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African Climate Change: 1900-2100 Revised Manuscript for Climate Research Hulme et al., 12 April 2000

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African Climate Change: 1900-2100

Mike Hulme1, Ruth Doherty1, Todd Ngara2, Mark New3 and David Lister1

1 Climatic Research Unit, School of Environmental Sciences, University of East Anglia,Norwich NR4 7TJ, UK

2 Climate Change Office, Ministry of Mines, Environment and Tourism, P.Bag 7753 Causeway,Harare, Zimbabwe

3 School of Geography, Mansfield Road, University of Oxford, Oxford OX1 3TB

Corresponding Author: [email protected]

Revised for Climate Research, 12 April 2000

Abstract

This paper reviews observed (1900-2000) and possible future (2000-2100) continent-wide changes intemperature and rainfall for Africa. For the historic period we draw upon a new observed global climatedata set which allows us to explore aspects of regional climate change related to diurnal temperature rangeand rainfall variability. The latter includes an investigation of regions where seasonal rainfall is sensitiveto El Niño climate variability. This review of past climate change provides the context for our scenariosof future greenhouse gas-induced climate change in Africa. These scenarios draw upon the newpreliminary emissions scenarios prepared for the Intergovernmental Panel on Climate Change's ThirdAssessment Report, a suite of recent global climate model experiments, and a simple climate model to linkthese two sets of analyses. We present a range of four climate futures for Africa, focusing on changes inboth continental and regional seasonal-mean temperature and rainfall. Estimates of associated changes inglobal CO2 concentration and global-mean sea-level change are also supplied. These scenarios drawupon some of the most recent climate modelling work. We also identify some fundamental limitations toknowledge with regard to future African climate. These include the often poor representation of El Niñoclimate variability in global climate models, and the absence in these models of any representation ofregional changes in land cover and dust and biomass aerosol loadings. These omitted processes may wellhave important consequences for future African climates, especially at regional scales. We conclude bydiscussing the value of the sort of climate change scenarios presented here and how best they should beused in national and regional vulnerability and adaptation assessments.

Key Words: African climate, climate scenarios, rainfall variability, climate modelling, seasonal forecasting, land cover changes


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1 Introduction

The climates of Africa are both varied and varying; varied, because they range from humid equatorial

regimes, through seasonally-arid tropical regimes, to sub-tropical Mediterranean-type climates, and

varying because all these climates exhibit differing degrees of temporal variability, particularly with

regard to rainfall. Understanding and predicting these inter-annual, inter-decadal and multi-decadal

variations in climate has become the major challenge facing African and African-specialist climate

scientists in recent years. Whilst seasonal climate forecasting has taken great strides forward, both its

development and application (Folland et al., 1991; Stockdale et al., 1998; Washington and Downing,

1999; SARCOF, 1999), the ultimate causes of the lower frequency decadal and multi-decadal rainfall

variability that affects some African climate regimes, especially in the Sahel region, remain uncertain (see

Rowell et al., 1995 vs. Sud and Lau, 1996; also Xue and Shukla, 1998). This work examining the

variability of African climate, especially rainfall, is set in the wider context of our emerging understanding

of human influences on the larger, global-scale climate. Increasing greenhouse gas accumulation in the

global atmosphere and increasing regional concentrations of aerosol particulates are now understood to

have detectable effects on the global climate system (Santer et al., 1996). These effects will be manifest at

regional scales although perhaps in more uncertain terms (Mitchell and Hulme, 1999; Giorgi and

Francisco, 2000).

Africa will be no exception to experiencing these human-induced changes in climate. Much work remains

to be done, however, in trying to isolate those aspects of African climate variability that are ‘natural’ from

those that are related to human influences. African climate scientists face a further challenge in that in this

continent the role of land cover changes - some natural and some human-related - in modifying regional

climates is perhaps most marked (Xue, 1997). This role of land cover change in altering regional climate

in Africa has been suggested for several decades now. As far back as the 1920s and 1930s theories about

the encroachment of the Sahara and the desiccation of the climate of West Africa were put forward

(Stebbing, 1935; Aubreville, 1949). These ideas have been explored over the last 25 years through

modelling studies of tropical north African climate (e.g. Charney, 1975; Cunnington and Rowntree, 1986;

Zheng and Eltahir, 1997). It is for these two reasons - large internal climate variability as driven by the

oceans and the confounding role of human-induced land cover change - that climate change ‘predictions’

(or the preferable term scenarios) for Africa based on greenhouse gas warming remain highly uncertain.

While global climate models (GCMs) simulate changes to African climate as a result of increased

greenhouse gas concentrations, these two potentially important drivers of African climate variability – for

example El Niño/Southern Oscillation (poorly) and land cover change (not at all) - are not well

represented in the models.


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Nevertheless, it is of considerable interest to try and explore the magnitude of the problem that the

enhanced greenhouse effect may pose for African climate and for African resource managers. Are the

changes that are simulated by GCMs for the next century large or small in relation to our best estimates of

‘natural’ climate variability in Africa? How well do GCM simulations agree for the African continent?

And what are the limitations/uncertainties of these model predictions? Answering these questions has a

very practical relevance in the context of national vulnerability and adaptation assessments of climate

change currently being undertaken by many African nations as part of the reporting process to the UN

Framework Convention on Climate Change. This paper makes a contribution to these assessments by

providing an overview of future climate change in Africa, particularly with regard to simulations of

greenhouse gas warming over the next 100 years. We start the paper (Section 2) by reviewing some

previous climate change scenarios and analyses for regions within Africa. Such studies have been far

from comprehensive. Section 3 explains the data, models and approaches that we have taken in

generating our analyses and constructing our climate change scenarios for Africa. In Section 4 we

consider the salient features of African climate change and variability over the last 100 years, based on the

observational record of Africa climate. Such a historical perspective is essential if the simulated climates

of the next century are to be put into their proper context. Section 5 then presents our future climate

change scenarios for Africa, based on the preliminary Special Report on Emissions Scenarios range of

future greenhouse gas emissions (SRES, 2000) and the GCM results deposited with the Inter-

governmental Panel on Climate Change (IPCC) Data Distribution Centre (DDC, 2000). Changes in mean

seasonal climate are shown as well as some measures of changed interannual variability. Section 6 then

discusses these future climate simulations in the light of modelling uncertainties and in the context of

other causes of African climate variability and change. We consider how much useful and reliable

information these types of studies yield and how they can be incorporated into climate change impacts

assessments. Our key conclusions are presented in Section 7.

2 Review of Previous African Climate Change Scenario Work

There has been relatively little work published on future climate change scenarios for Africa. The various

IPCC assessments have of course included global maps of climate change within which Africa has

featured and in Mitchell et al. (1990) the African Sahel was identified as one of five regions for which a

more detailed analysis was conducted. Kittel et al. (1998) and Giorgi and Francisco (2000) also identify

African regions within their global analysis of inter-model differences in climate predictions, but no

detailed African scenarios are presented.


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Tyson (1991) published one of the first scenario analyses specifically focused on an African region. In

this case some climate change scenarios for southern Africa were constructed using results from the first

generation GCM equilibrium 2xCO2 experiments. In a further development, Hulme (1994a) presented a

method for creating regional climate change scenarios combining GCM results with the newly published

IPCC IS92 emissions scenarios and demonstrated the application of the method for Africa. In this study

mean annual temperature and precipitation changes from 1990 to 2050 under the IS92a emission scenario

were presented.

Some more recent examples of climate scenarios for Africa use results from transient GCM climate

change experiments. Hernes et al. (1995) and Ringius et al. (1996) constructed climate change scenarios

for the African continent that showed land areas over the Sahara and semi-arid parts of southern Africa

warming by the 2050s by as much as 1.6ºC and the equatorial African countries warming at a slightly

slower rate of about 1.4ºC. These studies, together with Joubert et al. (1996), also suggested a rise in

mean sea-level around the African coastline of about 25cm by 2050. A more selective approach to the use

of GCM experiments was taken by Hulme et al. (1996a). They describe three future climate change

scenarios for the Southern African Development Community (SADC) region of southern Africa for the

2050s on the basis of three different GCM experiments. These experiments were selected to deliberately

span the range of precipitation changes for the SADC region as simulated by GCMs. Using these

scenarios, the study then describes some potential impacts and implications of climate change for

agriculture, hydrology, health, biodiversity, wildlife and rangelands. A similar approach was adopted by

Conway et al. (1996) for a study of the impacts of climate change on the Nile Basin. More recently, the

Africa chapter (Zinyowera et al., 1998) in the IPCC Assessment of Regional Impacts of Climate Change

(IPCC, 1998) also reported on some GCM studies that related to the African continent.

Considerable uncertainty exists in relation to large-scale precipitation changes simulated by GCMs for

Africa (Hudson and Hewitson, 1997; Hudson, 1997; Joubert and Hewitson, 1997; Feddema, 1999).

Joubert and Hewitson (1997) nevertheless conclude that, in general, precipitation is simulated to increase

over much of the African continent by the year 2050. These GCM studies show, for example, that parts of

the Sahel could experience precipitation increases of as much as 15 per cent over the 1961-90 average by

2050. A note of caution is needed, however, concerning such a conclusion. Hulme (1998) studied the

present-day and future simulated inter-decadal precipitation variability in the Sahel using the HadCM2

GCM. These model results were compared with observations during the twentieth century. Two

problems emerge. First, the GCM does not generate the same magnitude of inter-decadal precipitation

variability that has been observed over the last 100 years, casting doubt on the extent to which the most


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important controlling mechanisms are being simulated in the GCM. Second, the magnitude of the future

simulated precipitation changes for the Sahel is not large in relation to 'natural' precipitation variability for

this region. This low signal:noise ratio suggests that the greenhouse gas-induced climate change signals

are not well defined in the model, at least for this region. We develop this line of reasoning in this paper

and illustrate it in Section 5 with further examples from Africa.

Although there have been studies of GCM-simulated climate change for several regions in Africa, the

downscaling of GCM outputs to finer spatial and temporal scales has received relatively little attention in

Africa. Hewitson and Crane (1998) and Hewitson and Joubert (1998) have applied empirical downscaling

methods to generate climate change scenarios for South Africa using Artificial Neural Networks and

predictors relating to upper air circulation and tropospheric humidity. The usual caveats, however, apply

to these downscaled scenarios (Hulme and Carter, 1999a) - they are still dependent on the large-scale

forcing from the GCMs and they still only sample one realisation of the possible range of future possible

climates, albeit with higher resolution. The application of Regional Climate Models is still in it's infancy,

although some initiatives are now under way for East Africa (Sun et al., 1999), West Africa (Wang and

Eltahir, 2000) and southern Africa (Bruce Hewitson, pers.comm.). These initiatives have not yet

generated experimental results from regional climate change simulations for use in scenario construction.

