INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 12, 685-699 (1992) 551.577.34:551.577.38(6)

RAINFALL CHANGES IN AFRICA: 1931-1960 to 1961-1990

MIKE HULME

Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, U K

Received 28 November 1991 Accepted 7 April 1992

ABSTRACT

It remains common for 1941-1970, or even 1931-1960, climatological rainfall normals to be used in applied climate studies in Africa. Often this is due simply to the easier availability of station means or rainfall maps for these periods. Such rainfall statistics, however, are unrepresentative of recent decades, especially the new World Meteorological Organization standard period, 1961-1990. In this paper two independent 30-year rainfall climatologies for Africa are constructed from 572 quality controlled station time series of monthly rainfall. These climatologies are for the periods 1931-1960 and 1961-1990 and are constructed on a 5" grid. Differences between these two 30-year periods are determined with respect to mean seasonal rainfall, interannual variability and rainfall seasonality. Latitudinal profiles of mean seasonal rainfall emphasize the dominance of reduced Sahelian rainfall in the rainfall changes occurring over this period. This decline in boreal summer rainfall is shown to be statistically significant using field comparison statistics. Annual time series of regional rainfall anomalies for the Sahel, East Africa and south-western Africa are constructed; these three time series possess quite different interannual rainfall characteristics. Possible explanations for these observed rainfall changes are discussed with respect to land cover changes, global sea-surface temperature patterns, and greenhouse gas forcing.

KEY WORDS Climatic change African rainfall Rainfall seasonality Rainfall variability The Sahel drought

INTRODUCTION

Rainfall is one of the most important natural resources for many of mainland Africa's 48 nations. Achieving food security in these nations has been a continual struggle in recent years, hampered by civil war, political volatility, worsening terms of international trade, rapid population growth, and drought (de Waal, 1988). Of these hindrances drought is frequently given a pre-eminence that is not always deserved. Careful assessment of the magnitude and extent of African rainfall changes over recent decades is needed (Farmer and Wigley, 1985), together with an appreciation of those characteristics of the rainfall supply that are most essential for its effective utilization as a resource and those which are most sensitive to change (Hulme, 1990).

Several rainfall climatologies for Africa have been constructed in recent years (e.g. Griffiths, 1972; Leroux, 1983; Legates and Willmott, 1990; Leemans and Cramer, 1990). These have generally relied on climatological normals representing a variety of time periods. These climatologies are not therefore homogeneous and may be termed 'timeless'. Furthermore, the above climatologies do not incorporate temporal variability since they have been constructed from mean monthly rainfall totals rather than from station time series of monthly rainfall. Yet it is rainfall variability that is the most critical characteristic of African rainfall for resource applications. Failure to appreciate fully the greater and more complex temporal variability of African rainfall compared with rainfall in temperate latitudes has in the past led to misjudgements being made about the viability of hydrological and agricultural schemes (e.g. the South Chad Irrigation Project; Mott McDonald, 1991). The data set compiled and analysed by Nicholson et al. (1988), and analysed by Nicholson (1985,1986, 1989) and Janowiak (1988) among others, represents the most thorough treatment to date of African rainfall variability.

0899-841 8/92/070685- 15$12.50 0 1992 by the Royal Meteorological Society

686 M. HULME

With the passing of 1990, a new 30-year World Meteorological Organization (WMO) normal period is complete enabling two successive 30-year climatologies for Africa to be compared for the first time: 1931-1960 and 1961-1990. This comparison is important for two reasons. First, it provides a continent-wide illustration of the dependence of so-called climatological ‘normals’ upon the 30-year period that is chosen. This is a point that has been made frequently in recent years with respect to Africa (e.g. Todorov, 1985; Quinlan, 1986; Farmer, 1989), yet one which requires a more comprehensive spatial illustration. Second, such a comparison reveals the extent and pattern of rainfall change that has occurred over this 60-year period thereby quantifying the variability of African rainfall on multi-decadal time-scales. The extent to which this variability can be termed ‘natural’ as opposed to ‘anthropogenic’ remains uncertain. The last 60 years have seen about a 20 per cent increase in the concentration of greenhouse gases in the global atmosphere and also substantial changes in African land cover (Myers, 1991). These two aspects of recent environmental change are suspected of forcing change in global (e.g. Houghton et al., 1990) and regional (e.g. Nicholson, 1988) climates respectively. It is also noteworthy that the 1931-1960 climatology is broadly representative of the later era of colonialism in Africa, while 1961-1990 has coincided with the emergence of independent African states (Griffiths, 1984). Contrasting these climatologies will reveal the extent to which independent African governments have had to manage their economies with a rainfall resource quite different from that prevailing during the latter decades of colonialism.

This paper provides a comparison of these two time-dependent rainfall climatologies for Africa in terms of mean rainfall, temporal variability, and seasonality. This is accomplished through the use of maps, latitudinal rainfall gradients, and historical time series for selected regions. Differences between the climatologies are tested for statistical significance. The analysis makes use of the monthly precipitation global data set held in the Climatic Research Unit, which is an extended and updated version of the daLa set used by, among others, Bradley et al. (1987) and documented by Eischeid et al. (1991). The paper concludes by discussing some of the mechanisms that may have caused African rainfall variability on these decadal time-scales.

DATA

Data were extracted from the global monthly precipitation data set held by the Climatic Research Unit (CRU). This data set consists of historical time series of monthly precipitation for over 7500 stations world-wide of which 2099 lie within Africa. These data have been compiled from a variety of sources including World Weather Records, the National Centre for Atmospheric Research (this included S. E. Nicholson’s African data .set), Monthly Climatic Data for the World, and through bilateral contacts between CRU and a number of African scientists and National Meteorological Agencies. All data entering the data set are screened for outliers and the resulting time series are tested for homogeneity using Pettit’s change-point test (Pettit, 1979). Although this test cannot distinguish between abrupt changes due to climatic and non-climatic causes, comparison between stations that failed the test and others nearby which passed the test provided an indication of the likely cause of the discontinuity. This was sometimes due to inconsistencies in the conversion from imperial to metric units, sometimes to the conventional rainfall year commencing in different months in different countries, and occasionally to quite different station records having been patched together. Quite often discontinuities were deemed to reflect genuine climatic changes. More complete details on the data set and its quality control are available in Eischeid et al. (1991) and Hulme (1992).

