# Spatial and seasonal variability of clouds over the southwest Indian Ocean based on the DARDAR mask product

Hélène Vérèmes, Constantino Listowski, Julien Delanoë, Christelle Barthe, Pierre Tulet, François Bonnardot, Denis Roy

<figure><img src="image_1.png" /></figure>

## To cite this version:

Hélène Vérèmes, Constantino Listowski, Julien Delanoë, Christelle Barthe, Pierre Tulet, et al., Spatial and seasonal variability of clouds over the southwest Indian Ocean based on the DARDAR mask product. Quarterly Journal of the Royal Meteorological Society, 2019, 145 (725), pp.3561-3576. 10.1002/qj.3640 . insu-02285578

HAL Id: insu-02285578

https://insu.hal.science/insu-02285578v1

Submitted on 23 Oct 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

Quarterly Journal of the
Royal Meteorological Society

<figure><img src="image_3.png" /></figure>

RMetsS

# Spatial and seasonal variability of clouds over the southwest Indian Ocean based on the DARDAR mask product

Hélène Vérèmes<fnref n="1" /> ID | Constantino Listowski<fnref n="2" /> ID | Julien Delanoë<fnref n="2" /> | Christelle Barthe<fnref n="1" /> ID |
Pierre Tulet<fnref n="1" /> | François Bonnardot<fnref n="3" /> ID | Denis Roy<fnref n="3" />

**Correspondence**

Hélène Vérèmes, UMR8105, LACy,
Université de La Réunion, 15 avenue René
Cassin, CS92003, 97744 Saint-Denis Cedex
9, La Réunion, France.

Email: helene.veremes@univ-reunion.fr

**Funding information**

Centre National d'Etudes Spatiales (CNES);
French national program LEFE/INSU,
LIMA-TROPIC; Région Réunion, European
Commission (FEDER program) and
Université de La Réunion

**Abstract**

We present a characterization of the variability of clouds over the southwest Indian Ocean between 2007 and 2010. Cloud occurrence is derived from the DARDAR (raDAR/liDAR) mask, a synergistic product based on Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat measurements. It provides a target classification for hydrometeors. We demonstrate that this product is suitable for studying the vertical, spatial and seasonal cloud distribution in the southwest Indian Ocean. The variability of cloud occurrence increases approaching the tropics: the average maximum amplitude of monthly occurrence is ~7% between 30°S and 60°S and ~14% between 10°S and 30°S. The 10–30°S latitudinal band exhibits the largest contrasts: summer (winter) total cloud occurrence is mainly driven by high- (low-) level clouds. The vertical distribution of clouds differs on either side of 55°E and this is related to a land–ocean contrast and to large-scale influence. In winter, east of 55°E, the maximum of cloud occurrence corresponds to warm and mixed-phased precipitating clouds associated with the Mascarene High. In summer, west of 55°E, the vertical distribution of cloud occurrence is driven by deep convection associated with the InterTropical Convergence Zone and the Near Equatorial Trough. The vertical distribution of cloud occurrence shows an interannual variability that is related to El Niño events. The influence of other oscillation modes should be further investigated.

**KEYWORDS**

clouds, DARDAR, Mascarene High, Near Equatorial Trough, rain, southwest Indian Ocean

# 1 | INTRODUCTION

If the Indian Ocean has long been considered as a passive component of the climate system, that idea is gone. Studies have shown that anomalies in the Indian Ocean can have a climate impact on a regional scale but also beyond the basin (Chang et al., 2006). The sea-surface temperature of the Indian Ocean is divided zonally into two dipoles: a tropical one, known as the Indian Ocean Dipole (IOD: Yamagata et al., 2003) and, further south, a subtropical dipole

referenced in the literature as the Subtropical Indian Ocean Dipole (SIOD: Behera and Yamagata, 2001). The interannual oscillations of the coupled ocean–atmosphere system in the tropics are known to affect large-scale circulation, clouds and their radiative effect. The IOD is closely linked to El Niño–Southern Oscillation (ENSO) and the Indian monsoon (Terray et al., 2005). There are teleconnections with the other basins (like the Atlantic or the Pacific one) based on interactions between IOD, ENSO, SIOD, and even other modes of intraseasonal and interannual variability (Fauchereau et al.,

<figure><img src="image_4.png" /></figure>

<footnote n="1">¹Laboratoire de l'Atmosphère et des Cyclones, UMR8105, CNRS, Université de La Réunion, Météo-France, Saint-Denis, La Réunion, France</footnote>

<footnote n="2">²Laboratoire ATmosphères, Milieux, Observations Spatiales-IPSL, UVSQ Université Paris-Saclay, UPMC Université, Sorbonne Université, CNRS, Guyancourt, France</footnote>

<footnote n="3">³DIROI, Météo-France, Saint-Denis, La Réunion, France</footnote>
2003; Saji and Yamagata, 2003; Hermes and Reason, 2005; Chang et al., 2006). Due to interhemispheric exchanges (Carrera and Gyakum, 2003), anomalies in the Indian Ocean can influence climate on a global scale. Therefore it is important to assess the average state of the Indian Ocean basin in order to identify its anomalies. This ocean basin has a specific geographical characteristic: it is closed to the north. Many studies have been carried out in the northern part of the Indian Ocean basin (Dey et al., 2015), in particular to study the Indian monsoon (Rajeevan et al., 2013; Das et al., 2017). The northeastern Indian Ocean is well documented due to the presence of the Maritime Continent (Yamanaka et al., 2018) and to the strong influence of the Madden-Julian Oscillation (Riley et al., 2011; Sakaeda et al., 2017; Hung and Sui, 2018). However, it is important to consider the ocean as a whole as it is rather in the southern part of the basin that the positioning of the Mascarene High will partly drive the SIOD (Terray et al., 2003). The southwest Indian Ocean appears to be a key area for understanding the place of the Indian Ocean in the global climate machine but this is an area with a lack of climatological study to estimate its average state.

The southwest Indian Ocean includes several islands: Comoros, Madagascar, the Seychelles and the Mascarene Islands (consisting of Réunion Island, Mauritius and Rodrigues), which can potentially suffer significant damage due to the passage of cyclonic systems (Jury et al., 1993; Leroux et al., 2018) or if submitted to heavy precipitation that would lead to a flood issue. Some territories in the southwest Indian Ocean, such as Madagascar and Mozambique, have a precarious economy that can be destabilized by major natural disasters. For example, Madagascar's population is dependent on agriculture, and the societal consequences of floods can be catastrophic (Brown, 2009). To limit human losses due to extreme weather events and societal impact, it is important to understand the meteorological processes at the root of deep convection and heavy rainfall events in the Indian Ocean basin so that forecasts can be improved (Roux et al., 2004). In this region, satellite measurements are essential for the observation of convective systems because they develop largely in ocean regions with less conventional observations.

Satellite passive radiometers' products were successfully used to retrieve spatial and seasonal cloud cover (also known as cloud fraction) at global scale, such as MODerate-resolution Imaging Spectroradiometer (MODIS: King et al., 2013), High-resolution Infrared Radiation Sounder (HIRS: Wylie et al., 2005) and the International Satellite Cloud Climatology Project (ISSCP: Rossow and Schiffer, 1999) datasets. Some climatologies were obtained by differentiating clouds (low vs. high clouds, cirrus clouds...) with criteria on cloud top height (Stubenrauch et al., 2005), optical thickness (Stubenrauch et al., 2006) and emissivity (Wylie et al., 1994). Spaceborne active remote sensors sample the atmosphere with narrower swaths but they

are able to retrieve the vertical distribution of clouds (Stubenrauch et al., 2013) with a higher vertical resolution (Dey et al., 2015). The synergy of instruments such as lidar and radar allows the investigation of the spatial and vertical distribution of total and specific clouds. The A-Train (Afternoon Train) satellite constellation is a group of coordinated satellites on a Sun-synchronous polar orbit, crossing the Equator at around 1330 and 0130 hr local time (LT). Different instruments on board these satellites can be combined to obtain information on cloud microphysics and on aerosol properties. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat measurements can be processed in synergy to retrieve the thermodynamic phase of the clouds (Delanoë and Hogan, 2010; Stein et al., 2011). The DARDAR (raDAR/liDAR) mask is a simplified target classification based on that synergy (Ceccaldi et al., 2013). It has been used for the monthly and seasonal study of all cloud types and, more specifically, of mixed-phase clouds in polar and border regions (Huang et al., 2012; Mioche et al., 2015; Listowski et al., 2019).

There are few studies on cloud variability focusing on the southwest Indian Ocean (Miyamoto et al., 2018) and, up to now, none of them investigated the vertical distribution of clouds in the whole troposphere. Li et al. (2014) have shown some evidence of linkage between the cloud vertical structure and large-scale climate by exploring large-scale atmospheric dynamics, meteorological processes and tropospheric cloudiness. To improve our understanding of the meteorology in this region and the climate, the spatial distribution of clouds must be studied by taking into account cloud altitude and phase. Using the DARDAR mask product, we present a characterization of the spatial, seasonal and vertical variability of clouds over the whole southwest Indian Ocean. The distribution of clouds is particularly investigated in the latitudinal band between 10 and 30°S where most of the land surface is located, and which exhibits high monthly variability. Methodology and data are presented in section 2. In section 3, the seasonal occurrence of clouds over the southwest Indian Ocean is analysed. Section 4 compares the western and the open ocean regions between 10°S and 30°S in terms of cloud and rain occurrence. Section 5 concludes with the main findings of this study.

