The West African country of Côte D'Ivoire (approximately 4-10^oN, 8-2^oW) lies at the western edge of a region (approximately south of 10^oN, 7.5^oW-7.5^oE) whose July-September rainfall total has a strong positive correlation (approximately 0.7) with Equatorial Atlantic sea-surface temperature (SST; Ward et al. 1990; Janicot 1992; Rowell et al. 1995; Ward 1998). The association is robust, remaining steady through the complete historical record, beginning early this century. The July-September rains in this region have not shown large multi-decadal fluctuations.

This association is used here to make an experimental rainfall forecast for Côte D'Ivoire. Investigations have shown that a robust predictor is the May SST anomaly averaged for the region Equator-10^oS, 20^oW-10^oE (Aligbe et al. 1997). A prediction was also made in 1997 (Berte and Ward, 1997). The forecast this year follows on from the PRESAO-1 (First West African Forum on Climate Variability and Prediction and its Application in Early Warning Systems for Food Security) West Africa forecast made in early May 1998 for the July-September season across the whole of West Africa (see African Center of Meteorological Applications and Development web site http://www.acmad.ne). The forecast reported now in this document serves to update the PRESAO-1 prediction with the latest SST information and also provide an example of the type of downscaling of the PRESAO map product that can be made by National Meteorological Services to better serve the needs of users.

First, a forecast is made for a rainfall index based on the 10 synoptic stations in Côte D'Ivoire with long records. The observed mean percentage of normal July-September rainfall is calculated for each year 1955-90, and then a regression is constructed between this rainfall index and the May SST anomaly predictor. With one predictor, problems of overfitting are much less than is the case with multiple candidates. For independent verification we have fitted the model on the first half of the record and tested on the second half, and then reversed the process. Table 1 shows that the relationship holds well in both sub-periods.

The May SST anomaly predictor in 1998 is +0.623^oC. This is a substantial warm anomaly, and contrasts with the cooler than normal conditions experienced at this time last year. The forecast for July-September 1998 is for a clearly above normal rainfall total (119.4% of the 1961-90 normal). The regression analysis has been repeated for individual stations (Table 2). The skill is generally better for the central and southern stations, and poorer for the northern and western stations, which are on the geographical edge of the teleconnection with SST.

The region normally experiences two rainfall peaks, one during March-June, the other during October-November, which are exploited for two crops. In years when the rains during July-September are forecast to be good, there is the potential for innovative farmers to plant a second crop in early July, and have produce to take to market in early October at a time when fresh produce is in short supply and prices are high.

With this application in mind, we have made a further set of forecasts that can be viewed as forecasts for the "rainfall season's characteristics that are relevant to agriculture", including such features as dry spell lengths. For this, we used the Frere and Popov (1979) crop model to calculate a crop index in each year 1955-90, using 10-day rainfall totals for each station. The index effectively estimates the amount of water stress suffered by the crop (both excess and deficit). A crop index value of 100 indicates no stress. Thus, if there is a 30-day dry spell, the index will drop dramatically, and it cannot recover if there are subsequently very heavy rains - in fact the index may fall further due to excess water. Results are here shown assuming 100-day maize is planted on the 1st July in each year, and that each year has a potential evapotranspiration of 50 mm per 10 day period. Other crops and other choices of evapotranspiration have also been studied (Aligbe et al., 1997) and the results (Table 3) are found to be robust. To make a forecast, a regression between the crop index and the May SST anomaly index is calculated. An example of the relationship was shown in Figure 1 of Berte and Ward (1997), demonstrating that a linear assumption is reasonable, though more sophisticated regressions are possible. Results in Table 3 are presented in the same form as Table 2. On average, there is more skill in predicting the crop index than in predicting the seasonal rainfall total. With the warm SST anomaly this year in the Equatorial Atlantic, the forecasts for the crop index are generally good. This contrasts greatly with the forecasts that were made for 1997 (Berte and Ward, 1997). While a detailed verification of the 1997 forecast is still to be made, available rainfall data indicate that the forecast was generally accurate (Omar Baddour, Climate Division, African Centre of Meteorological Applications for Development, Niamey , personal communication).

Acknowledgments: The authors are grateful to Dr Roger Stern (formerly ICRISAT, Niamey) for supervision on using the crop model and to the SODEXAM climate data section for assistance with accessing the daily precipitation data held at SODEXAM, Abidjan. The SST data were kindly provided by the UK Meteorological Office (through Andrew Colman). The work was supported by the WMO project CLIPS and the African Centre of Meteorological Applications for Development, Niamey, Niger.

References:

Aligbe, O., Y. Berte and M.N. Ward, 1997: The procedure for forecasting seasonal rainfall amount and crop performance in the Guinea Coast (West Africa), using Equatorial Atlantic Sea Surface Temperature Anomalies. African Centre of Meteorological Applications for Development, Niamey, Niger. Research Report, pp50. (French version available from Y. Berte).

Berte, Y. and M.N. Ward, 1997: Experimental Forecast of Seasonal Rainfall and Crop Index for July-September 1997 in Cote D’Ivoire. NOAA Experimental Long-Lead Forecast Bulletin, Vol. 6, No. 3, 27-29.

Frere, M. and G. Popov, 1979: Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection Paper no. 17. pp64.

Janicot, S., 1992: Spatiotemporal variability of West African rainfall. Part II: Associated surface and airmass characteristics. J. Climate, 5, 499-511.

Rowell, D.P., C.K. Folland, K. Maskell and M.N. Ward, 1995: Variability of summer rainfall over tropical North Africa (1906-92): Observations and modelling. Quart. J. Roy. Meteor. Soc., 121, 669-704.

Ward, M.N., J.A. Owen, C.K. Folland and G. Farmer, 1990: The relationship between sea surface temperature anomalies and summer rainfall in Africa 4-20^oN. Long Range Forecasting and Climate Memorandum No. 32. Available from the National Meteorological Library, Meteorological Office, Bracknell, Berkshire, UK.

Ward, M.N., 1998: Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multi-decadal timescales. J. Climate (in press).