For a station time series to contribute to the analysis the record had to possess a minimum number of monthly rainfall totals between 1931 and 1990. The selection of this minimum number required a trade-off between station coverage and the robustness of the resulting climatologies. Only 30 stations possessed all 720 monthly totals between 1931 and 1990. The relationship between station and area coverage and the missing data criterion is shown in Figure 1. A selection criterion of 83 per cent for both 30-year periods was adopted, i.e. a station required at least 300 monthly totals in each period to be included in the analysis. This threshold number is in keeping with WMO guidelines for calculating 30-year normals (World Meteorological Organization, 1989).

AFRICAN RAINFALL CHANGES

2200

2000

1800

1600

1400

1200

1000

800

600

400

200

0

Per cent of months between 1931 ond 1990 with data

687

Figure 1. Number of stations and the area of Africa covered by 5" grid boxes under different missing monthly data criteria for the period 1931-1990. Nearly complete coverage of the continent could be obtained using a 50 per cent missing data threshold for station inclusion.

An 83 per cent criterion was selected, however, in keeping with WMO guidelines; this is shown by the vertical dotted line

Figure 2. (left) Distribution of stations that were included in the gridding analysis. A station time series required at least 300 out of 360 monthly rainfall totals in both 1931-1960 and 1961-1990 to be included. The three regions are those used in the construction of the rainfall anomaly indices displayed in Figure 7. (right) Distribution of 5" grid boxes that possessed monthly time series for 1931-1990.

These boxes represent about 70 per cent of the land area of Africa

A total of 572 stations were thus selected and their distribution is shown in Figure 2. The absence of stations from Zaire and Angola is due to very little data being reported after the mid-1970s. Elsewhere station coverage is reasonable, with the exception of parts of the central Sahara, although somewhat uneven. To ensure robust time series covering the period 1931-1990 these station data were converted into a gridded time series on a 5" latitude/longitude resolution. The rationale and methodology for this gridding procedure

688 M. HULME

are fully outlined in Hulme (1992). For the present analysis, the procedure generates 5" grid-box estimates of monthly rainfall in millimetres covering about 70 per cent of Africa for the period 1931-1990 (Figure 1). The distribution of the 101 five degree grid boxes is shown in Figure 2 with missing grid boxes occurring in parts of west equatorial Africa and the central Sahara. Subsequent analysis uses both the gridded and individual station time series.

SPATIAL PATTERNS OF RAINFALL CHANGE, 1931-1960 TO 1961-1990

Changes in mean rainfall

The change between 1931-1960 and 1961-1990 in mean seasonal rainfall rates over Africa is shown in Figure 3 for boreal summer (June-July-August (JJA) and austral summer (December-January-February (DJF). Rainfall change has been dominated by the reduction in JJA rainfall in the Sahel with widespread decreases of well over 0-4 mm day-' (locally over 30 per cent) between these two 30-year climatologies. Throughout the Sahel this magnitude of change represents more than 0.5 of the 1931-1960 standard deviation and in parts of Mali a decrease of over 1.0 standard deviations. Such a large relative rainfall change between these two 30-year climatologies is unparalleled elsewhere in the world (Hulme et al., 1992). In contrast, JJA rainfall rates have increased by over 0 4 mm day-' in the southern coastal regions of West Africa and parts of west equatorial Africa. However, this represents an increase of only ca. 10 per cent in rainfall and generally is less than 0.5 of the 1931-1960 standard deviation. Partly compensating the summer increase in these regions has been a decline in winter (DJF) rains of about 0.2mm day-' (Figure 3), representing a decrease of cu. 15 per cent which again is more than 0.5 standard deviations. Autumn (September-October-November (SON)) rainfall rates also have declined around the Gulf of Guinea coast by more than 0.4 mm day-' (not shown).

30 f A

20- 20

10- 10

m - u . r =I D) = 0- 0 2 d - A -

c a m

-10- -10

-20

-30

.

. -20-

p d p change Imnldayl

m Above 0 4

0.2 - 0 4

-0.2 - 0 2

-0 4 --0 2

Below -0 4

-30-

-20 yL_L

Longitude 10 0 10 20 30 40 50 60

Figure 3. Change in mean seasonal rainfall rates between 1931-1960 and 1961-1990 for JJA (left) and DJF (right). The fields are generated from the gridded dataset. Only changes greater than k0.2 mm day-' are shown (n.d.=no data)

AFRICAN RAINFALL CHANGES 689

Austral summer (DJF) rainfall rates have declined in the Southern Hemisphere tropical margins with decreases of more than 0-4mm day-' (ca. 10 per cent) over parts of Botswana and Zimbabwe. Similar magnitude increases in DJF rainfall rates have been restricted to the interior of Tanzania and northern Madagascar. None of these changes in DJF rainfall are greater than 0 5 of the 1931-1960 standard deviation and are therefore less important than those changes affecting tropical north Africa. The greatest percentage increases in seasonal rainfall rates have occurred over equatorial East Africa with SON rainfall increasing by ca. 25 per cent (not shown), about 0.5 standard deviations. This latter change largely reflects the series of wet years that occurred in the early 1960s (Flohn, 1987). These maps of rainfall change should be further interpreted with respect to the regional time series of rainfall anomalies shown later.

