SUMMARY. The nature and causes of seasonal (July to September (JAS)) rainfall variability over tropical north Africa are investigated using a combination of ...
Q. J . R. Meteorol. Soc. (199S), 121, pp. 669-704
S51.577.32:5Sl.S77.34(66)
Variability of summer rainfall over tropical north Africa (1906-92): Observations and modelling By DAVID P. ROWELL*, CHRIS K. FOLLAND, KATHY MASKELL and M. NEIL WARD Hadley Centre for Climate Prediction and Research, Meteorological Ofice, UK (Received 28 February 1994; revised 28 September 1994)
SUMMARY The nature and causes of seasonal (July to September (JAS)) rainfall variability over tropical north Africa are investigated using a combination of empirical and modelling approaches. Concentration is focused on three regions: the Sahel, Soudan and Guinea Coast. The variability of seasonal rainfall through the twentieth century is analysed for each region. The well known dipole of anomalies between the Sahel and Guinea Coast is evident, and new analysis reveals that this fluctuates primarily on time-scales of five years or less. Attention is then focussed on the causes of rainfall variability in each region, by examining the relationships with global sea-surface-temperature (SST) patterns; a partitioning of the data into low- and high-frequency components is found to be particularly useful. So as to attribute more convincingly the primary cause of seasonal rainfall variability to global SST forcing, a suite of general circulation model experiments are performed, aimed at simulating JAS rainfall anomalies for ten past years between 1949 and 1990. Each is forced by the observed SST patterns for the appropriate year. In almost every case, the model quite skilfully simulates the magnitude and pattern of JAS rainfall anomalies across tropical north Africa, thus strengthening the idea that global SST variations are indeed responsible for most of the variability of seasonal rainfall. The relative impact of two additional sources of variability is also investigated. First the role of internal atmospheric variability is quantified (using an ‘analysis of variance’ technique), and found to be small in all three regions. Second, and perhaps more controversially, the possible effects of land-surface-moisture feedback are explored. This is done by replacing the normal interactive soil-moisture scheme with a model-derived climatology; results suggest that in some years moisture evaporated from the land surface may play a key role, but that in general SST forcing still dominates. Finally, an assessment of the model’s skill at sub-seasonal time-scales reveals that fluctuations of monthly rainfall about each year’s seasonal mean (intruseasonal variations) are insensitive to SST forcing, in part due to a larger influence of internal atmospheric variations.
1. INTRODUCTION Variations of tropical precipitation occur on a wide range of time-scales, and in some regions can have immense societal and economic impact. In this paper we concentrate on seasonal to decadal time-scales, and upon one particular region-tropical north Africa. Attempts to understand its fluctuations of seasonal rainfall have received considerable attention, but there still exist many fundamental issues which are debated or poorly understood (reviews by Druyan (1989), Nicholson (1989), Hastenrath (1991 pp. 312322) and Lamb and Peppler (1991)). Further advances in understanding must be made in order to improve the predictability of drought and flood events, as well as improving our overall understanding of the natural variability of the climate system. Tropical north Africa encompasses a variety of rainfall regimes. On the southern flanks of the Sahara lies the Sahel, receiving the bulk of its rainfall during July to September, with almost no rain falling outside May to October. The low annual totals of this area (averages of approximately 100 to 600 mm) make it particularly sensitive to the vagaries of the climate system. Further south, the wet season lengthens, annual totals increase, and towards the equator the seasonal cycle takes on an element of bimodality. Research over the last fifteen years or so has concentrated mainly upon the vulnerable Sahel region, not least because of its recent experience of long and persistent drought (e.g. Folland e l al. 1991; Lamb and Peppler 1992; Nicholson and Palao 1993).
* Corresponding author: Hadley Centre for Climate Prediction and Research, Meteorological Office, London Road, Bracknell, Berkshire RG12 2SY, UK.
