1 Introduction

Climate change and extreme weather events have been triggered by the impact of certain natural disasters, more and which can intensify their risks. They result in serious destruction to the environment and infrastructure, ecosystem, and human health (Wehner 2017b; Hayhoe et al. 2018; Ebi et al. 2018; Lipton 2018). In recent years, global warming has been one of the main factors that led to climate change. It has meant such persistent heat waves, wildfires, droughts, water shortages, storms, and deadly floods (Westerling et al. 2006; Schubert 2011; Armstrong et al. 2014; Abatzoglou and Williams 2016; Angélil et al. 2017; Fazel-Rastgar 2020; Fazel-Rastgar and Sivakumar 2022). Human activities have consciously changed the Earth’s atmosphere over the past hundred years (Maibach et al. 2014; Wang et al. 2019; https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions; Hung 2022). This has triggered the rise in greenhouse gases concentration leading to global warming, and subsequent variations in climatic indexes plus increasing both the intensity and frequency of climatic extreme events (Botero and Frances 2007; Mirhosseini et al. 2012; Wang et al. 2014; Nwaogazie 2017; Sadegh et al. 2018; AghaKouchak et al. 2020; Almahrouqi 2022). So, global warming is one of the key in shift pace of the climate in present century (Mahlstein et al. 2013). Scientists are working to know how extreme weather events with an adverse sequence of influences occur into act together like local weather structures and changes in atmospheric circulation(Westra, et al. 2014; Ummenhofer et al. 2017; Zscheischler et al. 2020). Heat waves are one of the most hazardous climate events causing dramatic influences on both human health and natural ecosystems as well as on anthropogenetic actions, communications, and the economy (Buzan and Huber 2020; Pascaline and Rowena 2018; von Buttlar et al. 2018; Auffhammer et al. 2017; McFarland, et al. 2015; Zuo et al. 2015; Sathaye et al 2013).

Heat waves are often identified as persistent periods of abnormally hot weather compared to the expected condition in a specified place and time (Guldberg et al. 2018; Horton et al. 2016; Mora et al. 2017). They are also evidenced an increase in temperature for short duration (https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves). Heat waves occur in summer mostly during the peak warmer months. They often result in temperature increases beyond the expected historical value. This is indicated by several days when the temperatures are substantially above the long-term normal mean, together day and night (Wedler et al 2023; Papalexiou et al. 2018; Zhang et al. 2015; Son et al. 2014; Anderson and Bell 2011; https://www.britannica.com/science/heat-wave-meteorology). The harsh hot air mass in an urban condition can cause many lives lost, notably among the elderly, sick and very young people including children (https://www.who.int/health-topics/heatwaves/#tab=tab_1). Extreme high temperatures could affect human causalities such as stress infrastructure, water resources, transport and electricity requirements, farms and forest ecosystems, and animals, including mortality. Also, the most fatal condition of hyperthermia can be affected by high ambient temperatures (Smoyer 2003; De Stéphan et al. 2005; Tillett 2011; Chen and Liang Zhang 2012; Cheng et al. 2018; Conti 2019; Troeyer et al. 2020; Royé et al. 2020). For example, during the summer months of 2003, much of Western Europe was influenced by major European heat waves (Beniston 2004; Conti 2005; Kosatsky 2005; Cassardo et al. 2007; Feudale and Shukla 2011). It has resulted in major life loss and wildfires, notably in Italy, France, Spain, the United Kingdom, Portugal, and the Netherlands, the heat waves affected several people with many fatalities. The major European heat wave of 2003 was one of the first well-recorded both in terms of its finding as particularly remarkable, and designation with the anthropogenic climate change followed by the climate models. The Russian heat wave of 2010 was another example of a major extreme heat wave leading to extensive agricultural damages, wildfires, smoke along life loss (https://www.metoffice.gov.uk/weather/learn-about/weather/case-studies/russian-heatwave). The European major heat waves of 2003 and the heat wave of 2010 in Russia affected about 70,000 and 55,000 deaths respectively (Robine 2008; Hoag 2014).