Changes in seasonality

Rainfall over most of Africa is strongly seasonal, reflecting the dominant role of the migrating Inter Tropical Convergence Zone (ITCZ) in determining the rainfall seasons. Seasonality is predominantly unimodal (i.e. a single wet season), with a dual wet season restricted to parts of the immediate equatorial zone especially in East Africa. One measure of seasonality is the amplitude of the first harmonic describing the annual cycle of rainfall (the zeroth harmonic being the mean annual rainfall, Lau and Sheu (1988)). This amplitude can be standardized by expressing it as a percentage of the mean monthly rainfall for the year. Seasonality thus defined varies in Africa from close to zero (central Sahara), to about 50 per cent (equatorial Africa), to well over 100 per cent (the Sahel).

Figure 4 presents the change in this index of seasonality between 1931-1960 and 1961-1990 expressed as the change in the percentage of the mean monthly rainfall for the respective period. Seasonality has increased

-30 40--1

30-

20-

10 -

m - u - 3 .= 0- m - 1 -

-10-

-20-

-30-

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

gYJBelow - 8 0

-40 I I , , , , , , . , I , . I , , I , 1 1 1 , , , , . , / -40 -20 -20 -10 0 10 20 30 40 50 60

Longitude

Figure 4. Change in rainfall seasonality between 1931-1960 and 1961-1990. Seasonality is measured as the amplitude of the first harmonic expressed as a percentage of the mean monthly rainfall for the respective time period. The field is generated from the gridded

data set. Only changes greater than +4 per cent are shown (n.d.=no data)

690 M. HULME

most substantially ( > 8 per cent) in the southern coastal region of West Africa reflecting the increased JJA and decreased DJF rainfall noted above. Elsewhere in Africa, seasonality has generally decreased, most notably in the coastal regions of East Africa and parts of the North African littoral where decreases have been greater than 8 per cent. In the Sahel, rainfall seasonality has remained constant despite the decrease in the JJA rainfall rate. This reflects the continuing dominant role even during periods of lower rainfall of the northerly summer migration of the ITCZ in generating rainfall in this region.

Changes in variability

The interannual variability of rainfall is an important indicator of the reliability of the rainfall resource in Africa. There are difficulties in defining an adequate measure which reflects changes in variability rather than changes in the mean rainfall. For example, since the standard deviation closely covaries with mean rainfall, the difference between, or ratio of, two standard deviations is not necessarily the best measure to use when mean rainfall conditions are themselves changing. Instead, a measure of relative variability (the coefficient of variation-which is the standard deviation standardized by the mean) is used to represent changes in reliability. Figure 5 presents this measure of annual rainfall variability change over the continent and shows that areas of increased variability outweigh areas of reduced variability. The latter are restricted to Egypt and eastern Libya and a small area of northern Somalia. The Sahel shows an increased rainfall variability, although with a contrast between the western (increases generally of less than 5 per cent) and eastern Sahel (increases over 5 per cent and locally over 15 per cent). Tunisia and western Libya and the extreme south of Africa also have seen increases in relative variability of over 5 per cent. Most of equatorial Africa has seen little change in relative annual variability.

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

40

30

20

ia

r

0: c Q

-la

-30

-2\ -40 -30

Coefficient of variation (%I

Above 15.0

5.0 - 15.0

0 -5.0 - 5.0

n-15.0 - -5.0

Below -15 0

I ' ' - ' I ' * ' ' I - ~ ' ~ -20 -10 0

0 ;-20

- -30

Longitude

Figure 5. Change in relative variability of annual rainfall between 1931-1960 and 1961-1990. Variability is expressed as the coefficient of variation. The field is generated from the gridded data set. Only changes greater than f 5 per cent are shown (n.d. =no data)

AFRICAN RAINFALL CHANGES 69 1

Significance of changes

The above analysis has identified the character and magnitude of rainfall change over Africa over the last 60 years, but has not attributed statistical significance to these changes. Are the differences in mean rainfall and rainfall variability substantial enough to suggest that the 1931-1960 and 1961-1990 rainfall climatolo- gies represent two different statistical populations? Wigley and Santer (1990) recently have suggested a suite of test statistics to determine differences between climatological fields using the pool permutation procedure (PPP) first introduced by Preisendorfer and Barnett (1983). The PPP methods provide a means of circumventing problems commonly encountered in field significance testing, namely spatial autocorrelation and unknown sampling distributions. These methods were therefore used to perform an analysis of the statistical significance of the differences between the 1931-1960 and 1961-1990 climatologies and a subset of the result is shown in Table 1. The comparisons were performed month by month and only those grid boxes with complete time series for the two periods were used in the PPP analysis. The number of grid boxes used therefore varied from 45 to 55.

The difference in Africa-mean rainfall is tested using T1, a f-test of the field-mean monthly rainfalls. Very significant declines are revealed for May, August, and September (June and July are only significant at p < 0.1). NT1 is a test of the significance of the total number of locally significant t-tests (i.e. where t-tests are performed on n individual grid boxes and local significance is defined as p < 0.01). Again, the boreal summer months are clearly shown to contain significantly large numbers of grid boxes where the rainfall decline has been significant at p<O.OI. In the case of August more than 22 per cent of grid boxes showed a locally significant decline in rainfall at this level of significance, a result that is highly significant in terms of field differences. The result for November was interesting since there were significant numbers of grid boxes that had both significant increases and decreases in rainfall between the two time periods. This largely reflects the contrast between equatorial western (drying) and eastern (wetting) Africa noted above.

