The Recent Droughts of 2019/20 in Southern Africa and Its Teleconnection with ENSO Events ()
1. Introduction
ENSO events dramatically alter oceanic conditions, climate and weather patterns across the globe and significant correlations between large-scale regional precipitation patterns and ENSO episodes have been identified for several specific regions around the world (e.g., [1] - [6]. On a seasonal time-scale, the ENSO phenomenon affects the atmospheric circulation outside the tropics [7] [8]. South-eastern Africa tends to experience dry conditions during warm ENSO events [2] [6]. ENSO is an atmospheric phenomenon that has long been known to have a characteristic manifestation in Southern Africa whereby warm-phase episodes are associated with droughts while cold-phase episodes lead to wetter than normal conditions. Mechanisms linking above-normal sea-surface temperature (SST) anomalies over the central Indian Ocean with Southern African droughts have also been explored by Jury and Pathack [9] [10]; Landman [11]; Jury et al., [12]; Tennant [13]; Landman and Klopper [14]; Rautenbach [15].
The easterly and westerly shifts of ENSO activity in the Walker circulation induce warming and cooling in remote ocean basins such as the Indian Ocean and the tropical north Atlantic, with a time lag of one to two seasons [16], with ENSO events recurring in periodical patterns. ENSO can also affect regions outside the tropics, via large-scale atmospheric waves [17]. The influence of ENSO events is profoundly felt outside the tropics as well including southern Africa as noted in recent literature on evolution of southern African summer precipitation as noted by Monerie et al. [18] Hoell et al. [19] [20]; on ENSO teleconnections e.g. Jury [2], Archer et al. [21] Winsemius et al. [22] Alemaw & Chaoka [23] and seasonal rainfall predictions as noted by Rautenbach [15] Landman & Klopper [14].
The ENSO phenomenon is one of the biggest players in the game of year-to-year climatic variability. As many researchers have now come to appreciate, these two phenomena typically occur in conjunction, about once every few years. A predictive model for the December to March rainfall season simulation for South Africa that considers ENSO influence in a canonical correlation analysis is provided by [14] Landman and Klopper (1998). Due to the heterogeneous nature of rainfall, a large number of measurement stations are required for accurate characterization of rainfall patterns over large areas. Furthermore, understanding large-scale global atmospheric dynamics will enhance our understanding of regional and local systems of rainfall occurrence, which could improve understanding of precipitation characteristics. Moreover, tropical and extra-tropical atmospheric teleconnections of ENSO have an influence on extreme hydrological phenomena such as the intensity and occurrence of extreme events such as storms, heatwaves, droughts and floods as noted by Yeh et al. [24] and Cai et al. [16].
Effect of ENSO on rainfall over large areas can be felt as integrators of river systems and the ability to predict flow patterns in rivers will be highly enhanced if the relationship between precipitation and ENSO is predicted (Alemaw and Chaoka) [23]. In earlier studies, an attempt was made to establish the relationship between ENSO and the natural variability in the flow of tropical rivers such as Amazon, Congo, Parana and Nile Rivers as noted [25]. These authors reported the existence of a stronger correlation between the seasonal and annual precipitations of these rivers and seasonal ENSO indices.
A prevalence of below-normal rainfall occurrences in several regions of South Africa during El Niño years has been reported (e.g. [14] [15] ). Recent studies indicate that ENSO events can be accurately predicted one to two years in advance using a physical model of the coupled ocean-atmospheric system [26], and lately in Atmosphere-Ocean General Circulation Models (AOGCM) under the Climate Model Inter-comparison Project, phase 5 (CMIP5) [1] [27].
Therefore, the motivation of this paper is to explore whether a relationship exists between precipitation and ENSO and to quantify the relationship in the Southern African rainfall drought regimes that occurred during ENSO events. Emphasis is given to the drier ENSO events to examine the possible link with the decline in rainfall regimes in this region in the recent drought period of 2019/20 and those between 2005 and 2019/20.
