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© University of Hamburg 2018
All rights reserved
Klaus Hess Publishers
Göttingen & Windhoek
www.k-hess-verlag.de
ISBN: 978-3-933117-95-3 (Germany), 978-99916-57-43-1 (Namibia)
Language editing: Will Simonson (Cambridge), and Proofreading Pal
Translation of abstracts to Portuguese: Ana Filipa Guerra Silva Gomes da Piedade
Page desing & layout: Marit Arnold, Klaus A. Hess, Ria Henning-Lohmann
Cover photographs:
front: Thunderstorm approaching a village on the Angolan Central Plateau (Rasmus Revermann)
back: Fire in the miombo woodlands, Zambia (David Parduhn)
Cover Design: Ria Henning-Lohmann
ISSN 1613-9801
Printed in Germany
Suggestion for citations:
Volume:
Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens, N. (eds.) (2018)
Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and
solutions. Biodiversity & Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek.
Articles (example):
Archer, E., Engelbrecht, F., Hänsler, A., Landman, W., Tadross, M. & Helmschrot, J. (2018) Seasonal
prediction and regional climate projections for southern Africa. In: Climate change and adaptive land
management in southern Africa – assessments, changes, challenges, and solutions (ed. by Revermann, R.,
Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens, N.), pp. 14–21, Biodiversity
& Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek.
Corrections brought to our attention will be published at the following location:
http://www.biodiversity-plants.de/biodivers_ecol/biodivers_ecol.php
Biodiversity & Ecology
Journal of the Division Biodiversity, Evolution and Ecology of Plants,
Institute for Plant Science and Microbiology, University of Hamburg
Volume 6:
Climate change and adaptive land management
in southern Africa
Assessments, changes, challenges, and solutions
Edited by
Rasmus Revermann1, Kristin M. Krewenka1, Ute Schmiedel1,
Jane M. Olwoch2, Jörg Helmschrot2,3, Norbert Jürgens1
1 Institute for Plant Science and Microbiology, University of Hamburg
2 Southern African Science Service Centre for Climate Change and Adaptive Land Management
3 Department of Soil Science, Faculty of AgriSciences, Stellenbosch University
Hamburg 2018
RPlease cite the article as follows:
Archer, E., Engelbrecht, F., Hänsler, A., Landman, W., Tadross, M. & Helmschrot, J. (2018)
Seasonal prediction and regional climate projections for southern Africa. In: Climate change and
adaptive land management in southern Africa – assessments, changes, challenges, and solutions
(ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens,
N.), pp. 14-21, Biodiversity & Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek.
doi:10.7809/b-e.00296
14 C A
Climate
Abstract: Temperatures over southern Africa have been increasing rapidly over the last fi ve decades, at a rate of about twice
the global rate of temperature increase. Further drastic increases, in the order of 6°C by the end of the century relative to the
present-day climate, may occur over the central and western interior regions under low-mitigation futures. Moreover, south-
ern Africa is projected to become generally drier under low-mitigation climate change futures. Such changes will leave little
room for adaptation in a region that is already characterised as dry and hot. Impacts on crop and livestock farming may well
be devastating, and signifi cant changes may occur in terms of vegetation cover in the savannas, particularly in the presence
of human-induced land degradation. Under modest to high mitigation, southern Africa will still experience further climate
change, but amplitudes of change will be reduced, potentially leaving more room for adaptation. Skilful seasonal forecasts
may become an increasingly important adaptation tool in southern Africa, especially when combined with a robust weather
station monitoring network.
Resumo: A temperatura no Sul de África tem vindo a aumentar rapidamente ao longo das últimas cinco décadas, a uma
taxa de cerca do dobro da global. Aumentos adicionais drásticos, na ordem dos 6°C até ao fi nal do século em relação ao
clima actual, poderão ocorrer nas regiões interiores centrais e ocidentais sob cenários futuros de baixa mitigação. Além dis-
so, prevê-se que o Sul de África irá tornar-se geralmente mais seco sob cenários futuros de baixa mitigação das alterações
climáticas. Tais alterações deixarão pouco espaço para a adaptação numa região que já é caracterizada como seca e quente.
Os impactos na agricultura e na pecuária poderão ser devastadores, e alterações signifi cativas poderão ocorrer em termos
de cobertura vegetativa nas savanas, particularmente na presença de degradação da terra induzida pelo Homem. Com uma
mitigação média-alta, o Sul de África continua infl uenciado pelas alterações climáticas, mas as amplitudes são reduzidas,
deixando potencialmente mais espaço para a adaptação. Previsões sazonais competentes poderão tornar-se numa ferramenta
de adaptação cada vez mais importante no Sul de África, especialmente quando combinadas com redes robustas de moni-
torização por estações meteorológicas.
Seasonal prediction and regional climate
projections for southern Africa
Emma Archer1,5*, Francois Engelbrecht3,7, Andreas Hänsler2, Willem Landman4, Mark Tadross6, Jörg Helmschrot 8,9
1 CSIR Natural Resources and the Environment, Building 1, cnr Carlow and Rustenburg Roads, Emmarentia 2193,
Johannesburg, South Africa
2 Climate Service Center Germany (GERICS), Chilehaus – Eingang B, Fischertwiete 1, 20095 Hamburg, Germany
3 CSIR Natural Resources and the Environment, Building 33, Meiring Naude Road, Pretoria 0001, South Africa
4 Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Private Bag X20,
Hatfi eld 0028, South Africa
5 Global Change Institute, University of the Witwatersrand, 1 Jan Smuts Avenue, Braamfontein 2000, Johannesburg,
South Africa
6 UNDP/Climate Systems Analysis Group, University of Cape Town, Pvt Bag, Rondebosch 7700, South Africa
7 School of Geography, Archaeology and Environmental Sciences, University of the Witwatersrand, Private Bag 2,
WITS 2050, South Africa
8 SASSCAL Regional Secretariat, 28 Robert Mugabe Avenue, Windhoek, Namibia
9 Stellenbosch University, Faculty of AgriSciences, PO Box X1, Stellenbosch 7602, South Africa
* Corresponding author: earcher@csir.co.za
B E 6 2018 15
Climate
Introduction
The past few years in southern Africa (in
both the summer and winter rainfall re-
gions) have demonstrated yet again the
vulnerability of the subcontinent to cli-
mate variability. Multi-year below-nor-
mal summer rainfall has had a severe im-
pact on key sectors, including agriculture
and water, as have multiple more recent
winters with below-normal rainfall (see,
for example, Archer et al., 2017).
