# The ERA5's diurnal cycle of low-level clouds over Western Central Africa during June–September: Dynamic and thermodynamic processes

A Dommo, Derbetini A Vondou, N Philippon, R Eastman, V. Moron, N Aloysius

► To cite this version:

A Dommo, Derbetini A Vondou, N Philippon, R Eastman, V. Moron, et al.. The ERA5's diurnal cycle of low-level clouds over Western Central Africa during June–September: Dynamic and thermodynamic processes. Atmospheric Research, 2022, 280, pp.106426. 10.1016/j.atmosres.2022.106426. hal-03793124

HAL Id: hal-03793124

https://hal.science/hal-03793124v1

Submitted on 28 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished 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_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 **Title: The ERA5's diurnal cycle of low-level clouds over Western Central Africa during**
2 **June-September: dynamic and thermodynamic processes.**

4 **A. Dommo**

5 Laboratory of Environmental Modeling and Atmospheric Physics, University of Yaoundé 1,
6 Yaoundé, Cameroon, 0000-0001-8667-4791

7 **Derbetini A. Vondou**

8 Laboratory of Environmental Modeling and Atmospheric Physics, University of Yaoundé 1,
9 Yaoundé, Cameroon, 0000-0002-8681-5328

10 **N. Philippon**

11 IGE, University of Grenoble Alpes, CNRS, IRD, Grenoble INP, Grenoble, France, 0000-0003-3519-539X

13 **R. Eastman**

14 Department of Atmospheric Sciences, University of Washington, Seattle, Washington, 0000-0001-8696-4211

16 **V. Moron**

17 Aix Marseille Univ, CNRS, IRD, INRAE, Coll. de France, CEREGE, Aix en Provence,
18 France and IRI, Columbia University, Palisades, NY, USA, 0000-0002-4981-9530

19 **N. Aloysius**

20 Department of Natural Resources, University of Missouri, Columbia, Missouri, USA, 0000-0002-9094-427X

22

23 **Corresponding author:** Atanas DOMMO. Laboratory of Environmental Modelling and
24 Atmospheric Physic (LAMEPA), University of Yaoundé 1, Yaoundé, Cameroon

25 Email: dmmatanas@gmail.com

26
27

**Abstract**

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

This paper analyzes the diurnal cycle of low-level cloud cover (LLC) and its atmospheric drivers over Western Central Africa (WCA) during the cloudiest season (June-September). Moderate Resolution Imaging Spectroradiometer (MODIS) observations, Extended Edited Clouds Reports Archive (EECRA) and the fifth generation of reanalysis of the European Centre for Medium Range Weather Forecasts (ERA5) are used. LLC peaks between 04 LT and 07 LT and tends to be less extensive during the afternoon. Strong low-level (below 1000 m) southwesterly flow in the evening supplies the region with humidity from the ocean and leads to cloud formation. Relative humidity (RH) tendencies show that temperature contributes to 100% of RH changes during the night: the strong cooling observed after sunset increases RH in WCA by about 8%/h in the layer below 1000 m. Strong cooling rates of about -1.4K/h occur indeed from sunset until 22 LT, then decrease then after to reach a value of about -0.3K/h at 07 LT. Cloud formation is mostly related to horizontal moisture flux, strong convergence in the lower layer and turbulent upward mixing of moisture. The reinforcement of the radiative cooling at the cloud top helps to maintain the cloud deck once it has formed. During daytime, latent and sensible heat flux from the surface, due to downward shortwave radiation, contributes to increase the vertical turbulence and deepen the boundary layer and thus, combined with decreasing RH in low levels ~ 1000 m mostly due to the diurnal warming, act to partly destroy the cloud deck and decrease the cloud fraction.

48

49

**1 Introduction**

50

51

52

53

54

55

56

57

58

59

60

**keywords:** radiative cooling, relative humidity, convergence, dry season

Low-level clouds (LLCs) cover large regions of the Earth's surface. Worldwide, LLCs cover approximately 28% of the earth's land surface (Hahn and Warren 2007) and strongly impact earth's surface energy balance (Chen et al. 2000) mostly by reflecting incoming solar radiation away from the surface. LLCs are close to the earth's surface and therefore exert a relatively small effect on the top of atmosphere outgoing longwave radiation (Stephens and Greenwald 1991; Hartmann et al. 1992). Consequently, LLCs produce a strong net cooling at the surface by scattering the shortwave radiation back to space. In general, low stratiform clouds form in environments with strong lower tropospheric static stability, in response to cooling of the boundary layer (Wood et al. 2012), which may be driven by radiative processes, buoyancy and advection. In many circumstances, saturation can be
generated by turbulent mixing. According to Garratt (1992), the primary source of turbulence
may be either vertical shear of horizontal wind, or buoyancy flux due to the release of latent
heat from the surface. Paluch and Lenschow (1991) argued that the exchange with the surface
is critically dependent upon the buoyancy of the air immediately close to the surface.

65

The understanding of climatic processes over Western Central Africa (WCA) suffers from the
weak sampling of in situ observations. This holds particularly true for LLCs while its
misrepresentation in climate models biases temperature and rainfall simulations and
projection (Knippertz et al. 2011; Schuster et al. 2013; Camberlin et al. 2022). Most of the
studies dedicated to the climate of Central Africa revolve around numerical simulations with
the main objectives of evaluating climate models (Fotso et al. 2016, 2017; Vondou et al.
2017; Mba et al. 2018; Sonkoue et al. 2018; Tamoffo et al. 2019a). For instance, Tamoffo et
al. (2021) used process-based evaluations to assess the performance of two versions of
Rossby Centre Regional Climate Model (RCA4) in simulating rainfall over Congo Basin. The
two versions are RCA4 version 1 (RCA4-v1) with strong turbulent mixing and the modified
one, RCA4 version 4 (RCA4-v4), with reduced turbulent mixing. These authors shown that
RCA4-v4, with reduced turbulent mixing, injects less moisture to the free atmosphere, and
thus leads to an anomalous moistening of the lower layers and an increasing LLCs cover. This
increasing LLCs cover is more pronounced over WCA than over the Congo Basin (Tamoffo
et al. 2021). Consequently, the downward surface solar radiation is reduced over WCA, which
in turn enhances the surface temperature gradient between WCA and the Congo Basin, then
possibly increasing the low-level westerlies over WCA.

83

Dommo et al. (2018) have produced the first assessment of the mean spatio-temporal
evolution of LLCs over WCA using satellite data, ground-based observations and reanalysis.
These authors find that WCA is mostly covered by low stratiform clouds during the June-
September (JJAS) dry season. These LLCs are particularly important for forest growth
(Philippon et al. 2019; Rejou-Mechain et al. 2021). Stratiform low clouds are generally
observed in coastal areas windward of anomalously cold sea surface temperatures (SST)
(Cermak 2012; Pilié et al. 1979), where they sustain vegetation by lowering temperature and
bringing substantial amounts of water. These ecosystems may not be present without LLC
(Garcia-Santos et al. 2004; Del-Val et al. 2006). In WCA, LLCs extend inland up to around
15°E, then dissipates just westward of the Congo basin leeward of low-level W-SW winds
(i.e. westward of Chaillu Mts, Cristal Mts and Batéké plateaus; Dommo et al. 2018). The
coastal plains approximately between Libreville (Gabon) and Pointe Noire (Congo) and the ocean facing valleys have the most extensive LLCs with a maximum intra-daily low-level cloud fraction (LCF) at 04 LT (Dommo et al. 2018). Over hills and plateaus, LLCs are less extensive, with a maximum at 10 LT. The dissipation of LLC east of 15°E may be at least partly due to the foehn effect (Dommo et al. 2018). At the interannual time scale, recent studies show that LLCs formation over WCA is related to low tropospheric stability (Camberlin et al. 2022), the El Nino southern Oscillation and the shallow meridional and zonal overturning circulations over the southern and equatorial Atlantic Ocean, favors by anomalously sea surface temperature (Moron et al. 2022). While the above few mentioned studies helped to underline the diurnal evolution, the spatial extension, and the drivers of the formation of low clouds over WCA at the interannual timescale, very little attention has been paid to these clouds. In WCA, there are not an in-depth analysis on cloud controlling mechanisms such as thermodynamic (relative humidity change, radiative cooling, heating at the surface) and dynamic (moisture flux and convergence, turbulence, vertical mixing) processes that control low cloud formation and dissipation at the diurnal scale. This incompleteness in the study of processes driving LLCs formation, maintenance and dissipation is a crucial limitation for climate studies and modeling in WCA, demanding substantial improvements.

113

Previous studies (Hill et al. 2018; Wild 2011, 2020; Wild et al. 2021) have reported that clouds severely modulate the surface downward longwave and shortwave radiation which is fundamental for the energy budget of the whole earth-atmosphere system. Quantifying properly the longwave radiation under all-sky conditions remains a challenge for the scientific community (Kato et al. 2011), and the derivation of more accurate longwave radiation datasets taking into account not only the clouds properties but the complex terrain characteristics (Schmit et al. 2005; Letu et al. 2018; Zhang et al. 2018) are needed. Using the MODIS level-3 products to characterize the spatio-temporal variations of clouds over China, Zhao et al. (2018) showed that the spatial pattern and seasonal variability of clouds depends on both the cloud's optical properties and the landform. For instance, at the seasonal time scale, the latter authors show that the cloud optical thickness-which is mostly associated to the type of cloud and the amount of the water vapor- almost exhibits the same regional distribution pattern as the cloud fraction. Furthermore, the regional distribution of the largest cloud fraction shows some similarities with the spatial patterns of the coldest cloud tops temperature and the lowest cloud tops pressure. At the diurnal time scale, the release of the
129

new generation of geostationary data Himawari-8 helps also to increase the detection of cloud fraction over Tibetan Plateau (Zhang et al. 2018).