In this paper, the relationship between seasonal quarter warm ENSO events in terms of the SLP anomalies data and 1 degree resolution precipitation data of southern Africa has been retrieved from the global gridded data base of the Global Precipitation Climatology Centre (GPCC) [28]. The monthly datasets were converted to seasonal and annual ensembles for each 1deg grid covering the region of southern Africa. The seasonal ensembles were the quarterly ENSO indices of the three-month aggregate mean values of December to February (DJF), March to May (MAM), June to August (JJA) and September to November (SON).
The global gridded precipitation datasets are produced by methods developed to produce real-time rain gauge-satellite merged analyses of global monthly precipitation as noted in [29]. The datasets contain global monthly precipitation analyses that are composed of a mix of rain gauge observations and satellite estimates. These are based on the Global Precipitation Climatology Project (GPCP) combined analysis products described by [29] and the Climate Prediction Center Merged Analysis of Precipitation (CMAP) presented in [30].
The objective of this manuscript is to document results of correlation and spatial analysis specifically aimed at investigating the type and magnitude of possible correlations between warm ENSO activity and the regional implications of the seasonal and annual precipitation over the southern African region. Possible explanations are also presented for the possible link between the long-term ENSO patterns and trends in annual precipitation pattern and the spatial coherence of the ENSO-precipitation correlation over the region explained in terms of confidence interval of the prevailing rainfall-ENSO correlation coefficients.
2. Data Used
Gridded datasets at a spatial scale of 1 degree were extracted for the region of southern Africa from global database of the Global Precipitation Climatology Centre (GPCC) [28]. The GPCC dataset from 2005 to February 2020 was extracted to investigate the teleconnection between the rainfall in the region of southern Africa and the most recent ENSO activity that occurred from early 1950s to date. The annual precipitation of the study region for 2019/2020, resampled from the GPCC dataset using the nearest neighborhood method is presented in Figure 1.
To assess the effect of ENSO phenomenon on the rainfall regime of the region with the concurrent period of the ENSO data available from NOAA that extends as early as 1951 (Figure 2), rain-gauge data for stations nearby selected grid points
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Figure 1. Southern Africa variation of 2019/2020 Annual Rainfall and selected rainfall Stations from FRIEND data base. Data source: [31].
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Figure 2. Time series plots of aggregated 3-month ENSO mean sea-level pressure (SLP) anomalies (solid bars) and three month moving averages (dashed lines) for January 1951 to February 2020. Data source: [32].
in the region are also further explored. The Flow Regimes from International Experiments and Network Data (FRIEND) data stations are also shown in Figure 1. Precipitation time series data of nine gauging stations from southern African countries were acquired from the database of FRIEND Project coordinated at the University of Dar es Salaam [31]. Quality of daily and monthly precipitation discharge data was checked and then annual precipitation time series data was extracted to complement the GPCC gridded dataset rainfall data of the study region. Details of the rainfall data used are summarized in Table 1.
Even though the period of available record is not sufficiently long enough, the authors thrust that the data set is fairly sufficient to draw some conclusions regarding spatial variability of precipitation in the region and its possible correlation with ENSO phenomena. It is also fairly speculated that the data can be used to build on the similar studies conducted in the regional focusing to some extent on few countries and sub-regions of the region of southern Africa.
Monthly dataset of SLP anomalies are acquired from database of the NOAA and are acquired from the Climate Prediction Centre (CPC) official web site [32]. Figure 2 depicts the monthly mean Southern Oscillation Index in terms of sea-level pressure (SLP) anomalies expressed in standard units for the period from January 1951 to February 2020 expressed in standard units, which is a measure of the pressure difference between the central (Tahiti) and western Pacific (Darwin).