## 2 | DATA AND METHODOLOGY

### 2.1 | DARDAR products

A variational algorithm has been developed based on the complementarity of the Cloud Profiling Radar (CPR) of CloudSat and the two-wavelength lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) of CALIPSO to produce the DARDAR dataset (Delanoë and Hogan, 2008; 2010; Ceccaldi et al., 2013). The radar is more sensitive
<figure><img src="image_5.png" /><figcaption>TABLE 1 DARDAR v2 classes</figcaption></figure>

<table><thead><tr><td>Class number</td><td>Definition</td></tr></thead><tbody><tr><td>-2</td><td>Presence of liquid unknown</td></tr><tr><td>-1</td><td>Surface and subsurface</td></tr><tr><td>0</td><td>Clear sky</td></tr><tr><td>1</td><td>Ice clouds</td></tr><tr><td>2</td><td>Spherical or 2D ice</td></tr><tr><td>3</td><td>Supercooled water</td></tr><tr><td>4</td><td>Supercooled water + ice</td></tr><tr><td>5</td><td>Cold rain</td></tr><tr><td>6</td><td>Aerosol</td></tr><tr><td>7</td><td>Warm rain</td></tr><tr><td>8</td><td>Stratospheric clouds</td></tr><tr><td>9</td><td>Highly concentrated ice</td></tr><tr><td>10</td><td>Top of convective towers</td></tr><tr><td>11</td><td>Liquid clouds</td></tr><tr><td>12</td><td>Warm rain + liquid clouds</td></tr><tr><td>13</td><td>Cold rain + liquid clouds</td></tr><tr><td>14</td><td>Rain may be mixed with liquid</td></tr><tr><td>15</td><td>Multiple scattering due to supercooled water</td></tr></tbody></table>

to the largest particles and thus able to penetrate optically thick clouds. The lidar is sensitive to the concentration of small particles (e.g. droplets) and can detect optically thin clouds. However, it suffers from strong attenuation in optically thick clouds. The co-located 94 GHz radar reflectivity from CloudSat and the 532 nm backscatter coefficient from CALIOP, along with some auxiliary products from the European Centre for Medium-range Weather Forecasts (ECMWF) (such as temperature, pressure and humidity) are interpolated on the same grid to provide the vertical distribution of detailed cloud properties (Delanoë and Hogan, 2010). The DARDAR_MASK product includes a target categorization and provides cloud phase information. Table 1 summarizes the different DARDAR retrieval classes that are derived from a decision tree presented in Ceccaldi (2014). DARDAR_MASK version 2 (Ceccaldi et al., 2013; Ceccaldi, 2014) is used in this study. The horizontal and vertical resolutions are 1.4 km (cross-track) × 1.7 km (along-track) and 60 m, respectively.

## 2.2 | Methodology

CloudSat and CALIPSO were launched at the end of 2006 and are still operating in 2018. Since a technical issue on the battery of the spacecraft system in April 2011, CPR measurements are made only when CloudSat is in the sunlight. Consequently, only measurements performed between 2007

<figure><img src="image_6.png" /><figcaption>FIGURE 1 Domain and subareas. The southwest Indian Ocean (black solid line) is subdivided into four latitudinal bands: A (cyan), B (yellow), C (red) and D (purple), and two small areas: Mozambique Channel and Madagascar region (B1, green hatches) and the maritime region enclosing Réunion and Mauritius Islands (B2, blue hatches) [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

and 2010 are used hereinafter in order to cover the largest part of the diurnal cycle. The seasons are defined as follows: December–January–February (DJF, austral summer), March–April–May (MAM), June–July–August (JJA, austral winter) and September–October–November (SON). For the summer of a given year, December corresponds to the previous year. Note that December 2006 data are used for 2007 summer calculations.

In this study, we define the southwest Indian Ocean as the area between the Equator and 60°S, and between 35°E and 95°E (Figure 1). The domain has been subdivided into four latitudinal bands. A (0–10°S) and B (10–30°S) correspond to the tropics, A including the southern part of the equatorial band. C (30–40°S) can be defined as a subtropical area and D (40–60°S) is on the northern edge of the Southern Ocean (Figure 1). There are about 2,000 single overpasses in the whole domain each year. The time range of the satellites' overpasses in the domain is 0630–1230 UTC and 1730–2330 UTC. The spatial and vertical distributions are investigated for altitudes ranging between 500 m and 20 km. The minimum altitude of 500 m is chosen to avoid potential ground contamination due to CloudSat radar echoes (Marchand et al., 2008) and CALIOP attenuated backscattered signal. The instrument's shortcomings are discussed in section 2.3.

The methodology used to compute the spatial and vertical distributions of clouds is extracted from Mioche et al. (2015). The domain is subdivided into boxes of 5° in longitude and 2° in latitude. For the 2007–2010 period, there were 450 to 650 satellites' overpasses in each box. The representativity of this dataset is discussed in section 2.3. Following Mioche
<table><caption>**TABLE 2** Specific sets of classes defined for this study (i.e. all cloud types, low-level, ice, warm or mixed-phase clouds and all rain included). The DARDAR class numbers (from Table 1) used for this classification are marked with black filled squares. Note that “clouds” refers to all classes of hydrometeors. High-level and mid-level clouds are defined as the “clouds” category and, as for low-level clouds, occurrences are calculated for the altitudes specified in section 2.2</caption><thead><tr><th></th><th>-2</th><th>-1</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th></tr></thead><tbody><tr><th>Clouds</th><td></td><td></td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td></td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td></tr><tr><th>Low clouds</th><td></td><td></td><td></td><td>■</td><td>■</td><td>■</td><td>■</td><td></td><td></td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td></tr><tr><th>Ice clouds</th><td></td><td></td><td>■</td><td>■</td><td></td><td></td><td></td><td></td><td></td><td></td><td>■</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Warm clouds</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>■</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Mixed clouds</th><td></td><td></td><td></td><td></td><td>■</td><td>■</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Rain</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>■</td><td>■</td><td></td><td></td><td></td><td></td><td>■</td><td>■</td><td>■</td><td>■</td><td>■</td></tr></tbody></table>

et al. (2015), the cloud occurrence for each month or season ($F_{\text{cloud}}$) in each box of 2° latitude ($\Delta lat$), 5° longitude ($\Delta lon$) and between $z_{\text{min}} = 500$ m and $z_{\text{max}} = 20$ km is:

$$F_{\text{CLOUD}}(\Delta lat, \Delta lon) = \frac{\sum_{i,j} n_{\text{CLOUD}}(i, j)}{N_{\text{footprints}}(\Delta lat, \Delta lon)} \\ \text{with } N_{\text{CLOUD}}(i, j) = 1 \text{ if } \sum_{z=z_{\text{min}}}^{z_{\text{max}}} n_{\text{CLOUD}}(i, j, z) \ge 3 \\ = 0 \text{ if } \sum_{z=z_{\text{min}}}^{z_{\text{max}}} n_{\text{CLOUD}}(i, j, z) < 3 \quad (1)$$

where i and j are the latitudes and longitudes of granules falling into the box ($\Delta lat, \Delta lon$). $n_{\text{CLOUD}}(i,j,z)$ represents the detection of clouds at the latitude i and longitude j for an altitude z. Clouds are identified if hydrometeors (classes 1 to 14 in Table 1, except class 6) are detected at point (i,j,z). Note that class 15 (initially omitted by Ceccaldi et al. (2013)) that indicates backscatter signal caused by the multiple scattering of photons in the supercooled liquid layer (Ceccaldi, 2014, section III.3.5; Listowski et al., 2019), is excluded from the calculation. $N_{\text{CLOUD}}$ is the cloud occurrence at (i,j) and tagged as 1 if at least three DARDAR vertically adjacent pixels are found on the atmospheric column located at (i,j). $N_{\text{footprints}}$ is the total number of footprints in the box ($\Delta lat, \Delta lon$) for the considered period. $F_{\text{CLOUD}}$ can be calculated for specific layers of the atmosphere: low-level clouds (with $z_{\text{max}} = 3$ km above sea level (asl) in the subtropics and 4 km asl for tropics), mid-level clouds (where $z_{\text{min}} = 3$ and 4 km asl and $z_{\text{max}} = 6$ and 10 km asl in the subtropics and tropics, respectively) and high-level clouds (where $z_{\text{min}} = 6$ and 10 km in the subtropics and tropics, respectively). Note that, in this study, low-level cloud (or “low cloud”) means presence of hydrometeors in at least three vertical adjacent pixels at “low altitudes”, that is, from 500 m and up to 3 km (4 km) above the surface for subtropics (tropics). In order to exclude cases where rain formed above $z_{\text{max}}$ will fall below this limit, and cases where rain originates from melting snow, rain-only

classes (i.e. classes 5 and 7 in Table 1) are removed from the calculation of low-level cloud occurrence. We specifically target clouds with this definition. It should also be noted that a deep convective cloud can be counted as low-level cloud, mid-level cloud and high-level cloud.

The vertical distribution of cloud occurrence is computed at each altitude level z, within grid boxes of 60 m height, and averaged for different latitudinal bands (Figure 1), following:

$$F'_{\text{CLOUD}}((\Delta lat, \Delta lon), z) = \frac{\sum_{i,j} n_{\text{CLOUD}}(i, j, z)}{N_{\text{footprints}}(\Delta lat, \Delta lon)} \quad (2)$$

In this approach, $n_{\text{CLOUD}}$ refers to only one class (Table 1) or to an addition of classes (Table 2).

