# METEOSAT Long-Term Observations Reveal Changes in Convective Organization Over Tropical Africa and Atlantic Ocean

Rémy Roca, Thomas Fiolleau, Viju O. John, Jörg Schulz

► To cite this version:

Rémy Roca, Thomas Fiolleau, Viju O. John, Jörg Schulz. METEOSAT Long-Term Observations Reveal Changes in Convective Organization Over Tropical Africa and Atlantic Ocean. Surveys in Geophysics, 2024, 45, pp.1979-1998. 10.1007/s10712-024-09862-8. insu-04835177

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# METEOSAT Long-Term Observations Reveal Changes in Convective Organization Over Tropical Africa and Atlantic Ocean

Rémy Roca<fnref n="1" /> Ⓒ · Thomas Fiolleau<fnref n="1" /> · Viju O. John<fnref n="2" /> · Jörg Schulz<fnref n="2" />

Received: 24 October 2023 / Accepted: 5 September 2024
© The Author(s) 2024

## Abstract

In the tropics, deep convection, which is often organized into convective systems, plays a crucial role in the water and energy cycles by significantly contributing to surface precipitation and forming upper-level ice clouds. The arrangement of these deep convective systems, as well as their individual properties, has recently been recognized as a key feature of the tropical climate. Using data from Africa and the tropical Atlantic Ocean as a case study, recent shifts in convective organization have been analyzed through a well-curated, unique record of METEOSAT observations spanning four decades. The findings indicate a significant shift in the occurrence of deep convective systems, characterized by a decrease in large, short-lived systems and an increase in smaller, longer-lived ones. This shift, combined with a nearly constant deep cloud fraction over the same period, highlights a notable change in convective organization. These new observational insights are valuable for refining emerging kilometer-scale climate models that accurately represent individual convective systems but struggle to realistically simulate their overall arrangement.

**Keywords** Deep convective systems · Satellite observations · Climate change · Africa

## Article Highlights

* Long-term observations of deep convective cloud systems over continental Africa and the Atlantic Ocean reveal a robust shift in the frequency of these systems' occurrences

* Relatively small and long-lived systems tend to increase over the last three decades

* Relatively large and short-lived systems tend to decrease over the last three decades

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

<footnote>Rémy Roca
remy.roca@cnrs.fr</footnote>

<footnote n="1">¹ Observatoire Midi-Pyrénées, Laboratoire d'Études en Géophysique et Océanographie Spatiales, Université de Toulouse III, CNRS, CNES, IRD, 14 Avenue Edouard Belin, 31400 Toulouse, France</footnote>

<footnote n="2">² EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany</footnote>
# 1 Introduction

In the tropics, deep convection—a buoyancy-driven dynamical process—transports moist air from the lower levels of the atmosphere upwards into the free troposphere, producing ice-topped upper-level clouds. These clouds are at the heart of climatic feedbacks (Stephens et al. 2024). Deep cloud systems (DCS), ranging from individual cumulonimbus to extensive cloud decks, cover scales from a few to 300 km and last from several hours to a few days (Houze 1993). These features of the tropical atmosphere, scattered throughout the intertropical belt, are pivotal for understanding climate dynamics (Fig. 1).

Due to their significant societal impacts and their anticipated evolution under climate change, Mesoscale convective systems (MCS)—a major category of these DCS—have received renewed attention. This focus is supported by advances in convection-permitting simulations of climate change (Prein et al. 2022) and due to the maturity and length of the observational record. Indeed recent changes in the occurrence and various intensity metrics of MCS have been documented, using satellite observations mainly, so far for the whole tropics (Tan et al. 2015) as well as for numerous large regions: United States (Feng et al. 2016; Prein et al. 2017; Hu et al. 2020), East Asia (Guo et al. 2023), Africa (Hart et al. 2019), the Atlantic Ocean (Bennartz and Schroeder 2012) or with specific regional emphasis such as the Sahel during monsoon months (Bennartz and Schroeder 2012; Taylor et al. 2017), the Congo basin (Alber et al. 2021; Raghavendra et al. 2018), eastern South Africa (Morake et al. 2021). All of these studies point of more intense MCS metrics that have occurred in recent decades, even if the attribution of these changes and the relative role of thermodynamics vs. dynamical drivers of such trends are still disputed (Maybee et al. 2024; Zhao et al. 2024).