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i n order to understand seasonal variability, one must take a global view of the atmosphere. Indeed, the 'ultimate viewpoint' is to consider the entire coupled landocean-atmosphere system, and the nature of its internal variations. However, interaction and feedbacks between each component of the coupled system are highly complex and not well modelled at present, so the approach often taken is to consider them as separate systems. The variability of the atmosphere may then be regarded as due to changes in fluxes of heat, moisture and momentum across the ocean-atmosphere and land-atmosphere boundaries, plus a component of variability independent of this boundary forcing (i.e. internal to the atmosphere). It is generally thought that the influence of boundary forcing on seasonal climate variability is greatest at low latitudes, where the impact of thermodynamic instabilities on the large-scale flow is largest and the impact of nonlinear dynamical flow instabilities is generally smaller (e.g. Charney and Shukla 1981). In this paper, one of our prime aims is to investigate the relative importance of each of these sources of variability (ocean, land and internal) for the tropical north African region. First we review and evaluate current opinions in the research community.
( a ) Oceanic forcing It was originally proposed by Lamb (1978a, b) that fluctuations of seasonal Sahel rainfall are associated with sea-surface-temperature (SST) variability in the tropical Atlantic, and this was later confirmed by numerous other studies (Lough 1980, 1986; Hastenrath 1984; Semazzi et a f . 1988; Adedoyin 1989; Hastenrath 1990; Druyan 1991; Folland et a f . 1991; Lamb and Peppler 1992; Shinoda and Kawamura 1994). However, the interaction between distant tropical (and extratropical) regions through global teleconnections is now widely recognized (e.g. Glantz et a f . 1991), and so it seems natural to also look to more distant ocean basins for an influence on the Sahel. El Nino Southern Oscillation (ENSO) is always a prime candidate, but for the Sahel, research has suggested its influence to be relatively weak (but probably significant) (e.g. Nicholson and Entekhabi 1986; Ropelewski and Halpert 1987; Semazzi el af. 1988; Folland et af. 1991; Lamb and Peppler 1991). Further south (to 5"N, say), Nicholson and Entekhabi (1986) and Janowiak (1988) suggest that seasonal rainfall may be more strongly related to ENSO events, but in a complex manner with perhaps distinct impacts on different spatial modes of seasonal rainfall and within different bands of the frequency domain. Variability over the Indian Ocean is also thought to be important, where surface warming often coincides with Sahel drought (Palmer 1986; Folland et a f . 1991; Hastenrath and Wolter 1992; Shinoda and Kawamura 1994). To some extent, the temporal variability of SSTs in these individual ocean basins is interdependent, and this is borne out by a near-global-scale pattern of SST anomalies particularly related to Sahelian rainfall variations on decadal time-scales (Folland el al. 1986, 1991; Wolter 1989). Furthermore, the importance of global (as well as regional) patterns of SST has also been demonstrated by forcing general circulation models (GCMs) with observed SST data for individual years, and successfully simulating the observed anomalies of seasonal Sahel rainfall for those years (Folland et a f . 1989, 1991; Druyan and Hastenrath 1991; Palmer et al. 1992; Rowel1 et al. 1992). These experiments provide a much stronger physical basis for the influence of global SSTs on Sahel drought. ( b ) Land-surface forcing Feedback mechanisms involving land-surface characteristics have also been proposed (reviewed by Nicholson (1988)), often with the suggestion that these may explain most of the unusually large fraction of decadal variability in the Sahel. This may occur through one or more of the following effects.
