1. Introduction
Previous studies of the global distribution of favorable severe weather environments (e.g., Brooks et al. 2003) and of intense thunderstorms (e.g., Zipser et al. 2006) often identify southeastern Africa and the adjacent ocean as a “convective hotspot.” As a recent example, convective storms along the east coast of South Africa during austral summer 2019/20 caused 44 deaths, destroyed 3000 houses, and led to considerable damage to infrastructure. This region as well as much of southern Africa receives most of its rainfall in summer through convective systems (Tyson and Preston-Whyte 2000). The onset of the summer rains take place as the Mascarene high retreats southeastward over the south Indian Ocean, the Saint Helena high moves southeast toward the southwestern tip of South Africa, and the tropical convergence zones move poleward, facilitating the inflow of relatively moist and unstable air into southern Africa from the western Indian Ocean and tropical southeast Atlantic (Reason et al. 2006). Elsewhere, southwestern South Africa is a winter rainfall region (Mediterranean-type climate) whereas the south coast receives rainfall nearly all year round (Weldon and Reason 2014; Engelbrecht et al. 2015).
Studies of severe weather in southern Africa have mainly focused on synoptic-scale systems such as tropical–extratropical cloud bands (e.g., Hart et al. 2010, 2013), cutoff lows (e.g., Singleton and Reason 2006, 2007; Favre et al. 2012), or landfalling tropical cyclones (e.g., Reason and Keibel 2004; Mawren et al. 2020). Smaller systems, such as mesoscale convective systems (MCSs), are often overlooked. MCSs are convective storms that are organized, through induced mesoscale circulations, into a single cloud system (Zipser 1982; Houze 2004; Houze 2018). Although there are various criteria to classify MCSs, they may be loosely defined as a long-lived (≥3 h) cumulonimbus cloud system that contain a precipitation area extending at least 100 km or more in at least one direction (Houze 2004). There are numerous types of MCSs, but the most well-known are squall lines (linearly organized systems) and mesoscale convective complexes (MCCs) defined as large, long-lasting quasi-circular systems (Maddox 1980; Velasco and Fritsch 1987; Fritsch and Forbes 2001; Houze 2004; Houze 2018).
Occurrences of MCS activity are common in tropical as well as midlatitude land and ocean areas. This activity has been documented over parts of North America (Maddox 1980; Ashley et al. 2003; Ashley and Ashley 2008; Feng et al. 2019; Haberlie and Ashley 2019; Cheeks et al. 2020), South America (Velasco and Fritsch 1987; Durkee and Mote 2009; Durkee et al. 2009; Rasmussen et al. 2016), Europe (Laing and Fritsch 1997; Morel and Senesi 2002; García-Herrera et al. 2005; Kolios and Feidas 2010), Asia (Laing and Fritsch 1993b; Virts and Houze 2016; Yang et al. 2019; Zhao et al. 2020), Australia (Keenan and Carbone 1992; Cifelli and Rutledge 1998; Perrin and Reason 2000), and Africa (Laing and Fritsch 1993a; Laurent et al. 1998; Mathon et al. 2002; Blamey and Reason 2009, 2012; Liu et al. 2019). These systems mainly occur in summer but can occur during the transition seasons of autumn and spring.
The typical development of an MCS is through a single cell storm growing from the organization and triggering of secondary new cells or the amalgamation of smaller convective storms into a single cloud system. Understanding all the processes that result in convection being sustained and organized into an MCS remains a key scientific challenge. In general, the large-scale environments of such long-lived systems are associated with considerable horizontal temperature, moisture, and stability gradients, along with considerable variations in both horizontal and vertical wind shear (Laing and Fritsch 2000). Factors like environmental wind shear play an important role in the organization and maintenance of an MCS through influencing storm features such as updraft tilt (Parker and Johnson 2000; 2004). Such environmental conditions can further be influenced by topography (e.g., Mulholland et al. 2019) with Fritsch and Forbes (2001) noting that most MCS populations occur downstream (typically within 1500 km) of north–south extending mountain ranges. Thus in the Americas, MCSs are often found on the leeward sides of the Rocky and Andes Mountains (Laing and Fritsch 1997; 2000; Machado et al. 1998; Nesbitt et al. 2006; Rasmussen et al. 2016).
Mesoscale convective systems play a key role in the hydrological cycle and global circulation through the redistribution of energy, heat, and moisture in the atmosphere (Brooks and Dotzek 2008; Yang et al. 2017; Feng et al. 2019). These systems often make large contributions to seasonal rainfall totals (e.g., Fritsch et al. 1986; Ashley et al. 2003; Nesbitt et al. 2006; Durkee et al. 2009; Blamey and Reason 2013), producing rainfall that is important for sustaining people’s livelihoods and the regional economy. Slow-moving or long-lasting MCSs often lead to extreme flooding, hail, and strong winds (Maddox 1980; Maddox et al. 1986; Velasco and Fritsch 1987; García-Herrera et al. 2005; Blamey and Reason 2009; Durkee and Mote 2009; Nuryanto et al. 2019).
Research on MCSs within Africa is mostly confined to the Sahel (e.g., Laurent et al. 1998; Laing et al. 1999; Mathon and Laurent 2001; Goyens et al. 2012) and the equatorial region (e.g., Taylor et al. 2018; Hartman 2020). The southern Africa region has received relatively limited attention. Individual case studies of MCSs in South Africa have revealed that a single system can produce a considerable amount of rainfall (e.g., Rouault et al. 2003; Blamey and Reason 2009). The only relatively long-term analysis of MCSs in southern Africa, based on Meteosat-7 data, was performed by Blamey and Reason (2012). In a subsequent study, Blamey and Reason (2013) showed that these MCSs can make a considerable contribution to warm season rainfall totals across eastern South Africa and southern Mozambique. However, two limitations to the Blamey and Reason (2012, 2013) analysis were that only a subset of MCSs, namely MCCs, were considered and that it only covered a 9-yr period (1998–2006). The limitation of the short study period of Blamey and Reason (2012) resulted in no clear evidence of any relationship between MCCs and the main modes of climate variability.
El Niño–Southern Oscillation (ENSO) is considered the dominant driver behind interannual summer rainfall variability over southern Africa, with widespread drought typically occurring during El Niño events (Lindesay 1988; Rocha and Simmonds 1997; Reason et al. 2000; Cook 2001; Reason and Jagadheesha 2005). Other large-scale modes of climate variability that are known to influence regional summer rainfall include the southern annular mode (SAM) and the subtropical south Indian Ocean dipole (SIOD). The SAM, a zonally symmetric mode of variability in the Southern Hemisphere, consists of an out-of-phase geopotential height variations pattern between the midlatitudes and high latitudes (Hartmann and Lo 1998; Thompson and Wallace 2000). Gillett et al. (2006) attribute the increase in summer rainfall in eastern South Africa during a positive phase in SAM (decrease in geopotential height over the Antarctic and increase in the midlatitudes) to anomalous easterly winds advecting more moisture from the Indian Ocean. The SIOD, on the other hand, is a regional mode of variability characterized by warm (cool) sea surface temperature anomalies in the southwest Indian Ocean and cool (warm) in the southeast Indian Ocean together with anticyclonic (cyclonic) wind anomalies over the basin and has been linked with increased (decreased) summer rains over large areas of southeastern Africa during the positive (negative) phase (Behera and Yamagata 2001; Reason 2001a, 2002).
Here, we use data spanning 24 years and study a wide group of MCSs, not just MCCs. Numerous flood and drought events, including multiyear droughts (Blamey et al. 2018), have occurred during this period so it is of considerable interest to investigate variability in MCS characteristics in this region. Furthermore, the nearby location of the Drakensberg Mountains and warm Agulhas Current (Fig. 1) with its large latent heat fluxes (Rouault et al. 2003), have been previously found to be favorable for severe weather (Rouault et al. 2002; Singleton and Reason 2006; Blamey and Reason 2009; Blamey et al. 2017), hence the focus on this domain, leaving the tropical southern Africa for future work.
(a) SSTs (°C; based on MUR SST) surrounding southern Africa for a particular summer day (19 Jan 2019), with extremely warm SSTs along the east coast and cold upwelled water along the west coast. JFM mean sea level pressure (hPa), for the period 1985–2008 and based on ERA5 reanalysis, is shown in black contours (over ocean only) and used to illustrate the location of the St. Helena high (SH) and Mascarene high (MH). The approximate location of the Mozambique Channel trough (MCT) during JFM is depicted by the white circle. The location of the KwaZulu-Natal (KZN) province (eastern South Africa) is shaded in light gray. The light blue box shows the domain used for (b). (b) Topography over eastern South Africa (shading; m), which is derived from ETOPO2. The blue polygon (D1) depicts the domain used in this study (referred to as domain 1 in the text) to build the MCS climatology.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
2. Data and methods
a. Domain
Figure 1 shows that the Drakensberg and Maluti Mountains rise up to about 2500–3500 m within a few hundred kilometers of the warm Agulhas Current. Inland from these mountains are mainly plains with interspersed low mountain ranges that gradually slope up toward the escarpment. East of the mountains, the KwaZulu-Natal (KZN) province contains a narrow coastal plain with numerous stepped terraces, steep valleys, and smaller mountain ranges before rising sharply up to the eastern edge of the Drakensberg escarpment. Extreme rainfall events in this region are poorly understood despite the region containing a large rural population and making substantial agricultural and tourism contributions to the national economy.
