African climate change: 1900-2100

CLIMATE RESEARCH Clim Res Vol. 17: 145–168, 2001 Published August 15 African climate change: 1900–2100 Mike Hulme1,*, Ruth Doherty3, Todd Ngara4, Mark New 5, David Lister 2 1 Tyndall Centre for Climate Change Research and 2Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom 3 Environmental and Societal Impacts Group, NCAR, Boulder, Colorado 80307, USA 4 Climate Change Office, Ministry of Mines, Environment and Tourism, Postal Bag 7753 Causeway, Harare, Zimbabwe 5 School of Geography, Mansfield Road, University of Oxford, Oxford OX1 3TB, United Kingdom ABSTRACT: This paper reviews observed (1900–2000) and possible future (2000–2100) continentwide 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 experiments, 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 climate. 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 Resale or republication not permitted without written consent of the publisher 1. INTRODUCTION The climates of Africa are both varied and varying: varied because they range from humid equatorial regimes, through seasonally-arid tropical regimes, to sub-tropical Mediterranean-type climates, and varying because all these climates exhibit differing degrees of temporal variability, particularly with regard to rainfall. Understanding and predicting these inter-annual, inter-decadal and multi-decadal variations in climate has become the major challenge facing African and African-specialist climate scientists in recent years. *E-mail: m.hulme@uea.ac.uk © Inter-Research 2001 Whilst seasonal climate forecasting has taken great strides forward, in both its development and application (Folland et al. 1991, Stockdale et al. 1998, Washington & Downing 1999, also see http://www.ogp. noaa.gov/enso/africa.html [SARCOF: Southern Africa Regional Climate Outlook Forum]), the ultimate causes of the lower frequency decadal and multi-decadal rainfall variability that affects some African climate regimes, especially in the Sahel region, remain uncertain (see Rowell et al. 1995 vs Sud & Lau 1996, also Xue & Shukla 1998). This work examining the variability of African climate, especially rainfall, is set in the wider context of our emerging understanding of human influences on the larger, global-scale climate. Increasing greenhouse gas accumulation in the global atmo- 146 Clim Res 17: 145–168, 2001 sphere and increasing regional concentrations of aerosol particulates are now understood to have detectable effects on the global climate system (Santer et al. 1996). These effects will be manifest at regional scales although perhaps in more uncertain terms (Mitchell & Hulme 1999, Giorgi & Francisco 2000). Africa will not be exempt from experiencing these human-induced changes in climate. Much work remains to be done, however, in trying to isolate those aspects of African climate variability that are ‘natural’ from those that are related to human influences. African climate scientists face a further challenge in that on this continent the role of land cover changes — some natural and some human-related — in modifying regional climates is perhaps most marked (Xue 1997). This role of land cover change in altering regional climate in Africa has been suggested for several decades now. As far back as the 1920s and 1930s, theories about the encroachment of the Sahara and the desiccation of the climate of West Africa were put forward (Stebbing 1935, Aubreville 1949). These ideas have been explored over the last 25 yr through modelling studies of tropical north African climate (e.g. Charney 1975, Cunnington & Rowntree 1986, Zheng & Eltahir 1997). It is for these 2 reasons — large internal climate variability driven by the oceans and the confounding role of human-induced land cover change — that climate change ‘predictions’ (or the preferable term scenarios) for Africa based on greenhouse gas warming remain highly uncertain. While global climate models (GCMs) simulate changes to African climate as a result of increased greenhouse gas concentrations, these 2 potentially important drivers of African climate variability — for example El Niño/Southern Oscillation (ENSO) (poorly) and land cover change (not at all) — are not well represented in the models. Nevertheless, it is of considerable interest to try and explore the magnitude of the problem that the enhanced greenhouse effect may pose for African climate and for African resource managers. Are the changes that are simulated by GCMs for the next century large or small in relation to our best estimates of ‘natural’ climate variability in Africa? How well do GCM simulations agree for the African continent? And what are the limitations/uncertainties of these model predictions? Answering these questions has a very practical relevance in the context of national vulnerability and adaptation assessments of climate change currently being undertaken by many African nations as part of the reporting process to the UN Framework Convention on Climate Change. This paper makes a contribution to these assessments by providing an overview of future climate change in Africa, particularly with regard to simulations of greenhouse gas warming over the next 100 yr. We start the paper (Section 2) by reviewing some previous climate change scenarios and analyses for regions within Africa. Such studies have been far from comprehensive. Section 3 explains the data, models and approaches that we have taken in generating our analyses and constructing our climate change scenarios for Africa. In Section 4 we consider the salient features of African climate change and variability over the last 100 yr, based on the observational record of Africa climate. Such a historical perspective is essential if the simulated climates of the next century are to be put into their proper context. Section 5 then presents our future climate change scenarios for Africa, based on the draft Special Report on Emissions Scenarios range of future greenhouse gas emissions (http://sres.ciesin.org/index.html [SRES]) and the GCM results deposited with the Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre (http://ipcc-ddc.cru.uea.ac.uk/index.html [DDC]). Changes in mean seasonal climate are shown as well as some measures of changed interannual variability. Section 6 then discusses these future climate simulations in the light of modelling uncertainties and in the context of other causes of African climate variability and change. We consider how much useful and reliable information these types of studies yield and how they can be incorporated into climate change impacts assessments. Our key conclusions are presented in Section 7. 2. REVIEW OF PREVIOUS AFRICAN CLIMATE CHANGE SCENARIO WORK There has been relatively little work published on future climate change scenarios for Africa. The various IPCC assessments have of course included global maps of climate change within which Africa has featured, and in Mitchell et al. (1990) the African Sahel was one of 5 regions for which a more detailed analysis was conducted. Kittel et al. (1998) and Giorgi & Francisco (2000) also identify African regions within their global analysis of inter-model differences in climate predictions, but no detailed African scenarios are presented. Tyson (1991) published one of the first scenario analyses specifically focused on an African region. In this case some climate change scenarios for southern Africa were constructed using results from the first generation GCM equilibrium 2 × CO2 experiments. In a further development, Hulme (1994a) presented a method for creating regional climate change scenarios combining GCM results with the newly published IPCC IS92 emissions scenarios and demonstrated the application of the method for Africa. In this study mean Hulme et al.: African Climate Change: 1900–2100 annual temperature and precipitation changes from 1990 to 2050 under the IS92a emission scenario were presented. Some more recent examples of climate scenarios for Africa use results from transient GCM climate change experiments. Hernes et al. (1995) and Ringius et al. (1996) constructed climate change scenarios for the African continent that showed land areas over the Sahara and semi-arid parts of southern Africa warming by the 2050s by as much as 1.6°C and the equatorial African countries warming at a slightly slower rate of about 1.4°C. These studies, together with Joubert et al. (1996), also suggested a rise in mean sea-level around the African coastline of about 25 cm by 2050. A more selective approach to the use of GCM experiments was taken in Hulme (1996a). They described 3 future climate change scenarios for the Southern African Development Community (SADC) region of southern Africa for the 2050s on the basis of 3 different GCM experiments. These experiments were selected to deliberately span the range of precipitation changes for the SADC region as simulated by GCMs. Using these scenarios, the study then described some potential impacts and implications of climate change for agriculture, hydrology, health, biodiversity, wildlife and rangelands. A similar approach was adopted by Conway et al. (1996) for a study of the impacts of climate change on the Nile Basin. More recently, the Africa chapter (Zinyowera et al. 1998) in the IPCC Assessment of Regional Impacts of Climate Change (IPCC 1998) also reported on some GCM studies that related to the African continent. Considerable uncertainty exists in relation to largescale precipitation changes simulated by GCMs for Africa (Hudson 1997, Hudson & Hewitson 1997, Joubert & Hewitson 1997, Feddema 1999). Joubert & Hewitson (1997) nevertheless conclude that, in general, precipitation is simulated to increase over much of the African continent by the year 2050. These GCM studies show, for example, that parts of the Sahel could experience precipitation increases of as much as 15% over the 1961–90 average by 2050. A note of caution is needed, however, concerning such a conclusion. Hulme (1998) studied the present-day and future simulated inter-decadal precipitation variability in the Sahel using the HadCM2 GCM. These model results were compared with observations during the 20th Century. Two problems emerge. First, the GCM does not generate the same magnitude of inter-decadal precipitation variability that has been observed over the last 100 yr, casting doubt on the extent to which the most important controlling mechanisms are being simulated in the GCM. Second, the magnitude of the future simulated precipitation changes for the Sahel is not large in relation to ‘natural’ precipitation vari- 147 ability for this region. This low signal:noise ratio suggests that the greenhouse gas-induced climate change signals are not well defined in the model, at least for this region. We develop this line of reasoning in this paper and illustrate it in Section 5 with further examples from Africa. Although there have been studies of GCM-simulated climate change for several regions in Africa, the downscaling of GCM outputs to finer spatial and temporal scales has received relatively little attention in Africa. Hewitson & Crane (1998) and Hewitson & Joubert (1998) have applied empirical downscaling methods to generate climate change scenarios for South Africa using artificial neural networks and predictors relating to upper air circulation and tropospheric humidity. The usual caveats, however, apply to these downscaled scenarios (Hulme & Carter 1999) — they are still dependent on the large-scale forcing from the GCMs and they still only sample one realisation of the possible range of future possible climates, albeit with higher resolution. The application of regional climate models is still in its infancy, although some initiatives are now under way for East Africa (Sun et al. 1999), West Africa (Wang & Eltahir 2000) and southern Africa (B. Hewitson pers. comm.). These initiatives have not yet generated experimental results from regional climate change simulations for use in scenario construction. 