPC layers as the latter form at higher altitudes on average and have some active glaciation process suggestive of these lower temperatures. Note that, in the DARDAR-MASK, the two criteria using temperature in the identification of supercooled liquid is −40 ^∘C, taken as the homogeneous nucleation temperature, below which the lidar backscatter will be considered to come from highly concentrated small ice crystals, and 0 ^∘C, above which the liquid layer will be considered warm liquid (Ceccaldi et al., 2013). Apart from that it is the combination of lidar and radar observations that determines whether or not liquid and ice are simultaneously present. Hence, our observations of systematic higher average temperatures (and lower altitude – from Sect. 4.2) of the USLC are, while being independent, in line with the identification of these layers. Marine SLC top temperatures range between −22 and −10 ^∘C. Continental SLC top temperatures range between −38 and −22 ^∘C. The average lowest SLC top temperature occurs on the plateau (−35 in summer and −38^∘ in winter). The statistics based on the highest number of samples, i.e. those for the whole Antarctic region (black lines in Fig. 11a), give a 1.5–2 ^∘C warmer Ttop for USLCs than for MPCs. This temperature difference is significant at the 99.9 % level (using a t test), while the differences between Qtop for MPCs and USLCs is significant at the 90 % level only. There are no statistically significant Antarctic-wide differences in the near-surface temperature and water vapour mixing ratio between the MPCs and USLCs (Fig. 11c and d). This shows that the average near-surface conditions are the same for both types of SLC and more particularly over water. The only exception is the winter near-surface temperature on the plateau, which corresponds to extremely low and almost null SLC occurrences (Fig. 10g and j).

4.4.1 Ceilometer cloud base observations at Rothera and Halley between 2007 and 2010

In order to get a better perspective of the monthly evolution of cloud fraction illustrated by Fig. 9a, we performed qualitative comparisons with ceilometer data collected at the British Antarctic Survey's stations Rothera and Halley (Fig. 1) for the period 2007–2010. These were introduced in Sect. 3.3. We compare these with our low-level cloud fraction as the average height of cloud bases detected by the ceilometers is ∼1600 m at Rothera and ∼1000 m at Halley. When using the data from the ceilometers, we plot cloud base occurrences as measured starting from above the surface (>0 m) and from above 500 m above the surface (>500 m). In order to have enough monthly statistics from the satellite overpasses we extend the analyses to larger regions than the grid box containing the respective station (see Fig. 4a). Hence, in addition to the stations' grid boxes we derive the low-level cloud fraction using – for Rothera Station – the Bellingshausen sea (i.e. upwind of the station) and – for Halley – the Weddell Sea (off the Brunt Ice Shelf where Halley sits). From Fig. 5a–d, both station grid boxes experience similar seasonal cloud fractions to the one from these nearby areas. Thus it is legitimate to use those larger areas as proxies for both stations. Rothera is on the lee side of Adelaide Island's mountains, though, meaning that part of the clouds observed over Rothera are orographic in nature and that local effects should be more pregnant than at Halley.

Figure 12a shows the monthly evolution of the low-level cloud fraction derived for the grid boxes corresponding to Rothera (triangles) and Halley (circles) using solid lines, as well as for the Bellingshausen Sea and the Weddell Sea using dotted lines. Figure 12b shows the cloud fraction restrained to and not restrained to ceilometer detections above 500 m above the surface for both stations (using the same distinct markers as for Fig. 12a). In each figure the shaded area indicates the maximum and the minimum monthly average over the 4 years (the interannual variability). The monthly evolution of fog occurrences (reported in the ceilometer data set as “full obscuration but no cloud base detected”) is also reported for both stations.

Figure 12(a) Four-year (2007–2010) average time series of the monthly mean low-level cloud fraction plotted for the grid box corresponding to Rothera (triangles, solid line) and Halley Station (circles, solid line), and the Bellingshausen Sea (triangles, dotted line) and the Weddell Sea (circles, dotted line). (b) Four-year (2007–2010) average time series of the monthly mean of the cloud base detections by the ceilometers at Rothera (triangles) and Halley (circles), for detections above 0 m (dashed line) and 500 m (solid line) above surface. Dashed light-blue lines show the fog detections for Rothera and Halley.

