Global climate change is likely to lead to significant changes across the southern African biomes, and seasonal rainfall will have implication on the ecosystems services they provide through the alteration of existing habitats, organism extinctions, water scarcity, and biodiversity and vegetation loss (Chaps. 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 and 23). Southern Africa is anticipated to become hotter and dryer and discussed in detail in (Chaps. 6 and 7).

Extreme weather events together with anthropogenic activities might threaten the sustainability of Southern African ecosystem affecting species distribution through shifting habitat, changing the migration patterns, geographic range, emerging alien species and changing organisms' seasonal activity by altering life cycles of many terrestrial and marine species (Chaps. 14 and 26; UNEP 2012).

Projected increases in the likelihood of floods suggest a possibility of changes to the flow regimes in rivers (Dudgeon et al. 2006). Groundwater, which is critical to maintaining "low flows" and aquatic habitats during the drier periods, is likely to be impacted by changes in recharge rates due to increases in floods and droughts. Changes in flow regimes may affect channel geomorphology, longitudinal and lateral connectivity, aquatic habitat and biotic composition. Systematic quantification of loss and damage to ecological and coastal infrastructure around the riverbanks and coastal regions in Southern Africa could significantly inform estimation of adaptation needs and hence bolster attainment of SDG 15.

Regardless of which Shared Socioeconomic Pathway is used for predicting climate futures, current best estimates are that the southern Africa regions are likely to experience increased drought relative to 1850–1900 (IPCC 2021). Hydrological impacts from increased drought include reduced stream flow, resulting in degreased water storage in dams (Forzieri et al. 2014; Trambauer et al. 2014), long-term declines in rainfall (Rahman et al. 2015; Kruger and Nxumalo 2017), increased evaporation from water bodies and increased plant transpiration (Meybeck 2003). The reductions in stream flow and storage will be accompanied by climatically induced increases in the demand for water in environments such as agriculture (especially in terms of irrigation) and, to a lesser extent, power generation (Brown et al. 2013).

The Southern African region is considered to be water stressed, with South Africa and Namibia being the worst affected. In South Africa, more than half of the country's water management areas are in deficit (Alcamo and Henrichs 2002). Southern Africa has also been identified as a region characterized by a relatively high degree of flow alteration caused by the construction of dams (Döll et al. 2009). A review of studies on the impacts of climate change on stream flow found that reductions are projected for many basins in southern Africa including the Zambezi, Pungwe, Limpopo, Thukela, Okavango, Ruvhuma, Orange, Gwayi, Odzi and Sebakwe (Kusangaya et al. 2014). Given these patterns and the growing populations and economies in the region, the ability of aquatic ecosystems to provide ecosystem

services is considered under threat. A challenge to the management of water in the region is the transboundary nature of many of the river basins, with 12 such basins existing across the Southern African Development Community countries (Kusangaya et al. 2014).

In the southern African region, drought occurred simultaneously with heat waves and the combined contributes to crop losses (Engelbrecht and Scholes 2021), stresses on regional water supplies and to widespread livestock mortality (Sivakumar 2007). In addition, reduced rainfall and increased drought frequency could result in a reduction in forage quality and quantity, hence affecting the dynamics and ecosystem function for wildlife and the vegetation (Nangombe 2015).

Heat waves are defined as warm extreme temperature events or excessively hot weather (Nairn and Fawcett 2013) that have socioeconomic and ecological impacts.  The region experiences increased frequency of fires due to drastic increases in heat waves events (Engelbrecht et al. 2015; Garland et al. 2015; Mbokodo et al. 2020). Garland et al. (2015) reported warming over southern African region using the Conformal Cubic Atmospheric Model (CCAM) forced with the A2 emission scenario. Their model results suggested that extreme apparent temperature days in Africa are projected to increase in the future climate. This was in accord with Engelbrecht et al. (2015) who also projected substantial increases in the annual number of heat waves days over southern Africa. Moreover, a case study by Mbokodo et al. (2020) projected that there will be an increase in the number of hot extreme events in the most parts of the interior of South Africa throughout the year 2070–2099, while the number of cold events is decreasing. As a developing region (southern Africa), the substantial changes in the number of extreme temperature and heat wave events are a threat to a number of sectors including ecosystems, agriculture, water resources, energy demand and human health (Zuo et al. 2015).

For tropical and subtropical biomes, studies suggest that large landscapes in sub-Saharan Africa are prone to relatively fast shifts in vegetation structure and biodiversity due, in part, to shifts in fire regimes (Chap. 14, Lehmann et al. 2011; Bond and Midgley 2012; Moncrieff et al. 2014).

Foden et al. (2007) conducted a study on the distribution of *Aloe dichotoma*  and observed changes in species distributions based on ~100-year (1904–2002) observational records. This study provides evidence that the range of a Namib Desert tree is shifting poleward, with extinction along trailing edge exceeding colonization along leading edge. Similar impacts are anticipated for countless other species.

Decision by African policy makers and stakeholders toward the attainment of SDG 15 goals must consider making urgent choices relating to trade-offs between biodiversity, carbon sequestration capacity of biomes and their direct ecosystems service. Such a decision could benefit from mechanistic studies that consider important plant functional types, herbivory and climate feedbacks (Huntley et al.

2014). Investment in early warning systems could help curb some of the major losses on ecosystems, especially around ecosystems that host critically endangered species.

#### **3.7 An Analysis of SDGs and Ecosystem Threats**

Changes in the biophysical environment, including droughts, floods, water quantity and quality, and degrading ecosystems, are expected to affect opportunities for people to generate income thus altering the synergistic nature of the SDGs. Interactions between targets for SDG 14 and SDG 15 with other SDGs show that generally, there are more synergies between goals and targets than there are trade-offs. Cumulative impacts from direct and indirect (via climate change) human pressures on marine and coastal ecosystems are potentially large and require concerted action in attaining both SDG 14 and SDG 13 (Griggs et al. 2017). The pursuit of food (SDG 2) and energy (SDG 7) goals can cause significant trade-offs with other SDGs, especially water (SDG 6) and ecosystems (SDG 15) (Pham-Truffert et al. 2020). Examples of interactions between SDG 14 and SDG 15 with other SDGs are outlined in Table 3.2.

#### **3.7.1 Policy Implications for Ecosystem Protection and Restoration**

Implementation of the adaptation component of the global climate policy is intractably linked to progress the SDG 14 and 15. This chapter posits that adaptation to the impacts of climate change-induced threats, in the least and developing countries in Southern Africa would be constrained without improved access to global climate finance by both private and public institution actors. Adaptation support in a form of technologies and means of implementation, of the global climate policy, would therefore accelerate the two SDGs through: fast tracking of ecosystem restoration and rehabilitation, e.g., nature-based solutions, intensify "working for" projects; protection of coastal settlements, ecological infrastructure and other uses of natural or seminatural ecosystems and landscapes for the delivery of ecosystem services; implementation of climate risk informed land-use planning. Reversal of some of the loss and damage and climate-proofing of infrastructure for all sectors of development (e.g., through improved design of dams, flood drainage and water reservoirs); and tailoring of climate services informed by researchers, service providers and fellow users' communities should not be negotiable.

#### **Table 3.2** Examples of interactions between SDG 14 and SDG 15 with other SDGs

#### **3.8 Conclusion**

There is an inextricable link between ecosystem function and the various SDG goals and targets. Invariably global climate change and the projected outlook threaten both the terrestrial and aquatic ecosystems impacting people's livelihoods. It is anticipated that climate and environmental change will hamper poverty reduction, or even exacerbate poverty in some or all of its dimensions. These changes, together with a shortage of adequate coping mechanisms and innovations to adapt to climate change, are to result in a surge in economic and social vulnerability of communities, particularly among the poor. While pollution could be regarded as a historical

threat to aquatic biota, and thus outside the realm of a review focused on emerging threats, the scale of pollution impacts is accelerating in parallel with exponential human population growth and demographic population shifts to nearby surface water sources and coastal cities.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **4 Overview of the Macroeconomic Drivers of the Region**

## Johann F. Kirsten, Bernhard Dalheimer, and Bernhard Brümmer

#### **Abstract**

The ecosystems in Southern Africa are impacted by economic activity and population growth and pressure. There are several macro-economic drivers shaping these economic and population pressures and it is for this reason that this chapter unpacks the macro-economic drivers in the region. With the economy of South Africa dominating the regional economy (90% of Gross value added) it makes sense to discuss to the macroeconomic situation in Southern Africa by referring to policy and macro indicators in South Africa as a proxy of the regional situation. We also focus on the Limpopo province which shares boundaries and an ecosystem with three other countries in Southern Africa. Starting from the general macropolicy situation, major macro indicators for the region, the country and Limpopo are presented, jointly with the specific challenges, regulatory frameworks and policies that govern the development processes in the region. We focus on environmental, agricultural and trade policy measures, including their interlinkages, and illustrate that they provide a volatile and uncertain environment for structural development of the agricultural sector.

J. F. Kirsten (-)

B. Dalheimer · B. Brümmer

© The Author(s) 2024

Bureau for Economic Research, Stellenbosch University, Stellenbosch, South Africa e-mail<EMAIL_ADDRESS>
Department of Agricultural Economics and Rural Development & Centre for Biodiversity and Sustainable Land Use, University of Göttingen, Göttingen, Germany e-mail<EMAIL_ADDRESS>
G. P. von Maltitz et al. (eds.), *Sustainability of Southern African Ecosystems under Global Change*, Ecological Studies 248, https://doi.org/10.1007/978-3-031-10948-5\_4

#### **4.1 Introduction**

The economy of the Southern Africa is dominated by the sheer size, diversity, and magnitude of the South African economy. South Africa is responsible for 90% of all gross value added in the region and its markets, policies, infrastructure have an important impact and effect on all the other economies and people of the region. The study region of Limpopo is imposed in the region of Southern Africa, which is a point in case of the heterogeneous economic conditions that can be found in Africa. Most predominantly, the stark contrast of the value addition in South Africa compared with those of neighboring countries, but also the different importance of sectors in other economies of the region is striking. However, these macroeconomic conditions critically determine economic, social and environmental value creation in the study region of Limpopo. Moreover, as Limpopo borders with Botswana, Zimbabwe and Mozambique, the macroeconomic developments of these regions as much as potential differentials therein—are important to consider.

This chapter sets the stage by providing an overview of the macroeconomic drivers in the region using an analysis of South Africa indicators and trends as the reference point. We focus on environmental, agricultural and trade policy measures, including their interlinkages, and illustrate that they to provide a volatile and uncertain environment for structural development of the agricultural sector. Given the geographical location of the Limpopo region, the chapter adopts a hierarchical structure and provides insights at the supranational, the national and the regional level. In the rest of the chapter, we describe the macroeconomic trends starting from the broader region and subsequently narrowing down to the national and finally the regional level. Similarly, we provide key agricultural and trade policy insights again on these three levels. Finally, we conclude the chapter with recommendations for policy reforms.

#### **4.2 Macroeconomic Trends in Southern Africa**

Southern Africa1 is responsible for about one third of Africa's GDP. Average per capita incomes are higher than the average per capita income of Africa. In the more recent past, growth in the region was slower than in most other parts of

1 Southern Africa may refer to three different regional entities, each with a different composition of countries. First, in a geographical sense, it comprises countries that are south of the Congo river basin, but usually excluding the Democratic Republic of the Congo (DRC). Second, in a political sense, Southern Africa is often used to refer to members of the Southern African Development Community (SADC)—the regional economic and trade community—headquartered in Gaborone, Botswana. Third, Southern Africa also groups the countries of Botswana, Eswatini, Lesotho, Namibia and South Africa into a subgroup of the United nations (UN) geoscheme for Africa. In this chapter, we adopt a slightly modified denition of Southern Africa where we extend the UN subgroup with Zimbabwe and Mozambique to more adequately reflect the surrounding economies of our focus region, the province of Limpopo in the Republic of South Africa (RSA).

Africa, due to the already relatively large economies compared with others on the continent. In further comparison with the continent at large, the economies of southern Africa are characterized by homogeneously strong mining sectors and comparably large contributions from the secondary and tertiary sectors. Table 4.1 shows the relative contributions to total value added by sector in Southern Africa and selected southern African countries. With the exception of Eswatini, mining and utilities are responsible for between 10% and 15%, and in all countries, the secondary and tertiary sectors mostly make up for more than 70% of GDP. The importance of agriculture to national income on the other hand is the most diverse indicator, ranging from 2.1% in South Africa to almost one third in Mozambique (UNCTADstat 2022).