3. DATA AND METHODS For our analyses of observed climate variability in Africa we use the global gridded data sets of Jones (1994, updated; mean temperature), Hulme (1994b, updated; precipitation), and New et al. (1999, 2000; 10 surface climate variables). These data sets are all public domain and are available, along with some documentation on their construction, from the following Web sites: GCM results were taken from the IPCC Data Distribution Centre (DDC) (http://ipcc-ddc.cru. uea.ac.uk); most of the observed data sets used here can be obtained from the Climatic Research Unit (http://www.cru.uea.ac.uk); the draft (February 1999; non-IPCC approved, but used with permission) SRES emissions scenarios were obtained from the SRES (http://sres.ciesin.org/index.html). The data sets of Jones (1994) and Hulme (1994b) exist on a relatively coarse grid (5° latitude/longitude and 2.5° latitude by 3.75° longitude respectively), while the data set of New et al. (1999, 2000) exists with a 0.5° latitude/longitude resolution. These observed data are resolved only to monthly time steps and we therefore undertake no original analyses of observed daily climate variability. For Ethiopia and Zimbabwe we analyse unpublished 148 Clim Res 17: 145–168, 2001 monthly mean maximum and minimum temperature data for a number of stations in each country. These data originate from the respective national meteorological agencies. For the index of the Southern Oscillation we use the updated index of Ropelewski & Jones (1987), calculated as the normalised mean sea-level pressure difference between Tahiti and Darwin and available from Climate Monitor online (http://www. cru.uea.ac.uk/cru/climon). Other climate-related and continent-wide data sets also have value for some climate analyses, whether these data are derived from satellite observations (e.g. Normalised Difference Vegetation Index or satellitederived precipitation estimates) or from numerical weather prediction model re-analyses (e.g. the NCEP [National Centers for Environmental Prediction] reanalysis from 1948 to present). Although these alternative data sets have some real advantages in particular environmental or modelling applications (e.g. modelling malaria; Lindsay et al. 1998; evaluating dust forcing; Brooks 1999), we prefer to limit our analysis here to the use of conventional observed climate data sets derived from surface observations. The GCM results used in this study are mainly extracted from the IPCC DDC archive. This archive contains results from climate change experiments performed with 7 coupled ocean-atmosphere global climate models (Table 1). All these experiments were conducted using similar greenhouse gas or greenhouse gas plus aerosol forcing. In this study only the results from the greenhouse gas-forced simulations are used for reasons outlined below. We also use results from the 1400 yr control simulation of the HadCM2 climate model (Tett et al. 1997) to derive model-based estimates of natural multi-decadal climate variability. The data were re-gridded using a Gaussian spacefilter onto a common grid, namely the HadCM2 grid. Later results are presented on this common grid. Climate can be affected by a number of other agents in addition to greenhouse gases; important amongst these are small particles (aerosols). These aerosols are suspended in the atmosphere and some types (e.g. sulphate aerosols derived from sulphur dioxide) reflect back solar radiation; hence they have a cooling effect on climate. Although there are no measurements to show how these aerosol concentrations have changed over the past 150 yr, there are estimates of how sulphur dioxide emissions (one of the main precursors for aerosol particles) have risen and scenarios of such emissions into the future. A number of such scenarios have been used in a sulphur cycle model to calculate the future rise in sulphate aerosol concentrations (Penner et al. 1998). When one of these scenarios was used, along with greenhouse gas increases, as input to the DDC GCMs, the global-mean temperature rise to 2100 was reduced by between a quarter and a third. The reductions over Africa were less than this. These are very uncertain calculations, however, due to a number of factors. First, the old 1992 IPCC emissions scenario on which it was based (IS92a; Leggett et al. 1992) contains large rises in sulphur dioxide emissions over the next century. Newer emissions scenarios, including the draft SRES scenarios, estimate only a small rise in sulphur dioxide emissions over the next couple of decades followed by reductions to levels lower than today’s by 2100 (SRES). Over Africa, sulphur emissions remain quite low for the whole of this century. The inclusion of such modest sulphur dioxide emissions scenarios in GCM experiments would actually produce a small temperature rise relative to model experiments that excluded the aerosol effect (Schlesinger et al. 2000). Results from GCM experiments using these revised sulphur scenarios are not yet widely available. Second, more recent sulphur cycle models generate a lower sulphate burden per tonne of 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 origin CCSR-NIES CGCM1 CSIRO-Mk2 ECHAM4 GFDL-R15 HadCM2a NCAR1 a Japan Canada Australia Germany USA UK USA Approximate resolution (lat. × long.) Climate sensitivity (°C) Integration length Source 5.62° × 5.62° 3.75° × 3.75° 3.21° × 5.62° 2.81° × 2.81° 4.50° × 7.50° 2.50° × 3.75° 4.50° × 7.50° 3.5 3.5 4.3 2.6 3.7 2.5 4.6 1890–2099 1900–2100 1881–2100 1860–2099 1958–2057 1860–2099 1901–2036 Emori et al. (1999) Boer et al. (2000) Hirst et al. (2000) Roeckner et al. (1996) Haywood et al. (1997) Mitchell & Johns (1997) Meehl & Washington (1995) An ensemble of 4 climate change simulations were available from the HadCM2 model 149 Hulme et al.: African Climate Change: 1900–2100 4. TWENTIETH CENTURY CLIMATE CHANGE 4.1. Temperature The continent of Africa is warmer than it was 100 yr ago. Warming through the 20th century has been at the rate of about 0.5°C century–1 (Fig. 1), with slightly larger warming in the June–August (JJA) and September–November (SON) seasons than in December– February (DJF) and March–May (MAM). The 6 warmest years in Africa have all occurred since 1987, with 1998 being the warmest year. This rate of warming is not dissimilar to that experienced globally, and the periods of most rapid warming — the 1910s to 1930s and the post-1970s — occur simultaneously in Africa and the rest of the world. Few studies have examined long-term changes in the diurnal cycle of temperature in Africa. Here, we show results for 4 countries for which studies have been published or data were available for analysis — for Sudan and South Africa as published by Jones & Lindesay (1993) and for Ethiopia and Zimbabwe (unpubl.). While a majority of the Earth’s surface has experienced a decline in the mean annual diurnal temperature range (DTR) as climate has warmed (Nicholls et al. 1996), our examples here show contrasting trends for these 4 African countries. Mean annual DTR decreased by between 0.5 and 1°C since the 1950s in Sudan and Ethiopia, but increased by a similar amount in Zimbabwe (Fig. 2). In South Africa, DTR decreased during the 1950s and 1960s, but has remained quite stable since then. Examination of the seasonal variation in these trends (not shown) suggests that different factors contribute to DTR trends in different seasons and in different countries. For example, in Sudan DTR shows an 1900 1920 1940 1960 1980 2000 1920 1940 1960 1980 2000 Annual 0.6 0 -0.6 DJF 0.6 0 Mean temperature anomaly ( degC) the sulphate particles in more sophisticated radiation models is smaller than previously calculated. Third, in addition to their direct effect, sulphate aerosols can also indirectly cool climate by changing the reflectivity and longevity of clouds (Schimel et al. 1996). These indirect effects are now realised as being at least as important as the direct effect, but were not included in the present DDC GCM climate change simulations. Fourth, there are other types of aerosols (e.g. carbon or soot) which may also have increased due to human activity, but which act to warm the atmosphere. Finally and above all, the short lifetime of sulphate particles in the atmosphere means that they should be seen as a temporary masking effect on the underlying warming trend due to greenhouse gases. For all these reasons, model simulations of future climate change using both greenhouse gases and sulphate aerosols have not been used to develop the climate change scenarios illustrated in this paper. The future greenhouse gas forcing scenario used in the DDC experiments approximated a 1% annum–1 growth in greenhouse gas concentrations over the period from 1990 to 2100. Since the future growth in anthropogenic greenhouse gas forcing is highly uncertain, it is important that our climate scenarios for Africa reflect this uncertainty; it would be misleading to construct climate change scenarios that reflected just one future emissions growth curve. We therefore adopt the 4 draft marker emissions scenarios of the IPCC SRES: B1, B2, A1 and A2. None of these emissions scenarios assume any climate policy implementation; the differences result from alternative developments in global population, the economy and technology. Our method of climate change scenario construction follows that adopted by Hulme & Carter (2000) in their generation of climate change scenarios for Europe as part of the ACACIA assessment of climate impact in Europe. Full details may be found there, but we provide a short summary of the method in Section 5 below. -0.6 MAM 0.6 0 -0.6 JJA 0.6 0 -0.6 SON 0.6 0 -0.6 1900 Fig. 1. Mean surface air temperature anomalies for the African continent, 1901–98, expressed with respect to the 1961–90 average; annual and 4 seasons — DJF, MAM, JJA, SON. The smooth curves result from applying a 10 yr Gaussian filter Clim Res 17: 145–168, 2001 150 1940 1950 1960 1970 1980 1990 2000 Sudan 1 1 0 0 -1 -1 degC anomaly Ethiopia 1 1 0 0 -1 -1 Zimbabwe 1 1 0 0 -1 -1 To illustrate something of this variability we present an analysis for the 3 regions of