Such conditions have highlighted the
need for climate science in the region
that truly enables us to both predict con-
ditions of climatic risk in the shorter to
the longer term and to use such infor-
mation to improve short- and long-term
readiness (Winsemius et al., 2014). In
this overview article, we describe work
in climate prediction undertaken on both
longer-term climate change projections
and seasonal early warning. We conclude
by a brief discussion of the essentials be-
yond climate science, where we may po-
tentially eff ectively translate information
into real utility.
Projections of future
climate change over
southern Africa
Later in this chapter, we consider climate
observations and data availability; and it
should be noted at the start of discuss-
ing the latest fi ndings in terms of climate
change projections for the continent that
observation and data gaps remain a sig-
nifi cant concern. Observed data also con-
strain our work in the area of seasonal
forecasting and early warning (see sec-
tion to follow). Figure 4, for example,
shows the uneven coverage of observed
climate data for the continent, particu-
larly outside of South Africa. That limi-
tation notwithstanding (and we provide
more detail later in the chapter), substan-
tive work has been undertaken in terms of
climate change projections on the conti-
nent. Climate change is projected to have
widespread impacts in southern African
during the 21st century, particulalrly un-
der low-mitigation futures (Niang et al.,
2014). Temperatures are projected to rise
rapidly, at 1.5 to 2 times the global rate
of temperature increase (James & Wash-
ington, 2013; Engelbrecht et al., 2015).
Indeed, the observed rate of temperature
increase is particularly high over the in-
terior regions of southern Africa. Here
temperature trends as high as a 2 to 3.6°C
increase per century have been recorded
over the period 1961–2010 (Engelbrecht
et al., 2015; Kruger & Sekele, 2013). In
addition to the projected increases in sur-
face temperature, the southern African
region is also projected to become gener-
ally drier under e nhanced anthropogenic
forcing (Christensen et al., 2007; Engel-
brecht et al., 2009; Haensler et al., 2010,
2011; James & Washington, 2013; Niang
et al., 2014). These regional changes
will plausibly have a range of impacts
in southern Africa, including impacts on
energy demand (in terms of achieving hu-
man comfort in buildings and factories),
agriculture (e.g., reductions of yield in
the maize crop under higher tempera-
tures and reduced soil moisture; Land-
man et al., 2017), livestock production
(e.g., higher cattle mortality as a result of
oppressive temperatures), water security
(through reduced rainfall and enhanced
evapotranspiration; Engelbrecht et al.,
2015) and human health (through oppres-
sive temperatures; Garland et al., 2015).
Moreover, climate change is to take
place not only through changes in aver-
age temperature and rainfall patterns, but
also through changes in the attributes of
extreme weather events. For the southern
African region, generally drier condi-
tions and the more frequent occurrence of
dry spells are plausible over most of the
interior (Christensen et al., 2007; Engel-
brecht et al., 2009; Haensler et al., 2011).
Tropical cyclone tracks are projected
to shift northward, bringing more fl ood
events to northern Mozambique and
fewer to the Limpopo province in South
Africa (Malherbe et al., 2013). Cut-off
low related fl ood events are also pro-
jected to occur less frequently in South
Africa (e.g., Engelbrecht et al., 2013) in
response to a poleward displacement of
the westerly wind regime. Intense thun-
derstorms plausibly may occur more
frequently over South Africa in a gener-
ally warmer climate (e.g., Engelbrecht
et al., 2013). Perhaps most important is
that the regional changes in circulation
that are plausible over southern Africa,
in particular an increase in the frequency
and intensity of mid-level high-pressure
systems, may plausibly induce the more
frequent occurrence of heat-wave events
over the region (e.g., Engelbrecht et al.,
2015; Garland et al., 2015).
It is against this background that a fo-
cused eff ort was made to further explore
the climate change futures of southern
Africa through a coordinated SASSCAL
research programme, in addition to other
research active in the subcontinent and
on the continent more broadly. At the
CSIR in South Africa and at the Cli-
mate Service Center Germany (GER-
ICS), the most recent global circulation
model (GCM) projections of the Coupled
Model Intercomparison Project Phase
Five (CMIP5) and Assessment Report
Five (AR5) of the I ntergovernmental
Panel on Climate Change (IPCC) were
downscaled to 50 km resolution over Af-
rica. These simulations are for the period
1961 to 2100, follow the experimental
design recommended by the Coordinated
Downscaling Experiment (CORDEX),
and have been derived for low- (Rep-
resentative Concentration Pathway 8.5
[RCP8.5]), modest-high- (RCP4.5) and
high-mitigation (RCP2.6) scenarios. The
data of these simulations are also made
available to the international science
community via the CORDEX databases.
The regional climate model used at the
CSIR is the conformal-cubic atmospher-
ic model (CCAM), a variable-resolution
global climate model (GCM) developed
by the Commonwealth Scientifi c and In-
dustrial Research Organisation (CSIRO)
(McGregor, 2005). At GERICS the
simulations have been conducted with
the REMO regional climate model. For
each of the RCPs, six diff erent GCMs
were downscaled, so that the results pre-
sented below are based on an ensemble
of possible future developments. The
CCAM simulations were performed on
supercomputers at the Centre for High-
Performance Computing (CHPC) of the
Meraka Institute of the CSIR in South
Africa; the REMO simulations were con-
ducted at the German Climate Comput-
ing Center in Hamburg, Germany.
The CCAM projected changes in an-
nual rainfall over southern Africa are
16 C A
Climate
shown in Figure 1 for the far-future
period 2080–2099 compared to the
present-day (1971–2000). A general pat-
tern of rainfall decreases is projected for
subtropical southern Africa. An excep-
tion is Mozambique, where rainfall in-
creases are projected for the central and
northern parts in particular. There is some
uncertainty in the projections over the in-
terior of the central subcontinent, where
a minority of projections indicate rainfall
increases over specifi c regions, or de-
creases that are small in amplitude. The
largest rainfall decreases are projected
for Angola and over the southern parts of
South Africa. The projected decreases in
Angola may be occurring in conjunction
with changes in the Angola low-pressure
system and the general strengthening of
the subtropical high-pressure belt over
southern Africa (e.g., Engelbrecht et al.,
2009). Over South Africa, the rainfall
decreases projected for the southwest-
ern Cape are occurring in association
with a poleward displacement of the
westerlies and frontal systems under low
mitigation (e.g., Christensen et al., 2007;
Engelbrecht et al., 2009).