In the past, the development of manufacturing, predominantly in South Africa and Zimbabwe, benefited from larger investments in infrastructure, education and healthcare and led to improvements in related development indicators. Both regions were classified as lower-middle income countries since the 1980s. The region as a whole developed better than other countries in other parts of Africa in terms of income and other developmental outcomes. However, since the 1990s, increased competition with East Asia and South East Asia led to staggering manufacturing sectors and even deindustrialization in Zimbabwe. Today, poverty, inequality, corruption, HIV/AIDS and skilled workers emigration are the predominant impediments to GDP growth in the region (Nshimbi and Fioramonti 2014; Moyo et al. 2014).

As evidenced in Table 4.1, South Africa is by far the most important economy in the region with about 90% of the GDP of the political Southern Africa. Also compared with other countries in the region its GDP exceeds others by a factor of 10 or more in some cases. Given the predominant economic role of South Africa in the region, as well as the fact that Limpopo is a region within South Africa, the following section focuses on the macroeconomic situation in RSA in more detail.

#### **4.2.1 The Situation in South Africa**

Statistics South Africa (StatsSA) has in August 2021 released a new set of GDP numbers for South Africa. Real gross domestic product (GDP) is now measured at constant 2015 prices instead of 2010 prices. The revised estimate of GDP in 2020 is R5 521 billion, an increase of 11% compared with the previous estimate of R4 973 billion. The annual growth rate for 2020 was revised from −7.0% to −6.4%. With this GDP revised numbers, the South African GDP per capita is now R79 913 per person.

Based on the latest estimates by StatsSA, shown in Table 4.1, the tertiary sector makes up 65% of the South African economy—while the primary sector—mining and agriculture—which is dominant in the Limpopo region, is responsible for only 8.9% of gross value added in the South African economy.

The biggest concern for the South African economy is the alarming unemployment statistics. According to the most recent Quarterly Labour Force Survey

(QLFS), 7.8 million people were unemployed in the second quarter of 2021. The reality is even worse: besides the almost eight million officially classified as unemployed, we should add people who are not seen as part of the labor market because they have given up looking for work. These discouraged work seekers totaled 3.3 million in the second quarter. So, in broad terms there were 11.1 million people of working age not employed during the second quarter. Given an estimated labor force of 22.8 million, this means that the expanded unemployment rate in South Africa is now heading toward a staggering 50% (48.9% in the second quarter).

For some perspective, during the first quarter of 2008, which was the first time that the current version of the QLFS was published, 4.2 million people were unemployed. At the time, discouraged work seekers were 1.2 million. Therefore, 5.4 million were unemployed if the broader measure is used. In terms of the expanded definition of unemployment, roughly 5.5 million more people did not have a job in the second quarter of 2021 compared to early 2008. In the first quarter of 2008, the expanded unemployment rate was at an already worrisome 30%. For the expanded unemployment rate to deteriorate by roughly another 19 percentage points to almost 50% over the following decade suggests a major crisis.

A multitude of factors, including two very severe external shocks in the form of the global financial crisis (GFC) in 2007/8 (and its aftermath) and the COVID-19 pandemic since 2020, more than a decade of periodic Eskom (the South African power utility) load-shedding (switching of electricity to municipalities on a rotational basis to prevent total power grid collapse), and the devastating period of state capture help to explain why the labor market has deteriorated so significantly over the last decade and more. Not long after the release of the first iteration of the current QLFS in early 2008, the aftermath of the GFC resulted in a severe global economic downturn in 2009. As a result, real GDP in South Africa contracted by 1.5% in the same year. At the time, this was by far the worst GDP performance of the democratic era since 1994. As a result, 1.2 million private sector jobs were lost between 2008Q4 and 2010Q3, before a recovery followed.

The South African economy was already shedding jobs before the Covid-19 hard lockdown and the associated massive private sector job losses of 2.2 million in the second quarter of 2020. The weak pre-Covid labor market followed a sustained period where domestic real GDP growth was unable to keep up with the rate of population growth. Even after incorporating StatsSA's GDP rebasing and historical revisions that were released this week, real GDP growth averaged only 1% in the six years between 2014 and 2019. The weak growth meant that the economy was unable to absorb most of the new entrants into the job market. Compared to this period of weak output expansion, real GDP growth averaged 4.5% between 2003 and 2008, translating into average total annual private sector employment growth of 3.4% during these years. During a period of sustained robust GDP growth, the economy was able to generate jobs. One therefore needs to ask the critical questions such as what in government policy, political processes and general governance contributed to this weak growth performance. If policy levers, policy incentives and clean and effective government were in place (and which ones), would South Africa would have had better growth performance?

It is important to appreciate that the paltry GDP growth between 2014 and 2019 and the sharp rise in unemployment since 2008 was despite significantly more accommodate macropolicy settings than in the preceding period. Between 1994 and 2007, the SA Reserve Bank's repo policy interest rate averaged 12%. On average, the policy rate was almost halved to an average of 6.7% between 2008 and 2019. The policy rate was subsequently reduced dramatically further, amid the Covid shock last year and remains accommodate. Indeed, after adjusting for forward-looking inflation, the policy rate is negative in real terms.

In terms of fiscal policy, expressed as a share of the economy's size, main budget noninterest expenditure rose from just below 23% of GDP during the 2006/7 fiscal year to 26.5% in 2019/20. Expenditure rose further to 28.5% of GDP in 2020/21. The quoted numbers already incorporate the higher denominator (nominal GDP) after the recent GDP revisions. The key here is that despite a much lower policy interest rate and more government spending, real GDP growth severely underperformed in the years before Covid-19. This again flies against the argument that more government spending would bring about higher economic growth. While the pandemic has been a major blow to GDP and the local job market, weak output and employment growth precede it. Importantly, the sustained poor growth performance of recent years should not be laid at the door of excessively restrictive macroeconomic policy settings.

Except for gold, the prices of SA's major export commodities continued to rise in 2021Q1. Relative to 2020Q1, all South Africa's major export commodity prices were notably higher. **Rhodium** remained the star performer, but there were strong annual gains across the board. In some cases, the price gains continued in the early part of 2021Q2, with **palladium** reaching an all-time (nominal) high above \$2900/oz. in the week ending 23 April. The platinum group metals (platinum, palladium and rhodium) continue to be supported by stricter vehicle emission standards in places like Europe and China. This increases the demand for these metals, which are used in catalytic converters.

The price movements have had a major impact on the contribution of the individual commodities to SA's total mineral export sales. In a nutshell, while the contribution of gold (long-term trend, 2020 being an exception), platinum (since 2015) and coal has declined, the opposite is true for iron ore, palladium and rhodium.

Regarding SA's import bill, the **Brent crude oil** price has increased at a faster tempo than anticipated. The sharp price gains in 2021Q1 were again driven by an improved outlook for global GDP growth and oil demand. Developments on the supply side also supported the price. In early March, the OPEC+ grouping of major oil producers agreed to extend oil output curbs. In addition, one-off events affected the oil price through the quarter. These included disruptive weather in Texas, which shut-in oil production totaling more than 10% of US oil supply**,** drone attacks on Saudi Arabia oil facilities and delays caused by the blockage in the Suez Canal. This increased shipping costs, as well as the oil price.

Focusing on the agricultural sector amidst all of the negative impacts and projections as a result of the COVID-19 pandemic, the South African agricultural sector has emerged as a shining light, growing by 13% in 2020 (StatsSA 2022). This represents a sharp turnaround and an illustration of the sector's ability to recover from extremely tough conditions over the past five years where the agricultural real growth rate averaged negative 1.3% per annum.

Compared to other economic sectors, the agriculture and food sector has been relatively insulated from the effects of the COVID-19 crisis because as an essential service, operations were allowed to continue, with the exception of alcoholic beverages and tobacco and initially also wool, mohair and cotton. Overall, agriculture was mainly affected in the short-run by a decline in sales due to the closure of hospitality, take-away-food outlets and informal trading and the ban on alcohol and tobacco sales greatly impacted the liquor and tobacco value chains. Moreover, the devaluation of the South Africa Rand during lockdown affected the cost of imported inputs like agrochemicals but also benefited exporting industries.

#### **4.2.2 The Situation in Limpopo**

The economy of Limpopo province has developed along similar trends as South Africa at large. Some differences emerge, however, because of the differences in the sectoral composition of GDP (Fig. 4.1).

As Fig. 4.1 illustrates, the primary sector has a much larger role in Limpopo than in South Africa at large. Mining and quarrying, as the largest industry, contributes about 28% of the provincial level GDP while agriculture, forestry and fishing add another 4%, totaling to about a third of provincial level GDP from the primary

**Fig. 4.1** Composition of Limpopo province's GDP (2019 and 2020). Source: StatsSA

#### **Employment**

*Source: before 2008 - Quantec, since 2008 - StatsSA QLFS*

**Fig. 4.2** Number of employed persons over the past 15 years in the Limpopo province. Source: before 2008—Quantec, since 2008—StatsSA QLFS

sector. Nationwide, the contribution of the primary sector is below 10%. The tertiary sector contributes to the GDP of Limpopo province to a similar extent as in national GDP but the secondary sector, with only 2.6% in 2020, is very underdeveloped compared to the rest of the country. This situation persisted already before the Covid-19 crisis and must thus be viewed as a structural property of Limpopo province.

The important role of the primary sector is visible in the trade statistics for Limpopo, too. Limpopo's share in the total value of South African exports was at 4.5% in 2020 and almost exclusively consisted of raw materials. Since the population of Limpopo province amounts to 5.8 million people, equivalent to 9.8% of the total South African population, the per capital export value from the province is less than half of the average number for South Africa. The most important agricultural exportables were fruit and nuts, which alone contributed 5.6% of the export value of the province.

The lackluster development of Limpopo province can at least to a large extent again be explained by the difficult conditions on the labor market. Figure 4.2 depicts the number of employed persons over the past 15 years. While a mild positive trend in employment numbers is present over the years between 2005 and 2018, when employment grew on average by 3.3% per year, this development came to an end already in 2019, before the Covid-19 shock hit the world economy. This shock, however, had a very strong negative effect on the labor market in Limpopo, even when compared to the rest of South Africa. With a drop of 10.7%, the loss of jobs was most marked among all provinces in South Africa.

The agricultural sector in Limpopo is dominated by horticultural production, followed by animal production activities, and field crop production. The contribution of horticulture to gross farming income amounts to about 60%, while the contributions of livestock and crop production are about 20% and 15%, respectively. Limpopo is well known for its substantive production of tomatoes, potatoes, onions, mangoes, avocadoes, citrus fruits and various nuts, confirming the dominance of the horticultural industry, which is water intensive and labor intensive. For tomatoes, pumpkins and subtropical fruits, Limpopo has the largest planted area in South Africa. In particular, Mopani district in Limpopo, where many of these crops are grown, is ranked sixth among all districts in SA with the highest agricultural employment numbers (29,000 workers).

The farm structure in Limpopo is characterized by a mixture of commercial, emerging and smallholder farms. There are about 3000 commercial farming units (540 of these are owned /operated by black farmers) in the province employing 97,400 farm workers, (63,000 are full-time employees).

#### **4.3 The Policy Arena**

At the Southern African level, the economic, trade and agricultural policies in RSA are embedded within the regional integration framework of the Southern Africa Development Community (SADC). The organization is one of the eight Regional Economic Communities (RECs) in Africa. Its current form emerged in the early 1990s with the aim of an intensified socioeconomic, political and security cooperation among its members. Between 1996 and 2012 a total of 27 protocols were ratified, which define the cooperation activities in various subfields, including agriculture and trade. In 2008, an additional free trade agreement (FTA) was implemented with the long-term targets to implement a customs union, a common market, a monetary union and eventually a single currency. However, besides the FTA, none of these milestones have been achieved to date (SADC 2022).

The main challenge of further integration within to SADC is overlapping memberships of its economies. For example, RSA and Botswana are also members in the Southern Africa Customs Union, Zambia joined the Common Market for Eastern and Southern Africa, and Tanzania is also part of the East African Community. Since all of these organizations target integration in overlapping or even identical areas, a number of regulatory, organizational and political conflicts of aims have arisen as impediments to negotiations and integration processes as a whole. In particular with regards to trade policy, where for instance tariff rates, quotas and rules of origin are regulated differently across these different economic integration endeavors, inducing contradicting trade governance that are not solved generically, but rather on a case by case basis.