3 Data and Methods

For our analyses of observed climate variability in Africa we use the global gridded data sets of Jones

(1994, updated; mean temperature), Hulme (1994b, updated; precipitation), and New et al. (1999, 2000;

ten surface climate variables). These data sets are all public domain and are available, along with some

documentation on their construction, from the web sites listed in the acknowledgements to this paper. The

former two data sets exist on a relatively coarse grid (5° latitude/longitude and 2.5° latitude by 3.75°

longitude respectively), while the data set of New et al. exists on a 0.5° latitude/longitude resolution.

These observed data are resolved only to monthly time-steps and we therefore undertake no original

analyses of observed daily climate variability. For Ethiopia and Zimbabwe we analyse unpublished

monthly mean maximum and minimum temperature data for a number of stations in each country. These

data originate from the respective National Meteorological Agencies. For the index of the Southern

Oscillation we use the updated index of Ropelewski and Jones (1987), calculated as the normalised mean

sea-level pressure difference between Tahiti and Darwin and available from CRU (2000).

Other climate-related and continent-wide data sets also have value for some climate analyses, whether

these data are derived from satellite observations (e.g. Normalised Difference Vegetation Index or


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satellite-derived precipitation estimates), or from numerical weather prediction model re-analyses (e.g. the

NCEP re-analysis from 1948 to present). Although these alternative data sets have some real advantages

in particular environmental or modelling applications (e.g. modelling malaria; Lindsay et al., 1998;

evaluating dust forcing; Brooks, 1999), we prefer to limit our analysis here to the use of conventional

observed climate data sets derived from surface observations.

The GCM results used in this study are mainly extracted from the IPCC Data Distribution Centre (DDC,

2000). This archive contains results from climate change experiments performed with seven coupled

ocean-atmosphere global climate models (Table 1). All these experiments have been conducted using

similar greenhouse gas or greenhouse gas plus aerosol forcing. In this study only the results from the

greenhouse gas forced simulations for reasons outlined below. We also use results from the 1400-year

control simulation of the HadCM2 climate model (Tett et al., 1997) to derive model-based estimates of

natural multi-decadal climate variability. The data were re-gridded using a Gaussian space-filter onto a

common grid, namely the HadCM2 grid. Later results are presented on this common grid.

Country oforigin

Approximateresolution

(lat. x long.)

Climatesensitivity

(degC)

Integrationlength

Reference

CCSR-NIES Japan 5.62º by 5.62º 3.5 1890-2099 Emori et al. (1999)CGCM1 Canada 3.75º by 3.75º 3.5 1900-2100 Boer et al. (2000)CSIRO-Mk2 Australia 3.21º by 5.62º 4.3 1881-2100 Hirst et al. (2000)ECHAM4 Germany 2.81º by 2.81º 2.6 1860-2099 Roeckner et al. (1996)GFDL-R15 USA 4.50º by 7.50º 3.7 1958-2057 Haywood et al. (1997)HadCM2** UK 2.50º by 3.75º 2.5 1860-2099 Mitchell and Johns (1997)NCAR-DOE USA 4.50º by 7.50º 4.6 1901-2036 Meehl et al. (2000)** An ensemble of four climate change simulations were available from the HadCM2 model.

Table 1: Characteristics of the seven global climate models available at the IPCC Data Distribution Centre fromwhich experimental results were used in this study. Only the greenhouse gas forced integrations were used here.The climate sensitivity describes the estimated equilibrium global-mean surface air temperature change of eachmodel following a doubling of atmospheric carbon dioxide concentration.

Climate can be affected by a number of other agents in addition to greenhouse gases; important amongst

these are small particles (aerosols). These aerosols are suspended in the atmosphere and some types (e.g.

sulphate aerosols derived from sulphur dioxide) reflect back solar radiation, hence they have a cooling

effect on climate. Although there are no measurements to show how these aerosol concentrations have

changed over the past 150 years, there are estimates of how sulphur dioxide emissions have risen (one of

the main precursors for aerosol particles) and scenarios of such emissions into the future. A number of

such scenarios have been used in a sulphur cycle model to calculate the future rise in sulphate aerosol


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concentrations (Penner et al., 1998). When one of these scenarios was used, along with greenhouse gas

increases, as input to the DDC GCMs, the global-mean temperature rise to 2100 was reduced by between

a quarter and a third. The reductions over Africa were less than this.

These are very uncertain calculations, however, due to a number of factors. First, the old 1992 IPCC

emissions scenario on which it was based (IS92a; Leggett et al., 1992) contains large rises in sulphur

dioxide emissions over the next century. Newer emissions scenarios, including the preliminary SRES

scenarios, estimate only a small rise in sulphur dioxide emissions over the next couple of decades

followed by reductions to levels lower than today's by 2100 (SRES, 2000). Over Africa, sulphur

emissions remain quite low for the whole of next century. The inclusion of such modest sulphur dioxide

emissions scenarios into GCM experiments would actually produce a small temperature rise relative to

model experiments that excluded the aerosol effect (Schlesinger et al., 2000). Results from GCM

experiments using these revised sulphur scenarios are not available yet. Second, more recent sulphur

cycle models generate a lower sulphate burden per tonne of sulphur dioxide emissions and the radiative

effect of the sulphate particles in more sophisticated radiation models is smaller than previously

calculated. Third, in addition to their direct effect, sulphate aerosols can also indirectly cool climate by

changing the reflectivity and longevity of clouds (Schimel et al., 1996). These indirect effects are now

realised as being as at least as important as the direct effect, but were not included in the present DDC

GCM climate change simulations. Fourth, there are other types of aerosols (e.g. carbon or soot) which

may also have increased due to human activity, but which act to warm the atmosphere. Finally and above

all, the short lifetime of sulphate particles in the atmosphere means that they should be seen as a

temporary masking effect on the underlying warming trend due to greenhouse gases. For all these

reasons, model simulations of future climate change using both greenhouse gases and sulphate aerosols

have not been used to develop the climate change scenarios illustrated in this paper.

The future greenhouse gas forcing scenario used in the DDC experiments approximated a 1% per annum

growth in greenhouse gas concentrations over the period from 1990 to 2100. Since the future growth in

anthropogenic greenhouse gas forcing is highly uncertain, it is important that our climate scenarios for

Africa reflect this uncertainty; it would be misleading to construct climate change scenarios that reflected

just one future emissions growth curve. We therefore adopt the four preliminary marker emissions

scenarios of the IPCC Special Report on Emissions Scenarios (SRES, 2000): B1, B2, A1 and A2. None of

these emissions scenarios assumes any climate policy implementation, the differences resulting from

alternative developments in global population, the economy and technology. Our method of climate

change scenario construction follows that adopted by Hulme and Carter (1999b) in their generation of


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climate change scenarios for Europe as part of the ACACIA assessment of climate impact in Europe. Full

details may be found there, but we provide a short summary of the method in Section 5 below.

4 Twentieth Century Climate Change

Temperature

The continent of Africa is warmer than it was 100 years ago. Warming through the twentieth century has

been at the rate of about 0.5ºC/century (Figure 1), with slightly larger warming in the June-August and

September-November seasons than in December-February and March-May. The six warmest years in

Africa have all occurred since 1987, with 1998 being the warmest year. This rate of warming is not

dissimilar to that experienced globally and the periods of most rapid warming - the 1910s to 1930s and the

post-1970s - occur simultaneously in Africa and the world.

Few studies have examined long-term changes in the diurnal cycle of temperature in Africa. Here, we

show results for four countries for which studies have been published or data were available for analysis -

for Sudan and South Africa as published by Jones and Lindesay (1993) and for Ethiopia and Zimbabwe

(unpublished). While a majority of the Earth's surface has experienced a decline in the mean annual

diurnal temperature range (DTR) as climate has warmed (Nicholls et al., 1996), our examples here show

contrasting trends for these four African countries. Mean annual DTR decreased by between 0.5º and 1ºC

since the 1950s in Sudan and Ethiopia, but increased by a similar amount in Zimbabwe (Figure 2). In

South Africa, DTR decreased during the 1950s and 1960s, but has remained quite stable since then.

Examination of the seasonal variation in these trends (not shown) suggests that different factors contribute

to DTR trends in different seasons and in different countries. For example, in Sudan DTR shows an

increasing trend during the July-September wet season, probably caused by trends towards reduced

cloudiness, while DTR decreased during the rest of the year, probably due to trends for increased

dustiness (Brooks, 1999). Both of these two factors are related to the multi-decadal drought experienced

in Sudan since the 1950s (Hulme, 2000). The long-term increase in annual DTR in Zimbabwe is due

almost entirely to increases during the November-February wet season; trends during the rest of the year

have been close to zero. We are not aware of published analyses of diurnal temperature trends in other

African countries.

Rainfall

Interannual rainfall variability is large over most of Africa and for some regions, most notably the Sahel,

multi-decadal variability in rainfall has also been substantial. Reviews of twentieth century African

rainfall variability have been provided by, among others, Janowiak (1988), Hulme (1992) and Nicholson


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(1994). To illustrate something of this variability we present an analysis for the three regions of Africa

used by Hulme (1996b) - the Sahel, East Africa and southeast Africa (domains shown in Figure 4). These

three regions exhibit contrasting rainfall variability characteristics (Figure 3): the Sahel displays large

multi-decadal variability with recent drying, East Africa a relatively stable regime with some evidence of

long-term wetting, and southeast Africa also a basically stable regime, but with marked inter-decadal

variability. In recent years Sahel rainfall has been quite stable around the 1961-90 annual average of

371mm, although this 30-year period is substantial drier (about 25 per cent) than earlier decades this

century. In East Africa, 1997 was a very wet year and, like in 1961 and 1963, led to a surge in the level of

Lake Victoria (Birkett et al., 1999). Recent analyses (Webster et al., 1999; Saji et al., 1999) have

suggested these extreme wet years in East Africa are related to a dipole mode of variability in the Indian

Ocean. In southeast Africa, the dry years of the early 1990s were followed by two very wet years in

1995/96 and 1996/97. Mason et al. (1999) report an increase in recent decades in the frequency of the

most intense daily precipitation over South Africa, even though there is little long-term trend in total

annual rainfall amount.

Figure 3 also displays the trends in annual temperature for these same three regions. Temperatures for all

three regions during the 1990s are higher than they have been this century (except for a period at the end

of the 1930s in the Sahel) and are currently between 0.2º and 0.3ºC warmer than the 1961-90 average.

There is no simple correlation between temperature and rainfall in these three regions, although Hulme

(1996b) noted that drying in the Sahel was associated with a moderate warming trend.

Spatial Patterns

Our analysis is summarised further in Figure 4 where we show mean linear trends in annual temperature

and precipitation during the twentieth century. This analysis first filters the data using a 10-point gaussian

filter to subdue the effects on the regression analysis of outlier values at either end of the time period.