Phase occurrence ($F_{\text{PHA}}$) is studied through the vertical distribution of cold, warm or mixed-phase cloud occurrence relative to all clouds (also called “relative occurrence”), and is computed as:

$$F_{\text{PHA}}((\Delta lat, \Delta lon), z) = \frac{\sum_{i,j} n_{\text{PHA}}(i, j, z)}{\sum_{i,j} n_{\text{CLOUD}}(i, j, z)} \quad (3)$$

$n_{\text{PHA}}$ refers to the detection of one type of cloud (either cold, warm or mixed-phase). Cold, warm and mixed-phase clouds are differentiated as DARDAR pixels classified as ice-only (classes 1, 2 and 9), supercooled only and mixed with ice (classes 3 and 4) and liquid water clouds (class 11).

Rain occurrence is investigated in section 4 with the DARDAR classes of rain-only (warm and cold types of rain, classes 5 and 7) and the mixture of rain and liquid water (classes 12, 13 and 14). One of the limitations of most of the rain-measuring spaceborne sensors is the detection of light rainfall. Behrangi et al. (2012) have shown that among various sensors, CloudSat is the most sensitive to detect this light rainfall over ocean. This benefits DARDAR.
<figure><img src="image_7.png" /></figure>

## 2.3 | Representativeness of the dataset

When carrying out climatological type studies from satellite data, the question of the representativeness of the data arises. Is the domain sufficiently covered by the data? Are there enough data to make the analysis meaningful? Firstly, it is necessary to look at the time of passage of the A-Train in the domain. The time range of the satellites overpasses in the domain is 1030–1630 and 2130–0330 hr LT. The data cover only part of the diurnal cycle: late evening–early morning, and late morning–early afternoon. The variability of tropical ocean convection is mainly modulated at synoptic and intraseasonal scales, while continental convection is mainly driven by the diurnal cycle (Yang and Slingo, 2001; Nesbitt and Zipser, 2003). Tropical oceanic convection (detected on brightness temperature data) peaks in the early morning while continental convection exhibits a maximum in the evening (Yang and Slingo, 2001). Over the ocean, Yang and Smith (2006) have shown that precipitation has a maximum between late evening and early morning. Nesbitt and Zipser (2003) showed that the development of isolated convection occurs in the late morning and early afternoon over the islands (0800–1600 hr LT) and that during the evening and night many convective mesoscale systems grow (2000–0400 hr LT) over the Maritime Continent (region between the Indian and Pacific Oceans including the archipelagos of Indonesia, the Philippine Islands, New Guinea, Borneo, Malaysia, and the surrounding shallow seas) (Ramage, 1968). In winter, the Indian Ocean exhibits only one maximum which occurs in late evening–early morning (Yang and Smith, 2006). Moreover, comparisons between Cloud–Aerosol Transport Systems (CATS) spaceborne lidar and CALIOP data suggest that over oceans the A-Train measurements (at 0130 and 1330 hr LT) catch the daily extremes of the cloud fraction profiles and are relevant to study the cloud diurnal cycle (Noel et al., 2018). Given that the domain selected for this study is mainly oceanic, the dataset should be representative of the main features of the diurnal cycle of convection and precipitation.

Secondly, the spatial coverage of the domain by the satellites must be analysed. There are between 450 and 650 single overpasses in each grid box over the 2007–2010 period. The number of CloudSat/CALIPSO overpasses in the southwest Indian Ocean is low compared to the polar regions where the DARDAR_MASK product was validated (Mioche et al., 2015). So, the representativeness of our dataset needs to be scrutinized. The average cloud occurrence over 2007–2010 and over the whole domain derived using DARDAR is 69%. From radiometric satellite data, King et al. (2013) evaluated the ocean cloudiness as 72%. Considering that the southwest Indian Ocean is mostly oceanic, our result is in agreement with King et al. (2013). Figure 2 shows the seasonal occurrence of clouds over the southwest Indian Ocean derived from DARDAR. The main features are in agreement with Figure 1

<figure><img src="image_8.png" /><figcaption>FIGURE 2 (a–d) Average seasonal cloud occurrence (%) between 2007 and 2010 derived from DARDAR v2 product [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

in Pincus *et al.* (2012) which shows that the highest cloud fraction (>90%) is located around 60°S and in the equatorial band while the lowest total cloud occurrence is found between 10 and 30°S with an average around 55%. Presence and location of minima (maxima) of cloud occurrence in summer (winter), close to 10% (90%), are in agreement with King *et al.* (2013). Thus, the spatial distribution derived from DARDAR is in agreement with MODIS climatologies which justifies the methodology applied in this study considering that passive radiometry provides optimal horizontal coverage compared to current active techniques. The synergistic dataset can be used to study clouds outside of the polar regions. The asset of active remote sensing compared to passive radiometry is its ability to restitute vertical distribution.

Finally, it should be noted that there are some limitations due to both lidar and radar measurement techniques (Mioche et al., 2015; Listowski et al., 2019). The lidar may have difficulties in detecting low-level clouds because of overlapping optically thick clouds. The radar echo at the surface blurs the signal and prevents the instrument from detecting low-level clouds; that is why we start using DARDAR at 500 m asl. In the case of a supercooled liquid layer, the lidar signal is quickly extinguished and unable to detect underlying clouds. In view of these limitations, studies on the Southern Ocean and on Antarctica have shown that some of the near-surface clouds may not be detected (Huang et al., 2012; Listowski et al., 2019). The bias induced by this limitation in the quantification of the low-cloud fraction in case of multi-layered
<figure><img src="image_9.png" /></figure>

FIGURE 3 (a–d) Mean cumulative seasonal rainfall (colours; in mm) in the Indian Ocean from ERA-Interim (1979–2017). The yellow arrows represent the wind at 850 hPa and the green lines represent the isocontours of the mean-sea-level pressure (hPa). The letter “H” indicates the mean position of the Mascarene High, while the yellow and green dashed lines show the mean position of the ITCZ and of the NET, respectively [Colour figure can be viewed at wileyonlinelibrary.com].

systems can be investigated through class –2 (presence of liq-
uid unknown, Table 1) which is part of the statistics presented
in this article. The presence of large cloud systems associ-
ated with deep convection might induce an underestimation
of low-level clouds in summer over the tropics. It will be
investigated in section 3.

3 | SEASONAL CLOUD DISTRIBUTION OVER THE SOUTHWEST INDIAN OCEAN

In this section, the seasonality and distribution of the total
cloud occurrence (F<sub>CLOUD</sub>) is investigated across the whole
southwest Indian Ocean, as well as separately for distinct
regions (latitudinal bands) south (C and D, Figure 1) and north
(A, Figure 1) of our main region of interest (B, Figure 1). The
InterTropical Convergence Zone (ITCZ), the Near Equatorial
Trough (NET) and the Mascarene High (also known as the
Indian Ocean subtropical high) have seasonal variability that
may affect cloud distribution in the Indian Ocean. Figure 3
shows the seasonal average cumulative rainfall, the 850 hPa
wind and the mean-sea-level pressure in the Indian Ocean
based on 1979–2017 ERA-Interim data (with a horizontal
resolution of 2.5°). The position of the ITCZ corresponds to
the zone of wind convergence and large rainfall. In the aus-
tral summer, the ITCZ shifts into the Southern Hemisphere

(Waliser and Gautier, 1993). The convergence of air masses
at the ITCZ resolves into strong ascents, these wet masses
expand under the effect of the heat, which can lead to the
formation of large convective systems. The NET is a thalweg
that exists near the Equator (Ramage, 1968; Hobbs, 1974). It
is characterized by westerlies and a low-pressure region near
the Equator, and is located around 3°N and 3°S during aus-
tral winter and summer, respectively (Beucher, 2010). The
NET is associated with low-level convergence and the devel-
opment of convective clouds. The Mascarene High is a zone
of high pressure located on average between 25°S and 35°S
and between 40°E and 90°E in austral winter. It is strongest
in austral winter but exists all the year round, and moves east-
ward in austral summer (Lee *et al.*, 2013; Miyamoto *et al.*,
2018).

3.1 | Cloud distribution in the southern part (regions C and D)

Figure 4 represents the monthly evolution of the cloud occur-
rence averaged over regions A to D. The 2007–2010 average
monthly cloud occurrence between 40°S and 60°S (region D)
is the highest of the whole domain (between 79 and 86%;
purple lines in Figure 4) and has little monthly variability
(with an average maximum amplitude lower than 3% when
excluding February). In this region, persistent high cloud
<figure><img src="image_10.png" /><figcaption>**FIGURE 4** Monthly evolution of the average cloud occurrence (%) in the whole domain (in black) and in regions A (cyan), B (yellow), C (red) and D (purple) when referring to Figure 1. The lines with dots, diamonds, squares and triangles represent the average cloud occurrence for years 2007, 2008, 2009 and 2010, respectively [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

occurrence values are found (65–95%, Figure 2). Cloud occurrences are higher than 80% all year long, south of 50°S (±4°). This region is adjacent to the Southern Ocean where values higher than 80% are common and where the cloud cover is essentially driven by low-level clouds (Huang *et al.*, 2012). Low clouds are predominant in region D but high clouds show an occurrence around 14% (not shown). Low-level cloud occurrence exhibits some seasonality contrary to high cloud occurrence (with an average maximum amplitude of 10 versus 4%, respectively; not shown).