In contrast, few analyses, if any, document the recent observed evolution of the full DCS spectrum as well as the arrangement between the DCS (Tan et al. 2015). As illustrated in Fig. 1, the DCS are spatially arranged in diverse geometries, often loosely referred to as *convective organization*. Climate and its future evolution is influenced by the two facets of *convective organization*: the collective effect of the deep cloud systems population and the resulting upper-level cloudiness, as well as the properties of the individual systems (Stephens et al. 2023). *Convective organization* is crucial for extreme precipitation (Pendergrass 2020) and the tropical radiation budget (Bony et al. 2020), among others. More generally, for a given water cycle intensity, *convective organization* can modify the associated energy cycle, like radiation and surface fluxes (Tobin et al. 2012) offering a key element of the ongoing efforts discussed in this Special Issue. Furthermore, modeling efforts have recently highlighted that the spread in climate sensitivity estimates among a large number of models can be related to each model’s handling of *convective organization* (Becker and Wing 2020; Wing et al. 2020). Despite its well-recognized importance, the theoretical foundations for *convective organization* are still being intensely debated.

<figure><img src="image_3.png" /><figcaption>**Fig. 1** A multi satellite mosaic depicting the ubiquitous deep clouds, shown as white clusters, on August 8, 2019 at 06 UT. Source https://satmos.aeris-data.fr/products/xsat/</figcaption></figure>
Current frameworks addressing the distribution of the individual convective systems range from wave dynamics (Mapes et al. 2006) to radiatively driven self-aggregation (Muller et al. 2022) or statistical mechanics paradigm (Peters and Neelin 2006), but no definitive proposition has yet emerged. Frameworks addressing the evolution of upper level cloud cover include the fixed anvil temperature (Hartmann and Larson 2002) or the “iris effect” (Lindzen and Choi 2021; Mauritsen and Stevens 2015). One reason for this diversity of perspectives may lie in the lack of observational data to confront these theoretical developments (Stephens et al. 2024).

The main objective of this study is to propose an observational constraint on the evolution of both cold cloud cover and the distribution of individual systems. Utilizing geostationary infrared imagery and a cloud tracking algorithm, we document changes in *convective organization* over tropical Africa and the Atlantic Ocean by analyzing the evolution of both upper-level cloud cover and the arrangement of the full spectrum of DCS over the last four decades. This region has been extensively surveyed by the METEOSAT system since the early 1980s, providing a unique long-term record of high-resolution (< 5 km) and high-frequency (≤ 30 min) thermal infrared imagery. From these radiation measurements, deep convective clouds can be easily identified (Duvel 1989). High temporal sampling further allows us to delineate the life cycle of deep convective cloud systems (Fiolleau and Roca 2013a). The geostationary field of view spans a wide area, enabling not only the estimation of DCS morphology but also the analysis of their convective organization. Tropical Africa encompasses well-known regions of deep convective activity (Hart et al. 2019), as revealed by the 1983–2020 climatology of the cold cloud cover associated with deep convection (Fig. 2). The Congo basin hosts deep convective systems (Mohr and Zipser 1996; Nguyen and Duvel 2008); orography-linked deep convection spans the Guinea coast,

<figure><img src="image_4.png" /><figcaption>**Fig. 2** The map of annual cold cloud fraction (in %) over 1983–2023 shows the active convective areas. The black rectangles delineate the two regions used in the study. Note that, for the African box, oceanic regions are excluded</figcaption></figure>
Mount Cameroun (Vondou 2012) and the edge of the Ethiopian plateau (Jackson et al. 2009). The Sahel experiences monsoon deep convection (Lafore et al. 2017). The adjacent tropical Atlantic Ocean, where numerous DCS are found in the Intertropical Convergence Zone (Houze and Betts 1981) can further be used to assess the sensitivity of our results to the type of surface-atmosphere interactions. In short, this region is populated by a wide spectrum of deep convective systems and a very diverse geography, making it an appropriate area for investigating *convective organization*.

Various metrics have been proposed to quantify aspects of convective organization, such as the occurrence of convective structures, their morphological characteristics, and the arrangements among them (Weger et al. 1992). The simplest of these metrics scales with the occurrence of the convective clusters in the region of interest (Tobin et al. 2012), while others test the departure from a random arrangement to quantify clustering effects (Tompkins and Semie 2017). Some metrics emphasize the geometry of the scene, including or excluding the morphology of the cluster (White et al. 2018). Despite these various attempts, interpreting convective organization remains challenging, and no definitive definition or metric has emerged (Cheng et al. 2018; Retsch et al. 2020). Consequently, in this study, we opt for a straightforward approach by relating the occurrence of deep convective systems to the mean cold cloud fraction. Unlike previous efforts that only considered snapshots of deep cloud clusters, we include the time-related aspects of convective systems in our analysis. Our metric for convective organization incorporates the life cycle information of DCS.

The next section presents the dataset used in this study, along with the climatological features of cold cloud fraction and DCS distribution. The rationale for our characterization of convective organization is introduced in Sect. 3, where we also present the results of the linear trend analysis. The paper concludes with a discussion and suggestions for future research.