The study region (domain 1, the dark blue polygon in Fig. 1b) extends from 38°E (offshore of the Agulhas Current) to well inland of the Drakensberg Mountains and from 23° to 30°S. The southern latitude is determined by the limit of the available data (30°S–30°N) whereas 23°S is roughly the northern extent of the Drakensberg Mountains. The western boundary of domain 1 was subjectively determined by first getting the longitude of the highest point along the entire stretch of the eastern escarpment and then moving 2° farther west. MCSs that are initiated outside the domain but then subsequently track through the domain are excluded from the analysis.
b. MCS database
This study uses the Huang (2017) MCS dataset, which extends over the domain 30°N–30°S for 1985–2008 and contains basic trajectory information along with the intensity, area, eccentricity, speed, direction, and duration of each MCS. The climatology is derived from European Union Cloud Archive User Service (CLAUS) project data (Hodges et al. 2000)–a global dataset based on the calibrated International Satellite Cloud Climatology Project (ISCCP) B3 radiance data (Rossow and Schiffer 1999). The CLAUS dataset has been widely used to detect convective activity globally from 1985 to 2008 (e.g., Nguyen and Duvel 2008; Dias et al. 2012; Dong et al. 2016) and provides a 3-hourly global brightness temperature (BT) with intervals sampled at a 30-km (1/3°) resolution.
The Huang et al. (2018) MCS identification is based upon combining a Kalman filter with the conventional area-overlapping method. The area-overlapping method is one of the most commonly used techniques in MCS tracking (e.g., Williams and Houze 1987; Chen et al. 1996). A system is first identified in an image (based on a set of criteria); then, using the geographic overlapping (with overlap thresholds) between the original identified system and cloud clusters on a subsequent image, the systems are matched and linked. Initiation is determined when no overlapping occurs in the preceding image and termination occurs when no cloud cluster is found in the next image. During this process, if the convective system satisfies the various criteria (discussed below) for a set amount of time, it is defined as an MCS. Other automated methods used in the literature for MCS detection/tracking include, but not limited to, centroid tracking (e.g., Johnson et al. 1998), maximum spatial correlation (e.g., Carvalho and Jones 2001), clustering methods (e.g., Fiolleau and Roca 2013), and graph theory (e.g., Whitehall et al. 2015).
A challenge for MCS identification is the lack of consensus on the thresholds used to track the system as it evolves. The main defining criteria for MCS identification are based on the cold cloud shield BT and the size of the cold cloud shield at the respective BT threshold. A low BT is used as a proxy for convection taking place (i.e., used to delineate areas of deep, continuous convection) and this threshold varies between −18° and −65°C (255 and 208 K) in the literature (Machado et al. 1998). Even colder BT thresholds are at times used to identify the convectively active areas of a particular system and can reveal the presence of overshooting tops. For most MCS studies in Africa (mostly West Africa), slightly narrower BT thresholds ranging from −40°C (233 K) to −60°C (213 K) have been used, with the lower threshold (−40°C) being commonly used (see Table 1 of Goyens et al. 2012). In the Huang et al. (2018) methodology, MCSs are identified as convective systems with a brightness temperature less than −40°C (233 K) and a size threshold of 5000 km2. However, because the focus of this study is on long-lived, intense MCSs, a filter is applied to the Huang (2017) database to extract such systems. Here, only systems that contained BT values colder than a threshold of −52°C (221 K) and contain a corresponding area covering at least 10 000 km2 are considered. Furthermore, the system is required to meet these two thresholds for at least 6 h. This more constrained BT threshold is similar to that applied to MCSs in the Americas (e.g., Maddox 1980; Cotton et al. 1989; Anderson and Arritt 1998, 2001; Durkee and Mote 2009; Cheeks et al. 2020) and previously applied to MCCs in South Africa (Blamey and Reason 2012). The 10 000 km2 size threshold has previously been used in the literature (e.g., García-Herrera et al. 2005; Rafati and Karimi 2017). Given such thresholds, smaller and short-lived MCSs are omitted from the analysis although they may sometimes produce substantial rainfall. MCSs identified in January 1985 or December 2008 are excluded since those two months lie outside the period covered by the CLAUS dataset.
c. Environmental conditions
Convective available potential energy (CAPE) and u and υ winds from the high-resolution ERA5 reanalysis (Copernicus Climate Change Service 2017) are used to better understand seasonal conditions favoring MCS development. CAPE is a nonlinear combination of two of the ingredients described by Doswell et al. (1996) in identifying deep, moist convection, which are moisture and conditionally unstable lapse rates. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350-hPa level. The maximum CAPE produced by the different parcels is the value retained. The calculation of this CAPE in ERA5, due to computational efficiency considerations, assumes that (i) the parcel of air does not mix with surrounding air, (ii) ascent is pseudoadiabatic (all condensed water is instantaneously removed by precipitation), and (iii) other simplifications are related to the mixed-phase condensational heating. It is recognized that the quality of ERA5-derived CAPE remains untested for southern Africa due to the lack of sounding observations. Elsewhere, biases in the predecessor of ERA5, ERA-Interim, with some of the convective parameters, possibly related to boundary layer representation, have been identified in parts of Europe (Taszarek et al. 2018). For the same European domain, ERA5 appears to produce a better agreement between pseudosoundings and radiosonde data (Ukkonen and Mäkelä 2019).
The development and evolution of a MCS and associated severe weather is also dependent on the vertical shear profile (Weisman and Klemp 1982, 1984; Bluestein and Jain 1985; Parker and Johnson 2000; Coniglio et al. 2006; Coniglio et al. 2010). ERA5 u and υ winds are used to calculate the 0–6-km (deep layer) wind shear. For the surface, winds at 100 m above ground level are used, while for the 6-km level winds from two pressure levels (400 and 500 hPa) are used. A caveat with using reanalysis-derived winds and not radiosonde observations (not available for the domain) is that the model may not be able to resolve orographically induced mesoscale circulations that may modify the environment around mountains (e.g., Púčik et al. 2017). As described earlier, eastern South Africa contains a narrow coastal plain, rising sharply up toward the eastern escarpment (2500–3500 m), while the interior of South Africa is a plateau, sitting around 1.6–1.9 km above sea level. Given the topography, the upper level to calculate the vertical wind shear in the coastal area was determined using the 500-hPa level and 400 hPa for the high-lying regions. Only data covering the MCS period (1985–2008) are used and the 1500 UTC (1700 LST) data are presented here since local convective activity in South Africa typically occurs in the afternoon (Tyson and Preston-Whyte 2000).
To better understand variability in MCS frequency, conditions favorable for MCS development are analyzed through a composite analysis using monthly ERA5 data for years with anomalously high MCS activity. Given that there is an apparent transition in atmospheric conditions over South Africa from an extratropical nature in early summer [October–December (OND)] to a more tropical nature in late summer [January–March (JFM)] (Dyson et al. 2015), the composite analysis is divided into OND and JFM. Moisture fluxes were computed from the horizontal winds and specific humidity. The National Oceanic and Atmospheric Administration (NOAA) daily Optimum Interpolation Sea Surface Temperature (OISST) data, available from 1981 to present on a 1.0° × 1.0° horizontal grid (Reynolds et al. 2007), are used to analyze seasonal SST anomalies in the Indian and Atlantic Ocean for the composites. The statistical significance of the composite anomalies was determined using a two-tailed nonparametric Monte Carlo bootstrap statistical significance test.
The oceanic Niño index based on SST anomalies in the Niño-3.4 region (5°N–5°S, 120°–170°W) from OISST data is used to identify ENSO events. The Marshall (2003) index is used for the SAM while the SIOD index is defined as the SST anomaly difference between the eastern Indian Ocean (90°–100°E; 18°S–28°N) and the southwestern Indian Ocean (55°–65°E, 27°–37°S) (Behera and Yamagata 2001). The Mozambique Channel trough (MCT) index used in this study is based on the area average of the relative vorticity at 850 hPa over 35°–44°E, 16°–26°S (Barimalala et al. 2018).
3. The MCS climatology
a. Spatial distribution
Figure 2 shows that south of 15°S, there are two regions in southern Africa where MCSs preferentially develop. One is the eastern South Africa/Lesotho/Agulhas Current region focused on in this study and the other is the tropical belt (except over the northern Namibian/southern Angolan coast with its cool offshore SST due to upwelling). The South Africa/Lesotho maximum is clearly associated with topography with most systems tending to initiate near the eastern escarpment where there are sharp topographic gradients. Garstang et al. (1987) described how the interaction between topography of the northeastern escarpment and the upper air westerly waves propagating across the southern tip of Africa can lead to the development of strong convection over the east coast of South Africa.