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The ceilometer cloud fractions (Fig. 12b) are systematically lower than those derived from DARDAR products (Fig. 12a). This has two potential causes. First, ceilometers record thousands of observations per day at one single point, while the satellite has two observations per grid box per day in the best case at these latitudes and the cloud cover over the grid box may not be uniform. Also, the ceilometer has a much smaller footprint than the satellite and it samples the “patchiness” of the cloud on small scales. At least we found that, using only the ceilometer observations corresponding to the satellite overpasses, no difference appears in the annual cycle (not shown). This is consistent with the fact that the amplitude of diurnal cycles at both stations is negligible and therefore does not bias our study. Nonetheless, the problem of detecting much finer structures in the cloud cover with the ceilometer, which the satellite cannot resolve, spatially or temporally, is still a likely cause for mismatches. The second explanation is that fog and blowing snow – particularly at Halley for the latter – can lead to signal obscuration and prevent the ceilometer from observing the clouds from the surface, thus lowering the number of observations. This is discussed below.

Consistently, the cloud fraction at Halley is lower than at Rothera for both the DARDAR and the ceilometer data sets. A similar pattern appears between the ceilometers' cloud fractions at Halley (>0 m), and the DARDAR cloud fraction across the Weddell Sea: a decrease in the cloud fraction with a minimum in September, followed by a steeper increase. However, this feature is much dampened in the ceilometer's data set when restricting detections to altitudes above 500 m above the surface.

A similar seasonal evolution is detected at Rothera for both the ceilometer and the DARDAR products. For both data sets the minimum in cloud fractions at Rothera occurs in July. This minimum is 60 % for the DARDAR products and 40 % (30 %) for the >0 m (>500 m) ceilometer detections. Also, for both data sets the maximum cloud fraction values occur in summer (75 % with DARDAR and 50 % with the ceilometers). Note that the local maximum observed in March with the DARDAR data set is not observed with the ceilometers. Absolute differences in cloud fractions between ceilometer detections above 0 m and above 500 m at Halley (more than 20 % in summer, and down to 10 % in the winter) are larger than at Rothera (less than 10 %). This suggests a less vertically homogeneous distribution of hydrometeors at Halley.

Focusing on the detection of fog at both stations, we find fog occurrences of 10 % to 30 % at Halley with the maximum reached in July, and 20 % to 37 % at Rothera, with a maximum in June (Fig. 12b). Interestingly the average difference between the cloud fractions from DARDAR and from the ceilometers ranges between 22 % and 43 % at Rothera, and between 32 % and 40 % at Halley. Hence, the fog occurrences can possibly explain a large part of the lower cloud fraction seen by the ceilometers and help reconcile both data sets, particularly at Rothera. At Halley, however, blowing snow events (Mann et al., 2000) are an additional likely source of ceilometer obscuration. It is probable that the signal of the seasonal cycle in cloud fraction seen in the DARDAR data set is masked in the ceilometer data set because of the seasonal cycle in fog occurrences and blowing snow events. This would explain the reduced seasonal cycle at Halley when restricting the ceilometer observations to altitudes above 500 m. Another study comparing the DARDAR v2 data set with ground-based measurements of SO clouds at Cape Grim, Australia (Alexander and Protat, 2018) used a space-averaging technique based on typical wind speeds for the DARDAR observations. However, the high occurrence of obscuration of the ceilometer signal (Fig. 12b) makes it unlikely that using more sophisticated averaging techniques will improve our comparisons at both stations.

Finally, one should also note that the ceilometer detects a cloud base and we work with cloud phase fraction on a vertical grid from the DARDAR and do not derive cloud base values here. Our low-level cloud fraction statistics could include clouds with a base below 500 m, which are counted in the >0 m ceilometer statistics but not in the >500 m statistics. It is difficult to assess the bias induced by this difference since the cloud base of low-level clouds detected with the DARDAR products is difficult to determine because of the lidar signal extinction or the radar ground clutter. We have shown qualitative agreements between DARDAR and ceilometer observations at both stations. The use of a polar-optimised algorithm for ceilometer observations (VanTricht et al., 2014) for further cloud vertical distributions comparisons is needed but it would not have affected our conclusions in the present study, as explained in Sect. 3.3. More generally, there is a need for more systematic comparisons of ground-based measurements of cloud occurrences with combinations of lidars and cloud profiling radars in Antarctica. This will be the topic of a future study using the DARDAR products for more recent years, when such ground instruments were deployed.