Africa used by Hulme (1996b) — the Sahel, East Africa and southeast Africa (domains shown in Fig. 4). These 3 regions exhibit contrasting rainfall variability characteristics (Fig. 3): the Sahel displays large multi-decadal variability with recent drying, East Africa a relatively stable regime with some evidence of long-term wetting, and southeast Africa also a basically stable regime, but with marked inter-decadal variability. In recent years Sahel rainfall has been quite stable around the 1961–90 annual average of 371 mm, although this 30 yr period is substantial drier (about 25%) than earlier decades this century. In East Africa, 1997 was a very wet year and, as in 1961 and 1963, led to a surge in the level of Lake Victoria (Birkett et al. 1999). Recent analyses (Saji et al. 1999, Webster et al. 1999) have suggested these extreme wet years in East Africa are related to a dipole South Africa 1 1 0 0 -1 -1 1880 80 1900 1920 1940 1960 1980 2000 60 1960 1970 1980 1990 2000 Fig. 2. Mean annual diurnal temperature range (Tmax – Tmin) for a number of African countries: Sudan (data end 1987), Ethiopia (1990), Zimbabwe (1997) and South Africa (1991). The smooth curves result from applying a 10 yr Gaussian filter. Data for Sudan and South Africa are from Jones & Lindesay (1993), while data for Ethiopia and Zimbabwe are unpublished increasing trend during the July–September wet season, probably caused by trends towards reduced cloudiness, while DTR decreased during the rest of the year, probably due to trends for increased dustiness (Brooks 1999). Both of these factors are related to the multi-decadal drought experienced in Sudan since the 1950s (Hulme 2001). The long-term increase in annual DTR in Zimbabwe is due almost entirely to increases during the November–February wet season; trends during the rest of the year have been close to zero. We are not aware of published analyses of diurnal temperature trends in other African countries. +0.4 20 +0.2 0 0.0 -20 -0.2 -60 Interannual rainfall variability is large over most of Africa and for some regions, most notably the Sahel, multi-decadal variability in rainfall has also been substantial. Reviews of 20th Century African rainfall variability have been provided by, among others, Janowiak (1988), Hulme (1992) and Nicholson (1994). The Sahel 80 -80 60 40 +0.4 20 +0.2 0 0.0 -20 -0.2 -0.4 -40 -60 East Africa 80 -80 60 40 +0.4 20 +0.2 0 0.0 -20 -0.2 -40 -0.4 -60 4.2. Rainfall -0.4 -40 -80 1880 Southeast Africa 1900 1920 1940 1960 1980 2000 Fig. 3. Annual rainfall (1900–98; histograms and bold line) and mean temperature anomalies (1901–98; dashed line) for 3 African regions, expressed with respect to the 1961–90 average: Sahel, East Africa and southeastern Africa (regional domains marked on Fig. 4). Note: for southeast Africa year is July to June. The smooth curves for rainfall and temperature result from applying a 10 yr Gaussian filter Annual temperature anomaly (degC) 1950 Annual rainall anomaly (per cent) 1940 40 151 Hulme et al.: African Climate Change: 1900–2100 mode of variability in the Indian Ocean. In southeast Africa, the dry years of the early 1990s were followed by 2 very wet years in 1995/96 and 1996/97. Mason et al. (1999) report an increase in recent decades in the frequency of the most intense daily precipitation over South Africa, even though there is little long-term trend in total annual rainfall amount. Fig. 3 also displays the trends in annual temperature for these same 3 regions. Temperatures for all 3 regions during the 1990s are higher than they were earlier in the century (except for a period at the end of the 1930s in the Sahel) and are currently between 0.2 and 0.3°C warmer than the 1961–90 average. There is no simple correlation between temperature and rainfall in these 3 regions, although Hulme (1996b) noted that drying in the Sahel was associated with a moderate warming trend. 4.3. Spatial patterns Our analysis is summarised further in Fig. 4, where we show mean linear trends in annual temperature and precipitation during the 20th century. This analysis first filters the data using a 10-point Gaussian filter to subdue the effects on the regression analysis of outlier values at either end of the time period. While warming is seen to dominate the continent (see Fig. 1 above), some coherent areas of cooling are noted, around Nigeria/Cameroon in West Africa and along the coastal margins of Senegal/Mauritania and South Africa. In contrast, warming is at a maximum of nearly 2°C century–1 over the interior of southern Africa and in the Mediterranean countries of northwest Africa. Mean Annual Temperature Annual Precipitation 30N 30N 15N 15N 0 0 15S 15S 30S 30S 15W -3.0 -2.5 0 -2.0 -1.5 15E -1.0 -0.5 0.0 o C 30E 0.5 1.0 45E 1.5 2.0 2.5 15W 3.0 -60 -50 0 -40 -30 15E -20 -10 0 30E 10 20 45E 30 40 50 60 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 regional analysis shown in Fig. 3, with drying of up to 25% century–1 or more over some western and eastern parts of the Sahel. More moderate drying — 5 to 15% century–1 — is also noted along the Mediterranean coast and over large parts of Botswana and Zimbabwe and the Transvaal in southeast Africa. The modest wetting trend noted over East Africa is seen to be part of a more coherent zone of wetting across most of equatorial Africa, in some areas of up to 10% century–1 or more. Regions along the Red Sea coast have also seen an increase in rainfall, although trends in this arid/semi-arid region are unlikely to be very robust. 4.4. ENSO influence on rainfall With regard to interannual rainfall variability in Africa, the ENSO is one of the more important controlling factors, at least for some regions (Ropelewski & Halpert 1987, 1989, 1996, Janowiak 1988, Dai & Wigley 2000). These studies have established that the 2 regions in Africa with the most dominant ENSO influences are in eastern equatorial Africa during the short October-November rainy season and in southeastern Africa during the main November–February wet season. Ropelewski & Halpert (1989) also examined Southern Oscillation and rainfall relationships during La Niña or high index years. We have conducted our own more general analysis of Southern Oscillation rainfall variability for the African region over the period 1901–98 using an updated and more comprehensive data set (Hulme 1994b) than was used by these earlier studies. We also use the Southern Oscillation Index (SOI) as a continuous index of Southern Oscillation behaviour rather than designating discrete ‘warm’ (El Niño; low index) and ‘cold’ (La Niña; high index) Southern Oscillation events as was done by Ropelewski & Halpert (1996). We defined an annual average SOI using the June– May year, a definition that maximises the coherence of individual Southern Oscillation events, and correlated this index against seasonal rainfall in Africa. We performed this analysis for the 4 conventional seasons (not shown) and also for the 2 extended seasons of June to October (Year 0) and November (Year 0) to April (Year 1; Fig. 5a). This analysis confirms the strength of the previously identified relationships for equatorial east Africa (high rainfall during a warm ENSO event) and southern Africa (low rainfall during a warm ENSO event). The former relationship is strongest during the September–November rainy season (the ‘short’ rains; not shown), with an almost complete absence of ENSO sensitivity in this region during the February–April season (‘long’ rains) as found by Ropelewski & Halpert (1996). The southern African sensitivity is strongest over South Africa during December–February before migrating northwards over Zimbabwe and Mozambique during the March–May season (not shown). There is little rainfall sensitivity to ENSO behaviour elsewhere in Africa, although weak tendencies for Sahelian June–August drying (Janicot et al. 1996) and northwest African March–May drying (El Hamly et al. 1998) can also be found. 5. TWENTY-FIRST CENTURY CLIMATE CHANGE For a comprehensive assessment of the impact and implications of climate change, it is necessary to apply a number of climate change scenarios that span a reasonable range of the likely climate change distribution. The fact that there is a distribution of future climate changes arises not only because of incomplete understanding of the climate system (e.g. the unknown value of the climate sensitivity, different climate model responses, etc.), but also because of the inherent unpredictability of climate (e.g. unknowable future climate forcings and regional differences in the climate system response to a given forcing because of chaos). The ‘true’ climate change distribution is of course unknown, but we can make some sensible guesses as to 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/ Climate sensitivity B1-low / 1.5°C B2-mid / 2.5°C A1-mid / 2.5°C A2-high / 4.5°C Population (billions) C emissions from energy (GtC) Total S emissions (TgS) Global ∆T (°C) Global ∆SL (cm) pCO2 (ppmv) 8.76 9.53 8.54 11.67 9.7 11.3 16.1 17.3 51 55 58 96 0.9 1.5 1.8 2.6 13 36 39 68 479 492 555 559 153 Hulme et al.: African Climate Change: 1900–2100 Time series correlations of SOI (Jun-May) vs. Precip (Jun-Oct) a 1 30N 0.5 Time series correlations of SOI (Jun-May) vs. Precip (Nov-Apr) 1 b 0.5 30N 0.4 0.4 0.3 0.3 0.26 0.26 EQ EQ 30S -0.26 -0.26 -0.3 -0.3 -0.4 -0.4 -0.5 a 0 30S -0.5 b -1 30E 0 Time series correlations of SOI (Jun-May) vs. Precip (Jun-Oct) -1 30E Time series correlations of SOI (Jun-May) vs. Precip (Nov-Apr) c 1 30N 0.5 1 d 0.5 30N 0.4 0.4 0.3 0.3 0.17 EQ 0.17 EQ 30S -0.17 -0.17 -0.3 -0.3 -0.4 -0.4 -0.5 c 0 30S -0.5 d -1 30E 0 -1 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 so as to sample a reasonable part of its range. We have done this at a global scale by making choices about future greenhouse gas forcings and about the climate sensitivity (see Table 1 for definition). We follow Hulme & Carter (2000) and Carter et al. (2001) in this procedure, yielding the 4 global climate scenarios shown in Table 2. We have chosen the SRES A2 emis- sions scenario combined with a high climate sensitivity (4.5°C), SRES A1 and SRES B2 combined with medium climate sensitivities (2.5°C) and SRES B1 combined with a low climate sensitivity (1.5°C). These 4 scenarios are subsequently termed A2-high, A1-mid, B2-mid and B1-low, respectively, and yield a range of global warming by the 2050s of 0.9 to 2.6°C. We chose the 2 middle cases deliberately because, even though the 154 Clim Res 17: 145–168, 2001 global warming is similar, the worlds which underlie the B2 and A1 emissions scenarios are quite different (SRES). The impacts on Africa of what may be rather similar global and regional climate changes could be quite different in these 2 cases. For example, global (and African) population is lower in the A1 world than in the B2 world, but carbon and sulphur emissions and CO2 concentrations are higher (Table 2). Having defined these 4 global climate scenarios, we next consider the range of climate changes for Africa that may result from each of these 4 possible futures. Again, we have a distribution of possible regional outcomes for a given global warming. We use results from the 7 GCM experiments (see Table 1) to define this range. (Note: for HadCM2 there are 4 simulations for the same scenario thus the total GCM sample available to us is 10; this gives more weight in our final scenarios to the HadCM2 responses than to the other 6 GCMs.) We present the scenario results for seasonal mean temperature and precipitation for the 2020s, 2050s and 2080s in 2 different ways: Africa-wide maps and national-scale summary results for 4 representative countries within Africa. 