While warming is seen to dominate the continent (cf. Figure 1 above), some coherent areas of cooling are

noted, around Nigeria/Cameroon in West Africa and along the coastal margins of Senegal/Mauritania and

South Africa. In contrast, warming is at a maximum of nearly 2ºC/century over the interior of southern

Africa and in the Mediterranean countries of northwest Africa.

The pattern of rainfall trends (Figure 4) reflects the regional analysis shown in Figure 3 with drying of up

to 25 per cent/century or more over some western and eastern parts of the Sahel. More moderate drying -

5 to 15 per cent/century - is also noted along the Mediterranean coast and over large parts of Botswana

and Zimbabwe and the Transvaal in southeast Africa. The modest wetting trend noted over East Africa is


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seen to be part of a more coherent zone of wetting across most of equatorial Africa, in some areas of up to

10 per cent or more per century. Regions along the Red Sea coast have also seen an increase in rainfall,

although trends in this arid/semi-arid region are unlikely to be very robust.

ENSO Influence on Rainfall

With regard to interannual rainfall variability in Africa, the El Niño/Southern Oscillation (ENSO) is one

of the more important controlling factors, at least for some regions (Janowiak, 1988; Ropelewski and

Halpert, 1987; 1989; 1996; Dai and Wigley, 2000). These studies have established that the two regions in

Africa with the most dominant ENSO influences are in eastern equatorial Africa during the short October-

November rainy season and in southeastern Africa during the main November-February wet season.

Ropelewski and Halpert (1989) also examined Southern Oscillation and rainfall relationships during La

Niña or high index years. We have conducted our own more general analysis of Southern Oscillation-

rainfall variability for the African region over the period 1901-1998 using an updated and more

comprehensive data set (Hulme, 1994b) than was used by these earlier studies. We also use the Southern

Oscillation Index (SOI) as a continuous index of Southern Oscillation behaviour rather than designating

discrete 'warm' (El Niño; low index) and 'cold' (La Niña; high index) Southern Oscillation events as was

done by Ropelewski and Halpert (1996).

We defined an annual average SOI using the June-May year, a definition that maximises the coherence of

individual Southern Oscillation events, and correlated this index against seasonal rainfall in Africa. We

performed this analysis for the four conventional seasons (not shown) and also for the two extended

seasons of June to October (Year 0) and November (Year 0) to April (Year 1; Figure 5a). This analysis

confirms the strength of the previously identified relationships for equatorial east Africa (high rainfall

during a warm ENSO event) and southern Africa (low rainfall during a warm ENSO event). The former

relationship is strongest during the September-November rainy season (the 'short' rains; not shown), with

an almost complete absence of ENSO sensitivity in this region during the February-April season ('long'

rains) as found by Ropelewski and Halpert (1996). The southern African sensitivity is strongest over

South Africa during December-February before migrating northwards over Zimbabwe and Mozambique

during the March-May season (not shown). There is little rainfall sensitivity to ENSO behaviour

elsewhere in Africa, although weak tendencies for Sahelian June-August drying (Janicot et al., 1996) and

northwest African March-May drying (El Hamly et al., 1998) can also be found.

5 Twenty-first Century Climate Change


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For a comprehensive assessment of the impact and implications of climate change it is necessary to apply

a number of climate change scenarios that span a reasonable range of the likely climate change

distribution. The fact that there is a distribution of future climate changes arises not only because of

incomplete understanding of the climate system (e.g. the unknown value of the climate sensitivity,

different climate model responses, etc.), but also because of the inherent unpredictability of climate (e.g.

unknowable future climate forcings and regional differences in the climate system response to a given

forcing because of chaos). The 'true' climate change distribution is of course unknown, but we can make

some sensible guesses as to its magnitude and shape and then make some choices so as to sample a

reasonable part of its range.

We have done this at a global scale by making choices about future greenhouse gas forcings and about the

climate sensitivity (see Table 1 for definition). We follow Hulme and Carter (1999b) and Carter et al.

(1999) in this procedure, yielding the four global climate scenarios shown in Table 2. We have chosen the

SRES A2 emissions scenario combined with a high climate sensitivity (4.5°C), SRES A1 and SRES B2

combined with medium climate sensitivities (2.5°C) and SRES B1 combined with a low climate

sensitivity (1.5°C). These four scenarios are subsequently termed A2-high, A1-mid, B2-mid and B1-low,

respectively, and yield a range of global warming by the 2050s of 0.9° to 2.6°C. We chose the two middle

cases deliberately because, even though the global warming is similar, the worlds which underlie the B2

and A1 emissions scenarios are quite different (SRES, 2000). The impacts on Africa of what may be

rather similar global and regional climate changes could be quite different in these two cases. For

example, global (and African) population is lower in the A1 world than in the B2 world, but carbon and

sulphur emissions and CO2 concentrations are higher (Table 2).

Scenario/Climate sensitivity

Population(billions)

C emissionsfrom energy

(GtC)

Total Semissions

(TgS)

Global ∆T(°C)

Global ∆SL(cm)

pCO2

(ppmv)

B1-low / 1.5°C 8.76 9.7 51 0.9 13 479

B2-mid / 2.5°C 9.53 11.3 55 1.5 36 492

A1-mid / 2.5°C 8.54 16.1 58 1.8 39 555

A2-high / 4.5°C 11.67 17.3 96 2.6 68 559

Table 2: The four climate scenarios. Estimates shown here are for the 2050s (i.e., 2055), but the values for the2020s and 2080s were also calculated. Temperature and sea-level changes assume no aerosol effects and arecalculated from a 1961-90 baseline using the MAGICC climate model (Wigley and Raper, 1992; Raper et al., 1996;Wigley et al., 2000). C is annual carbon emissions from fossil energy sources, S is annual sulphur emissions, ∆T ischange in mean annual temperature, ∆SL is change in mean sea-level and pCO2 is the atmospheric carbon dioxideconcentration.


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Having defined these four global climate scenarios, we next consider the range of climate changes for

Africa that may result from each of these four possible futures. Again, we have a distribution of possible

regional outcomes for a given global warming. We use results from the seven GCM experiments (cf.

Table 1) to define this range (note: for HadCM2 there are four simulations for the same scenario thus the

total GCM sample available to use is 10. This gives more weight in our final scenarios to the HadCM2

responses than the other six GCMs). We present the scenario results for seasonal mean temperature and

precipitation for the 2020s, 2050s and 2080s in two different ways: Africa-wide maps and national-scale

summary results for four representative countries within Africa.

African Scenario Maps

The construction of the scenario maps follows the approach of Hulme and Carter (1999b) and Carter et al.

(1999). We first standardise the 2071-2100 climate response patterns - defined relative to the 1961-90

model average - in the DDC GCMs using the global warming values in each respective GCM. These

standardised climate response patterns are then scaled by the global warming values for our four scenarios

and three time periods calculated by the MAGICC climate model (see Table 2). Scaling of GCM response

patterns in this way assumes that local greenhouse gas-induced climate change is a linear function of

global-mean temperature. This may be a poor assumption to make, especially for rainfall. Only a

selection of the full set of maps is shown here. For each scenario, season, variable and time-slice we

present two maps representing the change in mean seasonal climate for the respective 30-year period

(Figures 6 to 11). One map shows the Median change from our sample of ten standardised and scaled

GCM responses (left panels) and the other map shows the absolute Range of these ten model responses

(right panels).

We also introduce the idea of signal:noise ratios by comparing the Median scaled GCM change against an

estimate of natural multi-decadal climate variability. In the maps showing the Median change we only

plot these values where they exceed the one standard deviation estimate of natural 30-year time-scale

climate variability. These estimates were extracted from the 1400-year unforced simulation of the

HadCM2 model (Tett et al., 1997). We use a climate model simulation to quantify the range of natural

climate variability rather than observations because the model gives us longer and more comprehensive

estimates of natural climate variability. This has the disadvantage that the climate model may not

accurately simulate natural climate variability, although at least for some regions and on some time-scales,

HadCM2 yields estimates of natural variability quite similar both to observations (Tett et al., 1997) and to


13

climatic fluctuations reconstructed from proxy records over the past millenium (Jones et al., 1998). We

discuss this problem further in Section 6.

The resulting African scenario maps therefore inform at a number of levels:

• Africa-wide estimates are presented of mean seasonal climate change (mean temperature and

precipitation) for the four adopted climate change scenarios;

• These estimates are derived from a sample (a pseudo-ensemble) of ten different GCM simulations,

rather than being dependent on any single GCM or GCM experiment;

• Only Median changes that exceed what may reasonably be expected to occur due to natural 30-year

time-scale climate variability are plotted;

• The extent of inter-model agreement is depicted through the Range maps.

For our scenarios, future annual warming across Africa ranges from below 0.2°C per decade (B1-low

scenario; Figure 6) to over 0.5°C per decade (A2-high; Figure 7). This warming is greatest over the

interior semi-arid tropical margins of the Sahara and central southern Africa, and least in equatorial

latitudes and coastal environments. The B2 and A1 scenarios (not shown) fall roughly in between these

two extremes. All of the estimated temperature changes exceed the one sigma level of natural temperature

variability (as defined by unforced HadCM2 simulation), even under the B1-low scenario. The inter-

model range (an indicator of the extent of agreement between different GCMs) is smallest over northern

Africa and the Equator, and greatest over the interior of central southern Africa. For example, the inter-

model range falls to less than 25 per cent of the model median response in the former regions, but rises to

over 60 per cent of the model median response in the latter areas.

Future changes in mean seasonal rainfall in Africa are less well defined. Under the B1-low scenario,

relatively few regions in Africa experience a change in either DJF or JJA rainfall that exceeds the one

sigma level of natural rainfall variability simulated by the HadCM2 model (Figures 8 and 9). The

exceptions are parts of equatorial East Africa where rainfall increases by 5 to 30 per cent in DJF and

decreases by 5 to 10 per cent in JJA. Some areas of Sahelian West Africa and the Mahgreb also

experience 'significant' rainfall decreases in JJA season under the B1-low scenario. The inter-model range

for these rainfall changes is large and in the cases cited above always exceeds the magnitude of the

Median model response. Over the seasonally-arid regions of Africa, the inter-model range becomes very

large (>100 per cent) because of relatively large per cent changes in modelled rainfall induced by very

small baseline seasonal rainfall quantities.


14

With more rapid global warming (e.g. the B2, A1 and A2-high scenarios), increasing areas of Africa

experience changes in DJF or JJA rainfall that do exceed the one sigma level of natural rainfall variability.