# 2 Data and Method

## 2.1 The METEOSAT Record

Table 1 summarizes the characteristics of the METEOSAT observing system over the last 42 years. The first-generation and second-generation METEOSAT satellites (Schmetz et al. 2002) share a broad thermal infrared channel, although the spectral response function has slightly changed over the decades. The spatial and temporal resolution has also evolved throughout the record. Consequently, combining measurements taken from multiple sensors on various platforms for climate monitoring necessitates recalibration and homogenization efforts (Fiolleau et al. 2020).

The Meteosat visible infra-red imager (MVIRI) and Spinning enhanced visible and infrared imager (SEVIRI) measurements have been consistently recalibrated using a method described by John et al. (2019). The method relies on reference measurements from the Infrared Atmospheric sounding interferometer (IASI), the Atmospheric infrared sounder (AIRS), and the High-Resolution Infrared Radiation Sounder (HIRS/2) on polar orbiting satellites. The recalibration method can be applied directly to the lowest level of geostationary measurements available, that is, digital counts, to obtain recalibrated radiances. A significant reduction in biases (~5%) have been observed for the IR channel, considered in this article, compared to the operational radiances. IASI hyperspectral
<table><caption>**Table 1** The characteristics of the METEOSAT observing system and of the thermal infrared channel over the last four decades</caption><thead><tr><th>period</th><th>Platform</th><th>Nadir location</th><th>Instrument</th><th>Central wave-length (μm)</th><th>Spectral interval (μm)</th><th>Spatial resolution at nadir (km)</th><th>Temporal resolution (min)</th></tr></thead><tbody><tr><td>1981/08–1988/08</td><td>METEOSAT-2</td><td>0°</td><td>MVIRI</td><td>11.5</td><td>10.5–12.5</td><td>5</td><td>30</td></tr><tr><td>1988/08–1990/11</td><td>METEOSAT-3</td><td>0°</td><td>MVIRI</td><td>11.5</td><td>10.5–12.5</td><td>5</td><td>30</td></tr><tr><td>1989/06–1994/02</td><td>METEOSAT-4</td><td>0°</td><td>MVIRI</td><td>11.5</td><td>10.5–12.5</td><td>5</td><td>30</td></tr><tr><td>1991/11–1997/02</td><td>METEOSAT-5</td><td>0°</td><td>MVIRI</td><td>11.5</td><td>10.5–12.5</td><td>5</td><td>30</td></tr><tr><td>1997/02–1998/06</td><td>METEOSAT-6</td><td>0°</td><td>MVIRI</td><td>11.5</td><td>10.5–12.5</td><td>5</td><td>30</td></tr><tr><td>1998/06–2004/10</td><td>METEOSAT-7</td><td>0°</td><td>MVIRI</td><td>11.5</td><td>10.5–12.5</td><td>5</td><td>30</td></tr><tr><td>2004/10–2007/05</td><td>METEOSAT-8</td><td>0°</td><td>SEVIRI</td><td>10.8</td><td>9.8–11.8</td><td>3</td><td>15</td></tr><tr><td>2007/05–2013/02</td><td>METEOSAT-9</td><td>0°</td><td>SEVIRI</td><td>10.8</td><td>9.8–11.8</td><td>3</td><td>15</td></tr><tr><td>2013/02–2017/12</td><td>METEOSAT-10</td><td>0°</td><td>SEVIRI</td><td>10.8</td><td>9.8–11.8</td><td>3</td><td>15</td></tr><tr><td>2018/01–2023/03</td><td>METEOSAT-11</td><td>0°</td><td>SEVIRI</td><td>10.8</td><td>9.8–11.8</td><td>3</td><td>15</td></tr><tr><td>2023/03–2023/12</td><td>METEOSAT-10</td><td>0°</td><td>SEVIRI</td><td>10.8</td><td>9.8–11.8</td><td>3</td><td>15</td></tr></tbody></table>
measurements are used to determine spectral band adjustment factors (SBAF) that account for the spectral differences among the different instruments on geostationary satellites. Radiances for all instruments have been adjusted as if they had been measured by METEOSAT-5.

As a result, the record is expected to be homogeneous throughout, with a small residual ranging from 0.2 to 0.5 K, increasing with the age of the satellite. The stability of the resulting equivalent record of METEOSAT-5 is exemplified in Fig. 3, which shows the difference between the BT derived from METEOSAT-7 (the last MFG satellite) and METEOSAT-8 (the first MSG series). The analysis was conducted over 20 days between July 1 and 20, 2004, during which METEOSAT-7 and MSG-1 simultaneously monitored the African region and the Atlantic Ocean. The IR data from the two platforms have been first remapped from their native formats to a regular lon/lat 0.5° grid and the biases were computed on the region spanning 40° W and 40° E and between 0° and 30° S to minimize the impact of the time delay between the scanning of METEOSAT-7 and MSG-1 (Fiolleau et al. 2020). The results show an overall residual bias of -0.18 K for BT<sub>MSG-1</sub> < 235 K and confirm the high stability of the record over this major change in the time series.