However, efforts with regards to regional integration in SADC and neighboring RECs in the more recent past are likely to be outpaced by developments on the continental level. Since the foundation of the African Union (AU) in 2002, a number of socioeconomic, justice, political and security cooperation agreements have been negotiated at the continental level. On the one hand, this added a further layer of complexity to the already cumbersome regional integration processes. On the other hand, these AU-based initiatives allowed for some overarching, continent wide regulations. For example, in 2021, the African Continental Free Trade Area (AfCFTA) was launched, which aims at the implementation of free trade relationships across all of the African continent. Even though many of the details of the agreement remain to be negotiated, it is expected that in the medium term, the AfCFTA subsumes most of the regionally regulated trade relationships, such as those of SADC, and eventually forms a viable umbrella for most of intra-African trade.

In South Africa, the National Development Plan (NDP) identifies the key challenges facing South Africa as a country but argues that the country can eliminate poverty and reduce inequality by 2030. It emphasizes the importance of hard work, leadership and unity. It furthermore identifies Infrastructure Development, Job Creation, Health, Education, Governance, Inclusive Planning and the Fight against Corruption as key focus areas and spells out specific projects for each.

The NDP was developed after a detailed diagnostic assessment of the issues constraining economic development and improvement in social wellbeing. It identifies the critical interventions needed to improve education and health outcomes as well as grow the economy and reduce unemployment and alleviate poverty.

As the primary economic activity in rural provinces such as Limpopo, the NDP identifies agriculture as having the potential to create close to one million new jobs by 2030, a significant contribution to the overall employment target. To achieve this, the NDP proposes the following policy imperatives:

The NDP makes the following detailed recommendations, in achieving the goal of 1 million new jobs by 2030:

Although the NDP is very ambitious with the 1 million jobs in agriculture target, the plan and vision for the agricultural sector is much more inspiring and innovative than many of government's current action programs.

From our review of all the relevant policy documents and frameworks relevant to the agricultural and food sector, it is evident that the main focuses of these policies and plans are job creation in agriculture and dealing with process of empowerment and redistribution of resources and opportunities in the sector. Although food security and nutrition issues are mentioned and discussed as one of the main challenges it is merely seen as an important outcome if the agricultural and food sector performs optimally.

The National Development Plan is the only policy framework reviewed here that presents specific plans and suggestions on how to tackle the problems of food insecurity and malnutrition. To reduce the acute effects of poverty on millions of South Africans over the short term, the plan proposes to:

It has to be stated that these are just recommendations and suggestions and have yet to be translated into real policies with action programs and budgets. This point applies equally to most of the policy frameworks discussed above. The translation of strategic plans and policy frameworks into well-funded and efficiently executed government programs remain a major omission in the South African government. Implicitly most of the policies to promote increase agricultural production are listed and well argued—but never executed. South Africa has always the best plans and the most modern legislation but political rhetoric and nondelivery and general political insecurity flies against all these noble plans resulting in further job shedding and decrease in production.

#### **4.3.1 Agricultural Policies**

At the Southern African level, the key role of agricultural policies has been recognized in various of the regional organizations. SADC has recognized the importance of agriculture to food security in the region as the incomes of about 70% of the population in SADC countries depends on agriculture. Moreover, as agriculture in the region is relatively labor intensive, it is also heavily affected by the HIV/AIDS pandemic, which SADC identified as a major challenge for agricultural development. Given the mainly smallholder-based agricultural sectors of most member states, another pertinent issue is the access of smallholders to functioning product and factor markets, exacerbated by problems of road and marketing infrastructure. On the other hand, agricultural products account for about 13% of the total export value SADC, highlighting the key role of agricultural development from yet another perspective (SADC 2022).

The Dar-Es-Salaam declaration of agriculture and food security in the SADC region of 2004 addressed both market access as well as labor shortage problems. The declaration furthermore aimed at streamlining regional agricultural and food security policy with the Millennium Development Goals (MDG) of the United Nations (UN). In the short term, members agreed to facilitate smallholder access to improved seed varieties, fertilizers, agrochemicals, tillage services, farm implements and the construction of irrigation systems. For the medium and long terms, the SADC member countries also mandated the allocation of at least 10% of national budgets to agriculture and decided to implement national food reserves programs to buffer negative food supply shocks that are also pertinent in the region (SADC 2004).

In 2008, the SADC Multi-country Agricultural Productivity Programme implemented further measures to achieve the targets of the Dar-Es-Salaam declaration. The most overarching concern was low agricultural productivity in the region. The program stretches over a 15 year period and targets to enhance (1) food (crop, livestock and fishery) production and productivity through improved access to and sustainable use of agricultural productive assets, (2) diversification and value added through the establishment of a supportive policy and an adequate institutional environment for the development of efficient agroindustrial commodity chains, (3) disaster prevention, preparedness and mitigation through the implementation of a comprehensive strategy involving the development of drought and pest tolerant crop and livestock varieties, and (4) institutional collaboration in the region. These targets are mandated to be pursued and implemented by the member states.

The Government of National Unity (GNU) that was formed in April 1994 set in motion radical changes in the political economy of South Africa, but in agriculture policy changes had to wait until 1996 when the GNU was replaced by the ANC government whose policies over the following 27 years focused on the delivery of basic services, reducing poverty and expanding the payment of social grants to poor communities, all of which have implications for the demand for agricultural products.

In the agricultural sector, the most important policy initiatives in the post 1994 years included land reform; institutional restructuring in the public sector; the promulgation of new legislation, including the Marketing of Agricultural Products Act (No 47 of 1996) and trade policy and water and labor policies and laws within the framework of wider macroeconomic policy reform. At the same time, the political decision was made to dismantle the support services that favored white commercial farmers as part of the restructuring of government services to the population at large and reprioritization of government expenditure. The combined effect was that South African agriculture was exposed to all the volatilities of international commodity markets. The reduced levels of subsidization were supposed to reduce land values and hence to support land reform and the transformation of the agrarian economy. In reality, the impact was different: it made the process of integrating new and previously disadvantaged farmer communities into commercial agricultural value chains very difficult and was one of the main reasons for the failure of agrarian transformation in general and of land reform in particular (e.g., Hebinck and Cousins 2013). In an effort to correct these weaknesses, the government initiated various support programs to help land reform beneficiaries, including the Comprehensive Agricultural Support Programme (CASP), the Recapitalization and Development Programme (RECAP) and the Micro Finance Scheme for Agriculture (MAFISA), but these have met with little success (Kirsten et al. 2019).

The withdrawal of this support to white farmers had two consequences. First, it allowed the growth of very large-scale ("mega") farming operations (about 2600 (or 6.5%) of them), especially (but not exclusively) in intensive irrigated horticulture production (StatsSA 2020). Second, it was accompanied by the abolition of support measures, from direct subsidies to indirect market interventions, from funding of research and extension to the withdrawal of subsidies on conservation works (Vink 2000). The result was that black farmers were bereft of the support services that they had been denied under the previous regimes.

Unfortunately, the many attempts to remedy this situation (e.g., through the Comprehensive Agricultural Support Programme CASP and other programs) have been ex post, piecemeal and unsuccessful. Unless this is changed, commercial agriculture will remain white dominated, only slightly less racially segregated than in the 1980s, but with a strong bias against those smaller scale family farming operations whose development can do much to initiate growth and employment opportunities throughout the country, in the manner envisaged in the National Development Plan.

In the last three decades, the value of South African agricultural output more than doubled in real terms (DALRRD 2020). This growth has largely been driven by increased productivity, which has been underpinned by technological innovation, as well as growth in traditional export markets as well as access to new ones and has spanned across all subsectors of agriculture (livestock, horticulture and field crops).

Black farmers were largely excluded from the benefits of this agricultural growth while the various programs and plans were not sufficiently broad based to foster an inclusive and prosperous sector and, combined with the slow pace of land reform, contributed to frustrations among black farmers.

Despite remarkable growth during the last three decades, South Africa's agricultural sector remained plagued by dualism, mistrust and suboptimal performance. On the one hand, South Africa agriculture has surpassed the NDP targets in expanding a number of high-value commodities (citrus, macadamias, apples, table grapes, avocados, dairy and pork), but on the other hand the country has not fully achieved the jobs target and expansion of agriculture in the former homelands. The dualistic nature of the sector remains therefore entrenched. The only way this can change is through a capable and effective state (provincial and national), stable and conducive policy and investment environment; infrastructure development and services including electricity and water; and effective farmer support programs, among other support measures.

The expansion and growth of the industry over the last three decades was driven by new technology (irrigation, cultivation techniques, genetic material, etc.) and by the 2607 large farm enterprises that have capital and systems in place to invest, to expand and to export. Although these large enterprises are responsible for 67% of total farm output, cornerstone of the agricultural sector remains the many small family farms. More than 90% of all commercial farming units in South Africa are small family-based operations at different levels of commercial activity.

A number of prevailing and perpetuating cross-cutting factors are hindering inclusive growth and investment in the agriculture and agroprocessing value chains to a greater degree. These factors include continued policy ambiguities, mainly related to access to land and ownership as well as water rights, diminishing/unreliable infrastructural capacity (electricity, water, rail, roads, fresh produce markets and ports), major safety concerns for farmers and farm workers living in rural areas and a sharp increase in theft of stock and farming equipment, limited drive to opening new export markets and the inability to comply with stringent market access protocols, deteriorating biosecurity management, rising concentration and market power at food production, processing and distribution levels partly due to growing barriers to entry that limit access to key routes to market, and skills shortages and decaying research capacity in the country.

Moreover, coupled with consistently low and ineffective farmer support, in particular to subsistence and emerging farmers and high barriers to entry, these constraints contribute to an uncertain and unstable investment environment, resulting in limited growth and job creation in the sector, and thereby perpetuating inequality and exclusion of historically disadvantaged farmers and agripreneurs in agriculture and food value chains in the country.

The constraints and factors hindering the agricultural sector to grow and to transform to be more inclusive, are not new and have been identified in 1995 (White Paper on Agriculture), affirmed in 2001 (Agricultural Strategic Plan) and again in the diagnostics leading up to the National Development Plan (NDP) in 2011 and more recently by the High Level Panel Report led by former president Motlanthe and the Presidential Panel Report on Agriculture Land led by the late Vuyo Mahlati. They have not changed, and while some progress in some areas has been achieved since the adoption of the NDP, there are still areas of substantial underperformance. These include:

#### **4.3.2 Trade Policies**

Trade policies constitute the key policy framework for the development of the South African economy. This holds in particular for Limpopo province, with its relatively strong reliance on the primary sector. Unleashing the full potential of agriculture in particular requires improved market access to high-value export chains. The development in this regard has been quite remarkable over the past decades, starting from the free trade agreement with the European Union (1999, fully implemented since 2004) and the regional free trade agreement within the Southern African Development Community (fully implemented since 2012) up to the African Continental Free Trade Area (2020). Nevertheless, macroeconomic instability and policy incoherence remain substantial risk factors for agricultural value chains (SADC 2022).

Trade policy in South Africa has always had a strong regional focus. From the beginning of the Southern Africa Customs Union (SACU) more than 50 years ago, South Africa has been the driving and dominating force in regional integration. Currently, regional trade relations are mainly governed by the SADC free trade area. The loss of national decision power over tariffs and other trade-related policies that usually goes hand in hand with the formation of a free trade area or a customs union, has been not a major challenge for South Africa since the country accounts for about half of the current GDP of the SADC FTA. This retained national autonomy might explain why the regional integration within SADC FTA has remained somewhat subdued, with only 10% of the bloc's total trade being intra-FTA trade. While lack of physical infrastructure and the strong role of nontariff measures are common to many, in particular smaller, developing countries (Fiankor et al. 2021), the role of the policy process seems to be specific to SADC (Sikuko 2018), where a dominating influence seems to be exerted by South Africa.

The meager intraregional trade development within SADC has been present in many of the continents regional trade agreements. In the early 2000s, SADC and two more regional blocs (COMESA and EAC) aimed at forming a larger regional trade agreement, the Tripartite Free Trade Area. However, this initiative was never ratified but was replaced by a continent-wide initiative for boosting intra-African trade, the African Continental Free Trade Area (AfCFTA). This agreement is effective since 2019, and the expected effects on intra-African trade are huge. For instance, the World Bank (2020) estimates that intra-AfCFTA imports will double relative to the baseline by 2035. For total exports in agricultural products, the study expects an increase of more than 30% relative to the baseline. While the main effects for South Africa will materialize in increasing trade opportunities in manufacturing and services, the important role of agriculture in Limpopo suggests that for this province, new opportunities for intra-African exports might open up.