Thus for the A2-high scenario, large areas of equatorial Africa experience ‘significant’ increases in DJF

rainfall of up to 50 or 100 per cent over parts of East Africa (Figure 10), while rainfall decreases

‘significantly’ in JJA over parts of the Horn of Africa and northwest Africa (Figure 11). Some

‘significant’ JJA rainfall increases occur over the central Sahel region of Niger and Chad, while

‘significant’ decreases in DJF rainfall (15 to 25 per cent) occur over much of South Africa and Namibia

and along the Mediterranean coast. The inter-model range for these rainfall changes remains large,

however, and with very few exceptions exceeds the magnitude of the Median model response. Even for

the seasonally wet JJA rainfall regime of the Sahel, inter-model ranges can exceed 100 per cent,

suggesting that different GCM simulations yield (sometimes) very different regional rainfall responses to

a given greenhouse gas forcing. This large inter-model range in seasonal mean rainfall response is not

unique to Africa and is also found over much of south and southwest Asia and parts of Central America

(Carter et al., 1999).

National Scenario Graphs

To condense this scenario information further, we also constructed ‘national’-scale summary graphs for

four smaller regions – centred on the countries of Senegal, Tunisia, Ethiopia and Zimbabwe. These

chosen domains are shown in Figure 6 (top left panel) and reflect the diversity of existing climate regimes

across the continent from north to south and from west to east. Each country graph shows, for the 2050s,

the distribution of the mean annual changes in mean temperature and precipitation for each GCM

simulation and for each of our four scenarios. As with the continental maps, these changes are compared

with the natural multi-decadal variability of annual-mean temperature and precipitation extracted from the

HadCM2 1400-year unforced simulation. These graphs provide a quick assessment at a ‘national’-scale of

the likely range and significance of future climate change and again shows the extent to which different

GCMs agree in their regional response to a given magnitude of global warming.

For each country there is a spread of results relating to inter-model differences in climate response. For

example, in Tunisia the change in annual rainfall is predominantly towards drying (only ECHAM4

displays wetting), although the magnitude of the drying under the A2-high scenario is between 1 per cent

and 30 per cent. Natural climate variability is estimated to lead to differences of up to ±10 per cent

between different 30-year mean climates, therefore the more extreme of these scenario outcomes would

appear to be 'significant' for Tunisia. The picture would appear at first sight to be less clear for Zimbabwe


15

where four of the GCMs suggest wetting and three - including the HadCM2 ensemble of four simulations

- suggest drying. However, the range of natural variability in annual rainfall when averaged over 30-years

is shown to be about ±6 per cent and most of the wetting scenarios fall within this limit. It is the drying

responses under the more extreme A2-high, B2 and A1 scenarios that would appear to yield a more

'significant' result.

It is also important to point out that inter-ensemble differences in response at these national scales can also

be large. The four-member HadCM2 ensemble for Tunisia yields differences in rainfall change of 15 per

cent or more, while for Ethiopia inter-ensemble differences can lead to a sign change in the rainfall

scenario. In this latter case, however, few of these HadCM2 rainfall changes are larger than the estimate

of natural rainfall variability for Ethiopia. It is also worth noting that the relative regional response of

different GCMs is not always the same. Thus for Ethiopia, the CCSR-NIES GCM generates the most

extreme wetting scenario, whereas for Tunisia the same model yields the most extreme drying scenario.

We discuss the significance of some of these differences and similarities between different GCMs in our

discussion of uncertainties in Section 6.

Changes in ENSO-related Rainfall Variability

Given the important role that ENSO events exert on interannual African rainfall variability, at least in

some regions, determining future changes in interannual rainfall variability in Africa can only be properly

considered in the context of changes in ENSO behaviour. There is still ambiguity, however, about how

ENSO events may respond to global warming. This is partly because global climate models only

imperfectly simulate present ENSO behaviour. Tett et al. (1997) demonstrate that HadCM2 simulates

ENSO-type features in the Pacific Ocean, but the model generates too large a warming across the Tropics

in response to El Niño events. Timmermann et al. (1999), however, have recently argued that their

ECHAM4 model (cf. Table 1) has sufficient resolution to simulate 'realistic' ENSO behaviour. They

analyse their greenhouse gas forced simulations to suggest that in the future there are more frequent and

more intense 'warm' and 'cold' ENSO events, a result also found in the HadCM2 model (Collins, 2000).

What effects would such changes have on interannual African rainfall variability? This not only depends

on how ENSO behaviour changes in the future, but also upon how realistically the models simulate the

observed ENSO-rainfall relationships in Africa. Smith and Ropelewski (1997) looked at Southern

Oscillation-rainfall relationships in the NCEP atmospheric GCM, where the model is used to re-create

observed climate variability after being forced with observed sea surface temperatures (SSTs). Even in

this most favourable of model experiments the model relationships do not always reproduce those


16

observed. Over southeastern Africa, the simulated rainfall percentiles are consistent with the observations

reported by Ropelewski and Halpert (1996), but over eastern equatorial Africa the model simulates an

opposite relationship to that observed. The recently elucidated role of the Indian Ocean dipole (Webster et

al., 1999; Saji et al., 1999) in modulating eastern African rainfall variability may be one reason simple

ENSO-precipitation relationships are not well replicated by the GCMs in this region.

We analysed 240 years of unforced simulated climate made using the HadCM2 GCM (cf. Table 1) to see

to what extent this model can reproduce observed relationships. We performed the identical analysis to

that performed on the observed data in Section 4 and the results are plotted in Figure 5b. The two

strongest ENSO signals in African rainfall variability are only imperfectly reproduced by the model. The

East African negative correlation in November to April is rather too weak in the model and also too

extensive, extending westwards across the whole African equatorial domain. The positive correlation over

southern Africa is too weak in HadCM2 and displaced northwards by some 10º latitude. The absence of

any strong and coherent relationship during the June to October season is reproduced by the model (Figure

5b).

On the basis of this analysis, and our assessment of the literature, we are not convinced that quantifying

future changes to interannual rainfall variability in Africa due to greenhouse gas forcing is warranted. At

the very least, this issue deserves a more thorough investigation of ENSO-rainfall relationships in the

GCMs used here, and how these relationships change in the future. Such an analysis might also be useful

in determining the extent to which seasonal rainfall forecasts in Africa that rely upon ENSO signatures

may remain valid under scenarios of future greenhouse gas forcing.

6 Uncertainties and Limitations to Knowledge

In the introduction to this paper we alluded to some of the limitations of climate change scenarios for

Africa and those shown in this paper are no exception. These limitations arise because of, inter alia, (1)

the problem of small signal-to-noise ratios in some scenarios for precipitation and other variables, (2) the

inability of climate model predictions to account for the influence of land cover changes on future climate,

and (3) the relatively poor representation in many models of some aspects of climate variability that are

important for Africa (e.g. ENSO). Some of these limitations have been revealed by analyses presented

earlier.

Even though we have presented a set of four climate futures for Africa, where the range reflects unknown

future global greenhouse gas emissions and three different values for the global climate sensitivity, we


17

cannot place probability estimates on these four outcomes with much confidence. While this conclusion

may well apply for most, or all, world regions, it is particularly true for Africa where the roles of land

cover change and dust and biomass aerosols in inducing regional climate change are excluded from the

climate change model experiments reported here.

This concern is most evident in the Sahel region of Africa. None of the model-simulated present or future

climates for this region displays behaviour in rainfall regimes that is similar to that observed over recent

decades. This is shown in Figure 13 where we plot the observed regional rainfall series for 1900-1998, as

used in Figure 3, and then append the ten model-simulated evolutions of future rainfall for the period

2000-2100. These future curves are extracted directly from the ten GCM experiments reported in Table 1

and have not been scaled to our four scenario values (this scaling was performed in the construction of

Figures 8 to 11 as discussed in Section 5). One can see that none of the model rainfall curves for the Sahel

displays multi-decadal desiccation similar to what has been observed in recent decades. This conclusion

also applies to the multi-century unforced integrations performed with the same GCMs (Brooks, 1999).

There are a number of possible reasons for this. It could be that the climate models are poorly replicating

'natural' rainfall variability for this region. In particular the possible role of ocean circulation changes in

causing this desiccation (Street-Perrott and Perrott, 1990) may not be well simulated in the models. It

could also be that the cause of the observed desiccation is some process that the models are not including.

Two candidates for such processes would be the absence of a dynamic land cover/atmosphere feedback

process and the absence of any representation of changing atmospheric dust aerosol concentration. The

former of these feedback processes has been suggested as being very important in determining African

climate change during the Holocene by amplifying orbitally-induced African monsoon enhancement

(Kutzbach et al., 1996; Claussen et al., 1999; Doherty et al., 2000). This feedback may also have

contributed to the more recently observed desiccation of the Sahel (Xue, 1997). The latter process of

elevated Saharan dust concentrations may also be implicated in the recent Sahelian desiccation (Brooks,

1999).

Without such a realistic simulation of observed rainfall variability, it is difficult to define with confidence

the true magnitude of natural rainfall variability in these model simulations and also difficult to argue that

these greenhouse gas-induced attributed rainfall changes for regions in Africa will actually be those that

dominate the rainfall regimes of the twentyfirst century. Notwithstanding these model limitations due to

omitted or poorly represented processes, Figure 13 also illustrates the problem of small signal-to-noise

ratios in precipitation scenarios. The ten individual model simulations yield different signs of


18

precipitation change for these three regions as well as different magnitudes. How much of these

differences are due to model-generated natural variability is difficult to say. In our scenario maps (Figures

8 to 11) we presented the median precipitation change from these ten (scaled) model simulations,

implying that we can treat these climate change simulations as individual members of an ensemble. The

ensemble-mean or median therefore yields our 'best' estimate of the true response to greenhouse gas

forcing, much as in numerical weather prediction the ensemble-mean forecast is often taken as the 'best'

short-range weather forecast. In our example, for the Sahel and southern African the median response was

for annual drying, whereas for East Africa the median response was for wetting (Figure 13).

One other concern about the applicability in Africa of climate change scenarios such as those presented

here is the relationship between future climate change predictions and seasonal rainfall forecasts. There is

increasing recognition (e.g. Downing et al., 1997; Ringius, 1999; Washington and Downing, 1999) that

for many areas in the tropics one of the most pragmatic responses to the prospect of long-term climate

change is to strengthen the scientific basis of seasonal rainfall forecasts. Where forecasts are feasible, this

should be accompanied by improvements in the management infrastructure to facilitate timely responses.

Such a research and adaptation strategy focuses on the short-term realisable goals of seasonal climate

prediction and the near-term and quantifiable benefits that improved forecast applications will yield. At

the same time, the strengthening of these institutional structures offers the possibility that the more slowly

emerging signal of climate change in these regions can be better managed in the decades to come. It is

therefore an appropriate form of climate change adaptation. This means that two of the objectives of

climate change prediction should be to determine the effect global warming may have on seasonal

predictability - will forecast skill levels increase or decrease or will different predictors be needed? - and

to determine the extent to which predicted future climate change will impose additional strains of natural

and managed systems over and above those that are caused by existing seasonal climate variability. For

both of these reasons we need to improve our predictions of future climate change and in particular to

improve our quantification of the uncertainties.

