CLIMATE RESEARCH Vol. 17: 145–168, 2001 Published August 15 Clim Res

African climate change: 1900–2100

Mike Hulme1,*, Ruth Doherty3, Todd Ngara4, Mark New5, David Lister2

1Tyndall Centre for Climate Change Research and 2Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom 3Environmental and Societal Impacts Group, NCAR, Boulder, Colorado 80307, USA 4Climate Change Office, Ministry of Mines, Environment and Tourism, Postal Bag 7753 Causeway, Harare, Zimbabwe 5School of Geography, Mansfield Road, University of Oxford, Oxford OX1 3TB, United Kingdom

ABSTRACT: This paper reviews observed (1900–2000) and possible future (2000–2100) continent- wide changes in temperature and rainfall for Africa. For the historic period we draw upon a new observed global climate data set which allows us to explore aspects of regional climate change related to diurnal temperature range and rainfall variability. The latter includes an investigation of regions where seasonal rainfall is sensitive to El Niño climate variability. This review of past climate change provides the context for our scenarios of future greenhouse gas-induced climate change in Africa. These scenarios draw upon the draft emissions scenarios prepared for the Intergovernmental Panel on Climate Change’s Third Assessment Report, a suite of recent global climate model experi- ments, and a simple climate model to link these 2 sets of analyses. We present a range of 4 climate futures for Africa, focusing on changes in both continental and regional seasonal-mean temperature and rainfall. Estimates of associated changes in global CO2 concentration and global-mean sea-level change are also supplied. These scenarios draw upon some of the most recent climate modelling work. We also identify some fundamental limitations to knowledge with regard to future African cli- mate. These include the often poor representation of El Niño climate variability in global climate models, and the absence in these models of any representation of regional changes in land cover and dust and biomass aerosol loadings. These omitted processes may well have important consequences for future African climates, especially at regional scales. We conclude by discussing the value of the sort of climate change scenarios presented here and how best they should be used 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 Whilst seasonal climate forecasting has taken great strides forward, in both its development and applica- The climates of Africa are both varied and varying: tion (Folland et al. 1991, Stockdale et al. 1998, Wash- varied because they range from humid equatorial ington & Downing 1999, also see http://www.ogp. regimes, through seasonally-arid tropical regimes, to noaa.gov/enso/africa.html [SARCOF: Southern Africa sub-tropical Mediterranean-type climates, and varying Regional Climate Outlook Forum]), the ultimate causes because all these climates exhibit differing degrees of of the lower frequency decadal and multi-decadal temporal variability, particularly with regard to rain- rainfall variability that affects some African climate fall. Understanding and predicting these inter-annual, regimes, especially in the Sahel region, remain uncer- inter-decadal and multi-decadal variations in climate tain (see Rowell et al. 1995 vs Sud & Lau 1996, also Xue has become the major challenge facing African and & Shukla 1998). This work examining the variability of African-specialist climate scientists in recent years. African climate, especially rainfall, is set in the wider context of our emerging understanding of human influences on the larger, global-scale climate. Increas- *E-mail: [email protected] ing greenhouse gas accumulation in the global atmo-

© Inter-Research 2001 146 Clim Res 17: 145–168, 2001

sphere and increasing regional concentrations of We start the paper (Section 2) by reviewing some aerosol particulates are now understood to have previous climate change scenarios and analyses for detectable effects on the global climate system (Santer regions within Africa. Such studies have been far from et al. 1996). These effects will be manifest at regional comprehensive. Section 3 explains the data, models scales although perhaps in more uncertain terms and approaches that we have taken in generating our (Mitchell & Hulme 1999, Giorgi & Francisco 2000). analyses and constructing our climate change scenar- Africa will not be exempt from experiencing these ios for Africa. In Section 4 we consider the salient human-induced changes in climate. Much work re- features of African climate change and variability over mains to be done, however, in trying to isolate those the last 100 yr, based on the observational record of aspects of African climate variability that are ‘natural’ Africa climate. Such a historical perspective is essen- from those that are related to human influences. tial if the simulated climates of the next century are African climate scientists face a further challenge in to be put into their proper context. Section 5 then that on this continent the role of land cover changes— presents our future climate change scenarios for some natural and some human-related—in modifying Africa, based on the draft Special Report on regional climates is perhaps most marked (Xue 1997). Emissions Scenarios range of future greenhouse gas This role of land cover change in altering regional cli- emissions (http://sres.ciesin.org/index.html [SRES]) mate in Africa has been suggested for several decades and the GCM results deposited with the Intergovern- now. As far back as the 1920s and 1930s, theories mental Panel on Climate Change (IPCC) Data Distrib- about the encroachment of the Sahara and the desicca- ution Centre (http://ipcc-ddc.cru.uea.ac.uk/index.html tion of the climate of West Africa were put forward [DDC]). Changes in mean seasonal climate are shown (Stebbing 1935, Aubreville 1949). These ideas have as well as some measures of changed interannual vari- been explored over the last 25 yr through modelling ability. Section 6 then discusses these future climate studies of tropical north African climate (e.g. Charney simulations in the light of modelling uncertainties and 1975, Cunnington & Rowntree 1986, Zheng & Eltahir in the context of other causes of African climate vari- 1997). It is for these 2 reasons—large internal climate ability and change. We consider how much useful and variability driven by the oceans and the confounding reliable information these types of studies yield and role of human-induced land cover change—that cli- how they can be incorporated into climate change mate change ‘predictions’ (or the preferable term sce- impacts assessments. Our key conclusions are pre- narios) for Africa based on greenhouse gas warming sented in Section 7. remain highly uncertain. While global climate models (GCMs) simulate changes to African climate as a re- sult of increased greenhouse gas concentrations, 2. REVIEW OF PREVIOUS AFRICAN CLIMATE these 2 potentially important drivers of African climate CHANGE SCENARIO WORK variability—for example El Niño/Southern Oscillation (ENSO) (poorly) and land cover change (not at all)— There has been relatively little work published on are not well represented in the models. future climate change scenarios for Africa. The various Nevertheless, it is of considerable interest to try and IPCC assessments have of course included global explore the magnitude of the problem that the maps of climate change within which Africa has fea- enhanced greenhouse effect may pose for African tured, and in Mitchell et al. (1990) the African Sahel climate and for African resource managers. Are the was one of 5 regions for which a more detailed analy- changes that are simulated by GCMs for the next cen- sis was conducted. Kittel et al. (1998) and Giorgi & tury large or small in relation to our best estimates of Francisco (2000) also identify African regions within ‘natural’ climate variability in Africa? How well do their global analysis of inter-model differences in cli- GCM simulations agree for the African continent? And mate predictions, but no detailed African scenarios are what are the limitations/uncertainties of these model presented. predictions? Answering these questions has a very Tyson (1991) published one of the first scenario practical relevance in the context of national vulnera- analyses specifically focused on an African region. In bility and adaptation assessments of climate change this case some climate change scenarios for southern currently being undertaken by many African nations Africa were constructed using results from the first as part of the reporting process to the UN Framework generation GCM equilibrium 2 × CO2 experiments. In Convention on Climate Change. This paper makes a a further development, Hulme (1994a) presented a contribution to these assessments by providing an method for creating regional climate change scenarios overview of future climate change in Africa, particu- combining GCM results with the newly published larly with regard to simulations of greenhouse gas IPCC IS92 emissions scenarios and demonstrated the warming over the next 100 yr. application of the method for Africa. In this study mean Hulme et al.: African Climate Change: 1900–2100 147

annual temperature and precipitation changes from ability for this region. This low signal:noise ratio sug- 1990 to 2050 under the IS92a emission scenario were gests that the greenhouse gas-induced climate change presented. signals are not well defined in the model, at least for Some more recent examples of climate scenarios for this region. We develop this line of reasoning in this Africa use results from transient GCM climate change paper and illustrate it in Section 5 with further exam- experiments. Hernes et al. (1995) and Ringius et al. ples from Africa. (1996) constructed climate change scenarios for the Although there have been studies of GCM-simu- African continent that showed land areas over the lated climate change for several regions in Africa, the Sahara and semi-arid parts of southern Africa warming downscaling of GCM outputs to finer spatial and tem- by the 2050s by as much as 1.6°C and the equatorial poral scales has received relatively little attention in African countries warming at a slightly slower rate of Africa. Hewitson & Crane (1998) and Hewitson & about 1.4°C. These studies, together with Joubert et al. Joubert (1998) have applied empirical downscaling (1996), also suggested a rise in mean sea-level around methods to generate climate change scenarios for the African coastline of about 25 cm by 2050. A more South Africa using artificial neural networks and pre- selective approach to the use of GCM experiments was dictors relating to upper air circulation and tropos- taken in Hulme (1996a). They described 3 future cli- pheric humidity. The usual caveats, however, apply to mate change scenarios for the Southern African Devel- these downscaled scenarios (Hulme & Carter 1999)— opment Community (SADC) region of southern Africa they are still dependent on the large-scale forcing from for the 2050s on the basis of 3 different GCM experi- the GCMs and they still only sample one realisation of ments. These experiments were selected to deliber- the possible range of future possible climates, albeit ately span the range of precipitation changes for the with higher resolution. The application of regional SADC region as simulated by GCMs. Using these climate models is still in its infancy, although some scenarios, the study then described some potential initiatives are now under way for East Africa (Sun et impacts and implications of climate change for agri- al. 1999), West Africa (Wang & Eltahir 2000) and south- culture, hydrology, health, biodiversity, wildlife and ern Africa (B. Hewitson pers. comm.). These initiatives rangelands. A similar approach was adopted by Con- have not yet generated experimental results from way et al. (1996) for a study of the impacts of climate regional climate change simulations for use in scenario change on the Nile Basin. More recently, the Africa construction. chapter (Zinyowera et al. 1998) in the IPCC Assess- ment of Regional Impacts of Climate Change (IPCC 1998) also reported on some GCM studies that related 3. DATA AND METHODS to the African continent. Considerable uncertainty exists in relation to large- For our analyses of observed climate variability in scale precipitation changes simulated by GCMs for Africa we use the global gridded data sets of Jones Africa (Hudson 1997, Hudson & Hewitson 1997, Jou- (1994, updated; mean temperature), Hulme (1994b, bert & Hewitson 1997, Feddema 1999). Joubert & updated; precipitation), and New et al. (1999, 2000; Hewitson (1997) nevertheless conclude that, in gen- 10 surface climate variables). These data sets are all eral, precipitation is simulated to increase over much public domain and are available, along with some of the African continent by the year 2050. These GCM documentation on their construction, from the follow- studies show, for example, that parts of the Sahel could ing Web sites: GCM results were taken from the IPCC experience precipitation increases of as much as 15% Data Distribution Centre (DDC) (http://ipcc-ddc.cru. over the 1961–90 average by 2050. A note of caution uea.ac.uk); most of the observed data sets used here is needed, however, concerning such a conclusion. can be obtained from the Climatic Research Unit Hulme (1998) studied the present-day and future (http://www.cru.uea.ac.uk); the draft (February 1999; simulated inter-decadal precipitation variability in the non-IPCC approved, but used with permission) SRES Sahel using the HadCM2 GCM. These model results emissions scenarios were obtained from the SRES were compared with observations during the 20th (http://sres.ciesin.org/index.html). The data sets of Century. Two problems emerge. First, the GCM does Jones (1994) and Hulme (1994b) exist on a relatively not generate the same magnitude of inter-decadal coarse grid (5° latitude/longitude and 2.5° latitude by precipitation variability that has been observed over 3.75° longitude respectively), while the data set of New the last 100 yr, casting doubt on the extent to which et al. (1999, 2000) exists with a 0.5° latitude/longitude the most important controlling mechanisms are being resolution. These observed data are resolved only to simulated in the GCM. Second, the magnitude of the monthly time steps and we therefore undertake no future simulated precipitation changes for the Sahel original analyses of observed daily climate variability. is not large in relation to ‘natural’ precipitation vari- For Ethiopia and Zimbabwe we analyse unpublished 148 Clim Res 17: 145–168, 2001

monthly mean maximum and minimum temperature Climate can be affected by a number of other agents data for a number of stations in each country. These in addition to greenhouse gases; important amongst data originate from the respective national meteoro- these are small particles (aerosols). These aerosols are logical agencies. For the index of the Southern Oscilla- suspended in the atmosphere and some types (e.g. sul- tion we use the updated index of Ropelewski & Jones phate aerosols derived from sulphur dioxide) reflect (1987), calculated as the normalised mean sea-level back solar radiation; hence they have a cooling effect pressure difference between Tahiti and Darwin and on climate. Although there are no measurements to available from Climate Monitor online (http://www. show how these aerosol concentrations have changed cru.uea.ac.uk/cru/climon). over the past 150 yr, there are estimates of how sul- Other climate-related and continent-wide data sets phur dioxide emissions (one of the main precursors for also have value for some climate analyses, whether aerosol particles) have risen and scenarios of such these data are derived from satellite observations (e.g. emissions into the future. A number of such scenarios Normalised Difference Vegetation Index or satellite- have been used in a sulphur cycle model to calculate derived precipitation estimates) or from numerical the future rise in sulphate aerosol concentrations (Pen- weather prediction model re-analyses (e.g. the NCEP ner et al. 1998). When one of these scenarios was used, [National Centers for Environmental Prediction] re- along with greenhouse gas increases, as input to the analysis from 1948 to present). Although these alterna- DDC GCMs, the global-mean temperature rise to 2100 tive data sets have some real advantages in particular was reduced by between a quarter and a third. The environmental or modelling applications (e.g. model- reductions over Africa were less than this. ling malaria; Lindsay et al. 1998; evaluating dust forc- These are very uncertain calculations, however, due ing; Brooks 1999), we prefer to limit our analysis here to a number of factors. First, the old 1992 IPCC emis- to the use of conventional observed climate data sets sions scenario on which it was based (IS92a; Leggett et derived from surface observations. al. 1992) contains large rises in sulphur dioxide emis- The GCM results used in this study are mainly sions over the next century. Newer emissions scenar- extracted from the IPCC DDC archive. This archive ios, including the draft SRES scenarios, estimate only a contains results from climate change experiments small rise in sulphur dioxide emissions over the next performed with 7 coupled ocean-atmosphere global couple of decades followed by reductions to levels climate models (Table 1). All these experiments were lower than today’s by 2100 (SRES). Over Africa, sul- conducted using similar greenhouse gas or green- phur emissions remain quite low for the whole of this house gas plus aerosol forcing. In this study only the century. The inclusion of such modest sulphur dioxide results from the greenhouse gas-forced simulations are emissions scenarios in GCM experiments would actu- used for reasons outlined below. We also use results ally produce a small temperature rise relative to model from the 1400 yr control simulation of the HadCM2 cli- experiments that excluded the aerosol effect (Schles- mate model (Tett et al. 1997) to derive model-based inger et al. 2000). Results from GCM experiments estimates of natural multi-decadal climate variability. using these revised sulphur scenarios are not yet The data were re-gridded using a Gaussian space- widely available. Second, more recent sulphur cycle filter onto a common grid, namely the HadCM2 grid. models generate a lower sulphate burden per tonne of Later results are presented on this common grid. sulphur dioxide emissions and the radiative effect of