131

132

In southern West Africa (SWA), along the north coast of the Gulf of Guinea, LLCs form during the night and persist during the following day (Babić et al. 2019a). They affect the energy balance at the surface and the diurnal evolution of the atmospheric boundary layer (Knippertz et al. 2011; Hannack et al. 2017). Schrage et al. (2007) showed that cloudy nights in Parakou (2.62°E, 9.33°N) are characterized by strong moisture convergence at 925 hPa and low static stability. In SWA, intensifying nocturnal low-level jets drive cloud formation (Knippertz et al. 2011; Schrage and Fink 2012; Schuster et al. 2013; Babić et al. 2019a; Alder et al. 2019; Lehou et al. 2020; Zouzoua et al. 2021). Prior to low cloud formation, the main factors leading to saturation are firstly a cool and moist air advection bringing large amounts of humidity from the sea, and then, a strong radiative cooling over land after sunset. After cloud formation, the cloud deck is maintained by a well-mixed sub-cloud layer due to vertical shear driven turbulence associated with the intensity of the nocturnal low-level jets. Schuster et al. (2013) showed that, in very stable conditions, turbulent energy is dissipated in the area of strong radiative cooling after sunset. Schrage et al. (2007) found that, if the radiative cooling is anomalously strong, the surface layer can decouple from the lower atmosphere, impeding cloud formation, whereas turbulent fluxes of moisture and heat can support cloud formation depending on vertical stability. Babić et al. (2019a) shown that nighttime cooling increases relative humidity, leading to saturation and cloud formation. After sunrise, solar heating at the surface drives positive sensible heat flux into the boundary layer and upwards transport of warm air, thus deepening the atmospheric boundary layer (Babić et al. 2019a; Alder et al. 2019; Lehou et al. 2020) and lifting the cloud base.

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

The main objective of this study is to analyze the relative role of key physical processes that control the diurnal cycle of LLC during the JJAS dry season in WCA (Fig 1), thus deepening the preliminary study by Dommo et al. (2018). To that end, we diagnose the dynamic and thermodynamic conditions of the atmosphere relevant to LLCs formation and dissipation at daily time scale. We also address in this paper the main factors leading to relative humidity changes, the surface energy and radiative balance along the diurnal cycle. Because of the lack of in situ, radiosoundings and aircraft measurements in WCA, our analysis is mostly based on ERA5 reanalysis that provides high spatio-temporal datasets and various atmospheric
parameters useful to assess the dynamic and thermodynamic conditions relevant for LLCs
formation and dissipation. The paper is organized as follows: in section 2, we make a brief
description of the data used and statistical analysis applied on ground and satellite
observations and ERA5. Since ERA5 will be used to diagnose the atmospheric conditions
relevant to LLCs, in Section 3, we made a brief validation of ERA5 LCF against satellite and
ground observations to test the accuracy of ERA5. In section 4, we depict the atmospheric
conditions relevant to the LLCs diurnal cycle and section 5 addresses the surface and radiative
balance. We end in section 6 with a conclusion and discussion.

170

171

## 2 Data

In this section, we briefly describe and characterize the three cloud products used in this
study. The first two are the daily Moderate Resolution Imaging Spectroradiometer (MODIS)
Satellite data from National Aeronautics and Space Administration (NASA) and the in-situ
observations (Extended Edited Clouds Reports Archive (EECRA) collected from ground-
based observations). The last dataset is the fifth generation of reanalysis of the European
Centre for Medium Range Weather Forecasts (ECMWF), i.e., ERA5, which is the improved
version of ERA Interim and have been successfully used in many research studies (Urraca et
al. 2018; Hoffmann et al. 2019; Sun and Zhao 2020, 2021). ERA5 reanalysis are used to
investigate the atmospheric and thermodynamic conditions under which LLCs grow in WCA,
whereas EECRA and MODIS observations are used in this study for spatial and diurnal
comparisons of LLC with ERA5. ERA5 is in general comparable to satellite-derived products
(Urraca et al. 2018; Sun and Zhao 2020; Letu et al. 2022) and most inlands stations (Urraca et
al. 2018).

185

The topographic aspects of the study area are displayed in **Fig 1**. The region is characterized
by moderate topographic elevation (**Fig 1**) with plateaus, coastal plains and valleys. Details
about the geographical aspects of the study area can be found in Dommo et al. (2018).

189

190

### 2.1 Satellite and observational data of low clouds

191

#### 2.1.1 MODIS low clouds cover
MODIS is a key instrument of NASA's Earth Observing System (EOS) on board the Terra and Aqua satellites (Sun et al. 2012). With a sun-synchronous polar orbit nearly perpendicular to the equator, both Terra and Aqua satellites offer a daytime and nighttime view of the Earth's surface for a swath width of 2330 km. Their equator crossing times are 1:30 am and 13:30 pm LT for Aqua and 10:30 am and 22:30 pm LT for Terra. The MODIS data used are averaged from 1km resolution to a 1°x1° latitude-longitude grid (called L3) separately for daytime and nighttime. This study uses both Aqua (MYD08 D3) and Terra (MOD08 D3) daily mean low cloud amounts for the period 2014-2015. MODIS LLC is obtained by reprocessing the MODIS (https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/) day and night cloud mask data using a cloud top temperature filter, removing cloud scenes with a cloud top temperature below 0°C. The filter aims to eliminate all observations with cloud top temperature below 0°C, given that the study area is a warm area.

204

## 2.1.2 EECRA

The EECRA data used in this study are ground based observations i.e. the amount and type of cloud is determined by a trained human observer, based on cloud base height and physical characteristics (www.atmos.washington.edu/CloudMap/). The land based EECRA data are from 5388 weather stations on land areas spread over all continents, as well as a few stations on Arctic Sea ice. EECRA offers a climatology for the 1971-2009 period of different cloud types within the low, middle and high levels of the atmosphere in addition to cloud base height and present weather conditions.

Several quality controls were applied on raw reports so that inconsistent reports are excluded. Nighttime observations are screened for adequate lunar illuminance (Hahn et al. 1995) to correct underestimation of clouds due to inadequate nighttime illumination of cloud cover. The dataset includes monthly averages, seasonal, multi-year annual averages as well as 3-hourly reports for eight reporting UTC times per day from 00 UTC (corresponding to 01, 04, 07, 10, 13, 16, 19, 22 LT). For this study, we use 3-hourly mean LCF during JJAS season for the period 1971-2009 over the WCA. Low cloud types (their base height is generally below 600 m) are defined as follows: fog, stratus, stratocumulus, cumulus and cumulonimbus. In EECRA, stratocumulus as well as cumulus and cumulonimbus are classified as low clouds because their base height is close to the surface despite a (possibly) larger vertical extent. Most of Gabon and SW Republic of the Congo do not receive any rain in JJAS, thus limiting
radically the fraction of cumuliform clouds in the LLC cover (Dommo et al. 2018). The number of reports varies considerably. To avoid bias due to missing records, we selected the 18 stations which provide at least 25% of 3-hourly data during JJAS season over the 39-year EECRA span (red triangles on Fig 1). The diurnal cycle is obtained by averaging LCF over each of the eight-time steps for the JJAS season and for the 18 stations.

229

## 2.2 ERA5 data

Within Copernicus Climate Change Services, the European Centre for Medium-range Weather Forecasts (ECMWF) currently produces the Fifth Generation Reanalysis (ERA5), which includes a detailed record of the global atmosphere, land surface and ocean waves from 1950 to present. This new reanalysis replaces the interim ERA reanalysis that was launched in 2006. ERA5 is based on the 41r2 cycle of the integrated operational forecasting system (IFS) since 2016. ERA5 has thus benefited from a decade of development in model physics and data assimilation methods are now based on Ensemble Data Assimilation. ERA5 provides many improvements in temporal (hourly) and spatial resolution (0.25° x 0.25°) compared to ERA interim. It also extends to 137 altitude levels (from the surface to 0.01 hPa). ERA5 provides hourly data for a large number of atmospheric and oceanic variables and includes uncertainty information for all variables (Hersbach et al. 2018). Based on the number of input parameters (https://confluence.ecmwf.int/display/CKB/ERA5+data+documentation) and many new or reprocessed observations, ERA5 is appropriate for climate study (Hoffmann et al. 2019; Lei et al. 2020; Sun and Zhao 2020, 2021). The use of ERA5 has many advantages: 1) it offers data at a high spatiotemporal resolution, which is suitable for regions lacking in situ observation, and 2) it offers a wide range of parameters that allow for in-depth study of dynamic and thermodynamic conditions. In addition to different atmospheric parameters provided by ERA5, it also provides cloud variables in 2-D and 3-D dimensions. For our present study, we used hourly data on their native spatial resolution for the period 2014-2015. Due to limited local computational resources, only two years are chosen for thermodynamic and dynamic analyses. Considering land only pixels over the box (6°S-5°N/8°E-15°E), the representativeness of the two selected years (2014 and 2015) is checked by computing the diurnal cycle of LCF for each of the years 1979-2019 (Fig 2). All years are shown as gray lines, with 2014 and 2015 represented as thicker red and black lines.

255
Both years 2014 and 2015 are cloudier than normal probably due to an anomalously cold coastal and equatorial upwelling over the adjacent Atlantic (Moron et al. 2022) but the phase and the amplitude of the diurnal cycle are almost constant from one year to the other, so the analysis of the diurnal cycle of the above-mentioned years is probably a good approximation of what may happen on average over WCA. For our present study, LCF (3-D and 2-D), total column water vapor and total column liquid water content are extracted for low cloud characteristics. Also, wind (horizontal and vertical components), temperature, relative and specific humidity, net shortwave and longwave radiation, surface latent and sensible heat are used for dynamic and thermodynamic processes.

## 3 ERA5 LLCs evaluation against MODIS and EECRA

Here we compare the mean spatial distribution as well as the diurnal evolution of LCF in ERA5 during JJAS season with MODIS and EECRA over WCA. This will reveal the capability of ERA5 to capture the spatial distribution as well as the diurnal cycle of LLCs in WCA. We restricted our analysis on the land area 6°S-5°N and 8°E-15°E (Fig 1). The LLCs spatial distribution is partly controlled by topography and a rapid decrease in cloud fraction is observed eastward from 15°E, corresponding to the downward slopes facing toward the Congo basin (Dommo et al. 2018).