The results of previous and recent studies based on observations and ensemble model simulations have explained that the temperature is one of the key important signs of the climate trend rise in southern Africa (Kruger and Shongwe 2004; New et al. 2006; Kruger and Sekele 2012; Engelbrecht et al. 2015; Mbokodo 2023). Southern Africa has experience several large weather-related natural disasters in recent decades. Natural disasters including the heatwaves in South Africa have caused major social and economic failures linked to climate change. From 1900–2017, more than 100 disasters were reported causing 2200 deaths and around 4.5 billion USD in financial loss (https://climateknowledgeportal.worldbank.org/country/south-africa/vulnerability). Therefore, natural disasters occur nearly every year in South Africa. Life loss, the suffering of the survivors, health issues, freshwater access, food availability, housing problems, and contagious illnesses are all main concerns in management. Relevant research is needed to understand the weather patterns/disasters scientifically and to further aid in pre-informing and issuing warnings to help better survive such disasters. The 5th Intergovernmental Panel on Climate Change (IPCC) Assessment Report Africa has reported during the last 50 to 100 years, the temperature over Africa has intensified by at least 0.5 °C (specifically in vulnerable areas to climate change). The average temperature across continental Africa is at around 0.56 °C to 0.63 °C above the climatological mean value (https://library.wmo.int/doc_num.php?explnum_id=10421) obtained from 1981 to2010. It is noted here the WMO baseline refer until to the end of 2020 (https://library.wmo.int/doc_num.php?explnum_id=10421). Also, the year 2019 was reported as the most possibly the third warmest year on record, after the year of 2010 and 2016 (https://earthobservatory.nasa.gov/images/146154/2019). It should be noted that both 2010 and 2016 were also warm years in the world and those were influenced by the El Niño conditions by the beginning of the corresponding year. North Africa experienced a very severe heat wave in April 2010 with a daily maximum temperature of 40 ℃ and a minimum of 27 ℃ for more than five consecutive days in the Saharan and Sahelian areas (Largeron et al. 2020). However, there were local temperature anomaly changes on a subcontinental scale during 2019 (WMO 2020). The temperatures have been beyond 2 °C above the long-term average of the WMO baseline until to the end of 2020 (1981–2010) in South Africa, Namibia, and some parts of Angola. Based on climatic situations mostly in the western part of Africa, the urban areas, emerge to be vulnerable to heat waves (Engdaw et al. 2020; Ngoungue Langue 2023). Large areas extending from the south to the north of the continent were more than 1 °C above normal (https://library.wmo.int/doc_num.php?explnum_id=10421). Only some small areas in the north-west, containing Mauritania, and adjacent ocean areas, were faintly cooler than the 1981–2010 (WMO baseline until end of 2020) long-term average (https://library.wmo.int/doc_num.php?explnum_id=10421). Also, it has been anticipated that land temperatures over southern African areas to increase faster than the global average, particularly in the more arid areas like Madagascar (Watterson 2009; Orlowsky and Seneviratne  2011; James and Washington 2012). It is possible that broad areas of Africa may surpass the 2 °C temperature increase by 2080, in comparison to the mean temperature of the late twentieth century (5th IPCC Assessment Report Africa). The mean surface warming in Southern Africa areas is possible to go beyond the global mean land surface temperature increase in all seasons. Furthermore, by the end of the twenty-first century, it is projected that an increase of temperatures between 3.4 ºC and 4.2 ºC with respect to the 1981–2000 mean based (WMO baseline until 2020) on the A2 (defining a very heterogeneous world) scenario (Moise and Hudson 2008). Also, high warming rates are predicted over the semi-arid parts northwestern of South Africa, Botswana, and Namibia (Moise and Hudson 2008; Shongwe et al. 2011; Watterson 2009).

Heatwaves and their consequences can act at several scales, from global to local, and involve dynamical and thermodynamical processes (Rostami et al. 2024; Guigma et al. 2020; Vautard, et al. 2016). There is a limited and incomplete comprehension of the connections among these processes on local scales, and their shifts under global warming (Halpin 1997; Ibáñez et al. 2006; Hagen et al 2012; Oliver et al. 2014).

Despite the valuable studies related to heat waves and extreme heat events such as other extreme weather events, some aspects are still unclear. For example, the absence of a globally accepted definition of heatwave would allow a better comparison between studies conducted in different regions of the world (Annex 2022). Also, it there is a lack or limitation on studies focused on some specific regions of the globe. Therefore, the studies related to heatwave over regional and global are not well detailed and and which needs more further thorough understanding. Also, more advanced technological experiences, such as more sophisticated models and other techniques such as machine learning will assist in solving these relevant challenges.

Following the previous and recent studies as described above, our study aims to investigate the characteristics of the recent heat wave occurred in South Africaduring Januar 2023. Based on the South African Weather Service (SAWS) report (ISSN 1992–2566 Volume: 34 Number 1), during January 2023, the maximum temperature deviations were found to be largely positive over South Africa. Also, extremely hot, and dry conditions have been reported in some parts of South Africa. Unfortunately, there have been reported eight lives lost because of heat stroke in Kakamas, Northern Cape after the maximum temperature pointed to 41 ºC and two others on the 12th and five people on the 17th of January 2023. Besides, the Nelson Mandela Bay municipality due to the lack of rain, has faced a persistent drought. The subject of our interest in the current study is related to more recent abnormal warmer air temperatures observed during January 2023. This work aims to explore and indicate the background atmospheric dynamical influences to cause this most recent hot weather in South Africa.

2 Methodology and data selections

This study examines the recent abnormal hot weather situation in South Africa, influenced by abnormal atmospheric weather conditions. The study uses the National Centers for Environmental Prediction (NCEP)/ National Center for Atmospheric Research (NCAR) meteorological reanalysis datasets. The NCEP/NCAR Reanalysis Project is a joint project between NCEP and NCAR. The NCEP/NCAR Reanalysis is a continually modernized gridded dataset that indicates the state of the Earth's atmosphere, containing observational data and numerical weather prediction (NWP) model outputs from 1948 to the present. The spatial coverage of the NCEP model is 2.5 degrees × 2.5 degrees with global (Lat/Lon) grids of 144 × 73 covering from 0.0 E to 357.5 E and 90.0 N to 90.0 S.