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(i) Reduced soil moisture during drought periods lessening the supply of evaporated moisture to help feed convection (e.g. Cunnington and Rowntree 1986; Sud and Molod 1988; Rowell and Blondin 1990; Rowell et al. 1992). On seasonal time-scales, however, SST forcing appears to dominate (Rowell et al. 1992). (ii) A feedback involving increased surface albedo, through lack of vegetation, which leads to reduced rainfall (Otterman 1974; Charney 1975; Charney et al. 1977; Lava1 and Picon 1986; Cunnington and Rowntree 1986; Xue and Shukla 1993). However, observational studies of Sahelian rainfall and albedo variations appear inconclusive (Courel et al. 1984). (iii) Reduced vegetative cover may sufficiently lower values of surface roughness so as to alter precipitation patterns through changes to low-level wind convergence (Sud and Smith 1985; Sud et al. 1988; Xue and Shukla 1993). (iv) A further effect of a drier less vegetated surface may be to increase the generation of atmospheric dust (Prosper0 and Ness 1977, 1986), although possible mechanisms by which this could reduce rainfall have yet to be thoroughly assessed (Nicholson 1988, 1989). One problem with many of the GCM studies used to support these ideas is that the perturbations are often quite unrealistic of year-to-year variability, both in terms of their magnitude and spatial extent. Another problem with some of these ideas concerning land-surface feedback is that the proposed mechanisms do not explain convincingly the ‘memory’ from one year to the next. Soil wetness, for example, is near-zero before the rainfall season, whatever the previous year’s rains. In summary, further and more realistic experimentation is required to assess accurately the importance of land-surface feedbacks. ( c ) Internal variability A final impact on seasonal variability arises from internal atmospheric variations, although for many tropical regions this is thought to be less important than the effects of boundary forcing. Its small effect on Sahel rainfall has been indicated by the studies of Rowell et al. (1992) and Palmer et al. (1992), who tested the sensitivity of their GCM integrations to changes of initial atmospheric data, keeping the SST forcing constant. Little difference was found between seasonal Sahel rainfall totals, suggesting that although some internal atmospheric variability at seasonal time-scales exists, it is dominated by the oceanic forcing.
The primary aim of this paper is to elucidate further the character of seasonal (July to September (JAS)) rainfall variability over tropical north Africa, and in particular its relationship with global SST patterns. As well as investigating variability over the Sahel, we will also focus attention on two areas south of the Sahel (the Soudan and Guinea Coast), which have previously received rather less attention. In section 2 we provide a detailed empirical analysis of observed data, with particular emphasis on a separation in the frequency domain of the nature and forcing of the rainfall variability. The following sections then describe the results of a number of GCM simulations aimed at strengthening the idea that the global SST variability is responsible for the major part of the seasonal rainfall fluctuations (section 5, with an introduction in sections 3 and 4). These experiments considerably extend the work of Folland et al. (1991) and Rowell et al. (1992), including: an analysis of the Soudan and Guinea Coast; a methodology for quantifying the importance of internal atmospheric variability (section 5(b)); a more detailed discussion of the role of land-surface-moisture feedbacks (section 5(c)); a study of recurring spatial patterns of rainfall anomalies (previously explored by, for example, Janicot (1992a) and Nicholson and Palao (1993)) (section 5(d)); and finally an assessment of the possible
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causes of intraseasonal monthly variability (section 5(e)). In a later paper we shall provide further hypotheses about the local (African) atmospheric circulation changes associated with the seasonal rainfall anomalies, making use of the data provided by the simulation experiments. ANALYSIS 2. EMPIRICAL
(a) Rainfall data The observed rainfall data used throughout this paper were provided by M. Hulme (personal communication, 1993), as monthly means for the period 1900-92 on a regular 2.5" latitude by 3.75" longitude grid (identical to the grid of the GCM used in sections 3 to 5). One advantage of using gridded data is that it enables computation of more reliable estimates of areal average rainfall amounts. The data construction method (for more detail refer to Hulme (1994)) involved the creation of two rainfall data sets, which were then merged. These data sets employ differing 'base periods', 1931-70 and 1951-90, with a station network defined for each; the earlier period includes many stations which ceased to operate during the early 1970s, and the latter period includes stations opened during the early 1950s. An individual station qualified if data were available for more than 83% of months during the base period, with a grid-box value set to missing if no stations qualified. For each station network, grid-box means were computed for all months during 1900-92 using Thiessen weighted values. Missing values for any station in an individual month were estimated from the percentage anomalies of nearby (within 278 km) stations using an angular/inverse-distance weighting function (if less than two stations were within 278 km of the missing station, the station value for that month was set to missing). Next, in order to merge the two data sets, the 1931-70 base data were scaled by the ratios of the means and standard deviations from the two data sets computed for the overlapping 1951-70 period (this attempts to correct for the effects of using different station networks, so that the final merged data set is as homogeneous as possible). The final data set is then constructed using the rescaled 1931-70 base data for 1900-50, and the unaltered 1951-90 base data for 1951-92. Figure 1 summarizes the combined station network of the two base periods, the grid-box locations, and the boundaries of the three analysis regions, the definitions of which will be discussed later. Our analysis will use only the period 1906-92, since for the years 1900-05 very few grid boxes are available across our regions of interest.