Overall, the trade policy of South Africa can be viewed as relatively liberalized, with open import and export markets. However, access to high-value export markets remains hampered by sanitary and phytosanitary measures (e.g., Kalaba et al. 2016) and the increasing role of private standards (e.g., Fiankor et al. 2020) that are increasingly used by developed countries' food importers and retailers.

#### **4.4 Recommendations for Reform**

In terms of **macroeconomic policies**, the hindrances to economic development are related to labor markets, monetary stability and external trade. The basic lessons to pursue these objectives can be learnt from other countries' experience and have frequently been put on the policy agenda. The willingness of policy makers to commit to these objectives, however, seems to be much less of a clear issue.

In light of these structural realities in the field of **agricultural policies**, the necessary guidelines to facilitate the growth and financial sustainability of the agricultural sector at large should be obvious and clear. What has been lacking over the past two decades is the practical implementation of the government policy frameworks and legislation, which, in turn, reinforced the lack of access among black farmers, and few opportunities within the input supply, agroprocessing and food retail sectors. The underlying factors behind this lack of implementation can be categorized into four broad streams. First, the limited government capacity to execute government programs together with a misalignment of functions and priorities between the three spheres of government. Second, the misallocation of the budget by the national and provincial governments. Third, the poor and uncoordinated transformation programs between government, private sector and civil society. Fourth, the abolishment of crucial institutions such as the Agricultural Credit Board and corporatization of farmer cooperates that were essential in coordinating and providing financial and nonfinancial support to farmers prior to 1998.

**Biosecurity for plants and animals** are becoming a critical issue as South Africa expands its production of exportable products. Government systems have time and again failed the agricultural sector in this regard with them unable to deal with animal diseases and plant diseases. At the same time, these impact South Africa and especially the Limpopo province to trade with other countries in livestock products such as beef, game meat, and chicken and eggs. Stronger emphasis on developing and implementing standards and technical regulations in the field of biosecurity is therefore warranted, including cooperation with trading partners like the EU to both ensure the mutual acceptance of standards and technical regulations and to develop solutions for managing isolated outbreaks of pests or diseases in a better way. Promising ideas, e.g., "green corridor" zones with stricter regulation that could remain in export business even in case of phytosanitary problems elsewhere in the country, should be explored by bringing policy makers and private actors in the value chain from both South Africa and important destination markets together.

#### **References**

SADC (2004) Dar-es-salaam declaration on agriculture and food security in the SADC region SADC (2022) SADC website. Retrieved in March 2022 at https://sadc.int.

Sikuko K (2018) Policy making in SADC: the missing link to advancing integration. In: Centre TL (ed) Monitoring regional integration in southern Africa, yearbook 2017/18. RSA, Tralac, Stellenbosch

StatsSA (2020) Census of Commercial Agriculture 2017, Pretoria: Statistics South Africa

StatsSA (2022) Statsitics South Africa. Retrieved in February 2022 at http://www.statssa.gov.za/

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

**Part II**

**Drivers of Climatic Variability and Change in Southern Africa**

# **5 Past Climate Variability in the Last Millennium**

## Eduardo Zorita, Birgit Hünicke, Nele Tim, and Matthieu Rouault

#### **Abstract**

We review our knowledge of the climate variability in southern Africa over the past millennium, based on information provided by proxy data and by climate simulations. Since proxy data almost exclusively record past temperature and/or precipitation, the review is focused on those two variables. Proxy data identify three thermal phases in the region: a medieval warm period around year 1000 CE (common era), a Little Ice Age until about the eighteenth century, and a clear warming phase since that temperature minimum until the present period. Variations of precipitation are different in the summer-rainfall and winter-rainfall regions. In the former, precipitation tends to accompany the temperature, with warm/humid and cold/dry phases. In the winter-rainfall zone, the variations are opposite to temperature. Thus, past precipitation variations display a seesaw pattern between the summer- and winter-rainfall zones. However, climate simulations do not display these three different hydroclimatic periods. Instead, the simulations show a clearly warm twentieth century and punctuated cooling due to volcanic eruptions, with otherwise little variations during the preindustrial period. Also, the simulations do not indicate an anticorrelation between precipitation in the summer- and winter-rainfall zones. Possible reasons for these discrepancies are discussed.

E. Zorita (-) · B. Hünicke · N. Tim

M. Rouault

© The Author(s) 2024

Institute of Coastal Systems, Helmholtz-Centre Hereon, Geesthacht, Germany e-mail<EMAIL_ADDRESS>
Nansen Tutu Center for Marine Environmental Research, Department of Oceanography, University of Cape Town, Cape Town, South Africa

G. P. von Maltitz et al. (eds.), *Sustainability of Southern African Ecosystems under Global Change*, Ecological Studies 248, https://doi.org/10.1007/978-3-031-10948-5\_5

#### **5.1 Introduction**

Climatology is a branch of science where experiments are very difficult, if not impossible, to perform. Therefore, we are bound to use as much information from observations as possible, to try to understand the mechanisms of climate change and its possible impacts on society and ecosystems. Although climate models and basic physical considerations unambiguously agree that an increase in atmospheric greenhouse gases must lead to generally warmer temperatures, regional climate changes are more uncertain, in particular concerning other climate variables. For instance, questions such as whether warmer climates will lead to increased or decreased precipitation over a particular region are much more difficult to answer. The climate of the past can help find an answer.

The observational record is, however, usually too short, spanning at most the last 200 hundred years and more usually only the last few decades. Many relevant questions about the present climate, such as the unprecedented character of current temperatures, climate trends, and climate extremes, are better addressed by looking beyond the period covered by observations. This goal can be partially achieved by analysing indirect climate information from the so-called proxy data—tree rings, lake sediments, etc.—that are natural archives, sensitive to past environmental conditions. The period covered by these natural archives can be vast, but the past few centuries span climate conditions that are, from the geological perspective, not very much different from the present and future climate, so that the lessons learnt there may find applications for the understanding of present climate trends.

Simulations with Earth System Models that cover the past few hundred years can, together with proxy data, provide useful insights about the relevant climate mechanisms. Here, each of these two sources of information serve as independent confirmation (or rebuttal) of the other. Both are inherently uncertain, displaying different sources of error, and their combination leads to more robust conclusions than each of them taken in isolation would be able to provide.

In the following sections in this chapter, we will review the existing literature on climate variability in southern Africa during approximately the past millennium. We start with a summary of evidence available from proxies, both at the global scale and more specifically for southern Africa. These sections are followed by selected results obtained from climate simulations. Finally, we discuss their agreements and inconsistencies and conclude with the main take-home implications for future climate changes in this region.

#### **5.2 The Climate of the Past Millennium: Global Background**

Our knowledge of the climate of the past millennium is derived from indirect indicators that archive information about past environmental conditions. Trees tend to form thicker annual growth rings or produce wood of higher density in years with more suitable environmental conditions, usually warmer and/or wetter. These

biological characteristics can be calibrated to reconstruct past physical magnitudes such as temperature or precipitation variations by statistically comparing recent tree rings to meteorological observations. Apart from dendroclimatological data, other proxy records also contain information from past environmental conditions: carbon and oxygen stable isotopes in old wood, oxygen isotopes in stalagmites, pollen assemblages in lake sediments, historical documents, etc. In this fashion, global networks of proxy data can be translated by means of complex statistical methods to annually resolved climate patterns in past periods (Li et al. 2010). These climate reconstructions, however, rely on some general assumptions that may not be always fulfilled. For instance, dendrochronological proxy records reflect the environmental conditions during the growing season, i.e., are seasonally biased. Also, other nonclimatic factors may affect the growth of trees, such as availability of nutrients, fires, etc. Other types of records suffer from other corresponding caveats, so that it is not totally surprising that discrepancies between reconstructions derived from different proxy records arise. This highlights the need to combine different sources of information to reach robust conclusions.

In addition, depending on the method applied to translate the proxy information and on the network of proxy data, the reconstructions of past climate may differ on the amplitude of past climate variations and on the specific regional details. However, most of the temperature reconstructions published so far indicate that the Earth's climate of the past millennium can be described by relatively warm centuries around year 1000 CE (common era)—the Medieval Climate Anomaly or Medieval Warm Period, followed by colder centuries between around 1500 CE and 1850 CE—usually denoted as the Little Ice Age—which in turn were followed by a warming trend that has strongly intensified from around 1980 CE onwards until present (Crowley 2000).

These centennial climate fluctuations have been attributed to different *external climate forcings* (Schmidt et al. 2012). One is *volcanic activity* that tends to cool the global climate due to the volcanic aerosols ejected to the stratosphere, dimming the incoming solar radiation. *Solar output* itself is also variable through time. Landuse and forest cover can modulate the regional climate due to the implied changes in the surface reflectivity. Concerning *land use*, major changes have occurred in some regions such as Europe and East Asia over the past few centuries. Finally, *greenhouse gases* since the industrial revolution have contributed to the warming trend since the end of the Little Ice Age and are the single most important factor for the warming since the mid-twentieth century.

The variations of these external factors can be reconstructed by the analysis of polar ice cores. They archive the composition of the past atmosphere. Ice acidity records show sharp peaks due to the deposition of volcanic aerosols, allowing for an accurate dating and estimation of the strength of eruptions. The concentrations of cosmogenic isotopes such as 10Be are indicative of past solar activity. These records show that around the Medieval Climate Anomaly volcanic activity was sparse and the Sun was stronger than in the ensuing centuries. By contrast, the Little Ice Age witnessed an intense and frequent volcanic activity and a weaker Sun. The

recent decades are characterised by an almost constant solar output, relatively weak volcanism, and a very strong forcing due to greenhouse gases.

In addition, internal climate variations, not caused by any particular external factor but due to the slow variations of ocean currents and the inter-play between ocean and atmosphere, could have also contributed to some of these past variations. For instance, it is unclear yet as to whether the external factors could have been solely responsible for the Medieval Climate Anomaly or whether some sort of slowly varying internal mechanism might have contributed to generally warmer temperatures. This is perhaps more relevant at regional scales, for which internal climate patterns such as the El Niño-Southern Oscillation (ENSO) may have a stronger immediate influence than global external forcings.

#### **5.3 The Climate of the Past Millennium: Southern Africa**

Earlier literature reviews on the paleoclimate of southern Africa during the last 2000 years (Tyson and Lindesay 1992; Hannaford and Nash 2016) have identified the warm and cold climate phases in the past millennium previously mentioned in Sect. 5.2. A long 3000-year-long stalagmite record from Cold Air Cave in the Makapansgat Valley, which displays colour banding that is correlated to local temperature, also confirms the sequence of warm–cold–warm periods over the past millennium (Holmgren et al. 2001). According to this record, the Little Ice Age would have been about 1 ◦C colder than present. This is approximately confirmed by a stalagmite-based 18O isotope record from the same site spanning the past 350 years, which has been interpreted as indicators of a sharp and well-defined cold multi-decadal period centred around 1720 CE (Sundqvist et al. 2013). The cooling may have amounted to 1*.*4 ◦C colder than present. The rise and fall in temperature in these different thermal phases would have been roughly homogeneous over the whole region.

The picture derived for precipitation is more nuanced. As explained in Chap. 6, southern Africa is characterised by two regions with different annual precipitation regimes: a (mostly) winter-precipitation region around Cape Town and a summerprecipitation region located further to the east and northeast (Reason 2017). During the Little Ice Age, the winter-precipitation region may have received more precipitation due to a northward displacement of the belt of westerly winds (Dunwiddie and LaMarche 1980), whereas the summer-precipitation zone of southern Africa would have faced a generally drier climate due to diminished evaporation from the Indian ocean leading to lower air humidity (Woodborne et al. 2015). The reversed precipitation pattern is found in the prior warmer centuries during the Medieval Climate Anomaly. In this period, proxy indicators of precipitation in the summerrainfall region based on stable carbon isotopes in baobab trees show increased rainfall (Woodborne et al. 2015).

Thus, an important feature of the pattern of the variability of annual precipitation totals in southern Africa, as derived from proxy information, appears to be a see-saw pattern between the winter-precipitation zone in the southwest and the summer-

precipitation zone in the northeast (Fig. 5.1). Whether or not this see-saw type of variability recurs over time no matter which is the main external climate driver is indeed an interesting question, relevant for future climate changes.