This model applies and assimilates, a good capacity of observational data to create long-term weather configurations globally. The large data can be assimilated due to the initialization of the model to the real-world situation. The model inputs the meteorological data from radiosondes, surface, and upper atmospheric observations, dropsondes, radiosonde balloons, pilot balloons, polar orbit, and geostationary satellites. A large subset of this data is obtained from the Physical Sciences Division (PSD) of the NOAA (National Oceanic and Atmospheric Administration) Earth System Research Laboratory (ESRL) in its primary four times daily basis and as a daily mean. The composite mean charts along with the anomaly maps were created with the NOAA/ERSL Physical Sciences Division (www.ersl.noaa.gov/psd) support. Here, the hourly, daily and monthly mean composites along with anomalies and climate mean maps are used.. The anomalies were determined as the daily and monthly averages departure from the climate normal (1991–2020) as an updated standard reference work period for the long-term climate change computations which is recommended by the World Meteorological Organization (WMO). All calculations were based on the NCEP/NCAR Reanalysis model and other datasets. Daily composite maps are the averages of the mean (which are calculated every six hours a day) for each grid point in the model and the anomalies are calculated (mean – total base mean). The long-term climatology means are based values from 1991–2020 as a reference base. It is noted that earlier WMO baseline was 1981–2010 and has been updated to 1991–2020.. Until to the end of 2020, the standard reference period for calculating climate normal was 30 years span from 1981 to2010. However, the WMO’s Services Commission meeting recommended that the use of new 30-year baseline, 1991–2020, should be implemented globally and undertook support to all members to update their calculations. It is noted in the WMO baseline that the 30-year standard reference periods should be updated every decade to better show the changing climate and its impact on day-to-day weather face (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). Also , the mean and anomaly structural patterns in the composite maps have been synoptically and dynamically analyzed. In addition, the monthly, or daily mean latitude–longitude averaged for the different study cases from NASA´s Modern-Era Retrospective Analysis for Research and Applications (MERRA) database (Rienecker et al. 2011) has been obtained and examined. Also, the daily mean cloud fraction has been obtained from MODIS-Aqua data. Furthermore, the real-time observational data from the South African Air Quality Information System (SAAQIS). Besides the data from the Iowa State University of Science and Technology have been examined.

In the current work, we have used the baseline map of the heatwave threshold for South Africa (https://www.weathersa.co.za/home/weatherques) defined by the South African Weather Service (SAWS). SAWS declares the heatwave episode when the maximum temperature at a specific town is predicted to exceed 5 ℃ above the average maximum temperature of “the hottest month” for that specific place and stand remainingfor a continuous 3 days or more. More details are available on the following site, A detailed map of the “heat wave threshold” has been issued (https://www.weathersa.co.za/home/weatherques. Simultaneously, the current study has also looked at some specific areas for the observational data reported for the hourly, daily maximum, and minimum temperatures and relative humidity during the extreme weather situation.

3 Results

3.1 Yearly January average of the daily max 2-m air temperature anomalies

Figure 1a shows a long-term anomalies during (1980–2023) of the daily maximum 2-m air temperatures during January on the area-averaged over South Africa shape file (see Fig. 1b). Figure 1b displays the same parameter , showing the study area. Data obtained from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) database. This figure shows remarkable positive deviation values larger than 0.5 K( for the from the long-term mean (1991–2020) over the study area (South Africa). The time series analysis of the daily max 2-m air temperatures during January on the area-averaged of the South Africa shape file shows 10 events where the temperature anomalies exceeded 0.5 C in January i.e. 1983 (2.1 K), 1988 (0.8 K), 1992 (1.3 K), 1993 (1.2 K),1995(0.9 K), 2003 (1.1 K), 2007 (1.3 K), 2012 (0.9 K), 2015(1 K), 2016(1.3 K), 2018(1.2 K), 2019 (1.4 K) and, 2023 (0.9 K).Two-period moving averages have been shown in blue dot lines in Fig. 1a. Two-period moving averages commonly can be used for the prediction of the average of the real values in two past periods. A moving average is typically applied with time series data to smooth out the short-term variations and emphasize longer-term trends. We have briefly discussed the comparison between the different dynamical procedures causing the January 2023 heat waves, in conjunction with 2016 which was correlated to severe El Niño Southern Oscillation (ENSO). This specific event in 2016 brought severe drought seasons (Mbokodo et al. 2023) in Southern Africa. Also, the pressure fields for the other years where long-term maximum temperature deviations exceeded 0.5 K these years have been analyzed and presented in the 4.2 section. Figure 1c represents the geographical map of the study area (South Africa) including the coordinates and provinces.

Fig. 1
figure 1

a) Long-term 2-m maximum air temperature anomaly over South Africa obtained from the MERRA-2 database during January (1980 to 2023). b) Latitude–longitude color map of the same parameter. c)  A geographical map of the study area including the coordinates and provinces

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Figures 2 show the Hovmöller diagrams for the maximum and minimum air temperature at 2 m (a and c) and their anomalies with respect to normal climatology mean (b and d) for January 2023 over the study area. A Hovmöller diagram is a typical method to plot a large quantity of meteorological data with no time averaging to indicate the wave formation patterns. These figures show the remarkable existence of the warm air mostly from 10 to 18th January 2023 with the maximum anomaly at around 7 K during this time in comparison with long-term climate mean (1991–2020) values for both maximum and minimum air temperatures.