5.1. African scenario maps The construction of the scenario maps follows the approach of Hulme & Carter (2000) and Carter et al. (2001). We first standardise the 2071–2100 climate response patterns — defined relative to the 1961–90 model average — in the DDC GCMs using the global warming values in each respective GCM. These standardised climate response patterns are then scaled by the global warming values for our 4 scenarios and 3 time periods calculated by the MAGICC climate model (see Table 2). Scaling of GCM response patterns in this way assumes that local greenhouse gas-induced climate change is a linear function of global-mean temperature. (See Mitchell et al. 1999 for a discussion of this asumption). Only a selection of the full set of maps is shown here. For each scenario, season, variable and time slice we present 2 maps representing the change in mean seasonal climate for the respective 30 yr period (Figs 6 to 11). One map shows the Median change from our sample of 10 standardised and scaled GCM responses (left panels) and the other map shows the absolute Range of these 10 model responses (right panels). We also introduce the idea of signal:noise ratios by comparing the Median GCM change with an estimate of natural multi-decadal climate variability. In the maps showing the Median change we only plot these values where they exceed the 1 standard deviation estimate of natural 30 yr time-scale climate variability. These estimates were extracted from the 1400 yr unforced simulation of the HadCM2 model (Tett et al. 1997). We use a climate model simulation to quantify the range of natural climate variability rather than observations because the model gives us longer and more comprehensive estimates of natural climate variability. This has the disadvantage that the climate model may not accurately simulate natural climate variability, although at least for some regions and on some time-scales, HadCM2 yields estimates of natural variability quite similar both to observations (Tett et al. 1997) and to climatic fluctuations reconstructed from proxy records over the past millennium (Jones et al. 1998). We discuss this problem further in Section 6. The resulting African scenario maps are therefore informative at a number of levels: • Africa-wide estimates are presented of mean seasonal climate change (mean temperature and precipitation) for the 4 adopted climate change scenarios; • These estimates are derived from a sample (a pseudo-ensemble) of 10 different GCM simulations, rather than being dependent on any single GCM or GCM experiment; • Only Median changes that exceed what may reasonably be expected to occur due to natural 30 yr timescale climate variability are plotted; • The extent of inter-model agreement is depicted through the Range maps. For our scenarios, future annual warming across Africa ranges from below 0.2°C decade–1 (B1-low scenario; Fig. 6) to over 0.5°C decade–1 (A2-high; Fig. 7). This warming is greatest over the interior semi-arid tropical margins of the Sahara and central southern Africa, and least in equatorial latitudes and coastal environments. The B2-mid and A1-mid scenarios (not shown) fall roughly in between these 2 extremes. All of the estimated temperature changes exceed the 1 sigma level of natural temperature variability (as defined by unforced HadCM2 simulation), even in the B1-low scenario. The inter-model range (an indicator of the extent of agreement between different GCMs) is smallest over northern Africa and the Equator and greatest over the interior of central southern Africa. For example, the inter-model range falls to less than 25% of the model median response in the former regions, but rises to over 60% of the model median response in the latter areas. Future changes in mean seasonal rainfall in Africa are less well defined. Under the B1-low scenario, relatively few regions in Africa experience a change in either DJF or JJA rainfall that exceeds the 1 sigma level of natural rainfall variability simulated by the HadCM2 model (Figs 8 & 9). The exceptions are parts 155 Hulme et al.: African Climate Change: 1900–2100 10W 30N 0.8 20N 0.8 10N 2020s 0.8 0.8 0.8 0.8 0.9 0.9 1.0 1.0 1.0 0.9 0.9 0.8 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.8 0.7 0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.8 0.7 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.8 10E 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.8 0.8 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.8 0.8 0.7 EQ 0.8 0.8 0.9 0.9 1.0 1.0 1.0 1.0 0.9 0.8 0.8 0.8 0.7 0.8 0.7 10S 20S 20E 0.8 0.8 0.9 0.9 1.0 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0.9 0.8 0.8 0.8 30S 30N 1.2 20N 1.3 10N 2050s 1.3 1.3 1.3 1.3 1.3 1.5 1.6 1.6 1.5 1.5 1.4 1.2 1.4 1.4 1.5 1.5 1.6 1.6 1.6 1.6 1.5 1.4 1.3 1.2 1.5 1.5 1.5 1.5 1.6 1.6 1.6 1.6 1.6 1.5 1.4 1.3 1.2 1.4 1.4 1.5 1.5 1.6 1.6 1.6 1.6 1.6 1.5 1.4 1.2 1.4 1.4 1.5 1.5 1.5 1.6 1.6 1.6 1.5 1.5 1.3 1.2 1.3 1.4 1.4 1.5 1.5 1.5 1.5 1.5 1.5 1.4 1.3 1.2 1.1 EQ 1.3 1.3 1.4 1.5 1.5 1.5 1.5 1.5 1.4 1.3 1.3 1.2 1.2 1.2 1.2 10S 20S 1.2 1.3 1.4 1.5 1.5 1.5 1.5 1.4 1.3 1.3 1.3 1.2 1.2 1.3 1.3 1.3 1.3 1.4 1.4 1.4 1.3 1.3 1.2 30S 30N 1.5 20N 1.6 10N 2080s 1.6 1.6 1.6 1.6 1.7 1.8 1.9 1.9 1.9 1.8 1.7 1.5 1.8 1.8 1.8 1.8 2.0 2.0 2.0 2.0 1.9 1.8 1.6 1.4 1.9 1.9 1.9 1.9 1.9 2.0 2.0 2.0 2.0 1.9 1.8 1.6 1.4 1.8 1.8 1.9 1.9 1.9 2.0 2.0 2.0 1.9 1.8 1.7 1.5 EQ 1.7 1.8 1.8 1.9 1.9 1.9 1.9 1.9 1.9 1.8 1.7 1.5 1.6 1.7 1.8 1.9 1.9 1.9 1.9 1.9 1.9 1.8 1.6 1.5 1.4 1.6 1.6 1.7 1.8 1.9 1.9 1.9 1.8 1.8 1.6 1.6 1.5 1.4 1.5 1.4 10S 20S 30S 10W 0 1.5 1.6 1.7 1.8 1.8 1.8 1.8 1.8 1.6 1.6 1.6 1.5 1.5 1.6 1.6 1.6 1.6 1.7 1.7 1.7 1.6 1.6 1.5 30E 40E 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.9 0.9 1.0 1.0 1.0 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.7 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.9 0.9 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.0 0.9 0.8 0.8 0.8 0.7 0.7 0.8 0.9 0.8 0.8 0.9 0.9 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.8 0.8 0.7 0.6 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.6 0.9 0.8 0.7 0.6 0.9 0.8 0.8 0.7 0.9 0.9 0.8 0.7 1.0 0.9 0.8 0.7 1.0 0.9 0.9 0.7 1.0 1.0 0.9 0.7 1.0 1.0 0.9 1.0 1.0 0.8 1.0 0.9 0.8 1.0 0.8 0.9 0.8 0.8 1.2 1.3 1.3 1.4 1.5 1.5 1.5 1.4 1.3 1.3 1.3 1.2 1.2 1.3 1.4 1.5 1.5 1.6 1.6 1.6 1.6 1.5 1.4 1.3 1.2 1.1 1.1 1.2 1.3 1.4 1.4 1.4 1.5 1.4 1.3 1.3 1.3 1.2 1.2 1.3 1.4 1.4 1.6 1.6 1.7 1.7 1.7 1.7 1.6 1.6 1.4 1.3 1.1 1.2 1.3 1.4 1.4 1.4 1.4 1.5 1.4 1.3 1.2 1.2 1.2 1.2 1.3 1.4 1.5 1.5 1.6 1.6 1.6 1.6 1.6 1.5 1.4 1.2 1.2 1.3 1.3 1.4 1.4 1.4 1.5 1.5 1.4 1.3 1.3 1.2 1.1 1.2 1.2 1.3 1.4 1.4 1.5 1.5 1.5 1.5 1.4 1.3 1.3 1.3 1.4 1.4 1.4 1.4 1.5 1.4 1.4 1.3 1.2 1.1 1.1 1.0 1.0 1.1 1.2 1.3 1.3 1.3 1.4 1.4 1.3 1.2 1.5 1.6 1.6 1.8 1.8 1.8 1.8 1.8 1.6 1.6 1.6 1.5 1.5 1.6 1.7 1.8 1.9 2.0 2.0 2.0 2.0 1.9 1.8 1.6 1.5 1.3 1.4 1.5 1.6 1.7 1.8 1.8 1.8 1.8 1.7 1.6 1.6 1.5 1.5 1.6 1.7 1.8 1.9 2.0 2.0 2.1 2.1 2.1 2.0 1.9 1.7 1.6 1.4 1.5 1.6 1.7 1.7 1.7 1.8 1.8 1.7 1.6 1.5 1.5 1.5 1.5 1.7 1.8 1.8 1.9 1.9 2.0 2.0 2.0 2.0 1.9 1.7 1.5 1.4 1.6 1.7 1.7 1.7 1.7 1.8 1.8 1.7 1.6 1.6 1.5 1.4 1.4 1.5 1.6 1.7 1.7 1.8 1.9 1.9 1.9 1.8 1.6 1.5 1.6 1.7 1.7 1.7 1.7 1.8 1.8 1.7 1.7 1.5 1.4 1.3 1.3 1.3 1.4 1.5 1.6 1.6 1.7 1.7 1.7 1.6 1.5 10E 20E 30E 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.3 1.3 1.4 1.5 1.5 1.4 1.4 1.4 1.5 1.5 1.5 1.5 1.4 1.4 1.3 1.3 1.2 1.1 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.2 1.2 0.8 0.8 0.7 0.7 0.6 0.6 50E 0.9 0.9 0.9 0.9 0.9 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 1 0.9 0.9 1.0 0.9 0.8 0.5 0.4 0.3 0.3 0.4 0.7 0.7 0.4 0.5 0.5 0.4 0.4 0.5 0.6 0.5 0.3 0 0.4 0.4 0.4 0.4 0.4 0.3 0.4 0.5 0.6 0.5 0.4 0.4 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.4 0.5 0.6 0.5 0.4 0.4 0.5 0.3 0.2 0.3 0.3 0.4 0.4 0.5 0.5 0.5 0.5 0.4 10E 0.4 0.3 0.2 0.2 0.3 0.4 0.4 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.5 0.4 0.4 0.4 0.2 0.2 0.2 0.3 0.3 0.4 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.6 0.6 0.6 0.7 0.6 1.3 1.3 1.2 1.1 1.0 1.0 1.4 1.5 1.4 1.4 1.5 1.6 1.6 1.5 1.4 1.3 1.2 1.2 1.1 1.0 1.5 1.4 1.4 1.5 1.6 1.5 1.3 0.8 0.7 0.5 0.5 0.6 1.1 1.1 0.7 0.8 0.7 0.6 0.6 0.8 0.9 0.7 0.5 0.6 0.6 0.7 0.6 0.5 0.5 0.7 0.8 0.9 0.8 0.6 0.6 0.7 0.7 0.6 0.6 0.6 0.5 0.5 0.7 0.7 0.9 0.8 0.6 0.6 0.7 0.5 0.3 0.4 0.5 0.6 0.6 0.8 0.8 0.9 0.8 0.7 0.6 0.4 0.4 0.4 0.5 0.6 0.7 0.7 0.8 0.8 0.8 0.6 0.5 0.4 0.4 0.3 0.4 0.5 0.6 0.7 0.7 0.8 0.7 0.6 0.6 0.3 0.4 0.3 0.4 0.5 0.6 0.7 0.7 0.7 0.7 0.6 0.7 0.7 0.7 0.6 0.9 1.0 1.0 1.1 1.0 1.6 1.6 1.7 1.8 1.8 1.7 1.7 1.7 1.6 1.5 1.3 1.2 1.2 1.2 1.2 1.3 1.4 1.4 1.4 1.4 1.7 1.7 1.8 1.8 1.9 1.9 1.8 1.6 1.6 1.4 1.3 1.2 1.2 1.8 1.8 1.8 1.7 1.8 1.9 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.8 1.8 1.7 1.8 1.9 1.8 1.6 0.9 0.8 0.6 0.6 0.7 1.4 1.3 0.9 1.0 0.9 0.8 0.7 0.9 1.2 0.9 0.6 0.7 0.7 0.8 0.8 0.7 0.6 0.8 0.9 1.2 1.0 0.7 0.7 0.9 0.9 0.8 0.8 0.7 0.7 0.7 0.8 0.9 1.1 0.9 0.7 0.7 0.9 0.6 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.0 0.8 0.8 0.6 0.4 0.5 0.6 0.7 0.8 0.9 0.9 1.0 0.9 0.8 0.6 0.5 0.4 0.4 0.5 0.7 0.8 0.9 0.9 0.9 0.9 0.8 0.7 0.4 0.5 0.4 0.5 0.6 0.7 0.9 0.8 0.9 0.8 0.8 0.9 0.8 0.8 0.8 1.1 1.2 1.2 1.3 1.2 40E 50E o 0.5 10W 0.9 0.9 1.5 10W 0 10E 20E 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.3 0.4 0.4 0.4 0.5 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.7 0.7 0.8 0.9 0.8 0.8 0.7 0.5 0.4 0.4 0.5 0.6 0.7 0.9 0.9 0.9 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.1 1.0 1.0 0.8 0.7 30E 40E 0.2 0.2 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.5 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.5 0.6 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.7 0.7 0.7 0.6 0.5 0.5 0.5 0.5 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.6 0.5 0.6 0.6 0.5 0.5 0.4 0.4 0.4 0.4 0.5 0.4 0.4 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.4 0.5 0.5 0.4 0.4 0.5 0.5 0.2 0.4 0.6 0.4 0.2 0.5 0.5 0.5 0.3 0.5 0.6 0.5 0.4 0.6 0.6 0.6 0.5 0.7 0.7 0.7 0.5 0.7 0.7 0.7 0.5 0.7 0.7 0.6 0.6 0.6 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.3 0.4 0.3 0.4 0.4 0.5 0.5 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 1.0 1.0 0.9 0.8 0.6 0.6 0.6 0.4 0.5 0.4 0.4 0.5 0.8 0.9 0.8 0.8 0.7 0.7 0.7 0.6 0.6 0.7 0.8 0.8 0.9 1.0 1.1 1.0 1.0 0.8 0.8 0.7 0.7 0.5 0.5 0.3 0.3 0.5 0.9 0.9 0.9 0.8 0.7 0.7 0.6 0.6 0.6 0.7 0.7 0.8 1.0 1.1 1.1 1.1 1.0 0.8 0.7 0.7 0.7 0.6 0.4 0.3 0.4 0.5 0.9 0.9 0.8 0.8 0.7 0.8 0.7 0.8 0.8 0.9 0.8 0.9 1.0 1.1 1.1 1.0 0.9 0.7 0.6 0.5 0.5 0.3 0.3 0.5 0.6 0.9 1.0 0.9 0.6 0.7 0.8 0.8 0.8 0.8 