Drastic temperature increases of 4–7°C
are projected to occur over the western
interior regions of southern Africa under
low mitigation (Fig. 2). Relatively small-
er increases are projected for Mozam-
bique (where general increases in rainfall
and cloud cover are projected) and along
the coastal areas (due to the moderating
eff ects of the ocean).
Incorporating 16 regional climate
change projections conducted by GER-
ICS (using the REMO model) and other
institutions in the frame of the CORDEX
initiative for the southern African region,
analyses of projected changes for a set
of climate indices along various tran-
sects over the SASSCAL region have
been conducted based on larger regional
climate model ensembles for RCP4.5
and RCP8.5. The median projection of
change in annual maximum temperature
is about 3°C (RCP4.5) to 5°C (RCP8.5)
in the interior and about 1.5 to 2°C less
at the coastal areas in the west, east, and
south. The spread between the diff erent
simulations is about 2°C (RCP4.5) to
3°C (RCP8.5), leading to a maximum
projected increase in maximum annual
temperature of about 7°C (RCP8.5) over
the semi-arid to arid western parts of the
SASSCAL region (Fig. 3). The CCAM
projections (Fig. 2) are consistent with
the range of changes projected by the
CORDEX ensemble (Fig. 3).
The SASSCAL projections and analy-
ses convey a clear message that a low-
mitigation climate future may have
devastating impacts on the southern
African region. Drastically rising aver-
age temperatures and related extreme
events (e.g., very hot days, heat-wave
days, and high fi re danger) are plausi-
ble to have a negative impact on crop
yield, livestock production, and human
health. The general reductions in rain-
fall may induce further stress for rainfed
agriculture in the region. For example,
the Kalahari Desert receives annual pre-
cipitation rates of about 250 mm in the
arid south-western parts and rising to
more than 600 mm towards the centre
and north-east of Botswana. For the end
of the century, not only rising tempera-
tures are projected but also a reduction
in the annual rainfall rate. With a later
onset of the rainy season and an earlier
cessation, the number of dry days out-
side the rainy season increases and the
rainy season itself shortens. As a result,
semi-arid and arid domains are estimated
to expand by 5–8%, infl uencing the eco-
system and its vegetation, hydrology,
Figure 1: CCAM projected change in the annual average rainfall totals (units 10*mm/
day) over southern Africa at 50 km resolution, for the time period 2080–2099 relative to
1971–2000. The downscalings were obtained from six diff erent CMIP5 GCM projections
under low mitigation (RCP8.5).
B E 6 2018 17
Climate
and human proceedings (Stringer et al.,
2009). Climate change has already been
noticeably present during the past dec-
ades (Kusangaya et al., 2014), and the
associated intensifi cation and expansion
of agriculture and livestock farming has
reinforced land use pressure. Due to the
absence of suffi cient surface water re-
sources, groundwater resources are used
to address the rising demand for water
and, consequently, the number of wells
and boreholes in the Kalahari Desert
has increased remarkably during the
last century (Christelis & Struckmeier,
2011). Projected climate extremes, in
combination with population growth,
may cause an overutilization of limited
Figure 2: CCAM projected change in the annual average temperature (°C) over southern
Africa at 50 km resolution, for the time period 2080–2099 relative to 1971–2000. The
downscalings were obtained from six diff erent CMIP5 GCM projections under low mitigation
(RCP8.5).
resources in central Botswana, which in
turn may cause migration to other areas.
Also, coastlines may be aff ected.
While the west coast of southern Af-
rica is comparably dry (< 500 mm), the
east coast receives more rainfall (700–
1200 mm), with a decreasing trend from
north to south. As shown by Oltmanns
(2015), projections indicate a decline in
precipitation for most coastlines, except
for northern Mozambique, for which an
increase by approximately 10% is pro-
jected. A similar tendency can be seen
for rainfall intensity. The west coast will
barely experience extreme events (more
than 20 mm/day), but an increase in ex-
treme events is projected for the north-
ern Mozambican coastline (declining
slightly towards the south). Although
aspects of agriculture in Mozambique
may benefi t from an increase in rainfall,
the country simultaneously needs to pre-
pare for the likelihood of an increasing
number of fl ood events associated with
landfalling tropical lows and cyclones
under climate change. The plausibility
of a signifi cant reduction of rainfall over
the mega-dam region of South Africa is
a further cause for concern. Even under
modest-high mitigation, southern Africa
will experience potentially signifi cant
changes in the regional climate. Over the
interior regions, temperature increases
may well still reach values of 3–4°C, and
it remains plausible that the region will
become generally drier. Nevertheless,
temperature increases under modest-high
mitigation, though signifi cant, are on the
order of half the amplitude of changes
under low mitigation. This implies the
availability of more options for adapta-
tion and more time to adapt before criti-
cal temperature thresholds are exceeded
for the fi rst time.
It is important to consider what the
implications of the projected changes in
climate may be for vegetation in south-
ern Africa, particularly in the savannas,
where complex interactions occur be-
tween grasses, trees, fi re, and CO2 (Bond
& Midgley, 2012). In fact, rising levels
of CO2 strongly favour trees over grasses
in the savannas, potentially causing bush
encroachment and spawning the hypoth-
esis of the “forestation of Africa” under
climate change (West et al., 2012). How-
ever, the substantial reductions in rainfall
projected for southern Angola and Zam-
bia in particular, in combination with
more frequent fi res occurring under dras-
tic temperature increases (Engelbrecht et
al., 2015) and human-induced land deg-
radation, may in fact result in decreasing
tree cover in the savannas (Engelbrecht
& Engelbrecht, 2016). Dynamic vegeta-
tion-fi re models that can also incorporate
scenarios of human-induced changes
in land use are required to objectively
project the vegetation future of south-
ern Africa, yet few such models have to
date been developed and applied over the
region.