SPRETl and NFl are the F-test equivalents to TI and NTI and test for differences in the temporal variability of the Africa-mean rainfall (SPRETl) and in the number of locally significant F-test results (NF1). The changes in variability shown in Figure 5 are generally not statistically significant (Table I). April and

Table I . Results from the application of a suite of test statistics that quantify differences in means and temporal and spatial variance between the 1931-1960 and 1961-1990 monthly rainfall fields over Africa. T1, overall field means; NT1, number of local t-tests significant at p i 0.01; SPRETl, overall field temporal variance; NF1, number of local F-tests significant at p < 0.01; SPREXl, overall field spatial variance (tests are defined in

Wigley and Santer, 1990)

n T1 NTl SPRETl NF1 SPREXl

January February March April

June July August September October November December

May

51 52 55 51 54 45 46 49 45 48 46 51

_ _

_ _ _ _

- + + + + + - =decrease significant at p i 005. - - =decrease significant at p < 0.01. + =increase significant at p i 0.05. + + =increase significant at p i 0.01. - + =the number of locally significant t-tests displaying both increases and decreases are significant

at p < 0.05.

692 M. HULME

L

a -1.5

December display a significant increases in Africa-mean temporal variability, while only November reveals a significant number of grid boxes with locally significant differences in variability. In this case, these local differences reflect increased variability of the monthly rainfall fields. The decline in boreal summer rainfall over tropical north Africa has resulted in a significant decrease in the spatial variability of the rainfall field (SPREX1) over Africa during the months May, August, and September (June and July are only significant at p < 0.1 5). In contrast, November and December have seen an increase in the spatial variability of rainfall over the continent, probably reflecting the increased November rainfall over eastern Africa (not shown) and the more variable pattern of rainfall change over southern Africa during DJF (Figure 3, right).

- 1950-54 0 1903-87

-

I I I I I I I

LATITUDINAL CHANGES IN RAINFALL, 1931-1960 to 1961-1990

Latitudinal profiles of mean rainfall rates over Africa show the distinctive characteristic of a tropical rainfall regime-alternate hemispheres yielding rainfall maxima in boreal summer and winter. Examination of the mean latitudinal rainfall profiles for 1931-1960 and 1961-1990 provides a further assessment of the changes that have occurred in African rainfall. Latitude-means were constructed from the gridded data set in 5" bands, with only those grid boxes with valid data contributing to the latitude average. The equatorial latitude-means are therefore somewhat lower than their true value because of the absence of several high-rainfall grid boxes over equatorial West Africa (Figure 2). The change in latitudinal profiles between 1931-1960 and 1961-1990 is shown in Figure 6 for JJA and DJF. Two 5-year periods were also identified to

1.0

JJA season

- 0 5

00 E E 5 -0.5

a -1.0 a, - 1961-90 a

0 x U

c 0 + .- L

-1 5

- 2%0 -30 -20 -10 0 10 20 30 40

South Latitude Nor th

1.0 , 1 I

0.5 I 1 DJF season

x 0 U

0.0 E E 6 -0.5 n t 0 ._ c

1961-90 j

Figure 6. Changes in latitude-mean rainfall rates for the periods 1961-1990,195&1954, and 1983-1987 expressed relative to 1931-1960 latitude means, for JJA (top) and DJF (bottom)

AFRICAN RAINFALL CHANGES 693

highlight shorter term rainfall variations: 1950-1954 (a wet period within the 1931-1960 climatology) and 1983-1987 (a dry period within the 1961-1990 climatology). The changes for all three periods are expressed relative to the 1931-1960 mean.

These latitudinal profiles are again dominated by the decline in Sahel rainfall during JJA. At 12.5"N, 1961-1990 rainfall rates were 0.6 mm day-' lower than between 1931-1960, this decline increasing to 1.5 mm day-' for the period 1983-1987. This latter change represents about a 50 per cent decrease from 1931-1960 rainfall. At the same latitude the period 1950-1954 received ca. 0 3 m m day-' more rainfall than the 1931-1960 mean. Because of the strong latitudinal alignment of rainfall gradients over much of Africa, these changes in latitude-means can be converted into equivalent latitudinal shifts in isohyets. The maximum migration has occurred at about 123"N with southward shifts of JJA rainfall zones of just over 1" latitude (ca. 120 km) between 1931-1960 and 1961-1990, and just under 3" latitude (ca. 330 km) between 1950-1954 and 1983-1987. This compares with shifts of up to 2.2" latitude (ca. 240 km) in the position of the 200 mm annual isohyet in the Sahel during the decade 1980-1990 estimated by Tucker et al. (1991) using normalised difference vegetation index (NDVI) data.

The increase in JJA equatorial rains noted from Figure 3 is also evident from Figure 6 with an increase of 0.4mm day-' occurring at 2.5"N. The DJF profile shows a similar magnitude increase over these two 30-year periods just south of the Equator (0.4 mm day-' at 7*5"S), but there is no DJF reduction in tropical southern African rainfall comparable to the JJA decrease in the Sahel. The period 1983-1987, however, did experience reductions of 0.8 mm day-' at 23"N and 1.0 mm day-' at 223"S, emphasizing the continent- wide rainfall deficits of the period 1983-1987.

REGIONAL RAINFALL ANOMALY TIME SERIES

The above comparison of 30-year climatologies needs to be placed within a longer historical context and this can be done by examining time series of regional rainfall anomalies over the last 100 years. Rainfall anomaly indices (RAIs; Katz and Glantz, 1986; Barring and Hulme, 1991) were constructed for three regions in Africa (see Figure 1) using individual station time series. The three regions are the Sahel (defined as the area that received 100 mm to 600mm mean annual rainfall for 1931-1990), East Africa (Uganda, Kenya and Tanzania), and south-western Africa (100 mm to 600 mm mean annual rainfall for 1931/32-1990/91). For the latter region the rainfall year was defined as July to June. A station required data for 83 per cent of months between 1931 and 1990 for it to be included in the regional series. The numbers of station time series contributing to each of these RAIs were, respectively, 65, 48, and 40.