4.4.2 Precipitation measurements at Dome C in 2009 and 2010

A study of in situ precipitation measurements over the plateau showed that snowfall over winter at Concordia Station at Dome C (75.1^∘ S; 123^∘ E, Fig. 1) was about 5 times less important in winter 2010 (∼1 mm water equivalent – w.e.) than in winter 2009 (∼5 mm w.e.) (Schlosser et al., 2016, their Fig. 4). Schlosser et al. (2016) related this change to the changing strength of the westerly wind belt around Antarctic coasts quantified by the Southern Annular Mode (SAM) index (Marshall, 2003): winter 2009 was a low-SAM-index season, allowing more intrusions of moisture, while winter 2010 was a high-SAM-index season.

We do not investigate interannual variability here, but these measurements are an opportunity to assess the consistency of our cloud fraction variability with changes in winter precipitation measured from the ground over 2009 and 2010. Recall that the ice phase in the DARDAR-MASK products include both cloud ice and precipitating ice, so that increased precipitation is expected to cause an increase in our low-level cloud fraction. From the 2009 and 2010 cloud fraction winter averages we subtract the 4-year average introduced in Sect. 4.1. Thus, we derive an anomaly for the grid box centred on Concordia Station (120–125^∘ E and 74–76^∘ S). Given that synoptic-scale systems are the ones causing the substantial increases in precipitations from one year to the next in these high-altitude regions of the continent (Schlosser et al., 2016), it is reasonable to think that, given the absence of any topographical feature for hundreds of kilometres around Concordia Station, the grid box of size 280×100 km is representative of the location.

The cloud fraction anomalies in winter 2009 and 2010 are +5 % and 0 %. The relative increase in winter 2009 (ratio of the winter 2009 value to the winter 4-year average) is 15 %. Looking at different levels, the anomaly (relative increase) in low-level cloud fraction is +7 % (+32 %) in JJA 2009 against −4 % (−20 %) in JJA 2010; for the high-level clouds it is +5 % (+74 %) in JJA 2009 against +0.2 % (+4 %) in JJA 2010. The picture differs only for the mid-level clouds with −0.3 % (−2 %) in JJA 2009, and +4 % (+20 %) in JJA 2010. The increase in the low-level cloud fraction in JJA 2009 is consistent with the increased snowfall observed on the ground. The simultaneous increase in high-level cloud fractions illustrates the more numerous deep (thick) clouds reaching Dome C in JJA 2009. The decrease in low-level cloud fraction in winter 2010 consistently shows the less numerous precipitating clouds in agreement with the lower precipitation measured at the surface that year. No SLW is involved in this change in cloud fraction, as the SLC fraction is null above Dome C during winter (Fig. 5g). Overall, a 15 % relative increase in the all-ice fractions in winter 2009 (+32 % for low-level all-ice and +75 % for high-level all-ice) is consistent with the increased snowfall measured on the ground by Schlosser et al. (2016) during this winter.

4.4.3 Supercooled liquid-water observations at the South Pole in 2009

On the continent the DARDAR data are limited to latitudes lower than 82^∘ S. In this respect, measurements of SLW like the ones done by Lawson and Gettelman (2014) at the Amundsen–Scott South Pole Station (SPS) are essential for better constraining the distribution of the Antarctic-wide liquid phase. During the summer 2009, they used a tethered balloon to calibrate their mixed-phase clouds detections made with a micropulsed lidar (MPL) and to subsequently deduce the number of mixed-phase clouds detection in comparison to the ice cloud detections throughout the year. From Fig. 2d of (Lawson and Gettelman, 2014), we can extract the ratio of mixed-phase cloud occurrences over the total number of cloud occurrences. The authors show the number of 10 min detections of mixed-phase clouds and pure ice clouds each month. We divide their numbers of monthly mixed-phase cloud occurrences by their numbers of monthly cloud detections to build a monthly relative fraction of mixed-phase clouds. We attempt here a comparison with our low-level SLC relative fraction. We use our low-level fraction as the detections by the MPL are all below 3 km above the surface. The ground clutter prevents the CPR from correctly assessing the presence of ice mixed with SLW close to the surface. At the same time it is not clear in which case the strong backscatter signal of their MPL was indeed a signature of a MPC or just of a USLC layer, and the authors do not distinguish between them. Since we detect MPCs and USLCs in the interior of the continent we consider both in our case. Thus, we use our low-level SLC relative fraction. Additionally, the cloud detections by the MPL range between 200 and 2200 m above the surface (Lawson and Gettelman, 2014, their Fig. 2c). Only the lidar can detect the lowest layers because of the CPR blind zone.