Table 1. Characteristics of the 7 global climate models available at the IPCC Data Distribution Centre from which 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 each model following a doubling of atmospheric carbon dioxide concentration

Country of Approximate resolution Climate sensitivity Integration Source origin (lat. × long.) (°C) length

CCSR-NIES Japan 5.62° × 5.62° 3.5 1890–2099 Emori et al. (1999) CGCM1 Canada 3.75° × 3.75° 3.5 1900–2100 Boer et al. (2000) CSIRO-Mk2 Australia 3.21° × 5.62° 4.3 1881–2100 Hirst et al. (2000) ECHAM4 Germany 2.81° × 2.81° 2.6 1860–2099 Roeckner et al. (1996) GFDL-R15 USA 4.50° × 7.50° 3.7 1958–2057 Haywood et al. (1997) HadCM2a UK 2.50° × 3.75° 2.5 1860–2099 Mitchell & Johns (1997) NCAR1 USA 4.50° × 7.50° 4.6 1901–2036 Meehl & Washington (1995) aAn ensemble of 4 climate change simulations were available from the HadCM2 model Hulme et al.: African Climate Change: 1900–2100 149

the sulphate particles in more sophisticated radiation and the post-1970s—occur simultaneously in Africa models is smaller than previously calculated. Third, in and the rest of the world. addition to their direct effect, sulphate aerosols can Few studies have examined long-term changes in the also indirectly cool climate by changing the reflectivity diurnal cycle of temperature in Africa. Here, we show and longevity of clouds (Schimel et al. 1996). These results for 4 countries for which studies have been pub- indirect effects are now realised as being at least as lished or data were available for analysis—for Sudan important as the direct effect, but were not included in and South Africa as published by Jones & Lindesay the present DDC GCM climate change simulations. (1993) and for Ethiopia and Zimbabwe (unpubl.). While Fourth, there are other types of aerosols (e.g. carbon or a majority of the Earth’s surface has experienced a de- soot) which may also have increased due to human cline in the mean annual diurnal temperature range activity, but which act to warm the atmosphere. Finally (DTR) as climate has warmed (Nicholls et al. 1996), our and above all, the short lifetime of sulphate particles in examples here show contrasting trends for these 4 the atmosphere means that they should be seen as a African countries. Mean annual DTR decreased by be- temporary masking effect on the underlying warming tween 0.5 and 1°C since the 1950s in Sudan and trend due to greenhouse gases. For all these reasons, Ethiopia, but increased by a similar amount in Zim- model simulations of future climate change using both babwe (Fig. 2). In South Africa, DTR decreased during greenhouse gases and sulphate aerosols have not been the 1950s and 1960s, but has remained quite stable used to develop the climate change scenarios illus- since then. Examination of the seasonal variation in trated in this paper. these trends (not shown) suggests that different factors The future greenhouse gas forcing scenario used in contribute to DTR trends in different seasons and in dif- the DDC experiments approximated a 1% annum–1 ferent countries. For example, in Sudan DTR shows an growth in greenhouse gas concentrations over the period from 1990 to 2100. Since the future growth in 1900 1920 1940 1960 1980 2000 Annual anthropogenic greenhouse gas forcing is highly uncer- 0.6 tain, it is important that our climate scenarios for Africa reflect this uncertainty; it would be misleading to con- 0 struct climate change scenarios that reflected just one -0.6 future emissions growth curve. We therefore adopt the

4 draft marker emissions scenarios of the IPCC SRES: DJF B1, B2, A1 and A2. None of these emissions scenarios 0.6 assume any climate policy implementation; the differ- 0 ences result from alternative developments in global population, the economy and technology. Our method -0.6 of climate change scenario construction follows that adopted by Hulme & Carter (2000) in their generation MAM 0.6 of climate change scenarios for Europe as part of the

ACACIA assessment of climate impact in Europe. Full (degC) e anomaly 0 details may be found there, but we provide a short -0.6 summary of the method in Section 5 below.

JJA 0.6

4. TWENTIETH CENTURY CLIMATE CHANGE Mean temperatur 0

4.1. Temperature -0.6

The continent of Africa is warmer than it was 100 yr SON 0.6 ago. Warming through the 20th century has been at –1 the rate of about 0.5°C century (Fig. 1), with slightly 0 larger warming in the June–August (JJA) and Sep- tember–November (SON) seasons than in December– -0.6

February (DJF) and March–May (MAM). The 6 1900 1920 1940 1960 1980 2000 warmest years in Africa have all occurred since 1987, Fig. 1. Mean surface air temperature anomalies for the African with 1998 being the warmest year. This rate of warm- continent, 1901–98, expressed with respect to the 1961–90 average; ing is not dissimilar to that experienced globally, and annual and 4 seasons—DJF, MAM, JJA, SON. The smooth curves the periods of most rapid warming—the 1910s to 1930s result from applying a 10 yr Gaussian filter 150 Clim Res 17: 145–168, 2001

1940 1950 1960 1970 1980 1990 2000 To illustrate something of this variability we present an Sudan analysis for the 3 regions of Africa used by Hulme 1 1 (1996b)—the Sahel, East Africa and southeast Africa 0 0 (domains shown in Fig. 4). These 3 regions exhibit con- trasting rainfall variability characteristics (Fig. 3): the -1 -1 Sahel displays large multi-decadal variability with recent drying, East Africa a relatively stable regime Ethiopia 1 1 with some evidence of long-term wetting, and south- east Africa also a basically stable regime, but with 0 0 marked inter-decadal variability. In recent years Sahel rainfall has been quite stable around the 1961–90 -1 -1 annual average of 371 mm, although this 30 yr period is substantial drier (about 25%) than earlier decades Zimbabwe 1 1 this century. In East Africa, 1997 was a very wet year

degC anomaly degC and, as in 1961 and 1963, led to a surge in the level of 0 0 Lake Victoria (Birkett et al. 1999). Recent analyses (Saji et al. 1999, Webster et al. 1999) have suggested these -1 -1 extreme wet years in East Africa are related to a dipole

South Africa 1 1

1880 1900 1920 1940 1960 1980 2000 80 0 0 60

-1 -1 40 +0.4

20 +0.2 1940 1950 1960 1970 1980 1990 2000 0 0.0

Fig. 2. Mean annual diurnal temperature range (Tmax – Tmin) -20 -0.2 for a number of African countries: Sudan (data end 1987), Ethiopia (1990), Zimbabwe (1997) and South Africa (1991). -40 -0.4

The smooth curves result from applying a 10 yr Gaussian -60 The Sahel filter. Data for Sudan and South Africa are from Jones & -8080 Lindesay (1993), while data for Ethiopia and Zimbabwe are unpublished 60 40 +0.4

20 +0.2 increasing trend during the July–September wet 0.0 season, probably caused by trends towards reduced 0 cloudiness, while DTR decreased during the rest of the -20 -0.2 year, probably due to trends for increased dustiness -40 -0.4

(Brooks 1999). Both of these factors are related to the -60 East Africa nnual rainall anomaly (per cent) (per anomaly rainall nnual multi-decadal drought experienced in Sudan since the A -8080 Annual temperature anomaly (degC)

1950s (Hulme 2001). The long-term increase in annual 60

DTR in Zimbabwe is due almost entirely to increases 40 +0.4 during the November–February wet season; trends 20 +0.2 during the rest of the year have been close to zero. We 0 0.0 are not aware of published analyses of diurnal tem- perature trends in other African countries. -20 -0.2 -40 -0.4

-60 Southeast Africa 4.2. Rainfall -80 1880 1900 1920 1940 1960 1980 2000

Interannual rainfall variability is large over most of Fig. 3. Annual rainfall (1900–98; histograms and bold line) and Africa and for some regions, most notably the Sahel, mean temperature anomalies (1901–98; dashed line) for 3 African multi-decadal variability in rainfall has also been regions, expressed with respect to the 1961–90 average: Sahel, East Africa and southeastern Africa (regional domains marked on substantial. Reviews of 20th Century African rainfall Fig. 4). Note: for southeast Africa year is July to June. The smooth variability have been provided by, among others, curves for rainfall and temperature result from applying a 10 yr Janowiak (1988), Hulme (1992) and Nicholson (1994). Gaussian filter Hulme et al.: African Climate Change: 1900–2100 151

mode of variability in the Indian Ocean. In southeast 4.3. Spatial patterns Africa, the dry years of the early 1990s were followed by 2 very wet years in 1995/96 and 1996/97. Mason et Our analysis is summarised further in Fig. 4, where al. (1999) report an increase in recent decades in the we show mean linear trends in annual temperature frequency of the most intense daily precipitation over and precipitation during the 20th century. This ana- South Africa, even though there is little long-term lysis first filters the data using a 10-point Gaussian trend in total annual rainfall amount. filter to subdue the effects on the regression analysis Fig. 3 also displays the trends in annual temperature of outlier values at either end of the time period. for these same 3 regions. Temperatures for all 3 regions While warming is seen to dominate the continent (see during the 1990s are higher than they were earlier in the Fig. 1 above), some coherent areas of cooling are century (except for a period at the end of the 1930s in the noted, around Nigeria/Cameroon in West Africa and Sahel) and are currently between 0.2 and 0.3°C warmer along the coastal margins of Senegal/Mauritania and than the 1961–90 average. There is no simple correlation South Africa. In contrast, warming is at a maximum between temperature and rainfall in these 3 regions, al- of nearly 2°C century–1 over the interior of southern though Hulme (1996b) noted that drying in the Sahel Africa and in the Mediterranean countries of north- was associated with a moderate warming trend. west Africa.

Mean Annual Temperature Annual Precipitation

30N 30N

15N 15N

0 0

15S 15S

30S 30S

15W 0 15E 30E 45E 15W 0 15E 30E 45E

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 oC Percent

Fig. 4. Mean linear trends in annual temperature (°C century–1) and annual rainfall (% century–1), calculated over the period 1901–95 from the New et al. (1999, 2000) data set. Data were filtered with a 10-point Gaussian filter before being subject to regression analysis. The 3 regions shown are those depicted in Figs 3 & 13 152 Clim Res 17: 145–168, 2001

The pattern of rainfall trends (Fig. 4) reflects the individual Southern Oscillation events, and correlated regional analysis shown in Fig. 3, with drying of up to this index against seasonal rainfall in Africa. We per- 25% century–1 or more over some western and eastern formed this analysis for the 4 conventional seasons (not parts of the Sahel. More moderate drying—5 to 15% shown) and also for the 2 extended seasons of June to century–1—is also noted along the Mediterranean October (Year 0) and November (Year 0) to April (Year coast and over large parts of Botswana and Zimbabwe 1; Fig. 5a). This analysis confirms the strength of the and the Transvaal in southeast Africa. The modest previously identified relationships for equatorial east wetting trend noted over East Africa is seen to be part Africa (high rainfall during a warm ENSO event) and of a more coherent zone of wetting across most of southern Africa (low rainfall during a warm ENSO equatorial Africa, in some areas of up to 10% century–1 event). The former relationship is strongest during the or more. Regions along the Red Sea coast have also September–November rainy season (the ‘short’ rains; seen an increase in rainfall, although trends in this not shown), with an almost complete absence of ENSO arid/semi-arid region are unlikely to be very robust. sensitivity in this region during the February–April season (‘long’ rains) as found by Ropelewski & Halpert (1996). The southern African sensitivity is strongest 4.4. ENSO influence on rainfall over South Africa during December–February before migrating northwards over Zimbabwe and Mozam- With regard to interannual rainfall variability in bique during the March–May season (not shown). Africa, the ENSO is one of the more important control- There is little rainfall sensitivity to ENSO behaviour ling factors, at least for some regions (Ropelewski & elsewhere in Africa, although weak tendencies for Halpert 1987, 1989, 1996, Janowiak 1988, Dai & Sahelian June–August drying (Janicot et al. 1996) and Wigley 2000). These studies have established that the northwest African March–May drying (El Hamly et 2 regions in Africa with the most dominant ENSO al. 1998) can also be found. influences are in eastern equatorial Africa during the short October-November rainy season and in south- eastern Africa during the main November–February 5. TWENTY-FIRST CENTURY CLIMATE CHANGE wet season. Ropelewski & Halpert (1989) also exam- ined Southern Oscillation and rainfall relationships For a comprehensive assessment of the impact and during La Niña or high index years. We have con- implications of climate change, it is necessary to apply ducted our own more general analysis of Southern a number of climate change scenarios that span a rea- Oscillation rainfall variability for the African region sonable range of the likely climate change distribution. over the period 1901–98 using an updated and more The fact that there is a distribution of future climate comprehensive data set (Hulme 1994b) than was used changes arises not only because of incomplete under- by these earlier studies. We also use the Southern Os- standing of the climate system (e.g. the unknown value cillation Index (SOI) as a continuous index of Southern of the climate sensitivity, different climate model re- Oscillation behaviour rather than designating discrete sponses, etc.), but also because of the inherent unpre- ‘warm’ (El Niño; low index) and ‘cold’ (La Niña; high dictability of climate (e.g. unknowable future climate index) Southern Oscillation events as was done by forcings and regional differences in the climate system Ropelewski & Halpert (1996). response to a given forcing because of chaos). The We defined an annual average SOI using the June– ‘true’ climate change distribution is of course un- May year, a definition that maximises the coherence of known, but we can make some sensible guesses as to