These three regions of Africa display quite different characteristics of rainfall variability over the twentieth century (Figure 7). This can be summarized conveniently by the autocorrelation structures of the respective time series (Figure 8). The striking persistence of Sahelian rainfall anomalies is clearly revealed by its autocorrelation function and is in marked contrast to both the other two regions. This persistence has increased in recent decades (Folland et al., 1991). Despite the near average year of 1988 in the Sahel, the subsequent 3 years have again seen below average rainfall, continuing the drought sequence of the last 25 years. Equatorial East Africa shows little temporal organization in rainfall variability although, as remarked earlier, the extremely wet years of the early 1960s are noteworthy as is the cluster of wet years around 1905. The autocorrelation function for East Africa shows little organized behaviour (Figure 8). The quasi- periodicity of southern African rainfall over about an 18-year period, well documented by Tyson et al. (1975) and Tyson (1991), is only weakly revealed by its autocorrelation function. The alternate wet and dry phases over recent decades are more clearly evident from Figure 7. The coincidence in the early 1980s of a dry phase in south-western Africa, with severe rainfall deficits in the Sahel and two dry years in equatorial East Africa in 1983 and 1984, resulted in the near continent-wide drought between 1983-1987 noted previously, with related food security problems for the continent (Borton and Clay, 1988).

DISCUSSION AND CONCLUSIONS

African rainfall has changed substantially over the last 60 years. This change has been most notable over tropical north Africa where rainfall during 1961-1990 declined by up to 30 per cent compared with

694

2 -

M. HULME

n

1 Sahel

U -2 I t 1 East Africa

southwestern Africa -= t Year

Figure 7. Annual rainfall anomaly indices (RAIs) for three regions in Africa: the Sahel (top), equatorial East Africa (middle), and south-western Africa (bottom). See the text and Figure 2 for a definition of the regions. Anomalies are with respect to mean 1931-1990 rainfall. The top two time series show annual anomalies using January to December totals, the bottom time series using July to June. The smooth curves are 10-point Gaussian filters fitted through the data, which suppress variations on time-scales of less than a decade

1931-1960. For the boreal summer months these changes in mean rainfall have been shown to be significant statistically. The tropical margins of southern Africa have seen rainfall reduced by ca. 5 per cent. Rainfall increases have occurred in some areas, most notably in equatorial East Africa (+ 15 per cent) and in the southern coastal region of West Africa (+ 10 per cent). The variability of annual rainfall is high over much of the continent and this variability, when measured with respect to mean rainfall amount, has generally increased in Africa between these two 30-year periods. This increase in relative variability has been greatest

AFRICAN RAINFALL CHANGES

0.40

695

I 1 I I I I I I

~ Sohel

_ _ _ _ _ _ East A f r i ca -

southwestern A f r i ca -

-0.20

-0.40

I I I I I I I I I

0 5 10 15 20 25 30 35 40 45 50

lag (years)

Figure 8. The autocorrelation functions of the three RAIs shown in Figure 7. Curves are smoothed with a five-point Gaussian filter

over Tunisia and Algeria, the Nile Basin, and the extreme south of the continent. These variability changes, however, are not statistically significant when considering the continent as a whole. Rainfall seasonality is a fundamental characteristic of African rainfall and changes in seasonality also have occurred over the last 60 years. One striking aspect of these changes is the contrast between the southern coastal region of West Africa (increased seasonality) and much of eastern Africa (decreased seasonality). Rainfall in north-west Africa has also become somewhat less seasonal.

What explanations may be offered for the rainfall changes identified above? Possible causes may be summarized conveniently as falling into three broad areas: those related to land cover changes within the continent; those related to changes in the global ocean circulation and associated with patterns of sea-surface temperatures (SSTs); and those related to the changing composition of the global atmosphere.

Land cover changes in Africa have occurred within two main biomes-tropical rain forests and acacia savannah. The destruction of tropical rain forest is primarily for logging, plantations, and first-generation cultivation, and Myers (1991) has estimated that the process is occurring at an annual rate of about 0.5 per cent for tropical Africa. Theoretical and empirical evidence that contemporary tropical rain forest destruc- tion significantly reduces regional-scale rainfall remains inconclusive for Africa, although clearer for the Amazon (Lean and Warrilow, 1989; Salati and Nobre, 1991). The progressive clearing of savannah-type acacia vegetation in the more subhumid and semi-arid parts of the continent for fuelwood and increased intensity cultivation is a harder process to quantify. Callaghan et al. (1985) estimated the annual clearance rate of acacia woodland in central Sudan to be about 3.6 per cent, although this is likely to represent an upper limit. Reduction of dryland vegetation cover has led to the proposition of a land-surface-atmosphere feedback whereby reduced vegetation leads to decreases in soil moisture, increased sensible heat flux and hence reduced rainfall (see review in Nicholson, 1988). The Sahel is the prime candidate region for such a feedback mechanism to operate and the strong persistence of the recent rainfall decline (Figure 7) provides circumstantial evidence in support of the idea. However, in such a strongly seasonal regime as the Sahel where one wet season is effectively decoupled from the next, it is hard to see how soil moisture can act as a year-to-year hydrological 'memory' and induce such persistence. Recent experiments by Rowel1 et al. (1991) suggest that the soil moisture feedback is only a minor factor in determining regional rainfall anomalies in the African Sahel.

An additional feedback mechanism involving vegetation change may operate through changes in surface albedo and roughness associated with vegetation succession. Modelling experiments of the Amazon Basin by Lean and Warrilow (1989) suggest that replacement of forest with grassland will reduce regional rainfall owing to reduced moisture convergence (through higher albedos) and decreased evaporation (through decreased surface roughness). The importance of the roughness mechanism for the Sahel is likely to be

696 M. HULME

substantially less than for the Amazon, however, since only a small proportion of Sahelian rainfall is derived from local evaporation.