The 2007–2010 average monthly cloud occurrence between 30°S and 40°S (region C) varies around 68% (Figure 4, red lines) (except in January and February) which is very close to the average monthly cloud occurrence over the whole domain (69%). Little monthly variability is observed: the average maximum amplitude is ~7%. However, the spatial distribution of the cloud occurrence in region C is heterogeneous and varies seasonally. The average difference in cloud occurrence (regarding latitude) between the eastern and western boundaries of the region is 6% in summer (DJF) and in SON and 13% in MAM and in winter (JJA) (Figure 2). It should also be noted that the location of the 70% cloud occurrence limit moves from 36°S in summer to 32°S in winter, in the western part of the domain.

The variability of clouds over regions C and D will not be studied further herein. The overflow of the Mascarene High from region C to region B (Figure 3) makes it a meteorological factor to be taken into account when studying the occurrence

of clouds in the (sub)tropical region. This will be described in section 4. Hereafter, we will focus on the tropical band (regions A and B) that shows the highest seasonal, spatial and vertical variability.

## 3.2 | Cloud distribution in the near-equatorial region (region A)

In the most equatorial area (region A), the 2007–2010 average monthly cloud occurrence varies roughly between 62 and 72% throughout the year (average between cyan lines in Figure 4). This region exhibits two local maxima of similar value (~70%) in February and June and two local minima (~65%) in May and September. This region is also illustrative of a high interannual variability.

In summer (DJF, Figure 2a), cloud occurrences greater than 65% form a band (~10° wide in latitude) that extends from the eastern part of the study area at the Equator to Madagascar. The left column of Figure 5 represents the 2007–2010 meridionally averaged vertical distribution of cloud occurrence for region A. The area of largest cloud occurrence (>25%) is located around 90–95°E in DJF (Figure 5a). The maximum is found at around 13.5 km asl, which is consistent with the presence of high clouds associated with deep convection in the ITCZ (Waliser and Gautier, 1993). The relative weakening of the cloud occurrence west of 55°E is explained by the position of the ITCZ which is further south in this region (Figures 2a and 3a).

In winter (JJA), east of 50°E, the large occurrence of clouds (>70%) between the Equator and 6°S (Figure 2c) and the vertical distribution of cloud occurrence representative of deep convection (Figure 5e) are assumed to be associated with the NET (Figure 3).

During the transition seasons (MAM and SON), a maximum of cloud occurrence (>75%) appears over the ocean, in the northeast of region A (Figure 2b,d). It is associated with deep convective or cirrus clouds as shown in Figure 5c,g. This maximum is higher (91%) and extends over a larger region in SON (Figure 2d). The curvature of the southern trade winds into westerlies is linked to the balance between the pressure dipole between the two extremities of the Indian Ocean, the trade winds and the Coriolis force (Hastenrath and Polzin, 2004). Equatorial strong westerly winds, the equatorial Wyrtki jet (Wyrtki, 1973), and therefore the NET, are best established during the transition seasons, and particularly in SON (Figure 3). Combined with westerlies, the easterly jet (Wyrtki, 1973) contributes to creating a powerful zonal circulation with ascent on the Maritime Continent (strong cloud occurrence east of the Indian Ocean) and a subsidence region near the African coast (weak cloud occurrence west of the Indian Ocean).

Near the eastern African coast, there is a persistent area with intermediate values of cloud occurrence (around 50%)
<figure><img src="image_11.png" /></figure>

**FIGURE 5** Vertical distribution of zonal cloud occurrence (%) averaged over (a,c,e,g) 0–10°S (region A) and over (b,d,f,h) 10–30°S (region B) for the (a,b) DJF, (c,d) MAM, (e,f) JJA and (g,h) SON periods between 2007 and 2010. All hydrometeors (including rain) are used in the calculation of the cloud occurrence [Colour figure can be viewed at wileyonlinelibrary.com].

(Figure 2a–d), which contrasts with the strong cloud occurrence within region A (>65%). This region corresponds to the annual mean position of the descending branch of the Walker circulation in the western part of the Indian Ocean (Lau and Yang, 2003) with strong descent in the mid-troposphere. Cloud occurrence is very low (<10%) all year long between 5 and 8 km altitude in the western part of the area (35°–55°E) (Figure 5a,c,e,g). The cloud occurrence shows a minimum in winter with values lower than 1% between 8 and 10 km altitude, between 35 and 45°E.

Large convective systems developing in the equatorial region (region A, Figure 1) can extend beyond the boundaries of region A and influence the occurrence of clouds in region B.

## 3.3 | Variability of rain and cloud occurrence between 10°S and 30°S

In the 10–30°S band (region B), the monthly average cloud occurrence varies between 49 and 64% (yellow lines in Figure 4). The main maximum occurs in February (~64%) and a secondary maximum occurs in July (~55%). The factors explaining these two peaks of cloud occurrence are analysed in this section. In this region, two areas can be distinguished, one under “terrestrial” influence (west of 55°E, region B1 in Figure 1) and the other one under “oceanic” influence (east of 55°E, region B2 in Figure 1).

### 3.3.1 | The western part

The highest cloud occurrence (85%) is found over land in summer, in the northern part of Madagascar and over Mozambique (Figure 2a). As shown in the mean vertical distribution of cloud occurrence in region B (right column of Figure 5), deep convection is mainly confined to the western part of the basin during the DJF and MAM periods (Figure 5b,d, respectively). The maximum of cloud occurrence in the north of the Mozambique Channel and Madagascar is between 5 and 15 km asl, with values of cloud occurrence between 17 and 25% at longitude 45°E. In Madagascar, the wet season lasts from November to April (Nassor and Jury, 1998). This deep convection in the northwest of Madagascar results from the convergence of northwesterly monsoon flow with easterly trade winds (Jury et al., 1995; Nassor and Jury, 1998; Macron et al., 2016). High occurrence of clouds northwest of Madagascar can also be associated with mesoscale convective systems of the ITCZ (Figure 3). The location of the maximum of cloud occurrence (12°S–50°S; 45°E–50°E) north of Madagascar (Figures 2a and 5b) is consistent with the location of the most rainy region in summer found by Macron et al. (2016). Figure 6 represents the 2007–2010 average monthly evolution of total rain occurrence (i.e. the DARDAR classes rain only and mixture of rain and liquid water cloud relatively to all footprints) averaged over the whole southwest Indian Ocean and over regions B1 and B2. In the Mozambique Channel and Madagascar area (region B1), the monthly evolution of rainfall occurrence shows one maximum (reaching 23%) in January (green line in Figure 6). On the east coast of Africa, factors driving convection are similar to those influencing convection north of Madagascar. Manatsa et al. (2013) have shown that precipitating convective systems on the African coast are mainly associated with the ITCZ, but are also influenced by local factors (orography, presence of lakes, oceanic influence) and by the southeasterly trade winds associated with the variability of the Mascarene High (Figure 3). These two regions of strong deep convective activity also correspond to the location of the maximum lightning activity in the
<figure><img src="image_12.png" /></figure>

**FIGURE 6** Monthly evolution of the average occurrence of rain-only and mixture of rain and liquid water (relative to all footprints) in the whole domain (black), and in regions B1 (green) and B2 (blue) when referring to Figure 1 [Colour figure can be viewed at wileyonlinelibrary.com].

southwest Indian Ocean (Bovalo *et al.*, 2012), at (8°S–16°S; 35°E–40°E) and at (12°S–20°S; 45°E–50°E).

Figure 7 shows the relative 2007–2010 meridionally averaged vertical distribution of ice occurrence relative to cloud occurrence between 10 and 30°S in summer and winter. In summer, the vertical column is divided into three distinct parts. Above 11 km altitude (~-38 °C isotherm), cloud droplets homogeneously freeze (Rosenfeld and Woodley, 2000; Herbert *et al.*, 2015) and the ice occurrence reaches 100% over the whole region. This ice occurrence above 11 km is likely representative of cirrus clouds and anvils of deep convective systems. Between 4.5 and 11 km, the ice occurrence ranges between 80 and 98% (Figure 7a). The transition of the occurrence of ice between 0 and 80% is abrupt around the 0 °C isotherm. The prevalence of ice clouds around the 0 °C isotherm could be explained by the fact that no specific distribution of mixed-phase clouds is seen in middle and low levels (not shown). This could be due to a potential limitation of either the DARDAR product which, in case of strong convective activity, would limit the detection of mixed-phase layers in the low troposphere, or the temporal or spatial resolution of our dataset which would not allow us to sample it. The lidar could be strongly attenuated or even extinguished because of the optically very thick clouds which could limit the detection of underlying supercooled water and thus the detection of mixed and warm low-altitude clouds. The mask would then only see the ice or rain thanks to the radar. The vertical distribution of lidar extinction occurrence in regions A and B for altitude below 10 km shows a seasonal pattern correlated (not shown) with the occurrence of thick clouds in

<figure><img src="image_13.png" /></figure>

**FIGURE 7** 2007–2010 seasonal average ice occurrence (%, relatively to all hydrometeors) between 10 and 30°S (region B, Figure 1) for (a) summer and (b) winter: zonal transect [Colour figure can be viewed at wileyonlinelibrary.com].

the upper troposphere. In region B, the highest occurrence of lidar extinction (>25%) is found between 45 and 50°E below 5 km and between 35 and 70°E below 2 km. It corresponds to the regions where the maximum of cloud occurrence (>20%) is found above 8 km in Figure 5b. To estimate the impact of deep convection on low-level cloud detection, we compared the cloud occurrence calculated according to the methodology described in section 2.2 (i.e. $N_{\text{footprints}}$ corresponds to the total number of footprints in the box) with the cloud occurrence calculated with a new $N_{\text{footprints}}$ where the number of observations in each vertical pixel is reduced, by subtracting all the instances of the “presence of liquid unknown” class. The difference between both is only a few per cent (<5%) above 5 km in regions A and B. Nevertheless, this difference reaches 15% below 2 km (not shown) in case of cloud occurrence of 40% in the upper troposphere as seen in SON in region A. The lidar extinction in the lower and middle troposphere shows a seasonal variability in agreement with the variability of deep convection (not shown). Moreover, the “presence of liquid unknown” class is greater than 25% in the lower and middle troposphere in the case of cloud occurrence of 40% in the upper troposphere (not shown). Therefore, this leads to the conclusion that there is a potential underestimation of low- and mid-level liquid-bearing clouds associated with deep convection.