An alternative geostationary fundamental data record is the GRIDSAT-1B product (Knapp et al. 2011), derived from the ISCCP data and the operational HIRS data. However, the GRIDSAT-1B product does not benefit from reprocessed HIRS observations or the hyperspectral sounder calibrations that have reduced calibration errors in the HIRS/2 measurements. This limitation makes the GRIDSAT record less appealing for long-term analysis. Further discussion and comparison of this new Fundamental Climate Data Record (FCDR) with the GRIDSAT-1B record are provided in Supplementary Note 1.

Quality control is performed using a number of tests (Liefhebber et al. 2020) aimed at flagging corrupted images. Once flawed images are identified, sequence of missing

<figure><img src="image_5.png" /><figcaption>**Fig. 3** Distribution of the differences of instantaneous brightness temperature between MSG1 and MET7 as a function of MSG1 brightness temperature for a common 20 days long period in July 2004 over Africa and the Atlantic Ocean. The blue line is the mean and the gray shading corresponds to plus minus one standard deviation</figcaption></figure>
images which can be detrimental to the cloud tracking algorithm (see below) are sought. A very conservative approach is employed here: any corrupted image is considered missing, regardless of where the missing data are within the image. Any day with a missing sequence of images is simply removed from the analysis. Figure S1 illustrates how the quality of the monitoring improves over time, from the early preoperational phase of the METEOSAT up to the highly functional METEOSAT Second Generation era. To account for inhomogeneities in data availability, all the statistics are first computed based on the available imagery and scaled up with a simple coefficient. This coefficient corresponds to the ratio of the number of days in the year divided by the number of actually available days. The impact of using this scaling technique on our analysis is assessed in the Discussion section of the paper. The imagery is re-gridded to a 0.04° regular grid to ensure continuity from the MFG to MSG era. Finally, brightness temperatures are limb corrected following the method of Fiolleau et al. (2020).

In summary, the long-term METEOSAT observations have been carefully homogenized over the length of the record, making them compliant with climate monitoring standards. Possible remaining sources of inhomogeneities have been characterized and should be kept in mind when interpreting the trends analysis.

## 2.2 Deep Cloud Fraction

The deep cloud fraction is calculated by estimating the fraction of observations colder than 235 K, a well-suited threshold for delineating deep convective cloudiness in the tropics (Duvel 1989; Fiolleau and Roca 2013a; Bouniol et al. 2016). As discussed in the introduction, we use two contrasting regions in this study: tropical land Africa (referred to as AFR) and a large region of the Atlantic ITCZ representing open Atlantic Ocean conditions (ATL), as depicted in Fig. 2. Annual means are constructed from the full-resolution record over the 1983–2020 period.

## 2.3 Deep Cloud Systems (DCS)

Deep Cloud Systems are identified using the Tracking of organized convection algorithm through a 3D segmentation (TOOCAN) algorithm (Fiolleau and Roca 2013). TOOCAN is designed to overcome one major limitation of the traditional overlap-based tracking technique by analyzing the 3D (2D+time) imagery. It is based on a conceptual model of a convective system, where the cloud deck is composed of a convective and stratiform part (Houze 2004) particularly suited for tropical Africa and the Atlantic Ocean (Futyan and Del Genio 2007; Schumacher and Houze 2006; Lafore et al. 2017). The TOOCAN algorithm first performs a multithreshold, multistep screening on the space and time infrared image volume to detect convective seeds. Then, it grows these seeds towards the edges of their stratiform extension, defined the upper level cloudiness threshold of 235 K. The algorithm outputs various morphological parameters of the cold cloud shield (geolocation, size, brightness temperature distribution) as a function of the cloud system life cycle, with a temporal resolution of 30 min. From these raw outputs, numerous integrated DCS parameters are built: duration, distance of propagation, average speed of propagation. Earlier versions of this MCS database have been derived from various geostationary archives, and used in a number of studies to investigate the morphology of the cold cloud shield (Roca et al. 2017; Elsaesser et al. 2022), the distribution of DCS and tropical rainfall (Roca et al. 2014) or rainfall extreme (Roca and Fiolleau 2020); the radiative properties of the MCS
(Bouniol et al. 2016). In this study, we focus on the duration parameter and the maximum extent of the cloud shield to characterize the spectrum of DCS.

Due to the large volume of data under consideration, a data reduction step is first per-
formed by projecting the raw systems data onto a regular 1°×1° grid, where all informa-
tion about the systems is retained (see the Methods section of Roca and Fiolleau 2020 for
more details). Statistics are then built from this reduced dataset, also known as CACA-
TOES (http://toocan.ipsl.fr).