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( b ) Temporal and spatial averaging methods We concentrate our investigation on JAS seasonal mean data; Ward (1994) illustrates how these three months exhibit broadly similar time series and SST relationships, whereas May, June and October behave quite differently, particularly on time-scales of less than 10 years. The seasonal means were constructed by simply summing the constituent monthly values; if any monthly value was missing the seasonal value was set to missing. Figure 2 illustrates the contribution of the JAS period to annual rainfall totals across northern Africa. Clearly it is an important season throughout tropical north Africa, but especially so in the Sahel where up to 90% of annual rainfall occurs during these three months. 20" N
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For the spatial variability of the nature and causes of rainfall fluctuations, much of our investigation focuses on anomalies averaged over the three regions marked in Fig. 1. These were defined as follows. (i) The northernmost region, the Sahel, is identical to that used by Rowel1 et al. (1992), and was originally chosen to be broadly similar to Nicholson's (1980) Sahel region, except that its western half does not extend as far north. (ii) The next row of grid boxes to the south we call the Soudan region (note the two grid points south of the westernmost Sahel are omitted because of a lack of rainfall data in an older version of the data set (on which the research reported here was originally based), and the easternmost point omitted because its mountainous topography results in poor correlations with the rest of the Soudan). This is similar to Nicholson's Soudan region in the east, but in the west contains only the southern part of her Soudan region and some of the northern parts of her Soudano-Guinean zone. (iii) The region we define as Guinea Coast includes all model land grid boxes south of the western Soudan. The eastern and western boundaries were chosen to give a homogeneous region of JAS rainfall anomalies. The area is similar to Nicholson and Palao's (1993) Guinea Coast region (their Fig. 2), but also includes some of the southern parts of their Soudano-Guinean zone. Area-mean rainfall amounts were computed by averaging standardized anomalies for available grid boxes. For the Sahel, average values were first computed for the western and eastern halves separately and then these averaged together, to minimize bias against the larger proportion of missing data in the east Sahel. Note also that data from some additional coastal boxes were included in the area averages used for this
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section of the paper (two boxes west of the Sahel, and one in the southern part of the Guinea Coast), and that all coastal boxes were weighted by the proportion of land in each box. The last aspect of our analysis requiring description is the separation of data in the frequency domain into essentially sub-decadal and decadal time-scales. This is a key facet of this paper, since the large component of low-frequency variability in the Sahel and Soudan regions may have a different causal chain to the high-frequency component. The filter used here is the integrated random-walk smoothing algorithm described by Ng and Young (1990) and Young et al. (199-l), with noise variance ratio set to 0.1, corresponding to a 50% amplitude cut-off. ( c ) S S T data
SSTs are taken from the fourth version of the Meteorological Office Historical SST data set (MOHSST4), which contains monthly SST anomalies (from 1951 to 1980) on a 5" by 5" grid, and is described by Bottomley et af. (1990). The data have been corrected for biases before 1942, using techniques described by Folland and Parker (1995). For use in the correlation analysis presented in section 2(e), the 5" monthly SST anomalies were spatially and temporally averaged, with equal weighting, to form a lo" by 10" seasonal (JAS) data set. Since there is high persistence of local SST anomalies during July to September, as little as one constituent monthly 5" anomaly was allowed when calculating a seasonal lo" anomaly. The data were also filtered (high pass and low pass), using the same method