Table 2. The 4 climate scenarios. Estimates shown here are for the 2050s (i.e., 2055), but the values for the 2020s and 2080s were also calculated. Temperature and sea-level changes assume no aerosol effects and are calculated from a 1961–90 baseline using the MAGICC climate model (Wigley & 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 is change in mean annual temperature, ∆SL is change in mean sea-level and pCO2 is the atmospheric carbon dioxide concentration

Scenario/ Population C emissions from Total S Global ∆T Global ∆SL pCO2 Climate sensitivity (billions) energy (GtC) emissions (TgS) (°C) (cm) (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 Hulme et al.: African Climate Change: 1900–2100 153

Time series correlations of SOI (Jun-May) vs. Precip (Jun-Oct) Time series correlations of SOI (Jun-May) vs. Precip (Nov-Apr) a 1 b 1 30N 0.5 30N 0.5

0.4 0.4

0.3 0.3

0.26 0.26 EQ EQ -0.26 -0.26

-0.3 -0.3

-0.4 -0.4 30S 30S -0.5 -0.5 ab -1 -1 0 30E 0 30E

Time series correlations of SOI (Jun-May) vs. Precip (Jun-Oct) Time series correlations of SOI (Jun-May) vs. Precip (Nov-Apr) cd 1 1

30N 0.5 30N 0.5

0.4 0.4

0.3 0.3

0.17 0.17 EQ EQ -0.17 -0.17

-0.3 -0.3

-0.4 -0.4 30S 30S -0.5 -0.5 cd -1 -1 0 30E 0 30E

Fig. 5. Correlation between annual (June–May) Southern Oscillation Index (SOI) and seasonal rainfall; (a,c) June–October (Year 0) rainfall; (b,d) November–April (Year 0 to +1) rainfall. (a,b) Observed relationship over the period 1901–98; (c,d) HadCM2 model-simulated relationship over a 240 yr unforced simulation. Correlations are only plotted where they are significant at 95% and in regions where the respective seasonal rainfall is greater than 20 mm and greater than 20% of annual total

its magnitude and shape and then make some choices sions scenario combined with a high climate sensitivity so as to sample a reasonable part of its range. (4.5°C), SRES A1 and SRES B2 combined with medium We have done this at a global scale by making choices climate sensitivities (2.5°C) and SRES B1 combined about future greenhouse gas forcings and about the with a low climate sensitivity (1.5°C). These 4 scenarios climate sensitivity (see Table 1 for definition). We fol- are subsequently termed A2-high, A1-mid, B2-mid low Hulme & Carter (2000) and Carter et al. (2001) in and B1-low, respectively, and yield a range of global this procedure, yielding the 4 global climate scenarios warming by the 2050s of 0.9 to 2.6°C. We chose the shown in Table 2. We have chosen the SRES A2 emis- 2 middle cases deliberately because, even though the 154 Clim Res 17: 145–168, 2001

global warming is similar, the worlds which underlie These estimates were extracted from the 1400 yr the B2 and A1 emissions scenarios are quite different unforced simulation of the HadCM2 model (Tett et al. (SRES). The impacts on Africa of what may be rather 1997). We use a climate model simulation to quantify similar global and regional climate changes could be the range of natural climate variability rather than quite different in these 2 cases. For example, global observations because the model gives us longer and (and African) population is lower in the A1 world than more comprehensive estimates of natural climate vari- in the B2 world, but carbon and sulphur emissions and ability. This has the disadvantage that the climate

CO2 concentrations are higher (Table 2). model may not accurately simulate natural climate Having defined these 4 global climate scenarios, we variability, although at least for some regions and on next consider the range of climate changes for Africa some time-scales, HadCM2 yields estimates of natural that may result from each of these 4 possible futures. variability quite similar both to observations (Tett et al. Again, we have a distribution of possible regional out- 1997) and to climatic fluctuations reconstructed from comes for a given global warming. We use results from proxy records over the past millennium (Jones et al. the 7 GCM experiments (see Table 1) to define this 1998). We discuss this problem further in Section 6. range. (Note: for HadCM2 there are 4 simulations for The resulting African scenario maps are therefore the same scenario thus the total GCM sample available informative at a number of levels: to us is 10; this gives more weight in our final scenarios • Africa-wide estimates are presented of mean sea- to the HadCM2 responses than to the other 6 GCMs.) sonal climate change (mean temperature and pre- We present the scenario results for seasonal mean tem- cipitation) for the 4 adopted climate change sce- perature and precipitation for the 2020s, 2050s and narios; 2080s in 2 different ways: Africa-wide maps and • These estimates are derived from a sample (a national-scale summary results for 4 representative pseudo-ensemble) of 10 different GCM simulations, countries within Africa. rather than being dependent on any single GCM or GCM experiment; • Only Median changes that exceed what may reason- 5.1. African scenario maps ably be expected to occur due to natural 30 yr time- scale climate variability are plotted; The construction of the scenario maps follows the • The extent of inter-model agreement is depicted approach of Hulme & Carter (2000) and Carter et al. through the Range maps. (2001). We first standardise the 2071–2100 climate response patterns—defined relative to the 1961–90 For our scenarios, future annual warming across model average—in the DDC GCMs using the global Africa ranges from below 0.2°C decade–1 (B1-low sce- warming values in each respective GCM. These stan- nario; Fig. 6) to over 0.5°C decade–1 (A2-high; Fig. 7). dardised climate response patterns are then scaled by This warming is greatest over the interior semi-arid the global warming values for our 4 scenarios and 3 tropical margins of the Sahara and central southern time periods calculated by the MAGICC climate model Africa, and least in equatorial latitudes and coastal (see Table 2). Scaling of GCM response patterns in this environments. The B2-mid and A1-mid scenarios (not way assumes that local greenhouse gas-induced cli- shown) fall roughly in between these 2 extremes. All of mate change is a linear function of global-mean tem- the estimated temperature changes exceed the 1 sigma perature. (See Mitchell et al. 1999 for a discussion of level of natural temperature variability (as defined by this asumption). Only a selection of the full set of maps unforced HadCM2 simulation), even in the B1-low sce- is shown here. For each scenario, season, variable and nario. The inter-model range (an indicator of the extent time slice we present 2 maps representing the change of agreement between different GCMs) is smallest in mean seasonal climate for the respective 30 yr over northern Africa and the Equator and greatest over period (Figs 6 to 11). One map shows the Median the interior of central southern Africa. For example, the change from our sample of 10 standardised and scaled inter-model range falls to less than 25% of the model GCM responses (left panels) and the other map shows median response in the former regions, but rises to the absolute Range of these 10 model responses (right over 60% of the model median response in the latter panels). areas. We also introduce the idea of signal:noise ratios by Future changes in mean seasonal rainfall in Africa comparing the Median GCM change with an estimate are less well defined. Under the B1-low scenario, rela- of natural multi-decadal climate variability. In the tively few regions in Africa experience a change in maps showing the Median change we only plot these either DJF or JJA rainfall that exceeds the 1 sigma values where they exceed the 1 standard deviation level of natural rainfall variability simulated by the estimate of natural 30 yr time-scale climate variability. HadCM2 model (Figs 8 & 9). The exceptions are parts Hulme et al.: African Climate Change: 1900–2100 155

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E 1.0 0.9 0.9 0.8 0.8 0.9 0.9 0.9 0.5 0.5 0.4 0.3 0.3 0.4 0.4 0.4 0.9 1.0 0.9 0.9 0.9 0.8 0.8 0.9 0.9 0.9 0.4 0.4 0.3 0.3 0.3 0.2 0.3 0.4 0.4 0.4 30N 0.9 1.0 1.0 0.9 0.9 0.8 0.8 0.8 0.7 0.7 0.7 0.8 0.9 0.9 0.9 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.3 0.2 0.2 0.2 30N 0.8 0.9 1.0 1.0 1.0 1.0 0.9 0.8 0.8 0.8 0.8 0.8 0.9 0.9 1.0 0.9 0.4 0.4 0.4 0.3 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.2 0.1 0.2 0.9 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.8 0.8 0.8 0.9 0.9 0.9 1.0 0.9 0.9 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.8 0.9 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0 0.9 0.5 0.5 0.4 0.3 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.3 0.5 0.5 0.6 20N 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 1.0 1.0 1.0 0.4 0.4 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.5 0.6 0.6 20N 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.6 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.5 0.8 0.9 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.4 0.6 0.6 0.6 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.7 0.7 10N 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.7 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.4 0.4 0.6 0.7 0.5 10N 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.4 0.5 0.4 0.6 0.5 0.4 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.4 0.4 0.4 0.4 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.4 0.3 0.7 0.8 0.8 0.8 0.8 0.8 0.7 0.6 0.6 0.6 0.4 0.5 0.4 0.4 0.4 0.4 0.5 0.5 0.3 0.3 2020s EQ 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.6 0.6 0.4 0.5 0.4 0.4 0.4 0.5 0.5 0.4 0.3 EQ 0.7 0.8 0.8 0.8 0.8 0.7 0.7 0.6 0.4 0.5 0.5 0.4 0.4 0.5 0.5 0.2 0.8 0.9 0.9 0.9 0.8 0.7 0.6 0.5 0.5 0.5 0.4 0.6 0.4 0.2 0.8 0.9 0.9 0.9 0.8 0.8 0.7 0.5 0.5 0.5 0.5 0.5 0.5 0.3 10S 0.8 1.0 1.0 0.9 0.9 0.8 0.7 0.5 0.5 0.5 0.5 0.6 0.5 0.4 10S 0.9 1.0 1.0 1.0 0.9 0.8 0.7 0.5 0.6 0.6 0.6 0.6 0.6 0.5 0.9 1.0 1.1 1.0 0.9 0.9 0.7 0.6 0.5 0.6 0.7 0.7 0.7 0.7 0.5 0.4 0.9 1.1 1.1 1.0 1.0 0.9 0.7 0.6 0.5 0.6 0.7 0.7 0.7 0.7 0.5 0.4 20S 0.8 1.0 1.1 1.0 1.0 0.9 0.6 0.5 0.6 0.7 0.7 0.7 0.6 0.4 20S 0.8 1.0 1.1 1.0 1.0 0.8 0.7 0.4 0.6 0.6 0.6 0.6 0.5 0.4 0.8 0.9 1.1 1.0 0.9 0.8 0.6 0.3 0.5 0.5 0.5 0.4 0.4 0.4 0.8 1.0 1.0 0.8 0.4 0.5 0.4 0.4 30S 0.8 0.9 0.9 0.8 0.4 0.5 0.4 0.3 30S 0.7 0.8 0.8 0.4 0.5 0.4

1.5 1.4 1.4 1.3 1.3 1.4 1.4 1.5 0.7 0.7 0.6 0.5 0.5 0.6 0.7 0.7 1.4 1.5 1.4 1.4 1.4 1.3 1.3 1.4 1.5 1.4 0.6 0.7 0.5 0.4 0.4 0.3 0.5 0.6 0.6 0.6 30N 1.4 1.5 1.5 1.5 1.4 1.3 1.2 1.2 1.1 1.1 1.2 1.3 1.4 1.4 1.4 0.6 0.6 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.5 0.6 0.5 0.3 0.3 0.3 30N 1.3 1.5 1.5 1.5 1.5 1.5 1.4 1.3 1.3 1.2 1.2 1.3 1.4 1.5 1.5 1.4 0.7 0.7 0.6 0.4 0.4 0.3 0.3 0.4 0.4 0.5 0.5 0.4 0.3 0.2 0.2 0.3 1.3 1.5 1.6 1.6 1.5 1.5 1.5 1.4 1.3 1.3 1.3 1.3 1.4 1.5 1.5 1.5 1.4 0.8 0.6 0.6 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.4 0.5 0.6 1.2 1.5 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.4 1.4 1.4 1.4 1.4 1.5 1.6 1.5 0.8 0.7 0.5 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.4 0.3 0.4 0.5 0.8 0.9 0.9 20N 1.3 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.4 1.4 1.4 1.4 1.5 1.6 1.6 0.7 0.6 0.5 0.5 0.6 0.7 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.6 0.8 1.0 0.9 20N 1.3 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.4 1.4 1.4 1.5 1.4 1.5 1.5 0.5 0.6 0.7 0.7 0.8 0.7 0.7 0.7 0.7 0.8 0.8 0.9 0.9 0.9 0.9 1.1 0.9 1.3 1.5 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.5 1.5 1.4 1.5 1.4 1.4 1.4 1.3 0.5 0.8 0.8 0.7 0.8 0.8 0.7 0.7 0.7 0.8 0.9 0.9 0.9 1.0 1.1 1.1 0.8 1.3 1.5 1.5 1.5 1.5 1.5 1.4 1.4 1.4 1.4 1.4 1.5 1.5 1.4 1.3 1.3 1.3 0.6 0.9 0.9 0.9 0.9 0.8 0.8 0.7 0.7 0.8 0.8 0.9 0.8 0.9 0.9 1.1 1.0 10N 1.4 1.4 1.4 1.4 1.3 1.3 1.3 1.3 1.3 1.3 1.4 1.4 1.3 1.3 1.3 1.2 1.1 0.7 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.6 0.6 0.9 1.1 0.8 10N 1.2 1.3 1.3 1.2 1.2 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.2 1.2 1.2 1.2 1.1 0.5 0.6 0.6 0.7 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.6 0.9 0.7 0.6 1.2 1.2 1.1 1.2 1.3 1.3 1.3 1.2 1.3 1.1 1.1 1.1 1.1 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.6 0.4 1.2 1.2 1.2 1.2 1.2 1.2 1.1 1.0 1.0 1.0 0.7 0.7 0.7 0.7 0.6 0.7 0.8 0.7 0.4 0.4 2050s EQ 1.2 1.2 1.2 1.2 1.2 1.1 1.0 1.0 1.0 0.7 0.7 0.7 0.6 0.6 0.8 0.8 0.6 0.5 EQ 1.2 1.3 1.3 1.3 1.2 1.2 1.0 1.0 0.6 0.7 0.7 0.6 0.6 0.8 0.8 0.4 1.3 1.4 1.4 1.3 1.2 1.1 1.0 0.8 0.8 0.7 0.7 0.9 0.7 0.3 1.3 1.5 1.4 1.4 1.3 1.2 1.0 0.7 0.8 0.8 0.7 0.8 0.7 0.5 10S 1.3 1.5 1.6 1.5 1.4 1.3 1.1 0.7 0.8 0.8 0.8 0.9 0.8 0.6 10S 1.4 1.6 1.6 1.5 1.4 1.3 1.1 0.8 0.9 0.9 1.0 1.0 1.0 0.8 1.4 1.6 1.7 1.6 1.5 1.3 1.2 0.9 0.9 0.9 1.0 1.1 1.1 1.0 0.8 0.7 1.4 1.6 1.7 1.6 1.5 1.4 1.2 1.0 0.8 1.0 1.1 1.1 1.1 1.0 0.8 0.7 20S 1.3 1.6 1.7 1.6 1.5 1.4 1.0 0.8 1.0 1.0 1.1 1.0 0.9 0.6 20S 1.3 1.5 1.7 1.6 1.5 1.3 1.1 0.7 0.9 1.0 1.0 0.9 0.8 0.7 1.2 1.4 1.6 1.6 1.4 1.2 1.0 0.5 0.8 0.8 0.8 0.7 0.6 0.7 1.3 1.6 1.5 1.3 0.6 0.8 0.7 0.6 30S 1.2 1.4 1.4 1.3 0.6 0.7 0.7 0.5 30S 1.1 1.3 1.2 0.6 0.7 0.7