18 C A
Climate
Seasonal variability and
early warning
Southern African seasonal climate
anomalies are (generally) predictable
(Barnston et al., 1996), although work
in this area remains challenged by the
lack of observational data in certain ar-
eas (see section to follow). The notion of
a predictable climate, further supported
by the discovery in the 1980s of the El
Niño-Southern Oscillation (ENSO) phe-
nomenon as a primary driver of seasonal-
to-interannual variability over the region
(Ropelewski & Halpert, 1987, 1989),
led to the development of operational
seasonal prediction systems for rainfall
(Mason, 1998; Jury et al., 1999) and for
temperature (Klopper et al., 1998). The
initial modelling in southern Africa was
undertaken mainly from the early 1990s
by a number of institutions that devel-
oped statistical seasonal forecast models
(Mason, 1998; Jury et al., 1999; Land-
man & Mason, 1999). A few years later,
in the early 2000s, atmospheric general
circulation models (AGCMs) for opera-
tional seasonal forecasting and research
began to be used (e.g. Landman et al.,
2001). Major advances in seasonal fore-
cast system and infrastructure develop-
ment have occurred since then, including
the World Meteorological Organisation’s
recognition of the South African Weather
Service (SAWS) as a Global Producing
Centre for Long-Range Forecasting, the
development of objective mult i-model
forecasting systems for southern Africa
(Landman & Beraki, 2012), and, signifi -
cantly, the development of a fully cou-
pled ocean-atmosphere model at SAWS
for operational seasonal forecast produc-
tion (Beraki et al., 2014). Nested regional
climate models as seasonal forecasting
tools were also investigated (Landman et
al., 2005, 2009; Kgatuke et al., 2008; Rat-
nam et al., 2011). A review on aspects of
seasonal forecast development in South
Africa can be found in Landman (2014).
After forecasts were demonstrated to
obtain the highest levels of skill when
statistical methods and global model
forecasts are blended into a multi-tiered
forecast system (Landman et al., 2001), a
move away from compiling operational
forecasts subjectively through consensus
discussions was introduced by making
use of objective multi-model forecast
systems (Landman & Beraki, 2012). Over
the past 10 years or so, modelling advanc-
es obtained locally were largely focused
on the development, testing, and use of
fully coupled ocean-atmosphere models
in seasonal forecast production (Beraki et
al., 2012; Landman et al., 2012), the dem-
onstrated potential of forecasts through
the development of objective applications
models (Malherbe et al., 2014), and the
modelling of intra-seasonal characteris-
tics (Engelbrecht et al., 2017).
Notwithstanding these developments,
a number of caveats regarding season-
al forecasting in South Africa may be
identifi ed that require the attention of
modellers, forecast producers, and us-
ers of forecasts. These include (but are
not limited to) the need to demonstrate
the benefi ts derived from using seasonal
forecasts, including fi nancial benefi ts;
expanding on the knowledge of current
skill levels and identifying factors limit-
ing forecast skill; the development and
testing of forecast systems for areas of
southern Africa largely neglected up to
now (i.e., the south-western and south-
ern Cape); the development of schemes
40°0'0"E30°0'0"E20°0'0"E10°0'0"E
10°0'0"S20°0'0"S30°0'0"S
Location of the Transects
¯
0 500 1.000
kilometres
Cartography:
V. Oltm anns
2015-06-12
Figure 3: Range of projected changes in annual maximum temperature along an east-west
(25S) and a north-south (20E) transect for the time period 2071–2100 relative to 1971–2000
for the RCP4.5 and RCP8.5 scenarios. For each of the scenarios, the projections are based
on an ensemble of 16 transient regional climate change simulations from the CORDEX Af-
rica database. The black line represents the median change. The dark-grey area refl ects the
range defi ned by the 25th to 75th percentiles of all simulations centred on the median. The
light grey area spans the range between the ensemble minimum and maximum. Figures are
taken from Oltmanns (2015).
B E 6 2018 19
Climate
for process-based verifi cation; the build-
ing of so-called earth system models for
improved forecasts through, for example,
data assimilation systems and tropical-
extra-tropical ocean-land-atmosphere
coupling; the operational production of
forecasts to address seasonal character-
istics such as onset, cessation, and sub-
seasonal variations; the production and
testing of high spatial and temporal reso-
lution forecasts; operational applications
model development; and, through co-
production, the development of method-
ologies to better communicate seasonal
forecast information to a variety of users
in terms of complexity and application.
Data gaps and needs
Lötter et al. (2018) describe a key chal-
lenge in the SADC region as being the
lack of long-term reliable climate re-
cords, particularly outside of South
Africa. Such records are essential both
for measurement and interpretation of
current trends (e.g., Kruger & Sekele,
2013; Engelbrecht et al., 2015) and for
providing the critical ability to interpret
the occurrence of extreme events against
the historical record. In addition, a robust
observation network supports a range of
tasks from shorter-term forecasting to
seasonal predictions to multi-decadal cli-
mate change projections (Engelbrecht et
al., 2011) through the process of model
evaluation and validation and by provid-
ing options for statistical downscaling
(e.g., Landman et al., 2017). It also sup-
ports adaptation eff orts, such as climate
index–driven insurance schemes (e.g.,
Malherbe et al., 2018).
Figure 4 (from Lötter et al., 2018)
shows the sparseness of climate records,
making it evident that certain areas are
particularly poorly served. It may be
noted that in this regard SASSCAL has
in recent years made a considerable ef-
fort to rescue historic climate data and to
expand the weather station observational
network in Namibia, Botswana, Zambia,
and Angola (Kaspar et al., 2015; Muche
et al., 2018; Posada et al., 2018).
Moving forward
At a time of recent and current drought
in both southern Africa’s summer and
winter rainfall periods, it is an opportune
moment to consider the role of climate
prediction in supporting both shorter-
term coping and longer-term adaptation
to climate variability and change. While
improved prediction can by no means
stand alone in support of improved re-
sponse, improvements are essential at
both a national and regional level. It is
hoped that such improvements in pre-
diction as those detailed here (including
attention to gaps in data and the obser-
vational network) might be matched with
improved support for response and adap-
tation to support the evolution of a more
resilient subcontinent.
a
b
Figure 4: The locations of NOAA’s Global Historical Climate Network (GHCN) weather
stations, as used by CRU, across Africa (a) and the number of weather stations collecting
daily temperature records across southern Africa from 1850 to 2014 used in the gridded
CRUTEM4 product (b). Station density increased consistently from the start of the 20th
century and peaked in the 1970s, after which it began to decline. Source: Davis & Vincent,
2017 (reproduced with the permission of the authors).