as described for the rainfall data. ( d ) Temporal and spatial Variability of rainfall anomalies Figure 3 illustrates the time series of JAS rainfall anomalies for each region, and Fig. 4 the spectra of these series (using a fast-Fourier-transform method on detrended data). The three areas are considered in turn. The temporal character of Sahelian rainfall variability (Fig. 3(a)) is well known (e.g. Folland et al. 1991; Lamb and Peppler 1992; Nicholson and Palao 1993), with the recent long-running drought, the wetter decade of the 1 9 5 0 ~and ~ an earlier period of reduced low-frequency variability. The importance of this decadal variability is borne out by the spectrum (Fig. 4), which shows a dominance of low-frequency variations (greater than about 10 years). The Soudan time series of JAS rainfall anomalies (Fig. 3(b)) is similar to that of the Sahel, and the spectrum is again dominated by low-frequency variations (Fig. 4). Interestingly, it also reveals a broad peak at 3 to 8 years, which although failing to achieve significance, hints that the E N S 0 time-scale is a little more important in the Soudan than the Sahel (see also section 2(e)). Table 1 shows the correlations between the three regions: low-frequency variations in the Sahel and Soudan are almost perfectly matched, and the two regions have about a third of their high-frequency variance in common (this increases to about a half for the 1949-90 period). In the Guinea Coast region, the character of JAS rainfall variability (Fig. 3(c)) is quite different from the two areas further north. The low-frequency component accounts for a much smaller fraction of the JAS variance (lo%, 46% , 54% for the Guinea Coast, Soudan and Sahel respectively), and this is borne out by the spectrum in Fig. 4. Most of the variability during JAS occurs on a time-scale of 2-3 years. Note, however, that other seasons have a larger low-frequency component (Ward 1994), leading to the more substantial low-frequency component of annual totals found by Nicholson and Palao (1993). This emphasizes the need to study different phases of the annual cycle separately, since rainfall behaviour and presumably mechanisms may differ substantially.
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Figure 3 . Time series from 1906 to 1992 of the July to September standardized rainfall anomalies from a 1941-70 climatology, and its low-frequency component (continuous curve), for each of the three regions marked in Fig. 1.
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TABLE 1.
STANDARD CORRELATION BETWEEN OBSERVED SEASONAL (JULYTO SEPTEMBER) RAINFALL SERIES FOR THE THREE REGIONS SHOWN IN FIG.1 FOR 1906-92
Unfiltered data Low-frequency data High-frequency data
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0.77 0.96 0.60*
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0.09 -0.34 0.18
Statistical significance at the 1% level, for the high-frequency data, is marked by ‘*’.
Table 1 also confirms the anticorrelation of rainfall anomalies between the Sahel and Guinea Coast found by, for example, Janowiak (1988), Janicot (1992a) and Nicholson and Palao (1993), and suggests that it may result mainly from the high-frequency component of variability (although the low-frequency correlation is slightly higher, it has too few degrees of freedom to achieve significance).This is clarified by a cross-spectrum of the two series, shown in Fig. 5 , which suggests the dipole primarily operates on a time-scale of 2 to 3 years. The spatial pattern of this mode is illustrated by the first correlation empirical orthogonal function (EOF) of the high-frequency component of tropical north African JAS rainfall (Fig. 6). Weights are opposite, but of the same magnitude, in the Sahel and Guinea Coast, with the eastern Soudan varying in phase with the Sahel, and the western Soudan having almost no common variance with the EOF dipole. Unfiltered EOFs are discussed further in section 5(d), and the EOFs of filtered data are described more fully by Ward (1994).
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Figure 6. First correlation empirical orthogonal function (EOF) of high-pass filtered July to September rainfall, using 1949-90 data. Units are correlation with the EOF time coefficient.