1.9 1.8 1.7 1.6 1.6 1.7 1.8 1.8 0.9 0.9 0.8 0.6 0.7 0.7 0.8 0.8 1.8 1.9 1.8 1.8 1.7 1.6 1.6 1.7 1.8 1.8 0.7 0.9 0.6 0.6 0.5 0.4 0.6 0.7 0.7 0.7 30N 1.8 1.9 1.9 1.8 1.8 1.6 1.5 1.5 1.4 1.4 1.4 1.6 1.7 1.8 1.8 0.7 0.8 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.7 0.7 0.6 0.4 0.4 0.4 30N 1.6 1.8 1.9 1.9 1.9 1.9 1.7 1.6 1.6 1.5 1.5 1.6 1.7 1.8 1.8 1.7 0.9 0.8 0.8 0.5 0.5 0.4 0.4 0.4 0.5 0.6 0.6 0.5 0.4 0.3 0.3 0.4 1.7 1.8 1.9 1.9 1.9 1.9 1.8 1.7 1.6 1.6 1.6 1.7 1.7 1.8 1.9 1.8 1.7 1.0 0.8 0.7 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.3 0.5 0.6 0.7 1.5 1.8 2.0 2.0 2.0 1.9 1.9 1.9 1.8 1.8 1.7 1.7 1.7 1.7 1.9 1.9 1.8 0.9 0.9 0.7 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.4 0.5 0.6 0.9 1.1 1.1 20N 1.6 1.9 2.0 2.0 2.0 1.9 1.9 1.9 1.8 1.8 1.8 1.7 1.7 1.7 1.8 1.9 1.9 0.8 0.8 0.6 0.7 0.8 0.8 0.8 0.7 0.7 0.6 0.6 0.6 0.7 0.7 1.0 1.2 1.1 20N 1.6 1.9 2.0 2.0 2.0 1.9 1.9 1.9 1.8 1.8 1.8 1.7 1.7 1.8 1.7 1.8 1.8 0.6 0.7 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.1 1.1 1.2 1.2 1.3 1.1 1.6 1.9 2.0 2.0 1.9 1.9 1.9 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.7 1.7 1.6 0.6 0.9 0.9 0.9 1.0 0.9 0.9 0.8 0.9 1.0 1.1 1.2 1.2 1.2 1.4 1.3 1.0 1.6 1.8 1.9 1.9 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.7 1.7 1.6 1.6 0.7 1.2 1.2 1.1 1.1 1.0 0.9 0.9 0.9 0.9 1.0 1.1 1.0 1.1 1.1 1.3 1.3 10N 1.7 1.8 1.8 1.7 1.7 1.6 1.6 1.6 1.6 1.7 1.7 1.7 1.7 1.6 1.6 1.5 1.4 0.9 1.0 0.9 1.0 0.9 0.9 0.8 0.8 0.9 1.0 1.0 1.0 0.7 0.8 1.2 1.3 1.0 10N 1.5 1.6 1.6 1.5 1.5 1.5 1.6 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.4 1.4 1.3 0.6 0.7 0.7 0.8 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.7 1.1 0.9 0.7 1.4 1.4 1.4 1.5 1.6 1.6 1.6 1.5 1.6 1.4 1.3 1.3 1.3 0.7 0.7 0.7 0.9 0.9 0.9 0.8 0.8 1.0 1.0 1.0 0.7 0.6 1.4 1.5 1.5 1.5 1.5 1.5 1.3 1.2 1.2 1.2 0.8 0.9 0.9 0.8 0.7 0.8 0.9 0.9 0.5 0.5 2080s EQ 1.5 1.5 1.5 1.5 1.5 1.4 1.3 1.2 1.2 0.8 0.9 0.9 0.8 0.7 1.0 1.0 0.7 0.6 EQ 1.4 1.6 1.6 1.6 1.5 1.4 1.3 1.2 0.8 0.9 0.9 0.8 0.7 1.0 0.9 0.4 1.6 1.7 1.7 1.7 1.5 1.4 1.2 0.9 0.9 0.9 0.8 1.1 0.9 0.4 1.6 1.8 1.8 1.8 1.6 1.5 1.3 0.9 1.0 0.9 0.9 1.0 0.9 0.6 10S 1.6 1.9 1.9 1.8 1.7 1.6 1.4 0.9 1.0 1.0 1.0 1.2 1.0 0.7 10S 1.7 2.0 2.0 1.9 1.7 1.6 1.4 1.0 1.1 1.1 1.2 1.2 1.2 1.0 1.7 2.0 2.0 1.9 1.8 1.7 1.4 1.1 1.1 1.1 1.3 1.3 1.4 1.3 1.0 0.8 1.7 2.0 2.1 2.0 1.9 1.7 1.4 1.2 1.0 1.2 1.3 1.4 1.4 1.3 1.0 0.8 20S 1.6 2.0 2.1 2.0 1.9 1.7 1.2 1.0 1.2 1.3 1.3 1.3 1.1 0.8 20S 1.6 1.9 2.1 2.0 1.9 1.6 1.3 0.8 1.1 1.2 1.2 1.1 0.9 0.8 1.5 1.8 2.0 2.0 1.8 1.5 1.2 0.7 1.0 1.0 0.9 0.9 0.8 0.8 1.6 1.9 1.9 1.6 0.8 1.0 0.9 0.8 30S 1.5 1.7 1.7 1.5 0.8 0.9 0.8 0.7 30S 1.3 1.6 1.5 0.7 0.9 0.8

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

0.5 1 1.5 2 3 4 5

Fig. 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 7 GCM experiments. Right panels: inter-model range in mean annual temperature change. See text for further explanation. Selected domains in the top left panel are the 4 ‘national’ regions used in Fig. 12 156 Clim Res 17: 145–168, 2001

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E 2.2 2.1 2.0 1.9 1.9 2.0 2.1 2.1 1.1 1.0 0.9 0.8 0.8 0.9 0.9 1.0 2.1 2.2 2.1 2.0 2.0 1.8 1.9 2.0 2.1 2.1 0.8 1.0 0.8 0.6 0.6 0.5 0.8 0.8 0.8 0.8 30N 2.1 2.2 2.2 2.1 2.1 1.9 1.8 1.7 1.6 1.6 1.7 1.8 2.0 2.1 2.1 0.8 0.9 0.5 0.5 0.5 0.6 0.5 0.5 0.6 0.8 0.8 0.7 0.5 0.4 0.5 30N 1.9 2.1 2.2 2.2 2.2 2.2 2.0 1.9 1.8 1.8 1.8 1.8 2.0 2.1 2.2 2.0 1.0 1.0 0.9 0.6 0.6 0.5 0.5 0.5 0.6 0.7 0.7 0.6 0.4 0.3 0.3 0.4 1.9 2.1 2.3 2.3 2.2 2.2 2.1 2.0 1.9 1.9 1.9 1.9 2.0 2.1 2.2 2.1 2.0 1.1 0.9 0.9 0.7 0.7 0.6 0.6 0.6 0.6 0.6 0.5 0.4 0.4 0.4 0.6 0.7 0.9 1.8 2.1 2.3 2.3 2.3 2.3 2.2 2.2 2.1 2.1 2.0 2.0 2.0 2.0 2.2 2.2 2.1 1.1 1.1 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.6 0.5 0.5 0.7 1.1 1.2 1.3 20N 1.9 2.2 2.3 2.3 2.3 2.3 2.2 2.2 2.2 2.1 2.1 2.0 2.0 2.0 2.2 2.3 2.3 0.9 0.9 0.7 0.8 0.9 0.9 0.9 0.9 0.8 0.7 0.7 0.7 0.8 0.9 1.2 1.4 1.3 20N 1.9 2.3 2.4 2.3 2.3 2.2 2.2 2.2 2.1 2.1 2.1 2.0 2.0 2.1 2.0 2.1 2.1 0.7 0.9 1.0 1.0 1.1 1.1 1.0 1.0 1.1 1.1 1.2 1.3 1.3 1.4 1.4 1.5 1.3 1.9 2.2 2.3 2.3 2.3 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.0 2.0 1.9 0.7 1.1 1.1 1.1 1.1 1.1 1.1 1.0 1.1 1.2 1.3 1.4 1.4 1.4 1.6 1.5 1.1 1.8 2.1 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.0 1.9 1.9 1.8 0.8 1.4 1.4 1.3 1.2 1.1 1.1 1.0 1.0 1.1 1.2 1.2 1.2 1.3 1.3 1.5 1.5 10N 2.0 2.1 2.1 2.0 1.9 1.8 1.9 1.9 1.9 1.9 2.0 2.0 1.9 1.9 1.8 1.7 1.6 1.0 1.2 1.1 1.2 1.1 1.0 0.9 1.0 1.1 1.2 1.2 1.1 0.9 0.9 1.4 1.5 1.1 10N 1.7 1.9 1.9 1.8 1.7 1.8 1.8 1.8 1.9 1.8 1.9 1.9 1.8 1.7 1.7 1.7 1.6 0.7 0.8 0.9 1.0 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.0 0.8 1.2 1.0 0.8 1.7 1.7 1.6 1.7 1.8 1.8 1.8 1.8 1.8 1.6 1.5 1.5 1.5 0.9 0.9 0.9 1.0 1.1 1.0 1.0 1.0 1.2 1.1 1.1 0.8 0.6 1.7 1.8 1.8 1.8 1.8 1.7 1.6 1.4 1.5 1.4 0.9 1.0 1.0 0.9 0.9 1.0 1.1 1.1 0.6 0.6 2020s EQ 1.7 1.8 1.8 1.8 1.8 1.6 1.5 1.4 1.4 1.0 1.0 1.0 0.9 0.8 1.1 1.2 0.8 0.7 EQ 1.7 1.9 1.9 1.8 1.8 1.7 1.5 1.4 0.9 1.1 1.0 0.9 0.9 1.2 1.1 0.5 1.9 2.0 2.0 1.9 1.8 1.6 1.4 1.1 1.1 1.0 1.0 1.2 1.0 0.5 1.9 2.1 2.1 2.1 1.9 1.7 1.5 1.0 1.1 1.1 1.1 1.2 1.0 0.7 10S 1.9 2.2 2.2 2.1 2.0 1.8 1.6 1.0 1.2 1.2 1.2 1.4 1.2 0.9 10S 2.0 2.3 2.3 2.2 2.0 1.9 1.7 1.2 1.3 1.3 1.4 1.4 1.4 1.1 2.0 2.4 2.4 2.3 2.1 1.9 1.7 1.3 1.2 1.3 1.5 1.5 1.6 1.5 1.2 0.9 2.0 2.4 2.4 2.3 2.2 2.0 1.7 1.4 1.2 1.4 1.5 1.6 1.6 1.5 1.1 1.0 20S 1.9 2.3 2.4 2.4 2.2 2.0 1.4 1.1 1.4 1.5 1.5 1.5 1.3 0.9 20S 1.8 2.2 2.4 2.4 2.2 1.9 1.5 1.0 1.3 1.4 1.4 1.3 1.1 0.9 1.7 2.1 2.4 2.3 2.1 1.8 1.5 0.8 1.1 1.2 1.1 1.0 0.9 1.0 1.9 2.2 2.2 1.9 0.9 1.2 1.0 0.9 30S 1.7 2.0 2.0 1.8 0.9 1.1 1.0 0.8 30S 1.6 1.8 1.8 0.8 1.1 1.0