20 C A
Climate
Acknowledgements
The research was carried out in the
framework of SASSCAL and was spon-
sored by the German Federal Ministry of
Education and Research (BMBF) under
promotion number 01LG1201M. The
CCAM simulations contributed by the
CSIR as a contribution to the SASSCAL
climate tasks were performed on the
supercomputers of the Centre for High
Performance Computing (CHPC) of
the Meraka Institute of the CSIR. Co-
funding for this work was provided by a
CSIR Parliamentary Grant project on the
development of the fi rst African-based
Earth System Model. Further co-funding
was provided by the NERC project UM-
FULA, grant number NE/M02007X/1.
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Marumbwa, F. (2017) Understanding the evo-
lution of the 2014–2016 summer rainfall sea-
sons in southern Africa: key lessons. Climate
Risk Management, 16, 22–28.
Barnston, A.G., Thiao, W. & Kumar, V. (1996)
Long-lead forecasts of seasonal precipitation
in Africa using CCA. Weather Forecast, 11,
506–520.
Beraki, A.F., DeWitt, D., Landman, W.A. &
Oliver, C. (2014) Dynamical seasonal climate
prediction using an ocean-atmosphere cou-
pled climate model developed in partnership
between South Africa and the IRI. J Climate,
27, 1719–1741.
Bond, W.J. & Midgley, G.F. (2012) Carbon di-
oxide and the uneasy interactions of trees and
savannah grasses. Philosophical Transactions
of the Royal Society B, 367, 601–612.
Christelis, G. & Struckmeier, W. (2011) Ground-
water in Namibia. An explanation to the hy-
drogeological map. Namibian Ministry of
Agriculture, Water and Rural Development,
2, 1–132.
Christensen, J.H., Hewitson, B., Busuioc, A.,
Chen, A., Gao, X., Held, I., Jones, R., Kolli,
R.K., Kwon, W-T., Laprise, R., Magana Rue-
da, V., Mearns, L., Menendez, C.G., Raisanen,
J., Rinke, A., Sarr, A. & Whetton, P. (2007)
Regional climate projections. Climate change
2007: the physical science basis (ed. by S. Sol-
omon, D. Qin, M. Manning, Z. Chen, M. Mar-
quis, A.B. Averyt, M. Tignor and H.L. Miller).
Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge Univer-
sity Press, Cambridge, UK.
Davis-Reddy, C. & Vincent, K. (2017) Climate
risk and vulnerability: a handbook for south-
ern Africa, 2nd ed. Council for Scientifi c and
Industrial Research, Pretoria, South Africa.
Engelbrecht, C.J. & Engelbrecht, F.A. (2016)
Shifts in Köppen-Geiger climate zones over
southern Africa in relation to key global tem-
perature goals. Theoretical and applied clima-
tology, 123, 247–261. DOI 10.1007/s00704-
014-1354-1
Engelbrecht, C.J., Engelbrecht, F.A. & Dyson,
L.L. (2013) High-resolution model projected
changes in mid-tropospheric closed-lows and
extreme rainfall events over southern Africa.
International Journal of Climatology, 33,
173–187. doi:10.1002/joc.3420
Engelbrecht, C.J., Landman, W.A., Graham, R.J.
& McLean, P. (2017). Seasonal predictive
skill of intraseasonal synoptic type variability
over the Cape south coast of South Africa by
making use of the Met Offi ce Global Seasonal
Forecast system 5. International Journal of
Climatology, 37, 1998-2012.
Engelbrecht, F.A., Landman, W.A., Engelbrecht,
C.J., Landman, S., Roux, B., Bopape, M.M.,
McGregor, J.L. & Thatcher, M. (2011) Multi-
scale climate modelling over southern Africa
using a variable-resolution global model. Wa-
ter SA, 37, 647–658.
Engelbrecht, F.A., McGregor, J.L. & Engel-
brecht, C.J. (2009) Dynamics of the con-
formal-cubic atmospheric model projected
climate-change signal over southern Africa.
International Journal of Climatology, 29,
1013–1033.
Engelbrecht, F., Adegoke, J., Bopape, M.M.,
Naidoo, M., Garland, R., Thatcher, M.,
McGregor, J., Katzfey, J., Werner, M., Ichoku,
C. & Gatebe, C. (2015) Projections of rapidly
rising surface temperatures over Africa under
low mitigation. Environmental Research Let-
ters, 10. doi: 10.1088/1748-9326/10/8/085004
Garland, R., Matooane, M., Engelbrecht, F.A.,
Bopape, M., Landman, W., Naidoo, M., Van
der Merwe, J. & Wright, C. (2015) Regional
projections of extreme apparent temperature
days in Africa and the related potential risk
to human health. International Journal of En-
vironmental Research and Public Health, 12,
12577–12604.
Haensler, A., Hagemann, S. & Jacob, D. (2010)
How the future climate of the southern Afri-
can region might look like: results of a high-
resolution regional climate change projection.
Nova Acta Leopoldina, 112 , 183–193.
Haensler, A., Hagemann, S., & Jacob, D. (2011)
The role of the simulation setup in a long-term
high-resolution climate change projection for
the southern African region. Theoretical and
Applied Climatology, 106, 153-169.
James, R. & Washington, R. (2013) Changes in
African temperature and precipitation associ-
ated with degrees of global warming. Climatic
Change, 117, 859–872. doi:10.1007/s10584-
012-0581-7
Jury, M.R., Mulenga, H.M. & Mason, S.J. (1999)
Exploratory longrange models to estimate
summer climate variability over southern Af-
rica. Journal of Climate, 12, 1892–1899.
Kaspar, F., Helmschrot, J., Mhanda, A. et al.
(2015) The SASSCAL contribution to climate
observation, climate data management and
data rescue in Southern Africa. Advances in
Science and Research, 12, 171–177.