Coincident periods for both ENSO events and precipitation were selected and analyzed. Of the 24 El Niño events that have occurred in the century just ended, about 13 of the strongest historical El Niño events have occurred between 1950s and early 2000s, as documented by different authors for the various periods [7] and six additional ENSO events have occurred between 2000 and 20019/20. The prominent ENSO/El-Nino years are from December to January of 1951/52, 1953/54, 1957/58, 1963/64, 1965/66, 1969/70, 1972/73, 1977/78, 1982/83, 1987/88, 1991/92, 1994/95, 1997/98, 2002/03, 2004/05, 2006/07, 2009/10, 2015/16 and 2019/20.
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Table 1. Details of data used in the study.
These El Niño years are similar to those defined by other researchers [5] [21]. The selected monthly historical ENSO periods were each split into 4 consecutive three-month quarters of December to February (DJF), March to May (MAM), June to August (JJA) and September to November (SON). The main reason for creating different subsets was to better capture the influence of ENSO on the rainfall as a proxy for the occurrence and variability of precipitation in the region of southern Africa.
3. Methodology
3.1. Correlation Analysis
The methodology used in this study comprised correlation analysis of the mean annual runoff and three-month quarter warm ENSO indices during the concurrent El Niño years considered. The hydrological year or seasonal cycle defined as a 12-month period starting from the month of lowest average rainfall for each year was selected. For all the 1 degree grids covering southern Africa, the seasonal cycle of flow and the annual flow depths are determined. The type and magnitude of the correlation are also investigated. We have also attempted to explore the possible evidence of a link between the long-term ENSO patterns and trends in seasonal and annual runoff pattern across the region of Southern Africa.
The variance accounted for by El Niño in the natural variability of precipitation of selected locations in Southern Africa is established. In order to avoid possible lag correlation between seasonal precipitation and seasonal SLPs, a correlation analysis is conducted with the annual discharge, against eight quarters of SOI in terms of SLP anomalies. Eight quarters are formed from four ENSO/SOI quarters during El Niño years, and two quarters each before and after El Niño years.
We measured the association between ENSO and precipitation using the linear correlation coefficient, r (also called the product-moment correlation coefficient, or Pearson’s r). In order to assess whether a correlation is significant, we use the test statistic, t, to identify the confidence level of the correlations between ENSO and the terrestrial hydro-climatological variable of precipitation. The test statistic, t, is defined by [33] as:
(1)
which is distributed in the null hypothesis case (of no correlation) like the student’s t-distribution denoted by A(t/v) with v = N − 2 degrees of freedom. 1 − A(t/v) is the significance level at which the hypothesis that the means are equal is disproved. The mathematical definition of the function is given by:
(2)
with lower and upper limiting values, respectively given by:
and
(3)
A(t/v) is related to the incomplete beta function Ix(a; b) by:
(4)
The confidence level, γ for the t-statistic t(r, v) is then computed as given in [34] and further noted of its application in [23] as follows:
(5)
3.2. Sample Size Uncertainties
Sample size uncertainties and the Pearson correlation model capabilities are investigated to determine the contribution of internal ENSO variability on the rainfall trend and drought regime in the region [33] [34]. This is particularly true when the recent period ENSO events from 2005 to early 2020 only capture about 5 cycles that relate to lower rainfall depths obtained from the corresponding period of GPCC gridded rainfall data of the region of southern Africa.
The number of samples used to quantify the linkage between ENSO and precipitation in terms of the Pearson correlation model capabilities would affect degree of robustness of the model (Figure 3) [33] [34]. By combining Equations (1) and (5), the confidence interval, β = (1 − γ)100% is computed (see Figure 3 for the sample size of 5, which is the number of datasets attributed to the warm ENSO events that occurred during the period of analysis of date set from 2005 to February 2020). A confidence level γ indicates that (1 − γ)100% of the variability is attributable to random variability. FORTRAN routines provided in Numerical Recipes [34] are used to compute the confidence intervals. Before applying the test, we normalised the data and also demonstrated the possible loss of power of the t-test as a result of the small sample size considered as shown in Figure 3 for
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Figure 3. Relation of the correlation coefficients and the confidence intervals used in the study.
different sample sizes ranging between 5 to 15 samples. It can be noted that for correlation coefficient of 0.5 the confidence interval in the estimate of r = 0.5 would increase from 60% to nearly 85% as sample size increase from 5 to 15 sample sizes.