4.1 3.9 3.7 3.5 3.6 3.7 3.9 4.0 2.0 2.0 1.8 1.4 1.5 1.6 1.8 1.8 3.9 4.1 3.9 3.9 3.7 3.4 3.6 3.8 4.0 3.9 1.5 1.9 1.4 1.2 1.1 0.9 1.4 1.5 1.6 1.6 30N 3.9 4.2 4.1 4.0 3.9 3.6 3.4 3.2 3.1 3.1 3.2 3.5 3.8 3.9 3.9 1.6 1.7 0.9 1.0 1.0 1.1 1.0 0.9 1.1 1.4 1.5 1.2 0.9 0.8 0.9 30N 3.5 4.0 4.1 4.2 4.2 4.1 3.8 3.6 3.5 3.4 3.4 3.5 3.8 4.0 4.1 3.8 1.9 1.8 1.7 1.1 1.0 0.9 0.9 1.0 1.0 1.3 1.3 1.1 0.8 0.7 0.6 0.8 3.6 4.0 4.3 4.3 4.2 4.1 4.0 3.7 3.6 3.6 3.6 3.7 3.8 4.0 4.2 4.0 3.7 2.1 1.7 1.6 1.3 1.2 1.2 1.1 1.2 1.2 1.1 0.9 0.8 0.8 0.8 1.1 1.4 1.6 3.3 4.0 4.3 4.4 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.7 3.8 4.1 4.2 4.0 2.1 2.0 1.5 1.5 1.6 1.6 1.5 1.3 1.4 1.3 1.1 0.9 1.0 1.3 2.1 2.3 2.5 20N 3.5 4.2 4.4 4.4 4.3 4.3 4.2 4.1 4.1 4.0 3.9 3.8 3.8 3.8 4.1 4.2 4.3 1.8 1.7 1.4 1.5 1.7 1.8 1.7 1.6 1.6 1.4 1.2 1.3 1.5 1.6 2.3 2.6 2.4 20N 3.6 4.3 4.5 4.4 4.3 4.2 4.1 4.1 4.0 4.0 3.9 3.8 3.8 3.9 3.8 4.0 4.0 1.3 1.6 1.8 1.9 2.1 2.0 1.9 1.9 2.0 2.1 2.2 2.4 2.4 2.5 2.6 2.9 2.5 3.6 4.2 4.4 4.3 4.3 4.2 4.1 4.1 4.0 4.0 4.0 3.9 4.0 3.9 3.8 3.7 3.5 1.4 2.1 2.0 2.0 2.2 2.1 2.0 1.8 2.0 2.2 2.4 2.6 2.6 2.7 3.0 2.9 2.2 3.5 4.0 4.2 4.2 4.1 4.0 3.9 3.9 3.9 3.9 3.9 4.0 4.0 3.8 3.7 3.6 3.5 1.5 2.6 2.6 2.4 2.3 2.1 2.1 1.9 1.9 2.1 2.3 2.3 2.3 2.4 2.5 2.9 2.8 10N 3.7 3.9 3.9 3.8 3.6 3.5 3.6 3.6 3.6 3.7 3.7 3.8 3.6 3.6 3.4 3.3 3.0 1.9 2.3 2.0 2.2 2.1 1.9 1.8 1.8 2.0 2.2 2.2 2.1 1.6 1.7 2.5 2.9 2.1 10N 3.3 3.6 3.5 3.4 3.3 3.3 3.4 3.5 3.6 3.4 3.5 3.6 3.4 3.2 3.2 3.2 2.9 1.4 1.6 1.6 1.9 1.8 1.7 1.7 1.9 2.0 2.0 1.9 1.9 1.9 1.6 2.4 2.0 1.6 3.1 3.2 3.1 3.3 3.5 3.5 3.4 3.4 3.4 3.1 2.9 2.9 2.9 1.6 1.6 1.6 1.9 2.0 1.9 1.9 1.9 2.2 2.1 2.1 1.6 1.2 3.2 3.3 3.4 3.4 3.3 3.3 2.9 2.7 2.7 2.7 1.8 2.0 1.9 1.8 1.6 1.8 2.1 2.0 1.1 1.2 2050s EQ 3.2 3.4 3.4 3.4 3.3 3.1 2.8 2.7 2.7 1.8 1.9 1.9 1.7 1.5 2.1 2.2 1.5 1.2 EQ 3.2 3.6 3.6 3.4 3.4 3.1 2.8 2.7 1.8 2.0 1.9 1.8 1.6 2.2 2.1 1.0 3.6 3.8 3.7 3.6 3.4 3.0 2.7 2.1 2.1 1.9 1.8 2.3 1.9 0.9 3.5 4.0 3.9 3.9 3.6 3.2 2.8 1.9 2.1 2.1 2.0 2.2 2.0 1.3 10S 3.6 4.1 4.2 4.0 3.7 3.4 3.0 2.0 2.2 2.2 2.2 2.6 2.2 1.6 10S 3.7 4.3 4.4 4.1 3.8 3.6 3.1 2.2 2.4 2.5 2.6 2.7 2.7 2.1 3.7 4.5 4.5 4.3 4.0 3.6 3.1 2.5 2.3 2.5 2.8 2.9 3.0 2.8 2.3 1.8 3.7 4.5 4.6 4.4 4.1 3.7 3.2 2.6 2.3 2.7 2.9 3.0 3.0 2.8 2.2 1.8 20S 3.6 4.4 4.6 4.5 4.2 3.8 2.7 2.1 2.6 2.8 2.9 2.8 2.5 1.7 20S 3.4 4.2 4.6 4.4 4.1 3.6 2.9 1.8 2.5 2.7 2.6 2.5 2.1 1.8 3.3 3.9 4.5 4.3 3.9 3.3 2.7 1.5 2.2 2.3 2.1 1.9 1.7 1.8 3.5 4.2 4.2 3.6 1.8 2.2 1.9 1.7 30S 3.3 3.8 3.8 3.4 1.7 2.0 1.8 1.5 30S 3.0 3.4 3.3 1.6 2.0 1.8

6.3 6.0 5.7 5.4 5.5 5.7 6.0 6.1 3.0 3.0 2.7 2.2 2.3 2.5 2.7 2.8 6.0 6.3 6.0 5.9 5.7 5.3 5.5 5.8 6.1 6.0 2.4 2.9 2.2 1.9 1.7 1.4 2.2 2.3 2.4 2.4 30N 6.0 6.4 6.3 6.2 6.0 5.5 5.2 4.9 4.7 4.7 4.9 5.3 5.8 6.0 6.0 2.4 2.6 1.4 1.5 1.5 1.6 1.5 1.3 1.7 2.2 2.3 1.9 1.4 1.3 1.3 30N 5.4 6.1 6.3 6.4 6.4 6.3 5.9 5.5 5.3 5.1 5.2 5.3 5.8 6.1 6.2 5.8 2.9 2.8 2.6 1.6 1.6 1.5 1.4 1.5 1.6 1.9 2.0 1.7 1.2 1.0 0.9 1.2 5.6 6.2 6.5 6.6 6.5 6.3 6.1 5.8 5.5 5.5 5.5 5.6 5.9 6.1 6.4 6.1 5.7 3.2 2.6 2.5 1.9 1.9 1.8 1.7 1.8 1.9 1.7 1.4 1.2 1.3 1.2 1.7 2.1 2.5 5.1 6.1 6.6 6.7 6.7 6.6 6.4 6.3 6.2 5.9 5.8 5.7 5.7 5.8 6.3 6.5 6.1 3.2 3.1 2.3 2.2 2.4 2.4 2.3 2.0 2.1 2.0 1.7 1.3 1.5 2.0 3.2 3.6 3.9 20N 5.4 6.5 6.8 6.8 6.6 6.6 6.4 6.3 6.2 6.1 5.9 5.8 5.8 5.8 6.2 6.5 6.5 2.7 2.7 2.1 2.2 2.7 2.7 2.6 2.5 2.4 2.1 1.9 2.0 2.3 2.5 3.5 4.0 3.7 20N 5.5 6.6 6.9 6.8 6.6 6.5 6.4 6.3 6.2 6.1 5.9 5.8 5.9 6.1 5.8 6.1 6.1 2.1 2.5 2.8 2.8 3.2 3.1 2.9 3.0 3.1 3.2 3.4 3.7 3.7 3.9 4.0 4.5 3.8 5.5 6.4 6.7 6.7 6.6 6.4 6.2 6.2 6.2 6.1 6.1 6.0 6.1 6.1 5.8 5.7 5.4 2.1 3.2 3.1 3.0 3.3 3.2 3.1 2.8 3.1 3.3 3.7 3.9 3.9 4.2 4.6 4.4 3.3 5.3 6.1 6.4 6.5 6.2 6.1 5.9 6.0 6.0 5.9 6.0 6.1 6.1 5.9 5.6 5.5 5.3 2.4 4.0 3.9 3.6 3.6 3.3 3.1 2.9 2.9 3.2 3.5 3.6 3.5 3.6 3.9 4.4 4.3 10N 5.7 6.0 6.0 5.8 5.6 5.3 5.5 5.6 5.6 5.6 5.8 5.8 5.6 5.5 5.3 5.0 4.6 3.0 3.5 3.1 3.4 3.2 2.9 2.7 2.8 3.1 3.3 3.4 3.2 2.5 2.7 3.9 4.4 3.2 10N 5.0 5.5 5.4 5.2 5.0 5.1 5.2 5.3 5.5 5.2 5.4 5.5 5.2 4.9 4.9 4.8 4.5 2.1 2.4 2.5 2.8 2.7 2.6 2.7 2.9 3.0 3.0 2.9 2.9 2.9 2.4 3.6 3.0 2.4 4.8 4.9 4.7 5.0 5.3 5.3 5.3 5.2 5.3 4.8 4.5 4.5 4.5 2.5 2.5 2.5 2.9 3.1 3.0 2.9 2.8 3.4 3.2 3.2 2.4 1.9 4.8 5.1 5.2 5.2 5.1 5.0 4.5 4.2 4.2 4.2 2.7 3.0 2.9 2.7 2.5 2.8 3.2 3.1 1.7 1.8 2080s EQ 4.9 5.2 5.2 5.2 5.1 4.7 4.3 4.1 4.1 2.8 2.9 2.9 2.6 2.3 3.2 3.4 2.4 1.9 EQ 4.9 5.5 5.5 5.3 5.2 4.8 4.4 4.1 2.7 3.1 3.0 2.7 2.5 3.4 3.2 1.5 5.5 5.8 5.7 5.6 5.2 4.6 4.1 3.2 3.2 3.0 2.8 3.6 2.9 1.4 5.4 6.1 6.0 5.9 5.5 5.0 4.4 3.0 3.3 3.2 3.1 3.4 3.0 1.9 10S 5.5 6.3 6.5 6.2 5.7 5.2 4.6 3.0 3.4 3.4 3.4 3.9 3.4 2.5 10S 5.7 6.6 6.7 6.3 5.9 5.4 4.8 3.4 3.6 3.9 4.0 4.1 4.1 3.3 5.7 6.9 6.9 6.5 6.1 5.6 4.8 3.8 3.6 3.9 4.4 4.4 4.6 4.4 3.5 2.7 5.7 6.9 7.0 6.7 6.3 5.7 4.9 4.0 3.5 4.1 4.5 4.6 4.6 4.3 3.3 2.8 20S 5.5 6.8 7.0 6.8 6.4 5.8 4.1 3.3 4.0 4.3 4.4 4.3 3.8 2.5 20S 5.2 6.5 7.0 6.8 6.3 5.5 4.4 2.8 3.9 4.1 4.0 3.8 3.2 2.7 5.0 5.9 6.9 6.7 6.0 5.1 4.2 2.2 3.3 3.5 3.2 2.9 2.5 2.8 5.4 6.5 6.4 5.5 2.7 3.4 2.9 2.6 30S 5.0 5.8 5.9 5.2 2.5 3.1 2.8 2.3 30S 4.5 5.3 5.1 2.4 3.0 2.8

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

0.5 1 1.5 2 3 4 5

Fig. 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 7 GCM experiments. Right panels: Inter-model range in mean annual temperature change. See text for further explanation Hulme et al.: African Climate Change: 1900–2100 157

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E -6 16 18 18 17 13 13 12 10 22 20 24 33 40 13 14 15 14 12 30N 26 42 32 31 29 16 13 14 16 15 14 14 14 16 17 30N 29 24 37 76 47 43 43 27 21 57 32 32 30 18 18 18 36 40 47 62 66 38 33 52 31 33 32 41 29 29 17 14 15 19 28 29 64 117 62 48 36 35 32 36 34 41 29 29 18 17 20N 17 22 38 51 61 32 29 26 23 51 34 61 41 68 36 27 28 20N 22 37 43 45 58 56 44 37 57 54 92 43 98 126 64 40 60 34 37 50 59 66 55 48 98 77 55 99 118 59 76 49 38 46 40 48 40 45 50 48 103 82 40 61 63 66 40 35 20 55 82 10N 30 31 38 41 39 35 42 31 25 21 20 19 17 18 39 65 30 10N 10 24 27 35 27 25 25 31 31 30 30 27 22 14 22 48 82 24 10 13 25 25 26 30 30 30 29 27 21 14 31 86 59 4 6 11 21 17 14 14 15 12 19 46 64 68 2020s EQ 5 5 10 12 11 7 5 7 10 10 19 31 44 EQ 234 5 17 8 4 6 7 10 16 44 2 2 3 16 12 13 11 9 15 41 1 22 13 13 11 9 13 20 10S 28 11 9 7 8 11 13 10S 18 9 9 8 8 11 15 10 7 8 8 9 10 13 9 9 8 7 7 8 10 13 9 20S 10 7 8 6 7 9 9 20S 16 6 6 6 8 7 13 18 8 7 7 7 9 14 13 10 9 10 30S 11 7 7 8 30S 60 9 10