7 Conclusions

The climate of Africa is warmer than it was 100 years ago. Although there is no evidence for widespread

desiccation of the continent during this century, in some regions substantial interannual and multi-decadal

rainfall variations have been observed and near continent-wide droughts in 1983 and 1984 had some

dramatic impacts on both environment and some economies (Benson and Clay, 1998). The extent to

which these rainfall variations are related to greenhouse gas-induced global warming, however, remains


19

undetermined. A warming climate will nevertheless place additional stresses on water resources, whether

or not future rainfall is significantly altered.

Model-based predictions of future greenhouse gas-induced climate change for the continent clearly

suggest that this warming will continue and, in most scenarios, accelerate so that the continent on average

could be between 2º and 6ºC warmer in 100 years time. While these predictions of future warming may

be relatively robust, there remain fundamental reasons why we are much less confident about the

magnitude, and even direction, of regional rainfall changes in Africa. Two of these reasons relate to the

rather ambiguous representation in most global climate models of ENSO-type climate variability in the

tropics (a key determinant of African rainfall variability) and the omission in all current global climate

models of any representation of dynamic land cover-atmosphere interactions and dust and biomass

aerosols. Such interactions have been suggested to be important in determining African climate variability

during the Holocene and may well have contributed to the more recently observed desiccation of the

Sahel.

We suggest that climate change scenarios, such as those presented here, should nevertheless be used to

explore the sensitivity of a range of African environmental and social systems, and economically valuable

assets, to a range of future climate changes. Some examples of such exploration were presented by Dixon

et al. (1996), although in these studies there was little co-ordinated and quantified use of a coherent set of

climate futures. Further work can be done to elaborate on some of the higher order climate statistics

associated with the changes in mean seasonal climate shown here - particularly daily temperature and

precipitation extremes. It may also be worthwhile to explore the sensitivity of these model predictions to

the spatial resolution of the models - i.e., explore the extent to which downscaled scenarios differ from

GCM-scale scenarios - although such downscaling techniques do not remove the fundamental reasons

why we are uncertain about future African rainfall changes.

The exploration of African sensitivity to climate change must also be undertaken, however, in conjunction

with the more concrete examples we have of sensitivity to short-term (seasonal time-scale) climate

variability. These estimates may be based on observed reconstruction of climate variability over the last

century, or on the newly emerging regional seasonal rainfall forecasts now routinely being generated for

southern, eastern and western Africa (e.g. NOAA, 1999; SARCOF, 2000; IRI, 2000). Because of the

uncertainties mentioned above about future regional climate predictions for Africa, initial steps to reduce

vulnerability should focus on improved adaptation to existing climate variability (Downing et al., 1997;

Adger and Kelly, 1999; Ringius, 1999). Thus, emphasis would be placed on reducing vulnerability to


20

adverse climate-events and increasing capacity to adapt to short-term and seasonal weather conditions and

climatic variability. The likelihood of significant economic and social benefits from adaptation to short-

term climate variability in Africa justifies this activity. Additionally, and importantly, lessons from

adaptation to short-term climate variability would build capacity to respond incrementally to longer-term

changes in local and regional climates.

AcknowledgementsGCM results were extracted from the IPCC Data Distribution Centre web site at: http://ipcc-ddc.cru.uea.ac.uk/.Most of the observed data sets used here can be obtained from the Climatic Research Unit (seehttp://www.cru.uea.ac.uk/). The preliminary (February 1999; non-IPCC approved, but used with permission) SRESemissions scenarios were obtained from the SRES web site (http://sres.ciesin.org/index.html). The Climate ImpactsLINK Project (funded by DETR Contract No. EPG 1/1/68) contributed computing facilities and staff time for RMD.TN was supported by an IGBP/START Fellowship. The MAGICC climate model was used with the permission ofTom Wigley and Sarah Raper.

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26

Figures

Annual

-0.6

0

0.6

1900 1920 1940 1960 1980 2000

DJF

-0.6

0

0.6

MAM

-0.6

0

0.6

Me

an

tem

per

atur

e a

nom

aly

(de

gC)

JJA

-0.6

0

0.6

SON

-0.6

0

0.6

1900 1920 1940 1960 1980 2000

Figure 1: Mean surface air temperature anomalies for the African continent, 1901-1998, expressed with respect tothe 1961-90 average; annual and four seasons - DJF, MAM, JJA, SON. The smooth curves result from applying a10-year Gaussian filter.


27

Sudan

1940 1950 1960 1970 1980 1990 2000

-1

0

1

-1

0

1

Ethiopia

-1

0

1

-1

0

1

deg

C a

nom

aly

Zimbabwe

-1

0

1

-1

0

1

South Africa

1940 1950 1960 1970 1980 1990 2000

-1

0

1

-1

0

1

Figure 2: Mean annual diurnal temperature range (Tmax – Tmin) for a number of African countries: Sudan (dataend 1987), Ethiopia (1990), Zimbabwe (1997) and South Africa (1991). The smooth curves result from applying a10-year Gaussian filter. Sudan and South Africa are from Jones and Lindesay (1993), while data for Ethiopia andZimbabwe are unpublished.


28

The Sahel

0.0

+0.2

+0.4

-0.2

-0.4

-80

-60

-40

-20

0

20

40

60

801880 1900 1920 1940 1960 1980 2000

East Africa

0.0

+0.2

+0.4

-0.2

-0.4

-80

-60

-40

-20

0

20

40

60

80

An

nual

ra

ina

ll a

nom

aly

(per

cen

t)

Southeast Africa

0.0

+0.2

+0.4

-0.2

-0.4

-80

-60

-40

-20

0

20

40

60

80

1880 1900 1920 1940 1960 1980 2000

Figure 3: Annual rainfall (1900-98; histograms and bold line; per cent anomaly) and mean temperature anomalies(1901-98; dashed line; degC anomaly) for three African regions, expressed with respect to the 1961-90 average:Sahel, East Africa and southeastern Africa (regional domains marked on Figure 4). Note: for southeast Africa year isJuly to June. The smooth curves for rainfall and temperature result from applying a 10-year Gaussian filter.


29

Mean Annual Temperature

15W 0 15E 30E 45E

30S

15S

0

15N

30N

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0oC

15W 0 15E 30E 45E

30S

15S

0

15N

30N

Annual Precipitation

15W 0 15E 30E 45E

30S

15S

0

15N

30N

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60Percent

Figure 4: Mean linear trends in annual temperature (left; ºC/century) and annual rainfall (right; per cent/century),calculated over the period 1901-1995 from the New et al. (1999, 2000) data set. Data were filtered with a 10-pointGaussian filter before being subject to regression analysis. The three regions shown are those depicted in Figures 3and 13.


30

-1

-0.5

-0.4

-0.3

-0.26

0.26

0.3

0.4

0.5

1

0 30E

30S

EQ

30N

Time series correlations of SOI (Jun-May) vs. Precip (Jun-Oct)

-1

-0.5

-0.4

-0.3

-0.26

0.26

0.3

0.4

0.5

1

0 30E

30S

EQ

30N

Time series correlations of SOI (Jun-May) vs. Precip (Nov-Apr)

-1

-0.5

-0.4

-0.3

-0.17

0.17

0.3

0.4

0.5

1

0 30E

30S

EQ

30N

Time series correlations of SOI (Jun-May) vs. Precip (Jun-Oct)

-1

-0.5

-0.4

-0.3

-0.17

0.17

0.3

0.4

0.5

1

0 30E

30S

EQ

30N

Time series correlations of SOI (Jun-May) vs. Precip (Nov-Apr)

Figure 5: Correlation between annual (June-May) SOI and seasonal rainfall; left: June-October (Year 0) rainfall;right: November-April (Year 0 to +1) rainfall. Top panel (5a): observed relationship over the period 1901-1998.Bottom panel (5b): HadCM2 model-simulated relationship over a 240-year unforced simulation. Correlations areonly plotted where they are significant at 95% and in regions where the respective seasonal rainfall is greater than20mm and greater than 20 per cent of annual total.