Fig. 2
figure 2

Hovmöller maximum and minimum air temperatures (a and c) and its anomaly (b and d) maps during January 2023 over South Africa. The horizontal axis denotes the longitude in degree

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Figure 3 shows the daily mean cloud fraction (for January 2023) obtained from MODIS-Aqua MYD08_D3 v6.1 data which presents a nearly clear sky around January 5, 2023, over the study area with a peak around January 7 mostly before the extremely high-temperature occurrence. This has caused receiving the maximum sunshine daily and become to reach higher temperatures over the study area. Also, a yearly change for the daily average time series analysis for the cloud fraction from MODIS-Aqua MYD08_D3 v6.1 from 2003 to 2023 over the study region has been analyzed (the figure is not presented here). This analysis displays a negative trend associated with less cloudiness during the past two decades. The cloud fraction in the study area has decreased from 0.57 to 0.43 for January during the past 3 years.

Fig. 3
figure 3

Hovmöller diagram for the mean daily cloud fraction from MODIS-Aqua MYD08_D3 v6.1 during January 2023 for the study area. The horizontal axis denotes the longitude in degree

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Here, two cities have been selected to show the existence of abnormal maximum temperatures exceeding the threshold temperature for heat waves baseline defined by the South African Weather Service (SAWS). The national heat wave map has been extracted from the South African Weather Service report for a media release (https://www.weathersa.co.za/home/weatherques). The South African Weather Service (SAWS) has defined the heat wave situation, mostly for South African situations, when the maximum temperature at a specific station reaches or surpasses the average maximum temperature reported on the hottest month, for a few following days. A time series for the hourly ambient air temperature data reported from South African Air Quality Information Service (SAAQIS) for January 2023 for the air quality station in Edendale town in KwaZulu-Natal province has been analyzed (the figure is not presented here). Data analysis for hourly temperatures for this city shows two extremely abnormal maximum temperatures at 36.32 ℃ on January 13 at 14:00 UTC and 36.52℃ on January 24 at 1600 UTC with the relative humidity around 17.03% and 19.05% respectively. However, the maximum temperatures had been ranging from 33–36℃ from January 10 (with 36.08℃) to January 18 (36.15 ℃). Also, the calculated average temperature for this city during January 2023 indicates 23.45℃. However, the threshold temperature assumption for the heat wave consideration has been defined (by SAWS) for this area at around 26–28℃. Also, based on the climate weather report from the Global Historical Weather and Climate Data), the average high temperature on day 12 in the month of January 2023 was reported at 27.01℃ with a maximum record value of 34.92℃ for this location (Table 1).

Table 1 Maximum and threshold temperature temperatures in Edendale town in KwaZulu-Natal province
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Another time series for the hourly data reported from South African Air Quality Service (SAAQIS) for January 2023 for the metrological station in Middleburg in Mpumalanga province has been examined. Data analysis for the hourly temperatures in this city shows, an extreme maximum temperature of 33.5℃ on January 28 at 16:00 UTC and with a relative humidity of around 17.5%. Also, the maximum temperature for the next day has been reported at around 31℃ and the relative humidity of 37%. However, the calculated average temperature for this city during January 2023 shows an average of 21.3℃. The threshold temperature for heat waves has been reported from SAAQIS for this area at around 26–28℃. So, the maximum temperature reported for January 28 and 29 over this city has exceeded the threshold value (Table 2).

Table 2 Maximum and threshold temperature temperatures in Middleburg in Mpumalanga province
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Also, the daily observations for the maximum temperatures (data obtained from Iowa State University of Science and Technology) for Calvinia, a regional town in the Northern Cape province have been reported 37℃, 37℃, 33℃, 38℃ during 13–17 January 2023 respectively. However, the threshold temperature for this city is 37℃. Also, the daily observations for the maximum temperatures for Bloemfontein (with a threshold heat wave temperature of 35℃), the capital, and the largest city of the Free State province show variations from 35℃, 35℃, 34℃, 36℃, 35℃ for the days between 11 and 15 January 2023 (figures are not shown here). Thus, these are together displaying some observational data analysis indicating hot temperature occurrence in the study area during January 2023.

3.2 Synoptic and dynamics weather map analysis

Figure 4 shows the composite mean of sea level pressure (a), its climate normal (b), and its anomalies departure 1991–2020 (c) during 10–18 January 2023. During the more recent hot weather, an omega-blocking high-pressure system centered in the southern Indian Ocean has been extended toward South Africa and it has been intensified with a maximum of around 5 hPa (see the anomalies in Fig. 4c) rather than the climate mean (Fig. 4b). The anomaly map demonstrates an intensified blocking system (rather than climate normal) has been affected over the study area (see the anomalies in Fig. 4c). Further, the blocked system was settled over the study area for some time more than two weeks from 3 to 21 Jan 2023 (figures are not shown). The omega block high-pressure system has dominated in South Africa's eastern with the passage of an isobaric line of 1017.5 hPa (Fig. 4a). So, during a heatwave, an intensified blocking system has been affecting the study area. Also, as Fig. 4a shows a continental heat low-pressure centered over Namibia and Botswana with a trough of (1012.5 hPa in composite mean rather than 1007.5 hPa in climate normal map). This has changed with slightly the increase of an isobar line rather higher value at 0.5 hPa which shows weaker low-pressure system extended to the western parts of South Africa rather than the normal structure (see anomalies in Fig. 4c) has formed and extended to the western interior part of South Africa. In Figs. 4a, b and c, the horizontal and vertical axes indicate longitudes and latitudes in Degrees respectively.