The nineteenth century was relatively colder than the twentieth century. This period, with a more dense network of direct observations, offers the opportunity to test that working hypothesis suggested in the previous paragraph. Unfortunately, the available studies that provide more detailed analysis for the nineteenth century precipitation variations reach contradicting conclusions, indicating either drier (Nicholson et al. 2012) or wetter conditions (Neukom et al. 2014; Nash et al. 2016; Nash 2017) during the nineteenth century in the summer-precipitation zone. By contrast, precipitation in the winter-rainfall zone has likely remained temporally stable over the last two centuries (Nash 2017). An explanation for this discrepancy may lie in the different nature of the records analysed. Whereas Nicholson et al. analysed long instrumental and documentary records, the conclusion reached by the other studies is derived from a more comprehensive set of data, including, in addition to instrumental and documentary records and indirect proxies (dendroclimatological, corals). This highlights the difficulty of inferring past climates and the need to combine all available sources of information.

#### **5.4 Paleoclimate Simulations with Earth System Models**

Comprehensive climate models, very similar to those used to project the impact of greenhouse gases on future climate (Edwards 2011), have also been used to retrospectively simulate the climate of the past millennium (Fernández-Donado et al. 2013). These models, akin to weather prediction models, incorporate our knowledge of the main climate processes. They contain a representation not only of the atmosphere, but also of the ocean, of sea-ice, of soils and some of them also of the terrestrial and oceanic biosphere. All in all, they are one of the most complex software packages actually in use. Nevertheless, the climate system is very complex, with processes that typically occur over a vast range of spatial and temporal scales, from seconds to millennia and from millimetres to thousands of kilometres. Due

**Fig. 5.2** An example of the annual cycle of precipitation in the two southern African precipitation zones simulated by the climate model MPI-ESM-P in the pre-industrial centuries (850–1800 CE). The magnitude of precipitation depends on the location of the selected model grid cells

to computing limitations, some of these processes need to be represented in a more simplified form. Typically, a climate model has a spatial resolution of about 100 km, and all smaller-scale processes are represented in an averaged fashion. Climate models, for instance, do not directly simulate the formation of clouds, but only the average effect of clouds over an area of typically 100 km long and wide. This leads ˜ to inaccuracies and uncertainties in the simulation of precipitation and to differences in climate projections obtained with different models. In spite of mentioned model uncertainties, climate models are indeed able to reasonably replicate the two precipitation regimes observed in southern Africa. An example is shown in Fig. 5.2, which can be compared with the corresponding figure in Chap. 6. This means that the main mechanisms behind these two precipitation regimes, namely extratropical cyclones for the winter-precipitation zone and convective precipitation within the South Indian Convergence Zone for the summer-precipitation zone (Cook 2000; Reason 2017), are reasonably well simulated by global climate models.

To simulate the climate of the past, climate models need to be driven by the external factors that provide or modulate the energy that reaches the Earth.

As mentioned in Sect. 5.2, in the past millennium, these external factors were volcanism, solar output, and atmospheric greenhouse gases (Schmidt et al. 2012). The magnitude of these factors in the past millennium can be approximately reconstructed from chemical analysis of polar ice cores, including the air bubbles trapped in them, and then used for climate simulations. There exists a relatively large set of climate models that has been used to estimate future climate change, as included in the different reports by the Intergovernmental Panel on Climate Change (IPCC). Some of these models have also been used to simulate the climate of the past centuries, more precisely the period 850–2005 CE. These simulations are part of the Coupled Model Intercomparison Project CMIP5 (Taylor et al. 2012). As indicated in the Introduction, the results of these simulations may differ from model to model, so that it is necessary to consider several models not only to identify the robust results but also to be aware of the uncertainties inherent in these simulations.

Figure 5.3 displays the near-surface air temperature in southern Africa simulated by a suite of climate models in the period 850–2005 CE. Table 5.1 lists these

**Fig. 5.3** Near-surface annual mean air temperature averaged in southern Africa (land areas between 10S-40S and 10E-45W) as simulated by a suite of climate models from the Climate Model Intercomparison Project CMIP5 for the period 850–2005 CE (Taylor et al. 2012). The time series represent deviations from the twentieth century mean temperature and have been smoothed with a 20-year running-mean filter

**Table 5.1** List of global climate models and their spatial resolution (atmospheric model only) in geographical degrees used in this study

climate models and their spatial resolution. All simulated temperatures display clear similarities but also differences. Most models, with the only exception of HadCM3, estimate colder pre-industrial temperatures of the order of 0*.*5 ◦C relative to the twentieth century mean. There seems to exist little doubt, from the models' perspective, that temperatures in the twentieth century have clearly been above the pre-industrial average level. The model HadCM3 estimates twice as cold preindustrial temperatures with respect to the twentieth century mean.

The warming trend simulated during the twentieth century stands out compared to the trends in all other centuries, with the possible exception of the nineteenth century. The climate of the nineteenth century is, however, strongly impacted by the two first decades of intensive volcanism (see next paragraph), and by its associated strong cooling. The models that estimate the strongest impact of these eruptions (e.g., GISS-2-ER) are the ones for which the ensuing warming trend is also stronger. Even so, considering all models, the simulated twentieth century warming, about 1*.*2 ◦C, is larger than the bracket of nineteenth century warming of 0*.*5–0*.*8 ◦C spanned by the majority of models.

Further back in time, superposed to colder pre-industrial mean temperature, the models produce temperature variations that can be attributed to the impact of the external forcings. For instance, the clear cooling simulated after the mid-thirteenth century is due to the very strong eruption in Samalas (Indonesia) in 1258 CE (Guillet et al. 2017). Also clear in the figure are the series of eruptions in the early nineteenth century, one of them the famous Tambora eruption in 1815, which caused in Europe "the year without summer" (Raible et al. 2016) and serious societal disruptions there in 1816 CE. After around 1800 AD, the current warming trends set in, mainly caused by an increase of the solar output during the nineteenth century and by the increase of greenhouse gases in the second half of the twentieth century.

The succession of warm–cold–warm centennial or even multi-centennial periods (MCA-LIA-present) previously identified in the proxy-based reconstructions is not so clearly recognised in the simulations. Although the centuries around 1000 CE are slightly warmer in the simulations, the LIA is barely recognisable in the simulated series as a clearly cold differentiated centennial or multi-centennial period. As a result, the simulated temperature evolution during the pre-industrial period appears rather stable, with the interruptions caused by volcanism. In this respect, all models

**Fig. 5.4** Near-surface annual precipitation in southern Africa (land areas between 10S-40S and 10E-45W) as simulated by a suite of climate models from the Climate Model Intercomparison Project CMIP5 over the period 850–2005 CE, (**a**) for the winter-rainfall zone and (**b**) summerrainfall zone (see Chap. 6). The time series represent deviations from the twentieth century mean precipitation and are smoothed with a 20-year running-mean filter

seem to agree. The possible reasons for the discrepancies between models and reconstructions are later discussed in Sect. 5.5.

Concerning past changes in precipitation, we again need to differentiate between the winter-rainfall zone and the summer-rainfall zone (see Chap. 2, Fig. 2.2). Since the mechanisms behind precipitation in these two zones are different, their longterm evolution could also diverge. The evolutions of simulated precipitation in these two zones in the past millennium are displayed in Fig. 5.4. Precipitation in the winter-rainfall zone (around Cape Town and along southern coast in the preindustrial period) is in all simulations generally larger than in the twentieth century. The decadal variations of the pre-industrial mean precipitation are, however, much larger than for temperature, and a few decades in the simulations are indeed as dry as some decades of the twentieth century. Generally, the models do seem to indicate a tendency towards a drier climate in this region in the recent decades, in accordance with the analysis of observational data shown in Chap. 6. Precipitation in the summer-rainfall zone (continental northeast during the pre-industrial period, Fig. 5.4b) also shows a slight tendency to be larger than during the twentieth century. However, the suite of models displays a larger spread than for the winter-rainfall zone, so that the uncertainty is here also larger. One model, again the model HadCM3, behaves rather differently from the others and shows the opposite result.

Are the variations of the precipitation in the summer-rainfall and winter-rainfall zones mutually related? This is an interesting question regarding future climate change. If, for instance, the see-saw pattern suggested by proxy data (Sect. 5.3) is temporally stable, it is plausible that it will also be present in future climate, with enhanced rainfall in the summer zone and weaker in the winter zone, or vice

versa. The model simulations, however, do not support this result. The temporal correlations, within each simulation, between annual mean precipitation in the two zones are very small (all cases smaller than 0.2). This happens irrespective of the timescale, for instance, after decadal or multi-decadal smoothing of the time series. All in all, the simulated precipitation shows rather wide interannual (not shown) and decadal variations, compared to any centennial or multi-centennial trends. The possible reasons for the disagreement between models and reconstructions are discussed in Sect. 5.5.

Variables other than temperature or precipitation may also be important to characterise past climates and estimate the impact of climate change on ecosystems. This is the case for wind, and more particularly coastal winds. Winds flowing along the western southern Africa coast are the main drivers of coastal upwelling in the Benguela Upwelling System and, therefore, are critical for the biological productivity of that ocean region. How upwelling could change under anthropogenic climate change was the focus of the hypothesis put forward by Bakun (1990); Bakun et al. (2015). According to this hypothesis, the intensity of upwellingfavourable winds should increase in the future due to the widening temperature difference between continental and oceanic surface caused by global warming. The studies that have analysed this hypothesis using observations or climate simulations are, however, not univocally conclusive (Sydeman et al. 2014). A confirmation by possible paleoclimate data from oceanic sediment cores, which may record past ocean productivity, and paleoclimate simulations could in theory shed light on this question. However, the analysis of paleoclimate simulations over the past millennium does not indicate that the intensity of upwelling-favourable winds had in the past varied hand-in-hand with global temperatures (Tim et al. 2016). Thus, these simulations do not in principle support Bakun's hypothesis. This can happen because the effect predicted by Bakun's hypothesis has been in the past too small compared to the natural wind variations, or because the modelled variations of past temperatures are unrealistically too narrow (Sect. 5.5). Other possible explanation assumes that state-of-the-art global climate models cannot realistically represent the reaction of coastal wind to variations in external forcing yet, due to their too coarse spatial resolution (Small et al. 2015).

In summary, the results from the global climate simulations regarding southern Africa can be interpreted as follows. The past evolution of temperatures clearly shows the impact of volcanic eruptions and, in the twentieth century, the impact of anthropogenic greenhouse gases. The twentieth century would have been the warmest of the past millennium. By contrast, precipitation would have varied little along the whole millennium, with a tendency of the twentieth century to be somewhat drier than the previous centuries but only in the winter-rainfall zone. The simulations do not show clear, well-defined, warm, cold, wet, or dry centuries in the pre-industrial centuries. Thus, the pre-industrial period appears in the simulations as a rather stable climatic background against which the 20th temperatures, but not so much precipitation, clearly stand out.

#### **5.5 Comparison Between Proxy Data and Model Simulations**

The combined analysis of climate simulations and climate reconstructions aims at a better understanding of the processes of climate variability and change and helps identify robust model results but also deficiencies that need to be addressed to increase our confidence in spatially detailed future climate projections.

The simulated temperatures over the past millennium show agreements with proxy-based reconstructions but also clear discrepancies. Both clearly suggest that the twentieth century has been a very warm period against the backdrop of the preindustrial centuries. However, looking further back into the past, several climate reconstructions using independent data do show a well-defined cold period—the Little Ice Age, around 1700 CE—differentiated from a prior warmer period during medieval times. The simulations do not show these thermally differentiated periods. Instead, the simulations yield rather stable pre-industrial temperatures, punctuated by decadal cooling episodes caused by volcanic eruptions, which do not extend over several centuries.

One reason for this discrepancy may lie in the external forcing used to drive the climate models. The forcing used in the CMIP5 model suite of past millennium simulations follows a commonly agreed protocol that incorporates the knowledge of past variations of solar output and volcanism. These estimations are to some extent uncertain. Whereas volcanic forcing is relatively short-lived, the variations of solar output display longer timescales, with cycles of several hundred years. The assumed amplitude of those solar variations of past solar output in the CMIP5 protocol represents a temporally narrower version of previous solar output reconstructions, which tended to have much wider, even tenfold, amplitude of variations (Schmidt et al. 2012). It is conceivable that this assumed reduction of the amplitude of past solar output variability turns out to be somewhat unrealistic. Stronger solar output variability in the past could have led to wider temperature variability at centennial timescales.