The lowest cloud occurrences (<30%) are found in the southern part of Madagascar and in the Mozambique Channel between March and November (Figure 2b–d). The minimum cloud occurrence (~13%) is found between June and August south of the Mozambique Channel and on the western coast of Madagascar (Figure 2c). In winter, the vertical occurrence of clouds decreases to an average of less than 1% between 40 and 50°E above 5 km altitude (Figure 5f).

Figure 6 shows a secondary maximum of monthly rain occurrence around 10% in July for region B1 and two minima in September (~3%) and May (~6%). The average spatial
JJA

<figure><img src="image_14.png" /><figcaption>**FIGURE 8** 2007–2010 average winter occurrence of rain-only and mixture of rain and liquid water (relative to all hydrometeors) [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

distribution of total rain occurrence (relatively to all hydrometeors) in winter between 2007 and 2010 is shown in Figure 8. There is a contrast in rain occurrence around longitude 45°E: less than 15% on the western side and higher than 25% on the eastern side. This longitude corresponds to the mountainous region of Madagascar, which means that, in JJA, there is more precipitation over the windward side of Madagascar. The local maximum of rain occurrence in winter in region B1 (Figure 6) can be explained by the foehn effect.

### 3.3.2 | The maritime region

The maritime region (10–30°S, 50–95°E, including region B2) is characterized by values of cloud occurrence between 50 and 60% (Figure 2) whatever the season. The occurrence of clouds even exceeds 70% with the intrusions of the ITCZ and NET (depending on the season) from the north into this region.

Looking at the cloud occurrence in the vertical (Figure 5), region B2 experiences two different regimes. The first one occurs between December and May. The cloud occurrence can reach 10–15% above 15 km altitude, east of 50°E (Figure 5b,d). As shown in the previous sections, regions A and B1 have a high convective activity. The presence of anvils or cirrus clouds from deep convective systems that developed in regions A and B1 could explain the large occurrence of high clouds in region B2 above 15 km.

In contrast, between June and November, the cloud occurrence is mostly below 5% above 5 km in this region (Figure 5f,h). This is in agreement with Gettelman et al.

(2006) who showed that the relative humidity is very low (<20%) in winter over the Mascarene Islands. Below 3 km and east of 50°E, the cloud occurrence exceeds 15–20% whatever the season. Low-level clouds prevail: their monthly averaged occurrence is around 49% (against 7% for high-level clouds) (not shown). In winter, above 10 km, the eastern part of the southwest Indian Ocean is composed of more than 97% of ice (Figure 7b); the high-level clouds are probably cirrus clouds.

The maximum amplitude of the low-level cloud occurrence in the 10–30°S latitudinal band between winter and summer is 13% (not shown). The low-level cloud occurrence is larger in winter which is in agreement with Fig. 2a–b in Miyamoto et al. (2018). In region B2, the monthly averaged warm liquid cloud occurrence in July and August is 36% (relatively to all footprints) and 62% (of all clouds) (not shown); warm clouds predominate in winter. There is a gradual decrease of the relative occurrence of ice between 3.5 km (0 °C isotherm) and 10 km asl (Figure 7b). The decrease of the relative contribution of ice-only occurrences to cloud, from 80 to 0% downwards, occurs within a layer of less than 1 km in summer (Figure 7a) to approximately 3 km in winter (Figure 7b). Figure 9 represents the 2007–2010 meridionally averaged vertical distribution of mixed-phase cloud occurrence in the 10–30°S latitudinal band (region A in Figure 1) in winter. Whatever the longitude, around 5 km asl, the mixed-phase cloud occurrence (relatively to all clouds) is higher than 10% (Figure 9). This is consistent with the wintertime thickening of the gradient zone in ice occurrence seen in Figure 7b. Some mixed-phase clouds are found up to 8 km height in different locations. Such mixed-phase clouds with a top above the 0 °C isotherm could be associated with congestus-type convection (Kumar et al., 2013). It should be noted, however, that the occurrence of mixed-phase clouds relatively to all footprints is only a few per cent (but this is slightly higher than in summer) (not shown).

In the mainly oceanic region B2, the evolution of the monthly average rain occurrence shows two maxima (Figure 6). The main peak occurs in July–August with values around 25%. The second maximum occurs in February (~21%). The total rain occurrence represents more than 36% of all the hydrometeors in the area between 18°S and 38°S, and between 55°E and 95°E (Figure 8). It can be attributed to changes in the mean position of the Mascarene High, which is located further west of the basin during winter compared to summer (Fig. 3, Miyamoto et al., 2018). It is also in agreement with Feng et al. (2011) which explained that the conditions associated with a subtropical high are not necessarily dry and cloudless. Thus, the significant rain occurrence in winter (Figures 6 and 8) is associated with an enhanced presence of warm and, in very small quantities, mixed-phase clouds occurrence (Figures 7 and 9).
<figure><img src="image_15.png" /><figcaption>**FIGURE 9** 2007–2010 average ratio of the mixed-phase cloud occurrence (relative to all hydrometeors) between 10 and 30°S (region B, when referring to Figure 1) for winter: zonal transect [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

## 4 DISCUSSION: THE EAST–WEST CONTRAST

The zonal analysis of the vertical distribution of the cloud occurrence as well as the geographical distribution of the rain-fall occurrence revealed, in the 10–30°S latitudinal band, two regions with distinct characteristics (B1 and B2), on either side of longitude 55°E. The first difference between these two regions is the land–ocean contrast that explains the observed distributions. Their variability is explained by regional and large-scale processes. In this section, we focus our analysis on high-level clouds in summer and low-level clouds in winter for region B. Since the presence of high-level clouds is low in winter (<5%, Figure 5f), low-level cloud detection is likely to be less biased. To estimate the impact of deep convection on low-level cloud detection, we compared in section 3.3.1 the cloud occurrence calculated according to the methodology described in section 2.2 with the cloud occurrence calculated where the number of observations in each vertical pixel is reduced, by subtracting all the instances of the “presence of liquid unknown” class. The largest differences between both (>15%) are for region A where the occurrence of high clouds is significant all year round (>20%, Figure 5a,c,e,g). In that region, differences of a few per cent are observed up to 10 km asl. In region B, the largest difference is observed when the occurrence of clouds is higher than 20% in the upper troposphere which occurs only in summer (Figure 5b) and it is more localized (limited to the western part) than in region A. In winter, the difference is higher than 5% below 1 km asl, east of 60°E. The rest of the year, the difference is only a few per cent below 3 km asl and is almost zero above 3 km (not shown). Therefore, for region B, if the values of mid- and low-level cloud occurrences would be impacted by the instrumental

limitation due to high clouds, it would be mainly in summer. We focus our discussion on issues where the instrumental bias is expected to have the least impact.

### 4.1 Factors influencing the wintertime rain occurrence

While the rain occurrence peaks in austral summer in region B1, it reaches a maximum in winter in region B2 (Figure 6). In summer, the rain occurrence maximum has the same order of magnitude in regions B1 and B2, but in winter, the peak in region B2 is 2.5 higher than in region B1. This suggests that the processes controlling rain occurrence differ between these two regions.

Inside region B1, in austral winter, there is almost no cloud and no rain on the leeward side of Madagascar in opposition to high rain occurrence on the windward side. The low cloud occurrence in the Mozambique Channel might be explained by the presence of the descending branch of the Hadley cell (Lau and Yang, 2003). Moreover, the Malagasy orography with a plateau higher than 1,000 m extending from the north to the south of the island presents an obstacle to prevailing low-level easterly trade winds. In winter, the trade wind regime in Madagascar weakens (Donque, 1972), which could reinforce the foehn effect. The foehn effect produces an adiabatic heating of the descending air on the leeward side of the mountains and extremely dry conditions in the southwestern part of Madagascar (Logan, 1968). Thus the foehn effect is assumed to be at the origin of the local maximum of rain occurrence in winter in region B1 (Figure 6). In region B1, mixed-phase is observed (Figure 9) but relative cloud occurrences of 20% are mainly found below 5 km asl (Figure 5). This suggests that shallow convection is prevailing.