Fig. 4
figure 4

Composite mean of sea level pressure (a), climate normal (b), and anomaly map departure from 1991–2020 (c)during 10–18 January 2023. The color bar shows pressure in mb or hPa

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Figure 5 shows the composite mean for 500 hPa geopotential height (a), its climate normal (b), and its anomalies departure 1991–2020 (c) from 10 to 18 January 2023. This pattern and the structure show the domination of the subtropical Botswana subtropical high (Fig. 5a) centered (5900 hPa) along with the low-level omega blocking high pressure (Fig. 4a) over South Africa during the abnormally recent hot weather event. During this time the midlevel tropospheric ridge centered South Africa and has intensified (see the anomalies in Fig. 5c) in comparison with the climate normal pattern (see the climate normal map in Fig. 5b) with a maximum value at around 80 geopotential meters (gpm). The Botswana subtropical high is generally linked with the continental heat low (see Fig. 4a) which has affected the western parts of Southern Africa. However, during this time a mid-tropospheric deepened (rather than normal) trough (see the anomalies in Fig. 5c) has been shaped over the southern Atlantic Ocean which has caused some scatter instabilities over the southwestern marine areas during this time. Also, low and mid-tropospheric geopotential height maps analysis including 850 hPa and 700 hPa geopotential height (figures are not presented here) along with 500 hPa geopotential height structures indicate the stacked state of the omega blocking high corresponding with an abnormal warm air column (see Fig. 6) over the study area during the recent hot weather. Overall, an omega-blocking high system can disturb synoptic scale constructions from stable prevailing westerly development and result in irregular atmospheric circumstances during the blocking high event, and can form extreme weather circumstances such as heatwaves. Heat waves may almost be formed in connection with strong slow-moving or omega-blocking anticyclonic weather systems (Dole et al. 2011; Schubert 2011; Davini 2014; Brunner et al. 2017; Li et al. 2019; Li et al. 2020a, b; Bozkurt et al. 2022).

Fig. 5
figure 5

Composite mean for 500 hPa geopotential height (a), climate normal (b), and anomaly map (c) departure 1991–2020 (rather than normal) during 10–18 January 2023. The color bar shows pressure in gpm. In Figures, the horizontal and vertical axes indicate longitudes and latitudes in Degrees respectively

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Fig. 6
figure 6

Air temperature at surface (a), 850 hPa (b), 700 hPa (c), and 500 hPa (d) over the study area during the time period of heatwave that occurred during 10–18 January 2023. In Figures, the horizontal and vertical axes indicate longitudes and latitudes in Degrees respectively

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Figure 7a shows outgoing longwave radiation (OLR) anomalies (Wm−2) over the study area during the recent hot weather event. The figure shows abnormal positive values over South Africa mostly in the north and eastern areas with a maximum value of more than 40 Wattsm-2 which can imply a rather clear sky with diminished cloudiness or precipitation. Figure 7b shows the Skew-T diagram for the point area of 29ºS, 31ºE (considered as an area with a high value of OLR) for a day of 14 January 2023. The vertical temperature profile over the selected point shows an inversion layer associated with stable weather conditions associated with low-level subsidence. Therefore, by consideration of the dry (see Fig. 8e and f) and almost cloud-free weather (Fig. 3b) might lead to receiving the maximum sunshine daily and become higher temperatures day by day. So, during surface nighttime cooling, the intensified temperature inversion (Fig. 6b) coexists with a lower tropospheric dry air mass and the surface anticyclone amplification. Outgoing longwave radiation (OLR) is thermal energy that corresponds to total upward radiation from the Earth-Atmosphere system to space. In general, clouds and greenhouse gases can block or absorb specific wavelengths of OLR and then radiate back to the Earth. This causes an increasing total heat in the atmosphere.