A heat map showing the mean location of all MCSs over southern Africa between 15° and 30°S during the developing phase (first identification of the system in the dataset) for the period 1985–2008. The grid spacing is at 1° resolution. The domain used for the study is highlighted again by the green polygon, with no data available south of 30°S.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
A total of 1461 large, long-lived MCSs were identified and tracked in domain 1 (green polygon in Fig. 2 covering the South Africa/Lesotho maxima) for the period 1985–2008. Of these MCSs, the majority were located over land with 73% of the systems being categorized as continental and 27% as oceanic. Continental (oceanic) MCSs are defined here as systems that are located over land (ocean) during the developing stage of their evolution. This distribution is comparable with the global population of MCSs, with the most frequent occurrence being on land (e.g., Mohr and Zipser 1996; Zipser et al. 2006; Huang et al. 2018) and also for MCCs, a subset of MCSs (Laing and Fritsch 1997; Durkee and Mote 2009; Blamey and Reason 2012). It is likely that more systems occur on land than over the ocean due to more favorable thermal instability, orographic lift, and large diurnal thermal variations over land, related to land cover and topography (Houze 2018; Huang et al. 2018). If continental and oceanic systems are defined based on the location of the MCS at the maximum extent, then 892 systems are classified as continental and 569 as oceanic, almost a 60% continental and 40% oceanic split. Thus, MCSs in eastern South Africa typically initiate over the land and then track eastward toward the southwest Indian Ocean, described in more detail below.
b. MCS life cycle
Figure 3 shows the diurnal cycle of MCSs through three key stages of their lifespan, the developing, mature, and termination stages. The developing stage represents the time when the system is first identified in the data, the mature stage refers to when the system reaches the maximum spatial extent, and the termination stage is the last time step in which the systems are last identified. Given the low temporal resolution of the data, the timing of the first storms (i.e., before the system becomes classified as an MCS) is not easily identified and therefore not included. The stages are also restricted to intervals of three hours due to the temporal resolution of the data.
Diurnal variation of MCS occurrence (in percentage) over eastern South Africa. The key stages of the MCS life cycle denoted in the legend and the times are in UTC (local time is UTC + 2 h).
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
Based on the coarse temporal resolution, MCSs along the east coast and adjacent ocean tend to develop in the midafternoon (1500 UTC), reach a maximum spatial extent during the evening (between 1800 and 2100 UTC), and then mainly terminate during the night (2100–0300 UTC) (Fig. 3). Over 60% of the systems reach MCS status during the period between 1200 and 1500 UTC (a peak of 38% at 1500 UTC). Meanwhile, around 50% reached maximum spatial extent between 1800 and 2100 UTC, and over 50% terminated between 2100 and 0300 UTC. The nocturnal nature of MCSs over eastern South Africa, also identified by Blamey and Reason (2012) using a higher-resolution dataset, is consistent with the diurnal cycle of MCSs in other domains (Chen and Houze 1997; Machado et al. 1998; Morel and Senesi 2002; Kolios and Feidas 2010; Huang et al. 2018; Yang et al. 2019; Cheeks et al. 2020; Núñez Ocasio et al. 2020).
MCSs over land display a prominent diurnal cycle as described above with almost 50% of the systems first identified in the afternoon (1500 UTC), reaching maximum spatial extent in the evening (1800–2100 UTC) before terminating during the night (2100–0000 UTC). By contrast, MCSs over the ocean contain a less obvious diurnal cycle. Systems over the ocean appear to contain two favorable initiation times, one occurring at about 0300 UTC and the other occurring in the afternoon near 1500 UTC. The latter corresponds to the typical time of maximum SST in the diurnal cycle. It is not obvious what factors might contribute to the early morning initiation time. In general, SST is coolest around dawn and shortly thereafter but whether or not there is much of a diurnal range in SST depends on both dynamic (e.g., wind-induced mixing and Ekman pumping) and thermodynamic (shortwave and longwave radiation and latent heat fluxes) factors. Most of the oceanic MCSs tend to reach their maximum spatial extent in the late morning between 0900 and 1200 UTC before they terminate during the afternoon (1200–1500 UTC).
The duration of MCSs over the east coast of South Africa ranges from a minimum of 6 h (threshold) to over 24 h (Fig. 4). Due to the 3-hourly temporal resolution of the data, shorter-lived MCSs and the timing of the storms that become an MCS are not captured. Just over half the MCSs (53%) over domain 1 last between 6 and 9 h, with ~27% lasting between 12 and 15 h, and the remainder longer than 18 h. Around 7% of the systems (103 out of 1461 MCSs in the entire climatology) last longer than 24 h. Climatological studies of MCS in other regions (e.g., Velasco and Fritsch 1987; Machado et al. 1998) have also found cases where the systems last longer than 24 h if the environmental conditions and moisture inflows remain favorable over the storm region.
The duration of warm season MCSs (given in percentage) over eastern South Africa.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
A difference between continental versus oceanic based systems is that continental systems are shorter lived than the oceanic systems. More oceanic than continental systems have a duration longer than 18 h with around 13% of oceanic systems have a duration longer than 24 h. Previous studies have documented that MCSs developing over oceans are generally larger, shallower, and longer lasting than those over the continent (Mohr and Zipser 1996; Laing and Fritsch 1997; Mathon and Laurent 2001; Huang et al. 2018).
c. The seasonal cycle of MCSs
Figure 5 plots the monthly variability of MCS frequency over domain 1. There is a sharp increase through the early summer to reach a maximum in December followed by a gradual decrease through the late summer. It is also evident that considerable variability can occur within the different months, with December and January showing the largest range (Fig. 5b). The spatial distribution of the origins of the MCSs during the different summer months is shown in Fig. 6. In general, there are more systems near and downstream of strong topographic gradients or the Agulhas Current. There is a clear preference for systems to develop over land during the core summer period (November–February) compared to that over the ocean. When the systems are classed as continental versus oceanic systems, continental systems reveal a single peak in the season (December), due to favorable thermal instability over land during this period (Blamey et al. 2017), whereas there appear to be two minor peaks in ocean-based systems (December and April) (not shown). A December peak may arise because this is the month of maximum insolation while the circulation over eastern South Africa is still influenced by midlatitude systems, thus leading to relatively large airmass contrasts and baroclinicity in the environment within which the MCS develops. Over the ocean, April is close to the month (March) of maximum SST as well as the time when midlatitude circulation systems start to dominate over tropical influences. Thus, there is still a very warm ocean surface in April as well as a time of sharply increasing airmass contrasts and baroclinicity. The transitional months also show a tendency to have fewer systems in the northern land or ocean regions of the domain whereas a large number of systems are found in these areas in the main summer months.
(a) The total number of MCSs within the domain for each month over the period 1985–2008. The number above each bar represents the total number of systems for that particular month. (b) Boxplots illustrating the monthly mean and range in MCS activity for the period 1985–2008. The horizontal line in each box (showing the 25%–75% range) is the mean, while the vertical lines indicate the minimum and maximum number of MCSs, and outliers are denoted by plus signs.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
Spatial distribution of the origin of all MCSs for the extended summer starting in (a) September and ending in (h) April for the period 1985–2008. The grid spacing is 0.5° resolution, while the gray polygon is the outline of the domain.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
To highlight the seasonal evolution of favorable conditions for MCSs development, CAPE and deep layer vertical wind shear distributions produced with ERA5 data are analyzed. Figure 7 depicts the mean number of days per summer month exceeding 1000 J kg−1 across South Africa at 1500 UTC. This threshold choice follows since it has previously been identified as an indicator of extreme rainfall events over inland South Africa (Dyson et al. 2015). Two hotspots are evident in eastern South Africa, one along the northern part of the Drakensberg near the border with Eswatini, and the other along the southeast coast around 30°–32°S. Although high CAPE values in the two domains appear simultaneously at the start of the summer (October/November), high values of CAPE are more frequent along the northern parts (around Eswatini; ~27°S, 31°E) during the early/core summer months (November–January) reducing during the late summer as solar insolation weakens. In contrast, the maximum along the southeast coast (~30°–32°S) contains high values of CAPE that increase from October through February followed by a small decrease in spatial extent but not obviously in magnitude in March and April. Over the Agulhas Current region, the areas of high CAPE increase in extent through the summer as SSTs warm with the maximum occurring in March–April, the time of warmest SSTs.
The monthly mean of the number of days with CAPE at 1500 UTC (1700 LST) exceeding 1000 J kg−1 over the period 1985–2008. For reference, the green polygon illustrates the MCS domain used in the study.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
The summer cycle of vertical wind shear is presented as the mean number of days per month with deep layer vertical wind shear between 12 and 25 m s−1 at 1500 UTC (Fig. 8). Although southern South Africa generally experiences more days with favorable wind shear than the domain, revealing the presence of the subtropical jet stream, it does not contain the same convective activity as found along the east coast due to cooler adjacent SSTs and a more stable atmosphere there (Weldon and Reason 2014; Engelbrecht et al. 2015). Over the east coast of South Africa, favorable wind shear days are most frequent during early summer months (September–November) with a north–south gradient along the coast (more days with favorable shear in the south). The shear subsequently decreases in strength over the northern part of the east coast as the summer progresses, with the lowest values occurring between January and March. Due to the escarpment and the interior being at an altitude greater than 1000 m, vertical wind shear using a 400-hPa upper level was also calculated leading to a very similar spatial pattern as Fig. 8 but with more days with wind shear between 12 and 25 m s−1. Overall, the favorable storm environments seen throughout the summer over the southern part of the domain (immediately east of Lesotho and over the southern part of the east coast) help explain the maxima seen in MCS activity in this region (see Fig. 2) compared to the rest of the domain.