Region B2 is located in the northern part of the Mascarene High (Figure 3). The intensification of the Mascarene High (which happens in austral winter) increases the sensible-heat fluxes, which helps to form shallow convective clouds and thus increases low cloud fraction (Miyamoto et al., 2018). In anticyclonic regions, clouds develop preferentially under the temperature inversion, in the layer not affected by large-scale subsidence (Klein and Hartmann, 1993; Jumaux et al., 2011). Low-layer humidity will be even higher in oceanic environments, which favours the formation of low clouds. This can explain the prevalence of low clouds in this maritime region. Deep convection is inhibited because of the lowering of the trade inversion associated with the westward movement of the Mascarene High in winter (Figure 3). Some mixed-phase clouds are observed up to 8 km asl (between 10 and 20% of the total cloud occurrence, Figure 9). This suggests that under the Mascarene High, there is also some congestus-type convection. The limited vertical development of the convection to shallow and mid-topped cumulus clouds is correlated to
the presence of a dry air layer (Takemi *et al.*, 2004). Such dry mid-level air layer is typical in winter in the Indian Ocean (Figure 5f).

The location and prevalence of the low clouds in winter are driven by the seasonality of the Mascarene High, the existence of a dry layer in the middle troposphere and the foehn effect. The difference in winter rain occurrence between regions B1 and B2 is largely related to the land-ocean contrast, namely the orography of Madagascar on the one hand (at the origin of the foehn effect) and the maritime environment on the other (propitious to cloud formation in a layer already favourable due to its overlying dry anticyclonic layer).

## 4.2 | Factors influencing the summertime high-level cloud occurrence

Figure 10 represents the monthly evolution of the high-level cloud occurrence averaged over the whole domain and over regions B1 and B2. The monthly average high-level cloud occurrence of the whole domain is around 31% and exhibits a weak seasonal variability: 36% in January versus 26% in August (black lines in Figure 10). The meridionally averaged vertical distribution of cloud occurrence in the 10–30°S latitudinal band (Figure 5a,b) highlights the contrast between convection over land and over sea (e.g. Hong *et al.*, 2008; Noel *et al.*, 2018). The occurrence of clouds increases from 47 to 73% depending on the seasons in the 10–30°S latitudinal band (Figure 4). The average monthly high-level cloud occurrence is 66 and 36% higher in January than in August for region B1 and B2, respectively (Figure 10). The variability of the high-level cloud occurrence is important for both regions but is more important in the Mozambique Channel/Madagascar region where hotspots of land deep convection and the ITCZ (which can shift as far south as 20°S in summer, Figure 3) are located. The high occurrence of high-level clouds could also be explained by the presence of cirrus clouds disconnected from deep convective systems. However, the average vertical distribution of cloud occurrence in region B shows maxima around 14 km altitude (Figure 5b) and this altitude corresponds to the average altitude of the top of cirrus clouds induced by tropical convection in Hoareau *et al.* (2012), that is, 14.3 km with an average thickness of 1.4 km. We therefore assume that, in summer, for region B, the main source of cirrus clouds at the altitude of the maximum cloud occurrence is deep convection. The maritime region east of 55°E is on the margins of these processes, but it remains largely impacted by high clouds resulting from deep convective systems forming to the west and to the north. The distribution of high-level clouds shows an additional interannual variability (Figure 10) that should be further investigated.

<figure><img src="image_16.png" /><figcaption>**FIGURE 10** Monthly evolution of the average high-level cloud occurrence in the whole domain (in black), and in regions B1 (in green) and B2 (in blue) when referring to Figure 1 [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

## 4.3 | Link between ENSO and the vertical distribution of clouds

The monthly evolution of high-level cloud occurrence shows little interannual variability (around 4% on average) in the whole domain (Figure 10). This is different when focusing on regions B1 and B2, which exhibit some interannual variability. In region B1, the cloud occurrence above 10 km altitude is 69% on average in January and 3% in August (green lines, Figure 10). The average cloud occurrence in January varies between 61 and 80% over the four years and, in August between 1 and 4%. In region B2, the average cloud occurrence varies between 43% in January and 7% in August (blue lines, Figure 10). The monthly cloud occurrence in January is 28% in 2009 and 63% in 2010. In August, it is 1% in 2009 and 15% in 2008. There is a very strong contrast between wet and dry seasons in regions B1 and B2 (green and blue lines, respectively, in Figure 10). Both regions exhibit a peak in high-level cloud fraction in austral summer (wet season) and a minimum in austral winter (dry season). The maritime region B2 has a lower intraseasonal variability than region B1, but a higher interannual variability.

Figure 11 represents the meridionally averaged vertical distribution of cloud occurrence for the latitudinal band corresponding to zone B in Figure 1 for each year between 2007 and 2010. In summer 2008 (Figure 11a) and 2009 (Figure 11c), values of cloud occurrence higher than 15% are found across the whole tropospheric column between 45°E and 50°E, that is, over Madagascar. This is the signature of deep convective activity over this island as explained in section 3.3. In summer 2007 (Figure 11b), a maximum (~32%) is found at 45°E
<figure><img src="image_17.png" /><figcaption>**FIGURE 11** Annual variability in region B during austral summer (DJF). Average of cloud (all hydrometeors including rain) occurrence between 10 and 30°S (region B, Figure 1) for (a) 2008, (b) 2007, (c) 2009 and (d) 2010: zonal transect. For the summer (DJF) of a given year, December corresponds to the previous year. The right column (b,d) corresponds to years with an El Niño event [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

and 13.4 km. By then, the vertical and the meridional extent of the largest cloud occurrences (>25%) in 2007 were more important than in summer 2008 and 2009. In summer 2010, the cloud occurrence over Madagascar was lower than in other years but higher at 60–65°E (Figure 11d). The 2006–2007 and 2009–2010 years were characterized by a weak and moderate El Niño event, respectively. Using CALIPSO and CloudSat, Su and Jiang (2013) showed that the mean tropical vertical distribution of clouds and large-scale circulations are different for the 2006–2007 and 2009–2010 El Niño events. In 2006–2007, the tropical circulation was characterized by a reduction of the “Walker circulation” which could explain the strengthening of deep convection over the east coast of Africa and Madagascar (Figure 11b). In 2009–2010, there was an enhancement of the “Hadley circulation” (Su and Jiang, 2013). This could explain the decrease of the convective activity over Madagascar with a shift towards the east (Figure 11d).

Between 10 and 30°S, for the 2007–2010 period, the summer mean vertical distribution of cloud occurrence (Figure 5b) is representative of deep convection and shows a contrast on either side of a clearly apparent limit at 55°E (region B1-region B2). We find this representative pattern for the vertical distribution of clouds in the summers of 2008 (Figure 11a) and 2009 (Figure 11c), that is, years without an El Niño event. However, the occurrence of clouds shows a different distribution for the summers of 2007 (Figure 11b)

and 2010 (Figure 11d), when there were El Niño events. The east–west contrast around 55°E no longer applies: the boundary is shifted eastward in 2007 and the vertical distribution on either side of the limit changes in 2010. Thus, the vertical distribution of clouds in the Indian Ocean and ENSO appears to be related.

## 5 | CONCLUSION

This study is the first one that uses the DARDAR synergistic product to analyse the vertical distribution of clouds over the whole troposphere in the southwest Indian Ocean. This is also the first time that the use of the DARDAR product is discussed regarding deep convective systems. The main scientific insight of this study is the evidence of an atmospheric contrast (on cloud and rain occurrences) in the tropical part of the southwest Indian Ocean clearly apparent on either side of 55°E. This can be explained by the influence of different local and large-scale meteorological processes interacting with two types of environments: land and ocean.

The good agreement between the spatial distribution of cloud occurrence calculated from DARDAR in our study and MODIS climatologies from the literature validates the use of this product in the southwest Indian Ocean to study cloud variability. We have thus confirmed that the maximum values for cloud occurrence in the tropics are in summer, along the ITCZ, and in particular in the northern part of Madagascar and on the east coast of Africa. These values are associated with deep convection hotspots. The lowest values are found in the southern part of the Mozambique Channel in winter. We have pointed out that seasonal variations are greatest in the tropics throughout the southwest Indian Ocean. The main advantage of using DARDAR is its possibility to study the vertical distribution of clouds. In addition, its target classification makes it possible to study the occurrence of certain types of clouds (ice, mixed-phase, warm, supercooled ...) and of rain. Between 10 and 30°S, the seasonal variability of phase occurrence shows predominant types of microphysics: cold in summer from 4.5 km asl to the tropopause (western part) and warm and mixed in winter from 500 m to 8 km asl (open ocean into the east of the domain).

The 10–30°S region is characterized by two peaks in the monthly occurrence of clouds, in summer (~64%) and winter (~55%), respectively. The vertical distribution of clouds associated with the summer maximum occurrence shows a configuration representative of deep convection. West of 55°E, the convective activity is important: the position of the ITCZ lying in this region and deep convection developing on the land. East of 55°E, cloud cover is impacted by the cirriform parts of large convective systems developing to the north (between the Equator and 10°S, ITCZ and NET) and to the west. While this region is experiencing high cyclone activity, the representativeness of tropical cyclones in our results
remains to be determined and will be the subject of a specific study.