Concerning past precipitation, the proxy-based reconstructions in southern Africa indicate two important characteristics. One is the link between past precipitation and past temperature. Precipitation in the summer-rainfall zone varies, according to the reconstructions, in accordance with temperature, with lower precipitation during the Little Ice Age and higher precipitation during the Medieval Climate Anomaly. In the winter-rainfall zone, by contrast, the link between precipitation and temperature is opposite. Thus a second characteristic is the opposite variations in the summer-rainfall and winter-rainfall zones.

The simulations do not show this behaviour. Since the simulations do not present clearly defined thermal sub-periods, they cannot show a link between temperature and precipitation variations. Also, the simulated precipitation in the summer- and winter-rainfall zones does not appear anti-correlated in any of the model simulations. Should the see-saw character of precipitation variability be confirmed by further analysis of additional proxy records, it would raise the question

as to why the models fail in this respect, and whether this deficiency is critical for more robust projections of future precipitation.

It is known that the simulation of precipitation can be a challenge for global climate models. Due to their coarse resolution (Table 5.1), the much smaller-scale formation of clouds and the condensation of water vapour to liquid water cannot be explicitly represented in the models. Instead, they use heuristic equations that translate large-scale humidity convergence and atmosphere stability to average precipitation over a whole grid cell. These heuristic, empirically derived, equations are prone to errors. One possible explanation is, therefore, that precipitation in the summer-rainfall zone, which is caused by convective systems associated to the South Indian Convergence Zone (Cook 2000), might not be perfectly simulated. However, other studies raise the question as to whether climate models realistically represent the connection between large-scale patterns of climate variability, such as ENSO, and southern African rainfall. It has been found that the CMIP5 models do not replicate the observed link between southern African rainfall and ENSO (Dieppois et al. 2015, 2019). Therefore, a targeted investigation on whether this deficiency is solved in the next generation of climate models is needed to increase the confidence of future precipitation projections in this region.

Concerning climate reconstructions based on proxy data, we have to bear in mind that the amount of those types of data in the southern African region is sparse compared to other regions, such as Europe or North America. On the other hand, there exist a few sources of historical data that can be further exploited. One example is the collection of log books from the British Navy, which contain valuable information about wind intensity and duration at daily timescales. These data can be used to reconstruct atmospheric variables in the region over the few past centuries (Hannaford et al. 2015). A more dense network of proxy data could allow to construct spatially resolved gridded *climate field reconstructions* (Tingley et al. 2012) covering the whole region, instead of a collection of climate reconstructions that refer to the relatively smaller subregions where the proxy records are located. A gridded reconstruction covering the whole southern African region would allow a much better comparison with the output of climate models. This also remains a research focus for the future.

#### **5.6 Conclusions and Outlook**

The main conclusions that can be derived from this brief review of the climate of southern Africa over the past millennium are as follows:

• Proxy records, such as dendroclimatological data, indicate a succession of a relatively warm medieval period around 1000 CE, following by colder centuries denoted as the Little Ice Age, and a warm twentieth century.

These conclusions already indicate a possible way forward for future research. The disagreement between climate reconstructions and model simulations should be clarified in order to buttress the future climate projections obtained with those same climate models. Thereby, a particularly important aspect is the correlation between temperature and precipitation changes.

**Acknowledgments** This review was compiled in the frame of the project CASISAC (Changes in the Agulhas System and its impacts on southern African Coasts), funded by the German Federal Ministry of Education and Research (BMBF).

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **6 Southern Africa Climate Over the Recent Decades: Description, Variability and Trends**

Mathieu Rouault, Bastien Dieppois, Nele Tim, Birgit Hünicke, and Eduardo Zorita

#### **Abstract**

South of 15◦S, southern Africa has a subtropical climate, which is affected by temperate and tropical weather systems and comes under the influence of the Southern Hemisphere high-pressure systems. Most rainfall occurs in austral summer, but the southwest experiences winter rainfall. Much of the precipitation in summer is of convective origin forced by large-scale dynamics. There is a marked diurnal cycle in rainfall in summer. The El Niño Southern Oscillation (ENSO) influences interannual rainfall variability. In austral summer, drought tends to occur during El Niño, while above-normal rainfall conditions tend to follow La Niña. During El Niño, higher than normal atmospheric pressure anomalies, detrimental to rainfall, occur due to changes in the global atmospheric circulation. This also weakens the moisture transport from the Indian Ocean to the continent. The opposite mechanisms happen during La Niña. On top of the variability related to ENSO, the Pacific Ocean also influences the decadal variability of rainfall. Additionally, the Angola Current, the Agulhas Current, the Mozambique Channel and the southwest Indian Ocean affect rainfall variability. Over the last 40 to 60 years, near-surface temperatures have increased over almost the whole region, summer precipitation has increased south of 10◦S, and winter precipitation has mostly decreased in South Africa. Meanwhile, the

B. Dieppois (-)

Centre for Agroecology, Water and Resilience, Coventry University, Coventry, UK

N. Tim · B. Hünicke · E. Zorita Helmholtz-Zentrum Hereon, Institute for Coastal Systems – Analysis and Modeling, Geesthacht, Germany

M. Rouault

Nansen Tutu Center for Marine Environmental Research, Department of Oceanography, University of Cape Town, Cape Town, South Africa

Agulhas Current and the Angola Current have warmed, and the Benguela Current has cooled.

#### **6.1 Annual Cycle of Rainfall in Southern Africa**

Climatologically speaking, sub-Saharan Africa can be divided into four regions: (i) West Africa, a classic monsoon climate; (ii) East Africa, fed by an intense flux of moisture coming from the Indian Ocean; (iii) Central Africa, which has a tropical climate, hot and wet with rainfall maximum following an annual latitudinal pattern and (iv) southern Africa, a relatively dry and hot climate that can be found south of 15◦S to 20◦S depending on longitude (Fig. 6.1). Therefore, in this chapter, we will define southern Africa as the continental areas south of 15◦S. While there is a high range in annual rainfall and timing at the regional scale, southern Africa is characterized by distinct summer and winter rainfall regions (Figs. 6.2 and 6.3). There is also a region where rainfall is almost equal all year long to the far south. The longitudinal rainfall variation is due to a warm ocean to the east composed of the Agulhas Current, the Mozambique Channel and the southwest Indian Ocean. The longitudinal variation is also due to the South Atlantic high-pressure system's influence on the west, creating subsidence and preventing rainfall. The subsidence created to the east by the South Indian high-pressure system is offset by east-to-west moisture flux from the ocean to the continent and the presence of a convergence zone leading to air uplift and rain. Figure 6.2 shows the spatial distribution of the wettest month of the year in southern Africa. The red and blue domains are the area used to calculate the summer and winter rainfall total presented in Fig. 6.3 by averaging all grid points of each domain. It is also used to calculate the summer Standardized Precipitation Index (SPI) and calculate the Summer Rainfall Index (SRI), as was done by Dieppois et al. (2016, 2019).

**Fig. 6.1** Mean 1950–2016 Global Precipitation Climatology Centre (GPCC) January to December annual total rainfall (mm) for southern Africa

**Fig. 6.2** Spatial distribution of southern Africa's wettest month of the year (calendar month: red, summer: November to March; blue winter: April to September). Lines and colors delineate the area used to calculate summer and winter rainfall averages and indices (SRI and WRI)

**Fig. 6.3** Mean 1950–2016 annual cycle of rainfall in mm/month for southern Africa's southwest winter rainfall region (left) and summer rainfall region (right) as defined in Fig. 6.2

The greatest precipitation rate and maximum timing range are especially marked in South Africa. South Africa is divided into eight climatic regions by the South African Weather Service (Rouault and Richard 2003). The North-Western Cape has very little rainfall, with a winter maximum in June of 30 mm. The Southwestern Cape also has a maximum in June but differs with a maximum of 70 mm. Both regions constitute the winter rainfall region. The South Coast, which encompasses most of the Garden Route, experiences regular rainfall all year long, from about 30 mm to 40 mm per month, reaching about 100 mm in places over the Tsitsikamma region during October and November. There are even substantial spatial variations in rainfall within a region. For instance, Philippon et al. (2012) show that maximum monthly rainfall can vary from 240 mm to 30 mm per month for the Western Cape, primarily due to orography. The following five regions are part of the southern Africa summer rainfall region and have a maximum at different summer months. The Southern Interior has its maximum rainfall in late summer, March, with about 60 mm. The Western Interior has its rainfall maximum in January, with about 130 mm, but part of that region has a maximum in March (Dieppois et al. 2016). The Central Interior, KwaZulu-Natal and the North-Eastern Interior have maximum rainfall in January. KwaZulu-Natal is the wettest region, with a maximum in January of 130 mm. The North-Eastern Interior has the most significant difference between summer and winter. A comprehensive study of the annual cycle, regional differences and timings can be found in Favre et al. (2015). It indicates that while a rainfall deficit of 10 mm for a given month could be considered a drought in one region, it is not in another. Furthermore, the onset and demise of the rainy season are different for the eight areas making water management and mitigation of agricultural or hydrological drought difficult.

#### **6.2 Synoptic Drivers of Rainfall**

In southern Africa, much of the precipitation received in summer is of convective origin and forced by large-scale dynamics (Tyson and Preston-White 2000). Southern Africa has a subtropical climate and is affected by temperate and tropical weather systems. The country comes under the influence of the Southern Hemisphere high-pressure systems, but a heat low is found over the subcontinent in summer. This helps break the subsidence associated with high-pressure systems that prevent rain from occurring, allowing a diurnal cycle of rainfall to exist (Rouault et al. 2013). Most of the interior lies on an elevated plateau, and orography plays an important role in rainfall (Tyson and Preston-White 2000). The southwest region and the west coast receive most of their precipitation in austral winter through temperate systems such as cold fronts and cut-off lows, while the rest of the country gets most of its rainfall in austral summer. Summer rainfall is caused by large-scale synoptic systems leading to convection, but the diurnal cycle of rain has a substantial effect in the interior and along the East Coast. Such large rain-bearing systems include Tropical Temperate Troughs (TTTs; Chikoore and Jury 2010; Vigaud et al. 2012; Hart et al. 2012; Macron et al. 2014), Cut Off Lows (Favre et al. 2013), Mesoscale Convective Cloud systems (Blamey and Reason 2013) and occasionally tropical cyclones or tropical low-pressure systems associated with easterly waves (Malherbe et al. 2012). A substantial amount of rainfall in the summer rainfall region of southern Africa is due to TTTs and attendant cloud bands. The relative contribution of TTTs to rainfall during the summer ranges from 30% to 60%. While the annual cycle is dominant over southern Africa, intraseasonal rainfall oscillations occur with spectral peaks around 20 and 40 days associated with TTTs (Chikoore and Jury 2010). The Madden-Julian Oscillation also affects rainfall on a 40–60 days' timescale (Pohl et al. 2007). Over the South Coast, nearly half of the annual rainfall is due to ridging anticyclones (Engelbrecht et al. 2015). The South Indian Anticyclone over the southwest Indian Ocean, Mozambique Channel and the Agulhas Current also plays an essential role in the source of moisture transported onto the interior plateau (Rapolaki et al. 2020; Imbol et al. 2021). Severe weather and flooding can occur due to cut-off lows, especially when the South Indian Anticyclone is quasi-stationary or blocking. Cut-off lows contribute significantly to rainfall over South Africa, especially along the south and east coasts and the Karoo regions (Favre et al. 2013). With some regional exceptions, spring is the season of the most substantial contribution by cut-off lows to annual rainfall. Over the Kalahari region, the most considerable contribution by cut-off lows to rainfall occurs during late summer. In contrast, the coastal regions, particularly the south coast, receive the most considerable cut-off low contribution to rainfall during the winter season (Favre et al. 2013). Over the south coast, cut-off lows have been associated with the autumn rainfall peak (Engelbrecht et al. 2015). For South Africa, the standardized amplitudes, indicative of the strength of the diurnal cycle across a region, are most substantial over the interior and along the East Coast, with up to 70% explained variance of hourly rainfall associated with the diurnal cycle (Rouault et al. 2013). The time of maximum precipitation is late afternoon to early evening in the interior and midnight to early morning along the Agulhas Current and inland in the northeast of the country (Rouault et al. 2013). In general, annual mean rainfall is overestimated by the regional climate model in South Africa (Favre et al. 2015), and also ocean–atmosphere global climate models (Dieppois et al. 2015, 2019). However, despite these general overestimations of rainfall totals, regional climate models have been demonstrated to simulate the main features of the annual cycle in circulation patterns and rainfall realistically across the southern African region (Engelbrecht et al. 2015).