### 3.1 Mean seasonal pattern

Fig 3 displays the mean frequency of LLCs over WCA using Terra and Aqua MODIS (hereafter, MODIS) data (Fig 3a), ERA5 reanalysis (Fig 3b) and EECRA (Fig 3c) averaged for nighttime and daytime. Fig 3d displays the mean diurnal cycle of low clouds at EECRA stations and for corresponding ERA5 pixels. Also presented in Fig 3d are the spatial averages of MODIS LCF for the study region (shown as a green box in frames 3a-3c) at four times representing each MODIS crossing time as described in section 2.1.1. All three products show LLC maxima >= 80% along the Atlantic coast, namely the Cameroonian Coast, Gabon, and almost all of SW Congo, west of Brazzaville (Fig 1). There are some substantial differences between the spatial distribution of the three products: both MODIS and EECRA (Fig 3a, c) show LCF >= 65% in almost all parts of our area, whereas ERA5 (Fig 3b) struggles to represent low clouds (LCF <= 50%) in southeast Cameroon and eastern Gabon. To make
spatially comparable estimates with EECRA data, the diurnal cycle is computed by averaging
for each time step only the 18 ERA5 pixels co-located with the EECRA stations (Fig 1). Fig
3d displays the mean diurnal cycle of LLCs from EECRA stations and for the co-located
ERA5 and MODIS pixels. Diurnal cycles of LLCs from EECRA and ERA5 are largely in
phase despite some discrepancy from late afternoon to early night. Diurnal cycle amplitude of
LLCs in EECRA is about 15% compared to 35% in ERA5 and ~20% in MODIS. The
differences between EECRA and ERA5 during the diurnal cycle are larger (>20%) in the
early night, however this difference decreases from late night (about 10%) to late morning at
10 LT (about 4%).

In terms of LCF, EECRA shows a maximum cloud fraction of about 80% at 04 LT and a
minimum of about 65% at 22 LT. The maximum LCF remains constant until 10 LT. ERA5
shows a maximum (minimum) LCF of about 80% (40%) at 10 LT (19 LT), whereas MODIS
shows a maximum (minimum) of about 94% (74%) at 10.30 LT (22.30 LT). MODIS may
overestimate cloud fraction compared to EECRA observations. This is because MODIS
clouds are observed from all parts of the MODIS swath, including near edges where the wide
sensor view angle biases cloud fraction (Maddux et al. 2010).

## 3.2 Diurnal evolution of the spatial pattern

To compare the spatial evolution of ERA5 LLCs with that of EECRA along the diurnal cycle,
the eight observation times are retained for ERA5 analyses. The spatial evolution of LLC
along the diurnal cycle as simulated by ERA5 is represented in Fig 4a. LLCs begin forming in
the first half of the night at about 22 LT in the high-elevation areas of south Cameroon. LLCs
spread progressively along the Gabon coast and continue to spread during the course of the
night so that at 07 LT, low clouds cover the coastal areas and the west facing slopes with a
maximum cloud fraction reaching 95%. At 10 LT, LLCs start dissipating in south Cameroon
and Equatorial Guinea whereas LCF remains quasi-constant in the coastal plains and west
facing slopes of Gabon and the SW Republic of Congo, with amounts above 90% until 16 LT.
At 16 LT, clouds are dissipating, though substantial amounts (LCF = 55-60%) are still seen at
19 LT over west facing slopes and SW Republic of Congo. Fig 4b shows the three-hourly
spatial distribution of LLCs as observed at EECRA stations. The observed spatial distribution
shows that during the diurnal cycle, EECRA LCF are generally greater than 50% in almost all
stations of the green box, except for a few stations in SW Republic of Congo at 01 LT and 22
LT, showing amounts between 40 and 50%. Also observed in **Fig 4b** is the north-south dissipation of low clouds from 16 LT to 22 LT. All these features match what is seen in ERA5 even if there are some differences between them during the diurnal cycle. For instance, at 10 LT, LLCs diminish at coastal stations for EECRA (**Fig 4b**) while it is not observed in ERA5, where cloud fraction remains over 60%. Furthermore, between 19 LT and 22 LT, LLCs in EECRA are more abundant over southwest Gabon with a maximum cloud cover greater than 90%, whereas in ERA5, LCFs are barely above 70% in this area.

Despite a slight underestimation of LLCs by ERA5 compared to EECRA, ERA5 does show reasonable agreement with ground-based observations, capturing the major features of LLCs spatial and diurnal variability. The spatial pattern of LLCs in ERA5 also matches well with CALIOP and SAFNWC retrievals (see Fig 2a,b in Dommo et al. 2018). The LLCs underestimation in ERA5 is likely due to the weakly incorporated land assimilation system, combining in-situ and satellite observations (Hersbach et al. 2018). The relative agreement between satellite observations and ERA5 LLCs has already been drawn in West Africa (Danso et al. 2019) and over the Tibetan plateau and in eastern China (Lei et al. 2020). So, because of the improved spatial/temporal resolution of ERA5 compared to MODIS or the EECRA, the following analysis will make use of ERA5 to represent the LLCs throughout the diurnal cycle.

In the following analyses, only land pixels within the green box in **Fig 4a** are considered, as processes driving LLCs may be different over land compared to the ocean. To account only for the cloudiest areas, only the land pixels which recorded an average of at least 80% LLCs fraction at 07 LT are spatially averaged within the green box (Fig 4a) are considered in the following analyses. The threshold of 80% also reduces biases caused by spatial heterogeneity as the processes driving LLCs may be different over the coast, windward slopes, and plateaus.

## 4 Atmospheric conditions associated with the diurnal cycle of LLCs

It has been shown in SWA that moisture (Babić et al 2019a) needed for saturation, and strong low level wind speed driving vertical mixing (Knippertz et al. 2011; Schuster et al. 2013) acting to favors saturation are important factors for stratus formation there. Here, we investigate the dynamic and thermodynamic conditions driving low clouds in WCA by analyzing wind speed, relative humidity (RH), the moisture flux and moisture divergence along the diurnal cycle. Also analyzed in this section is turbulent kinetic energy (TKE) to
measure the stability or instability of the atmosphere and its relative role on low cloud formation and dissipation. All computations are done from ground level (pressure < 1000 hPa) to avoid influences of surface roughness.

## 4.1 Thermodynamic and dynamic conditions

### 4.1.1 Wind and humidity

We start the evaluation of atmospheric conditions relevant to LLCs by showing in Fig 5 the spatial averages of three-hourly time-height sections of horizontal wind direction (vectors) and horizontal wind speed (contour) together with relative humidity (RH, shading) in JJAS season. Southwesterly flow up to 1200 m (Fig 5) is a strong feature in the investigated area. Between 1500 m and 2000 m, a minimum in wind speed (about 0.5 m/s) corresponds to the veering from southwesterly below to easterly above this level. The vertical wind shear between 1500 m and levels > 2000 m fingerprints the predominant counterclockwise (viewed from the south) low level regional scale zonal circulation over Equatorial Africa (Longandjo et al. 2020). This shallow circulation extends up to 850 hPa with upward branch at 20°E and downward branch around 1°W (Longandjo et al. 2020). The lower branch of this shallow circulation cell consists of the low-level westerly between near surfaces. Above 2000 m, easterlies reach 5 m/s at 3200 m. Below 1000 m, the maximum wind speed is observed overnight between 22 LT and 04 LT (Fig 5). The vertical profiles of fraction of cloud cover (Fig 6a) plotted for corresponding times in the diurnal cycle show that it hardly reaches 25% between 22 LT and 01 LT. Between 04 LT and 07 LT, as saturation is reached, the cloud base height is the lowest (Fig S1) with a highest cloud fraction (~ 50% - below 1000m height) (Fig S2). After 10 LT, there is a simultaneous decrease in RH, cloud fraction and wind speed, which start to increase again in the evening (19 LT).

### 4.1.2 Horizontal moisture flux and divergence

Three-hourly vertical profiles of cloud fraction (hereafter, CF) are shown in Fig 6a, along with vertical profiles of moisture flux (Fig 6b). The horizontal moisture flux is computed as follows:

$$Q_{flux} = qw \quad (1)$$
where q is the specific humidity (g.kg⁻¹) and w is the sum of zonal (u) and meridional (v) wind components, all expressed in meters per second. The diurnal cycle of vertical profiles of the CF (Fig 6a) is in phase with Fig 4 if CF is summed over pressure levels in order to project the profiles onto a 2-D plane, as mentioned in section 2.2. The daily maximum of CFs is situated at about 800m of altitude at 07 LT, whereas the daily minimum in CF shows the thinner cloud deck elevated to about 1500m of altitude at 19 LT. The cloud deck mean height increases from 1000m at 10 LT to 1500m at 16 LT in response to an increase of the atmospheric boundary layer depth (not shown) due to entrainment of overlying air and thermal expansion. The low-level moisture flux (Fig 6b) diurnal variation seems to play a substantial role preceding cloud formation. Positive (negative) flux denotes flow driven by southwesterly (easterly) wind. During the course of the night, southwesterly wind supplies the area with cool and moist air from the nearby Atlantic (Pokam et al. 2014; Tamoffo et al. 2022), which enhances radiative cooling with increasing RH (Dong et al. 2019) which then reach saturation and form clouds. This occurs despite the temporal mismatch between the height and time of occurrence of the largest CF and those of moisture flux (hereafter, MF). Maximum MF observed between 22 LT and 04 LT - recorded at about 900m height - is associated with strong low-level westerlies (Fig 5), whereas the largest CF is observed later between 07 LT and 10 LT (Fig 6a) at about 800m. From the early afternoon to the evening, MF is weaker due to reduced southwesterlies, and simultaneously, low clouds are partly dissipating.