31

0.90.91.01.01.01.01.01.01.01.00.90.8

0.90.90.91.01.01.01.01.01.00.90.90.8

0.80.90.91.01.01.01.01.01.00.90.80.80.7

0.80.80.90.91.01.01.01.00.90.80.80.80.70.80.7

0.80.80.90.91.00.90.90.90.90.80.80.80.80.80.80.80.80.90.90.90.80.80.8

0.80.80.80.90.90.90.90.90.90.80.80.80.80.80.90.91.01.01.01.11.01.00.90.80.80.7

0.70.80.80.90.90.90.90.90.90.80.80.80.80.80.90.91.01.01.11.11.11.11.11.00.90.8

0.70.80.80.90.90.90.90.90.90.80.80.80.80.80.90.90.91.01.01.01.01.01.01.00.90.8

0.70.80.90.90.90.90.90.90.90.80.80.80.70.70.80.80.90.90.91.01.01.00.90.80.8

0.80.90.90.90.90.90.90.90.90.80.70.70.70.70.70.80.80.80.90.90.90.80.8

0.80.80.90.90.9

0.90.90.90.80.80.70.60.60.60.60.70.70.70.70.7

0.90.90.91.01.01.01.0

0.80.80.70.70.60.6

0.90.90.90.90.91.01.00.90.90.80.80.70.70.6

0.70.6

0.90.9

0.90.91.00.90.8

0.70.7

0.60.60.6

0.80.80.80.80.8

0.80.90.91.01.01.00.90.90.8

0.90.90.91.01.01.01.01.01.00.90.80.7

1.01.01.01.01.01.01.01.01.01.00.90.80.7

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

2020s

0.50.30.20.30.30.40.40.50.50.50.50.4

0.40.30.20.20.30.40.40.50.50.50.50.4

0.30.30.20.20.30.30.40.50.50.50.40.40.4

0.20.20.20.30.30.40.50.40.40.40.40.40.40.40.4

0.20.20.30.30.40.50.50.50.40.40.50.50.50.50.50.50.50.50.50.50.50.40.3

0.20.20.30.30.30.50.50.50.50.50.50.40.40.50.50.50.50.60.60.60.60.60.50.40.40.4

0.30.30.30.30.30.50.60.50.50.50.40.40.40.40.50.50.50.60.70.70.70.60.50.50.50.5

0.30.30.20.20.30.60.60.60.50.40.40.40.40.40.40.50.50.60.70.70.70.60.50.40.40.4

0.40.30.20.20.30.60.60.50.50.40.50.40.50.50.60.50.60.60.70.70.70.60.40.40.3

0.30.20.20.30.40.60.60.60.40.50.50.50.50.50.40.50.50.60.70.70.60.50.4

0.30.30.20.20.2

0.60.70.60.40.40.50.50.40.20.20.30.40.50.50.5

0.40.40.20.10.30.50.5

0.70.60.60.40.30.3

0.40.40.20.20.30.50.60.70.70.70.70.50.30.3

0.40.4

0.40.4

0.40.60.60.60.5

0.50.4

0.40.40.4

0.50.40.30.30.4

0.40.50.50.40.40.50.60.50.3

0.40.40.40.40.40.30.40.50.60.50.40.4

0.50.40.40.40.40.30.30.40.50.60.50.40.4

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

1.41.41.51.51.61.61.61.61.61.51.41.2

1.41.41.51.51.51.61.61.61.51.51.31.2

1.31.41.41.51.51.51.51.51.51.41.31.21.1

1.31.31.41.51.51.51.51.51.41.31.31.21.21.21.2

1.21.31.41.51.51.51.51.41.31.31.31.21.21.31.31.31.31.41.41.41.31.31.2

1.21.31.31.41.51.51.51.41.31.31.31.21.21.31.41.51.51.61.61.61.61.51.41.31.21.1

1.11.21.31.41.41.41.51.41.31.31.31.21.21.31.41.41.61.61.71.71.71.71.61.61.41.3

1.11.21.31.41.41.41.41.51.41.31.21.21.21.21.31.41.51.51.61.61.61.61.61.51.41.2

1.21.31.31.41.41.41.51.51.41.31.31.21.11.21.21.31.41.41.51.51.51.51.41.31.3

1.31.41.41.41.41.51.41.41.31.21.11.11.01.01.11.21.31.31.31.41.41.31.2

1.31.31.41.51.5

1.41.41.31.31.21.11.01.01.01.01.01.11.11.21.2

1.41.41.41.51.51.51.5

1.31.31.21.11.01.0

1.41.51.41.41.51.61.61.51.41.31.21.21.11.0

1.11.0

1.51.4

1.41.51.61.51.3

1.11.1

0.91.01.0

1.21.31.31.31.3

1.31.31.51.61.61.51.51.41.2

1.41.41.51.51.61.61.61.61.51.41.31.2

1.51.51.51.51.61.61.61.61.61.51.41.31.2

30S

20S

10S

EQ

10N

20N

30N

2050s

0.70.50.30.40.50.60.60.80.80.90.80.7

0.60.40.40.40.50.60.70.70.80.80.80.6

0.50.40.40.30.40.50.60.70.70.80.70.60.6

0.30.40.30.40.50.60.70.70.70.70.60.70.70.70.6

0.40.40.40.50.60.70.70.70.70.70.70.70.70.70.80.70.70.80.90.80.80.70.5

0.30.40.40.50.50.80.80.80.70.70.70.70.70.70.80.80.80.90.91.01.00.90.80.60.60.6

0.40.50.40.40.50.80.90.80.80.70.70.70.60.60.70.80.80.91.01.11.01.00.80.80.70.7

0.50.50.30.30.50.90.90.90.80.70.70.60.60.60.70.70.81.01.11.11.11.00.80.70.70.7

0.60.40.30.40.50.90.90.80.80.70.80.70.80.80.90.80.91.01.11.11.00.90.70.60.5

0.50.30.30.50.60.91.00.90.60.70.80.80.80.80.70.70.81.01.01.00.90.80.6

0.50.50.30.20.3

0.91.10.90.60.60.80.70.60.40.30.50.60.80.80.8

0.60.60.30.20.40.80.8

1.10.90.90.60.40.5

0.70.60.30.30.50.91.01.11.11.01.10.70.40.4

0.70.7

0.70.6

0.60.90.90.90.8

0.80.6

0.70.70.6

0.80.70.50.50.6

0.70.80.70.60.60.80.90.70.5

0.60.60.70.60.50.50.70.80.90.80.60.6

0.70.70.60.60.60.50.50.70.70.90.80.60.6

30S

20S

10S

EQ

10N

20N

30N

1.81.81.91.91.92.02.02.01.91.81.71.5

1.71.81.81.91.91.91.91.91.91.81.71.5

1.61.71.81.91.91.91.91.91.91.81.61.51.4

1.61.61.71.81.91.91.91.81.81.61.61.51.41.51.4

1.51.61.71.81.81.81.81.81.61.61.61.51.51.61.61.61.61.71.71.71.61.61.5

1.51.61.61.81.81.81.81.81.61.61.61.51.51.61.71.81.92.02.02.02.01.91.81.61.51.3

1.41.51.61.71.81.81.81.81.71.61.61.51.51.61.71.81.92.02.02.12.12.12.01.91.71.6

1.41.51.61.71.71.71.81.81.71.61.51.51.51.51.71.81.81.91.92.02.02.02.01.91.71.5

1.41.61.71.71.71.71.81.81.71.61.61.51.41.41.51.61.71.71.81.91.91.91.81.61.5

1.61.71.71.71.71.81.81.71.71.51.41.31.31.31.41.51.61.61.71.71.71.61.5

1.61.61.71.81.8

1.71.71.71.61.51.31.21.21.21.21.31.41.41.41.4

1.71.71.81.81.91.91.8

1.61.61.41.31.21.2

1.81.81.81.71.81.91.91.81.71.61.51.41.31.2

1.31.2

1.81.8

1.71.81.91.81.6

1.41.3

1.11.21.2

1.51.61.61.61.6

1.61.71.81.91.91.91.81.71.5

1.81.81.81.82.02.02.02.01.91.81.61.4

1.91.91.91.91.92.02.02.02.01.91.81.61.4

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

2080s

oC change

0.5 1 1.5 2 3 4 5

0.90.60.40.50.60.70.80.91.01.11.00.8

0.80.60.40.50.60.70.80.90.91.00.90.8

0.60.50.40.40.50.70.80.90.90.90.90.80.7

0.40.50.40.50.60.70.90.80.90.80.80.90.80.80.8

0.40.40.50.60.70.90.90.90.80.90.90.90.90.90.90.90.91.01.11.01.00.80.7

0.40.50.50.60.60.91.00.90.90.90.90.90.90.90.91.01.01.11.11.21.21.11.00.80.80.7

0.50.60.50.50.61.01.11.01.00.90.80.80.80.80.90.91.01.11.31.31.31.21.01.00.90.9

0.70.60.40.40.61.11.21.11.00.90.80.70.70.70.80.91.01.21.31.41.31.20.90.90.80.8

0.70.50.40.50.71.11.21.01.00.91.00.81.01.01.11.01.21.21.41.41.31.10.90.80.7

0.60.40.40.60.71.21.21.10.70.91.00.91.00.90.90.91.01.21.31.31.10.90.8

0.70.60.40.30.3

1.21.41.10.80.71.00.90.70.40.40.60.71.01.01.0

0.70.70.40.30.50.91.0

1.31.21.10.70.50.6

0.80.70.40.40.61.11.21.31.31.31.30.90.60.5

0.80.8

0.80.7

0.71.11.11.11.0

1.00.7

0.80.80.8

0.90.80.60.60.7

0.91.00.90.80.70.91.20.90.6

0.70.70.80.80.70.60.80.91.21.00.70.7

0.90.90.80.80.70.70.70.80.91.10.90.70.7

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

oC change

0.5 1 1.5 2 3 4 5

Figure 6: (Left panels) Change in mean annual temperature for the 2020s, 2050s and 2080s (with respect to 1961-90) for the B1-low scenario; median of seven GCM experiments. (Right panels) Inter-model range in mean annualtemperature change. See text for further explanation. Selected domains in the top left panel are the four 'national'regions used in Figure 12.


32

2.12.12.22.22.32.32.32.32.32.12.01.8

2.02.02.12.22.22.32.32.22.22.11.91.7

1.92.02.12.22.22.22.22.22.22.11.81.81.6

1.81.92.02.12.22.22.22.12.11.91.81.71.71.71.7

1.81.92.02.12.22.12.12.11.91.81.81.81.81.91.91.91.92.02.02.01.91.81.7

1.71.81.92.12.12.12.12.11.91.91.81.81.81.92.02.12.22.32.42.42.32.22.11.91.71.6

1.61.81.92.02.12.12.12.11.91.81.81.81.81.82.02.12.22.32.42.42.42.42.42.22.01.8

1.61.81.92.02.02.02.12.12.01.91.81.81.81.81.92.12.12.22.32.32.42.42.32.22.01.8

1.71.81.92.02.02.02.12.12.01.91.81.71.61.71.81.92.02.02.12.22.22.22.11.91.8

1.82.02.02.02.02.12.12.01.91.81.61.61.51.51.61.71.81.91.92.02.01.91.8

1.91.92.02.12.1

2.02.01.91.91.71.51.41.41.41.41.51.61.71.71.7

2.02.02.12.22.22.22.2

1.91.81.71.51.51.4

2.12.12.12.02.12.22.32.12.01.81.71.71.51.4

1.51.5

2.12.1

2.02.12.32.11.9

1.61.6

1.31.41.4

1.81.91.91.91.8

1.91.92.12.22.32.22.12.01.7

2.12.12.12.12.32.32.42.32.22.11.91.7

2.22.22.22.22.32.32.32.32.32.22.11.91.7

10W 0 10E 20E 30E 40E 50E

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1.00.80.50.60.70.80.91.11.11.21.21.0