Kgatuke, M.M., Landman, W.A., Beraki, A. &
Mbedzi, M. (2008) The internal variability of
the RegCM3 over South Africa. International
Journal of Climatology, 28, 505–520.
Klopper, E., Landman, W.A. and Van Heerden,
J. (1998) The predictability of seasonal maxi-
mum temperature in South Africa. Interna-
tional Journal of Climatology, 18, 741–758.
Kruger, A.C. & Sekele, S.S. (2013) Trends in
extreme temperature indices in South Africa:
1962–2009. International Journal of Clima-
tology, 33, 661–676.
Kusangaya, S., Warburton, M., Archer van
Garderen, E. & Jewitt, G. (2014) Impacts of
climate change on water resources in southern
Africa: a review. Physics and Chemistry of the
Earth, 67, 47–54.
Landman, W.A. (2014) How the International
Research Institute for Climate and Society has
contributed towards seasonal climate forecast
modelling and operations in South Africa.
Earth Perspectives, 1, 22.
Landman, W.A. & Beraki, A. (2012) Multi-
model forecast skill for mid-summer rainfall
over southern Africa. International Journal of
Climatology, 32, 303–314.
Landman, W.A., DeWitt, D. Lee, D.-E., Beraki,
A. & Lötter, D. (2012). Seasonal rainfall pre-
diction skill over South Africa: 1- vs. 2-tiered
forecasting systems. Weather and Forecast-
ing, 27, 489-501.
Landman, W.A., Engelbrecht, F.A., Hewitson,
B., Malherbe, J., & Van der Merwe, J. (2017)
Towards bridging the gap between climate
change projections and maize producers in
South Africa. Theoretical and Applied Clima-
tology, 11-17.
Landman, W.A., Kgatuke, M.M., Mbedzi, M.,
Beraki, A., Bartman, A. & du Piesanie, A.
(2009) Performance comparison of some dy-
namical and empirical downscaling methods
for South Africa from a seasonal climate mod-
elling perspective. International Journal of
Climatology 29, 1535-1549.
Landman, W.A. & Mason, S.J. (1999) Opera-
tional long-lead prediction of South African
rainfall using canonical correlation analysis.
International Journal of Climatology, 19,
1073–1090.
Landman, W.A., Mason, S.J., Tyson, P.D. &
Tennant, W.J. (2001) Retro-active skill of
multitiered forecasts of summer rainfall over
southern Africa. International Journal of Cli-
matology, 21, 1–19.
Landman, W.A., Seth, A., Camargo, S.J. (2005)
The eff ect of regional climate model domain
choice on the simulation of tropical cyclone-
like vortices in the southwestern Indian Ocean.
Journal of Climate, 18, 1263–1274.
Lötter, D., Davis, C., Archer, E., Vincent, L., Par-
doe, J., Tadross, M., Landman. W., Stuart-Hill,
S. & Tadross, M. (2018) Climate information
needs in southern Africa. CCCEP Working Pa-
per (under review).
Malherbe, J., Engelbrecht, F.A. & Landman,
W.A. (2013) Projected changes in tropical
cyclone climatology and landfall in the South-
west Indian Ocean region under enhanced an-
thropogenic forcing. Climate Dynamics, 40,
2867–2886.
Malherbe, J., Iyahen, E., Engelbrecht, F., Cha-
munorwa, M. & Helmschrot, J. (2018) The
Extreme Climate Index (ECI), a tool for moni-
toring regional extreme events. This volume.
Malherbe, J., Landman, W.A., Olivier, C., Sa-
kuma, H. & Luo, J.-J. (2014). Seasonal fore-
casts of the SINTEX-F coupled model applied
to maize yield and streamfl ow estimates over
B E 6 2018 21
Climate
north-eastern South Africa. Meteorological
Applications, 21, 733-742.
Mason, S.J. (1998) Seasonal forecasting of South
African rainfall using a non-linear discrimi-
nant analysis model. International Journal of
Climatology, 18, 147–164.
McGregor, J.L. (2005) C-CAM geometric as-
pects and dynamical formulation. CSIRO At-
mospheric Research Technical Paper, 70.
Muche, G., Kruger, S., Hillmann, T. et al. (2018)
SASSCAL WeatherNet: present state, chal-
lenges, and achievements of the regional cli-
matic observation network and database. This
volume.
Niang, I., Ruppel, O.C., Abdrabo, M.A., Essel,
A., Lennard, C., Padgham, J. & Urquhart, P.
(2014) Africa. Climate change 2014: impacts,
adaptation, and vulnerability. Part B: region-
al aspects (ed. by V.R. Barros, C.B. Field, D.J.
Dokken et al.), pp. 1199–1265. Cambridge
University Press, Cambridge, UK.
Oltmanns, V. (2015). Analysing regional rainfall
and temperature dynamics in Southern Africa
using CORDEX climate projections. MSc the-
sis, Friedrich Schiller University Jena (unpub-
lished).
Posada R., Riede, J., Kaspar, F., Mhanda, A.,
Radithupa, M., Stegling, J., Nascimento, D.,
Tima, L., Kanyanga, J., Nkonde, E., Swaswa,
M. & Waitolo, D. (2018) Cooperation of me-
teorological services within SASSCAL on im-
proving the management of observed climate
data. This volume.
Ratnam, J.V., Behera, S.K., Masumoto, Y., Taka-
hashi, K. & Yamagata, T. (2011) A simple re-
gional coupled model experiment for summer-
time climate simulation over southern Africa.
Climate Dynamics, 39, 2207–2217.
Ropelewski, C.F. & Halpert, M.S. (1987) Global
and regional scale precipitation patterns asso-
ciated with the El Niño–Southern Oscillation.
Monthly weather review, 115, 1606–1626.
Ropelewski, C.F. & Halpert, M.S. (1989) Pre-
cipitation patterns associated with the high
index of the Southern Oscillation. Journal of Cli-
mate, 2, 268–284.
Stringer, L., Dyer, J., Reed, M., Dougill, A.,
Twyman, C. & Mkwambisi, D. (2009) Adap-
tations to climate change, drought and deser-
tifi cation: local insights to enhance policy in
southern Africa. Environmental Science and
Policy, 12, 748–765.
West, A.G., Midgley, G.F. & Bond, W.J. (2012)
The reforestation of Africa? South African
Journal of Science, 108, 2–4.
