The Hadley Centre of the UK Meteorological Office has produced experimental forecasts for the Jul­Aug­Sep rainfall in tropical North Africa using multiple linear regression and linear discriminant analysis since 1986 (see Ward and Folland 1991 or the March 1993 issue of this Bulletin for information on these two statistical methodologies). Specifically, forecasts have been experimentally issued for the Sahel region of Africa since 1986, based preliminarily on March­April SST predictors (for an early May forecast) and then updated with the availability of May­June SST (for an early July forecast) for a more accurate zero­lead forecast for July­September rainfall. In 1992 the forecasts were expanded to cover four regions located from the Sahel southward to the Guinea Coast (Fig. 1).

African rainfall forecasts are made here using three distinct components of SST forcing as predictors. The first is an inter­hemispheric contrast (IHC) in SST anomaly. A warm Northern Hemisphere relative to Southern Hemisphere tends to accompany increased rainfall in the Sahel and Soudan. The second predictor is the local SST anomaly in the south tropical Atlantic, which accompanies more rain in the Guinea Coast region, less in the Sahel, and slightly less in the Soudan. Rainfall in the Guinea Coast region is best predicted, however, by the SST anomaly immediately adjacent to the west coast of Africa south of the Guinea Coast (Ward et al. 1993). The third SST predictor is related to ENSO, as the warm phase of ENSO accompanies reduced rainfall in the Sahel and especially the Soudan. The importance of each of these SST predictors (which are conveniently expressed as EOFs of the SST fields over various regions) for rainfall in each of the four regions is shown by standardized regression coefficients in multiple regression, which are discussed in the June 1993 issue of this Bulletin.

Independent forecast skill was estimated by developing multiple regression coefficients on a different period than that used for hindcast testing. Table 1 shows the skill of hindcasts for 1901­45 made using a regression model based on 1946­92 data, and vice versa, for two of the target regions.

Table 1. Independent hindcast skill (temporal correlation between forecasts and observations) in forecasting two of the four North African rainfall regions. The skill of persistence forecasts is shown in parentheses.

The correlations are fairly good, suggesting usable forecast skill. The high persistence skill for the SAH­N region in the later period arises because of the marked interdecadal component of variability, with relative wetness before the early 1970s versus relative dryness after that time.

The accuracy of the SAH­N forecasts issued since their inception in 1986 has generally been within one quint except for 1988 when there was a sudden development of La Nina after the forecast was made and the forecast was much too dry ("very dry" versus a verification of "wet"), and for 1994 when the forecast was again too dry.

In early work with an AGCM, it was found the model could simulate wet and dry Sahel years, driven by observed SSTs (Folland et al. 1991). Following a more recent period during which other UKMO models were unable to repeat that success, further study using ensemble simulations showed that the model rainfall anomaly over the Sahel region does indeed respond appropriately to significant SST forcing, as during ENSO episodes such as 1986-87 (warm), 1987-88 (rapid cooling), or 1988-89 (cold). For example, almost twice as much rain was correctly forecast in 1988 as in 1987, with no overlap of any ensemble members between the two years. Further detail on these findings are shown in the June 1995 issue of this Bulletin.

Real­time AGCM forecasts have been run with some success for the last several years, using persisted SST anomalies from May to forecast Jul-Aug-Sep northern African rainfall. The model forecasts have been considered in conjunction with the multiple regression and discriminant analysis forecasts even though the expected skill of the latter two are better known.

In March­May 1996, SST is above the 1961-90 average over most of the globe. Anomalies are particularly positive in the tropical south east Atlantic region (10E-20ES, 20EW-African coast), which usually favors below average seasonal rainfall in the Sahel and Soudan (regions 1, 2 and 3) and above average rainfall in the Guinea coast region (region 4). In March the average SST anomaly in the Southern Hemisphere was 0.24EC warmer than in the Northern Hemisphere, but this contrast decreased to 0.06EC in April. This interhemispheric contrast also favors drier than average conditions in regions 1, 2 and 3. Historically, warm SST anomalies in the central tropical Pacific are weakly associated with below normal rainfall in the Sahel. Central Pacific anomalies are presently small, so the effect of this factor is insignificant.