0.7 0.7 0.8 1.0 1.0 1.0 0.9 0.8 0.6 0.4 0.5 0.5 0.6 0.6 0.9 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0 1.1 1.1 1.2 1.2 1.1 1.0 0.8 0.8 0.7 0.5 0.6 0.5 0.5 0.6 1.0 1.1 1.0 1.0 0.9 0.8 0.8 0.8 0.8 0.9 0.9 1.0 1.1 1.3 1.3 1.3 1.2 1.0 1.0 0.9 0.9 0.7 0.6 0.4 0.4 0.6 1.1 1.2 1.1 1.0 0.9 0.8 0.7 0.7 0.7 0.8 0.9 1.0 1.2 1.3 1.4 1.3 1.2 0.9 0.9 0.8 0.8 0.7 0.5 0.4 0.5 0.7 1.1 1.2 1.0 1.0 0.9 1.0 0.8 1.0 1.0 1.1 1.0 1.2 1.2 1.4 1.4 1.3 1.1 0.9 0.8 0.7 0.6 0.4 0.4 0.6 0.7 1.2 1.2 1.1 0.7 0.9 1.0 0.9 1.0 0.9 0.9 0.9 1.0 1.2 1.3 1.3 1.1 0.9 0.8 20E 30E 0.4 0.4 0.2 0.1 0.3 0.5 0.5 0.5 0.5 0.3 0.2 0.3 0.6 0.6 0.3 0.2 0.4 0.8 0.8 0.9 1.1 0.9 0.6 0.6 0.8 0.7 0.6 0.4 0.3 0.5 0.6 0.8 0.8 0.8 0.7 0.6 0.6 0.4 0.3 0.3 50E 0.4 0.4 0.2 0.2 0.3 0.5 0.6 0.7 0.7 0.7 0.7 0.5 0.3 0.3 0.4 0.4 0.4 0.6 0.6 0.6 0.5 0.5 0.4 30N 20N 10N EQ 10S 0.4 0.4 0.4 20S 0.4 0.4 30S 1.1 0.9 0.9 0.6 0.4 0.5 0.7 0.6 0.3 0.3 0.5 0.9 1.0 1.1 1.1 1.0 1.1 0.7 0.4 0.4 0.7 0.6 0.6 0.9 0.9 0.9 0.8 0.8 0.6 30N 20N 10N EQ 10S 0.7 0.7 0.6 20S 0.7 0.7 30S 0.7 0.6 0.4 0.3 0.3 1.2 1.4 1.1 0.8 0.7 1.0 0.9 0.7 0.4 0.4 0.6 0.7 1.0 1.0 1.0 0.7 0.7 0.4 0.3 0.5 0.9 1.0 1.3 1.2 1.1 0.7 0.5 0.6 0.8 0.7 0.4 0.4 0.6 1.1 1.2 1.3 1.3 1.3 1.3 0.9 0.6 0.5 0.8 0.7 0.7 1.1 1.1 1.1 1.0 1.0 0.7 30N 20N 10N EQ 10S 0.8 0.8 0.8 20S 0.8 0.8 30S 40E 50E C change 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 30N 1.8 20N 1.9 10N 2020s 1.9 1.9 1.8 1.9 1.9 2.1 2.2 2.3 2.2 2.1 2.0 1.7 2.1 2.1 2.1 2.1 2.3 2.3 2.4 2.3 2.2 2.1 1.9 1.7 0 2.2 2.2 2.2 2.2 2.3 2.3 2.3 2.3 2.3 2.2 2.1 1.9 1.7 2.1 2.1 2.2 2.2 2.3 2.3 2.3 2.3 2.3 2.1 2.0 1.8 10E 2.0 2.0 2.1 2.2 2.2 2.3 2.3 2.2 2.2 2.1 1.9 1.7 1.9 2.0 2.1 2.2 2.2 2.2 2.2 2.2 2.2 2.1 1.8 1.8 1.6 EQ 1.8 1.9 2.0 2.1 2.2 2.2 2.2 2.1 2.1 1.9 1.8 1.7 1.7 1.7 1.7 10S 20S 20E 1.8 1.9 2.0 2.1 2.2 2.1 2.1 2.1 1.9 1.8 1.8 1.8 1.8 1.9 1.9 1.9 1.9 2.0 2.0 2.0 1.9 1.8 1.7 30S 30N 3.3 20N 3.5 10N 2050s 3.6 3.6 3.5 3.5 3.6 4.0 4.2 4.3 4.2 4.0 3.7 3.3 3.9 3.9 4.0 4.0 4.3 4.4 4.5 4.4 4.2 3.9 3.6 3.1 4.1 4.1 4.2 4.1 4.3 4.4 4.4 4.4 4.3 4.2 3.9 3.5 3.2 3.9 3.9 4.1 4.2 4.3 4.4 4.3 4.3 4.3 4.1 3.8 3.4 3.7 3.9 4.0 4.2 4.2 4.3 4.3 4.2 4.2 4.0 3.6 3.3 3.5 3.7 3.9 4.1 4.1 4.2 4.2 4.1 4.1 3.9 3.5 3.3 3.1 EQ 3.4 3.6 3.8 4.0 4.1 4.1 4.1 4.1 3.9 3.6 3.4 3.3 3.2 3.2 3.2 10S 20S 3.4 3.6 3.7 4.0 4.1 4.0 4.0 3.9 3.6 3.5 3.5 3.3 3.4 3.6 3.6 3.5 3.6 3.7 3.7 3.7 3.6 3.4 3.3 30S 30N 5.1 20N 5.4 10N 2080s 5.5 5.5 5.3 5.4 5.6 6.1 6.5 6.6 6.4 6.1 5.7 5.0 6.0 6.0 6.1 6.2 6.6 6.8 6.9 6.7 6.4 6.0 5.5 4.8 6.3 6.3 6.4 6.3 6.5 6.7 6.8 6.8 6.7 6.5 6.0 5.4 4.9 6.0 6.0 6.3 6.4 6.6 6.7 6.6 6.6 6.6 6.2 5.8 5.2 EQ 5.7 5.9 6.2 6.4 6.5 6.6 6.6 6.5 6.4 6.1 5.6 5.0 5.4 5.7 6.0 6.3 6.3 6.4 6.4 6.4 6.2 5.9 5.3 5.1 4.7 5.3 5.5 5.9 6.1 6.3 6.3 6.3 6.2 6.0 5.5 5.2 5.0 4.8 4.9 4.9 10S 20S 30S 10W 0 5.2 5.5 5.8 6.2 6.2 6.2 6.2 6.0 5.6 5.3 5.3 5.1 5.2 5.5 5.5 5.4 5.5 5.7 5.7 5.7 5.5 5.2 5.0 30E 40E 1.7 1.8 1.9 2.1 2.1 2.1 2.1 2.1 1.9 1.9 1.8 1.8 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.4 2.3 2.2 2.1 1.9 1.7 1.6 1.6 1.8 1.9 2.0 2.1 2.1 2.1 2.1 1.9 1.8 1.8 1.8 1.8 1.8 2.0 2.1 2.2 2.3 2.4 2.4 2.4 2.4 2.4 2.2 2.0 1.8 1.9 1.9 1.6 1.7 1.8 2.0 1.8 1.8 2.0 2.1 1.9 1.9 2.0 2.1 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.1 2.0 2.1 2.1 2.1 2.0 2.1 2.1 2.0 1.9 2.0 2.0 1.9 1.9 1.9 1.9 1.8 1.7 1.8 1.8 1.6 1.5 1.8 1.7 1.6 1.4 1.8 1.6 1.5 1.4 1.8 1.7 1.5 1.4 1.9 1.8 1.6 1.4 2.1 1.9 1.7 1.5 2.1 2.0 1.8 1.6 2.2 2.0 1.9 1.7 2.3 2.1 1.9 1.7 2.3 2.2 2.0 1.7 2.4 2.2 2.0 2.4 2.2 1.9 2.3 2.1 1.8 2.2 1.9 2.0 1.8 1.8 3.2 3.5 3.6 3.9 4.0 4.0 4.0 3.9 3.6 3.6 3.5 3.4 3.4 3.6 3.8 4.0 4.1 4.3 4.5 4.5 4.4 4.2 3.9 3.5 3.3 3.0 3.1 3.4 3.6 3.8 3.9 3.9 4.0 3.9 3.7 3.4 3.4 3.4 3.4 3.4 3.7 3.9 4.2 4.4 4.5 4.6 4.6 4.6 4.5 4.2 3.8 3.4 3.1 3.4 3.6 3.7 3.8 3.8 3.9 4.0 3.7 3.5 3.4 3.3 3.3 3.4 3.6 3.9 4.0 4.1 4.3 4.4 4.5 4.4 4.3 4.2 3.8 3.3 3.2 3.5 3.7 3.7 3.8 3.8 4.0 4.0 3.8 3.6 3.4 3.3 3.1 3.1 3.4 3.6 3.7 3.8 4.0 4.1 4.2 4.1 3.9 3.6 3.4 3.5 3.8 3.8 3.8 3.8 3.9 3.9 3.8 3.6 3.4 3.1 2.9 2.8 2.8 3.0 3.2 3.4 3.6 3.6 3.7 3.8 3.6 3.3 4.9 5.3 5.5 5.9 6.1 6.1 6.1 5.9 5.6 5.5 5.3 5.2 5.2 5.5 5.8 6.1 6.3 6.6 6.9 6.9 6.8 6.5 5.9 5.4 5.0 4.5 4.7 5.1 5.5 5.8 5.9 5.9 6.1 6.0 5.6 5.2 5.3 5.2 5.2 5.3 5.7 6.0 6.5 6.7 6.9 7.0 7.0 7.0 6.9 6.5 5.8 5.3 4.7 5.2 5.5 5.7 5.8 5.8 6.0 6.1 5.8 5.4 5.2 5.1 5.1 5.2 5.6 5.9 6.2 6.3 6.5 6.7 6.8 6.8 6.7 6.4 5.9 5.1 4.9 5.3 5.6 5.7 5.8 5.9 6.1 6.1 5.8 5.5 5.3 5.0 4.7 4.8 5.2 5.5 5.7 5.9 6.1 6.3 6.4 6.3 6.0 5.5 5.2 5.3 5.8 5.9 5.8 5.8 6.1 6.1 5.9 5.6 5.2 4.8 4.5 4.3 4.4 4.6 5.0 5.2 5.4 5.6 5.7 5.8 5.5 5.1 10E 20E 30E 2.0 2.0 2.1 2.2 2.2 2.2 2.2 3.6 3.6 3.8 4.0 4.0 3.7 3.8 3.9 4.1 4.2 4.1 4.1 3.8 3.8 3.7 3.6 3.2 2.9 2.7 2.7 2.7 2.7 2.8 3.0 3.1 3.1 3.2 1.9 1.8 1.7 1.5 1.5 1.4 50E 2.1 2.1 2.1 2.0 2.1 2.2 2.3 2.1 2.0 1.8 1.7 1.7 1.5 1.4 1 2.0 2.1 2.3 2.1 1.9 1.1 0.9 0.7 0.7 0.8 1.6 1.6 1.0 1.1 1.1 0.9 0.9 1.1 1.4 1.0 0.7 0 0.8 0.8 1.0 0.9 0.8 0.7 1.0 1.1 1.4 1.2 0.8 0.9 1.1 1.0 0.9 0.9 0.9 0.8 0.8 1.0 1.1 1.3 1.1 0.9 0.9 1.0 0.8 0.5 0.6 0.7 0.8 0.9 1.1 1.1 1.2 1.2 1.0 10E 0.9 0.6 0.5 0.6 0.7 0.8 0.9 1.1 1.1 1.1 1.1 0.9 0.8 0.6 0.5 0.5 0.6 0.8 0.9 1.0 1.1 1.1 1.0 0.9 0.9 0.5 0.6 0.5 0.6 0.7 0.9 1.0 1.0 1.0 0.9 0.9 1.0 0.9 1.0 0.9 1.3 1.4 1.4 1.5 1.5 3.6 3.4 3.2 2.9 2.7 2.7 3.9 4.0 3.9 3.8 4.0 4.2 4.2 4.0 3.7 3.5 3.3 3.2 2.9 2.7 4.0 3.9 3.7 4.0 4.3 4.0 3.5 2.1 1.8 1.3 1.4 1.5 3.0 2.9 1.9 2.1 2.0 1.7 1.6 2.1 2.6 1.9 1.4 1.5 1.6 1.8 1.7 1.5 1.4 1.8 2.0 2.6 2.3 1.6 1.6 2.0 1.9 1.7 1.7 1.6 1.5 1.5 1.9 2.0 2.4 2.0 1.6 1.6 2.0 1.4 0.9 1.1 1.3 1.6 1.7 2.1 2.2 2.3 2.2 1.9 1.8 1.2 1.0 1.0 1.2 1.6 1.8 2.0 2.1 2.1 2.1 1.8 1.4 1.1 1.0 0.9 1.2 1.5 1.7 1.9 2.0 2.1 1.9 1.7 1.6 0.9 1.1 0.9 1.1 1.3 1.6 1.9 1.8 1.9 1.8 1.7 1.9 1.8 1.8 1.8 2.5 2.6 2.7 2.9 2.7 5.5 5.5 5.8 6.1 6.1 5.8 5.8 5.6 5.5 4.9 4.5 4.2 4.1 4.1 4.1 4.4 4.6 4.8 4.8 4.9 5.7 5.8 6.0 6.2 6.4 6.3 6.2 5.5 5.3 4.9 4.5 4.2 4.1 6.0 6.1 6.0 5.8 6.1 6.5 6.5 6.1 5.7 5.3 5.0 4.8 4.5 4.2 6.1 6.0 5.7 6.1 6.5 6.1 5.4 3.2 2.7 2.1 2.1 2.4 4.6 4.5 2.9 3.2 3.1 2.7 2.5 3.2 4.0 3.0 2.1 2.4 2.4 2.8 2.6 2.3 2.1 2.8 3.1 3.9 3.5 2.4 2.5 3.0 2.9 2.6 2.6 2.5 2.2 2.2 2.8 3.0 3.6 3.1 2.5 2.5 3.0 2.2 1.4 1.6 1.9 2.4 2.7 3.2 3.3 3.6 3.4 2.8 2.7 1.9 1.5 1.6 1.9 2.4 2.7 3.1 3.2 3.3 3.2 2.7 2.2 1.7 1.5 1.5 1.8 2.3 2.6 2.9 3.1 3.1 2.9 2.6 2.5 1.4 1.6 1.4 1.7 2.0 2.5 3.0 2.8 2.9 2.7 2.7 2.9 2.7 2.8 2.7 3.8 4.0 4.1 4.4 4.2 40E 50E o 0.5 10W 2.1 2.1 1.5 10W 0 10E 20E 0.5 0.5 0.6 0.7 0.8 1.1 1.1 1.0 1.0 1.0 1.1 1.0 1.0 1.1 1.1 1.0 1.0 1.2 1.2 1.2 1.1 1.0 0.8 1.0 1.0 1.2 1.4 1.6 2.0 2.0 1.9 1.8 1.9 2.0 2.0 1.9 2.0 2.1 1.9 2.0 2.2 2.3 2.3 2.1 1.8 1.5 1.5 1.5 1.8 2.1 2.4 3.1 3.1 2.9 2.8 2.9 3.1 3.0 2.9 3.1 3.2 3.0 3.0 3.4 3.6 3.5 3.3 2.8 2.2 30E 40E 0.5 0.6 0.6 0.7 0.7 1.1 1.2 1.1 1.1 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.2 1.3 1.3 1.4 1.4 1.3 1.1 0.9 0.9 0.8 0.6 0.7 0.6 0.6 0.7 1.2 1.3 1.2 1.2 1.0 1.0 0.9 0.9 0.9 1.0 1.1 1.2 1.3 1.5 1.5 1.5 1.4 1.2 1.2 1.1 1.1 0.8 0.8 0.8 0.8 0.7 0.5 0.7 0.6 0.4 0.3 0.5 0.4 0.4 0.4 0.5 0.5 0.7 0.7 0.8 0.9 1.3 1.3 1.4 1.4 1.4 1.4 1.4 1.6 1.2 1.2 1.3 1.3 1.2 1.1 0.9 0.9 1.0 1.0 1.0 0.8 1.0 1.2 1.1 1.1 0.9 1.0 1.1 1.1 0.8 1.1 1.2 0.8 0.9 1.2 1.1 0.5 1.0 1.2 1.0 0.5 1.1 1.2 1.0 0.7 1.2 1.4 1.2 0.9 1.4 1.4 1.4 1.1 1.5 1.6 1.5 1.2 1.6 1.6 1.5 1.1 1.5 1.5 1.3 1.4 1.3 1.1 1.1 1.0 0.9 1.0 0.9 1.0 0.8 1.0 0.9 1.0 1.2 1.3 1.4 2.1 2.2 2.1 2.0 2.0 1.9 1.9 1.9 1.9 2.1 2.1 2.2 2.4 2.5 2.7 2.6 2.5 2.2 1.8 1.7 1.6 1.1 1.3 1.1 1.1 1.2 2.2 2.4 2.3 2.2 2.0 1.9 1.8 1.7 1.8 1.9 2.1 2.2 2.5 2.8 2.9 2.8 2.7 2.3 2.2 2.0 2.0 1.4 1.3 0.9 0.9 1.3 2.4 2.6 2.3 2.2 1.9 1.9 1.6 1.5 1.6 1.8 2.0 2.2 2.6 2.9 3.0 2.9 2.6 2.1 1.9 1.8 1.8 1.5 1.1 0.8 1.0 1.5 2.4 2.6 2.3 2.1 1.9 2.2 1.8 2.1 2.2 2.3 2.2 2.6 2.7 3.0 3.0 2.8 2.5 1.9 1.7 1.5 1.2 0.8 0.8 1.3 1.6 2.5 2.7 2.4 1.6 1.9 2.1 2.1 2.2 2.1 1.9 2.0 2.2 2.7 2.8 2.8 2.5 2.1 1.7 1.3 1.6 1.9 2.0 2.1 3.2 3.3 3.2 3.1 3.0 3.0 2.9 2.9 3.0 3.2 3.3 3.4 3.6 3.9 4.1 4.0 3.9 3.3 2.7 2.5 2.4 1.7 1.9 1.7 1.7 1.9 3.4 3.7 3.5 3.3 3.0 2.9 2.7 2.6 2.7 3.0 3.2 3.4 3.9 4.4 4.5 4.3 4.1 3.5 3.4 3.1 3.0 2.2 2.0 1.4 1.3 2.0 3.7 3.9 3.6 3.4 2.9 2.8 2.5 2.3 2.5 2.8 3.1 3.4 4.0 4.4 4.6 4.4 4.0 3.2 2.9 2.8 2.8 2.3 1.7 1.2 1.5 2.3 3.7 3.9 3.5 3.2 2.9 3.4 2.8 3.2 3.4 3.6 3.4 3.9 4.1 4.6 4.6 4.3 3.8 2.9 2.6 2.3 1.9 1.2 1.3 2.0 2.5 3.9 4.2 3.6 2.5 2.9 3.2 3.2 3.4 3.2 2.9 3.0 3.4 4.1 4.4 4.3 3.8 3.2 2.5 20E 30E 0.9 0.8 0.4 0.3 0.6 1.1 1.2 1.5 1.4 0.9 0.7 0.8 1.6 1.5 0.8 0.6 1.1 2.1 2.3 2.6 3.0 2.5 1.7 1.6 2.1 2.0 1.5 1.0 0.9 1.3 1.6 2.1 2.3 2.2 1.5 1.4 1.2 0.8 0.6 0.7 50E 0.9 0.8 0.5 0.4 0.7 1.2 1.4 1.5 1.5 1.5 1.5 1.0 0.6 0.6 1.0 0.8 0.9 1.3 1.3 1.3 1.1 1.1 0.8 30N 20N 10N EQ 10S 0.9 1.0 0.9 20S 0.9 1.0 30S 2.9 2.5 2.4 1.6 1.1 1.2 1.8 1.6 0.9 0.8 1.4 2.3 2.6 2.9 2.9 2.8 2.9 2.0 1.2 1.2 1.8 1.6 1.6 2.5 2.4 2.5 2.2 2.1 1.6 30N 20N 10N EQ 10S 1.8 1.8 1.7 20S 1.8 1.8 30S 2.3 2.2 1.4 1.0 1.2 4.0 4.6 3.9 2.7 2.4 3.2 3.1 2.4 1.5 1.4 1.9 2.5 3.3 3.5 3.3 2.5 2.3 1.3 0.9 1.7 3.2 3.5 4.4 3.9 3.6 2.4 1.7 1.9 2.7 2.4 1.3 1.2 2.1 3.6 4.0 4.5 4.4 4.3 4.4 3.0 1.9 1.8 2.8 2.4 2.5 3.9 3.7 3.8 3.3 3.2 2.4 30N 20N 10N EQ 10S 2.7 2.8 2.5 20S 2.7 2.8 30S 40E 50E C change 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 157 Hulme et al.: African Climate Change: 1900–2100 10W 0 10E 20E 30E 40E 10W 50E 0 -6 30N 19 17 22 34 40 20N 10N 2020s EQ 2 10S 3 2 1 4 5 4 2 6 5 5 3 10 10 13 11 10 12 29 36 28 22 37 37 48 30 24 16 20 42 37 47 64 51 45 59 45 38 35 25 22 26 24 40 29 38 43 50 40 31 27 25 18 24 32 76 62 117 61 58 66 50 41 27 10E 18 33 31 47 66 62 32 56 55 48 39 25 17 40 29 43 38 48 29 44 48 103 35 25 26 13 16 43 33 36 26 37 98 82 42 31 30 21 11 17 20S 20E 13 27 52 35 23 57 77 40 31 31 30 17 7 8 16 22 28 18 10 9 10 16 18 30S -9 -8 30N 29 26 35 53 62 20N 10N 14 19 20 5 23 7 2050s 5 6 5 EQ 4 3 10S 4 5 3 2 7 6 7 6 4 2 8 9 8 8 5 14 16 17 15 11 7 3 22 24 16 19 21 21 18 18 16 14 13 6 46 56 43 35 58 57 75 48 38 34 41 38 62 45 59 68 78 63 48 43 39 -3 -3 20S -3 -3 -3 -5 30S -11 -10 30N 36 33 43 65 77 20N 30 10N 2080s 17 23 25 21 26 28 13 11 6 8 6 4 EQ 4 10S 6 4 11 7 3 4 6 4 2 8 10 11 10 10 6 9 7 9 8 5 3 3 -2 13 17 20 21 19 14 9 4 13 20 26 27 23 20 17 16 8 28 30 23 24 23 17 57 69 54 43 72 71 93 59 47 42 51 46 77 56 73 84 96 78 60 53 48 -3 -3 20S -6 30S 10W 0 10E -2 -4 -4 -4 -5 -4 -6 20E 30E 40E 50E 25 28 28 27 31 38 52 63 21 65 50 48 45 26 21 58 118 74 68 67 43 73 97 104 59 51 81 100 183 96 74 56 55 79 96 50 46 41 37 71 91 88 69 58 89 92 103 86 75 154 120 71 78 75 161 128 63 60 64 62 55 66 48 55 42 39 40 48 48 40 46 48 39 32 26 17 10 27 13 26 34 44 28 15 15 16 25 28 -30 -20 -10 0 10 20 40E 13 14 15 14 14 14 32 32 30 18 32 41 29 29 34 41 29 61 41 68 43 98 126 64 118 59 76 49 66 40 35 20 20 19 17 18 27 22 14 22 27 21 14 31 15 12 19 46 10 10 19 31 7 10 16 44 11 9 15 41 11 9 13 20 7 8 11 13 8 8 11 15 8 9 10 13 7 8 10 13 6 7 9 6 8 7 7 7 9 9 10 7 8 10 13 15 16 18 17 29 36 50E 12 14 17 18 14 18 27 40 38 82 65 82 59 68 14 21 31 32 51 54 55 61 25 30 30 14 5 4 12 13 11 9 7 8 7 6 8 13 11 60 16 57 33 36 34 92 99 63 21 30 29 14 7 6 13 13 9 9 8 7 8 6 7 10 7 9 21 33 49 50 80 85 85 96 38 47 46 22 8 6 19 21 17 14 10 12 11 10 13 20 17 93 20 20 19 22 23 22 25 23 23 22 23 26 26 89 50 50 48 28 28 28 52 50 64 46 45 26 22 56 54 64 45 45 28 53 96 64 106 56 42 144 67 154 197 101 62 155 185 93 119 76 59 98 103 63 54 