Fig. 7
figure 7

Outgoing longwave radiation (OLR) anomalies (a) over the study area during the abnormal recent abnormal higher temperature event (the period used for computing the climatology is 1991–2020) and (b) Skew-T for (29ºS, 31ºE) for a day of 14 Jan. 2023 during the study time. In Fig. 7a, the horizontal and vertical axes indicate longitudes and latitudes in Degrees respectively

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Fig. 8
figure 8

Height (pressure)-longitude-cross section of air temperature (a), its anomaly (b), vertical pressure velocity (c), its anomaly (d), and relative humidity (e) and its anomaly (f). The period used for computing the climatology is from 1991 to 2020. In Figure, the horizontal and vertical axes indicate longitudes in Degrees and height in hPa respectively

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Figure 8 shows the height (pressure)-longitude cross-section of air temperature (a), its anomaly (b), omega, vertical pressure velocity (c), its anomaly (d), and relative humidity (e) and its anomaly (f) respectively. This figure clearly shows a positive abnormal warm air atmospheric column up to 300 hPa with a maximum of 4 K around 24ºE at 850 hPa. It is also noted that abnormal rather colder upper tropospheric air has been dominated between 200–100 hPa which infers rather stable cold conditions around the level of a jet stream associated with subsidence. Also, the vertical structure of the omega displays abnormal positive values associated with strong subsidence mostly in the eastern regions. However, in the western longitudes in the study region, there is negative abnormal omega (Fig. 8d) which accompanies a very atmospheric dry air mass (see Fig. 8e and f). Existence of the dry and warm air in the west but with negative omega can form strong gale warm winds in the western areas but very stable and hot air in the eastern part of South Africa during the heatwave event.

3.3 Wind vectors analysis

3.3.1 Surface wind vectors analysis

Figure 9 shows the composite mean (a), climate mean(b), and anomaly (c) maps for the surface wind vectors during 10–18 January 2023 over the study area. Figure 9a displays an anti-cyclonic wind pattern over the southern African countries including southern Angola, Namibia, Botswana, and South Africa. This is mostly associated with the dry continental currents (not from humid flows from the Southern Atlantic Ocean and the Indian Ocean). Figure 9b clearly shows that the surface wind vectors should be easterlies or south easterlies coming from the Indian Ocean in the normal case. This can cause to transport the moisture to the study areas in the long-term normal case situation. However, during the hot weather event, the pattern has been changed to an abnormal anticyclonic circulation mostly coming from the northern land areas (Botswana and Namibia) bringing the hot and dry air mass to the study area. This is accompanied by an anomalous positive surface pressure and anticyclonic circulation, with the anomalous north-westerly flow coming from northern land areas, thus reducing sea–land moisture transport and mostly drying the atmospheric low-level air over land.

Fig. 9
figure 9

Composite mean (a), climate mean(b) and the anomaly (c) maps for the surface wind speed wind vectors

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3.3.2 Upper tropospheric wind vectors analysis

Figure 10 shows the composite mean upper tropospheric wind vectors at a level of 200 hPa (a), climate normal (b), and the anomaly map departure from climate mean (c) during the study period. This figure displays the domination of the tropical easterly jet with the maximum value at the core of ~ 15 m/s around 5º-10ºS. But it has meandered to the southwest (to 20ºS) and intensified in comparison with the climate normal structure (Figs. 10a and b). The pattern shows the flows have been curved to westerly at around 25ºS due to the Coriolis force. Also, the westerly jet with the two remarkable maximums in jet cores of 40 m/s at 35 ºS-40ºS (with a meander pattern) and 45 ºS (zonal pattern) has formed. This is very different when compared with the climate normal case (Fig. 10b). Both easterly and westerly jets have been intensified at around 15 m/s and 18 m/s respectively (Fig. 10c). However, the normal structure for both easterly and westerly jets is associated with a zonal pattern. Both composite mean and anomaly patterns clearly show an anticyclonic (southern hemisphere) inflow wind pattern over the study area associated with an omega-blocking high. So, strengthener both easterly jet and westerly jets along with upper level (200 mb) confluent along with rather an upper level (200 mb) cold and dry air (Fig. 8a and e) causing strong subsidence and downward motion, particularly in the eastern areas. Therefore, it can be expected to increase the air temperatures by descending to lower-level temperatures (heatwave). When the westerly jet menders and shifts into a wavy pattern, such as Rossby waves. So, the warm air blocks the wave peaks, and cold air traps the troughs. Then, with longer waves along with higher amplitudes, the westerlies slant to move extra slowly and make the weather conditions to be more constant. Also, since the Rossby waves are easterlies, they create to shift in the low-pressure and high-pressure weather systems that delay them and so, forming a rather long time for the persistence of the heatwaves, droughts, or floods over the areas.

Fig. 10
figure 10

Composite mean upper tropospheric wind vectors at a level of 200 hPa (a), the climate normal (b), and anomaly departure climate mean (c) during the study period

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3.3.3 Zonal and meridional wind

Figure 11 shows the weakening of the upper tropospheric zonal flows (a) and an increase in meridional winds (b) in the upper troposphere during the study period. Negative zonal wind anomalies are associated with less westerlies and the positive and negative meridional wind stretching anomalies correlate to show as a signal forming of the Rossby wave meridional stretching. The Rossby waves turn into being stretched meridionally, permitting the wave break for the preservation of the blocking high omega system (Masato et al. 2011) during extreme events, for example, extreme heat weather events.

Fig. 11
figure 11

200 hPa zonal winds (a) and meridional winds (b) anomalies during the heat wave of Jan. 2023 event

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4 Discussion

Here, in this section, we shall discuss and compare the earlier heat waves that occurred in South Africa (especially during the years 2016 and 2022), in contrast to the current described in this study. The general dynamical characteristics of the South African heatwaves on 1–9 January 2016 are briefly investigated and a comparison based on the synoptic and dynamical analysis will be presented with the most recent hot weather during January 2023.