The monthly mean for the number of days (taken at 1500 UTC) with the vertical wind shear between 12 and 25 m s−1 across South Africa over the period 1985–2008. Here, deep layer vertical wind shear is calculated as the difference between winds at the 500-hPa pressure level with winds 100 m above ground level.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
d. Interannual variability and large-scale circulation favoring MCS development
On average 63 MCSs occur per summer, ranging from a minimum of 45 (1995/96) to a maximum of 81 (1987/88) with a standard deviation of 10 (Fig. 9a). Detrended correlations of MCS frequency over domain 1 are only significant with SAM during early summer (OND; r = 0.64, p < 0.01) and SIOD during late summer (JFM; r = −0.53, p < 0.01). The link between SAM and MCS frequency is consistent with the summer rainfall region of South Africa being typically wet (dry) during the positive (negative) phase of the SAM (Gillett et al. 2006). Thus, the shift in midlatitude westerlies linked to SAM could play a key role in MCS development in eastern South Africa (discussed in more detail below). Although ENSO is considered the dominant driver behind interannual rainfall variability over southern Africa during summer, there is no conclusive relationship with MCS frequencies over eastern South Africa. This inconsistency between ENSO and MCS activity may reflect the previously documented nonlinear relationship between ENSO and southern African climate (Reason and Jagadheesha 2005; Fauchereau et al. 2009; Boulard et al. 2013; Blamey et al. 2018; Driver et al. 2019).
(a) Summer totals of MCSs of extended summer September–April [SOND(0)JFMA(+1)] within the domain for period 1985–2008. The first bar is for the 1985/86 summer and the last is 2007/08. The numbers above each bar represent the total number of systems for each summer, while the horizontal line shows the summer mean, and the dashed lines are ±1 standard deviation. (b) The standardized anomalies for MCS frequency for the OND (black bars) and JFM (gray bars) periods. The first bars are for OND of 1985 and JFM of 1986.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
To better understand variability in MCS frequency, conditions that are favorable for MCS development are analyzed using composites based on ERA5 data. Only years with anomalously high MCS activity (seasons containing a standard deviation above 1 in Fig. 9b) for OND (1985, 1987, 1998, 1999, and 2001) and JFM (1987, 1990, 1992, 2005, and 2006) are included. Since some summers show OND and JFM with opposite signed anomalies (e.g., OND 1986 and JFM 1987 in Fig. 9b), early and late summer composites are derived. To gauge whether there is any difference in MCS location during these chosen OND/JFM composites, the domain is divided into four quadrants, roughly dividing it into southern/northern and land/ocean sections (Fig. 10a). In OND, the northeast quadrant shows a large percentage increase with the SW quadrant also showing a significant increase (Fig. 10b). Most of the latter (26% or ~4 more systems) mostly occur over land across KZN. For the northeast quadrant increase (60% or ~2 more systems than average), these changes occur over land/coastal areas in southern Mozambique. For the JFM composite, there is a larger percentage increase in MCS development in the southwest quadrant (mean is 13) compared to the other three (Fig. 10c). Although the other three quadrants have similar-sized percentage increases, only that in the northwest is significant.
(a) Quadrants used when referring to the composite anomalies of the spatial distribution of MCS during the developing phase for (b) OND and (c) JFM. Red (blue) values indicate an increase (decrease) in MCS activity (in percentage) within a given grid cell. The values in the corner of each quadrant represent the percentage anomaly in MCS frequency after spatially averaging over that quadrant. Values that are significant at the 95% confidence level are denoted by asterisks (*).
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
Figure 11 shows that there are significant increases in the number of days with favorable CAPE (Fig. 11a) and wind shear (Fig. 11b) for the JFM composite, particularly over the mainly land-based southwest and northwest quadrants that contained the largest increase in MCS frequency (Fig. 10c). For OND, the largest increase in MCS frequency occurred over parts of southern Mozambique in the northeast quadrant. Figure 11c shows a significant increase in favorable CAPE days over southern Mozambique as well as nearby land and ocean areas. While the CAPE changes are consistent with an increase in MCS numbers here, there is no obvious change in the number of shear days in this region (Fig. 11d). It is possible that in addition to CAPE, some other factors may have also influenced MCS frequency in the northeast quadrant.
JFM composites anomalies for (a) days with CAPE exceeding 1000 J kg−1 and (b) days with vertical wind shear between 12 and 25 m s−1. (c),(d) As in (a) and (b), but for OND. The mean is based on ERA5 data from 1985 to 2008 and only from the 1500 UTC time step. Stippling denotes values that are significant at or above 95% after applying a two-tailed nonparametric Monte Carlo bootstrap statistical significance test.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
All five cases in the OND composite coincide with positive SAM (Table 1) and with significant positive height anomalies over the east coast of South Africa and neighboring ocean (Fig. 12a). Associated with the positive SAM pattern is a weakening of the subtropical jet, particularly near 25°–30°S over South Africa and the southwest Indian Ocean (Fig. 12b), and a strengthening of the polar jet near Antarctica. These changes in the jet streams impact extratropical cyclones originating in the southwest Atlantic, which provide the midlatitude input to the dominant summer synoptic rainfall-producing system in southern Africa, tropical–extratropical cloud bands (Harrison 1984; Hart et al. 2013). Visual inspection of satellite imagery indicates that MCS systems are often embedded within these cloud bands, implying that if conditions are favorable for cloud bands, then they may also be for increased MCSs. During wet summers over subtropical southern Africa, these cloud bands are aligned northwest–southeast across the mainland (Fauchereau et al. 2009). Figure 13a, which shows composite anomalies in 500-hPa vertical velocity, indicates a northwest–southeast swath of relative upward motion extending from southeastern Angola, across Botswana and northern South Africa/northeastern South Africa, to the northern Agulhas Current region, consistent with enhanced convection and cloud band activity there including most of the northern part of the MCS domain. The stronger upward motion is accompanied by a decrease in OLR (increased cloud cover) for much of the domain and particularly over the northern half of the MCS domain (Fig. 13c), consistent with increased MCS activity.
The phase or strength of ENSO, SAM, SIOD, and MCT for the different OND and JFM periods with anomalously high frequency of MCSs within domain 1. Note that MCT strength is only shown for JFM. Years with a strong phase of the respective index are indicated in bold text.
OND composites anomalies for (a) 500-hPa geopotential height and (b) 300-hPa zonal wind. The mean is based on monthly ERA5 data from 1985 to 2008. Stippling denotes values that are significant at or above 95% after applying a two-tailed nonparametric Monte Carlo bootstrap statistical significance test.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
Composite anomalies of vertical velocity for (a) OND and (b) JFM. The mean is based on monthly ERA5 data from 1985 to 2008. The corresponding OLR anomalies, based on NOAA OLR, are shown in (c) OND and (d) JFM. Stippling denotes values that are significant at or above 95% after applying a two-tailed nonparametric Monte Carlo bootstrap statistical significance test.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
For JFM, the correlation between the SIOD and MCS variability appears to be partly explained through the relationship between SSTs in the Indian Ocean, the Mascarene high, and the MCT. Of the five JFMs, four (1987, 1990, 1992, and 2006) are associated with a weaker MCT, whereas JFM 2005 experienced a near-average MCT (Table 1). Barimalala et al. (2020) show that the MCT, which is most pronounced in JFM (see Fig. 1a for the general position of the MCT), is correlated with the strength and position of the Mascarene high over the Indian Ocean, and typically leads to more (less) moisture flux from the Mozambique Channel and from the south of Madagascar toward mainland southern Africa when both circulation features are weak (strong). Composite 850-hPa geopotential height and 850-hPa moisture flux anomalies confirm a weaker Mascarene high and weaker MCT in periods with high MCS activity (Fig. 14a). The weaker low-level winds and reduced cloud cover over the channel during these weak MCT late summers are favorable for warmer than average SSTs here, as well as in the oceanic parts of the northern MCS domain (Fig. 14b). These warm SSTs are conducive to increased convection and MCS activity over the domain since the flow will remain onshore, albeit weaker, but with more moisture than average. Indeed, low-level specific humidity anomalies (not shown) during the five JFMs reveal positive anomalies (moisture gain) within most areas of the MCS domain, particularly land areas and northern Agulhas Current, and negative anomalies over the Mozambique Channel (moisture loss). This anomalous specific humidity pattern also creates sharper than average gradients between the Agulhas Current and farther offshore, which may assist MCS development. The westerly moisture flux anomalies over Botswana extending into northern South Africa also imply more moisture flux convergence over the MCS domain with the onshore flow. Reason and Mulenga (1999) and Reason (2001b) have previously demonstrated through a set of idealized experiments using an atmospheric general circulation model that this anomalous circulation and convergence, linked to warm SST anomalies in the southwest Indian Ocean, plays a key role in above average rainfall in eastern South Africa. Furthermore, Blamey and Reason (2009) presented model evidence that SSTs and associated surface heat fluxes in the northern Agulhas Current were important for the development of a MCS over northern KZN.
Composites JFM anomalies for (a) 850-hPa geopotential height (shaded; m) and moisture flux (vectors; g kg−1 m s−1) and (b) SST. The SST composite is based on monthly OISST data from 1985 to 2008. In (a), geopotential values that are not significant are masked out, while in (b) stippling denotes values that are significant at or above 95% after applying a two-tailed nonparametric Monte Carlo bootstrap statistical significance test.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
Last, the favorable environmental conditions and increased MCSs activity during the OND composite seasons coincide with above average rainfall over the MCS domain and neighboring areas (Fig. 15b). For JFM, Fig. 15d shows that most of southern Africa experienced well below average rainfall. However, over much of the MCS domain, rainfall was average to above average, suggesting that the increased MCS numbers here in these late summers played an important role in preventing drought conditions. This suggestion is consistent with a detailed synoptic classification of extreme rainfall events over the Limpopo River basin, which lies mainly to the northwest of the MCS domain as well as covering the far northern part of it, in which MCSs contributed 14% of these events (Rapolaki et al. 2019). Future work is planned to determine the exact contribution of MCSs to rainfall totals over the domain.