The second maximum in the monthly evolution of cloud occurrence is winter and is associated with predominant liquid clouds at low altitudes. In winter, the region west of 55°E is driven by the foehn effect induced by the relief of Madagascar. On the leeward side, there are almost no cloud and no rain, while the windward coast shows a significant rain occurrence. East of 55°E, Réunion and Mauritius Islands are subject to a high occurrence of rain. This region shows a predominance of warm, precipitating low-level clouds resulting from the westward shift of the Mascarene High, the presence of dry air in the middle troposphere of the southwest Indian Ocean and benefiting from a marine (and therefore humid) environment. Influence of local versus large-scale processes on the occurrence and the intensity of rainfall under the Mascarene High and by the foehn effect should be further investigated.

It should be noted, however, that the time slot for the A-Train satellites to pass over the Indian Ocean limits the study of the distribution of clouds to two times in the diurnal cycle: late evening–early morning, and late morning–early afternoon. We have shown that there is an impact of instrumental limitations on the quantification of low clouds in case of a high presence of high-altitude convective clouds. In order to investigate the potential underestimation of the cloud occurrence of low clouds, comparisons with ground observations should be conducted. As the area is mainly oceanic, this remains a difficult exercise.

In addition to a marked seasonal variability in the southern tropics, we have detected interannual variability. The vertical distribution of clouds appears to be modulated by ENSO. We have shown different patterns according to the strength of the El Niño events. There are many drivers of interannual variability in the Indian Ocean (Schott et al., 2009): sea surface temperature, ENSO, IOD ... In order to fully understand the interannual variability of the southwest Indian Ocean vertical cloud distribution, it will be necessary to investigate the influences of different oscillation and teleconnection modes between tropics and midlatitudes.

## ACKNOWLEDGEMENTS

H. Vérèmes' research is funded by Région Réunion, the European Commission (FEDER programme) and Université de La Réunion. C. Listowski thanks CNES for postdoctoral fellowship funding. This work was partly supported by the French national programme LEFE/INSU (projet LIMA-TROPIC). Spaceborne data were provided by NASA/CNES. We thank the ICARE Data and Services Center (http://www.icare.univ-lille1.fr) (part of the AERIS Data Infrastructure for Atmosphere) for providing access to the data used in this study.

## ORCID

Hélène Vérèmes https://orcid.org/0000-0002-5205-3740
Constantino Listowski https://orcid.org/0000-0001-8693-8689

Christelle Barthe https://orcid.org/0000-0003-0790-7193
François Bonnardot https://orcid.org/0000-0002-8729-2020

## REFERENCES

Behera, S.K. and Yamagata, T. (2001) Subtropical SST dipole events in the southern Indian Ocean. *Geophysical Research Letters*, 28, 327–330.

Behrangi, A., Lebsock, M., Wong, S. and Lambrigtsen, B. (2012) On the quantification of oceanic rainfall using spaceborne sensors. *Journal of Geophysical Research: Atmospheres*, 117, D20105. https://doi.org/10.1029/2012JD017979.

Beucher, F. (2010) *Manuel de météorologie tropicale: des alizés au cyclone, Cours et manuels – Direction de la météorologie*, Vol. 1. Paris: Météo-France.

Bovalo, C., Barthe, C. and Bègue, N. (2012) A lightning climatology of the south-west Indian Ocean. *Natural Hazards and Earth System Sciences*, 12, 2659–2670.

Brown, M.L. (2009) Madagascar's cyclone vulnerability and the global vanilla economy. In: Jones, E.C. and Murphy, A.D. (Eds.) Society for Economic Anthropology. *The Political Economy of Hazards and Disasters, Ch.11*. New York: AltaMira Press, pp. 241–264.

Carrera, M.L. and Gyakum, J.R. (2003) Significant events of interhemispheric atmospheric mass exchange: composite structure and evolution. *Journal of Climate*, 16, 4061–4078.

Ceccaldi, M. (2014) *Combinaison de mesures actives et passives pour l'étude des nuages dans le cadre de la préparation à la mission EarthCARE*. PhD Thesis, Météorologie océanographie physique de l'Environnement Versailles-St Quentin en Yvelines. Available at: http://www.theses.fr/2014VERS0017 [Accessed 9th September 2019].

Ceccaldi, M., Delanoë, J., Hogan, R.J., Pounder, N.L., Protat, A. and Pelon, J. (2013) From CloudSat-CALIPSO to EarthCare: evolution of the DARDAR cloud classification and its comparison to airborne radar–lidar observations. *Journal of Geophysical Research: Atmospheres*, 118, 7962–7981.

Chang, P., Yamagata, T., Schopf, P., Behera, S.K., Carton, J., Kessler, W.S., Meyers, G., Qu, T., Schott, F., Shetye, S. and Xie, S.-P. (2006) Climate fluctuations of tropical coupled systems – the role of ocean dynamics. *Journal of Climate*, 19, 5122–5174.

Das, S.K., Golhait, R.B. and Uma, K.N. (2017) Clouds vertical properties over the Northern Hemisphere monsoon regions from CloudSat-CALIPSO measurements. *Atmospheric Research*, 183, 73–83.

Delanoë, J. and Hogan, R.J. (2008) A variational scheme for retrieving ice cloud properties from combined radar, lidar, and infrared radiometer. *Journal of Geophysical Research: Atmospheres*, 113, D07204. https://doi.org/10.1029/2007JD009000.

Delanoë, J. and Hogan, R.J. (2010) Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds. *Journal of Geophysical Research: Atmospheres*, 115, D00H29. https://doi.org/10.1029/2009JD012346.

Dey, S., Nishant, N., Sengupta, K. and Ghosh, S. (2015) Cloud climatology over the oceanic regions adjacent to the Indian subcontinent:
inter-comparison between passive and active sensors. *International Journal of Remote Sensing*, 36, 899–916.

Donque, G. (1972) The climatology of Madagascar. In: Battistini, R. and Richard-Vindard, G. (Eds.) *Biogeography and Ecology in Madagascar. Monographiae Biologicae*. Dordrecht: Springer Netherlands, pp. 87–144.

Fauchereau, N., Trzaska, S., Richard, Y., Roucou, P. and Camberlin, P. (2003) Sea-surface temperature co-variability in the Southern Atlantic and Indian Oceans and its connections with the atmospheric circulation in the Southern Hemisphere. *International Journal of Climatology*, 23, 663–677.

Feng, S., Liu, Q. and Fu, Y.-F. (2011) Cloud variations under subtropical high conditions. *Advances in Atmospheric Sciences*, 28, 623–635.

Gettelman, A., Collins, W.D., Fetzer, E.J., Eldering, A., Irion, F.W., Duffy, P.B. and Bala, G. (2006) Climatology of upper-tropospheric relative humidity from the atmospheric infrared sounder and implications for climate. *Journal of Climate*, 19, 6104–6121.

Hastenrath, S. and Polzin, D. (2004) Dynamics of the surface wind field over the equatorial Indian Ocean. *Quarterly Journal of the Royal Meteorological Society*, 130, 503–517.

Herbert, R.J., Murray, B.J., Dobbie, S.J. and Koop, T. (2015) Sensitivity of liquid clouds to homogenous freezing parameterizations. *Geophysical Research Letters*, 42, 1599–1605.

Hermes, J.C. and Reason, C.J.C. (2005) Ocean model diagnosis of inter-annual coevolving SST variability in the south Indian and South Atlantic Oceans. *Journal of Climate*, 18, 2864–2882.

Hoareau, C., Keckhut, P., Baray, J.-L., Robert, L., Courcoux, Y., Porteneuve, J., Vömel, H. and Morel, B. (2012) A Raman lidar at La Réunion (20.8°S, 55.5°E) for monitoring water vapour and cirrus distributions in the subtropical upper troposphere: preliminary analyses and description of a future system. *Atmospheric Measurement Techniques*, 5, 1333–1348.

Hobbs, J.E. (1974) Complex intertropical convergence zone – some examples from the Indian Ocean. *Weather*, 29(4), 122–143.

Hong, G., Heygster, G., Notholt, J. and Buehler, S.A. (2008) Interannual diurnal variations in tropical and subtropical deep convective clouds and convective overshooting from seven years of AMSU-B measurements. *Journal of Climate*, 21, 4168–4189.

Huang, Y., Siems, S.T., Manton, M.J., Protat, A. and Delanoë, J. (2012) A study on the low-altitude clouds over the Southern Ocean using the DARDAR-Mask. *Journal of Geophysical Research: Atmospheres*, 117, D18204.

Hung, C.-S. and Sui, C.-H. (2018) A diagnostic study of the evolution of the MJO from Indian Ocean to Maritime Continent: wave dynamics versus advective moistening processes. *Journal of Climate*, 31, 4095–4115.

Jumaux, G., Quetelard, H. and Roy, D. (2011) *Atlas Climatique de La Réunion*. Direction interrégionale de La Réunion de Météo-France, Bureau d'Etudes et Climatologie, Sainte-Clotilde, La Réunion.

Jury, M.R., Parker, B.A., Raholijao, N. and Nassor, A. (1995) Variability of summer rainfall over Madagascar: climatic determinants at inter-annual scales. *International Journal of Climatology*, 15, 1323–1332.

Jury, M.R., Pathack, B., Wang, B., Powell, M. and Raholijao, N. (1993) A destructive tropical cyclone season in the SW Indian Ocean: January–February 1984. *South African Geographical Journal*, 75, 53–59.

King, M.D., Platnick, S., Menzel, W.P., Ackerman, S.A. and Hubanks, P.A. (2013) Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites. *IEEE Transactions on Geoscience and Remote Sensing*, 51, 3826–3852.