-9 25 28 28 27 20 20 19 16 -8 34 31 38 52 63 21 22 23 22 19 30N 41 65 50 48 45 26 21 21 25 23 23 22 23 26 26 30N 46 38 58 118 74 68 67 43 33 89 50 50 48 28 28 28 56 62 73 97 104 59 51 81 49 52 50 64 46 45 26 22 24 29 43 45 100 183 96 74 56 55 50 56 54 64 45 45 28 27 20N 26 35 59 79 96 50 46 41 37 80 53 96 64 106 56 42 43 20N 35 58 68 71 91 88 69 58 89 85 144 67 154 197 101 62 94 53 57 78 92 103 86 75 154 120 85 155 185 93 119 76 59 72 22 62 75 63 71 78 75 161 128 63 96 98 103 63 54 31 87 128 10N 14 19 20 23 24 48 48 60 64 62 55 66 48 38 33 31 30 27 28 60 102 46 10N 5 14 16 19 38 43 55 42 39 40 48 48 47 47 42 34 22 34 75 128 37 7 5 6 7 8 16 21 39 39 40 46 48 46 45 42 32 21 48 134 93 5 6 9 17 21 18 32 26 22 22 24 19 30 72 100 107 2050s EQ 4 7 8 15 18 17 10 8 11 15 15 29 48 68 EQ 4 3 5 6 8 11 16 27 13 6 9 10 16 25 69 3 4 5 7 14 26 19 20 17 13 23 64 2 2 3 13 34 21 20 17 15 21 32 10S 6 44 17 14 12 13 17 20 10S -3 -3 28 14 15 12 13 17 23 15 10 12 12 14 16 20 13 15 12 11 11 13 16 21 15 20S 16 11 13 9 11 14 14 20S 25 10 9 10 12 11 20 -3 -3 28 13 12 12 12 15 22 -3 20 16 14 15 30S -5 17 10 11 12 30S 93 14 16

-11 31 34 35 34 25 25 24 20 -10 42 39 46 64 78 26 27 28 28 24 30N 51 80 61 59 56 32 26 26 31 28 28 28 28 32 33 30N 57 46 72 146 91 84 83 53 41 110 62 62 59 35 35 34 69 77 91 121 129 74 63 100 60 65 62 79 57 56 32 27 29 36 54 56 124 226 119 92 69 68 62 70 66 80 55 56 34 33 20N 33 43 73 98 119 62 57 50 45 99 66 119 79 131 69 52 53 20N 43 72 84 88 112 109 85 72 110 105 178 83 191 243 124 77 117 30 65 71 96 114 127 107 93 190 149 106 191 228 115 147 94 73 89 28 77 93 78 88 97 93 199 159 77 119 121 127 78 67 39 107 158 10N 17 23 25 21 26 28 13 13 30 59 60 74 80 76 68 82 59 47 40 38 37 33 35 75 126 57 10N 6 13 11 11 8 17 20 23 47 53 68 52 49 49 59 60 58 59 52 42 27 42 93 158 46 8 6 7 9 10 20 26 24 48 48 49 57 59 57 55 52 40 26 59 166 115 6 4 3 7 11 21 27 23 40 33 27 27 29 23 37 89 123 132 2080s EQ 4 9 10 19 23 17 20 13 10 14 19 19 36 59 85 EQ 4 4 6 8 10 14 20 33 16 7 11 13 20 31 85 4 5 6 9 17 32 24 25 22 17 29 79 2 3 4 16 42 26 25 21 18 25 40 10S 3 -2 8 54 21 17 14 16 22 24 10S -3 -3 34 18 18 15 16 21 29 19 13 15 15 18 19 25 16 18 15 14 13 16 19 25 18 20S 20 14 16 11 14 17 18 20S -6 -2 31 12 11 12 15 14 24 -4 -4 -4 34 16 14 14 15 18 27 -5 -4 25 20 17 19 30S -6 21 13 13 15 30S 116 17 20

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E Median of % changes Range of % changes

-50 -30 -20 -10 0 10 20 30 50 10 20 30 40 50 100

Fig. 8. Left panels: Change in mean DJF rainfall for the 2020s, 2050s and 2080s (with respect to 1961–90) for the B1-low scenario; median of 7 GCM experiments. For areas with no change shown the model median response fails to exceed the 1 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 158 Clim Res 17: 145–168, 2001

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E -9 16 13 14 14 315 173 110 120 -8 -8 29 29 16 18 15 17 314 222 149 119 30N 22 24 24 34 33 40 54 44 65 114 176 146 173 144 121 30N 22 29 28 56 107 118 137 186 207 93 119 133 139 139 133 110 32 27 38 80 87 104 93 105 100 112 104 100 96 98 88 75 70 27 25 31 32 68 75 85 88 91 104 107 91 83 75 67 61 53 20N 15 8 12 17 22 21 24 30 45 60 67 59 56 46 44 45 42 20N 21 12 7 10 16 14 13 17 24 35 45 39 31 22 34 28 25 23 14 9 11 15 13 11 12 16 20 24 23 18 19 22 25 23 -7 -5 22 14 13 13 10 10 11 12 13 10 8 10 13 12 15 24 22 10N -4 -3 4 -6 13 13 13 9 10 10 12 13 11 8 9 10 10 12 20 28 23 10N 33 2 -2-3 -5 13 13 12 10 9 11 13 12 11 8 8 11 9 9 20 24 25 7 5 -3 -5 19 17 14 17 14 11 9 9 9 9 16 19 19 -5 16 15 12 9 10 9 11 20 19 17 2020s EQ 9 14 14 10 10 10 18 27 34 EQ 9 14 16 14 10 12 16 23 18 24 22 18 13 16 14 14 27 29 26 16 41 16 10S 41 30 42 38 49 22 17 10S 41 41 59 71 40 23 16 53 47 69 80 30 17 9 17 123 58 64 68 42 22 11 18 20S 117 77 63 53 27 29 15 20S 91 57 67 54 45 31 13 156 65 49 48 29 31 12 102 38 39 23 30S 29 42 36 21 30S 9 9 22

-15 -15 -12 -11 25 20 22 22 493 271 172 188 -12 -12 -11 45 46 25 28 24 26 492 347 233 187 30N -13 35 37 37 53 52 62 85 69 102 178 275 229 271 225 190 30N 34 45 45 87 168 184 214 291 323 145 186 208 218 218 208 172 50 43 59 125 136 163 146 164 156 175 163 157 150 153 138 118 109 43 40 48 50 106 117 133 138 143 162 167 143 130 118 106 96 83 20N 24 12 18 26 35 33 37 47 70 94 105 93 87 73 69 70 65 20N -11 33 19 12 15 25 22 20 26 38 55 70 61 49 35 53 43 39 -12 -7 35 22 14 17 24 20 17 19 25 31 38 35 29 30 34 39 37 -12 -11 -8 -7 -4 34 22 20 21 15 16 16 18 20 16 13 16 20 20 24 38 34 10N -7 -5 -4 -3 2 2 6 -9 20 20 20 14 15 15 19 20 17 12 14 15 15 19 31 43 35 10N 3 3 3 3 54 3 -3 -5 -8 21 20 19 16 15 18 20 19 17 12 13 17 14 15 32 38 38 11 8 -4 -8 30 26 21 26 22 16 14 14 15 14 25 30 30 -8 25 23 19 14 16 14 18 32 29 26 2050s EQ -5 14 22 22 15 15 15 28 42 53 EQ -6 14 22 25 22 16 18 26 35 28 38 35 28 20 25 21 22 42 45 41 24 64 25 10S 64 46 66 59 77 34 27 10S 65 64 92 111 63 35 25 83 73 107 126 47 26 14 27 193 91 100 107 66 34 16 29 20S 183 120 99 84 42 46 23 20S 142 89 105 84 71 49 20 244 102 77 75 46 48 18 159 60 61 36 30S 45 65 57 34 30S 14 14 34

-19 -18 -15 -14 31 25 27 28 610 336 213 233 -17 -15 -15 -14 56 56 31 35 29 33 608 429 289 231 30N -16 -13 43 46 46 66 64 77 105 85 126 220 340 283 335 279 235 30N 42 56 55 108 208 228 264 360 400 180 230 258 270 269 258 213 62 53 73 155 169 202 181 203 193 216 202 194 186 190 171 145 135 53 49 60 61 132 145 164 170 177 200 206 177 160 146 131 119 102 20N -13 29 15 22 32 43 41 46 58 87 116 129 115 107 90 86 87 81 20N -14 41 24 14 19 32 27 25 33 47 68 87 76 60 43 66 53 49 -15 -15 -9 9 44 27 17 21 30 24 22 24 30 39 47 44 36 37 42 48 45 -14 -14 -9 -8 -5 42 28 25 26 19 20 20 23 25 20 16 20 25 24 30 47 43 10N -8 -6 -5 -4 3 2 7 -5 -11 24 25 24 17 19 19 23 25 21 15 17 19 19 23 39 53 44 10N 4 4 4 4 6 5 4 -4 -6 -10 -11 25 25 24 20 18 22 25 23 21 15 16 21 18 18 39 47 47 13 10 1 -6 -10 -9 38 33 26 32 27 20 17 17 18 18 31 37 37 -10 31 29 24 18 19 18 22 39 36 32 2080s EQ -7 17 27 27 19 19 19 34 52 65 EQ -7 17 27 31 27 20 23 32 44 35 47 43 35 24 31 26 28 51 56 51 30 79 31 10S 79 57 81 73 96 42 33 10S 80 79 114 137 77 44 31 103 90 133 155 58 32 18 34 239 113 124 132 82 42 20 36 20S 226 148 123 104 52 57 29 20S 176 110 130 104 88 61 25 302 126 95 93 57 59 22 197 74 76 44 30S 56 81 70 42 30S 18 17 42

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E Median of % changes Range of % changes

-50 -30 -20 -10 0 10 20 30 50 10 20 30 40 50 75 100

Fig. 9. Left panels: Change in mean JJA rainfall for the 2020s, 2050s and 2080s (with respect to 1961–90) for the B1-low scenario; median of 7 GCM experiments. For areas with no change shown the model median response fails to exceed the 1 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 Hulme et al.: African Climate Change: 1900–2100 159

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E -13 -7 37 40 40 39 29 30 28 23 -12 49 45 54 75 91 30 31 33 32 28 30N 59 94 72 69 65 37 30 31 37 33 33 32 33 37 38 30N 67 54 84 171 107 98 97 62 48 129 72 73 69 41 41 40 81 90 106 141 150 86 74 116 70 75 72 93 66 65 38 32 34 42 63 65 145 264 139 107 81 80 73 81 77 93 65 65 40 39 20N 38 50 86 114 139 72 66 58 53 116 77 139 92 153 80 61 62 20N 50 84 98 103 131 127 100 84 128 122 208 97 222 284 145 89 136 35 76 83 113 133 149 125 108 222 173 123 223 267 134 171 110 85 104 32 90 108 91 102 113 108 232 185 90 139 141 148 91 78 45 125 184 10N 20 27 29 25 30 33 21 16 15 35 69 69 86 93 89 80 95 69 55 47 45 43 38 41 87 147 67 10N 4 7 6 16 13 12 11 10 20 23 27 29 54 61 80 61 57 57 69 69 67 68 61 49 31 49 108 185 54 7 9 8 8 11 12 23 30 28 24 56 56 58 67 69 67 65 61 46 31 69 194 134 7 5 4 9 13 25 31 26 47 38 32 32 34 27 44 104 144 155 2020s EQ 3 5 11 11 22 27 20 24 15 12 16 22 22 42 69 99 EQ 5 4 7 9 12 16 23 38 19 8 13 15 24 36 99 4 6 7 11 20 37 28 29 25 19 34 93 3 3 2 4 19 49 30 29 24 21 30 46 10S 3 -3 9 63 24 20 17 19 25 28 10S -4 -4 40 21 21 17 19 24 34 22 15 18 18 21 23 29 19 21 18 16 15 18 23 30 21 20S 24 16 19 13 16 20 21 20S -6 -3 -3 36 14 13 14 17 16 28 -5 -5 -5 40 19 17 17 17 21 31 -6 -5 29 23 20 22 30S -7 24 15 16 18 30S -7 135 20 23

-11 -25 -14 -13 69 76 76 74 55 56 53 44 -10 -22 -17 93 85 102 141 171 57 59 62 61 53 30N -16 -23 -19 -19 112 177 135 131 123 70 56 58 69 62 61 61 61 70 72 30N 126 102 159 322 201 184 182 116 91 243 136 137 129 77 77 76 152 170 200 265 283 162 139 219 132 142 137 175 125 123 71 60 64 28 80 118 122 273 497 263 203 152 150 137 153 146 175 122 123 75 73 20N 25 27 72 94 161 216 261 136 125 110 100 218 145 262 174 288 152 115 118 20N 44 95 159 184 193 247 240 188 158 241 230 392 183 419 535 274 168 257 67 143 156 212 251 280 235 204 418 327 233 421 503 253 323 208 160 197 21 48 46 42 53 42 42 18 61 57 169 205 171 193 212 204 437 349 170 262 266 279 172 148 85 236 347 10N 15 37 52 55 47 56 62 40 33 34 22 29 28 66 59 130 131 162 176 168 150 180 130 104 89 84 81 72 77 164 277 126 10N 7 14 11 10 21 29 24 12 23 21 18 37 44 52 54 102 116 151 114 107 108 130 131 127 129 115 93 59 93 204 348 102 4 13 18 14 15 20 22 43 57 53 44 105 105 109 126 130 126 122 115 87 58 131 366 252 12 9 5 7 16 25 47 58 50 27 88 72 60 60 64 51 82 197 271 291 2050s EQ 9 5 5 10 20 22 41 50 38 45 28 23 30 41 42 80 131 186 EQ 19 10 8 13 17 22 30 43 72 36 15 25 28 44 67 187 2 8 10 13 20 37 70 53 54 47 36 63 175 3 5 6 3 8 36 92 56 55 46 40 56 87 10S 6 2 -5 17 119 46 38 31 36 47 53 10S 8 -7 -7 -3 76 39 39 33 35 46 64 -3 -3 -4 -6 41 28 34 33 39 43 55 36 -3 -6 40 33 30 29 34 42 56 40 20S -5 44 30 35 24 30 38 39 20S -12 -6 -5 68 27 25 27 33 30 54 -12 -10 -9 -9 76 36 32 31 32 40 59 -11 -9 54 44 38 42 30S -11 -4 -12 46 29 29 34 30S -17 -13 254 37 43