4. Results and Discussion
4.1. Correlation Coefficients and Confidence Intervals
A correlation coefficient between seasonal warm ENSO indices and annual of a typical station in western Botswana bordering SE Namibia and NW South Africa over 8 consecutive quarters is shown in Table 2. It shows that the mean annual precipitation is positively correlated with the four quarter ENSO periods observed during the El Niño years. Amongst the 8 quarters, a strong positive correlation (r > 0.52) seems to be apparent in the rainy season DJF and MAM quarters. The confidence level of the correlation coefficient for these quarters is above 95%. The positive correlation diminishes for the other seasonal quarters, and the correlation becomes negative in the quarters that precede and follow the El Niño years. For this reason, we studied the correlation between the DJF quarter warm ENSO indices and annual and concurrent seasonal precipitation of the region.
Table 2 and Table 3 show the correlation coefficients and confidence interval between the seasonal and annual rainfall at selected sites across the Southern African region and the three-month mean warm ENSO indices of December to February (DJF). The computations were made based on two datasets that represent the period before and after 2005 over a span of early 1960s to early 2000s.
The link between seasonal rainfall in the region of Southern Africa and ENSO of December to February (DJF) in terms of Pearson correlation coefficient and
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Table 2. Coefficients of cross-correlation between quarterly ENSO anomalies and mean annual precipitation during El Niño years of a station located in S Namibia and NW South Africa.
Key: [E]—El Niño year; [−]—Quarters preceding El Niño year; [+]—Quarters following El Niño year.
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Table 3. Correlation coefficient and confidence intervals based on based on gridded GPCC monthly rainfall data of 2005-2020.
the confidence interval is shown in Figure 4. The corresponding correlation coefficients and the confidence interval between annual rainfall in the region of Southern Africa and quarterly ENSO of December to February (DJF) is presented in Figure 5. There is a marked similarity between these two figures, which is a clear indication that DJF quarter is a rainy season whose rainfall represents substantial proportion of the annual rainfall amount in the region.
4.2. Annual and Seasonal Rainfall with ENSO Activity
Comparative investigations have been conducted to further investigate the association between annual rainfall and annual ENSO activity over annual time cycle presented in Table 4. It can be seen that this figure is still very much closer to the previous two figures highlighting the strong correlation between rainfall and ENSO activity, with a more pronounced effect observed during ENSO quarterly season of DJF.
Regional summary of correlation coefficients and confidence intervals of Seasonal and annual rainfall with DJF ENSO indices is provided in Table 5, which summarizes the correlation coefficient for the region of southern Africa in terms of percentage area distribution of the Pearson coefficient as well as confidence intervals.
4.3. Regional Rainfall and ENSO Activity
Of the entire 1 degree gridded area of southern Africa, 40.5%, 32.6% and 26.9% of the region has Pearson correlation coefficient with ranges of 0.25 to 1.0; −0.25 to 0.25 and −0.25 to −1.0 between the seasonal rainfall and ENSO during the quarters of December to February (DJF). The same corresponding correlation coefficient ranges in the region between annual rainfall and ENSO is found to be 41.2%, 33.0% and 26.0%, respectively.
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Figure 4. The link between seasonal rainfall and ENSO of December to February (DJF) in the region of Southern Africa (a) Correlation coefficient; (b) Confidence Interval in percent.
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Figure 5. The link between annual rainfall and ENSO of December to February (DJF) in the region of Southern Africa (a) Correlation coefficient; (b) Confidence Interval in percent.
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Table 4. Correlation coefficient and confidence intervals based on FRIEND monthly data of 1961 to early 2000s.