In order to quantify the inflow or outflow of moisture across our interested area, we calculate the divergence of MF expressed by

$$ \nabla Q_{adv} = \frac{dQ_{\alpha}}{dx} + \frac{dQ_{\beta}}{dy} \quad (2) $$

where Qα and Qβ are the horizontal zonal and meridional MF between 1000 and 700 hPa, respectively. When computing moisture divergence with equation 2, negative (positive) values represent convergence (divergence). Fig 6c shows a strong MF convergence in the low levels (< 600m) from 22 LT to 04 LT. Therefore, from the evening to mid/late night, southwesterly (i.e. positive) inflow of moist and cool air below 600m occurs prior to low clouds formation. Above 1500m, easterly flow acts to dry the upper layer (Kuete et al. 2019) since the air is drier over the Congo basin than over WCA at 700 hPa in JJAS season (not shown). Moreover, MF divergence is already present in layers between 700m and 900m (before the maximum MF) from 22 LT and 07 LT (Fig 6c).
With the aim to investigate the degree of dependency between MF divergence and LCF, scatter-plots are computed between 1) LCF anomaly (LCFA) and moisture flux at 925 hPa (i.e 790m above the ground) where the LCF seems to peak, and 2) between LCFA and MF divergence at 950 hPa (i.e 550m) where moisture convergence (Fig 6c) peaks. The LCFA is computed following two steps: first, the spatial average considering only the most overcast pixels (cf Fig 4a) over all timestep is performed to obtain a LLCs hourly time series. Secondly, the anomaly is computed by subtracting from each value of the time series the mean (for all hours) of the 1952 values (122 days x 8 time slots x 2 years) of LLC time series, for the same overcast pixels. The significance of the correlations is tested using a “random-phase” test (Janicot et al. 1996; Ebisuzaki 1997) at each timestep. All the correlation shown on Figs 7-8 are significant at 99% level of significance within the diurnal cycle.

The correlation coefficient (r) between LCFA and MF at 925 hPa (Fig 7) increases slightly from r=0.39 at 19 LT to r=0.41 (maximum value) at 01 LT simultaneously with an increase in MF as shown in Fig 6b. At the same time, LCFA switches from a negative value (LCFA = -14%) at 19 LT to a positive value at 01 LT (LCFA = 5%). From 01 LT to 07 LT, LCFA is positive and peaks at 07 LT (LCFA = 27%), indicating an expansion in cloud cover. During daytime, LCFA decreases from -0.24% at 10 LT to reach its minimum of -17% at 16 LT. At 950 hPa (Fig 8) there is strong moisture convergence along the diurnal cycle compared to 925 hPa (level of cloud maximum) where divergence has already started (Fig 6c) between 01 LT and 07 LT. Conversely, the largest LCFA at 07 LT is weaker at 950 hPa (Fig 8) compared to 925 hPa (Fig 7). This suggests that near surface moisture is transported to the upper level to reach saturation.

434

### 4.1.3 Turbulent kinetic energy and stability

The diurnal cycles of the vertical profile of turbulent kinetic energy (TKE) and static stability are now analyzed. Note that positive (negative) values of static stability denote a stable (unstable) atmosphere. In West Africa (Knippertz et al. 2011; Schrage and Fink. 2012; Babić et al. 2019b) turbulent mixing -that act to favors saturation during nighttime- driven by vertical wind shear plays an important role in stratus formation. At 19 LT mixing at the surface is suppressed - as shown by low TKE value (< 0.1 m².s⁻²) (Fig 9a) - by strong static stability (Fig 9b). Thereafter stability decreases during the night, leading to the moderate increase of TKE from 01 LT to 04 LT until sunrise (07 LT). This induces buoyancy of the
moist air and drives vertical mixing between the lower layers and contributes to saturation and 444 LLCs formation during late night. During daytime, especially in the early afternoon (13 LT), 445 TKE is the strongest (~ 0.8 m².s⁻²) (Fig 9a) -due to strong vertical motion (Fig S3) driven by 446 surface heating - leading to strong instability (Fig 9b). As a result, the cloud deck lifts 447 simultaneously with the LLCs dissolution. Thus, depending on its intensity, turbulence 448 contributes to low cloud formation during the night and dissipation during the day along the 449 diurnal cycle in WCA. 450

The time-height evolution of RH in Fig 5 shows large values > 95% below 1000m with 451 a maximum greater than 95% between 04 LT and 07 LT when low level winds are slowing 452 down. Near the surface, the moisture ratio is fairly close to saturation. Indeed, where RH is 453 greater than 80%, if such an air parcel is lifted, the pressure will decrease, and it will likely 454 form a cloud. The time series of specific humidity (Fig 10a), RH (black line) and temperature 455 (red line) (Fig 10b) at 790 m (i.e 925 hPa) where CFs peak at 07 LT are represented in Fig 456 10. Coinciding with the slight increase in RH from 94% to 98% between 01 LT and 07 LT, 457 the specific humidity and temperature respectively show decreases of about 0.2g/kg and 458 0.5°C. At 790 m of height, temperature reaches its minimum of ~19°C at 07 LT. From 07 LT, 459 RH drops from 98% and reaches its minimum of about 80% in the late afternoon due to the 460 diurnal heating beginning at ~07 LT. At 790 m of height, temperature reaches its maximum of 461 ~22°C at 16 LT. In the meantime, at 790 m of height, specific humidity continues to decrease 462 to reach its minimum (about 14g/kg) at 13 LT. This suggests a moderate drying of the air 463 mass over WCA during daytime. This drying is accompanied by a significant reduction in 464 cloud cover and an increase in the cloud deck height (Fig 6a). Thereafter, RH starts to 465 increase simultaneously with the increase in specific humidity until 01 LT while temperature 466 has been decreasing since 16 LT. After sunrise wind speed drops (Fig 10c) due to strong 467 turbulence (Fig 9a) which slows low level flow down compared to nighttime where the 468 atmosphere is radiatively stabilized (Fig 9b) and the flow can accelerate along the pressure 469 gradient. 470

LLCs grow overnight when air cools and becomes saturated with respect to liquid water. 471 Time series of total column cloud liquid water content (LWP hereafter) together with total 472 column water vapor (TWV) are shown in Fig 10d. The diurnal cycle of TWV varies only 473 slightly, by about 1%, compared to the larger diurnal cycle of LWP, which varies by about 474 ~50%. The minimum of LWP is observed at 22 LT. Thereafter and during the course of the 475 night, there is an increase of LWP with the maximum observed at 04 LT, coinciding with 476
clouds thickening and spreading over the region. During daytime, LWP decreases (together with LLC dissipation) to reach its daily minimum.

479

### 4.1.4 Relative humidity changes and cooling

Here, we analyze the changes in RH simultaneously with radiative cooling. Note that the cooling rate is expressed as the temporal evolution of potential temperature ($\theta$). Changes in RH may be the result of changes in either specific humidity (q) or temperature (T). In order to quantify which of q or T have the strongest contribution to RH changes and consequently to LLCs formation, vertical profiles of their respective contribution are computed. Assuming that relative humidity is expressed by

$$RH = \frac{e}{e_s} (3)$$

where e is water vapor pressure and $e_s$ is the saturated water vapor pressure. Note that $e_s$ is a function of temperature and is obtained according to Clausius Clapeyron relation

$$\frac{\partial e_s}{\partial T} = \frac{L_v e_s}{R_v T^2} (4)$$

and water vapor pressure is expressed according to

$$e = \frac{q}{0.378q+0.622} P(5).$$

In these expressions (equation 4 and 5), $L_v$ is the latent heat of vaporization ($2.5 \cdot 10^6 J \cdot kg^{-1}$) and $R_v = 461.5 J \cdot kg^{-1} \cdot K^{-1}$ is the gas constant of water vapor, T is the air temperature (in kelvin), P is air pressure (in hPa) and q is the specific humidity. Given the expression of RH, the contribution of T and q to RH changes is given (Hannak et al. 2016; Babić et al. 2019a) by equation

$$\frac{\partial RH}{\partial t} = \frac{P}{e_s (0.378q+0.622)^2} \frac{\partial q}{\partial t} - \frac{qL_v}{e_s (0.378q+0.622)R_v T^2} \frac{\partial T}{\partial t} (6)$$

where term I is the total RH change, term II is the q contribution and term III is the T contribution. Because of the minus sign before term III, a positive (negative) value of that term signifies cooling (heating). The calculation of tendencies is performed only from late evening to early morning to emphasize the time of cloud formation (i.e 19 LT to 07 LT).
Results are presented in **Fig 11**. During the course of the night, there is an increase in RH but the rate of increase is different across the three-hourly timesteps. In the early evening (at 19 LT), there is a strong increase in RH below 1000 m with a rate of about +8 %/h, driving a strong tendency to reach saturation and form clouds, though the increasing tendency decreases with height. After 22 LT, as clouds are forming, the increasing rate of RH gradually slows in the lower layer and reaches about +3 %/h at 07 LT at the end of the cloud development processes. But the reverse is observed above 1500 m where the RH tendency increases during the night. On average (**Fig 11f**) and during almost all the night, cooling contributes 100% to RH changes (**Fig 11f**) in the lower layer with an average cooling rate of about -0.9K/h (**Fig 11g**). The observed nighttime evolution of cooling (**Fig 11g**) shows a strong cooling rate (about -1.4K/h) in the early evening probably due to the rapid development of a thermal inversion over land after sunset coinciding with the increase in onshore winds and MF (**Fig 6b**). This cooling gradually decreases during the course of the night and reaches -0.3K/h at 07 LT when the CFs is at its maximum (**Fig 6a**), impeding heat loss to space.