#### **6.3 Interannual Variability**

The climate of southern Africa is highly variable and vulnerable to extremes such as droughts and floods. During the last decades, much has been gained on how the oceans can influence the climate of southern Africa on interannual scale (Richard et al. 2000, 2001; Fauchereau et al. 2003, 2009; Pohl et al. 2010; Crétat et al. 2012).

ENSO is the primary driver of southern Africa's rainfall interannual variability. Figure 6.4a shows the average normalized sea surface temperature and rainfall anomalies during the mature phase of El Niño in austral summer since 1982. This would be the average difference from normal during a typical El Niño event in austral summer. The normalized anomaly, which is presented in Fig. 6.4a, is a departure from the mean of November to March divided by the corresponding climatological standard deviation for 11 El Niño events. For instance, for values superior to one mean the anomaly is above one standard deviation. Roughly 66% of the values are between −1 and −1 standard if the data is normally distributed.

**Fig. 6.4** (**a**), top: Average November–March sea surface temperature seasonal standardized anomalies during the mature phase of El Niño in austral summer (anomaly from the mean of November to March divided by the climatological corresponding standard deviation for 11 El Niño events). Blue/green is colder than normal; yellow/red is warmer than normal. (**b**), bottom: Average November–March rainfall seasonal standardized anomalies during the mature phase of El Niño in austral summer. Blue/green is wetter than normal; yellow/red is dryer than normal

As shown in Fig. 6.4a, most sea surface temperature anomalies are due to changes in global atmospheric and regional oceanic circulation, which are associated with El Niño. The impact of ENSO on sea surface temperature, land temperature, wind speed and rainfall offers predictability at the seasonal scale. This is because ENSO events' starting months precede the southern African rainy season, and atmospheric models relatively well reproduce this lagged relationship.

Note that we use the Oceanic Niño Index (ONI) provided by the Climate Prediction Center to detect El Niño and La Niña years on Figs. 6.4 and 6.5. In addition, in Fig. 6.4b, the precipitation rate is estimated from satellite remote sensing using the 2.5-by-2.5 degrees resolution Global Precipitation Climate Project dataset available since 1979. For sea surface temperature, we use the 1-by-1-degree resolution Reynolds SST Optimally Interpolated, available only since 1982. For that reason, we have selected the following 11 El Niños, 1982/1983, 1986/1987, 1987/1988, 1991/1992, 1994/1995, 1997/1998, 2002/2003, 2004/2005, 2006/2007, 2009/2010 and 2014/2015.

Although early works suggested that there were no strong relation between the strength of El Niño and the intensity and spatial extension of the southern African drought (Rouault and Richard 2003, 2005), Ullah et al. (2023) highlight significant statistical relationships between El Niño and large-scale extreme rainfall events. Indeed, seven of the ten strongest droughts since the 1950s happened during the

**Fig. 6.5** November to March southern Africa summer rainfall Standardized Precipitation Index from 1950 to 2016. Red bars denote El Niño, and blue bars denote La Niña austral summers according to the ONI

mature phase of El Niño (Fig. 6.5), this provides ample early warning for drought monitoring. Droughts lasting two seasons always involve El Niño (Rouault and Richard 2003, 2005). The relationship between El Niño and drought occurrence has been particularly strong since the late 1970s (Fauchereau et al. 2003; Richard et al. 2000, 2001). However, this statistical relationship is not linear. For instance, the 1997/1998 strong El Niño did not lead to widespread drought (Lyon and Mason 2007), and severe drought can occur during a weak El Niño (Rouault and Richard 2005). Such nonlinear behavior in the El Niño-rainfall teleconnection has a significant impact on seasonal forecasts built on linear statistical models or in coupled or noncoupled general atmospheric circulation models, which, therefore, tends to be overconfident in predicting drought when El Niño occurs (Landman and Beraki 2012). Nevertheless, it may be noted that seasonal forecasts are skillful over the summer rainfall region of southern Africa during periods exhibiting significant ENSO forcing, even more so during La Niña, while forecasts are generally not skillful during neutral years (Landman and Beraki 2012).

The mechanisms linking the Pacific and southern Africa climate are relatively well understood and reasonably well simulated in state-of-the-art climate models (Dieppois et al. 2016, 2019). During El Niño events, near-surface divergence, detrimental to rainfall, is observed over southern Africa due to changes in the Walker and Hadley circulations. Along with the anomalous near-surface divergence, highpressure anomalies (Fig. 6.6) over the landmass during El Niño also reduce the maritime moisture transport from the Indian Ocean to southern Africa.

A mid-tropospheric anticyclone becomes established and persistent during El Niño leading (not shown) to the anomalies presented in Fig. 6.6. Large-scale midtroposphere disturbances (around 5000 m) are present globally during the mature

**Fig. 6.6** Average December–February standardized El Niño normalized anomalies of the 500 hPa geopotential height in austral summer indicating global anomalies of atmospheric circulation in the mid-troposphere

phase of El Niño. There is a significant positive correlation between geopotential height anomalies over southern Africa at 500 hPa and the ONI Index. A more robust than normal Botswana high-pressure system causes enhanced subsidence for prolonged periods over much of the subcontinent. Moreover, El Niño is associated with extratropical atmospheric Rossby waves propagating poleward, influencing the southern hemisphere mid-latitude storm track (Fig. 6.6), and that could be responsible for an eastward shift of the South Indian Convergence Zone (Cook 2000, 2001). The eastward shift of the South Indian Convergence Zone is visible in Fig. 6.4b (bottom), where most of the large-scale synoptic-scale rain-bearing systems that affect southern Africa, such as Tropical temperate Troughs, preferably develop. The Indian Ocean warming during El Niño (Fig. 6.4a) also shifts atmospheric convection and rainfall eastward above the ocean, increasing subsidence above southern Africa and reducing the maritime moisture flux to the continent. During La Niña, a low-pressure anomaly is found over southern Africa, which favors the transport of moisture into southern Africa from the Indian Ocean, and consequently, rainfall is enhanced. Cut-off lows occur more frequently in a latitudinal band situated at lower latitudes during the La Niña years compared to El Niño years, during which these systems are located too far south of the country to contribute to seasonal rainfall amounts (Favre et al. 2012). El Niño and La Niña also change the wind strength along the coast and influence the ocean. In the upwelling system of the West Coast of South Africa, the Southern Benguela Current, El Niño often triggers lower than normal wind, weaker upwelling and warmer sea surface temperature due to the mid-latitude low-pressure anomalies shown in Fig. 6.6 (Rouault et al. 2010; Dufois and Rouault 2012; Tim et al. 2015; Blamey et al. 2015) and the opposite effect happens in the Northern Benguela (Rouault and Tomety 2022). During La Niña, the opposite occurs. Refer to Chap. 9 in this volume for more information on the northern and southern Benguela systems. El Niño also impacts the Western Cape rainfall and wind patterns in winter (Philippon et al. 2012).

Other modes of climate variability have been correlated to southern African rainfall, such as the Antarctic Annular Oscillation in winter, which is also called Southern Annular Mode (Reason and Rouault 2005; Malherbe et al. 2016) and the

**Table 6.1** NDJFM synchronous cross-correlation between Summer Rainfall Index Standardized Precipitation Index (SRI SPI), ONI, South Indian Ocean Subtropical Dipole (SIOD) and Antarctic Annular Oscillation (AAO). In bold are correlations significant at the 95% confidence level

South Indian Ocean Subtropical Dipole in summer (Behera and Yamagata 2001; Hoell et al. 2017).

Table 6.1 shows the correlation between the southern rainfall summer Standardized Precipitation Index, ONI, SIOD and AAO for austral summer (November to March). The strongest correlation, −0.67, is between ONI and SRI. There is a weaker correlation, 0.42, between SIOD and SRI but there is also a correlation of the same magnitude, −0.45, between ONI and SIOD, which means that the SIOD does not necessarily impact southern African rainfall directly but could be a symptom of the effect of ENSO on both the Indian Ocean and southern African rainfall although Hoell et al. (2017) proposed that the phase of the SIOD can disrupt or augment the southern Africa precipitation response to ENSO.

Next, we investigate the impact of ENSO on Water Management Areas and stream flow of South Africa, focusing on the seasonal average measured flow of water in all rivers of a specific Water Management Area. Observed stream flow monthly time series data were summed in summer from November to March per Water Management Areas from 1969 to 2004. The most striking result is that for Water Management Areas of South Africa situated in the summer rainfall region (Fig. 6.2), flows are between 1.6 and 2.8 times higher during La Niña years than during El Niño years (Rouault 2014). This confirms and extends the findings of Landman et al. (2001). For instance, from the 1970s, for WMA 6, Usutu Mhlathuze Swazi WMA (including the following major rivers: the Usutu River, Pongola River, Mhlathuze River, Mfolozi River and Mkuze River and covers the Oedertrouw dam and the am Mhlathuze River dam; Fig. 6.7), the five driest years are all El Niño years

**Fig. 6.7** Total summer stream flow volume averaged for all stations of the Water Management Area 6 (Usutu Mhlathuze Swazi). El Niño years are in red, and La Niña years are in blue. Stream flow is summed from November to March

(2015/2016, 1982/1983, 1994/1995, 2014/2015 and 1991/1992), and four out of five wettest years are La Niña years (1983/1984, 1987/1988, 2000/2001, 1988/1989 and 2010/2011). Four of the five lowest stream flow volume summer seasons occurred during El Niño (1982/1983, 2015/2016, 1994/1995, 1984/1985 and 1991/1992), although one occurred during la Niña. Three out of five of the highest stream flow volume years happened during La Niña.

Regional oceanographic and atmospheric features that have been reported to influence the southern African climate are the Agulhas Current (Jury et al. 1993; Nkwinkwa Njouodo et al. 2018; Imbol Nkwinkwa et al. 2021), the Angola Current (Rouault et al. 2003; Desbiolles et al. 2020; Koseki and Imbol Koungue 2021), the Mozambique Channel (Barimalala et al. 2020), the Angola low-pressure system (Crétat et al. 2019; Desbiolles et al. 2020) and the Botswana high-pressure system (Driver and Reason 2017). The Southern Annular Mode (Reason and Rouault 2005; Malherbe et al. 2016), the South Indian Ocean Dipole (Behera and Yamagata 2001; Hoell et al. 2017; Dieppois et al. 2016, 2019; Ullah et al. 2023) have also been linked to climate variability in southern Africa.

#### **6.4 Decadal Variability of Southern Africa's Climate**

As illustrated in Fig. 6.8, while interannual variability (2–8 years) mainly related to ENSO explains approximately 70% of the total rainfall variance on average over southern Africa, a third of rainfall variability is explained by variations on quasi-decadal (8–13 years) and interdecadal timescales (15–23 years; Dieppois et al. 2016, 2019). In addition, we note that the amplitudes of those decadal variations strengthen and weakened over the twentieth century in summer and winter rainfall areas (Dieppois et al. 2016). Teleconnections with global sea surface temperature, in particular, the Pacific Ocean and atmospheric circulation anomalies, reminiscent of the impact of ENSO, were shown to impact southern Africa's summer and winter rainfall at the interdecadal and quasi-decadal timescales (Dieppois et al. 2016, 2019). This could help understanding why, during some periods, ENSO does not strongly influence southern Africa as a cold or warm sea surface temperature background in the Pacific could make the impact of ENSO on the atmosphere weaker or stronger (Fauchereau et al. 2009; Pohl et al. 2018). This might also help understand the development of various ENSO flavors and their contrasted effects on southern Africa (Ratnam et al. 2014; Hoell et al. 2017). Indeed, the development of ENSO flavors might partly result from interactions between Pacific SST anomalies occurring on different timescales (e.g.*,* Pacific Decadal Oscillation SST anomalies influencing ENSO anomalies). In austral summer, on interdecadal timescales, decadal ENSO-like forcing of the Pacific Decadal Oscillation (PDO) decadal variance indeed leads to shifts in the Walker circulation. At the regional scale, it contributes to ocean-atmosphere anomalies in the South Indian Ocean to shift the South Indian Convergence Zone toward the continent. According to Dieppois et al. (2016, 2019), combinations of ENSO forcing and ocean–atmosphere anomalies in the South Indian Ocean are thus needed in the relationship between Pacific Decadal

**Fig. 6.8** Timescale patterns of variability for summer (SRI) and winter (WRI) southern African rainfall index (**a**) Global wavelet spectra of the SRI (red) and the WRI (blue), and of every gridpoint used for their calculations (light red and blue). The dashed blue and red lines indicate the red noise spectra (**b–c**) Continuous wavelet power spectrum of the SRI and the WRI. Bold lines delineate the area under which power can be underestimated due to edge effects, wraparound effects and zero padding; thin contour lines show the 95% confidence limits. Note that SRI and WRI variability could be poorly represented from the mid-1990s due to a deficient number of rain gauges available in the Climatic Research Unit (CRU TS version 3.23) precipitation and temperature dataset that could substantially reduce interannual to interdecadal variability

Oscillation and southern African rainfall. The Interdecadal Pacific Oscillation (IPO) also drives such anomalies at the quasi-decadal timescale. At each timescale, colder or warmer Pacific SSTs result in changes to the Walker circulation: these interact with ocean-atmospheric modifications in the South Indian Ocean, which act together with varying degrees of importance to alter the intensity and longitudinal location of the South Indian Convergence Zone and, thus, to modulate the Tropical Trough developments and deep-convection over southern Africa (Dieppois et al. 2016, 2019; Pohl et al. 2018). The influence of tropical Pacific Ocean climate variability on austral winter southern African rainfall variability is almost negligible over the twentieth century. However, according to Philippon et al. (2012), the influence of tropical Pacific climate variability may have become more important since the 1970s. Decadal winter rainfall variability is strongly related to regional changes in the Southern hemisphere's subtropical high-pressure system and, thus, mid-latitude westerly low-pressure activity (Dieppois et al. 2016). This corroborates previous findings by Reason and Rouault (2005), highlighting that the Southern Annual Mode (SAM) influence is strong in austral winter.