The influence of regional and global SSTs upon African rainfall has been assessed most comprehensively by Folland ef at. (1991) for the Sahel, Nicholson and Entekhabi (1986) and Lindesay and Vogel (1990) for southern Africa, and Janowiak (1988) for the whole continent. The latter three studies found some association between regional rainfall anomalies and indices of the El Niiio/Southern Oscillation (ENSO) phenomenon. This association was most notable for south-eastern Africa (negative anomalies shortly following ENSO events) and equatorial East Africa (positive anomalies shortly following ENSO events). Such relationships account, however, for at most 25 per cent of the interannual variability in rainfall and are not sufficient to explain the quasi 18-year periodicity of south-west African rainfall shown in Figure 7. Work by Mason (1990), however, suggests that this periodicity may be related to local SST anomalies around the southern African coast.

The relationship between global SST anomalies and Sahel rainfall anomalies is even more convincing. The mode of SST variation associated most closely with negative rainfall anomalies in the Sahel is one where the southern oceans (plus the Indian Ocean) are warm and the northern oceans are cool. The coherence of this relationship has led to the development of a seasonal forecasting capability for Sahel rainfall using April to June global SST anomalies (Folland et al., 1991). The observed pattern of global SSTs over the last 30 years has predominantly expressed this mode of SST variation, and modelling studies have supported this explanation for interdecadal rainfall fluctuations in the Sahel (Rowell et a/., 199 1). Furthermore, the influence of South Atlantic SST anomalies on African rainfall suggests a strong dipole in rainfall anomalies between coastal West Africa and the interior Sahel (Druyan and Hastenrath, 1991; Janicot, 1992), very much as shown here in the JJA rainfall change observed between 1931-1960 and 1961-1990 (Figure 3). An outstanding question therefore concerns the reason for the recent pattern of observed global SST anomalies. Street- Perrott and Perrott (1990) have put forward the idea that changes in the strength of the thermo-haline circulation of the Atlantic Ocean may account for the build-up of warmth in the southern oceans relative to the north. They present evidence for this mechanism operating on different time-scales including both millenia and decades.

The third main candidate explanation for contemporary changes in African rainfall concerns the changing composition of the global atmosphere. Empirical evidence for global warming induced by rising greenhouse gas concentrations remains inconclusive, albeit suggestive (Wigley and Barnett, 1990). Global-mean precipi- tation is likely to increase with global warming at a rate of between 2 per cent and 4 per cent per 1°C of global-mean warming. This is a result of the intensification of the hydrological cycle, especially through increased evaporation over warmer ocean surfaces. The regional manifestation of such global precipitation change remains, however, highly uncertain. Rainfall decreases are anticipated in some regions for enhanced COz conditions by all general circulation model (GCM) experiments, although these regions tend to vary from model to model.

Does the pattern of observed rainfall change in Africa from 1931-1960 to 1961-1990 bear any resemblance to rainfall change patterns generated by recent GCM greenhouse experiments? In fact, none of the recent GCM experiments that have modelled the effects of increased COz concentrations have generated a pattern of rainfall change over Africa similar to that observed over the last 60 years. Pattern correlations between the annual, summer, and winter 1961-1990 minus 1931-1960 observed rainfall, and the 2 x COz minus 1 x COz rainfall for the seven GCM experiments listed in Table 11, range from 0.29 to -0.48 with a mean pattern correlation of 0.02. This negative result is not surprising for two main reasons. First, the relatively poor simulation of present-day regional rainfall patterns by current GCMs (Gates et al., 1990; Hulme, 1991) suggests that currently little confidence can be placed in simulations of future regional rainfall. If indeed, global SSTs determine much of the interannual and interdecadal variability of African rainfall, then a coupled ocean-atmosphere GCM that realistically simulates interannual variability in SSTs will be necessary to improve confidence. Second, the low signal-to-noise ratio anticipated for greenhouse-induced rainfall change (Wigley and Barnett, 1990) means that it will be very difficult to distinguish greenhouse- related rainfall anomalies from naturally induced variations in regional rainfall. It is premature therefore to attribute recent African rainfall changes to greenhouse-gas-induced climatic change.

AFRICAN RAINFALL CHANGES 697

Table 11. Pattern correlation coefficients between observed rainfall change (1931-1960 to 1961-1990) and modelled rainfall change (2 x CO, - 1 x CO,) for seven GCM greenhouse experi- ments for annual (ANN), boreal summer (JJA) and boreal winter (DJF) rainfall. Number of grid boxes correlated was 90. All experiments (with the exception of MPILSG) were equilib- rium experiments. MPILSG was a transient experiment with a coupled ocean-atmosphere model; the rainfall change was taken as the mean of the decade 2045-2054 minus 1981-1990

Experiment” ANN JJA DJF

GFDL 0.11 - 0.28 0.06 GISS 0.29 0.0 1 0.05 LLNL 0.19 0.26 0.10 MPILSG 0.22 0.20 - 0.03 osu - 0.06 0.07 -0.35 UKMO-L -0.12 - 048 0.13 UKMO-H 0.00 - 0.06 0.10

Mean 0.09 - 0.04 001

“GFDL, Geophysical Fluid Dynamics Laboratory (Wetherald and Manabe, 1986); GISS, Goddard Institute for Space Studies (Hansen et al., 1984); LLNL, Lawrence Livermore National Laboratory (L. Gates, pers. comm.); MPILSG, Max Planck Institute-Large Scale Geostrophic ocean (Cubasch et al., 1991); OSU, Oregon State Univer- sity (Schlesinger and Zhao, 1989); UKMO-L, UK Meteorological Office-Low resolution (Wilson and Mitchell, 1987); UKMO-H, UK Meteorological Office-High resolution (Mitchell et al., 1990).