The annual climatology of the occurrence of systems, based on the 1983–2023 period, is shown in a phase diagram built using the systems’ duration and maximum extent for the African and Atlantic ITCZ regions (Fig. 4). To first order, the distributions of each region are similar, with systems ranging from a few hours up to more than two days and from approximately 1000 km2 up to several million km². The mean system properties (~6.2 h; ~6300 km² for AFR and ~6.3 h; ~5000 km² for ATL) correspond to the most frequently observed systems. Comparatively, the larger and longer-lived systems are much less frequent, as observed for the entire tropical belt (Roca and Fiolleau 2020). Despite a strong relationship between maximum area and duration, the statistics highlight that for a given duration, the maximum area of the DCS cloud shield can vary by multiple orders of magnitude. Even systems lasting 24 h or more can span over two orders of magnitude in size. This result for African and Atlantic systems is consistent with previously reported tropical-wide analyses (Roca et al. 2017). This feature of the spectrum of DCS is often underappreciated, mainly due to the emphasis on MCS in the literature, but it remains an important aspect of convective organization in the tropics.

The distribution of the DCS cloud top characteristics can be summarized using the 90th percentile of the coldest pixels forming the cluster at the time of the maximum extent of the cold cloud shield (Roca and Ramanathan 2000). Overall, the larger the system, the deeper it is, with this trend being more pronounced over land than over ocean. This is particularly evident for systems exceeding 100,000 km² over land and those lasting longer than 15 h over the ocean (Fig. 5). A ‘V’ pattern characterizes both land and ocean distributions, with two branches associated with the less deep (warmer) systems: shorter-lived and slightly larger than the mean, and longer-lived and smaller than the mean. The largest and

<figure><img src="image_6.png" /><figcaption>**Fig. 4** The 1983–2023 climatology of annual occurrence of DCS. For Africa (left) and the Atlantic region (right). S<sub>max</sub> is the maximum extension of the DCS along its life cycle. The cross corresponds to the average of the 2D distribution</figcaption></figure>

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5** The 1983–2023 climatology of the 90th percentile of the cluster brightness temperature at the time of maximum extent. For Africa (left) and the Atlantic region (right). The cross corresponds to the average of the 2D distribution of Fig. 2</figcaption></figure>

longest-lived systems are deeper over land than over the ocean, primarily a feature associ-
ated with the Congo region (not shown), which is consistent with previous analyses (Liu
et al. 2007).

An aspect of the distribution of the DCS concerns the shape of the cloud shield, which
is summarized by computing the eccentricity of the cloud shield at the time of its maxi-
mum extent in the life cycle. The eccentricity is defined by fitting an equivalent ellipse to
the cloud shield and is expressed as the ratio of the small to the large axis of the ellipse.
The eccentricity distribution mirrors the cloud top characteristics (Fig. 6). Over both land
and ocean, the two 'warm' top populations of DCS tend to be more linear systems (eccen-
tricity < 0.5), while the 'coldest' top DCS are more circular, a feature often associated with
Mesoscale Convective Complexes (Jirak et al. 2003).

Finally, DCS are characterized by their movement speed, computed as the propagated
distance from beginning to end divided by the system duration. Fast systems, with speeds

<figure><img src="image_9.png" /><figcaption>**Fig. 6** The 1983–2023 climatology of eccentricity of the cluster at the time of maximum extent. For Africa (left) and the Atlantic region (right). The cross corresponds to the average of the 2D distribution of Fig. 2</figcaption></figure>
up to 15 m/s, are seen on the outer upper edge of the phase space diagram (Fig. 7), while the inner lower edge corresponds to slower systems. These features are more pronounced over the ocean compared to land, where a secondary population of fast systems is observed at the upper tail of the distribution in both size and duration. Over land, the most significant, longest-lived, and fastest propagating systems are likely squall lines (Lafore et al. 2017).

In summary, the climatological features of the DCS distribution over both Africa and the Atlantic Ocean encompass a wide range of system duration, size, depth, shape, and speed. This diversity confirms the suitability of this region for analyzing convective organization.

# 3 Results

## 3.1 Rationale for Convective Organization Monitoring

For a given region, assuming a constant cloud fraction over a given time period, any changes in the size and duration distributions of DCS during that period indicate a change in convective organization. This is due not only to changes in the occurrence of systems with different morphologies and properties but also to the arrangement of DCS relative to each other. Therefore, we first identify a time period in the long-term record during which the deep cloud fraction remains steady. For this period, we explore the changes in the DCS morphology distribution using phase-space diagrams.