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Table 5. Regional summary of correlation coefficients and confidence intervals of Seasonal and annual rainfall with DJF ENSO anomaly indices.
Considering the entire southern African region, 40.5% of the surface area exhibit strong positive correlation in terms of the December to February quarter rainfall and ENSO indices, including 17.4% of the area attributed to positive but weak correlation.
The confidence interval of the correlation coefficients between seasonal rainfall ENSO anomalies during DJF quarters is 74% whereas, between annual precipitation and DJF quarter ENSO indices reaches 84%, signifying substantial variance of precipitation during ENSO years is accounted for by the warm ENSO activity.
Strong positive correlation with ENSO activity is found to cover southern Zimbabwe, area around south eastern Namibia/North Western South Africa and southern Botswana/northern South Africa as well as North Zambia/Southern DRC. Most of these areas in the region represent areas with lower rainfall regime in general with lower water security, with implications of water scarcity which were hard-hit with water shortages in 2019 and early 2020.
Occurrence of below-normal rainfall amounts from December to March during El Niño years and warm ENSO positive association with drier rainfall conditions have been noted in South Africa in several studies [2] [14] [21] [22].
In particular, the results of the regression analysis (Figure 4) confirm a weak yet statistically significant association between ENSO and the latitudinal position of the Angola low as noted in [35]. This also agrees with the conclusions in [35] [36], specifically for January to March season of 1998, which further supports the notion that internal atmospheric extratropical variability is partly responsible of the Angola low low-frequency variability.
4.4. Recent Drought of 2019/20
Annual rainfall variation at a station located in an area centered on grid 25.50˚E/25.50˚S in southern Botswana and northern South Africa is shown in Figure 6. It clearly shows the 2019/20 rainfall signifies very low rainfall and drought that occurred in the region. Below-average production and drought impacts are felt in larger and some pockets of various countries in southern Africa, including but not limited to Angola, Botswana, Lesotho, Namibia, Zambia, Zimbabwe, South Africa and Mozambique. The areas identified in this study with below rainfall regimes associated with warm ENSO events are similar to recent observation and model simulation of North American Multi-Model Ensemble (NMME) probabilistic forecast for precipitation for January to March 2020 based on NOAA/CPC, which is widely used for forecasting outlook assessment of drought impacts by FEWSnet [37].
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Figure 6. Annual rainfall variation showing ENSO/La Nina years at a station located in an area centered on grid 25.50˚E/25.50˚S in southern Botswana and northern South Africa.
A number of climate indicators are noted to have changed in the year up to end of 1990s, especially around the Pacific Basin. As can be seen on Figure 2, before end of 1990s El Niño seems to have occurred with about equal frequency, each at intervals of about 3 to 7 years, and between 1960 and 1990 alone, there have been about 7 El Niños (almost one every 4 years). Figure 6 depicts standardized annual rainfall variation showing ENSO/La Nina years at a station located in an area centered on grid 25.50˚E/25.50˚S in southern Botswana and northern South Africa.
Below normal in the annual rainfall of 2019/20 in southern Africa, calculated from global gridded data base of the Global Precipitation Climatology Centre (GPCC) provided by Ziese et al. [28] is depicted in Figure 1. Below normal rainfall are recorded in southern Africa and higher rainfalls are recorded in equatorial parts of Southern Africa especially in the Congo River basin.
This analysis of the long-term trend of the El Niño phenomenon over the period 1950 to early 2020, depicted in Figure 2 and Figure 6, supports the findings of decline in precipitation in Southern Africa shown in Table 4 and Figures 4-6 and corroborated in other studies, e.g. [1] [19] [20] [38] [39].
The studies demonstrate the occurrence of low seasonal rainfall totals together with high seasonal temperatures during preceding seasons of El Nino/ENSO years are likely to be more frequent under future climate change as noted in Archer et al. [21] and [22]. Also the studies and the reported results also highlight the potential importance of the ENSO activity for southern African rainfall [36] [40] [41] [42]. ENSO events are also found to be an important phenomenon in modulating the skills of regional climate and CMIP5 models as illustrated by Dieppois et al. [1] and Taylor et al. [27].