( e ) Rainfall-SST relationships In this section we consider only the period 1949-90, when both the rainfall and SST data were most reliable; Ward (1994) discusses the earlier period, 190648. Figure 7 shows how JAS rainfall in each region is empirically and linearly related to the global JAS SST variability. The analysis is separated into high- and low-frequency components. Consider first the low-frequency variations. Significance values are not computed since the number of degrees of freedom is small; these maps should be viewed only as
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Figure 7. Correlation between filtered July to September (JAS) 10" by lo" SST anomalies and filtered standardized JAS rainfall anomalies, using 1949-90 data. (a) Low-frequency Sahel, (b) low-frequency Soudan, (c) high-frequency Sahel, (d) high-frequency Soudan, and ( e ) high-frequency Guinea Coast. Shading in (c) to (e) indicates where correlations are significant at the 5% level.
descriptive of the decadal SST variability which accompanied decadal rainfall variations during 1949-90. The Sahel and Soudan regions exhibit almost identical patterns (Figs. 7(a) and (b)), which is hardly surprising given the near-perfect correlation between the low-frequency components of their rainfall time series (Table 1). The pattern is reminiscent of the north-south quasi-hemispheric contrast described by Folland et al. (1986, 199l), which they also found to be closely associated with low-frequency variability over the Sahel. The correlation maps here show that the recent long-running drought has been associated with cooler than average waters in the extratropical North Pacific, the subtropical and extratropical North Atlantic, and the Caribbean, and warmer than average waters in parts of the tropical South Pacific, the equatorial and South Atlantic, and the entirety of the Indian Ocean. Many of these areas, with the probable exception of the tropical west Pacific, the Caribbean, and the tropical north-east Pacific, correspond
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to areas of high decadal SST variability (not shown), which is further suggestive of a likely influence of these regions on atmospheric low-frequency variability. However, this does not preclude additional influence from other areas, such as the Caribbean and possibly the tropical west Pacific, where small variations of the high mean SSTs are likely to have a proportionally larger impact on the overlying atmosphere. The patterns of low-frequency association between Guinea Coast rainfall and global SSTs are less coherent, and are not shown since these long time-scales represent such a small percentage of the total rainfall variability. We turn now to the high-frequency SST-rainfall relationships. First the Sahel, taking each ocean basin in turn. Local significance is computed using a standard t-test (note that no significant positive serial correlation exists which might affect the degrees of freedom), and field significance (Table 2) is computed following the technique of Livezey and Chen (1983). For the Sahel, each of the individual ocean regions achieves field significance to at least the 2% level. Figure 7(c) shows that the equatorial and eastern South Atlantic tend to be warmer (cooler) than average in Sahel drought (wet) years, which is similar to the results of other studies (e.g. Lamb 1978a, b; Lough 1986; Hastenrath 1990; Lamb and Peppler 1992). However, the extension of these negative correlations into the eastern North Atlantic differs from these earlier findings which combined associations on all time-scales. The Indian Ocean has small areas of significant (mainly negative) correlations, notably those close to the eastern coast of north and equatorial Africa. A reversal to positive correlations is seen through the Red Sea and into the eastern Mediterranean (note the similar pattern on decadal time-scales). In the Pacific, important areas linked to drought (wetness) are a warming (cooling) in the central and eastern equatorial Pacific (i.e. ENSO events), and a cooling (warming) in the west tropical Pacific. The former link appears considerably stronger on these interannual (high frequency) time-scales than has been suggested by the unfiltered data of, for example, Folland et al. (1991) or Ropelewski and Halpert (1987). In the west Pacific, although positive correlations occupy only a small area, they occur in a region of intense convective activity and tropospheric heating, and so may play a key role in forcing distant rainfall and circulation anomalies (see also discussion by Ward et aE. (1994)). A previous analysis by Folland et al. (1991), using unfiltered data, failed to highlight this region because of the opposite correlations found at low and high frequencies. The high-frequency links between Soudan rainfall and global SST variability (Fig. 7(d)) illustrate a broadly similar pattern to that for the Sahel. However, two