Klein, S.A. and Hartmann, D.L. (1993) The seasonal cycle of low stratiform clouds. *Journal of Climate*, 6, 1587–1606.

Kumar, V.V., Jakob, C., Protat, A., May, P.T. and Davies, L. (2013) The four cumulus cloud modes and their progression during rainfall events: a C-band polarimetric radar perspective. *Journal of Geophysical Research: Atmospheres*, 118, 8375–8389.

Lau, K.-M. and Yang, S. (2003) Walker circulation. In: Holton, J.R. (Ed.) *Encyclopedia of Atmospheric Sciences*. Oxford: Academic Press, pp. 2505–2510.

Lee, S.-K., Mechoso, C.R., Wang, C. and Neelin, J.D. (2013) Interhemispheric influence of the northern summer monsoons on southern subtropical anticyclones. *Journal of Climate*, 26, 10193–10204.

Leroux, M.-D., Meister, J., Mekies, D., Dorla, A.-L. and Caroff, P. (2018) A climatology of southwest Indian Ocean tropical systems: their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes. *Journal of Applied Meteorology and Climatology*, 57, 1021–1041.

Li, Y., Thompson, D.W.J., Stephens, G.L. and Bony, S. (2014) A global survey of the instantaneous linkages between cloud vertical structure and large-scale climate. *Journal of Geophysical Research: Atmospheres*, 119, 3770–3792.

Listowski, C., Delanoë, J., Kirchgaessner, A., Lachlan-Cope, T. and King, J. (2019) Antarctic clouds, supercooled liquid water and mixed phase, investigated with DARDAR: geographical and seasonal variations. *Atmospheric Chemistry and Physics*, 19, 6771–6808.

Logan, R.F. (1968) Chapter II - Causes, climates, and distribution of deserts. In: Brown, G.W. (Ed.) *Desert Biology*. New-York: Academic Press, pp. 21–50.

Macron, C., Richard, Y., Garot, T., Bessafi, M., Pohl, B., Ratiarison, A. and Razafindrabe, A. (2016) Intraseasonal rainfall variability over Madagascar. *Monthly Weather Review*, 144, 1877–1885.

Manatsa, D., Morioka, Y., Behera, S.K., Matarira, C.H. and Yamagata, T. (2013) Impact of Mascarene High variability on the East African 'short rains'. *Climate Dynamics*, 42, 1259–1274.

Marchand, R., Mace, G.G., Ackerman, T. and Stephens, G. (2008) Hydrometeor detection using Cloudsat – an Earth-orbiting 94-GHz cloud radar. *Journal of Atmospheric and Oceanic Technology*, 25, 519–533.

Mioche, G., Jourdan, O., Ceccaldi, M. and Delanoë, J. (2015) Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing. *Atmospheric Chemistry and Physics*, 15, 2445–2461.

Miyamoto, A., Nakamura, H. and Miyasaka, T. (2018) Influence of the subtropical high and storm track on low-cloud fraction and its seasonality over the south Indian Ocean. *Journal of Climate*, 31, 4017–4039.

Nassor, A. and Jury, M.R. (1998) Intra-seasonal climate variability of Madagascar. Part 1: Mean summer conditions. *Meteorology and Atmospheric Physics*, 65, 31–41.

Nesbitt, S.W. and Zipser, E.J. (2003) The diurnal cycle of rainfall and convective intensity according to three years of TRMM measurements. *Journal of Climate*, 16, 1456–1475.

Noel, V., Chepfer, H., Chiriaco, M. and Yorks, J. (2018) The diurnal cycle of cloud profiles over land and ocean between 51°S and 51°N, seen by the CATS spaceborne lidar from the International Space Station. *Atmospheric Chemistry and Physics*, 18, 9457–9473.

Pincus, R., Platnick, S., Ackerman, S.A., Hemler, R.S. and Hofmann, R.J.P. (2012) Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. *Journal of Climate*, 25, 4699–4720.
Rajeevan, M., Rohini, P., Kumar, K.N., Srinivasan, J. and Unnikrishnan,
C.K. (2013) A study of vertical cloud structure of the Indian summer
monsoon using CloudSat data. *Climate Dynamics*, 40, 637–650.

Ramage, C.S. (1968) Problems of a monsoon ocean. *Weather*, 23(1),
28–37.

Riley, E.M., Mapes, B.E. and Tulich, S.N. (2011) Clouds associated with
the Madden–Julian Oscillation: a new perspective from CloudSat.
*Journal of the Atmospheric Sciences*, 68, 3032–3051.

Rosenfeld, D. and Woodley, W.L. (2000) Deep convective clouds with
sustained supercooled liquid water down to –37.5 °C. *Nature*, 405,
440–442.

Rossow, W.B. and Schiffer, R.A. (1999) Advances in understanding
clouds from ISCCP. *Bulletin of the American Meteorological Society*, 80, 2261–2288.

Roux, F., Chane-Ming, F., Lasserre-Bigorry, A. and Nuissier, O. (2004)
Structure and evolution of intense tropical cyclone *Dina* near La Réunion on 22 January 2002: GB-EVTD analysis of single Doppler radar observations. *Journal of Atmospheric and Oceanic Technology*, 21, 1501–1518.

Saji, N.H. and Yamagata, T. (2003) Possible impacts of Indian Ocean
Dipole mode events on global climate. *Climate Research*, 25,
151–169.

Sakaeda, N., Kiladis, G. and Dias, J. (2017) The diurnal cycle of
tropical cloudiness and rainfall associated with the Madden–Julian
Oscillation. *Journal of Climate*, 30, 3999–4020.

Schott, F.A., Xie, S.-P. and McCreary Jr, J.P. (2009) Indian Ocean circulation and climate variability. *Reviews of Geophysics*, 47, RG1002.

Stein, T.H.M., Delanoë, J. and Hogan, R.J. (2011) A comparison among
four different retrieval methods for ice-cloud properties using data
from CloudSat, CALIPSO, and MODIS. *Journal of Applied Meteorology and Climatology*, 50, 1952–1969.

Stubenrauch, C.J., Chédin, A., Rädel, G., Scott, N.A. and Serrar, S.
(2006) Cloud properties and their seasonal and diurnal variability
from TOVS Path-B. *Journal of Climate*, 19, 5531–5553.

Stubenrauch, C.J., Eddounia, F. and Sauvage, L. (2005) Cloud heights
from TOVS Path-B: evaluation using LITE observations and distributions of highest cloud layers. *Journal of Geophysical Research: Atmospheres*, 110, D19203.

Stubenrauch, C.J., Rossow, W.B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A., Maddux, B.C., Menzel, W.P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S. and Zhao, G. (2013) Assessment of global cloud datasets
from satellites: project and database initiated by the GEWEX radiation panel. *Bulletin of the American Meteorological Society*, 94,
1031–1049.

Su, H. and Jiang, J.H. (2013) Tropical clouds and circulation changes
during the 2006/07 and 2009/10 El Niños. *Journal of Climate*, 26,
399–413.

Takemi, T., Hirayama, O. and Liu, C. (2004) Factors responsible for
the vertical development of tropical oceanic cumulus convection.
*Geophysical Research Letters*, 31, L11109.

Terray, P., Delecluse, P., Labattu, S. and Terray, L. (2003) Sea surface temperature associations with the late Indian summer monsoon. *Climate Dynamics*, 21, 593–618.

Terray, P., Dominiak, S. and Delecluse, P. (2005) Role of the southern
Indian Ocean in the transitions of the monsoon–ENSO system during
recent decades. *Climate Dynamics*, 24, 169–195.

Waliser, D.E. and Gautier, C. (1993) A satellite-derived climatology of
the ITCZ. *Journal of Climate*, 6, 2162–2174.

Wylie, D.P., Jackson, D.L., Menzel, W.P. and Bates, J.J. (2005) Trends
in global cloud cover in two decades of HIRS observations. *Journal
of Climate*, 18, 3021–3031.

Wylie, D.P., Menzel, W.P., Woolf, H.M. and Strabala, K.I. (1994) Four
years of global cirrus cloud statistics using HIRS. *Journal of Climate*,
7, 1972–1986.

Wyrtki, K. (1973) An equatorial jet in the Indian Ocean. *Science*, 181,
262–264.

Yamagata, T., Behera, S.K., Rao, S.A., Guan, Z., Ashok, K. and Saji,
H.N. (2003) Comments on “Dipoles, temperature gradients, and
tropical climate anomalies”. *Bulletin of the American Meteorological
Society*, 84, 1418–1422.

Yamanaka, M.D., Ogino, S.-Y., Wu, P.-M., Jun-Ichi, H., Mori, S., Matsumoto, J. and Syamsudin, F. (2018) Maritime Continent coastlines
controlling Earth's climate. *Progress in Earth and Planetary Science*,
5(1), 21.

Yang, G.-Y. and Slingo, J. (2001) The diurnal cycle in the Tropics.
*Monthly Weather Review*, 129, 784–801.

Yang, S. and Smith, E.A. (2006) Mechanisms for diurnal variability of
global tropical rainfall observed from TRMM. *Journal of Climate*,
19, 5190–5226.

How to cite this article: Vérèmes H, Listowski C, Delanoë J, et al. Spatial and seasonal variability of clouds over the southwest Indian Ocean based on the DARDAR mask product. *Q J R Meteorol Soc.* 2019;145:3561–3576. https://doi.org/10.1002/qj.3640