31 87 128 33 31 30 27 28 60 102 47 42 34 22 34 75 128 45 42 32 21 48 134 93 22 24 19 30 72 100 107 11 15 15 29 48 68 9 10 16 25 69 20 17 13 23 64 20 17 15 21 32 14 12 13 17 20 15 12 13 17 23 12 12 14 16 20 11 11 13 16 21 13 9 11 14 9 10 12 11 20 12 12 12 15 22 16 14 15 10 11 12 14 16 55 39 48 86 64 44 10 12 30N 15 17 28 60 46 20N 10N 30 24 EQ 10S 9 9 9 20S 13 14 30S 16 19 30N 24 27 43 94 72 20N 10N 46 37 EQ 10S 13 15 14 20S 30S 25 25 24 20 31 34 35 34 27 28 28 24 39 46 64 78 26 80 61 59 56 32 26 26 31 28 28 28 28 32 33 72 146 91 84 83 53 41 110 62 62 59 35 35 34 91 121 129 74 63 100 60 65 62 79 57 56 32 27 29 56 34 33 124 226 119 92 69 68 62 70 66 80 55 69 52 53 98 119 62 57 50 45 99 66 119 79 131 77 117 88 112 109 85 72 110 105 178 83 191 243 124 73 89 114 127 107 93 190 149 106 191 228 115 147 94 88 97 93 199 159 77 119 121 127 78 67 39 107 158 74 80 76 68 82 59 47 40 38 37 33 35 75 126 57 68 52 49 49 59 60 58 59 52 42 27 42 93 158 46 49 57 59 57 55 52 40 26 59 166 115 48 40 33 27 27 29 23 37 89 123 132 20 13 10 14 19 19 36 59 85 33 16 7 11 13 20 31 85 32 24 25 22 17 29 79 42 26 25 21 18 25 40 54 21 17 14 16 22 24 34 18 18 15 16 21 29 19 13 15 15 18 19 25 16 18 15 14 13 16 19 25 18 20 14 16 11 14 17 18 31 12 11 12 15 14 24 34 16 14 14 15 18 27 25 20 17 19 21 13 13 15 116 17 20 10W 0 10E Median of % changes -50 30E 20E 30E 40E 30N 20N 10N EQ 10S 20S 30S 50E Range of % changes 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 30N 27 15 21 23 22 20N 10N -7 -5 -4 -3 3 7 2020s 10W 50E -9 -8 -8 3 -6 4 -2 -3 -5 -3 -5 -5 2 5 EQ 22 32 25 8 12 14 14 13 13 29 22 29 27 31 12 7 9 13 13 13 19 0 16 29 24 28 38 32 17 10 11 13 13 12 17 13 16 24 56 80 68 22 16 15 10 9 10 10S 20S 30S -15 -15 -12 -11 -12 -12 -11 -13 30N 43 24 33 35 34 20N 10N 2050s -11 -12 -12 -11 -7 3 -7 -8 -7 -4 -5 -4 -3 3 3 11 8 2 3 5 4 2 6 -9 -3 -5 -8 -4 -8 -8 3 EQ 34 50 40 12 19 22 22 20 21 45 35 45 43 48 18 12 14 20 20 20 30 25 46 37 45 59 50 26 15 17 21 20 19 26 -5 -6 10S 20S 30S -19 -18 -15 -14 -17 -15 -15 -14 -16 -13 30N 20N 10N 2080s -13 -14 -15 -15 -14 -14 -8 4 -9 -9 -8 -5 -6 -5 -4 4 4 13 10 53 29 41 44 42 9 3 4 6 5 2 7 -5 -11 -4 -6 -10 -11 -6 -10 -9 -10 4 1 EQ 42 62 49 15 24 27 28 24 25 56 43 56 53 60 22 14 17 25 25 25 38 -7 -7 10S 20S 30S 10W 0 10E 20E 30E 40E 50E 10W 31 56 46 55 73 61 32 19 21 26 24 24 33 10E -30 -20 -10 0 10 20 30E 40E 50E 30N 20N 10N EQ 10S 20S 30S 20 22 22 493 271 172 188 25 28 24 26 492 347 233 187 37 53 52 62 85 69 102 178 275 229 271 225 190 87 168 184 214 291 323 145 186 208 218 218 208 172 125 136 163 146 164 156 175 163 157 150 153 138 118 109 106 117 133 138 143 162 167 143 130 118 106 96 83 35 33 37 47 70 94 105 93 87 73 69 70 65 25 22 20 26 38 55 70 61 49 35 53 43 39 24 20 17 19 25 31 38 35 29 30 34 39 37 15 16 16 18 20 16 13 16 20 20 24 38 34 14 15 15 19 20 17 12 14 15 15 19 31 43 35 16 15 18 20 19 17 12 13 17 14 15 32 38 38 21 26 22 16 14 14 15 14 25 30 30 25 23 19 14 16 14 18 32 29 26 14 22 22 15 15 15 28 42 53 14 22 25 22 16 18 26 35 28 38 35 28 20 25 21 22 42 45 41 24 64 25 64 46 66 59 77 34 27 65 64 92 111 63 35 25 83 73 107 126 47 26 14 27 193 91 100 107 66 34 16 29 183 120 99 84 42 46 23 142 89 105 84 71 49 20 244 102 77 75 46 48 18 159 60 61 36 45 65 57 34 14 14 34 30N 20N 10N EQ 10S 20S 30S 25 27 28 610 336 213 233 31 35 29 33 608 429 289 231 46 66 64 77 105 85 126 220 340 283 335 279 235 108 208 228 264 360 400 180 230 258 270 269 258 213 155 169 202 181 203 193 216 202 194 186 190 171 145 135 132 145 164 170 177 200 206 177 160 146 131 119 102 43 41 46 58 87 116 129 115 107 90 86 87 81 32 27 25 33 47 68 87 76 60 43 66 53 49 30 24 22 24 30 39 47 44 36 37 42 48 45 19 20 20 23 25 20 16 20 25 24 30 47 43 17 19 19 23 25 21 15 17 19 19 23 39 53 44 20 18 22 25 23 21 15 16 21 18 18 39 47 47 26 32 27 20 17 17 18 18 31 37 37 31 29 24 18 19 18 22 39 36 32 17 27 27 19 19 19 34 52 65 17 27 31 27 20 23 32 44 35 47 43 35 24 31 26 28 51 56 51 30 79 31 79 57 81 73 96 42 33 80 79 114 137 77 44 31 103 90 133 155 58 32 18 34 239 113 124 132 82 42 20 36 226 148 123 104 52 57 29 176 110 130 104 88 61 25 302 126 95 93 57 59 22 197 74 76 44 56 81 70 42 18 17 42 0 10E Median of % changes -50 20E 14 14 315 173 110 120 18 15 17 314 222 149 119 34 33 40 54 44 65 114 176 146 173 144 121 107 118 137 186 207 93 119 133 139 139 133 110 87 104 93 105 100 112 104 100 96 98 88 75 70 75 85 88 91 104 107 91 83 75 67 61 53 21 24 30 45 60 67 59 56 46 44 45 42 14 13 17 24 35 45 39 31 22 34 28 25 13 11 12 16 20 24 23 18 19 22 25 23 10 11 12 13 10 8 10 13 12 15 24 22 10 10 12 13 11 8 9 10 10 12 20 28 23 9 11 13 12 11 8 8 11 9 9 20 24 25 14 17 14 11 9 9 9 9 16 19 19 16 15 12 9 10 9 11 20 19 17 9 14 14 10 10 10 18 27 34 9 14 16 14 10 12 16 23 18 24 22 18 13 16 14 14 27 29 26 16 41 16 41 30 42 38 49 22 17 41 41 59 71 40 23 16 17 53 47 69 80 30 17 9 18 123 58 64 68 42 22 11 15 117 77 63 53 27 29 13 91 57 67 54 45 31 12 156 65 49 48 29 31 102 38 39 23 29 42 36 21 9 9 22 20E 30E 40E 30N 20N 10N EQ 10S 20S 30S 50E Range of % changes 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 159 Hulme et al.: African Climate Change: 1900–2100 10W 0 10E 20E 30E 40E 30N 50E 42 38 50 76 90 20N 10N 4 2020s EQ 10S 20 27 29 25 30 33 21 16 13 7 6 12 11 10 7 9 8 8 11 12 7 5 4 9 13 3 5 11 11 5 4 7 9 12 4 6 7 3 3 2 3 -3 -4 -4 16 20 23 25 22 16 11 4 15 23 30 31 27 23 20 19 9 35 32 35 27 29 28 24 26 20 20S -6 -3 -3 -5 -5 -5 -6 -5 -7 -7 30S -11 -16 28 25 27 20N 48 46 42 21 15 37 52 55 47 56 62 7 14 11 10 21 29 24 13 18 4 12 9 9 5 19 10 EQ 10S 20S 30S -9 -16 -10 42 33 23 15 7 10 13 8 5 22 18 22 25 22 22 13 3 -5 -3 2080s EQ 33 19 73 70 65 23 57 79 85 72 86 94 11 21 18 15 32 45 37 -6 20 27 6 19 14 14 7 30 15 82 61 19 22 7 8 12 4 5 9 -9 -9 -12 64 51 36 24 11 15 20 12 8 65 53 32 31 25 31 26 16 9 3 12 -11 -11 -4 -4 -6 -5 -8 -19 -9 -8 -18 -15 -13 -15 -16 -17 -6 -26 -20 10S 20S 30S 10W 0 61 66 52 53 50 38 -6 48 7 18 28 44 57 58 50 43 37 36 17 -6 27 43 44 23 38 42 27 10N 29 37 43 47 41 30 20 8 44 67 57 59 54 44 27 -38 -22 -19 -34 -26 -10 -20 -24 -18 -24 -35 -29 -29 -15 30N 20N 53 40 12 14 5 5 8 2 3 6 42 34 21 20 16 20 17 10 6 2 8 -7 -7 -3 -3 -4 -3 -5 -12 -6 -5 -12 -10 -9 -11 -11 -4 -17 -13 10N 2050s -25 -14 -13 -22 -17 -23 -19 -19 -10 30N 10E 20E 34 28 34 38 33 34 20 5 -8 -4 4 3 67 55 53 38 45 57 67 72 63 46 31 12 -3 64 27 44 68 87 89 77 67 57 55 25 22 42 28 31 46 68 51 103 51 93 87 101 90 37 79 83 47 81 68 76 42 58 -9 -5 -10 -14 -14 -19 30E 10W -7 -13 -12 40E 50E 0 10E -30 -20 -10 0 10 20 30E 40E 50E 30N 20N 10N EQ 10S 20S 30S 55 56 53 44 69 76 76 74 59 62 61 53 93 85 102 141 171 57 112 177 135 131 123 70 56 58 69 62 61 61 61 70 72 126 102 159 322 201 184 182 116 91 243 136 137 129 77 77 76 152 170 200 265 283 162 139 219 132 142 137 175 125 123 71 60 64 123 75 73 80 118 122 273 497 263 203 152 150 137 153 146 175 122 152 115 118 72 94 161 216 261 136 125 110 100 218 145 262 174 288 168 257 95 159 184 193 247 240 188 158 241 230 392 183 419 535 274 160 197 143 156 212 251 280 235 204 418 327 233 421 503 253 323 208 169 205 171 193 212 204 437 349 170 262 266 279 172 148 85 236 347 130 131 162 176 168 150 180 130 104 89 84 81 72 77 164 277 126 102 116 151 114 107 108 130 131 127 129 115 93 59 93 204 348 102 109 126 130 126 122 115 87 58 131 366 252 105 105 88 72 60 60 64 51 82 197 271 291 45 28 23 30 41 42 80 131 186 72 36 15 25 28 44 67 187 70 53 54 47 36 63 175 92 56 55 46 40 56 87 119 46 38 31 36 47 53 76 39 39 33 35 46 64 41 28 34 33 39 43 55 36 40 33 30 29 34 42 56 40 44 30 35 24 30 38 39 68 27 25 27 33 30 54 76 36 32 31 32 40 59 54 44 38 42 46 29 29 34 254 37 43 30N 20N 10N EQ 10S 20S 30S 84 85 81 67 106 116 117 114 91 96 94 81 143 130 157 216 263 88 172 272 207 200 189 107 86 89 106 95 94 93 94 107 110 193 157 244 493 308 282 279 178 139 372 209 211 198 118 118 116 233 261 306 407 434 248 214 337 203 218 210 268 191 189 109 92 98 188 115 113 123 181 188 418 763 403 311 234 230 210 235 224 269 187 233 176 180 110 145 248 331 401 209 191 169 153 335 223 401 267 442 258 394 146 244 282 297 379 369 288 242 370 353 602 280 643 821 420 246 302 220 240 326 385 430 361 313 642 502 357 645 771 388 495 319 260 314 263 296 326 312 671 535 261 401 409 428 264 227 131 362 533 199 201 249 269 258 230 276 200 160 136 129 124 111 119 252 425 194 157 178 231 176 164 166 200 201 194 198 177 143 91 142 313 534 156 167 193 199 194 187 177 134 89 201 561 387 161 162 135 110 93 92 98 78 126 302 416 447 69 44 35 46 63 64 123 201 286 111 55 24 38 43 68 103 287 107 81 83 73 56 97 268 142 86 84 71 61 86 133 183 71 59 48 55 73 82 116 60 61 50 54 70 98 64 43 52 51 60 65 85 56 61 51 46 44 53 65 86 62 68 46 54 37 46 58 60 104 41 39 41 50 47 82 116 55 49 48 49 62 91 83 67 59 64 70 44 45 52 390 57 67 10W 0 10E Median of % changes -50 20E 29 30 28 23 37 40 40 39 31 33 32 28 49 45 54 75 91 30 59 94 72 69 65 37 30 31 37 33 33 32 33 37 38 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 65 40 39 63 65 145 264 139 107 81 80 73 81 77 93 65 80 61 62 50 86 114 139 72 66 58 53 116 77 139 92 153 89 136 84 98 103 131 127 100 84 128 122 208 97 222 284 145 85 104 83 113 133 149 125 108 222 173 123 223 267 134 171 110 108 91 102 113 108 232 185 90 139 141 148 91 78 45 125 184 69 69 86 93 89 80 95 69 55 47 45 43 38 41 87 147 67 54 61 80 61 57 57 69 69 67 68 61 49 31 49 108 185 54 58 67 69 67 65 61 46 31 69 194 134 56 56 47 38 32 32 34 27 44 104 144 155 24 15 12 16 22 22 42 69 99 38 19 8 13 15 24 36 99 37 28 29 25 19 34 93 49 30 29 24 21 30 46 63 24 20 17 19 25 28 40 21 21 17 19 24 34 22 15 18 18 21 23 29 19 21 18 16 15 18 23 30 21 24 16 19 13 16 20 21 36 14 13 14 17 16 28 40 19 17 17 17 21 31 29 23 20 22 24 15 16 18 135 20 23 20E 30E 40E 30N 20N 10N EQ 10S 20S 30S 50E Range of % changes 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 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 160 Clim Res 17: 145–168, 2001 10W 30N 20N 10N 2020s 0 10E 20E 30E 40E 50E 10W -22 -21 -18 -16 -20 -17 -18 -16 -14 -18 -16 -15 -16 -16 -18 -17 -17 -16 -10 5 -11 -11 -11 -9 -6 -8 -6 -5 -3 5 5 4 16 12 62 34 47 51 49 10 12 12 3 3 -4 3 8 -6 -13 3 -5 -7 -12 -12 -6 -12 -11 -12 -8 -8 3 7 6 4 2 EQ 3 49 72 57 17 28 31 32 28 30 65 50 65 61 70 26 17 20 29 29 29 44 10S 20S -11 30S 30N 20N 10N 2050s -28 -29 -32 -30 -33 -32 -31 -30 -18 9 -37 -27 -26 -26 -20 -13 -20 -20 -21 -14 8 29 EQ -41 -32 -35 -28 -26 -18 -18 -40 -33 -29 -25 -33 -31 -26 -24 -29 -20 -31 -23 -26 -25 -28 -20 -34 -26 -23 -25 -28 -25 -33 -25 -28 -26 -26 17 17 22 9 12 20 22 23 -8 8 -18 -12 -6 6 3 -11 -9 -6 5 4 9 9 5 14 11 2 8 7 5 4 4 23 4 5 7 12 -21 -37 -28 -32 -21 -22 9 -7 -8 -4 -17 -4 5 16 -11 -24 -15 5 -9 -14 -22 -23 3 -12 -22 -20 -21 -2 -23 -16 -20 5 -15 -10 -15 4 -15 7 10S 27 -23 -20 -20 20S -15 -19 30S -17 -8 30N -43 20N -25 -45 10N 2080s -48 -46 -51 -49 -48 -46 -28 14 -57 -42 -39 -40 -31 -20 -31 -31 -32 -22 12 45 -63 -49 -53 -42 -39 -28 -27 -15 -12 -27 -17 5 35 -61 -51 -45 -38 -26 -19 -14 -9 -18 -13 14 -51 -47 -40 -37 -44 -31 -47 -35 -39 -38 -43 -30 14 26 13 -10 -9 7 13 8 10 EQ -35 -38 -44 -26 -30 -39 -50 -39 -43 -40 -30 -42 -21 -40 -35 26 34 22 19 30 34 36 5 5 8 12 3 4 9 4 21 17 3 13 7 6 6 -2 5 4 -10 6 7 -21 -10 10 11 -19 18 -52 -40 -21 -33 -37 -40 -32 -59 14 5 3 8 6 10 -10 -7 8 4 -3 -22 -24 10S -13 8 -14 -19 -35 -15 -52 -37 -45 -50 -56 -44 -35 -41 -43 -24 -49 -25 -32 -34 -7 -26 24 -17 -37 -23 -21 -34 -36 -34 -31 -32 -25 -31 -23 -26 -20 -32 -39 -27 24 23 41 20S -35 -31 -30 -18 -25 -23 -29 30S -26 -12 10W 0 10E 20E 30E 40E 50E 0 36 66 53 64 85 71 37 22 24 30 29 28 38 10E -30 -20 -10 0 10 20 30E 40E 50E 30N 20N 10N EQ 10S 20S 30S 1341738 469 513 69 56 59 61 123 124 68 76 64 72 1339943 635 508 95 101 101 145 142 170 231 186 278 484 748 623 738 613 517 93 122 121 237 457 501 582 792 880 395 505 567 593 592 567 468 136 116 160 340 371 445 398 446 425 476 445 427 409 417 376 320 298 287 261 225 116 108 132 135 290 318 362 374 389 441 454 389 353 321 188 191 178 64 32 49 70 94 90 101 127 190 256 285 253 236 198 118 107 89 52 31 42 69 59 55 72 103 150 190 167 132 94 145 106 100 96 59 38 46 65 54 48 52 67 85 104 96 79 81 92 93 61 55 56 42 43 45 50 54 43 35 44 55 53 65 103 94 53 54 54 37 41 42 51 55 47 32 37 42 41 50 86 118 96 56 54 52 43 40 49 54 52 45 34 34 47 39 40 86 103 104 58 71 59 45 37 37 40 39 69 82 81 83 72 68 64 53 39 42 39 48 87 80 71 38 60 59 42 41 42 75 114 144 37 60 68 60 43 50 70 96 77 104 94 77 54 68 58 61 113 122 113 66 175 68 174 126 178 161 210 92 72 176 175 251 302 170 96 67 227 199 292 342 128 71 39 74 525 248 273 291 181 92 45 78 497 326 270 228 115 125 64 388 243 285 228 193 133 55 665 278 209 204 125 130 49 433 163 167 97 122 177 154 91 39 37 92 30N 20N 10N EQ 10S 20S 30S 20571133719 787 105 85 91 93 20531447974 779 189 190 105 117 98 111 145 154 154 222 218 261 355 286 427 74211479561132941 794 142 188 186 364 702 769 89212141349606 775 870 910 908 870 719 208 178 246 522 569 682 610 684 651 730 682 655 627 640 576 491 457 440 400 345 178 165 202 207 444 488 555 574 596 677 697 596 541 493 289 293 273 99 49 76 108 145 139 154 194 292 392 437 389 363 304 181 164 137 80 48 65 106 90 85 110 157 230 292 256 203 144 223 162 153 148 91 58 70 100 83 73 79 103 130 159 147 121 124 142 142 93 85 86 64 66 69 77 83 66 54 68 85 81 100 158 144 82 83 82 57 63 65 79 85 72 49 57 64 63 77 131 180 148 86 83 81 66 62 74 83 79 70 52 53 71 59 61 132 158 160 89 109 91 69 56 57 61 59 106 126 124 127 110 104 98 81 60 65 60 73 133 123 108 58 93 90 64 63 65 115 175 221 57 92 104 91 66 77 108 148 119 160 145 118 82 105 89 93 173 188 173 102 268 104 267 193 274 247 323 141 111 270 268 385 463 261 147 103 348 305 448 525 197 110 60 113 805 380 418 446 277 141 69 120 763 500 415 349 176 192 98 595 372 438 350 296 205 84 1020426 321 313 192 200 76 664 250 256 149 187 272 236 140 60 57 141 10W 0 10E Median of % changes -50 20E 29 31 32 711 392 249 272 36 41 34 38 710 500 337 269 53 77 75 90 123 99 148 257 397 331 391 325 274 126 243 266 308 420 466 209 268 301 315 314 301 248 181 197 236 211 237 225 252 236 227 217 221 199 170 158 154 169 192 199 206 234 241 206 187 170 152 138 119 50 48 53 67 101 136 151 134 125 105 100 101 94 37 31 29 38 54 80 101 88 70 50 77 62 57 34 29 25 27 36 45 55 51 42 43 49 56 53 22 23 24 27 29 23 19 24 29 28 34 55 50 20 22 22 27 29 25 17 20 22 22 27 45 62 51 23 21 26 29 27 24 18 18 25 21 21 46 55 55 31 38 31 24 20 20 21 21 36 43 43 36 34 28 21 22 21 25 46 42 37 20 32 31 22 22 22 40 60 76 20 32 36 32 23 27 37 51 41 55 50 41 28 36 31 32 60 65 60 35 93 36 92 67 95 85 112 49 38 93 93 133 160 90 51 36 120 105 155 181 68 38 21 39 278 131 145 154 96 49 24 42 264 173 143 121 61 66 34 206 129 151 121 102 71 29 352 147 111 108 66 69 26 229 86 88 51 65 94 82 48 21 20 49 20E 30E 40E 30N 20N 10N EQ 10S 20S 30S 50E Range of % changes 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 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 of equatorial East Africa where rainfall increases by 5 to 30% in DJF and decreases by 5 to 10% in JJA. Some areas of Sahelian West Africa and the Mahgreb also experience ‘significant’ rainfall decreases in JJA season under the B1-low scenario. The inter-model range for these rainfall changes is large and in the cases cited above always exceeds the magnitude of the Median model response. Over the seasonally arid regions of Africa, the inter-model range becomes very large (>100%) because of relatively large percent changes in modelled rainfall induced by very small baseline seasonal rainfall quantities. With more rapid global warming (e.g. the B2-mid, A1-mid and A2-high scenarios), increasing areas of Africa experience changes in DJF or JJA rainfall that do exceed the 1 sigma level of natural rainfall variability. Thus for the A2-high scenario, large areas of equatorial Africa experience ‘significant’ increases in DJF rainfall of up to 50 or 100% over parts of East Africa (Fig. 10), while rainfall decreases ‘significantly’ in JJA over parts of the Horn of Africa and northwest Africa (Fig. 11). Some ‘significant’ JJA rainfall increases occur over the central Sahel region of Niger and Chad, while ‘significant’ decreases in DJF rainfall (15 to 25%) occur over much of South Africa and Namibia and along the Mediterranean coast. The inter-model range for these rainfall changes remains large, however, and with very few exceptions exceeds the magnitude of the Median model response. Even for the seasonally wet JJA rainfall regime of the Sahel, inter-model ranges can exceed 100%, suggesting that different GCM simulations yield (sometimes) very different regional rainfall responses to a given greenhouse gas forcing. This large inter-model range in seasonal mean rainfall response is not unique to Africa and is also found over much of south and southwest Asia and parts of Central America (Carter et al. 2001). 5.2. National scenario graphs To condense this scenario information further, we also constructed ‘national’-scale summary graphs for 4 smaller regions — centred on the countries of Senegal, Tunisia, Ethiopia and Zimbabwe. These chosen domains are shown in Fig. 6 (top left panel) and reflect the diversity of existing climate regimes across the continent from north to south and from west to east. Each country graph shows, for the 2050s, the distribution of the mean annual changes in mean temperature and precipitation for each GCM simulation and for each of our 4 scenarios (Fig. 12). As with the continental maps, these changes are compared with the natural multi-decadal variability of annual-mean temperature and precipitation extracted from the HadCM2 1400 yr 161 unforced simulation. These graphs provide a quick assessment at a ‘national’ scale of the likely range and significance of future climate change and again shows the extent to which different GCMs agree in their regional response to a given magnitude of global warming. For each country there is a spread of results relating to inter-model differences in climate response. For example, in Tunisia the change in annual rainfall is predominantly towards drying (only ECHAM4 displays wetting), although the magnitude of the drying under the A2-high scenario is between 1 and 30%. Natural climate variability is estimated to lead to differences of up to ±10% between different 30 yr mean climates; therefore the more extreme of these scenario outcomes would appear to be ‘significant’ for Tunisia. The picture would appear at first sight to be less clear for Zimbabwe, where 4 of the GCMs suggest wetting and 3 — including the HadCM2 ensemble of 4 simulations — suggest drying. However, the range of natural variability in annual rainfall when averaged over 30 yr is shown to be about ± 6% and most of the wetting scenarios fall within this limit. It is the drying responses under the more extreme A2-high, B2-mid and A1-mid scenarios that would appear to yield a more ‘significant’ result. It is also important to point out that inter-ensemble differences in response at these national scales can also be large. The 4-member HadCM2 ensemble for Tunisia yields differences in rainfall change of 15% or more, while for Ethiopia inter-ensemble differences can lead to a sign change in the rainfall scenario. In this latter case, however, few of these HadCM2 rainfall changes are larger than the estimate of natural rainfall variability for Ethiopia. It is also worth noting that the relative regional response of different GCMs is not always the same. Thus, for Ethiopia, the CCSR-NIES GCM generates the most extreme wetting scenario, whereas for Tunisia the same model yields the most extreme drying scenario. We discuss the significance of some of these differences and similarities between different GCMs in our discussion of uncertainties in Section 6. 5.3. Changes in ENSO-related rainfall variability Given the important role that ENSO events exert on interannual African rainfall variability, at least in some regions, determining future changes in interannual rainfall variability in Africa can only be properly considered in the context of changes in ENSO behaviour. There is still ambiguity, however, about how ENSO events may respond to global warming. This is partly because GCMs only imperfectly simulate present 162 Clim Res 17: 145–168, 2001 Senegal Tunisia 20 Precipitation change (%) Precipitation change (%) 20 0 -20 -40 -1 0 -20 -40 0 1 2 3 4 Temperature change ( oC) 5 -1 0 1 2 3 4 Temperature change ( oC) Ethiopia Zimbabwe 20 Precipitation change (%) Precipitation change (%) 20 0 -20 -40 -1 CCSR 5 0 -20 -40 0 1 2 3 4 Temperature change ( oC) CGCMI 5 CSIRO -1 ECHAM 0 1 2 3 4 Temperature change ( oC) GFDL HadCM2 5 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 HadCM2 simulates ENSO-type features in the Pacific Ocean, but the model generates too large a warming across the Tropics in response to El Niño events. Timmermann et al. (1999), however, have recently argued that their ECHAM4 model (see Table 1) has sufficient resolution to simulate ‘realistic’ ENSO behaviour. They analysed their greenhouse gas-forced simulations and suggested that in the future there will be more frequent and more intense ‘warm’ and ‘cold’ ENSO events, a result also found in the HadCM2 model (Collins 2000). What effects would such changes have on interannual African rainfall variability? This not only depends on how ENSO behaviour changes in the future, but also upon how realistically the models simulate the observed ENSO-rainfall relationships in Africa. Smith & Ropelewski (1997) looked at Southern Oscillationrainfall relationships in the NCEP atmospheric GCM, where the model is used to re-create observed climate variability after being forced with observed sea surface temperatures (SSTs). Even in this most favourable of model experiments, the model relationships do not Hulme et al.: African Climate Change: 1900–2100 always reproduce those observed. Over southeastern Africa, the simulated rainfall percentiles are consistent with the observations reported by Ropelewski & Halpert (1996), but over eastern equatorial Africa the model simulates an relationship opposite to that observed. The recently elucidated role of the Indian Ocean dipole (Saji et al. 1999, Webster et al. 1999) in modulating eastern African rainfall variability may be one reason simple ENSO-precipitation relationships are not well replicated by the GCMs in this region. We analysed 240 yr of unforced simulated climate made using the HadCM2 GCM (see Table 1) to see to what extent this model can reproduce observed relationships. We performed the identical analysis to that performed on the observed data in Section 4 and the results are plotted in Fig. 5c,d. The 2 strongest ENSO signals in African rainfall variability are only imperfectly reproduced by the model. The East African negative correlation in November–April is rather too weak in the model and also too extensive, extending westwards across the whole African equatorial domain. The positive correlation over southern Africa is too weak in HadCM2 and displaced northwards by some 10° latitude. The absence of any strong and coherent relationship during the June–October season is reproduced by the model (Fig. 5b). On the basis of this analysis, and our assessment of the literature, we are not convinced that quantifying future changes to interannual rainfall variability in Africa due to greenhouse gas forcing are warranted. At the very least, this issue deserves a more thorough investigation of ENSO-rainfall relationships in the GCMs used here and how these relationships change in the future. Such an analysis might also be useful in determining the extent to which seasonal rainfall forecasts in Africa that rely upon ENSO signatures may remain valid under scenarios of future greenhouse gas forcing. 