important differences should be noted. First, the influence of the equatorial and southern Atlantic is much weaker; we suggest that the tropical Atlantic pattern is a major part of the forcing of the rainfall dipole (see also next paragraph), and since the Soudan straddles the dipole its correlations with equatorial and South Atlantic SSTs are relatively weak. In the Pacific, however, a slightly stronger relationship with warm/cold ENSO events is found than for the Sahel, which is also consistent with the spectra (Fig. 4). Correlation patterns in the Indian Ocean are similar to those for the Sahel, but since slightly fewer individual squares are significant, the field significance is lower. For the Guinea Coast, coherent areas of significant correlation with SSTs are found only in the equatorial and south tropical Atlantic, where values of up to 0.77 are reached (Fig. 7(e)). This pattern is similar, but of opposite sign and larger amplitude, to the correlations with high-frequency Sahel rainfall, indicating (as noted above) that it may be important in years with a dipole of rainfall anomalies between the two regions (see also Janicot (1992b) and Fontaine and Bigot (1993)). This is also the only ocean basin achieving field significance (Table 2). We do not believe, however, that this is the only ocean basin which plays a significant role for the Guinea Coast; GCM experiments in
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which we have forced the model with observed SSTs in only limited parts of the global ocean, and used climatological SSTs elsewhere, suggest that Guinea Coast rainfall is simulated considerably better with observed global SSTs than only observed tropical Atlantic SSTs. Presumably the influence of other ocean basins occurs in a nonlinear fashion, and so is not revealed by the linear correlation analysis presented here (see also discussion by Ward (1994)). Having described the empirical character of north African rainfall variability, and its link with global and regional SST patterns, we now describe the results of a number of GCM experiments, forced with observed SSTs, aimed at simulating this rainfall variability. These provide a physical basis for the idea that much of the forcing of the seasonal rainfall variability does indeed result from global SST patterns. First, we describe the model and experimental design. 3. MODELAND
EXPERIMENTAL METHOD
The GCM and experimental design used in this paper are identical to that of Rowell et al. (1992), and an extension of the experiments carried out by Owen and Folland (1988) and Folland et al. (1991) for their studies of Sahel rainfall variability. The same suite of model experiments has also been used by Rowell (1992) to study interannual variations of the Indian monsoon. ( a ) Model description The GCM has a horizontal resolution of 2.5" latitude by 3.75" longitude, with 11 sigma levels in the vertical (a=p / p , = 0.987, 0.937, 0.844, 0.718, 0.577, 0.436, 0.317, 0.230, 0.157, 0.089, 0.022, where p is pressure and p s is surface pressure). Physical parametrizations include: a diurnally and seasonally varying radiation scheme, interactive with water vapour, but with prescribed zonal mean cloud amounts; a penetrative convection scheme with stability dependent closure; boundary-layer mixing in the lowest three layers; and a bucket-type model of the surface hydrology, where soil moisture content interacts with evaporation and precipitation. Gravity-wave drag is not included. A more detailed description of the model's formulation is given by Cunnington and Rowntree (1986); for a complete description readers should refer to Slingo (1985). We also note here that this is an older version of the UK Meteorological Office (UKMO) climate model, and is not the same as that currently used at the Hadley Centre (the reasons for this and its implications are discussed further in sections 4 and 6). ( b ) SST data and experimental design The aim of our simulation experiments is to investigate the response of the model to global SST forcing. The SST data used are based on two versions of the Meteorological Office Historical SST data sets, MOHSST3 and MOHSST4. Both are described by Bottomley et a/. (1990), with the latter incorporating a few additional data sources (this was used for the experiments carried out most recently). These 'raw' data are available as monthly means on a 5" by 5" grid, in the squares where ship or buoy measurements are available. To produce a globally complete data set, with which to force the GCM, data-sparse areas (particularly the South Pacific and Southern Ocean) were filled by blending the data with a 1951-80 SST climatology using an adaptation of Reynolds's (1988) Poisson-equation technique much as in Parker et al. (1995), except that only climatological ice extents (Alexander and Mobley 1976) were used with the SST set to
32.2 38.0 21.7 35.2 19.1 47.9 21.9 18.5
% Significant
Sahel