517

## 5 Surface energy and radiative balance

Because low clouds are mostly confined in the boundary layer, which is frequently thermodynamically connected to the Earth's surface, our understanding of cloud formation also demands the understanding of surface processes. Energy release from the surface in response to solar radiation is the primary source of vertical motion or convective instability (Wood 2012). According to Wood (2012), the diurnal cycle of stratocumulus (Sc) clouds is strongly modulated by the diurnal cycle of solar radiation which increase the near surface temperature then enhances heat fluxes and reduces near surface RH, thus preventing saturation. Note that net shortwave radiation ($R_{s}$) denotes the amount of solar radiation minus the amount reflected (or albedo) by the earth's surface whereas $R_{L}$ is the difference between the downward and upward thermal radiation at the earth's surface and net radiation (R*) is the sum of $R_{L}$ and $R_{s}$. The maximum in net shortwave radiation received at the surface (Fig 12a, red line) reaches 450 W/m$^2$ at 13 LT with an interquartile range of about 77 W/m$^2$ across the JJAS days in 2014-2015. This large range is probably due to day-to-day variability of the transparency of the atmosphere. The surface net longwave radiation ($R_{L}$) is negative along the diurnal cycle, ranging between -15 W/m$^2$ and -40 W/m$^2$. It reaches a value of about -40 W/m$^2$ at 13 LT. It decreases from -26 W/m$^2$ at 01 LT to -15 W/m$^2$ at 07 LT
indicating the decreasing of heat loss into space during LLCs formation. During the daytime as LLCs are dissipating and surface temperature increasing, the heat loss into space increases. $R_L$ increase from -26 W/m² at 07 LT to -38 W/m² in the late afternoon (16 LT). At 13 LT, $R^*= 403$ W/m² characterizing a warm surface which promotes evaporation (Fig 12c). At this time, $R_s$ enhances the turbulent sensible and latent heat flux (Fig 12b). As $R_s$ is at its maximum at 13 LT, the turbulent latent heat flux during the afternoon provides persistent turbulent updrafts to transport RH upwards and lower the RH in the lower altitude (Fig 5). This flux is maintained by the release of stored heat flux from the surface and hence continue to play a role even after sunset leading to the dissipation of LLCs as they gain altitude. At sunset (19 LT), the sensible heat flux becomes positive (~ +10.7 W/m²). It reaches a value of about +3W/m² at 01 LT. At the same time, $R^*$ becomes negative and remains so during the night. It varies from -28W/m² at 19 LT to -26W/m² at 01 LT indicating a cooling surface. Then, by offsetting negative $R^*$ (Fig 12a) from sunset to late night (01 LT), downward sensible heat contributes to the development of a stable nocturnal surface layer and the development of low clouds.

A conceptual model summarizing the processes of LLCs formation and dissipation along the diurnal cycle for WCA is proposed in Fig 13, showing evening (19, 22 LT), late night (01, 04, 07 LT) and daytime (10, 13, 16 LT) processes. In the evening, the horizontal advection of cool and moist air from the nearby Atlantic by the low-level southwesterlies is associated with strong moisture convergence of ~14 s⁻¹ g.kg⁻¹. This strong moisture convergence coincides with strong radiative cooling of ~1.4K/h below 500m which suppresses mixing and impeding LLCs formation, either due to low TKE or insufficient RH to reach saturation. The horizontal wind speed is faster at this time, with a maximum value of about 4 m/s coinciding with the downward sensible heat flux, which contributes to radiatively stabilize the near surface layer, likely aiding the acceleration of wind speed.

During the late night, wind speed weakens. Then, moisture convergence also weakens as moisture flux decreases, resulting in lower cooling rates. As the cloud deck is formed, the cooling weakens at the surface (Fig 11g), and the TKE moderately increases up to 0.4 m².s⁻² promoting vertical mixing for saturation and maintaining the cloud deck. After the cloud deck is formed, an increase of cooling at the cloud top (i.e above 1000 m, see Fig 11g) contributes to stabilization as it is partly compensated by latent heating. Indeed, the level at which cloud
fraction is maximum coincides with the level of strong moisture divergence (Fig 6c). Then, it seems like there is a competition between the outflow of moisture and the turbulent vertical transport of moisture. This is in agreement with the study by Schrage and Fink (2012), supporting that vertical transport of moisture contributes to cloud formation.

During daytime, solar radiation heats the cloud top. Consequently, cooling at the top is suppressed and cloud droplets evaporate. This reduces cloud thickness then leads to dissipation. Below the cloud deck, strong turbulence (>0.4 m².s⁻²) which is accompanied by the largest R* (~403 W.m⁻²) and sensible and latent heat acts also to destroy the cloud layer. As a consequence, the cloud layer rises and is less compact.

## 6 Conclusion and discussion

This study aims to picture out some physical processes leading to LLCs formation then dissipation across the diurnal cycle in WCA during the JJAS main dry season. Our analysis is based mostly on ERA5 reanalysis. Before any use of ERA5 data, a short validation with satellite (Terra and Aqua MODIS) and ground-based observations (EECRA) is made in order to show the capability of ERA5 to represent the diurnal cycle and spatial distribution of LLCs. Three-hourly spatial evolution of LLCs from ERA5 and EECRA in addition to cloud liquid water content (LWP) and total column water vapor (TWV) are drawn. We also explored the dynamic and thermodynamic conditions by analyzing RH, wind speed, temperature, and specific humidity. In addition, horizontal MF and divergence, cooling and static stability are also computed to strengthen our analysis. The main findings of our study are as follows:

1) Despite an underestimation of LLCs by ERA5, it captures well the mean spatial distribution of low clouds as compared to EECRA and MODIS observations. The LLCs diurnal cycles in EECRA and ERA5 are also well correlated although ERA5 struggles to produce enough clouds during the afternoon. The spatial evolution during the diurnal cycle shows that LCF is minimum at 19 LT (Fig 3d, Fig 4). Then, it starts to increase at 22 LT from south-Cameroon. LCF peaks during late night (04-07 LT) over windward slopes and coastal plains. After sunrise at 07 LT, LLC displays a north-south gradient (more LLCs over Gabon than Cameroon) which vanishes as LLCs diminishes overall until 19 LT. Accordingly, the cloud liquid water content shows similar evolution along the diurnal cycle.
2) WCA is subject in JJAS season to a southwesterly flow in the lower layers, a flow whose intensity increases from the evening to 01 LT and advects moisture from the ocean into the region. As shown in Fig 5, the large RH content in the low layers in late night is necessary for condensation and LLCs formation. There is strong moisture convergence in the lower layer (<500m) while strong MF is recorded from 19 LT to 01 LT at 900m of altitude.

3) We have also investigated the contributions of temperature and specific humidity to RH changes from the evening to early morning, i.e during cloud formation. The first insight shows that temperature contributes nearly 100% to RH changes during night. From 19 LT, the strong radiative cooling at the surface increases relative humidity by about 8%/h near the surface. During the course of the night, RH gradually increases. Conversely, while below 1000 m the rate of RH increase declines, the contrary is observed above 1000 m. Changes in RH below 1000 m are mostly due to variations in cooling rate.

4) While horizontal MF is shown to be a precondition to LLCs formation, another process is necessary for cloud formation. Vertical upward mixing of moisture from the lower layer during nighttime due to moderate TKE is necessary for cloud formation. This vertical mixing is important for saturation. Once the cloud deck is formed in stable conditions, cooling rate weakens at the surface, while the strong cooling rate at the top of the cloud deck is only partially compensated by latent heating. All these processes help to maintain and thicken the cloud deck, with cloud cover growing both upward and downward. During daytime, LLCs dissipate in response to strong thermodynamic instability due to the increase of downward shortwave radiation which warm the lower layer.

5) Finally, we can conclude that LLCs in WCA are formed owing to the combination of the nighttime cooling, moisture flux convergence from onshore winds and turbulent vertical mixing of moisture. During the night, radiative cooling at the top helps to maintain the cloud deck once it has formed. During daytime, the dissipation of LLCs is mainly due to strong turbulence due to turbulent sensible and latent heat release from the surface in response to downward shortwave radiation.

The calculation of RH tendencies directly using RH from ERA5 may lead to small discrepancies if we compute RH tendencies as the sum of term II and term III of equation 6. Therefore, total RH tendencies or changes in this study is the sum of term II and term III of
equation 6 rather than the RH variable itself. We assume these differences are the result of the complexity of the ERA5 data assimilation schemes. In addition to using only two years of data, another bias using ERA5 is the overestimation of LLCs over south Cameroon compared to what is found in Dommo et al. (2018) (see their figure 2d). This is because ERA5 does not yet distinguish between stratiform and cumuliform clouds. LLCs in south Cameroon may be mainly dominated by cumuliform clouds, particularly cumulonimbus. Therefore, in situ measurements and radio soundings are needed to disentangle and quantify precisely the atmospheric processes which drive observed LLCs formation, maintenance and dissipation over WCA. Nevertheless, the use of ERA5 allows us to analyze a panel of atmospheric processes relevant to LLCs in WCA. Results show some features similar to southern west Africa (SWA): for instance, horizontal cold air advection after sunset and radiative cooling (Babić et al. 2019a; Lehou et al. 2020) also drive the strong decrease in nighttime temperatures. Additionally, low level moisture convergence (Kniffka et al 2019) is similar between SWA and WCA. Also demonstrated in this study is the role of upward mixing of moisture that accumulates in the lower layers. This has also been highlighted by Schrage and Fink (2012) as a contributing process to LLCs formation in SWA. The added value of this work compared to what is found in SWA is the range of TKE. Indeed, if TKE is greater than $0.4 \ \text{m}^2 \cdot \text{s}^{-2}$ in WCA, it acts to destroy the cloud layer.