Recent studies by Monerie et al. [18], Archer et al. [21] and Winsemius et al. [22] corroborate occurrence of low seasonal rainfall totals together with high seasonal temperatures during preceding seasons during El Nino/ENSO years are likely to be more frequent under future climate change.
Predicting climate for future seasons to several decades is useful for decision makers to adapt policies to near-term climate change [43]. The effect of low precipitations would be a potential revelation for decline of runoff of some rivers in southern Africa which would determine water resources availability for societal demands as noted by Alemaw and Chaoka [23].
The need to anticipate damages due to climate variability is a stressful problem, especially in developing countries, which are more vulnerable to climate hazards. Climate projections are mostly provided by simulations performed with Atmosphere-Ocean General Circulation Models (AOGCM) under the Climate Model Intercomparison Project, phase 5 (CMIP5) [1] [18] [27].
The ability of CMIP5 models to simulate realistic summer rainfall variability of rainfall over southern African and noted the summer teleconnections with global SSTs from extended reconstructed SST version 4 (ERSST.v4) dataset of the National Climatic Data Centre is examined by Dieppois et al. [1]. The modelling study through various models demonstrated the impact of SST variability in the South Atlantic and South Indian Oceans on rainfall variability in southern Africa. With various degrees of association between SST and SA rainfall is also reported and documented for different time scales and time horizons.
These precipitation forecast products have been applied for forecasting and operational applications including the following: climate modelling [1] [21] [41]; precipitation extremes [38]; and regional agriculture and vulnerability assessment [37] [44]. Other applications also include rainfall and river flow analysis [5] [23]; real-time precipitation monitoring [43] [45]; fisheries and aquaculture [17]; and study of greenhouse gas forcing [24].
All together these results also corroborate the potential importance of the ENSO activity for southern African rainfall, as suggested in earlier studies in the region including [36] Nicholson and Kim [36], and others [19] [20] [38] [39] [40] [41] [42]; and ENSO events in modulating the skills of CMIP5 models reported by Dieppois et al. [1] and Taylor et al. [27].
5. Conclusions
This study revealed that a strong positive correlation exists between the 1951-2019/20 warm ENSO events (explained by the Southern Oscillation Index or Sea Level Pressure anomalies) and the annual and seasonal rainfall in the Southern African region. We have also found during the El Niño years that a significant variance of the annual flow regime is accounted for by the El Niño phenomenon. The correlation is moderately and strong positive strong (r > 0.25) in 41% of the surface area of the region during the rainy season of December to February (DJF).
Above 50% confidence interval in the correlation between seasonal rainfall and ENSO during DJF quarters is found in 74% of the surface area of the region and that of the annual rainfall and seasonal ENSO during DJF quarters is found in 84% of the surface area of the region of southern Africa.
It signifies the possible year-to-year variability of seasonal and annual precipitation during ENSO years is accounted for by the ENSO phenomena. More specifically, the recent droughts of 2015/16 and 2019/20 can be attributed to the ENSO activity.
Furthermore, the occurrence of low seasonal rainfall totals together with high seasonal temperatures during preceding seasons during El Nino/ENSO years are likely to be more frequent under future climate change as noted in recent literature.
Acknowledgements
The author thanks NOAA for the ENSO time series data, as well as Global Precipitation Climatology Centre (GPCC) for providing the gridded 1 Degree data covering the period of study used in the study. The author also thanks the coordinating Centre for the Southern Africa FRIEND Project based at the University of Dar es Salaam, Tanzania for providing daily rainfall data of Southern African stations used in this study as well previous data use in the 1990s for undertaking the PhD research of the author. The author also appreciates the constructive comments and suggestions of the anonymous reviewers of earlier versions of this paper.