#### **6.5 Current Climate Trends**

The increase in the concentration of atmospheric greenhouse gases is causing a global rise in temperatures and possibly also trends in precipitation, as the content of water vapor in the atmosphere has increased. However, regional climate change may be somewhat different from the global picture, since regional orography, proximity to water masses and atmospheric circulation may also modulate regional climate trends. In the following paragraphs, we present a summary of temperature and precipitation trends in southern Africa over the last decades, as these two variables are probably the most important for stakeholders. However, one should bear in mind that trends in water availability for societal needs may also be impacted by nonclimatic management decisions (Muller 2018), so water scarcity maybe not always linked to climate trends. In addition, and as discussed in the previous section, precipitation in southern Africa is prone to significant natural decadal variations (Dieppois et al. 2016, 2019; Mahlalela et al. 2019), so trends derived over the recent decades may result from the impact of greenhouse gas forcing but may also reflect shorter natural swings. Disentangling both, i.e., unambiguously attributing recent observed trends to anthropogenic climate forcing, requires a detailed analysis of observations and model simulations. In that sense, only a few studies highlighted an increase in the likelihood of extreme climate conditions, notably drought and fire-prone weather, in the southwestern regions of southern Africa (Otto et al. 2018; Zscheischler and Lehner 2022; Liu et al. 2023). In addition, according to climate future projections, Pohl et al. (2017) highlighted that southern Africa could expect fewer rainy days and more extreme rainfall by the end of the twenty-first century. This is also consistent with recent analyses identifying an increased severity of drought in South Africa from the mid-1980s (Phaduli 2018; Jury 2018). Therefore, an ongoing analysis of all available data sets to identify long-term trends in societal climate impacts due to climate change or other human impacts is paramount.

Beyond South Africa, southern Africa is not as densely covered by a net of meteorological stations as western Europe or North America, so estimating longterm climate trends from point observations may provide only a partial illustration of regional climate trends. Previous studies focusing on long-term trends were mainly based on meteorological stations' data (Jury 2018; Kruger and Nxumalo 2017; Ullah et al. 2021). Here, we use climate reanalysis and observations.

The reanalysis is essentially a retrospective hindcast using weather prediction models that incorporate the information from available meteorological observations. The trends in precipitation and air temperature over the last decades are shown here with the global reanalysis data sets, ERA5, of the ECMWF (European Centre for Medium-Range Weather Forecasts, Hersbach et al. 2020) and the observational data set CRU (Climate Research Units gridded time-series data set; Harris et al. 2020). Both are analyzed over the period 1979–2020. In Fig. 6.9, the trends in precipitation of the two data sets are shown for the two rainfall seasons, winter rainfall in June–August (JJA) and summer rainfall in December–February (DJF). The summer rainfall zone (SRZ) covers most of southern Africa, and the winter

**Fig. 6.9** Precipitation trends of CRU (**a**, **b**) and ERA5 reanalysis (**c**, **d**). Subfigures **a**, and **c**, left, are for the winter rainfall season (June–July–August), and subfigures **b** and **d**, right, are for the summer rainfall season (December–January–February). The winter rainfall zone is marked with black rectangular. Trends are given in mm/decade for the period 1979–2020. Based on typical standard deviation, trends of (**a**) 0.77, (**b**) 15.76, (**c**) 2.14 and (**d**) 31.8 mm/decade and larger are significant at a 95% level

rainfall zone (WRZ, black box in Figure 6.9a and c) covers the cape region in the southwest of South Africa.

For the winter rainfall zone, CRU and ERA5 provide somewhat different results. CRU shows a slight decrease in precipitation (Fig. 6.9a), while ERA5 shows an increase in the southwest of the winter rainfall zone (Fig. 6.9c), which has been found previously by Onyutha (2018) and MacKellar et al. (2014a, 2014b). In DJF in the summer rainfall zone, the precipitation has increased in most parts of southern Africa (Fig. 6.9b, d, Onyutha 2018; MacKellar et al. 2014a, 2014b; Kruger and Nxumalo 2017). Ullah et al. (2021) found a significant trend in the intensity of wet spells from 1965 to 2015 using daily observation and ERA5. Both CRU and ERA5 data sets show a drying at parts of the South African coast, which has also been found at the southeast coast by Jury (2018). Thus, the precipitation has mainly increased in the SRZ and decreased in the winter rainfall zone over the last 40 years. It must also be considered that the assessment provided by the sixth IPCC assessment report does not differentiate between summer and winter precipitation (Ranasinghe et al. 2021), so it is not directly comparable with the assessment provided here. Also, the available precipitation data sets do not always agree in this region, illustrating their inherent uncertainty.

Air temperature trends near the surface are shown in Fig. 6.10. The temperature has risen along the coast in both seasons. This has also been found in station data sets (Jury 2013) and in the pace of record-setting temperatures (McBride et al. 2021). In addition, ERA5 shows an area in central southern Africa where cooling or no trend occurred in DJF over the analyzed period. An even more pronounced cooling has been detected in other reanalysis data sets (e.g., ERA-Interim and JRA-55) and has also been found in annual mean trends by Jury (2013). Further analysis of the

**Fig. 6.10** 2 m air temperature trends of CRU (**a**, **b**) and ERA5 reanalysis (**c**, **d**). Subfigures **a**, and **c**, left, are for winter (June–July–August), and subfigures **b** and **d**, right, are for summer (December–January–February). Trends are given in ◦C/decade for the period 1979–2020. Based on typical standard deviation, trends of (**a**) 0.09, (**b**) 0.11, (**c**) 0.19 and (**d**) 0.16 ◦C/decade and larger are significant at a 95% level

**Fig. 6.11** Sea surface temperature (SST) trends of HadISST1, (**a**, left) for winter (June–July– August), (**b**, right) for summer (December–January–February). Trends are given in ◦C/period, which is 1958–2019. Based on typical standard deviation, trends of (**a**) 0.17 and (**b**) 0.35 degree/period and larger are significant at a 95% level

JRA-55 data set has shown that this cooling trend is likely an artifact of the data assimilation procedure. Fewer observational data can be assimilated in earlier years, and the introduction of increasingly more data can lead to this false-negative trend. The warming along the coast is accompanied by the warming of the adjacent oceans. The trends of sea surface temperature, as shown in Fig. 6.11, have been calculated using the interpolated observational data set, HadISST1 (Rayner et al. 2003). SSTs in the Agulhas Current and in the retroflexion, the region where the Agulhas Current turns eastward, have increased (Fig. 6.11 and Rouault et al. 2009), while SSTs in the Benguela Current have decreased (Fig. 6.11).

This warming in the Agulhas Current along the African coast of the Indian Ocean is upwind of the African continent. It may contribute to the positive trend in precipitation as the sea surface temperature of the Agulhas Current system influences southern Africa's rainfall (Nkwinkwa Njouodo et al. 2018; Imbol Nkwinkwa et al. 2021). The warming of the ocean off Angola could also have contributed to increased inland rainfall due to more significant atmospheric water content and subsequently increased moisture flux from the tropical Atlantic (Rouault et al. 2003).

To sum up, as found in basically all data sets analyzed here, the Agulhas Current, the Angola Current, the tropical Atlantic and the Indian Ocean have warmed, nearsurface air temperatures along the coasts have risen, and rainfall has increased over large parts of the summer rainfall zone of southern Africa during austral summer while it has decreased over coastal areas of South Africa in both rainfall seasons of the last few decades.

### **6.6 Further Research Questions**

Over the last ten years, much progress has been made in understanding the climate and weather of southern Africa, which has led to a significant number of publications in peer-reviewed journals, and which our paper cannot pay homage due to their sheer numbers. Seasonal forecasts of rainfall are used in the region and are more reliable during El Niño and La Niña. It is essential to maintain the precipitation and temperature observing system in South Africa and develop it in the other countries of southern Africa especially because we want to precisely quantify the effect of global climate change in southern Africa. It would also be essential to foster transdisciplinary studies involving social and economic sciences. Questions, which must be further explored in the future, remain, e.g.,

**Acknowledgments** This work was supported by the NRF SARCHI chair on "modeling oceanatmosphere-land interactions," the SPACE 2 project, the Nansen Tutu Centre, the EU H2020 TRIATLAS project under the grant agreement 817578, the Belmont Forum project EXEBUS and the PECO2 project. We like to thank NOAA for the NOAA OI V2 SST *data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA.* We also thank Copernicus and ECMWF for making the ERA5 dataset easily accessible.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## Francois A. Engelbrecht, Jessica Steinkopf, Jonathan Padavatan, and Guy F. Midgley

#### **Abstract**

Southern Africa is a climate change hotspot with projected warming and drying trends amplifying stresses in a naturally warm, dry and water-stressed region. Despite model-projected uncertainty in rainfall change over the eastern escarpment of South Africa, strong model agreement in projections indicates that southern African is likely to become generally drier. Sharply increased regional warming and associated strong reductions in soil-moisture availability and increases in heat-waves and high fire-danger days are virtually certain under low mitigation futures. Changes are detectible in observed climate trends for the last few decades, including regional warming, drying in both the summer and winter rainfall regions, and increases in intense rainfall events. The southern African climate is at risk of tipping into a new regime, with unprecedented impacts, such as day-zero drought in the Gauteng province of South Africa, collapse of the maize and cattle industries, heat-waves of unprecedented intensity and southward shifts in intense tropical cyclone landfalls. Many of these adverse changes could be avoided if the Paris Accord's global goal were to be achieved, but research is urgently required to quantify the probabilities of such tipping points in relation to future levels of global warming. Adaptation planning is an urgent regional priority.

F. A. Engelbrecht (-) · J. Steinkopf · J. Padavatan

Global Change Institute, Faculty of Science, University of the Witwatersrand, Witwatersrand, South Africa

e-mail<EMAIL_ADDRESS>
G. F. Midgley School for Climate Studies, Stellenbosch University, Stellenbosch, South Africa

#### **7.1 Introduction**

Southern Africa (here defined as Africa south of 10◦S) was classified as a climate change hotspot by the Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5◦C (SR1.5; Hoegh-Guldberg et al. 2018). It is a region with a warm climate and pronounced wet-dry seasonality, which is acknowledged to be water stressed in the context of naturally occurring droughts (Chap. 6), a growing population and the industrial ambitions of a developing economy (Chap. 4). Under low mitigation emission scenarios southern Africa is certain to become substantively warmer and likely drier (Engelbrecht et al. 2009, 2015b; Engelbrecht and Engelbrecht 2016; Hoegh-Guldberg et al. 2018; Lee et al. 2021), justifying its classification as a climate change hotspot, as under such scenarios, the options for adaptation will become limited. While drying is a general projection for the region, climate models also project likely subregional increases in intense rainfall events in eastern southern Africa, including the eastern escarpment region of South Africa and Mozambique, reflecting longer dry spells between more intense downpours (Ranasinghe et al. 2021).