0.90.60.50.60.70.80.91.11.11.11.10.9

0.80.60.50.50.60.80.91.01.11.11.00.90.9

0.50.60.50.60.70.91.01.01.00.90.91.00.91.00.9

0.50.50.60.70.81.11.11.01.01.01.11.01.01.11.11.01.01.21.21.21.11.00.8

0.50.60.60.70.71.11.21.11.11.01.01.01.01.01.11.11.21.31.31.41.41.31.10.90.90.8

0.60.70.60.60.71.21.31.21.21.01.00.90.90.91.01.11.21.31.51.51.51.41.21.21.11.1

0.80.70.50.50.71.31.41.21.21.01.00.90.80.91.01.11.21.41.51.61.51.41.11.01.01.0

0.80.60.40.50.81.31.41.21.11.01.21.01.11.21.21.21.41.41.61.61.51.31.00.90.8

0.70.40.40.70.91.41.41.30.91.01.11.11.21.11.01.01.21.41.51.51.31.10.9

0.80.80.50.30.4

1.41.61.30.90.81.11.10.80.50.50.70.91.11.21.1

0.90.80.40.30.61.11.2

1.51.41.20.80.60.7

0.90.80.50.40.71.21.41.51.51.51.51.00.60.6

0.91.0

1.00.8

0.91.31.31.31.1

1.10.8

0.91.00.9

1.10.90.70.70.8

1.01.11.10.90.91.11.41.00.7

0.80.81.00.90.80.71.01.11.41.20.80.9

1.11.00.90.90.90.80.81.01.11.31.10.90.9

10W 0 10E 20E 30E 40E 50E

30S

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EQ

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30N

3.93.94.14.24.34.44.34.34.34.13.83.4

3.73.94.04.24.24.34.34.24.24.03.63.3

3.53.73.94.14.14.24.24.14.13.93.53.33.1

3.43.63.84.04.14.14.14.13.93.63.43.33.23.23.2

3.43.63.74.04.14.04.03.93.63.53.53.33.43.63.63.53.63.73.73.73.63.43.3

3.23.53.63.94.04.04.03.93.63.63.53.43.43.63.84.04.14.34.54.54.44.23.93.53.33.0

3.13.43.63.83.93.94.03.93.73.43.43.43.43.43.73.94.24.44.54.64.64.64.54.23.83.4

3.13.43.63.73.83.83.94.03.73.53.43.33.33.43.63.94.04.14.34.44.54.44.34.23.83.3

3.23.53.73.73.83.84.04.03.83.63.43.33.13.13.43.63.73.84.04.14.24.13.93.63.4

3.53.83.83.83.83.93.93.83.63.43.12.92.82.83.03.23.43.63.63.73.83.63.3

3.63.63.84.04.0

3.83.83.73.63.22.92.72.72.72.72.83.03.13.13.2

3.73.83.94.14.24.14.1

3.63.43.22.92.72.7

3.94.03.93.84.04.24.24.03.73.53.33.22.92.7

2.92.7

4.03.9

3.74.04.34.03.5

3.02.9

2.52.62.7

3.33.53.63.63.5

3.53.64.04.24.34.24.03.73.3

3.93.94.04.04.34.44.54.44.23.93.63.1

4.14.14.24.14.34.44.44.44.34.23.93.53.2

30S

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EQ

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2050s

2.01.40.91.11.31.61.72.12.22.32.21.9

1.81.21.01.01.21.61.82.02.12.12.11.8

1.41.11.00.91.21.51.71.92.02.11.91.71.6

0.91.10.91.11.31.61.91.81.91.81.71.91.81.81.8

1.01.01.21.41.62.02.01.91.81.92.02.01.92.02.11.92.02.22.32.32.11.81.5

0.91.01.21.31.42.12.22.12.02.01.91.91.91.92.12.12.22.42.52.72.62.52.21.81.71.6

1.11.31.11.11.22.22.42.32.22.01.91.81.71.81.92.12.22.52.82.92.82.72.32.22.02.0

1.41.30.90.91.32.42.62.32.21.91.91.61.51.61.82.02.22.62.93.02.92.62.11.91.81.8

1.51.10.81.01.52.42.62.32.11.92.21.82.12.22.32.22.62.73.03.02.82.51.91.71.5

1.20.80.81.31.62.52.72.41.61.92.12.12.22.11.92.02.22.72.82.82.52.11.7

1.51.40.90.70.8

2.63.02.51.71.62.12.01.51.00.91.31.62.12.32.2

1.61.50.80.61.12.12.3

2.92.52.41.61.11.2

1.81.60.90.81.42.32.62.92.92.82.92.01.21.2

1.81.8

1.81.6

1.62.52.42.52.2

2.11.6

1.81.81.7

2.11.81.31.41.5

1.92.12.01.71.62.12.61.91.4

1.51.61.81.71.51.41.82.02.62.31.61.6

2.01.91.71.71.61.51.51.92.02.42.01.61.6

30S

20S

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EQ

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30N

6.06.06.36.46.66.76.66.66.66.25.85.2

5.75.96.26.46.56.66.66.56.46.15.65.0

5.45.76.06.36.36.46.46.46.25.95.35.14.7

5.35.55.96.16.36.36.36.26.05.55.25.04.84.94.9

5.25.55.86.26.26.26.26.05.65.35.35.15.25.55.55.45.55.75.75.75.55.25.0

4.95.35.55.96.16.16.15.95.65.55.35.25.25.55.86.16.36.66.96.96.86.55.95.45.04.5

4.75.15.55.85.95.96.16.05.65.25.35.25.25.35.76.06.56.76.97.07.07.06.96.55.85.3

4.75.25.55.75.85.86.06.15.85.45.25.15.15.25.65.96.26.36.56.76.86.86.76.45.95.1

4.95.35.65.75.85.96.16.15.85.55.35.04.74.85.25.55.75.96.16.36.46.36.05.55.2

5.35.85.95.85.86.16.15.95.65.24.84.54.34.44.65.05.25.45.65.75.85.55.1

5.55.55.86.16.1

5.85.85.65.54.94.54.24.14.14.14.44.64.84.84.9

5.75.86.06.26.46.36.2

5.55.34.94.54.24.1

6.06.16.05.86.16.56.56.15.75.35.04.84.54.2

4.44.2

6.16.0

5.76.16.56.15.4

4.64.5

3.84.04.1

5.15.45.55.55.3

5.45.66.16.56.66.46.15.75.0

6.06.06.16.26.66.86.96.76.46.05.54.8

6.36.36.46.36.56.76.86.86.76.56.05.44.9

10W 0 10E 20E 30E 40E 50E

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2080s

oC change

0.5 1 1.5 2 3 4 5

3.02.21.41.61.92.42.73.23.33.63.42.8

2.71.91.51.61.92.42.73.13.23.33.22.7

2.21.71.51.51.82.32.62.93.13.12.92.62.5

1.41.61.41.72.02.53.02.82.92.72.72.92.72.82.7

1.51.51.82.12.43.13.12.92.82.93.13.02.93.13.23.03.03.43.63.53.32.82.2

1.31.61.92.02.13.23.33.23.13.03.02.92.93.03.23.33.43.63.94.14.03.93.32.72.52.4

1.71.91.71.71.93.43.73.53.33.02.92.72.62.73.03.23.43.94.44.54.34.13.53.43.13.0

2.22.01.41.32.03.73.93.63.42.92.82.52.32.52.83.13.44.04.44.64.44.03.22.92.82.8

2.31.71.21.52.33.73.93.53.22.93.42.83.23.43.63.43.94.14.64.64.33.82.92.62.3

1.91.21.32.02.53.94.23.62.52.93.23.23.43.22.93.03.44.14.44.33.83.22.5

2.32.21.41.01.2

4.04.63.92.72.43.23.12.41.51.41.92.53.33.53.3

2.52.31.30.91.73.23.5

4.43.93.62.41.71.9

2.72.41.31.22.13.64.04.54.44.34.43.01.91.8

2.72.8

2.82.4

2.53.93.73.83.3

3.22.4

2.72.82.5

3.22.72.12.12.4

2.93.23.12.72.53.24.03.02.1

2.42.42.82.62.32.12.83.13.93.52.42.5

3.02.92.62.62.52.22.22.83.03.63.12.52.5

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

oC change

0.5 1 1.5 2 3 4 5

Figure 7: (Left panels) Change in mean annual temperature for the 2020s, 2050s and 2080s (with respect to 1961-90) for the A2-high scenario; median of seven GCM experiments. (Right panels) Inter-model range in mean annualtemperature change. See text for further explanation.


33

2 321

4542

6553

101110

-6

1013

12

10W 0 10E 20E 30E 40E 50E

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2020s

1824327662117615866504127

183331476662325655483925

174029433848294448103352526

131643333626379882423130211117

1327523523577740313130177816222818109101618

14213132515455612530301454121311978768131160

16573336349299632130291476131399878671079

1532323461431186620272715107111178876679710

1432414141985940192221121010998898787108

14302929681267635171414191916151311111010979

1314141829

644920182231463144412013151313

13151618172936

553948866444

1214171814182740388265825968

1314

1012

1517286046

3024

999

1917223440

293628223737483024

222624402938435040312725

16204237476451455945383525

10W 0 10E 20E 30E 40E 50E

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19 20

5

23

7

4

5

3

6

4532

-3

767642

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89885

-3-3-5

141617151173

-9-8

162121181614136

222419

18

145

30S

20S

10S

EQ

10N

20N

30N

2050s

283850118971839691103786442

2852487410496508886756239

276345685974466975161554040

21266751564158154128664846321727

21438155378912063484848261013263444281515162528

21334950808585963847462286192117141012111013201793

2589525653144155983347452211920201415121113912161014

23505054966718510331424224151017171212121191012141116

235064646415493633034321915161315131314131112121512

2248464510619711954272221302925232117171616141115

2022232845

1017631283448724869643220232021

20232628264556

87607513410068

19222628222842625912810212893107

2022

1619

2427439472

4637

131514

2926355362

465643355857754838

344138624559687863484339

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23 25 21 2613

6

281184

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6

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2080s

Median of % changes

-50 -30 -20 -10 0 10 20 30 50

344661146121226119112127978052

3564599112911962109107937649

347856847492578593199684949

26328363695072190159825957402033

2653100684511014977596059331316324254341918203134

2641606299105106119475857271072426211813151412162521116

31110657066178191121405955271411252517181514161114201317

2862626611983228127385252291913222114151513111214171320

2862798079191115783742402319201718161618161415151915

2859575513124314767332726373631292522211919171418

2527283556

1249439354259895985794024292525

25283235325669

107759316612385

242833342734527773158126158115132

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161818

3633436577

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425146775673849678605348

3139807291124988811488746848

10W 0 10E 20E 30E 40E 50E

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Range of % changes

10 20 30 40 50 75 100

Figure 8: (Left panels) Change in mean DJF rainfall for the 2020s, 2050s and 2080s (with respect to 1961-90) forthe B1-low scenario; median of seven GCM experiments. For areas with no change shown the model medianresponse fails to exceed the one sigma level of natural rainfall variability as defined by HadCM2. (Right panels)Inter-model range in mean annual temperature change. See text for further explanation.


34

-9-8

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131624568068221615109

10

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107877521141310109

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1181048524131111101114

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5418610591452416131312141514141814414153

12311791

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4420710010460352010111111121416242730414758775765

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101422294259696463674938429

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22921815011873353020151414182826256434352634464948

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Median of % changes

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854001932001166839202121202427314751577990

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126180216206129874716151517181927435681

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220230202177115764420171617191920355173

13715513210410493767042

3402581941601076036251921181819232430967758825288574442

28327018614690433724191818223432317942443242576159

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33642927925817113186

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2132892352131451198753484353473732

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Range of % changes

10 20 30 40 50 75 100

Figure 9: (Left panels) Change in mean JJA rainfall for the 2020s, 2050s and 2080s (with respect to 1961-90) forthe B1-low scenario; median of seven GCM experiments. For areas with no change shown the model medianresponse fails to exceed the one sigma level of natural rainfall variability as defined by HadCM2. (Right panels)Inter-model range in mean annual temperature change. See text for further explanation.