## 3.2 Identification of the Stable Period in the Deep Cloud Fraction

The time series of deep cloudiness for our two regions of interest exhibits up to 1% interannual variability, corresponding to ~10–20% variation in relative units (Fig. 8). The linear trend in deep cloudiness over time is calculated using the Theil-Sen slope estimator for various periods. The periods begin from a starting year and end in 2023 included. The result is shown in Fig. 8. The Theil-Sen slope estimator, based on the median of possible slopes

<figure><img src="image_10.png" /><figcaption>**Fig. 7** The 1983–2023 climatology of movement speed of the system. For Africa (left) and the Atlantic region (right). The cross corresponds to the average of the 2D distribution of Fig. 2</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 8** Time series (left) of the upper level cloudiness and linear trends estimates over the start year to 2023. The thin gray lines on the right panels correspond to the confidence interval at 95%. For Africa (top) and Atlantic Ocean (bottom)</figcaption></figure>

among pairs of points, is preferred over the classic ordinary least squares linear approach due to its robustness, although the results are not significantly sensitive to the choice of method (not shown). The significance of the trend is assessed by computing the 95% confidence interval of the Theil-Sen slope estimation (e.g., Helsel et al. 2020). The difference between the upper and lower confidence intervals reflects the slight skewness of the slope distribution. The results indicate that there are no significant trends for most of the record for both regions. Only for Africa does the analysis show a slightly significant positive trend if the record starts prior to 1990.

During the 1991–2023 period, deep cloudiness remains steady over our regions of interest. This stability is observed not only in the annual mean but also across each individual season (not shown). This finding aligns with previous regional studies focused on precipitation, which indicate a leveling off of annual Sahelian rainfall following a recovery period from the long drought of the 1980s (Biasutti 2019; Panthou et al. 2014). his trend is possibly linked to the evolution of sea surface temperature (SST) patterns (Chen et al. 2020). The 1991–2023 period will be examined in further detail in the next section to advance our analysis of convective organization.

## 3.3 Changes in the DCS Morphology Distributions

The linear trends in the DCS morphology in the phase space are also computed using the Theil-Sen estimator, with a 95% confidence interval as shown in Fig. 9. Significant changes are observed in the most frequent systems in both regions, comprising both negative trends (~-2%/year) and positive trends up to 3%/year. Systems lasting up to 15 h with a maximum extent of up to 20,000 km² are becoming more frequent. These systems are generally less circular, warmer, and slower than the rest of the DCS population. The negative trends are observed in the upper part of the distribution and concerns systems with extents up to 200,000 km² and durations up to 15 h. However, this negative trend area is not significant over the Atlantic Ocean.

The largest and longest-lived systems ($D > 15$ h; $S_{max} > 50000$ km²) show no significant trends over both Africa and the Atlantic Ocean. As shown in Supplementary Note 1, this is also the case for the Sahel region during the boreal summer. This finding contrasts with previous claims of significant changes in this region during 1983–2016 (Taylor et al. 2017). The divergence arises from differences in the datasets used, the definitions of Mesoscale
<figure><img src="image_12.png" /><figcaption>**Fig. 9** Linear trend in the occurrence over the 1991–2023 period in %/year. Black dots mark the significant trends. The green diamond corresponds to the average of the 2D distribution of Fig. 2. For Africa (left) and the Atlantic region (right)</figcaption></figure>

Convective Systems (MCS) employed, and the periods selected for regression analysis. In particular, the originally reported trends are still present when using the present METEOSAT FCDR but with a muted amplitude by a factor of approximately 2. These differences and their implications are discussed in more detail in Supplementary Note 1.

# 4 Discussion

These changes in convective organization could either result from artifacts in the observational record or be associated with large-scale climate evolution. Both possibilities are discussed next.

## 4.1 Possible Artifacts in the Record in the Trend's Estimation

The analysis of a well curated DCS database over two contrasted climatological regions, built on an advanced METEOSAT record carefully homogenized over the last four decades reveals a change of *convective organization*. Over the last 32 years, the deep cloudiness over both regions shows no trends despite pronounced interannual variability in each region. The analysis of the changes of the DCS morphology indicates a shift with less frequent short lived and large systems and more frequent mid lived and small systems. At a constant cloud fraction, this change of occurrences in the phase space translates in a change in arrangement of the systems in complement to a change in the most frequent morphology. Changes concern both the tropical Africa and the tropical Atlantic regions with stronger significance over land.