On the continent, from the geographical distribution of the SLC fractions (Fig. 5e–h) it is clear that it follows the topography as higher terrain experiences lower temperatures and moisture (due to the distance to the coast) and hence lower low-level SLC fractions. SPS is located at an altitude of 2840 m a.s.l. It is on the slope of the ice sheet that extends northwards towards the Transantarctic Mountains (diamond-shaped marker in Fig. 13a and c). Relying on the idea that the SLC fraction variations follow the changes in orography, the SLC fractions at the South Pole should be close to the ones of the EA's side of the Transantarctic Mountains, at similar altitudes (circle marker in Fig. 13a and c). Thus, we extract values of the SLC relative fractions from the few grid boxes verifying 20^∘ W < lon <20^∘ E and 80^∘ S < lat <82^∘ S with a surface height between 2500 and 3000 m a.s.l. (called area 1; north of the circle marker in Fig. 13a and c). As an element of comparison we also extract the monthly time series of the relative SLC fraction from the grid boxes on the southernmost boundary of the WAIS verifying 60^∘ E < lon <140^∘ E and 80^∘ S < lat <82^∘ S with a surface height between 2000 and 2500 m a.s.l. (called area 2; north of the square marker in Fig. 13a and c). Recall that our statistics is the best close to 82^∘ S, with ∼2.5 overpasses per grid box each day (Fig. 4b).

Figure 13a and c show the 4-year average summer and winter geographical distributions of the SLC relative fraction. Figure 13b shows the monthly time series of the SLC relative fraction for 2009 with (solid red line) and without (dotted red line) the 500 m lower altitude cut-off in area 1 and without the cut-off in area 2 (dotted green line). To give context, the interannual variability over 2007–2010 is also shown as a shaded area for each extraction. SPS observations (Lawson and Gettelman, 2014, their Fig. 2d) are shown in black (diamond markers).

Figure 13(a, c) Geographical distribution of the 2007–2010 average of the SLC relative fraction in DJF (a) and JJA (c). The diamond marker indicates the Amundsen–Scott South Pole Station (SPS). The circle and square markers indicate the approximate locations of the grid boxes – north of the markers – used to extract the time series in area 1 (circle) and area 2 (square), respectively (see text for exact definition of area 1 and 2). (b) column: Monthly time series of the DARDAR SLC relative fraction in 2009 with (solid red line) and without (dotted red line) the lower altitude cut-off of 500 m, extracted north of the circle marker shown in (a) and (c). The dotted green line shows the time series extracted north of the square marker shown in (a) and (c). The shaded areas represent the corresponding interannual variabilities. The black diamond markers show the relative fraction of mixed-phase clouds observed with the MPL at SPS (2850 m a.s.l.) in 2009 (Lawson and Gettelman, 2014, extracted from their Fig. 2d).

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It is remarkable how the seasonal cycles from DARDAR observations with or without cut-off in area 1 show a similar pattern to the ground-based observations despite the different locations investigated. This is in spite of the much larger temporal resolution of the MPL, which continuously observes at a single point and can detect features missed by the satellites (similarly to the ceilometers). It is also clear that area 1 is much more representative of SPS than area 2, where SLC relative fractions are far larger than at SPS during all seasons but SON. Expectedly, the SLC relative fractions in area 1 are larger without the altitude cut-off but only between March and July 2009. The 2009 DARDAR fraction is lower than the SPS observations by 10 % and 20 % (absolute difference) in January and February and by 35 % in November. For the rest of the months, the difference is less than 10 %. Importantly, though, the MPL observations lie in the interannual variability range of the DARDAR observations (with or without cut-off) throughout the year (except in November). These results suggest that cloud and SLW observations at the South Pole are representative of lower latitudes on the plateau and on its outskirts. The contribution of SLCs to clouds clearly maximises in summer with average values of 50 % in December and January for the MPL and 40 % for DARDAR (45 % for the 2007–2010 average). The minimum of the SLC relative fraction is in August for both data sets: 1 % for DARDAR (with and without the altitude cut-off) and 0 % at SPS.

Finally, note that in 2009 the occurrences of mixed-phase clouds at the South Pole were all below 5 km a.s.l. (Lawson and Gettelman, 2014). This is also the highest altitude at which we retrieve SLCs, for terrain as high as SPS, in summer (Fig. 7Bc). The temperatures reported by Lawson and Gettelman (2014) in summer are between −28 and −32 ^∘C and this is consistent with our values of −33 to −31 ^∘C derived for January on the plateau (Fig. 11a). This successful comparison, although made between different locations, validates the ability of DARDAR to capture the seasonal cycle of the SLC fraction, and more particularly in the southernmost regions probed by the satellites.