(a) Mean OND rainfall (shaded; mm day−1) and (b) OND composite anomaly of rainfall (shaded; mm day−1) for OND periods with high MCS activity (years given in bottom right-hand panel). The mean is based on CHIRPS daily data from 1985 to 2008. Stippling denotes values that are significant at or above 95% after applying a two-tailed nonparametric Monte Carlo bootstrap statistical significance test. (c),(d) As in (a) and (b), but for JFM.
Citation: Journal of Climate 34, 15; 10.1175/JCLI-D-20-0851.1
4. Discussion and conclusions
Based on the quasi-tropical (30°N–30°S) MCSs dataset covering the period 1985–2008 developed by Huang et al. (2018), an analysis of long-lived MCSs over eastern South Africa during the extended summer (September–April) was conducted. The identification of the MCSs is based on CLAUS 3-hourly global brightness temperatures (BTs) with intervals sampled at a 30-km (1/3°) resolution. A few of the limitations of Huang et al. (2018) are that it has a low temporal resolution (3 hourly) and spatial resolution of 30 km, and that it is restricted to 30°N and 30°S. Therefore, the southern part of South Africa (30°–34°S), which has very strong topographic gradients and is also strongly influenced by the Agulhas Current, is excluded. However, the earlier study of Blamey and Reason (2012) on MCCs over southern Africa, which was based on only 9 years of satellite data, found no MCCs south of about 32°S. Overall, the results presented here, which are based on 24 years of data, are consistent with those of Blamey and Reason (2012).
It is clear that over southern Africa between 15° and 30°S there is a pronounced local maximum in frequent MCS activity over the relatively small region extending from the Drakensberg Mountains to the Agulhas Current. A total of 1461 long-lived MCSs were identified over eastern South Africa and the adjacent ocean for the extended summer (September–April). Although it is not possible to make direct comparisons with other regions given the much smaller domain used in this study and the variations in the literature in MCSs threshold criteria used, it is apparent that the number of systems identified here is generally less than that compared to MCS regions in North America, Europe, Asia, and West Africa.
Mesoscale convective systems in eastern South Africa occur most frequently between November and January with a peak during December. The larger frequencies in November and December, particularly over land, compared to late summer could be linked to the large-scale environment since temperature and moisture gradients over South Africa tend to be stronger in early summer than in late summer (Todd et al. 2004). Our results reveal that more favorable CAPE and vertical shear profiles occur across the wider domain during November–December. Previous studies have highlighted the dominance of organized convection in early summer over eastern South Africa, for example through large-scale cloud bands (Hart et al. 2013), MCCs (Blamey and Reason 2012), and the frequency of days with favorable severe weather conditions (Blamey et al. 2017). In the late summer months (February–March), favorable CAPE and shear environments are more restricted to the southern parts of the east coast of South Africa and over the Agulhas Current. Relatively few MCSs occur during the transitional months of September/October and March/April (less than 10 per year in these months on average).
The systems generally reach MCS status (i.e., developing phase) during the midafternoon, a maximum extent during the evening hours and subsequently terminate in the middle of the night. This MCS life cycle closely matches the diurnal cycle of rainfall over the east coast of South Africa (Rouault et al. 2013) and is consistent with MCS timing in the U.S. Great Plains and South America. The possible role of nocturnal low level jets or land–sea breezes over eastern South Africa on MCS characteristics remains to be investigated. Although CAPE and deep layer vertical shear parameters have provided some insight into the distribution and frequency of the MCSs here, it is noted that further work is needed to understand upscale convective growth, where systems transition from isolated deep convection into organized MCSs, over eastern South Africa.
The results presented here suggest that most of the MCSs initiated over land as opposed to over the adjacent ocean. The tendency of systems to preferentially initiate over land has been noted in previous studies (e.g., Kolios and Feidas 2010; Durkee and Mote 2009). For eastern South Africa, the systems typically develop over land (near the escarpment) and then track eastward to the adjacent warm ocean (Agulhas Current). A similar pattern was documented in the limited climatology of MCCs over southern Africa by Blamey and Reason (2012). Although not completely understood, the complex terrain of the eastern escarpment could influence MCS development or evolution directly (e.g., modifying cold pool structure) or indirectly (e.g., changing vertical wind shear and CAPE) like that in the north–south running Sierras de Córdoba mountain range in Argentina (Mulholland et al. 2019). As indicated earlier, the northern Agulhas Current has been shown to contain favorable conditions (high latent heat fluxes) for local storm intensification. Although the findings here provide evidence that the northern Agulhas Current plays an important role in the development and intensification of MCSs, model simulations are required to investigate the exact mechanisms through which it does so.
Considerable interannual variability in the frequency of MCSs within the domain was also found, with an average of 63 systems occurring per season with the least in 1995/96 (45) and the most in 1987/88 (81). Significant correlations were observed between MCS frequency and SAM during OND (early summer) and the SIOD in JFM (late summer). No significant correlations with ENSO or IOD were found. The relationship between the SIOD and MCS activity in JFM appears to be linked through the strength of the Mascarene high and MCT. Late summers with more MCSs typically occurs when the Mascarene high is weaker, which as a result weakens the MCT. With a weaker MCT, warmer SSTs occur in the Mozambique Channel, which leads to a moister airmass being advected over the MCS region by the prevailing easterly winds. Westerly wind anomalies extending from west and northwest of the MCS domain lead to increased low-level convergence over the domain with this moister easterly inflow. Thus, favorable conditions occur for MCS development, which is illustrated with more days with sufficient CAPE and more favorable wind shear during these late summers.
Last, a composite analysis revealed that an increase in rainfall often occurred in regions containing anomalously high MCS activity during OND and JFM. To what extent MCSs influence the local hydrological cycle remains uncertain and warrants further investigation. It further highlights the need to better understand local environmental factors that play a role in the frequency, intensity, and evolution of these systems. Here, different environmental factors played a role in the increase in MCS activity between OND and JFM.
Acknowledgments
Dedricks Morake was funded by the National Research Foundation (NRF) through the Applied Centre for Climate and Earth System Science (ACCESS) and the University of Cape Town (UCT) Doctoral Research Scholarship. Ross Blamey received funding from the U.K.’s Natural Environment Research Council (NERC)/Department for International Development (DFID) Future Climate for Africa program, under the AMMA-2050/CLOVER project (Grant NE/M020428/1). We thank Joseph Woohoo for comments and PANGAEA for providing the MCS dataset.
Data availability statement
All mesoscale convective system data used for this study are openly available from PANGAEA–Data Publisher for Earth and Environmental Science website at https://doi.org/10.1594/PANGAEA.877914 as cited in Huang et al. (2018). Daily CHIRPS data (see Funk et al. 2015) have been downloaded from the Climate Hazards Group data website (https://www.chc.ucsb.edu/data/chirps). The ERA5 (fifth generation of ECMWF atmospheric reanalyses of the global climate) data are available at Copernicus Climate Change Service Climate Data Store (CDS), https://cds.climate.copernicus.eu/cdsapp#!/home. Daily OISST data are available from the National Oceanic and Atmospheric Administration (NOAA) website https://www.ncdc.noaa.gov/oisst/data-access. The Marshall SAM index data are available from http://www.nerc-bas.ac.uk/icd/gjma/sam.html.
REFERENCES
Anderson, C. J., and R. W. Arritt, 1998: Mesoscale convective complexes and persistent elongated convective systems over the United States during 1992 and 1993. Mon. Wea. Rev., 126, 578–599, https://doi.org/10.1175/1520-0493(1998)126<0578:MCCAPE>2.0.CO;2.
Anderson, C. J., and R. W. Arritt, 2001: Mesoscale convective systems over the United States during the 1997-98 El Niño. Mon. Wea. Rev., 129, 2443–2457, https://doi.org/10.1175/1520-0493(2001)129<2443:MCSOTU>2.0.CO;2.
Ashley, S. T., and W. S. Ashley, 2008: The storm morphology of deadly flooding events in the United States. Int. J. Climatol., 28, 493–503, https://doi.org/10.1002/joc.1554.
Ashley, W. S., T. L. Mote, P. G. Dixon, S. L. Trotter, E. J. Powell, J. D. Durkee, and A. J. Grundstein, 2003: Distribution of mesoscale convective complex rainfall in the United States. Mon. Wea. Rev., 131, 3003–3017, https://doi.org/10.1175/1520-0493(2003)131<3003:DOMCCR>2.0.CO;2.
Barimalala, R., F. Desbiolles, R. C. Blamey, and C. J. C. Reason, 2018: Madagascar influence on the South Indian Ocean convergence zone, the Mozambique Channel Trough and southern African rainfall. Geophys. Res. Lett., 45, 11 380–11 389, https://doi.org/10.1029/2018GL079964.
Barimalala, R., R. C. Blamey, F. Desbiolles, and C. J. C. Reason, 2020: Variability in the Mozambique Channel Trough and impacts on southeast African rainfall. J. Climate, 33, 749–765, https://doi.org/10.1175/JCLI-D-19-0267.1.