ACKNOWLEDGEMENTS

The monthly precipitation data set used in this analysis was compiled under grants from the US Department of Energy Carbon Dioxide research program and the UK Department of the Environment (contracts PECD 7/10/198 and PECD 7/12/78). The extensive work by Sharon Nicholson in the 1970s and early 1980s in compiling many of the original station time series is fully acknowledged. Additional data and extensive later updates have been possible through the generous provision of data by individuals and National Meteoro- logical Agencies throughout Africa too numerous to mention by name. Their co-operation is fully acknow- ledged. Suggestions by D. A. Warrilow and one referee led to an improved manuscript. The data set of gridded monthly precipitation time series from 1931 to 1990 is available from the author on request.

REFERENCES

Barring, L. and Hulme , M. 1991. Filters and approximate confidence intervals for interpreting rainfall anomaly indices’, J . Climate., 4,

Borton, J. and Clay, E. J. 1988. ‘The African food crisis of 1982-86, in Rimmer. D. (ed.), Rural Transformation in Tropical Africa Pinter, London.

Bradley, R. S., Diaz, H. F., Eisheid, J. K., Jones, P. D., Kelly, P. M. and Goodess, C. M. 1987. Precipitation fluctuations over Northern Hemisphere land areas since the mid-19th century’, Science, 237, 171-175.

Callaghan, T. V., Bacon, P. J., Lindley, D. K. and el Moghraby, A. I. 1985. ‘The energy crisis in the Sudan: alternative supplies of biomass’, Biomass, 8, 21 7-232.

Cubasch, U., Hasselmann, K., Hock, H., Maier-Reimer, E., Mikolajewicz, U., Santer, B. D. and Sausen, R. 1991. Time-dependent Greenhouse Warming Computations with a Coupled Ocean-Atmosphere Model, Max Planck Institute fur Meteorologie, Report No. 67, Hamburg, 18 pp.

837-847.

De Waal, A. 1988. ‘Famine early warning systems and the use of socio-economic data’, Disasters, 12, 81-91. Druyan, L. M. and Hastenrath, S., 1991. ‘Modelling the differential impact of 1984 and 1950 sea-surface temperatures on Sahel rainfall’,

Eischeid, J. K., Diaz, H. F., Bradley, R. S. and Jones, P. D. 1991. A Comprehensive Precipitation Data Setfor Global Land Areas, US I n [ . J . Climatol., 11, 367-380.

Department of Energy Report No. DOE/ER-69017T-H1, Washington, DC, 81 pp.

698 M. HULME

Farmer, G. 1989. ‘Rainfall’, in The IUCN Sahel Studies, 1989, IUCN, Gland, pp. 1-25. Farmer, G. and Wigley, T. M. L. (1985) Climatic Trends for Tropical Africa, Climatic Research Unit, University of East Anglia, Norwich,

135 pp. Flohn, H. 1987. ‘East African rains of 1961/62 and the abrupt change of the White Nile discharge’, in Coetzee, J. A. (ed.), Paleoecology of

Africa pnd the Surrounding Islands, 18, Balkema, Rotterdam, pp. 3-18. Folland, C. K., Owen, J. A,, Ward, M. N. and Colman, A. W. 1991. ‘Prediction of seasonal rainfall in the Sahel region using empirical and

dynamical methods’, J . Forecasting, 10, 21-56. Gates, W. L., Rowntree, P. R. and Zeng, Q.-C. 1990. ‘Validation of climate models’, in Houghton, J. T., Jenkins, G. J. and Ephraums, J. J.

(eds), Climate Change: the IPCC Scientific Assessment, Cambridge University Press, Cambridge, pp. 93-1 30. Griffiths, I. L1. 1984. An Atlas of African Affairs, Methuen, London, 200 pp. Griffiths, J. F. (ed.) 1972. Climates of Africa, Vol. 10, World Survey of Climatology, Elsevier, Amsterdam, 602 pp. Hansen, J., Lacis, A., Rind, D., Russell, L., Stone, P., Fung, I., Ruedy, R. and Lerner, J. 1984. ‘Climate sensitivity analysis of feedback

mechanisms’, in Hansen, J. and Takahashi, T. (eds), Climate Processes and Climate Sensitivity, Geophysical Monograph No. 29, American Geophysical Union, Washington, DC, pp. 130-163.

Houghton, J. T., Jenkins, G. J. and Ephraums, J. J. (eds.) 1990. Climate Change: the IPCC ScientiJic Assessment, Cambridge University Press, Cambridge, 364 pp.

Hulme, M. 1990. ‘The changing rainfall resources of Sudan’, Trans. Inst. Br. Geogr., 15,2l-34. Hulme, M. 1991. ‘An intercomparison of model and observed global precipitation climatologies’, Geophys. Res. Lett., 18, 1715-1718. Hulme, M. 1992. ‘A 1951-80 global land precipitation climatology for the evaluation of General Circulation Models’, Clim. Dyn., 7 ,

Hulme, M., Marsh, R. and Jones, P. D. 1992. ‘Global changes in a humidity index between 1931-60 and 1961-90, Climate Res., in press. Janicot, S. 1992. ‘Spatio-temporal variability of West African rainfall’, J . Climate, 5, 489-51 1. Janowiak, J. E. 1988. ‘An investigation of interannual rainfall variability in Africa’, J . Climate, 1, 240-255. Katz, R. W. and Glantz, M. H. 1986. ‘Anatomy of a rainfall index’, Mon. Wea. Rev., 114, 764-771. Lau, K. M. and Sheu, P. J. 1988. ‘Annual cycle, quasi-biennial oscillation, and Southern Oscillation in global precipitation’, J . Geophys.