-9 -16 -10 -38 -22 -19 106 116 117 114 84 85 81 67 -15 -34 -26 -10 143 130 157 216 263 88 91 96 94 81 30N -20 -24 -18 -24 -35 -29 -29 172 272 207 200 189 107 86 89 106 95 94 93 94 107 110 30N 193 157 244 493 308 282 279 178 139 372 209 211 198 118 118 116 27 22 233 261 306 407 434 248 214 337 203 218 210 268 191 189 109 92 98 43 44 42 28 123 181 188 418 763 403 311 234 230 210 235 224 269 187 188 115 113 20N 23 38 42 67 31 46 110 145 248 331 401 209 191 169 153 335 223 401 267 442 233 176 180 20N 27 55 64 68 51 146 244 282 297 379 369 288 242 370 353 602 280 643 821 420 258 394 48 53 103 51 220 240 326 385 430 361 313 642 502 357 645 771 388 495 319 246 302 33 19 73 70 65 82 64 65 38 27 93 87 260 314 263 296 326 312 671 535 261 401 409 428 264 227 131 362 533 10N 23 57 79 85 72 86 94 61 51 53 34 45 44 101 90 37 199 201 249 269 258 230 276 200 160 136 129 124 111 119 252 425 194 10N 7 11 21 18 15 32 45 37 19 36 32 28 57 68 79 83 47 157 178 231 176 164 166 200 201 194 198 177 143 91 142 313 534 156 6 -6 20 27 22 24 31 34 67 87 81 68 161 162 167 193 199 194 187 177 134 89 201 561 387 19 14 7 11 25 38 72 89 76 42 135 110 93 92 98 78 126 302 416 447 2080s EQ 14 7 8 15 31 33 63 77 58 69 44 35 46 63 64 123 201 286 EQ 30 15 12 20 26 34 46 67 111 55 24 38 43 68 103 287 4 12 16 20 31 57 107 81 83 73 56 97 268 5 8 9 5 12 55 142 86 84 71 61 86 133 10S 9 3 -8 -3 25 183 71 59 48 55 73 82 10S 12 -11 -11 -4 116 60 61 50 54 70 98 -4 -4 -6 4 -9 64 43 52 51 60 65 85 56 -5 3 -10 -5 61 51 46 44 53 65 86 62 20S -8 68 46 54 37 46 58 60 20S -19 -9 -8 104 41 39 41 50 47 82 -18 -15 -13 -14 116 55 49 48 49 62 91 -15 -16 -14 83 67 59 64 30S -17 -6 -19 70 44 45 52 30S -26 -20 390 57 67

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E Median of % changes Range of % changes

-50 -30 -20 -10 0 10 20 30 50 10 20 30 40 50 75 100

Fig. 10. Left panels: Change in mean DJF rainfall for the 2020s, 2050s and 2080s (with respect to 1961–90) for the A2-high sce- nario; median of 7 GCM experiments. For areas with no change shown the model median response fails to exceed the 1 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 160 Clim Res 17: 145–168, 2001

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E -22 -21 -18 -16 36 29 31 32 711 392 249 272 -20 -17 -18 -16 65 66 36 41 34 38 710 500 337 269 30N -14 -18 -16 50 53 53 77 75 90 123 99 148 257 397 331 391 325 274 30N 49 65 64 126 243 266 308 420 466 209 268 301 315 314 301 248 72 61 85 181 197 236 211 237 225 252 236 227 217 221 199 170 158 -15 62 57 70 71 154 169 192 199 206 234 241 206 187 170 152 138 119 20N -16 34 17 26 37 50 48 53 67 101 136 151 134 125 105 100 101 94 20N -16 -11 47 28 17 22 37 31 29 38 54 80 101 88 70 50 77 62 57 -18 -17 -11 10 12 12 51 31 20 24 34 29 25 27 36 45 55 51 42 43 49 56 53 -17 -16 -11 -9 -6 -4 49 32 29 30 22 23 24 27 29 23 19 24 29 28 34 55 50 10N -10 -8 -6 -5 -3 3 3 3 8 -6 -13 28 29 29 20 22 22 27 29 25 17 20 22 22 27 45 62 51 10N 5 4 5 5 376 4 3 -5 -7 -12 -12 30 29 28 23 21 26 29 27 24 18 18 25 21 21 46 55 55 16 12 2 -6 -12 -11 44 38 31 38 31 24 20 20 21 21 36 43 43 -12 36 34 28 21 22 21 25 46 42 37 2020s EQ 3 -8 20 32 31 22 22 22 40 60 76 EQ -8 20 32 36 32 23 27 37 51 41 55 50 41 28 36 31 32 60 65 60 35 93 36 10S 92 67 95 85 112 49 38 10S 93 93 133 160 90 51 36 120 105 155 181 68 38 21 39 278 131 145 154 96 49 24 42 20S -11 264 173 143 121 61 66 34 20S 206 129 151 121 102 71 29 352 147 111 108 66 69 26 229 86 88 51 30S 65 94 82 48 30S 21 20 49

-41 -40 -33 -31 -34 -26 -21 69 56 59 61 1341738 469 513 -37 -32 -33 -31 -23 -23 123 124 68 76 64 72 1339943 635 508 30N -27 -35 -29 -26 -26 -25 -28 95 101 101 145 142 170 231 186 278 484 748 623 738 613 517 30N -26 -28 -25 -24 -25 -25 -33 -25 93 122 121 237 457 501 582 792 880 395 505 567 593 592 567 468 -26 -26 -29 -28 -28 -26 -37 136 116 160 340 371 445 398 446 425 476 445 427 409 417 376 320 298 -28 -20 -18 -20 -20 -26 116 108 132 135 290 318 362 374 389 441 454 389 353 321 287 261 225 20N -29 -13 -18 -28 64 32 49 70 94 90 101 127 190 256 285 253 236 198 188 191 178 20N -32 -30 -20 9 17 17 22 -32 89 52 31 42 69 59 55 72 103 150 190 167 132 94 145 118 107 -33 -32 -20 -8 9 12 20 22 23 -21 -22 96 59 38 46 65 54 48 52 67 85 104 96 79 81 92 106 100 -31 -30 -21 -18 -12 -6 8 -7 -8 -4 -17 93 61 55 56 42 43 45 50 54 43 35 44 55 53 65 103 94 10N -18 -14 -11 -9 -6 5 6 3 -4 5 16 -11 -24 -15 53 54 54 37 41 42 51 55 47 32 37 42 41 50 86 118 96 10N 9 8 4 9 9 5 14 11 2 8 5 -9 -14 -22 -23 56 54 52 43 40 49 54 52 45 34 34 47 39 40 86 103 104 29 23 7 5 4 4 3 -12 -22 -20 -21 83 72 58 71 59 45 37 37 40 39 69 82 81 4 -2 -23 -16 -20 68 64 53 39 42 39 48 87 80 71 2050s EQ 5 5 -15 -10 -15 38 60 59 42 41 42 75 114 144 EQ 7 4 -15 37 60 68 60 43 50 70 96 12 7 77 104 94 77 54 68 58 61 113 122 113 66 175 68 10S 174 126 178 161 210 92 72 10S 176 175 251 302 170 96 67 27 -23 227 199 292 342 128 71 39 74 -20 525 248 273 291 181 92 45 78 20S -20 497 326 270 228 115 125 64 20S -15 388 243 285 228 193 133 55 -19 665 278 209 204 125 130 49 433 163 167 97 30S 122 177 154 91 30S -17 -8 39 37 92

-63 -61 -51 -47 -52 -40 -21 -33 105 85 91 93 20571133719 787 -57 -49 -51 -47 -35 -35 -37 -40 -32 189 190 105 117 98 111 20531447974 779 30N -42 -53 -45 -40 -39 -38 -44 -26 -30 145 154 154 222 218 261 355 286 427 74211479561132941 794 30N -39 -42 -38 -37 -38 -39 -50 -39 -59 -52 -37 -45 142 188 186 364 702 769 89212141349606 775 870 910 908 870 719 -40 -39 -26 -44 -43 -43 -40 -30 -42 -50 -56 208 178 246 522 569 682 610 684 651 730 682 655 627 640 576 491 457 -43 -31 -28 -19 -31 -30 -21 -40 -44 -35 178 165 202 207 444 488 555 574 596 677 697 596 541 493 440 400 345 20N -25 -45 -20 -27 -14 -35 -41 -43 -24 99 49 76 108 145 139 154 194 292 392 437 389 363 304 289 293 273 20N -48 -46 -31 -15 14 26 26 34 22 -49 -25 137 80 48 65 106 90 85 110 157 230 292 256 203 144 223 181 164 -51 -49 -31 -12 -9 13 19 30 34 36 14 -32 -34 148 91 58 70 100 83 73 79 103 130 159 147 121 124 142 162 153 -48 -46 -32 -27 -18 -10 5 5 8 12 -10 -13 -7 -26 142 93 85 86 64 66 69 77 83 66 54 68 85 81 100 158 144 10N -28 -22 -17 -13 -9 7 3 4 9 4 -7 8 24 -17 -37 -23 82 83 82 57 63 65 79 85 72 49 57 64 63 77 131 180 148 10N 14 12 5 14 13 8 21 17 3 13 8 -14 -21 -34 -36 86 83 81 66 62 74 83 79 70 52 53 71 59 61 132 158 160 45 35 10 7 6 6 -2 5 4 -19 -34 -31 -32 127 110 89 109 91 69 56 57 61 59 106 126 124 5 4 3 -3 -35 -25 -31 104 98 81 60 65 60 73 133 123 108 2080s EQ -10 6 7 8 -22 -15 -23 58 93 90 64 63 65 115 175 221 EQ -21 -10 10 11 6 -24 -26 57 92 104 91 66 77 108 148 -19 18 10 -20 119 160 145 118 82 105 89 93 173 188 173 102 268 104 10S -32 -39 267 193 274 247 323 141 111 10S 24 23 -27 270 268 385 463 261 147 103 41 -35 348 305 448 525 197 110 60 113 -31 805 380 418 446 277 141 69 120 20S -18 -30 763 500 415 349 176 192 98 20S -25 -23 595 372 438 350 296 205 84 -29 1020426 321 313 192 200 76 664 250 256 149 30S 187 272 236 140 30S -26 -12 60 57 141

10W 0 10E 20E 30E 40E 50E 10W 0 10E 20E 30E 40E 50E Median of % changes Range of % changes

-50 -30 -20 -10 0 10 20 30 50 10 20 30 40 50 75 100

Fig. 11. Left panels: Change in mean JJA rainfall for the 2020s, 2050s and 2080s (with respect to 1961–90) for the A2-high sce- nario; median of 7 GCM experiments. For areas with no change shown the model median response fails to exceed the 1 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 Hulme et al.: African Climate Change: 1900–2100 161

of equatorial East Africa where rainfall increases by 5 unforced simulation. These graphs provide a quick to 30% in DJF and decreases by 5 to 10% in JJA. Some assessment at a ‘national’ scale of the likely range and areas of Sahelian West Africa and the Mahgreb also significance of future climate change and again shows experience ‘significant’ rainfall decreases in JJA sea- the extent to which different GCMs agree in their son under the B1-low scenario. The inter-model range regional response to a given magnitude of global for these rainfall changes is large and in the cases cited warming. above always exceeds the magnitude of the Median For each country there is a spread of results relating model response. Over the seasonally arid regions of to inter-model differences in climate response. For Africa, the inter-model range becomes very large example, in Tunisia the change in annual rainfall is (>100%) because of relatively large percent changes predominantly towards drying (only ECHAM4 dis- in modelled rainfall induced by very small baseline plays wetting), although the magnitude of the drying seasonal rainfall quantities. under the A2-high scenario is between 1 and 30%. With more rapid global warming (e.g. the B2-mid, Natural climate variability is estimated to lead to dif- A1-mid and A2-high scenarios), increasing areas of ferences of up to ±10% between different 30 yr mean Africa experience changes in DJF or JJA rainfall that climates; therefore the more extreme of these scenario do exceed the 1 sigma level of natural rainfall vari- outcomes would appear to be ‘significant’ for Tunisia. ability. Thus for the A2-high scenario, large areas of The picture would appear at first sight to be less clear equatorial Africa experience ‘significant’ increases in for Zimbabwe, where 4 of the GCMs suggest wetting DJF rainfall of up to 50 or 100% over parts of East and 3—including the HadCM2 ensemble of 4 simula- Africa (Fig. 10), while rainfall decreases ‘significantly’ tions—suggest drying. However, the range of natural in JJA over parts of the Horn of Africa and northwest variability in annual rainfall when averaged over 30 yr Africa (Fig. 11). Some ‘significant’ JJA rainfall increases is shown to be about ±6% and most of the wetting sce- occur over the central Sahel region of Niger and Chad, narios fall within this limit. It is the drying responses while ‘significant’ decreases in DJF rainfall (15 to 25%) under the more extreme A2-high, B2-mid and A1-mid occur over much of South Africa and Namibia and scenarios that would appear to yield a more ‘signifi- along the Mediterranean coast. The inter-model range cant’ result. for these rainfall changes remains large, however, and It is also important to point out that inter-ensemble with very few exceptions exceeds the magnitude of the differences in response at these national scales can Median model response. Even for the seasonally wet also be large. The 4-member HadCM2 ensemble for JJA rainfall regime of the Sahel, inter-model ranges Tunisia yields differences in rainfall change of 15% or can exceed 100%, suggesting that different GCM sim- more, while for Ethiopia inter-ensemble differences ulations yield (sometimes) very different regional rain- can lead to a sign change in the rainfall scenario. In fall responses to a given greenhouse gas forcing. This this latter case, however, few of these HadCM2 rainfall large inter-model range in seasonal mean rainfall changes are larger than the estimate of natural rainfall response is not unique to Africa and is also found over variability for Ethiopia. It is also worth noting that the much of south and southwest Asia and parts of Central relative regional response of different GCMs is not America (Carter et al. 2001). 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 5.2. National scenario graphs extreme drying scenario. We discuss the significance of some of these differences and similarities between To condense this scenario information further, we different GCMs in our discussion of uncertainties in also constructed ‘national’-scale summary graphs for 4 Section 6. smaller regions—centred on the countries of Senegal, Tunisia, Ethiopia and Zimbabwe. These chosen do- mains are shown in Fig. 6 (top left panel) and reflect 5.3. Changes in ENSO-related rainfall variability the diversity of existing climate regimes across the continent from north to south and from west to east. Given the important role that ENSO events exert on Each country graph shows, for the 2050s, the distribu- interannual African rainfall variability, at least in some tion of the mean annual changes in mean temperature regions, determining future changes in interannual and precipitation for each GCM simulation and for rainfall variability in Africa can only be properly con- each of our 4 scenarios (Fig. 12). As with the continen- sidered in the context of changes in ENSO behaviour. tal maps, these changes are compared with the natural There is still ambiguity, however, about how ENSO multi-decadal variability of annual-mean temperature events may respond to global warming. This is partly and precipitation extracted from the HadCM2 1400 yr because GCMs only imperfectly simulate present 162 Clim Res 17: 145–168, 2001