The datasets used in this study are completely different from those used in similar studies over SWA where studies are mostly based on in situ observations and radiosoundings. They were able to distinguish different phases in LLCs evolution along the diurnal cycle (Babić et al. 2019a; Alder et al. 2019; Lehou et al. 2020), the atmospheric processes relevant to each phase, and to quantify them precisely. For instance, Alder et al. (2019) show that horizontal cold air advection contributes to 50% of cooling, whereas radiative flux divergence and sensible heat flux divergence contribute 20% and 22% (respectively) to cooling. Additionally, they highlighted the intensification of low-level jets during the jet phase. These are processes which we could not explore for WCA, due to the lack of observational datasets. Additionally, Knippertz et al. (2011) also show the effects of aerosols on LLCs formation, while Deetz et al. (2018) show that transitions from stratus to cumulus are highly sensitive to aerosols. On the other hand, the effects of gravity waves are not yet explored in WCA as they are shown by Babić et al. (2019b) for SWA. Therefore, many questions remain unanswered concerning the processes relevant to LLCs in WCA. For instance, in SWA, a clear characterization between cloudy and non-cloudy nights is made, followed by the main factors leading to these
differences. Nevertheless, this study has allowed us to lay the groundwork for an in-depth
study of LLCs in WCA, a region that is still very poorly documented. This study also allowed
us to considerably improve our knowledge of LLCs for the region compared to the work of
Dommo et al. (2018) and can help us to ameliorate our understanding of the climatic
characteristics of the region and the improvement of the regional climate model simulations.

672
673 Acknowledgements:

674 Authors thank NASA (National Aeronautics and Space Administration) for providing MODIS
675 low clouds data. They also acknowledge the ECMWF for providing reanalysis (ERA5) data.

676 We are also grateful to Andreas Fink and Peter Knippertz for their helpful comments. Authors
677 wish to thank the two anonymous reviewers whose comments and suggestions contributes to
678 improve the quality of the manuscript. This work is part of the International Joint Laboratory

679 "Dynamics of Land Eco-systems in Central Africa in a context of global changes" of the
680 Institut de Recherche pour le Développement (LMI DYCOFAC IRD).
705 Danso D K, Anquetin S, Diedhiou A, Lavaysse C, Kobea A, Touré NE (2019) Spatio-
706 temporal variability of cloud cover types in West Africa with satellite-based and
707 reanalysis data. Q J R Meteorol Soc 145:3715-3731. doi: 10.1002/qj.3651

708 Deetz K, Vogel H, Knippertz P, et al (2018) Numerical simulations of aerosol radiative
709 effects and their impact on clouds and atmospheric dynamics over southern West
710 Africa. Atmos Chem Phys 18:9767-9788. doi: 10.5194/acp-18-9767-2018

711 Dommo A, Philippon N, Vondou DA, Sèze G, Eastman R (2018) The June–September Low
712 Cloud Cover in Western Central Africa: Mean Spatial Distribution and Diurnal
713 Evolution, and Associated Atmospheric Dynamics. J Clim 31:9585–9603. doi:
714 10.1175/jcli-d-17-0082.1

715 Dong M, Chen N, Zhao X, Fan S and Chen Z (2019) Nighttime radiative cooling in hot and
716 humid climates, Optics express, 27(22) : 31587-31598. doi : 10.1364/OE.27.031587

717 Ebisuzaki, W. (1997). A method to estimate the statistical significance of a correlation when
718 the data are serially correlated. *J. Clim*, *10*(9), 2147–2153. doi.org/10.1175/1520-
719 0442(1997)010<2147:amtets>2.0.co;

720 Fotso-Nguemo TC, Vondou DA, Tchawoua C, Haensler A (2016) Assessment of simulated
721 rainfall and temperature from the regional climate model REMO and future changes
722 over Central Africa. Clim Dyn 48:3685–3705. doi: 10.1007/s00382-016-3294-1

723 Fotso-Nguemo TC, Vondou DA, Pokam WM, Yepdo Djomou Z, Diallo I, Haensler A,
724 Djiotang Tchotchou LA, Kamsu-Tamo PH, Gaye AT, Tchawoua C (2017) On the
725 added value of the regional climate model REMO in the assessment of climate change
726 signal over Central Africa. Clim Dyn 49:3813–3838. doi: 10.1007/s00382-017-3547-7

727 Garratt JR (1992) The atmospheric boundary layer. Cambridge University Press
728 Hahn, CJ and Warren, SG (1999) Extended Edicted Synoptic Cloud Reports from Ships and Land Stations over the globe, 1952-1996. Tennessee. University of North Texas Librairies. https://digital.library.unt.edu/ark:/67531/metadc670860/

731 Hahn CJ, Warren SG, London J (1995) The Effect of Moonlight on Observation of Cloud Cover at Night, and Application to Cloud Climatology. J Clim 8:1429-1446. doi: 10.1175/1520-0442(1995)008<1429:teomoo>2.0.co;2

734 Hahn C, Warren S (2007) A Gridded Climatology of Clouds over Land (1971-96) and Ocean (1954-97) from Surface Observations Worldwide. Carbon Dioxide Information Analysis Center (CDIAC) Datasets. doi: 10.3334/cdiac/cli.ndp026e

737 Hannak L, Knippertz P, Fink AH, et al (2017) Why Do Global Climate Models Struggle to Represent Low-Level Clouds in the West African Summer Monsoon? J Clim 30:1665-1687. doi: 10.1175/jcli-d-16-0451.1

740 Hartmann DL, Ockert-Bell ME, Michelsen ML (1992) The Effect of Cloud Type on Earth's Energy Balance: Global Analysis. J Clim 5:1281-1304. doi: 10.1175/1520-0442(1992)005<1281:teocto>2.0.co;2

743 Hersbach H, Rosnay Pde, Bell B (2018) Operational global reanalysis progress, future directions and synergies with NWP. European Centre for Medium Range Weather Forecasts

746 Hill, P. G., Allan, R. P., Chiu, J. C., Bodas-Salcedo, A., & Knippertz, P. (2018). Quantifying the Contribution of Different Cloud Types to the Radiation Budget in Southern West Africa. *J. Clim*, *31*(13), 5273-5291. https://www.jstor.org/stable/26496555

749 Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., et al. (2019). From ERA-Interim to ERA5: The considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations. *Atmos Chem Phys*, *19*(5), 3097-3124. https://doi.org/10.5194/acp-19-3097-2019
753 Kato, S., and Coauthors, (2011): Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties. J. Geophys. Res., **116**, D19209, doi: org/10.1029/2011JD016050.

756 Klein SA, Hartmann DL (1993) The Seasonal Cycle of Low Stratiform Clouds. J Clim 6:1587-1606. doi: 10.1175/1520-0442(1993)006<1587:tscols>2.0.co;2

758 Kniffka A, Knippertz P, Fink AH (2019) The role of low-level clouds in the West African monsoon system. Atmos Chem Phys 19:1623-1647. doi: 10.5194/acp-19-1623-2019

760 Knippertz P, Fink AH, Schuster R, Trentmann J, Schrage JM, Yorke C (2011) Ultra-low clouds over the southern West African monsoon region. Geophys Res Lett. doi: 10.1029/2011gl049278

763 Kuete G, Mba WP, Washington R (2019) African Easterly Jet South: control, maintenance mechanisms and link with Southern subtropical waves. Clim Dyn 54:1539-1552. doi: 10.1007/s00382-019-05072-w

766 Lei Y, Letu H, Shang H, Shi J (2020) Cloud cover over the Tibetan Plateau and eastern China: a comparison of ERA5 and ERA-Interim with satellite observations. Clim Dyn 54:2941-2957. doi: 10.1007/s00382-020-05149-x

769 Letu, H., Nakajima, T. Y., Wang, T., Shang, H., Ma, R., Yang, K., Baran, A. J., Riedi, J., Ishimoto, H., Yoshida, M., Shi, C., Khatri, P., Du, Y., Chen, L., & Shi, J. (2022). A New Benchmark for Surface Radiation Products over the East Asia-Pacific Region Retrieved from the Himawari-8/AHI Next- Generation Geostationary Satellite. BAMS, 103(3), E873-E888. doi.org/10.1175/BAMS-D-20-0148.1

774 Lohou F, Kalthoff N, Adler B, Babi K, Dione C, Lothon M, Pedruzo-Bagazgoitia X, Zouzoua M (2020) Conceptual model of diurnal cycle of low-level stratiform clouds over southern West Africa. Atmos Chem Phys 20:2263-2275. doi: 10.5194/acp-20-2263-2020
778 Longandjo, G. N. T., and M. Rouault, 2020: On the structure of the regional-scale circulation over central Africa: Seasonal evolution, variability, and mechanisms. J. Climate, 33, 145–162, https://doi.org/10.1175/JCLI-D-19-0176.1

781 Maddux BC, Ackerman SA, Platnick S (2010) Viewing Geometry Dependencies in MODIS Cloud Products. J Atmos Oceanic Technol 27:1519–1528. doi: 10.1175/2010jtecha1432.1

784 Mba WP, Longandjo G-NT, Moufouma-Okia W, Bell JP, James R, Vondou DA, Haensler A, Fotso Nguemo TC, Guenang GM, Djiotang Tchotchou AL, Kamsu-Tamo PH, Takong RR, Grigory N, Lennard CJ, Dosio A (2018) Consequences of 1.5 °C and 2 °C global warming levels for temperature and precipitation changes over Central Africa. Environ Res Lett 13:055011. doi: 10.1088/1748-9326/aab048

789 Moron V. and Coauthors (2022) Interannual variability of low-level cloud cover over western equatorial Africa, Inter. J. Climatology, submitted

791 Oreopoulos L (2005) The impact of subsampling on MODIS level-3 statistics of cloud optical thickness and effective radius. IEEE Transactions on Geoscience and Remote Sensing 43:366–373. doi: 10.1109/tgrs.2004.841247

794 Paluch IR, Lenschow DH (1991) Stratiform Cloud Formation in the Marine Boundary Layer. J Atmos Sci 48:2141–2158. doi: 10.1175/1520-0469(1991)048<2141:scfitm>2.0.co;2

797 Philippon N, Cornu G, Monteil L, Gond V, Moron V, Pergaud J, Sèze G, Bigot S, Camberlin P, Doumenge C, Fayolle A, Ngomanda A (2019) The light-deficient climates of western Central African evergreen forests. Environ Res Lett 14:034007. doi: 10.1088/1748-9326/aaf5d8
801 Pilié RJ, Mack EJ, Rogers CW, Katz U, Kocmond WC (1979) The Formation of Marine Fog

802 and the Development of Fog-Stratus Systems along the California Coast. J Appl Meteorol 18:1275-1286. doi: 10.1175/1520-0450(1979)018<1275:tfomfa>2.0.co;2

803 Meteorol 18:1275-1286. doi: 10.1175/1520-0450(1979)018<1275:tfomfa>2.0.co;2

804 Pokam WM, Bain CL, Chadwick RS, Graham R, Sonwa DJ, Mkankam Kamga F (2014)

805 Identification of Processes Driving Low-Level Westerlies in West Equatorial Africa. J Clim 27:4245-4262. doi: 10.1175/jcli-d-13-00490.1

806 Clim 27:4245-4262. doi: 10.1175/jcli-d-13-00490.1

807 Réjou-Méchain, M., and Coauthors, 2021: Unveiling African rainforest composition and vulnerability to global change. Nature, 593, 90-94.