Lean, J. and Warrilow, D. A. 1989. ‘Simulation of the regional climatic impact of Amazon deforestation’, Nature, 342, 41 1-413. Leemans, R. and Cramer, W. P. 1990. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on

Legates, D. R. and Willmott, C. J. 1990. ‘Mean seasonal and spatial variability in gauge-corrected, global precipitation’, Int. J . Climatol.,

Leroux, M. 1983. The Climare of Tropical Africa, Champion-Slatkine, Paris, 650 pp. Lindesay, J. A. and Vogel, C. H. 1990. ‘Historical evidence for Southern Oscillation-southern African rainfall relationships’, Int. J .

Climatol., 10, 679-690. Mason, S. J. 1990. ‘Temporal variability of sea surface temperatures around southern Africa: a possible forcing mechanism for the

18-year rainfall oscillation?’, South Afr. J . Sci., 86, 243-252. Mitchell, J. F. B., Manabe, S., Meleshko, V. and Tokioka, T. 1990. ‘Equilibrium climate change-and its implications for the future’, in

Houghton, J. T., Jenkins, G. J. and Ephraums, J. J. (eds), Climate Change: the IPCC Scient$c Assessment, Cambridge University Press, Cambridge, pp. 137-164.

Mott McDonald 1991. ‘The South Chad Irrigation Project’ in Modelling the Lake Chad Basin: Project Proposal, Mott McDonald plc, Cambridge, pp. 2-1 1 : 2-26.

Myers, N. 1991. ‘Tropical forests: present status and future outlook’, Climate Change, 19, 3-32. Nicholson, S. E. 1985. ‘Sub-saharan rainfall 1981-1984‘. J . Clim. Appl. Meteorol., 24, 1388-1391. Nicholson, S. E. 1986. ‘The nature of rainfall variability in Africa south of the equator’, J . Climatol., 6, 515-530. Nicholson, S. E. 1988. ‘Land surface-atmosphere interaction: physical processes and surface changes and their impact’, Prog. Phys.

Nicholson, S. E. 1989. ‘Long-term changes in African rainfall’, Weather, 44, 47-56. Nicholson, S. E. and Entekhabi, D. 1986. ‘The quasi-periodic behaviour of rainfall variability in Africa and its relationship to the

Southern Oscillation’, Arch. Meteorol. Geophys. Bioklimatol., Ser. A, 34, 3 11-348. Nicholson, S . E., Kim, J. and Hoopingarner, J. 1988. Atlas of African Rainfall and its Interannual Variability, Department of

Meteorology, The Florida State University, Tallahassee, FL, 237 pp. Pettit, A. N. 1979. ‘A non-parametric approach to the change-point problem’, Appl. Statistics, 28, 126135. Preisendorfer, R. W. and Barnett, T. P. 1983. ‘Numerical model-reality intercomparison tests using small-sample statistics’, J . Atmos.

Quinlan, F. T. 1986. ‘Comments on “Sahel: The changing rainfall regime and the ‘normals’ used for its assessment”’, J . Clim. Appl.

Rowell, D. P., Folland, C. K., Maskell, K., Owen, J. A. and Ward, M. N. 1991. ‘Causes and predictability of Sahel rainfall variability’,

Salati, E. and Nobre, C. A. 1991. ‘Possible climatic impacts of tropical deforestation’, Climate Change, 19, 177-196. Schlesinger, M. E. and Zhao, Z. C. 1989. ‘Seasonal climate changes induced by doubled CO, as simulated by the OSU atmospheric

Street-Perrott, F. A. and Perrott, R. A. 1990. ‘Abrupt climate fluctuations in the tropics: the influence of Atlantic Ocean circulation’,

Todorov, A. V. 1985. ‘Sahel: the changing rainfall regime and the “normals” used for its assessment’, J. Climate Appl. Meteorol., 24,

Tucker, C. J., Dregne, H. E. and Newcomb, W. W. 1991. ‘Expansion and contraction of the Sahara Desert from 1980 to 1990, Science,

57-72.

Res. Ser. D, 93, 10,975-10,988.

a Global Terrestrial Grid, Working Paper No. WP-90.-41, Biosphere Dynamics Project, IIASA, Laxenburg, 25 pp.

10, 111-128.

Geogr., 12, 3665.

Sci.. 40, 1884-1896.

Meteorol., 25, 257.

Climate Research Technical Note No. 12, Hadley Centre, Bracknell, 15 pp.

GCM mixed layer ocean model’, J . Climate, 2, 459-495.

Nature, 343, 607-612.

97-107.

253, 299-301.

AFRICAN RAINFALL CHANGES 699

Tyson, P. D. 1991. ‘Climatic change in southern Africa: past and present conditions and possible future scenarios’, Climatic Change, 18,

Tyson, P. D., Dyer;T. G. J. and Mametse, M. N. 1975. ‘Secular changes in South African rainfall: 188&1972’, Q. J . R. Meteorol. Soc.,

Wetherald, R. T. and Manabe, S. 1986. ‘An investigation of cloud cover change in response to thermal forcing’, Climate Change, 8,5-23. Wigley, T. M. L. and Barnett, T. P. 1990. ‘Detection of the greenhouse effect’ in, Houghton, J. T., Jenkins, G. J. and Ephraums, J. J. (eds),

Wigley, T. M. L. and Santer, B. D. 1990. ‘Statistical comparison of spatial fields in model validation, perturbation and predictability

Wilson, C. A. and Mitchell, J. F. B. 1987. ‘A doubled CO, climate sensitivity experiment with a global climate model including a simple

World Meteorological Organization 1989. Calculation of Monthly and Annual 30-year Standard Normals, WCDP-No. 10, WMO-

241 -258.

101, 817-833.

Climate Change: the IPCC Scientific Assessment, Cambridge University Press, Cambridge, pp 243-255.

experiments’, J . Geophys. Res., 95, 851-865.

ocean’, J. Geophys. Res., 92, 13,31513,343.

TD/No. 341, World Meteorological Organization, Geneva.