4.1 Heat wave of January 2016

From the last days of December 2015 and the first 9 days of January 2016 (within the strong ENSO signals), South Africa experienced the highest number and long-lasting heat wave from 1981 to2019 along with drought season (Mbokodo et al. 2023). Also, it is remarkable that during late 2015 and early 2016, Southern Africa was accorded the peak of a strong El Niño in the Pacific Ocean. This significantly affected on the global rainfall patterns both temporally and spatially over Southern Africa (Rembold et al. 2016). Also, a recent study shows that the El Nino Southern Oscillation (ENSO) is one of the major climate factors associated with different heatwaves over the southern African area (Meque et al. 2022).

The results from output climate models indicate a consistent increase in austral summer (December–February) temperatures in southern hemisphere along with an increase in drier frequency in comparison with climate normal (Lyon 2009). However, in the coastal areas with large populations, the local wind trajectories perform an important role in either preventing heat waves in South Africa with less heat wave–drought relationship (Lyon 2009). It is noted that the most fatal heat waves both physical (heat) and physiological problems (health) are not due to the high temperatures but are also the consequence of an increase in humidity (e.g. Guigma et al. 2020; de Freitas and Grigorieva 2015a, b, 2017; Evan et al. 2014; Steadman 1979a, b; Macpherson 1962). The hot and humid conditions (i.e., wet heat waves) specifically in the coastal areas can be more hazardous in comparison with the same equivalently hot case but with dry conditions (Wehner et al. 2017a, b). So, in addition to temperature, also humidity, wind, and incident radiation can act as important roles in happening and facing heat waves (Largeron et al. 2020; Li et al. 2020a, b; Sherwood 2018; Russo et al. 2017, 2016). Also, recent studies show that the East and South African regions experienced the highest number of heat wave days with the importance of humidity shown by most indices during 2016 (Engdaw 2020). Here, the following investigation has been presented to understand the weather structure related to the South African unique heat wave during January 2016.

The mean sea level pressure map (the figure is not presented here) displays the formation of an omega high-pressure system by the passage of a nearly flat isobaric ridge of 1016 hPa (1 hPa weaker than case 2023) over eastern South Africa. The middle tropospheric map at 500 hPa shows the persistence of a larger extended (in comparison with case 2023) Botswana subtropical high with same value of 5920.5 gpm over the study area. Also, the zonal flows are formed over the southwestern areas linked with more stable weather conditions in comparison with the case of 2023. This can be correlated with a rather strengthen stronger heat-low system that affected the study area during the strongest heatwave of 2016 triggering more drought seasons. During the case of 2023, the southwestern areas were affected by a rather slightly curved heat-low trough (see Fig. 4a). Also, the 850 hPa air temperature map (the figure is not presented here) clearly shows the more extended closed warm air area of 302.5 K (in comparison with case 2023) between the southern part of Botswana and Namibia. So, the general synoptical patterns for the same low-level and mid-level tropospheric parameters are the same. However, the synoptic analysis indicates a stronger heat-low system with larger expansion along with widened warm air in the case of the heat wave of 2016 in comparison to the case of 2023. Figure 12 shows the composite mean upper tropospheric wind vectors at a level of 200 hPa (a), the anomaly departure climate mean (b), and the climate normal (c) during the heatwave of 2016. This figure displays the southward shifting to the latitudes around 10ºS over Guinea (Fig. 11a) of the subtropical westerly jet where it has been intensified at ~ 28.5 m/s rather than climate mean (Fig. 11b). Also, the southern westerly jet has located at 45ºS with no change in position with the climate normal pattern (Fig. 12c). However, it has been strengthened at around 15 m/s in the core of the jet (Fig. 12b). It should be noted that the normal localized tropical south hemispheric easterlies (see Fig. 12c) are positioned at around 5ºS and have been diminished remarkably (see Fig. 12b). In the boundary of the South Atlantic Ocean with Namibia to the southwest of South Africa, the north westerlies from 10 ºS to 45 ºS have been intensified around 15 m/s with warmer air along with warmer water into the southern latitudes (figures are not presented here). A comparison of Figs. 10a and 11a shows the northward shift position (second core between 35–40 ºS) causing rather a moderate heat-increasing effect in the recent heatwave case of 2023 in comparison with the extreme heat wave of 2016. Also, by comparing Figs. 12 and 10, the high impact of the larger scale circulation (two hemispheric abnormal changes i.e., the northern sub-tropical jet impact) related to the case of 2016 and more internal dynamical changes linking to the more recent case.

Fig. 12
figure 12

Composite mean upper tropospheric wind vectors at a level of 200 hPa (a), the anomaly departure climate mean (b), and the climate normal (c) during the heatwave of 2016

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A comparison of the heat wave of January 2016 and the recent hot weather event in January 2023 shows relatively more southern hemispheric higher surface air temperatures for the year 2016 rather than the year 2023 (more localized). Also, the surface humidity analysis displays more moisture ~ 5–10% rather than the climate normal for the cases in 2016 associated with warmer moisture advected from the ocean to the study area. However, for the recent case of 2023, the surface is drier approximately from -5 to -12.5% rather than its climate normal values for most parts of South Africa.