Behera, S. K., and T. Yamagata, 2001: Subtropical SST dipole events in the southern Indian Ocean. Geophys. Res. Lett., 28, 327–330, https://doi.org/10.1029/2000GL011451.
Blamey, R. C. and C. J. C. Reason, 2009: Numerical simulation of a mesoscale convective system over the east coast of South Africa. Tellus, 61A, 17–34, https://doi.org/10.1111/j.1600-0870.2008.00366.x.
Blamey, R. C., and C. J. C. Reason, 2012: Mesoscale convective complexes over southern Africa. J. Climate, 25, 753–766, https://doi.org/10.1175/JCLI-D-10-05013.1.
Blamey, R. C., and C. J. C. Reason, 2013: The role of mesoscale convective complexes in southern Africa summer rainfall. J. Climate, 26, 1654–1668, https://doi.org/10.1175/JCLI-D-12-00239.1.
Blamey, R. C., C. Middleton, C. Lennard, and C. J. C. Reason, 2017: A climatology of potential severe convective environments across South Africa. Climate Dyn., 49, 2161–2178, https://doi.org/10.1007/s00382-016-3434-7.
Blamey, R. C., S. R. Kolusu, P. Mahlalela, M. C. Todd, and C. J. C. Reason, 2018: The role of regional circulation features in regulating El Niño climate impacts over southern Africa: A comparison of the 2015/2016 drought with previous events. Int. J. Climatol., 38, 4276–4295, https://doi.org/10.1002/joc.5668.
Bluestein, H. B., and M. H. Jain, 1985: Formation of mesoscale lines of precipitation: Severe squall lines in Oklahoma during the spring. J. Atmos. Sci., 42, 1711–1732, https://doi.org/10.1175/1520-0469(1985)042<1711:FOMLOP>2.0.CO;2.
Boulard, D., B. Pohl, J. Crétat, N. Vigaud, and T. Pham-Xuan, 2013: Downscaling large-scale climate variability using a regional climate model: The case of ENSO over Southern Africa. Climate Dyn., 40, 1141–1168, https://doi.org/10.1007/s00382-012-1400-6.
Brooks, H. E., and N. Dotzek, 2008: The spatial distribution of severe convective storms and an analysis of their secular changes. Climate Extremes and Society, H. F. Diaz and R. J. Murnane, Eds., Cambridge University Press, 35–53.
Brooks, H. E., J. W. Lee, and J. P. Craven, 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 67–68, 73–94, https://doi.org/10.1016/S0169-8095(03)00045-0.
Carvalho, L. M., and C. Jones, 2001: A satellite method to identify structural properties of mesoscale convective systems based on the maximum spatial correlation tracking technique (MASCOTTE). J. Appl. Meteor., 40, 1683–1701, https://doi.org/10.1175/1520-0450(2001)040<1683:ASMTIS>2.0.CO;2.
Cheeks, S. M., S. Fueglistaler, and S. T. Garner, 2020: A satellite-based climatology of central and southeastern U.S. mesoscale convective systems. Mon. Wea. Rev., 148, 2607–2621, https://doi.org/10.1175/MWR-D-20-0027.1.
Chen, S. S., and R. A. Houze Jr., 1997: Diurnal variation and life-cycle of deep convective systems over the tropical Pacific warm pool. Quart. J. Roy. Meteor. Soc., 123, 357–388, https://doi.org/10.1002/qj.49712353806.
Chen, S. S., R. A. Houze Jr., and B. E. Mapes, 1996: Multiscale variability of deep convection in relation to large-scale circulation in TOGA COARE. J. Atmos. Sci., 53, 1380–1409, https://doi.org/10.1175/1520-0469(1996)053<1380:MVODCI>2.0.CO;2.
Cifelli, R., and S. A. Rutledge, 1998: Vertical motion, diabatic heating, and rainfall characteristics in north Australia convective systems. Quart. J. Roy. Meteor. Soc., 124, 1133–1162, https://doi.org/10.1002/qj.49712454806.
Coniglio, M. C., D. J. Stensrud, and L. J. Wicker, 2006: Effects of upper-level shear on the structure and maintenance of strong quasi-linear mesoscale convective systems. J. Atmos. Sci., 63, 1231–1252, https://doi.org/10.1175/JAS3681.1.
Coniglio, M. C., J. Y. Hwang, and D. J. Stensrud, 2010: Environmental factors in the upscale growth and longevity of MCSs derived from Rapid Update Cycle analyses. Mon. Wea. Rev., 138, 3514–3539, https://doi.org/10.1175/2010MWR3233.1.
Cook, K. H., 2001: A Southern Hemisphere wave response to ENSO with implications for southern Africa precipitation. J. Atmos. Sci., 58, 2146–2162, https://doi.org/10.1175/1520-0469(2001)058<2146:ASHWRT>2.0.CO;2.
Copernicus Climate Change Service, 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), accessed 2020, https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset&text=ERA5.
Cotton, W. R., M. S. Lin, R. L. McAnelly, and C. J. Tremback, 1989: A composite model of mesoscale convective complexes. Mon. Wea. Rev., 117, 765–783, https://doi.org/10.1175/1520-0493(1989)117<0765:ACMOMC>2.0.CO;2.
Dias, J., S. N. Tulich, and G. N. Kiladis, 2012: An object-based approach to assessing the organization of tropical convection. J. Atmos. Sci., 69, 2488–2504, https://doi.org/10.1175/JAS-D-11-0293.1.
Dong, W., and Coauthors, 2016: Summer rainfall over the southwestern Tibetan Plateau controlled by deep convection over the Indian subcontinent. Nat. Commun., 7, 10925, https://doi.org/10.1038/ncomms10925.
Doswell, C. A., III, H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560–581, https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.
Driver, P., B. Abiodun, and C. J. C. Reason, 2019: Modelling the precipitation response over southern Africa to the 2009/2010 El Niño using a stretched grid global atmospheric model. Climate Dyn., 52, 3929–3949, https://doi.org/10.1007/s00382-018-4362-5.
Durkee, J. D., and T. L. Mote, 2009: A climatology of warm-season mesoscale convective complexes in subtropical South America. Int. J. Climatol., 30, 418–431, https://doi.org/10.1002/joc.1893.
Durkee, J. D., T. L. Mote, and J. M. Shepherd, 2009: The contribution of mesoscale convective complexes to rainfall across subtropical South America. J. Climate, 22, 4590–4605, https://doi.org/10.1175/2009JCLI2858.1.
Dyson, L. L., J. Van Heerden, and P. D. Sumner, 2015: A baseline climatology of sounding-derived parameters associated with heavy rainfall over Gauteng, South Africa. Int. J. Climatol., 35, 114–127, https://doi.org/10.1002/joc.3967.
Engelbrecht, C. J., W. A. Landman, F. A. Engelbrecht, and J. Malherbe, 2015: A synoptic decomposition of rainfall over the Cape south coast of South Africa. Climate Dyn., 44, 2589–2607, https://doi.org/10.1007/s00382-014-2230-5.
Fauchereau, N., B. Pohl, C. J. C. Reason, M. Rouault, and Y. Richard, 2009: Recurrent daily OLR patterns in the southern Africa/southwest Indian Ocean region, implications for South African rainfall and teleconnections. Climate Dyn., 32, 575–591, https://doi.org/10.1007/s00382-008-0426-2.
Favre, A., B. Hewitson, M. Tadross, C. Lennard, and R. Cerezo-Mota, 2012: Relationships between cut-off lows and the semiannual and southern oscillations. Climate Dyn., 38, 1473–1487, https://doi.org/10.1007/s00382-011-1030-4.
Feng, Z., R. A. Houze Jr., L. R. Leung, F. Song, J. C. Hardin, J. Wang, W. I. Gustafson Jr., and C. R. Homeyer, 2019: Spatiotemporal characteristics and large-scale environments of mesoscale convective systems east of the Rocky Mountains. J. Climate, 32, 7303–7328, https://doi.org/10.1175/JCLI-D-19-0137.1.
Fiolleau, T., and R. Roca, 2013: An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite. IEEE Trans. Geosci. Remote Sens., 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762.
Fritsch, J. M., and G. S. Forbes, 2001: Mesoscale convective systems. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 323–357.
Fritsch, J. M., R. J. Kane, and C. R. Chelius, 1986: The contribution of mesoscale convective weather systems to the warm-season precipitation in the United States. J. Climate Appl. Meteor., 25, 1333–1345, https://doi.org/10.1175/1520-0450(1986)025<1333:TCOMCW>2.0.CO;2.
Funk, C., and Coauthors, 2015: The Climate Hazards Infrared Precipitation with Stations—A new environmental record for monitoring extremes. Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66.
García-Herrera, R., E. Hernández, D. Paredes, D. Barriopedro, J. F. Correoso, and L. Prieto, 2005: A MASCOTTE-based characterization of MCSs over Spain, 2000–2002. Atmos. Res., 73, 261–282, https://doi.org/10.1016/j.atmosres.2004.11.003.
Garstang, M., B. E. Kelbe, G. D. Emmitt, and W. B. London, 1987: Generation of convective storms over the escarpment of northeastern South Africa. Mon. Wea. Rev., 115, 429–443, https://doi.org/10.1175/1520-0493(1987)115<0429:GOCSOT>2.0.CO;2.