6. UNCERTAINTIES AND LIMITATIONS TO KNOWLEDGE In the introduction to this paper we alluded to some of the limitations of climate change scenarios for Africa and those shown in this paper are no exception. These limitations arise because of, inter alia, (1) the problem of small signal:noise ratios in some scenarios for precipitation and other variables, (2) the inability of climate model predictions to account for the influence of land cover changes on future climate, and (3) the relatively poor representation in many models of some aspects of climate variability that are important for Africa (e.g. ENSO). Some of these limitations have been revealed by analyses presented earlier. 163 Even though we have presented a set of 4 climate futures for Africa, where the range reflects unknown future global greenhouse gas emissions and 3 different values for the global climate sensitivity, we cannot place probability estimates on these 4 outcomes with much confidence. While this conclusion may well apply for most, or all, world regions, it is particularly true for Africa, where the roles of land cover change and dust and biomass aerosols in inducing regional climate change are excluded from the climate change model experiments reported here. This concern is most evident in the Sahel region of Africa. None of the model-simulated present or future climates for this region displays behaviour in rainfall regimes that is similar to that observed over recent decades. This is shown in Fig. 13 where we plot the observed regional rainfall series for 1900–98, as used in Fig. 3, and then append the 10 model-simulated evolutions of future rainfall for the period 2000–2100. These future curves are extracted directly from the 10 GCM experiments reported in Table 1 and have not been scaled to our 4 scenario values (this scaling was performed in the construction of Figs 8 to 11 as discussed in Section 5). One can see that none of the model rainfall curves for the Sahel displays multidecadal desiccation similar to what has been observed in recent decades. This conclusion also applies to the multi-century unforced integrations performed with the same GCMs (Brooks 1999). There are a number of possible reasons for this. It could be that the climate models are poorly replicating ‘natural’ rainfall variability for this region. In particular the possible role of ocean circulation changes in causing this desiccation (Street-Perrott & Perrott 1990) may not be well simulated in the models. It could also be that the cause of the observed desiccation is some process that the models are not including. Two candidates for such processes would be the absence of a dynamic land cover/atmosphere feedback process and the absence of any representation of changing atmospheric dust aerosol concentration. The former of these feedback processes has been suggested as being very important in determining African climate change during the Holocene by amplifying orbitally induced African monsoon enhancement (Kutzbach et al. 1996, Claussen et al. 1999, Doherty et al. 2000). This feedback may also have contributed to the more recently observed desiccation of the Sahel (Xue 1997). The latter process of elevated Saharan dust concentrations may also be implicated in the recent Sahelian desiccation (Brooks 1999). Without such a realistic simulation of observed rainfall variability, it is difficult to define with confidence the true magnitude of natural rainfall variability in these model simulations and also difficult to argue 164 Clim Res 17: 145–168, 2001 natural variability is difficult to say. In our scenario maps (Figs 8 to 11) we pre60 sented the median precipitation change from these 10 (scaled) model simula40 tions, implying that we can treat these 20 climate change simulations as individual members of an ensemble. The ensem0 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 -80 80 the ‘best’ short-range weather forecast. In our example, for the Sahel and south60 ern African the median response was 40 annual drying, whereas for East Africa 20 the median response was wetting (Fig. 13). 0 One other concern about the applica-20 bility in Africa of climate change scenarios such as those presented here is -40 the relationship between future climate -60 E ast Africa change predictions and seasonal rainfall forecasts. There is increasing recogni80 -80 tion (e.g. Downing et al. 1997, Ringius 60 1999, Washington & Downing 1999) that 40 for many areas in the tropics one of the most pragmatic responses to the 20 prospect of long-term climate change is 0 a wish to strengthen the scientific basis of seasonal rainfall forecasts. Where -20 forecasts are feasible, this should be -40 accompanied by improvements in the management infrastructure to facilitate -60 Southeast Africa timely responses. Such a research and -80 1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 adaptation strategy focuses on the shortterm 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 tifiable benefits that improved forecast are for the 10 model simulations derived from the 7 DDC GCM experiments — applications will yield. At the same time, the 4 HadCM2 simulations are the dashed curves (see Table 1). All anomalies 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 exstructures offers the possibility that the periments and the results are not scaled to the 4 scenarios used in this paper. more slowly emerging signal of climate The smooth curves result from applying a 20 yr Gaussian filter 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 adapchanges 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 omiteffect global warming may have on seasonal predictted 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 deterprecipitation scenarios. The 10 individual model simumine 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 Annual rainall anomaly ( per cent) 1880 80 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 Hulme et al.: African Climate Change: 1900–2100 both of these reasons we need to improve our predictions of future climate change and in particular to improve our quantification of the uncertainties. 7. CONCLUSIONS The climate of Africa is warmer than it was 100 yr ago. Although there is no evidence for widespread desiccation of the continent during this century, in some regions substantial interannual and multi-decadal rainfall variations have been observed and near continent-wide droughts in 1983 and 1984 had some dramatic impacts on both the environment and some economies (Benson & Clay 1998). The extent to which these rainfall variations are related to greenhouse gasinduced global warming, however, remains undetermined. A warming climate will nevertheless place additional stresses on water resources, whether or not future rainfall is significantly altered. Model-based predictions of future greenhouse gasinduced climate change for the continent clearly suggest that this warming will continue and, in most scenarios, accelerate so that the continent on average could be between 2 and 6°C warmer in 100 yr time. While these predictions of future warming may be relatively robust, there remain fundamental reasons why we are much less confident about the magnitude, and even direction, of regional rainfall changes in Africa. Two of these reasons relate to the rather ambiguous representation in most GCMs of ENSO-type climate variability in the tropics (a key determinant of African rainfall variability) and the omission in all current GCMs of any representation of dynamic land coveratmosphere interactions and dust and biomass aerosols. Such interactions have been suggested to be important in determining African climate variability during the Holocene and may well have contributed to the more recently observed desiccation of the Sahel. We suggest that climate change scenarios, such as those presented here, should nevertheless be used to explore the sensitivity of a range of African environmental and social systems, and economically valuable assets, to a range of future climate changes. Some examples of such exploration were presented in Dixon et al. (1996), although in these studies there was little co-ordinated and quantified use of a coherent set of climate futures. Further work can be done to elaborate on some of the higher order climate statistics associated with the changes in mean seasonal climate shown here — particularly daily temperature and precipitation extremes. It may also be worthwhile to explore the sensitivity of these model predictions to the spatial resolution of the models — i.e. explore the extent to which downscaled scenarios differ from GCM-scale 165 scenarios — although such downscaling techniques do not remove the fundamental reasons why we are uncertain about future African rainfall changes. The exploration of African sensitivity to climate change must also be undertaken in conjunction with the more concrete examples we have of sensitivity to short-term (seasonal time scale) climate variability. These estimates may be based on observed reconstruction of climate variability over the last century or on the newly emerging regional seasonal rainfall forecasts now routinely being generated for southern, eastern and western Africa (e.g. NOAA 1999, SARCOF, see also http://iri.ldeo.columbia.edu [International Research Institute for Climate Prediction]). Because of the uncertainties mentioned above about future regional climate predictions for Africa, initial steps to reduce vulnerability should focus on improved adaptation to existing climate variability (Downing et al. 1997, Adger & Kelly 1999, Ringius 1999). 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