808 vulnerability to global change. Nature, 593, 90-94.

809 Shang, H., Letu, H., Nakajima, T. Y., Wang, Z., Ma, R., Wang, T., Lei, Y., Shenshen D. J.,

810 Shi, J. (2018). Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data.

811 Plateau as determined from Himawari-8 new-generation geostationary satellite data.

812 Sci Rep. 2018 Jan 18;8(1):1105 doi: 10.1038/s41598-018-19431-w

813 Schmit, T.J., Gunshor, M.M., Menzel, W.P., Gurka, J.J., Li, J., & Bachmeier, A.S. (2005). Introducing the next-generation Advanced Baseline Imager on GOES-R. BAMS, 86,

814 Introducing the next-generation Advanced Baseline Imager on GOES-R. BAMS, 86,

815 1079-1096. doi.org/10.1175/BAMS-86-8-1079

816 Schrage JM, Augustyn S, Fink AH (2007) Nocturnal stratiform cloudiness during the West Africa monsoon. Meteorol Atmos Phys 96: 73-86. doi: 10.1007/s00703-006-0194-7

817 Africa monsoon. Meteorol Atmos Phys 96: 73-86. doi: 10.1007/s00703-006-0194-7

818 Schrage JM, Fink AH (2012) Nocturnal Continental Low-Level Stratus over Tropical West Africa: Observations and Possible Mechanisms Controlling Its Onset. Mon Weather Rev 140:1794-1809. doi: 10.1175/mwr-d-11-00172.1

819 Africa: Observations and Possible Mechanisms Controlling Its Onset. Mon Weather Rev 140:1794-1809. doi: 10.1175/mwr-d-11-00172.1

820 Rev 140:1794-1809. doi: 10.1175/mwr-d-11-00172.1

821 Schuster R, Fink AH, Knippertz P (2013) Formation and Maintenance of Nocturnal Low-Level Stratus over the Southern West African Monsoon Region during AMMA 2006.

822 J Atmos Sci 70:2337-2355. doi: 10.1175/jas-d-12-0241.1

823 J Atmos Sci 70:2337-2355. doi: 10.1175/jas-d-12-0241.1

824 Janicot, S., Moron, V., Fontaine, B. (1996). Sahel droughts and Enso dynamics.

825 Geophysical Research Letters, 23(5), 515-518. doi.org/10.1029/96gl00246
826 Solomon S (2007) Climate change 2007: the physical science basis. Published for the IPCC
827 by Cambridge University Press

828 Sonkoué D, Monkam D, Fotso-Nguemo TC, Yepdo ZD, Vondou DA (2018) Evaluation and projected changes in daily rainfall characteristics over Central Africa based on a multi-model ensemble mean of CMIP5 simulations. Theor Appl Climatol 137:2167–2186.
831 doi: 10.1007/s00704-018-2729-5

832 Stephens GL (1978) Radiation Profiles in Extended Water Clouds. I: Theory. J Atmos Sc
833 35:2111–2122. doi: 10.1175/1520-0469(1978)035<2111:rpiewc>2.0.co;2

834 Stephens GL, Greenwald TJ (1991) The Earth's radiation budget and its relation to atmospheric hydrology: 2. Observations of cloud effects. J Geophys Res 96:15325.
835 doi: 10.1029/91jd00972

837 Stevens B (2002) Entrainment in stratocumulus-topped mixed layers. Q J R Meteorol Soc
838 128:2663–2690. doi: 10.1256/qj.01.202

839 Sun J, Angal A, Xiong X, et al (2012) MODIS reflective solar bands calibration improvements in Collection 6. Earth Observing Missions and Sensors: Development, Implementation, and Characterization II. doi: 10.1117/12.979733

842 Sun Y and Zhao, C. (2020). Influence of saharan dust on the large-scale meteorological environment for development of tropical cyclone over North Atlantic Ocean
843 Basin. *Journal of Geophysical Research: Atmospheres*, 125(23).
844 https://doi.org/10.1029/2020jd033454

846 Sun Y and Zhao, C. (2021). Distinct impacts on precipitation by aerosol radiative effect over three different megacity regions of eastern China. *Atmospheric Chemistry and Physics*, 21(21), 16555–16574. https://doi.org/10.5194/acp-21-16555-2021

849 Tamoffo AT, Vondou DA, Pokam WM, Haenler A, Yepdo ZD, Fotso-Nguemo TC, Djiotang Tchotchou LA, Nouayou R (2019a) Daily characteristics of Central African rainfall in
851 the REMO model. Theor Appl Climatol 137:2351-2368. doi: 10.1007/s00704-018-2745-5

852 Tamoffo AT, Nikulin G, Vondou DA, Dosio A, Nouayou R, Wu M, Igri PM (2021) Process-based assessment of the impact of reduced turbulent mixing on Congo Basin precipitation in the RCA4 Regional Climate Model. Clim Dyn. doi: 10.1007/s00382-020-05571-1

857 Tamoffo AT, Amekudzi L.K., Weber, T., Vondou, D.A., Yamba, E.I. and Jacob, D. (2022) Mechanisms of rainfall biases in two CORDEX-CORE regional climate models at rainfall peaks over central equatorial Africa. J. Clim, 35(2), 639-668. doi: 10.1175/JCL-D-21-0487.1

861 Urraca R, Huld T., Gracia-Amillo, A., Martinez-de-Pison, F. J., Kaspar, F., and Sanz-Garcia, A. (2018). Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data. *Solar Energy*, 164, 339-354. https://doi.org/10.1016/j.solener.2018.02.059

865 Vondou DA, Haensler A (2017) Evaluation of simulations with the regional climate model REMO over Central Africa and the effect of increased spatial resolution. Int J Climatol 37:741-760. doi: 10.1002/joc.5035

868 Wild, M. (2011). Enlightening Global Dimming and Brightening. *Bull. Amer. Meteor. Soc.*, 93, 27--37. doi: 10.1175/bams-d-11-00074.1

870 Wild, M (2020) The global energy balance as represented in CMIP6 climate models.” *Clim Dyn* 55: 553-577. doi.org/10.1007/s00382-020-05282-7

872 Wild, M., Wacker, S.W., Yang, S., Sanchez-Lorenzo, A. (2021). Evidence for Clear-Sky Dimming and Brightening in Central Europe. *Geophysical Research Letters*, 48.

874 Wood R (2012) Stratocumulus Clouds. Mon Weather Rev 140:2373-2423. doi: 10.1175/mwr-d-11-00121.1
876 Zhao C., Chen Y., Li J., Letu H., Su Y., Chen T., Wu X (2018) Fifteen-year statistical

877 analysis of cloud characteristics over China using Terra and Aqua Moderate

878 Resolution Imaging Spectroradiometer observations. Int J Clim. doi:

879 10.1002/joc.5975

880 Zouzoua M, Lohou F, Assamoi P, Lothon M, Yoboue V, Dione C, Kalthoff N, Alder B,

881 Babić K, Pedruzo-Bagazgoitia X, Derrien S (2021) Breakup of nocturnal low-level

882 stratiform clouds during the southern West African monsoon season. Atmos Chem

883 Phys 21:2027–2051. doi: 10.5194/acp-21-2027-2021
884 **List of figures**

885 **Fig 1** Topography of the study area. Red triangles indicate the 18 EECRA stations used in
886 this study

887 **Fig 2** Diurnal cycle of low cloud fraction for each year from 1979 to 2019 (grey lines) and in
888 2014 (red line) and 2015 (black line) spatially averaged over 6°S-5°N/8°E-15°E. Land-
889 only pixels are considered

890 **Fig 3** JJAS mean spatial distribution of low clouds cover in WCA from a) MODIS, b) ERA5
891 reanalysis, c) in-situ observation (EECRA) and d) diurnal cycle of LLCs. Triangles in
892 panel c indicate EECRA stations. For diurnal cycle (d), only ERA5 pixels documenting
893 the 18 EECRA stations are used, and the error bars indicate the standard deviation of
894 diurnal cycle across the grid points corresponding to EECRA stations. For EECRA
895 observations, only stations recording at least 25% of data in JJAS over the 39-year
896 ECCRA observation period have been used. The diurnal cycle is computed within the
897 green box considering only ERA5 pixels documenting EECRA stations (Fig 1). The red
898 dashed curve indicates the difference between EECRA and ERA5

899 **Fig 4** Mean spatial evolution of low clouds cover during the diurnal cycle for ERA5 (panel a),
900 and EECRA (panel b). Black dots on panel a) are pixels with at least 80% of low cloud
901 fraction at 07LT. Triangles (panel b) indicates EECRA stations. For EECRA, only
902 stations (18) recording at least 25% of data in JJAS over the 39-year EECRA
903 observation period have been used.

904 **Fig 5** Time-height evolution of ERA5 relative humidity (shadings in %) and horizontal wind
905 speed in m/s (contour) averaged over 6°S-5°N/8°E-15°E. For contours, only values in a
906 scale of 0.5 are displayed. Only land pixels with at least 80% of low clouds cover at
907 07LT are considered for spatial average. The wind speed values are indicated in red.
908 The arrows show the horizontal wind vectors. Computations are made from ground
909 level (pressure < 1000 hPa)

910 **Fig 6** ERA5 three-hourly vertical profiles from ground level to 3000 m of (a) cloud fraction
911 (%), (b) moisture flux, and (c) divergence of moisture flux. Spatial averages are made
912 over 6°S-5°N/8°E-15°E considering land-only pixels which record at least 80% of low
913 cloud at 07LT.