Gillett, N. P., T. D. Kell, and P. D. Jones, 2006: Regional climate impacts of the Southern Annular Mode. Geophys. Res. Lett., 33, L23704, https://doi.org/10.1029/2006GL027721.
Goyens, C., D. Lauwaet, M. Schröder, M. Demuzere, and N. P. Van Lipzig, 2012: Tracking mesoscale convective systems in the Sahel: Relation between cloud parameters and precipitation. Int. J. Climatol., 32, 1921–1934, https://doi.org/10.1002/joc.2407.
Haberlie, A. M., and W. S. Ashley, 2019: A radar-based climatology of mesoscale convective systems in the United States. J. Climate, 32, 1591–1606, https://doi.org/10.1175/JCLI-D-18-0559.1.
Harrison, M. S. J., 1984: A generalized classification of South African summer rain-bearing synoptic systems. Int. J. Climatol., 4, 547–560, https://doi.org/10.1002/joc.3370040510.
Hart, N. C. G., C. J. C. Reason, and N. Fauchereau, 2010: Tropical–extratropical interactions over southern Africa: Three cases of heavy summer season rainfall. Mon. Wea. Rev., 138, 2608–2623, https://doi.org/10.1175/2010MWR3070.1.
Hart, N. C. G., C. J. C. Reason, and N. Fauchereau, 2013: Cloud bands over southern Africa: Seasonality, contribution to rainfall variability and modulation by the MJO. Climate Dyn., 41, 1199–1212, https://doi.org/10.1007/s00382-012-1589-4.
Hartman, A. T., 2020: Tracking mesoscale convective systems in central equatorial Africa. Int. J. Climatol., 41, 469–482, https://doi.org/10.1002/joc.6632.
Hartmann, D. L., and F. Lo, 1998: Wave-driven zonal flow vacillation in the Southern Hemisphere. J. Atmos. Sci., 55, 1303–1315, https://doi.org/10.1175/1520-0469(1998)055<1303:WDZFVI>2.0.CO;2.
Hodges, K. I., D. W. Chappell, G. J. Robinson, and G. Yang, 2000: An improved algorithm for generating global window brightness temperatures from multiple satellite infrared imagery. J. Atmos. Oceanic Technol., 17, 1296–1312, https://doi.org/10.1175/1520-0426(2000)017<1296:AIAFGG>2.0.CO;2.
Houze, R. A., Jr., 2004: Mesoscale convective systems. Rev. Geophys., 42, 1–43, https://doi.org/10.1029/2004RG000150.
Houze, R. A., Jr., 2018: 100 years of research on mesoscale convective systems. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc., 17.11–17.54.
Huang, X., 2017: A comprehensive Mesoscale Convective System (MSC) dataset, links to files in MatLab and plain text format. Tsinghua University, Beijing, PANGAEA, accessed 12 October 2019, https://doi.org/10.1594/PANGAEA.877914.
Huang, X., C. Hu, X. Huang, Y. Chu, Y. H. Tseng, G. J. Zhang, and Y. Lin, 2018: A long-term tropical mesoscale convective systems dataset based on a novel objective automatic tracking algorithm. Climate Dyn., 51, 3145–3159, https://doi.org/10.1007/s00382-018-4071-0.
Johnson, J. T., P. L. MacKeen, A. Witt, E. D. W. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263–276, https://doi.org/10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.
Keenan, T. D., and R. E. Carbone, 1992: A preliminary morphology of precipitation systems in tropical northern Australia. Quart. J. Roy. Meteor. Soc., 118, 283–326, https://doi.org/10.1002/qj.49711850406.
Kolios, S., and H. Feidas, 2010: A warm season climatology of mesoscale convective systems in the Mediterranean basin using satellite data. Theor. Appl. Climatol., 102, 29–42, https://doi.org/10.1007/s00704-009-0241-7.
Laing, A. G., and J. M. Fritsch, 1993a: Mesoscale convective complexes in Africa. Mon. Wea. Rev., 121, 2254–2263, https://doi.org/10.1175/1520-0493(1993)121<2254:MCCIA>2.0.CO;2.
Laing, A. G., and J. M. Fritsch, 1993b: Mesoscale convective complexes over the Indian monsoon region. J. Climate, 6, 911–919, https://doi.org/10.1175/1520-0442(1993)006<0911:MCCOTI>2.0.CO;2.
Laing, A. G., and J. M. Fritsch, 1997: The global population of mesoscale convective complexes. Quart. J. Roy. Meteor. Soc., 123, 389–405, https://doi.org/10.1002/qj.49712353807.
Laing, A. G., and J. M. Fritsch, 2000: The large-scale environments of the global populations of mesoscale convective complexes. Mon. Wea. Rev., 128, 2756–2776, https://doi.org/10.1175/1520-0493(2000)128<2756:TLSEOT>2.0.CO;2.
Laing, A. G., J. M. Fritsch, and A. J. Negri, 1999: Contribution of mesoscale convective complexes to rainfall in Sahelian Africa: Estimates from geostationary infrared and passive microwave data. J. Appl. Meteor., 38, 957–964, https://doi.org/10.1175/1520-0450(1999)038<0957:COMCCT>2.0.CO;2.
Laurent, H., N. d’Amato, and T. Lebel, 1998: How important is the contribution of the mesoscale convective complexes to the Sahelian rainfall? Phys. Chem. Earth, 23, 629–633, https://doi.org/10.1016/S0079-1946(98)00099-8.
Lindesay, J. A., 1988: South African rainfall, the Southern Oscillation and a Southern Hemisphere semi-annual cycle. J. Climatol., 8, 17–30, https://doi.org/10.1002/joc.3370080103.
Liu, W., K. H. Cook, and E. K. Vizy, 2019: The role of mesoscale convective systems in the diurnal cycle of rainfall and its seasonality over sub-Saharan northern Africa. Climate Dyn., 52, 729–745, https://doi.org/10.1007/s00382-018-4162-y.
Machado, L. A. T., W. B. Rossow, R. L. Guedes, and A. W. Walker, 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126, 1630–1654, https://doi.org/10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2.
Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc., 61, 1374–1387, https://doi.org/10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2.
Maddox, R. A., K. W. Howard, D. L. Bartels, and D. M. Rodgers, 1986: Mesoscale convective complexes in the middle latitudes. Mesoscale Meteorology and Forecasting, American Meteorological Society, 390–413.
Marshall, G. J., 2003: Trends in the southern annular mode from observations and reanalyses. J. Climate, 16, 4134–4143, https://doi.org/10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2.
Mathon, V., and H. Laurent, 2001: Life cycle of Sahelian mesoscale convective cloud systems. Quart. J. Roy. Meteor. Soc., 127, 377–406, https://doi.org/10.1002/qj.49712757208.
Mathon, V., H. Laurent, and T. Lebel, 2002: Mesoscale convective system rainfall in the Sahel. J. Appl. Meteor., 41, 1081–1092, https://doi.org/10.1175/1520-0450(2002)041<1081:MCSRIT>2.0.CO;2.
Mawren, D., J. Hermes and C. J. C. Reason, 2020: Exceptional tropical cyclone Kenneth in the far northern Mozambique Channel and ocean eddy influences. Geophys. Res. Lett., 47, e2020GL088715, https://doi.org/10.1029/2020GL088715.
Mohr, K. I., and E. J. Zipser, 1996: Mesoscale convective systems defined by their 85-GHz ice scattering signature: Size and intensity comparison over tropical oceans and continents. Mon. Wea. Rev., 124, 2417–2437, https://doi.org/10.1175/1520-0493(1996)124<2417:MCSDBT>2.0.CO;2.
Morel, C., and S. Senesi, 2002: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Quart. J. Roy. Meteor. Soc., 128, 1953–1971, https://doi.org/10.1256/003590002320603485.
Mulholland, J. P., S. W. Nesbitt, and R. J. Trapp, 2019: A case study of terrain influences on upscale convective growth of a supercell. Mon. Wea. Rev., 147, 4305–4324, https://doi.org/10.1175/MWR-D-19-0099.1.
Nesbitt, S. W., R. Cifelli, and S. A. Rutledge, 2006: Storm morphology and rainfall characteristics of TRMM precipitation features. Mon. Wea. Rev., 134, 2702–2721, https://doi.org/10.1175/MWR3200.1.
Nguyen, H., and J. P. Duvel, 2008: Synoptic wave perturbations and convective systems over equatorial Africa. J. Climate, 21, 6372–6388, https://doi.org/10.1175/2008JCLI2409.1.
Núñez Ocasio, K. M., J. L. Evans, and G. S. Young, 2020: Tracking mesoscale convective systems that are potential candidates for tropical cyclogenesis. Mon. Wea. Rev., 148, 655–669, https://doi.org/10.1175/MWR-D-19-0070.1.
Nuryanto, D. E., H. Pawitan, R. Hidayat, and E. Aldrian, 2019: Characteristics of two mesoscale convective systems (MCSs) over the Greater Jakarta: Case of heavy rainfall period 15–18 January 2013. Geosci. Lett., 6, 1, https://doi.org/10.1186/s40562-019-0131-5.
Parker, M. D., and R. H. Johnson, 2000: Organizational modes of midlatitude mesoscale convective systems. Mon. Wea. Rev., 128, 3413–3436, https://doi.org/10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2.