Senegal Tunisia

20 20

0 0

-20 -20 Precipitation change (%) Precipitation change (%) Precipitation

-40 -40

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

Ethiopia Zimbabwe

20 20

0 0

-20 -20 Precipitation change (%) Precipitation change (%) Precipitation

-40 -40

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

CCSR CGCMI CSIRO ECHAM GFDL HadCM2 NCAR

Fig. 12. Change in mean annual temperature and precipitation for the 2050s (with respect to 1961–90) for regions centred on Senegal, Tunisia, Ethiopia and Zimbabwe (see Fig. 6, top left panel, for selected domains). Results from the 7 DDC GCMs are shown, scaled to reflect the 4 climate change scenarios adopted in this study: A2-high, A1-mid, B2-mid and B1-low. Note: the HadCM2 GCM has 4 results reflecting the 4-member ensemble simulations completed with this GCM. The bold lines centred on the origin indicate the 2 standard deviation limits of natural 30 yr time-scale natural climate variability defined by the 1400 yr HadCM2 control simulation

ENSO behaviour. Tett et al. (1997) demonstrate that What effects would such changes have on interan- HadCM2 simulates ENSO-type features in the Pacific nual African rainfall variability? This not only depends Ocean, but the model generates too large a warming on how ENSO behaviour changes in the future, but across the Tropics in response to El Niño events. Tim- also upon how realistically the models simulate the mermann et al. (1999), however, have recently argued observed ENSO-rainfall relationships in Africa. Smith that their ECHAM4 model (see Table 1) has sufficient & Ropelewski (1997) looked at Southern Oscillation- resolution to simulate ‘realistic’ ENSO behaviour. They rainfall relationships in the NCEP atmospheric GCM, analysed their greenhouse gas-forced simulations and where the model is used to re-create observed climate suggested that in the future there will be more frequent variability after being forced with observed sea surface and more intense ‘warm’ and ‘cold’ ENSO events, a temperatures (SSTs). Even in this most favourable of result also found in the HadCM2 model (Collins 2000). model experiments, the model relationships do not Hulme et al.: African Climate Change: 1900–2100 163

always reproduce those observed. Over southeastern Even though we have presented a set of 4 climate Africa, the simulated rainfall percentiles are consistent futures for Africa, where the range reflects unknown with the observations reported by Ropelewski & future global greenhouse gas emissions and 3 different Halpert (1996), but over eastern equatorial Africa the values for the global climate sensitivity, we cannot model simulates an relationship opposite to that place probability estimates on these 4 outcomes with observed. The recently elucidated role of the Indian much confidence. While this conclusion may well Ocean dipole (Saji et al. 1999, Webster et al. 1999) in apply for most, or all, world regions, it is particularly modulating eastern African rainfall variability may be true for Africa, where the roles of land cover change one reason simple ENSO-precipitation relationships and dust and biomass aerosols in inducing regional cli- are not well replicated by the GCMs in this region. mate change are excluded from the climate change We analysed 240 yr of unforced simulated climate model experiments reported here. made using the HadCM2 GCM (see Table 1) to see to This concern is most evident in the Sahel region of what extent this model can reproduce observed rela- Africa. None of the model-simulated present or future tionships. We performed the identical analysis to that climates for this region displays behaviour in rainfall performed on the observed data in Section 4 and the regimes that is similar to that observed over recent results are plotted in Fig. 5c,d. The 2 strongest ENSO decades. This is shown in Fig. 13 where we plot the signals in African rainfall variability are only imper- observed regional rainfall series for 1900–98, as used fectly reproduced by the model. The East African neg- in Fig. 3, and then append the 10 model-simulated ative correlation in November–April is rather too weak evolutions of future rainfall for the period 2000–2100. in the model and also too extensive, extending west- These future curves are extracted directly from the 10 wards across the whole African equatorial domain. GCM experiments reported in Table 1 and have not The positive correlation over southern Africa is too been scaled to our 4 scenario values (this scaling was weak in HadCM2 and displaced northwards by some performed in the construction of Figs 8 to 11 as dis- 10° latitude. The absence of any strong and coherent cussed in Section 5). One can see that none of the relationship during the June–October season is repro- model rainfall curves for the Sahel displays multi- duced by the model (Fig. 5b). decadal desiccation similar to what has been observed On the basis of this analysis, and our assessment of in recent decades. This conclusion also applies to the the literature, we are not convinced that quantifying multi-century unforced integrations performed with future changes to interannual rainfall variability in the same GCMs (Brooks 1999). Africa due to greenhouse gas forcing are warranted. There are a number of possible reasons for this. It At the very least, this issue deserves a more thorough could be that the climate models are poorly replicating investigation of ENSO-rainfall relationships in the ‘natural’ rainfall variability for this region. In particular GCMs used here and how these relationships change the possible role of ocean circulation changes in caus- in the future. Such an analysis might also be useful in ing this desiccation (Street-Perrott & Perrott 1990) may determining the extent to which seasonal rainfall fore- not be well simulated in the models. It could also be casts in Africa that rely upon ENSO signatures may that the cause of the observed desiccation is some pro- remain valid under scenarios of future greenhouse gas cess that the models are not including. Two candidates forcing. for such processes would be the absence of a dynamic land cover/atmosphere feedback process and the ab- sence of any representation of changing atmospheric 6. UNCERTAINTIES AND LIMITATIONS dust aerosol concentration. The former of these feed- TO KNOWLEDGE back processes has been suggested as being very im- portant in determining African climate change during In the introduction to this paper we alluded to some the Holocene by amplifying orbitally induced African of the limitations of climate change scenarios for Africa monsoon enhancement (Kutzbach et al. 1996, Claussen and those shown in this paper are no exception. These et al. 1999, Doherty et al. 2000). This feedback may limitations arise because of, inter alia, (1) the problem also have contributed to the more recently observed of small signal:noise ratios in some scenarios for pre- desiccation of the Sahel (Xue 1997). The latter process cipitation and other variables, (2) the inability of cli- of elevated Saharan dust concentrations may also be mate model predictions to account for the influence of implicated in the recent Sahelian desiccation (Brooks land cover changes on future climate, and (3) the rela- 1999). tively poor representation in many models of some Without such a realistic simulation of observed rain- aspects of climate variability that are important for fall variability, it is difficult to define with confidence Africa (e.g. ENSO). Some of these limitations have the true magnitude of natural rainfall variability in been revealed by analyses presented earlier. these model simulations and also difficult to argue 164 Clim Res 17: 145–168, 2001

1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 natural variability is difficult to say. In 80 our scenario maps (Figs 8 to 11) we pre- 60 sented the median precipitation change 40 from these 10 (scaled) model simula- tions, implying that we can treat these 20 climate change simulations as individual 0 members of an ensemble. The ensem- ble-mean or median therefore yields our -20 ‘best’ estimate of the true response to -40 greenhouse gas forcing; much as in numerical weather prediction the en- -60 The Sahel semble-mean forecast is often taken as -8080 the ‘best’ short-range weather forecast. 60 In our example, for the Sahel and south- ern African the median response was 40 annual drying, whereas for East Africa 20 the median response was wetting

omaly (per cent) (Fig. 13). 0 One other concern about the applica- -20 bility in Africa of climate change sce- -40 narios such as those presented here is the relationship between future climate -60 East Africa change predictions and seasonal rainfall Annual rainall an rainall Annual -8080 forecasts. There is increasing recogni- tion (e.g. Downing et al. 1997, Ringius 60 1999, Washington & Downing 1999) that 40 for many areas in the tropics one of

20 the most pragmatic responses to the prospect of long-term climate change is 0 a wish to strengthen the scientific basis -20 of seasonal rainfall forecasts. Where forecasts are feasible, this should be -40 accompanied by improvements in the

-60 Southeast Africa management infrastructure to facilitate timely responses. Such a research and -80 1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 adaptation strategy focuses on the short- term realisable goals of seasonal climate Fig. 13. Observed annual rainfall anomalies for 3 African regions, 1900–98 prediction and the near-term and quan- (see Fig. 3), and model-simulated anomalies for 2000–2099. Model anomalies are for the 10 model simulations derived from the 7 DDC GCM experiments— tifiable benefits that improved forecast the 4 HadCM2 simulations are the dashed curves (see Table 1). All anomalies applications will yield. At the same time, are expressed with respect to either observed or model-simulated 1961–90 the strengthening of these institutional average rainfall. The model curves are extracted directly from the GCM ex- structures offers the possibility that the periments and the results are not scaled to the 4 scenarios used in this paper. The smooth curves result from applying a 20 yr Gaussian filter more slowly emerging signal of climate change in these regions can be better managed in the decades to come. It is that these greenhouse gas-induced attributed rainfall therefore an appropriate form of climate change adap- changes for regions in Africa will actually be those that tation. This means that 2 of the objectives of climate dominate the rainfall regimes of the 21st century. change prediction should be: (a) to determine the Notwithstanding these model limitations due to omit- effect global warming may have on seasonal predict- ted or poorly represented processes, Fig. 13 also ability (will forecast skill levels increase or decrease or illustrates the problem of small signal:noise ratios in will different predictors be needed); and (b) to deter- precipitation scenarios. The 10 individual model simu- mine the extent to which predicted future climate lations yield different signs of precipitation change for change will impose additional strains on natural and these 3 regions as well as different magnitudes. How managed systems over and above those that are much of these differences are due to model-generated caused by existing seasonal climate variability. For Hulme et al.: African Climate Change: 1900–2100 165

both of these reasons we need to improve our predic- scenarios—although such downscaling techniques do tions of future climate change and in particular to not remove the fundamental reasons why we are un- improve our quantification of the uncertainties. certain about future African rainfall changes. The exploration of African sensitivity to climate change must also be undertaken in conjunction with 7. CONCLUSIONS the more concrete examples we have of sensitivity to short-term (seasonal time scale) climate variability. The climate of Africa is warmer than it was 100 yr These estimates may be based on observed recon- ago. Although there is no evidence for widespread struction of climate variability over the last century or desiccation of the continent during this century, in on the newly emerging regional seasonal rainfall fore- some regions substantial interannual and multi-de- casts now routinely being generated for southern, east- cadal rainfall variations have been observed and near ern and western Africa (e.g. NOAA 1999, SARCOF, continent-wide droughts in 1983 and 1984 had some see also http://iri.ldeo.columbia.edu [International dramatic impacts on both the environment and some Research Institute for Climate Prediction]). Because of economies (Benson & Clay 1998). The extent to which the uncertainties mentioned above about future these rainfall variations are related to greenhouse gas- regional climate predictions for Africa, initial steps to induced global warming, however, remains undeter- reduce vulnerability should focus on improved adapta- mined. A warming climate will nevertheless place tion to existing climate variability (Downing et al. 1997, additional stresses on water resources, whether or not Adger & Kelly 1999, Ringius 1999). Thus, emphasis future rainfall is significantly altered. would be placed on reducing vulnerability to adverse Model-based predictions of future greenhouse gas- climate events and increasing capacity to adapt to induced climate change for the continent clearly sug- short-term and seasonal weather conditions and cli- gest that this warming will continue and, in most sce- matic variability. The likelihood of significant eco- narios, accelerate so that the continent on average nomic and social benefits from adaptation to short- could be between 2 and 6°C warmer in 100 yr time. term climate variability in Africa justifies this activity. While these predictions of future warming may be rel- Additionally, and importantly, lessons from adaptation atively robust, there remain fundamental reasons why to short-term climate variability would build capacity we are much less confident about the magnitude, and to respond incrementally to longer-term changes in even direction, of regional rainfall changes in Africa. local and regional climates. Two of these reasons relate to the rather ambiguous representation in most GCMs of ENSO-type climate Acknowledgements. The Climate Impacts LINK Project variability in the tropics (a key determinant of African (funded by DETR Contract No. EPG 1/1/68) contributed com- rainfall variability) and the omission in all current puting facilities and staff time for RMD. T.N. was supported GCMs of any representation of dynamic land cover- by an IGBP/START Fellowship. The MAGICC climate model atmosphere interactions and dust and biomass aero- was used with the permission of Tom Wigley and Sarah Raper. sols. Such interactions have been suggested to be important in determining African climate variability during the Holocene and may well have contributed to LITERATURE CITED the more recently observed desiccation of the Sahel. Adger WN, Kelly PM (1999) Social vulnerability to climate We suggest that climate change scenarios, such as change and the architecture of entitlements. Mitig Adapt those presented here, should nevertheless be used to Strat Global Change 4:253–266 explore the sensitivity of a range of African environ- Aubreville A (1949) Climats, foréts et désertification de mental and social systems, and economically valuable l’Afrique tropicale. Société d’Editions Géographiques, Maritimes et Coloniales, Paris assets, to a range of future climate changes. 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Submitted: July 18, 1999; Accepted: October 11, 2000 Proofs received from author(s): February 12, 2001