4.2 Expanded heatwave comparison and synoptic conditions

As shown in Fig. 1, in addition to two recent January heat events (2016 and 2023), there were also revealed seven events where long-term temperature deviations exceeded 0.5 K (see Fig. 1). Here, the pressure fields (mean sea level pressure) where long-term temperature deviations exceeded 0.5 K are presented in Fig. 1 (from left to right respectively). It includes the monthly mean for the recent heat events (2016 and 2023) which were only analyzed in detail in the period 10–18 January 2023 and 1–9 January 2016 in previous sections (not the entire month of January). Figure 13 specifically reveals the presence of the Omega blocking pattern (noted with red vertical stretched circles) in all cases in January in the years 1983, 1988, 1992, 1993,1995, 2003, 2007, 2012, 2015, 2016, and 2018, 2019 along with the recent January monthly mean pressure in 2023 (Figs. 13a–m). This result determines that an Omega blocking pattern (noted with “H" in figures) can be a potential parameter for addressing the occurrence of heatwaves in the South African region.

Fig. 13
figure 13

Composite mean of sea level pressure during January of 1983, 1988, 1992, 1993,1995, 2003, 2007, 2012, 2015, 2016, and 2018 and 2023 from left to right and up to down respectively

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5 Summary and concluding remarks

This work studied the synoptic and dynamic characteristics of the more recent hot extreme weather during January 2023 in South Africa. This work has analyzed various atmospheric and meteorological parameters using different reanalysis models and observational data to understand the basis of the atmospheric circulation in mid and upper atmospheric in the study area. The composite mean and anomaly features for the surface and upper tropospheric atmospheric parameters have shown the domination of the anticyclonic pattern over the study area associated with an omega-blocking high. A strong omega-blocking high-pressure has been derived by warm and very dry air into the study region. The blocked system was formed and stayed over the study area for some time more than two weeks from 3 to late Jan 2023. It has been found that the dominant subtropical Botswana subtropical high along with the low-level omega blocking high pressure over South Africa was one of the main factors for the abnormally hot weather event. During the hot weather event, a hot and dry atmospheric column stayed over the study area too. Also, this has been correlated to abnormal both tropical easterly and southern westerly jets meandering around an omega-blocking pattern weather system over South Africa which causes warm air mass trapping over the study region. In addition, the characteristic of the upper-level atmospheric circulation over the study area has displayed a relationship with the Rossby wave anomalies. The upper-level anomaly wind vector analysis has demonstrated that the deterioration of the zonal wind comes with the Rossby wave meridionally stretching. The strengthening of both easterly jet and westerly jets along with upper level (200mb) confluent and with rather an upper level (200mb) cold and dry air caused strong subsidence and downward motion, particularly in the eastern areas. The outcome NCEP reanalysis model results have proven the anomalies of the surface higher temperature has happened close to the center of the blocking high, where an intensified southward shift of the easterly tropical jet along with the northward shift (jet entrance) of an intensified westerly jet has formed two strong cores forming confluent. This has caused air motions to descendant and then with the decrease of the cloudiness, all simultaneously have contributed to abnormally warm surface temperatures over the study area.

This research has also shown that the January hot weather case is demonstrated by an anomalous upper tropospheric anticyclonic inflow causing the strong subsidence, resulting in a surface temperature increase. Therefore, by a consequence of the dry and almost cloud-free weather it receive the maximum sunshine daily and turns into higher temperatures day by day. Then by the surface nighttime cooling, the intensified temperature inversion cooperates with a lower tropospheric dry air mass and the surface anticyclone amplification.

This study has indicated that the recent South African heatwave occurred mostly due to internal atmospheric dynamical procedures which shaped and continued with a strong and extensive omega-blocking high event like similar atmospheric patterns that happened in the heat wave of 2016 both in this region and other areas. The more recent case seems to be in a drier condition with less health risk rather than the other one.The pressure fields (mean sea level pressure) where long-term maximum air temperature anomaly exceeded 0.5 K were presented in this work. This research specifically revealed the presence of the Omega blocking high pattern in all these cases and could be a potential parameter for the occurrence of heatwaves in the South African region. It is remarkable that evidence analogies compared to other temporal cases and other areas show that the formation of the omega-blocking high is a potential parameter for heatwave occurrence elsewhere. For example, a recent study (Demirtaş 2017) showed that the June-July–August (JJA) of 2000, 2007, and 2010 heat wave events over the Balkan Peninsula and in Turkey were related to the blocking anticyclones. Also, during the blocking events in the summer of 2010 associated with abnormal weather conditions, extremely hot, and dry in Europe was observed (Hafez 2020). Also, a more recent study revealed that the strong atmospheric blocking headed the Pacific Northwest heat wave in late June 2021 (Neal 2022).

Besides, it can be noted that the gradually variable boundary situations can give a capability for early warning to cooperate a significant function in this such events for the future if may happen. Also, more advanced technological experiences, such as more sophisticated weather and climate models and other techniques such as machine learning will assist in solving these relevant challenges.