Winsemius, H.C., Dutra, E., Engelbrecht, F.A.,
Archer Van Garderen, E., Wetterhall, F.,
Pappenberger, F. & Werner, M.G.F. (2014)
The potential value of seasonal forecasts in a
changing climate in southern Africa. Hydrolo-
gy and Earth System Sciences, 18, 1525–1538.
References [CrossRef]
Archer, E.R.M., Landman, W., Tadross, M.,
Malherbe, J., Weepener, H., Maluleke, P.
and Marumbwa, F. (2017) Understanding
the evolution of the 2014–2016 summer
rainfall seasons in southern Africa: key
lessons. Climate Risk Management, 16, 22–
28. CrossRef
Barnston, A.G., Thiao, W. & Kumar, V. (1996)
Long-lead forecasts of seasonal precipitation
in Africa using CCA. Weather Forecast, 11,
506–520. CrossRef
Beraki, A.F., DeWitt, D., Landman, W.A. &
Oliver, C. (2014) Dynamical seasonal
climate prediction using an ocean-
atmosphere coupled climate model
developed in partnership between South
Africa and the IRI. J Climate, 27, 1719–
1741. CrossRef
Bond, W.J. & Midgley, G.F. (2012) Carbon
dioxide and the uneasy interactions of trees
and savannah grasses. Philosophical
Transactions of the Royal Society B, 367,
601–612. CrossRef
Christelis, G. & Struckmeier, W. (2011)
Groundwater in Namibia. An explanation to
the hydrogeological map. Namibian
Ministry of Agriculture, Water and Rural
Development, 2, 1–132.
Christensen, J.H., Hewitson, B., Busuioc, A.,
Chen, A., Gao, X., Held, I., Jones, R., Kolli,
R.K., Kwon, W-T., Laprise, R., Magana
Rueda, V., Mearns, L., Menendez, C.G.,
Raisanen, J., Rinke, A., Sarr, A. & Whetton,
P. (2007) Regional climate projections.
Climate change 2007: the physical science
basis (ed. by S. Solomon, D. Qin, M.
Manning, Z. Chen, M. Marquis, A.B.
Averyt, M. Tignor and H.L. Miller).
Contribution of Working Group I to the
Fourth Assessment Report of the
Intergovernmental Panel on Climate
Change. Cambridge University Press,
Cambridge, UK.
Davis-Reddy, C. & Vincent, K. (2017) Climate
risk and vulnerability: a handbook for
southern Africa, 2nd ed. Council for
Scientific and Industrial Research, Pretoria,
South Africa.
Engelbrecht, C.J. & Engelbrecht, F.A. (2016)
Shifts in Köppen-Geiger climate zones over
southern Africa in relation to key global
temperature goals. Theoretical and applied
climatology, 123, 247–261. CrossRef
Engelbrecht, C.J., Engelbrecht, F.A. & Dyson,
L.L. (2013) High-resolution model projected
changes in mid-tropospheric closed-lows
and extreme rainfall events over southern
Africa. International Journal of
Climatology, 33, 173–187. CrossRef
Engelbrecht, C.J., Landman, W.A., Graham,
R.J. & McLean, P. (2017). Seasonal
predictive skill of intraseasonal synoptic
type variability over the Cape south coast of
South Africa by making use of the Met
Office Global Seasonal Forecast system 5.
International Journal of Climatology, 37,
1998-2012. CrossRef
Engelbrecht, F.A., Landman, W.A.,
Engelbrecht, C.J., Landman, S., Roux, B.,
Bopape, M.M., McGregor, J.L. & Thatcher,
M. (2011) Multi-scale climate modelling
over southern Africa using a variable-
resolution global model. Water SA, 37, 647–
658. CrossRef
Engelbrecht, F.A., McGregor, J.L. &
Engelbrecht, C.J. (2009) Dynamics of the
conformal-cubic atmospheric model
projected climate-change signal over
southern Africa. International Journal of
Climatology, 29, 1013–1033. CrossRef
Engelbrecht, F., Adegoke, J., Bopape, M.M.,
Naidoo, M., Garland, R., Thatcher, M.,
McGregor, J., Katzfey, J., Werner, M.,
Ichoku, C. & Gatebe, C. (2015) Projections
of rapidly rising surface temperatures over
Africa under low mitigation. Environmental
Research Letters, 10. CrossRef
Garland, R., Matooane, M., Engelbrecht, F.A.,
Bopape, M., Landman, W., Naidoo, M., Van
der Merwe, J. & Wright, C. (2015) Regional
projections of extreme apparent temperature
days in Africa and the related potential risk
to human health. International Journal of
Environmental Research and Public Health,
12, 12577–12604. CrossRef
Haensler, A., Hagemann, S. & Jacob, D. (2010)
How the future climate of the southern
African region might look like: results of a
high-resolution regional climate change
projection. Nova Acta Leopoldina, 112, 183–
193.
Haensler, A., Hagemann, S., & Jacob, D.
(2011) The role of the simulation setup in a
long-term high-resolution climate change
projection for the southern African region.
Theoretical and Applied Climatology, 106,
153-169. CrossRef
James, R. & Washington, R. (2013) Changes in
African temperature and precipitation
associated with degrees of global warming.
Climatic Change, 117, 859–872. CrossRef
Jury, M.R., Mulenga, H.M. & Mason, S.J.
(1999) Exploratory longrange models to
estimate summer climate variability over
southern Africa. Journal of Climate, 12,
1892–1899. CrossRef
Kaspar, F., Helmschrot, J., Mhanda, A. et al.
(2015) The SASSCAL contribution to
climate observation, climate data
management and data rescue in Southern
Africa. Advances in Science and Research,
12, 171–177. CrossRef
Kgatuke, M.M., Landman, W.A., Beraki, A. &
Mbedzi, M. (2008) The internal variability
of the RegCM3 over South Africa.
International Journal of Climatology, 28,
505–520. CrossRef
Klopper, E., Landman, W.A. and Van Heerden,
J. (1998) The predictability of seasonal
maximum temperature in South Africa.