914 **Fig 7** Scatter plot between low level cloud fraction anomaly and moisture flux at 925 hPa
915 during the diurnal cycle. The scatter plot is obtained by averaging land only pixels
916 which record at least 80% in low cloud fraction at 07LT within the box 6°S-5°N/8°E-
917 15°E for the period 2014-2015. For each time slot (01, 04, 07, 10, 13, 16, 19 & 22 LT),
918 the mean of moisture flux (MF) and the mean low cloud fraction anomaly (LCFA),
919 correlation coefficient (r) and slope (m) are computed. The regression line between the
920 two variables is represented in black, and the reference line (y=0) is in cyan

921 **Fig 8** Same as figure 7 but between low cloud fraction anomaly and moisture divergence at
922 950 hPa. For each time slot, Div denotes the mean divergence. Negative (positive)
923 values of divergence expressed moisture inflow (outflow).

924 **Fig 9** As Figure 6 but for (a) turbulent kinetic energy, and (b) static stability.

925 **Fig 10** Diurnal cycle of a) specific humidity, b) relative humidity (black line) and temperature
926 (red line) and, c) horizontal wind speed at 925 hPa (i.e about 790m from ground level)
927 d) diurnal cycle of total column water vapor (TWV, blue line) and total column cloud
928 liquid water content (LWP, red line) within the box 6°S-5°N/8°E-15°E. Spatial
929 averages are made considering land-only pixels which record at least 80% of low cloud
930 at 07LT.

931 **Fig 11** a-e) ERA5 vertical profiles of specific humidity (q) and temperature (T) contribution
932 to relative humidity (RH) changes from early evening to early morning (19, 22, 01, 04,
933 and 07 LT). f) average for the five time slots. g) cooling rate from early evening to
early morning (19, 22, 01, 04, and 07 LT). The magenta line is the average of the 5
time-slots. Calculations are made from ground level to 3000 m considering land only
pixels with at least 80% of low cloud fraction at 07LT within 6°S-5°N/8°E-15°E for the
period 2014-2015

**Fig 12** Diurnal cycle of a) radiative balance (Net radiation: R*, surface net shortwave radiation: R<sub>S</sub>, Net long wave radiation: R<sub>L</sub>, b) energy balance (sensible heat, latent heat) and c) evaporation within 6°S-5°N/8°E-15°E for the period 2014-2015. Only land pixels recording at least 80% in low cloud fraction are considered for spatial average.

Error bars indicate the interquartile ranges

**Fig 13** Conceptual schematic model illustrating the cloud formation and dissipation processes over WCA along the diurnal cycle. The dark blue lines represent the vertical profiles of the horizontal wind with an indication of its maximum value at evening (19, 22 LT), late night (01, 04, 07 LT) and daytime (10, 13, 16 LT). The dark curled arrows symbolize mixing due to nocturnal turbulence. Maximum values for the contribution of each process are given. Each value is obtained from ERA5 within the area 6°S-5°N/8°E-15°E for the period 2014-2015. Only land pixels recording at least 80% in low cloud fraction are considered for spatial average.
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Topography of the study area. Red triangles indicate the 18 EECRA stations used in this study</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2** Diurnal cycle of low cloud fraction for each year from 1979 to 2019 (grey lines) and in 2014 (red line) and 2015 (black line) spatially averaged over 6°S-5°N/8°E-15°E. Land-only pixels are considered</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3** JJAS mean spatial distribution of low clouds cover in WCA from a) MODIS, b) ERA5 reanalysis, c) in-situ observation (EECRA) and d) diurnal cycle of LLCs. Triangles in panel c indicate EECRA stations. For diurnal cycle (d), only ERA5 pixels documenting the 18 EECRA stations are used, and the error bars indicate the standard deviation of diurnal cycle across the grid points corresponding to EECRA stations. For EECRA observations, only stations recording at least 25% of data in JJAS over the 39-year ECCRA observation period have been used. The diurnal cycle is computed within the green box considering only ERA5 pixels documenting EECRA stations (Fig 1). The red dashed curve indicates the difference between EECRA and ERA5</figcaption></figure>
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**Fig. 4** Mean spatial evolution of low clouds cover during the diurnal cycle for ERA5 (panel a), and EECRA (panel b). Black dots on panel a) are pixels with at least 80% of low cloud fraction at 07LT. Triangles (panel b) indicates EECRA stations. For EECRA, only stations (18) recording at least 25% of data in JJAS over the 39-year EECRA observation period have been used.
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Time-height evolution of ERA5 relative humidity (shadings in %) and horizontal wind speed in m/s (contour) averaged over 6°S-5°N/8°E-15°E. Only land pixels with at least 80% of low clouds cover at 07LT are considered for spatial average. For contours, only values in a scale of 0.5 are displayed. The wind speed values are indicated in red. The arrows show the horizontal wind vectors. Computations are made from ground level (pressure < 1000 hPa)</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 6 ERA5 three-hourly vertical profiles from ground level to 3000 m of (a) cloud fraction (%) , (b) moisture flux, and (c) divergence of moisture flux. Spatial averages are made over 6°S-5°N/8°E-15°E considering land-only pixels which record at least 80% of low cloud at 07LT.</figcaption></figure>

983

984

985

986

987

988

989
<figure><img src="image_9.png" /><figcaption>Fig 7 Scatter plot between low level cloud fraction anomaly and moisture flux at 925 hPa during the diurnal cycle. The scatter plot is obtained by averaging land only pixels which record at least 80% in low cloud fraction at 07LT within the box 6°S-5°N/8°E-15°E for the period 2014-2015. For each time slot (01, 04, 07, 10, 13, 16, 19 & 22 LT), the mean of moisture flux (MF) and the mean low cloud fraction anomaly (LCFA), correlation coefficient (r) and slope (m) are computed. The regression line between the two variables is represented in black, and the reference line (y=0) is in cyan</figcaption></figure>

990
991

992
993
994
995
996
997
998
<figure><img src="image_10.png" /><figcaption>**Fig 8** Same as figure 7 but between low cloud fraction anomaly and moisture divergence at 950 hPa. For each time slot, Div denotes the mean divergence. Negative (positive) values of divergence expressed moisture inflow (outflow).</figcaption></figure>

999

1000

1001

1002

1003

1004
1005

<figure><img src="image_11.png" /></figure>

1006

1007 **Fig. 9** As figure 6 but for (a) turbulent kinetic energy, and (b) static stability.
1008

<figure><img src="image_12.png" /></figure>

1009

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

1010

1011

1012

<caption>Fig 10 Diurnal cycle of a) specific humidity, b) relative humidity (black line) and temperature (red line) and, c) horizontal wind speed at 925 hPa (i.e about 790m from ground level) d) diurnal cycle of total column water vapor (TWV, blue line) and total column cloud liquid water content (LWP, red line) within the box 6°S-5°N/8°E-15°E. Spatial averages are made considering land-only pixels which record at least 80% of low cloud at 07LT.</caption>

1013

1014

1015

1016

1017

1018
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 11** a-e) ERA5 vertical profiles of specific humidity (q) and temperature (T) contribution to relative humidity (RH) changes from early evening to early morning (19, 22, 01, 04, and 07LT). f) average for the five time slots. g) cooling rate from early evening to early morning (19, 22, 01, 04, and 07LT). The magenta line is the average of the 5 time-slots. Calculations are made from ground level to 3000 m considering land only pixels with at least 80% of low cloud fraction at 07LT within 6°S-5°N/8°E-15°E for the period 2014-2019.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Fig. 12** Diurnal cycle of a) radiative balance (Net radiation: R*, surface net shortwave radiation: Rs, Net long wave radiation: RL, b) energy balance (sensible heat, latent heat) and c) evaporation within 6°S-5°N/8°E-15°E for the period 2014-2015. Only land pixels recording at least 80% in low cloud fraction are considered for spatial average.

Error bars indicate the interquartile ranges</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Fig. 13** Conceptual schematic model illustrating the cloud formation and dissipation processes over WCA along the diurnal cycle. The dark blue lines represent the vertical profiles of the horizontal wind with an indication of its maximum value at evening (19, 22 LT), late night (01, 04, 07 LT) and daytime (10, 13, 16 LT). The dark curled arrows symbolize mixing due to nocturnal turbulence. Maximum values for the contribution of each process are given. Each value is obtained from ERA5 within the area 6°S-5°N/8°E-15°E for the period 2014-2015. Only land pixels recording at least 80% in low cloud fraction are considered for spatial average.</figcaption></figure>

1038

1039

1040

1041

1042

1043

1044

1045

1046
Authors Contributions: **A. DOMMO**: Conceptualization, investigation, data visualization, editing, writing, software, revision. **DA. Vondou**: Data visualization, editing. **N.Philippon**: editing, shaping, reorganization of the paper, revision. **R. Eastman**: writing, editing, data curation, revision. **V. Moron**: Editing, writing, data curation, revision. **N. Aloysius**: editing, revision. All the authors contributed to the writing, editing, revision and validation of the paper.

1053

**Funding**: This research was supported by DAAD within the framework of the ClimapAfrica programme with funds of the Federal Ministry of Education and Research (funding ID 918 57610298 and ref 91815194)

1057

1058

**Conflicts of interest**: The authors declare that they have no conflicts of interest.

1060

**Data avaibility**: The data used in this study are available on ECMWF website (https://cds.climate.copernicus.eu/cdsapp#!/dataset) for ERA5 reanalysis. Terra and Aqua data can be found respectively at

https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD08_D3/ and
https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MYD08_D3/ and EECRA data
can be downloaded via https://rda.ucar.edu/datasets/ds292.2/index.html#!sfol-wl-/data/ds292.2

1068

1069

1070

1071

1072

1073

1074

1075

1076

1077