Cold cloudiness time series do not show obvious breaks, especially when the record transitions from the MFG to the MSG era, ruling out this problem as a possible artifact source. Both the Atlantic and the African region exhibit distinct interannual variability further indicative of the absence of a strong bias in the record but leaving room for an observing system-scale remaining trend residual that could corrupt our interpretation of the system morphology changes. Formulating a new null hypothesis, by which
there is no change in the system’s morphology but that the record contains an artificial residual trend (RT in K/y or K/decade), we can quantitatively assess the needed magnitude of such an artifact to be commensurate with the reported change in morphology. Following Fiolleau and Roca (2024), a 4-month period over tropical Africa and Atlantic Ocean is used. The TOOCAN algorithm is run on the data that are perturbed by a given amount to simulate the impact of a systematic bias on the statistics of the DCS occurrence. The sensitivity study indicates that for a +3 K systematic bias, the population of systems lasting 10 h would decrease for all the S<sub>max</sub> and the smallest system would be impacted more. A –3 K bias would yield to similar but opposite in sign, changes in the DCS population (Fig. 10). Similarly, for the systems reaching a size of 10<sup>4</sup> km<sup>2</sup>, the sign of the bias would correspond to a systematic under (or over) estimation across the various duration population. This is not consistent with our results that show positive and negative trends for short lived and large systems and mid lived and small systems, respectively. Figure 10 further indicates that over the 1991–2023 period, the magnitude of the trend is very much larger than that of a 3 K systematic bias. In other words, for the magnitude of the reported trends in the occurrence of DCS to be due to a residual trend in the METEOSAT FCDR, it would require such residual to be much larger than 3 K, or 1 K/decade. That is much larger than an order of magnitude larger than the estimated residual of 0.5 K/decade as discussed in the previous section and by consequence we rule out such a possible erroneous attribution of our reported trends.

The currently enforced quality control (QC) of the data is very conservative and could, in principle, introduce a possible source of inhomogeneity in the record. To assess the potential impact of such QC on our trend estimates, the computations are replicated for the more recent 1997–2023 period, during which data availability is consistently high. The results, shown in Figure S2, are very similar to the 1991–2023 analysis, further supporting the robustness of the observed changes in convective organization.

Additionally, these trend estimates and their significance are not sensitive to the sta-
tistical method used, whether it be ordinary least squares, nonparametric methods, boot-
strapping, or the Mann–Kendall test (not shown). Ruling out artifacts in our analysis
prompts a discussion of possible reasons for the observed changes in convective organi-
zation over the last few decades.

<figure><img src="image_13.png" /><figcaption>**Fig. 10** Sensitivity of the DCS population occurrence to a bias in the IR brightness temperature over tropical Africa and Atlantic Ocean. (left) For the systems lasting 10 h as a function of Smax. (right) For the systems with Smax = 10⁴ km² as a function of the duration. The thick gray lines are the 1991–2023 trends for Africa from Fig. 9</figcaption></figure>
## 4.2 Possible Climate-based Interpretation of the Trends in Morphology

The results have been aggregated for the tropical African continent to contrast with the Atlantic Ocean. But it should be kept in mind that these bulk results for Africa are mainly influenced by changes over the Congo region given its overwhelming role in the climatology (Fig. 1). This is also, albeit less significantly, the case for the Sahel region (See Supplementary Note 1). Thermodynamic and dynamical conditions are often invoked as controlling factors or drivers of the properties of the tropical systems (Yang et al. 2017; Schiro et al. 2020; Palmer et al. 2023). Trends in meridional gradient of surface temperature have been linked to regional changes in atmospheric circulation and in the environmental conditions and further connected with convective systems changes (Taylor et al. 2017, 2018; Cook et al. 2020; Klein et al. 2021; Baidu et al. 2022). Over tropical oceans, similar drivers are noted but with also stronger emphasis on water vapor and the link with the wind shear is less conclusive (Hagos et al. 2013; Duncan et al. 2014; Galarneau et al. 2023a; Chen et al. 2023; Berthet et al. 2017). There, instability is not limited as for the land-based conditions and CAPE is always available. Deep convection can be sustained more easily thanks to the moist maritime boundary layer compared to land (Houze 2004). Despite these contrasted environmental conditions between land and ocean, our study shows similar responses over both continental Africa and the tropical Atlantic regions suggest that the change in *convective organization* is taking place at large scale. The major large scale decadal change over this region concerns the interhemispheric surface temperature gradient (Hart et al. 2019). This large-scale meridional gradient encapsulates previous regional studies showing atmospheric circulations intensification and surface temperature gradient changes. Over the Atlantic sector, it is also associated with a warming of SST of ~0.5K¹ over the last 30 years and a concomitant water vapor increase (Schröder et al. 2016). This relatively modest moisture increase is consistent with small changes in maximum extent and would also be compatible with extended system duration reported here.