35

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29 25 301677

331395

5

21

8

34

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EQ

10N

20N

30N

2020s

4054721711412641391311491139361

407569107150139721271251088957

399165988610766100108232805758

30379774815884222185956967472438

3062116805312817390696969381519374963402221243640

31487073116122123139556767321282830242115181614192924135

37129758177208223141476865321613292920211816191317231520

3372727713997267148456161342215252417171815131417201623

3373939392222134914349462722241921191921181617172218

3269666515328417178383131444236343025242323201621

2931334165

145110454149691046999934628342930

30333741386580

1258710819414499

283238403240618985184147185134155

2831

2328

343962136104

6754

192121

4238507690

6781635084831086954

4959549065869811391696156

37459484106145114103133102868056

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

5211

-11

485510

464721

4256291312919

6224189510

8

53401214558236

-3

-5-12-12

-16

423323157101385

-7-3-3

-6-10-11-11-17

423421201620171062-7-4

-5-9

-4-13

221822252222133-5-3

-9-9-12

-23

293743474130208

-25-22-19

18284457585043373617

-6

-14-17-19

616652535038

-13

44675759544427

-6

2825

1574

-10

27

213714

30S

20S

10S

EQ

10N

20N

30N

2050s

76102135322265497261247280212176114

76141131201283263136240235204168107

74171123184162203125188204437150108109

5770182139152110158418349180130126884572

561162191501002413271701301311307228367092119764140446876

58911321372182302332621041271266023155356463928333027365446254

6924314215314539242126689129122603025545538393430352532442937

6213613714626218350327984115115644128474631333329242731382943

611371751751744192531728193875142443640363539343033324234

61129125122288535323148725958828067635647464342383040

55596177123

2742088577931311971311871758753645556

5662707771123152

236164204366271186

536172766075115168160347277348252291

5459

4453

6473118257197

126102

364039

807295143169

12615211894159156205130102

93112102170122161184212171131116105

6985177159200273216193251193162151105

30S

20S

10S

EQ

10N

20N

30N

-9

197918

-16

738515

-10

707232-6

65864520191430

94372714715

12

-20

826119227812459

-4

-8-19-18-15

-24

64513624111520128

-11-4-5

-9-15-16-17-26

-18

655332312531261693

-11-6

-8-13

-6-20

-24

48

342834383334205-8-443

-14-14-19

-35

675553384557677263463112-3

-38-34-29

64

27446887897767575525

-10

-22-26-29

4231

9310179817658

-19

681038790836842

-10

2228465151

3747

-9-5

23

7

27433827

23116

-15

4442

335721

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

2080s

Median of % changes

-50 -30 -20 -10 0 10 20 30 50

116157207493407763401379430326269176

117216200308434403209369361312258164

114263189282248311191288313671230166167

8810727921423416924264253527620019313569111

861783372301533705022612002011991104455107142183116646168104116

891392032103353533574011601941949335248186716043514641558370390

106372218235223602645409136198187924638838459615246543949674457

95209210224401280771428129177177986343737148505144374148594567

942112682692676433882641241431347864685661555460534650496452

931981911874428214952271119189126123103978673706565584762

849194118189

42031913111914220130220128726813382988586

8596107118109188233

362252313561416286

819411011692115176258246533425534387447

8291

6781

98113180394302

194156

566260

123110146220260

193233181145244240314199157

143172157261188248282326263201178161

106130272244306418331297385296249231162

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

Range of % changes

10 20 30 40 50 75 100

Figure 10: (Left panels) Change in mean DJF rainfall for the 2020s, 2050s and 2080s (with respect to 1961-90) forthe A2-high scenario; median of seven GCM experiments. For areas with no change shown the model medianresponse fails to exceed the one sigma level of natural rainfall variability as defined by HadCM2. (Right panels)Inter-model range in mean annual temperature change. See text for further explanation.


36

-21-18-16

-6-55

-18-16

-35

-16

33 7

10

6

12

3

12

42

3

3

-8-8

-43-5-6-12

8-7-12

-6-12-11

-13-12

-11

-18-17

-15-16-16-17-16-105

-20-14

-11-11-11-8416

-22-17-18

-9-6

12

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

2020s

293653

126181154503734222023

314177

243197169483129232221

323475

26623619253292524222631

3890

30821119967382727272938362020

12342023720610154362929273134323241329293

120278264206352

9946622523413680452325242428313655606793

1051311731291472296521

1482092522411511015519171820212232506595

133155145143151111869420

257268236206134885124201820222223416085

160181154121121108888249

3973012271871257042292225212122272835

11290689661

102665148

331315217170105504328222121254037369349513849667169

711710391314221

774934272136466051313638362124

392500325301199152100

554546434276

24933727424817013810162565062554337

2926

272269

158119945753

5155

394234

6234475149

497257172831322830

655065617026172029292944

36665364857137222430292838

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

-40-33-29-25

-12-99

-33-31-26-24-29-20

9

-6-69

-31-23-26-25-28-20

179

557

-23-25-25-28

1712

145

-28-33-26

2220

114

-25

-26

22

6244

27

-17

23838

5712

-8

3

547

-7-45

-2-15-15

-85-9-12-23-10

-34

-416-14-22-16-15

-26

-17-11-22-20-20

-37

-28-32-21

-24-23-21

-15-19

-21

-22

-15

-23-20-20

-32-33-31

-28-29-30-32-30-189

-37-27-26-26-20-13-20-20-21-14829

-41-32-35-28-26-18-18

-8-18-11423

30S

20S

10S

EQ

10N

20N

30N

2050s

5668

101237340290946965423743

5976

145457371318905954434140

6164

142501445362101554845424958

72170582398374127725250515471683837

23179244638919010367545552596460607761

174176227525497388665

1868804254412561508543474545535968

10411312617519924832624327843312239

2783954764542851901043532343739426094

12217825129227327028520916317737

484505445389253167964437343742414377

11316130234229122822820416715492

74856742735323613279554247403942505466

2101701281811151931259791

62359340932119894815341393948757068

17592967192

125133130

13411339738592417

145926550406987

11496586872673945

738943613567376287188

10386868280

144

46963551746832026119111810694

1181038171

5549

513508

298225178107100

96104

747864

11664899693

93136108325259615356

12395

12211613249313855545483

6912410112116013570424656545272

30S

20S

10S

EQ

10N

20N

30N

-61-51-45-38-26-19-14

-9-18-1314

-51-47-40-37-44-31

14

-10-913

-47-35-39-38-43-30

2613

7810

-35-38-39-43-21

261953217

-10-21

-44-50-40

343054176

-10-19

24

-26-39-30-40

223489365610

2341

-26

-30

-42

-35

3612413-2471118

-12

14

538610

-59

-10-784-3-22-24

-18

-138

-14-19-35-15

-32

-25

-52-37

-52

-724-21-34-25-23-26-20

-39-27

-40-40

-37-50-44-41

-26-17-34-31-31

-21

-45-56

-43-49-32

-37-36-32

-23-29

-33-32

-35-24-25-34

-23

-35-31-30

-25-48-51-48

-43-45-46-49-46-2814

-57-42-39-40-31-20-31-31-32-221245

-63-49-53-42-39-28-27-15-12-27-17535

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

2080s

Median of % changes

-50 -30 -20 -10 0 10 20 30 50

85

105154364522444145106100645766

911172227025694881399083666362

9398

218769682555154857369657489

11126189261057419411079777983

1091045857

355121468459629215710383857991989392

11993

267270348805763595

1020

2861349651677392230130667270698190

10416017319326830538050037242666418760

42760673069743729215954495256606491

14518827438544841841543832125027257

74277568259638925614768575357656366

118173247463525446349350313256236141

11478706555413632031218564716160657782

102323261197277176296192149140

9569106274933041441248163595973

115108105268141147110141192205200

205720531132908640

2231421007761

10613317514889

1041111036069

11331447941870576440289

158131132126123221

719974794719491400293181162144180158124108

8476

787779

457345273164153

148160

11312098

17899

137148142

142208165498091938286

189145188178202764858858383

127

1051901541862462071086570868281

110

10W 0 10E 20E 30E 40E 50E

30S

20S

10S

EQ

10N

20N

30N

Range of % changes

10 20 30 40 50 75 100

Figure 11: (Left panels) Change in mean JJA rainfall for the 2020s, 2050s and 2080s (with respect to 1961-90) forthe A2-high scenario; median of seven GCM experiments. For areas with no change shown the model medianresponse fails to exceed the one sigma level of natural rainfall variability as defined by HadCM2. (Right panels)Inter-model range in mean annual temperature change. See text for further explanation.


37

-1 0 1 2 3 4 5Temperature change (oC)

-40

-20

0

20

Pre

cipi

tatio

n ch

ange

(%

)

Senegal


-40

-20

0

20

Pre

cipi

tatio

n ch

ange

(%

)

Tunisia


-40

-20

0

20

Pre

cipi

tatio

n ch

ange

(%

)

Ethiopia


-40

-20

0

20

Pre

cipi

tatio

n ch

ange

(%

)

Zimbabwe

CCCSR CGCMI CSIRO ECHAM GFDL HCGG NCAR

Figure 12: Change in mean annual temperature and precipitation for the 2050s (with respect to 1961-90) for regionscentred on Senegal, Tunisia, Ethiopia and Zimbabwe (see Figure 6, top left panel, for selected domains). Resultsfrom the seven DDC GCMs are shown, scaled to reflect the four climate change scenarios adopted in this study: A2-high, A1-mid, B2-mid and B1-low. Note: the HadCM2 GCM has four results reflecting the four-member ensemblesimulations completed with this GCM. The bold lines centred on the origin indicate the two standard deviationlimits of natural 30-year time-scale natural climate variability defined by the 1400-year HadCM2 control simulation.


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The Sahel

-80

-60

-40

-20

0

20

40

60

801880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

East Africa

-80

-60

-40

-20

0

20

40

60

80

An

nual

ra

ina

ll a

nom

aly

(per

cen

t)

Southeast Africa

-80

-60

-40

-20

0

20

40

60

80

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100

Figure 13: Observed annual rainfall anomalies for three African regions, 1900-1998 (cf. Figure 3), and model-simulated anomalies for 2000-2099. Model anomalies are for the 10 model simulations derived from the seven DDCGCM experiments - the four HadCM2 simulations are the dashed curves (cf. Table 1). All anomalies are expressedwith respect to either observed or model-simulated 1961-90 average rainfall. The model curves are extracted directlyfrom the GCM experiments and the results are not scaled to the four scenarios used in this paper. The smooth curvesresult from applying a 20-year Gaussian filter.

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