Parker, M. D., and R. H. Johnson, 2004: Simulated convective lines with leading precipitation. Part II: Evolution and maintenance. J. Atmos. Sci., 61, 1656–1673, https://doi.org/10.1175/1520-0469(2004)061<1656:SCLWLP>2.0.CO;2.
Perrin, G. M., and C. J. C. Reason, 2000: Monsoonal influences on a mesoscale convective system over midlatitude South Australia. Meteor. Atmos. Phys., 74, 63–82, https://doi.org/10.1007/s007030070026.
Púčik, T., and Coauthors, 2017: Future changes in European severe convection environments in a regional climate model ensemble. J. Climate, 30, 6771–6794, https://doi.org/10.1175/JCLI-D-16-0777.1.
Rafati, S., and M. Karimi, 2017: Assessment of mesoscale convective systems using IR brightness temperature in the southwest of Iran. Theor. Appl. Climatol., 129, 539–549, https://doi.org/10.1007/s00704-016-1797-7.
Rapolaki, R. S., R. C. Blamey, J. C. Hermes, and C. J. C. Reason, 2019: A classification of synoptic weather patterns linked to extreme rainfall over the Limpopo River Basin in southern Africa. Climate Dyn., 53, 2265–2279, https://doi.org/10.1007/s00382-019-04829-7.
Rasmussen, K. L., M. M. Chaplin, M. D. Zuluaga, and R. A. Houze Jr., 2016: Contribution of extreme convective storms to rainfall in South America. J. Hydrometeor., 17, 353–367, https://doi.org/10.1175/JHM-D-15-0067.1.
Reason, C. J. C., 2001a: Subtropical Indian Ocean SST dipole events and southern African rainfall. Geophys. Res. Lett., 28, 2225–2227, https://doi.org/10.1029/2000GL012735.
Reason, C. J. C., 2001b: Evidence for the influence of the Agulhas Current on regional atmospheric circulation patterns. J. Climate, 14, 2769–2778, https://doi.org/10.1175/1520-0442(2001)014<2769:EFTIOT>2.0.CO;2.
Reason, C. J. C., 2002: Sensitivity of the southern African circulation to dipole sea surface temperature patterns in the south Indian Ocean. Int. J. Climatol., 22, 377–393, https://doi.org/10.1002/joc.744.
Reason, C. J. C., and H. Mulenga, 1999: Relationships between South African rainfall and SST anomalies in the southwest Indian Ocean. Int. J. Climatol., 19, 1651–1673, https://doi.org/10.1002/(SICI)1097-0088(199912)19:15<1651::AID-JOC439>3.0.CO;2-U.
Reason, C. J. C., and A. Keibel, 2004: Tropical cyclone Eline and its unusual penetration and impacts over the southern African mainland. Wea. Forecasting, 19, 789–805, https://doi.org/10.1175/1520-0434(2004)019<0789:TCEAIU>2.0.CO;2.
Reason, C. J. C., and D. Jagadheesha, 2005: A model investigation of recent ENSO impacts over southern Africa. Meteor. Atmos. Phys., 89, 181–205, https://doi.org/10.1007/s00703-005-0128-9.
Reason, C. J. C., R. J. Allan, J. A. Lindesay, and T. J. Ansell, 2000: ENSO and climatic signals across the Indian Ocean basin in the global context: Part I, Interannual composite patterns. Int. J. Climatol., 20, 1285–1327, https://doi.org/10.1002/1097-0088(200009)20:11<1285::AID-JOC536>3.0.CO;2-R.
Reason, C. J. C., W. Landman, and W. Tennant, 2006: Seasonal to decadal prediction of southern African climate and its links with variability of the Atlantic Ocean. Bull. Amer. Meteor. Soc., 87, 941–956, https://doi.org/10.1175/BAMS-87-7-941.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.
Rocha, A., and I. Simmonds, 1997: Interannual variability of southeastern African summer rainfall. Part 1: Relationships with air–sea interaction processes. Int. J. Climatol., 17, 235–265, https://doi.org/10.1002/(SICI)1097-0088(19970315)17:3<235::AID-JOC123>3.0.CO;2-N.
Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.
Rouault, M., S. A. White, C. J. C. Reason, J. R. E. Lutjeharms, and I. Jobard, 2002: Ocean–atmosphere interaction in the Agulhas Current region and a South African extreme weather event. Wea. Forecasting, 17, 655–669, https://doi.org/10.1175/1520-0434(2002)017<0655:OAIITA>2.0.CO;2.
Rouault, M., C. J. C. Reason, J. R. E. Lutjeharms, and A. C. M. Beljaars, 2003: Underestimation of latent and sensible heat fluxes above the Agulhas Current in NCEP and ECMWF analyses. J. Climate, 16, 776–782, https://doi.org/10.1175/1520-0442(2003)016<0776:UOLASH>2.0.CO;2.
Rouault, M., S. Sen Roy, and R. C. Balling, 2013: The diurnal cycle of rainfall in South Africa in the austral summer. Int. J. Climatol., 33, 770–777, https://doi.org/10.1002/joc.3451.
Singleton, A. T., and C. J. C. Reason, 2006: Numerical simulations of a severe rainfall event over the eastern Cape coast of South Africa: Sensitivity to sea surface temperature and topography. Tellus, 58A, 335–367, https://doi.org/10.1111/j.1600-0870.2006.00180.x.
Singleton, A. T., and C. J. C. Reason, 2007: Variability in the characteristics of cut-off low pressure systems over subtropical southern Africa. Int. J. Climatol., 27, 295–310, https://doi.org/10.1002/joc.1399.
Taszarek, M., H. E. Brooks, B. Czernecki, P. Szuster, and K. Fortuniak, 2018: Climatological aspects of convective parameters over Europe: A comparison of ERA-Interim and sounding data. J. Climate, 31, 4281–4308, https://doi.org/10.1175/JCLI-D-17-0596.1.
Taylor, C. M., A. H. Fink, C. Klein, D. J. Parker, F. Guichard, P. P. Harris, and K. R. Knapp, 2018: Earlier seasonal onset of intense mesoscale convective systems in the Congo basin since 1999. Geophys. Res. Lett., 45, 13 458–13 467, https://doi.org/10.1029/2018GL080516.
Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part II: Trends. J. Climate, 13, 1018–1036, https://doi.org/10.1175/1520-0442(2000)013<1018:AMITEC>2.0.CO;2.
Todd, M. C., R. Washington, and P. I. Palmer, 2004: Water vapour transport associated with tropical–temperate trough systems over southern Africa and the southwest Indian Ocean. Int. J. Climatol., 24, 555–568, https://doi.org/10.1002/joc.1023.
Tyson, P. D., and R. A. Preston-Whyte, 2000: Weather and Climate of Southern Africa. Oxford University Press, 396 pp.
Ukkonen, P., and A. Mäkelä, 2019: Evaluation of machine learning classifiers for predicting deep convection. J. Adv. Model. Earth Syst., 11, 1784–1802, https://doi.org/10.1029/2018MS001561.
Velasco, I., and J. M. Fritsch, 1987: Mesoscale convective complexes in the Americas. J. Geophys. Res., 92, 9591–9613, https://doi.org/10.1029/JD092iD08p09591.
Virts, K. S., and R. A. Houze Jr., 2016: Seasonal and intraseasonal variability of mesoscale convective systems over the South Asian monsoon region. J. Atmos. Sci., 73, 4753–4774, https://doi.org/10.1175/JAS-D-16-0022.1.
Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504–520, https://doi.org/10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.
Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 2479–2498, https://doi.org/10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2.
Weldon, D., and C. J. C. Reason, 2014: Variability of rainfall characteristics over the South Coast region of South Africa. Theor. Appl. Climatol., 115, 177–185, https://doi.org/10.1007/s00704-013-0882-4.
Whitehall, K., and Coauthors, 2015: Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets. Earth Sci. Inform., 8, 663–675, https://doi.org/10.1007/s12145-014-0181-3.
Williams, M., and R. A. Houze Jr., 1987: Satellite-observed characteristics of winter monsoon cloud clusters. Mon. Wea. Rev., 115, 505–519, https://doi.org/10.1175/1520-0493(1987)115<0505:SOCOWM>2.0.CO;2.
Yang, Q., R. A. Houze Jr., L. R. Leung, and Z. Feng, 2017: Environments of long-lived mesoscale convective systems over the central United States in convection permitting climate simulations. J. Geophys. Res., 122, 13 288–13 307, https://doi.org/10.1002/2017JD027033.
Yang, R., Y. Zhang, J. Sun, S. Fu, and J. Li, 2019: The characteristics and classification of eastward-propagating mesoscale convective systems generated over the second-step terrain in the Yangtze River Valley. Atmos. Sci. Lett., 20, e874, https://doi.org/10.1002/asl.874.
Zhao, Y., C. Liu, Y. Wang, and M. W. Moncrieff, 2020: Quasi-stationary extreme rain produced by mesoscale convective system on the Mei-Yu front. Meteor. Atmos. Phys., 132, 721–742, https://doi.org/10.1007/s00703-019-00717-1.
Zipser, E. J., 1982: Use of a conceptual model of the life cycle of mesoscale convective systems to improve very-short-range forecasts. Nowcasting, K. Browning, Ed., Academic Press, 191–204.
Zipser, E. J., D. J. Cecil, C. Liu, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Amer. Meteor. Soc., 87, 1057–1072, https://doi.org/10.1175/BAMS-87-8-1057.