Our analysis reveals a space/time scale dependence in the trends of DCS occurrence. The MCS subset of the DCS appears less altered than the core of the DCS distribution, indicating a need for new investigations into the drivers of these less explored systems. The extent to which the squall line (or MCS) conceptual model applies to the entire spectrum of DCS remains unclear. We can only speculate that the more frequent, not very deep, relatively small but long-lasting systems may not follow simple environmental control by dynamics. The increase in mean duration and decrease in extent suggest an enhanced atmospheric instability that sustains longer-lived systems without a significant increase in water vapor availability, which would favor larger systems. The maturity of the water and energy cycle depiction by the observing system, discussed at length in this Special Issue, along with concurrent advances in atmospheric reanalysis, now offers the opportunity to explore these speculative statements in greater detail. This will be the focus of future work.

## 5 Conclusions and Perspectives

A long-term analysis of convective organization was performed using a carefully homogenized four-decade-long METEOSAT archive. The analysis indicates that cold cloudiness has not exhibited any significant trends over the last 30 years. However, an investigation of the trends in DCS morphology reveals a significant shift in the occurrence of
deep convective systems, characterized by a decrease in large, short-lived systems and an increase in smaller, longer-lived ones.

The possibility that these results could be artifacts of the observational record has been ruled out through various tests. Trends in the large-scale interhemispheric surface temperature gradient may be related to changes in atmospheric circulation and environmental conditions, which in turn affect the morphology of DCS and lead to changes in convective organization.

Energetics considerations suggest that the tropical wide anvil cloud cover could be reduced under strong global warming through the “stability iris” effect (Hartmann and Larson 2002; Bony et al. 2016; Ito and Masunaga 2022). During that period, global warming of 0.6 K is observed<fnref n="1" /> that might not have triggered the proposed mechanisms. The restricted regional dimension of our analysis might prevent such an energy budget-based signal to emerge locally. Future work could consider the entire tropics for which DCS dataset 20+ years are emerging (Feng et al. 2021) or under construction (Heidinger et al. 2024). The IR-only anvil diagnostic might also suffer from limitation. The long observational record from the TRMM and GPM precipitation radar (Wang and Tang 2020) could be used in conjunction with the present METEOSAT data to better characterize the anvil cloudiness of the DCS (e.g., Elsaesser et al. 2022). Similarly, the characterization of deep convection might benefit from a multi-satellite perspective as well (Derras-Chouk and Luo 2024; Pilewskie et al. 2024; Li et al. 2024). Exploring the stability mechanism using these combined long satellite records is a venue for future work. Finally, our results highlight changes in the arrangement of the systems over the whole DCS spectrum and suggest that convective organization theory should also address this spectral dependency.

These findings have direct implications for climate modeling. Regional convection-permitting climate models, used to predict precipitation, rely on companion global parameterized models and are not directly impacted by the simulated DCS cloud shield properties. However, accurate representation of the cloud shield is crucial for kilometer-scale global climate models (Satoh et al. 2019) that requires a proper cloud-radiation feedback. Recent global simulations exhibit a significant spread in DCS morphology when compared to satellite observations (Feng et al. 2023). Our statistics and trend analysis provide an observed constraint on the evolution of DCS distribution over the last four decades. This data should be used to validate and improve the emerging multidecadal simulations using this new generation of kilometer-scale global models.

Finally, it is important to note that this active research topic will benefit in the near future from an enhanced observational basis. Field campaigns in the Atlantic ITCZ focusing on deep convection and surface interactions are planned for summer 2024: MAESTRO (S. Bony, personal communication, 2023; MAESTRO) and ORCESTRA (C. Hohenegger, personal communication, 2023). These campaigns will help understand the drivers behind the changes in DCS morphology reported here. Additionally, the existing satellite record will soon benefit from the third generation of METEOSAT (MTG) observations (Holmlund et al. 2021). The MTG's enhanced capabilities to monitor deep convection will be instrumental in better understanding the reasons for the evolution of DCS over this region.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10712-024-09862-8.

<footnote n="1">¹ See NOAA National Centers for Environmental information, Climate at a Glance: Global Time Series, from https://www.ncdc.noaa.gov/cag/</footnote>
**Acknowledgements** We thank S. Cloché for her assistance. This study benefited from the IPSL mesocenter ESPRI facility, which is supported by CNRS, UPMC, Labex L-IPSL, CNES, and Ecole Polytechnique. This study was also supported by CNES and CNRS under the Megha-Tropiques program. We extend our gratitude to Louis Netz for his valuable discussions on the organization metrics and to Graeme Stephens for stimulating discussions at the workshop. Data processing was performed on the European Weather Cloud infrastructure.

**Funding** Open access funding provided by Université Toulouse III - Paul Sabatier.

**Data Availability** The TOOCAN data are available at with https://doi.org/10.15770/EUM_SEC_CLM_1005. The CACATOES data are available at with https://doi.org/10.15770/EUM_SEC_CLM_1006.

## Declarations

**Conflict of interest** The authors do not have financial nor non-financial conflict of interest to declare.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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