International Journal of Climatology, 18,
741–758. CrossRef
Kruger, A.C. & Sekele, S.S. (2013) Trends in
extreme temperature indices in South Africa:
1962–2009. International Journal of
Climatology, 33, 661–676. CrossRef
Kusangaya, S., Warburton, M., Archer van
Garderen, E. & Jewitt, G. (2014) Impacts of
climate change on water resources in
southern Africa: a review. Physics and
Chemistry of the Earth, 67, 47–54. CrossRef
Landman, W.A. (2014) How the International
Research Institute for Climate and Society
has contributed towards seasonal climate
forecast modelling and operations in South
Africa. Earth Perspectives, 1, 22. CrossRef
Landman, W.A. & Beraki, A. (2012) Multi-
model forecast skill for mid-summer rainfall
over southern Africa. International Journal
of Climatology, 32, 303–314. CrossRef
Landman, W.A., DeWitt, D. Lee, D.-E.,
Beraki, A. & Lötter, D. (2012). Seasonal
rainfall prediction skill over South Africa: 1-
vs. 2-tiered forecasting systems. Weather
and Forecasting, 27, 489-501. CrossRef
Landman, W.A., Engelbrecht, F.A., Hewitson,
B., Malherbe, J., & Van der Merwe, J.
(2017) Towards bridging the gap between
climate change projections and maize
producers in South Africa. Theoretical and
Applied Climatology, 11-17. CrossRef
Landman, W.A., Kgatuke, M.M., Mbedzi, M.,
Beraki, A., Bartman, A. & du Piesanie, A.
(2009) Performance comparison of some
dynamical and empirical downscaling
methods for South Africa from a seasonal
climate modelling perspective. International
Journal of Climatology 29, 1535-1549.
CrossRef
Landman, W.A. & Mason, S.J. (1999)
Operational long-lead prediction of South
African rainfall using canonical correlation
analysis. International Journal of
Climatology, 19, 1073–1090. CrossRef
Landman, W.A., Mason, S.J., Tyson, P.D. &
Tennant, W.J. (2001) Retro-active skill of
multitiered forecasts of summer rainfall over
southern Africa. International Journal of
Climatology, 21, 1–19. CrossRef
Landman, W.A., Seth, A., Camargo, S.J.
(2005) The effect of regional climate model
domain choice on the simulation of tropical
cyclone-like vortices in the southwestern
Indian Ocean. Journal of Climate, 18, 1263–
1274. CrossRef
Lötter, D., Davis, C., Archer, E., Vincent, L.,
Pardoe, J., Tadross, M., Landman. W.,
Stuart-Hill, S. & Tadross, M. (2018) Climate
information needs in southern Africa.
CCCEP Working Paper (under review).
Malherbe, J., Engelbrecht, F.A. & Landman,
W.A. (2013) Projected changes in tropical
cyclone climatology and landfall in the
Southwest Indian Ocean region under
enhanced anthropogenic forcing. Climate
Dynamics, 40, 2867–2886. CrossRef
Malherbe, J., Iyahen, E., Engelbrecht, F.,
Chamunorwa, M. & Helmschrot, J. (2018)
The Extreme Climate Index (ECI), a tool for
monitoring regional extreme events. This
volume. CrossRef
Malherbe, J., Landman, W.A., Olivier, C.,
Sakuma, H. & Luo, J.-J. (2014). Seasonal
forecasts of the SINTEX-F coupled model
applied to maize yield and streamflow
estimates over north-eastern South Africa.
Meteorological Applications, 21, 733-742.
CrossRef
Mason, S.J. (1998) Seasonal forecasting of
South African rainfall using a non-linear
discriminant analysis model. International
Journal of Climatology, 18, 147–164.
CrossRef
McGregor, J.L. (2005) C-CAM geometric
aspects and dynamical formulation. CSIRO
Atmospheric Research Technical Paper, 70.
Muche, G., Kruger, S., Hillmann, T. et al.
(2018) SASSCAL WeatherNet: present
state, challenges and achievements of the
regional climatic observation network and
database. This volume. CrossRef
Niang, I., Ruppel, O.C., Abdrabo, M.A., Essel,
A., Lennard, C., Padgham, J. & Urquhart, P.
(2014) Africa. Climate change 2014:
impacts, adaptation, and vulnerability. Part
B: regional aspects (ed. by V.R. Barros,
C.B. Field, D.J. Dokken et al.), pp. 1199–
1265. Cambridge University Press,
Cambridge, UK.
Oltmanns, V. (2015). Analysing regional
rainfall and temperature dynamics in
Southern Africa using CORDEX climate
projections. MSc thesis, Friedrich Schiller
University Jena (unpublished).
Posada R., Riede, J., Kaspar, F., Mhanda, A.,
Radithupa, M., Stegling, J., Nascimento, D.,
Tima, L., Kanyanga, J., Nkonde, E.,
Swaswa, M. & Waitolo, D. (2018)
Cooperation of meteorological services
within SASSCAL on improving the
management of observed climate data. This
volume. CrossRef
Ratnam, J.V., Behera, S.K., Masumoto, Y.,
Takahashi, K. & Yamagata, T. (2011) A
simple regional coupled model experiment
for summer-time climate simulation over
southern Africa. Climate Dynamics, 39,
2207–2217. CrossRef
Ropelewski, C.F. & Halpert, M.S. (1987)
Global and regional scale precipitation
patterns associated with the El Niño–
Southern Oscillation. Monthly weather
review, 115, 1606–1626. CrossRef
Ropelewski, C.F. & Halpert, M.S. (1989)
Precipitation patterns associated with the
high index of the Southern Oscillation.
Journal of Climate, 2, 268–284. CrossRef
Stringer, L., Dyer, J., Reed, M., Dougill, A.,
Twyman, C. & Mkwambisi, D. (2009)
Adaptations to climate change, drought and
desertification: local insights to enhance
policy in southern Africa. Environmental
Science and Policy, 12, 748–765. CrossRef
West, A.G., Midgley, G.F. & Bond, W.J.
(2012) The reforestation of Africa? South
African Journal of Science, 108, 2–4.
CrossRef
Winsemius, H.C., Dutra, E., Engelbrecht, F.A.,
Archer Van Garderen, E., Wetterhall, F.,
Pappenberger, F. & Werner, M.G.F. (2014)
The potential value of seasonal forecasts in a
changing climate in southern Africa.
Hydrology and Earth System Sciences, 18,
1525–1538. CrossRef