We expect that the 1996 seasonal rainfall in all four forecast regions will be particularly sensitive to any changes in the substantial south east Atlantic SST anomalies through the forecast period.

When linear discriminant analysis and linear multiple regression are applied to the values of the March­April SST predictors summarized above, resulting rainfall forecasts are as shown in Tables 2 and 3, respectively. To reduce noise, forecasts made using models with two different training periods and (for Sahel, Soudan) two different sets of predictors are averaged.

Table 2. Probability of July through September 1996 rainfall in each of five equiprobable (with respect to 1941­85 data) categories in four regions in tropical North Africa, according to linear discriminant analysis prediction models.

Table 3. Prediction of July to September 1996 rainfall in four regions in tropical North Africa, based on multiple linear regression models.

Experimental predictions have also been made using the UKMO climate AGCM described above, using persisted April SST anomalies. An ensemble of three predictions was used. Their results for 1996, and the average of these, are expressed as a percentage of model climatology in Table 4.

Table 4. Prediction of July to September 1996 rainfall in three regions in tropical North Africa, based on the UKMO climate AGCM. Top row shows the ensemble mean forecast, bottom three rows the forecasts of the individual ensemble members (% of model climatology).

Because the predictive skill of the dynamical model in these African regions has not been fully tested, the overall forecast is based mainly on the linear discriminant and multiple regression results. The discriminant analysis forecasts indicated that for regions 1, 2 and 3 the very dry category has the largest probability. For region 4 the largest probabilities are for the wet and very wet categories. The multiple regression prediction lies on the dry/very dry boundary for regions 1, 2 and 3, and just above the wet/very wet boundary for region 4.

The statistical and dynamical model forecasts agree qualitatively in region 2, raising confidence in the best estimate forecast shown below, but disagree in region 3 (where the statistical methods predict below average rainfall and the dynamical model average rainfall), and disagree in region 4 (where the statistical methods predict above average rainfall but the dynamical model below average rainfall).

The SST anomalies have been increasing rapidly in the sensitive south east Atlantic area. Historical data indicate that a rapid increase to present levels is often followed by a rapid decrease. Such a decrease through the July-September season would tend to produce rainfall nearer to average in each region, so confidence in the forecast is reduced by this factor.

Our best estimate forecast, based on the prediction methods and the current evolution of SST, is:

o DRY/VERY DRY boundary for regions 1 and 2 (moderate confidence)

o DRY/VERY DRY boundary for region 3 (low to moderate confidence)

o WET for region 4 (low confidence)

Note: A forecast for a boundary between two categories means that each of them is equally likely. Also note that although well below average rainfall is forecast for regions 1, 2 and 3 relative to the 1951-80 mean, the forecast rainfalls are near average with respect to the drier 1971­90 means.

Folland, C.K., J.A. Owen, M.N. Ward, and A.W. Colman, 1991: Prediction of seasonal rainfall in the Sahel region of Africa using empirical and dynamical methods. J. Forecasting., 10, 21­56.

Nicholson, S.E., 1985: Sub­Saharan rainfall 1981­84. J. Clim. Appl. Met., 24, 1388­1391.

Ward, M.W. and C.K. Folland, 1991: Prediction of seasonal rainfall in the north Nordeste of Brazil using eigenvectors of sea­surface temperature. Int. J. Climatol., 11, 711­743.

Ward, M.N., C.K. Folland, K. Maskell, A.W. Colman, D.P. Rowell and K.B. Lane, 1993: Experimental Seasonal Forecasting of Tropical Rainfall at the U.K. Meteorological Office. In: Prediction of Interannual Climate Variations (J. Shukla, Ed.), NATO ASI Series, Vol. 16, Springer­Verlag, Berlin, 197­216.

Figures