Experimental Forecast of 2001 Seasonal Rainfall

in the Sahel and Other Regions of Tropical North Africa

contributed by Andrew Colman and Mike Davey

Met Office, Bracknell, UK

The Met Office is conducting research into the effects of sea surface temperatures and other climatic variables on tropical rainfall. As part of this research, experimental forecasts have been made of seasonal rainfall for the Sahel (region 1) for each year from 1986 onwards. Since 1992, forecasts of seasonal rainfall have also been made for a slightly redefined rectangular Sahel (region 2, 15W to 37.5E and 12.5N to 17.5N), for an area south of the Sahel (region 3, 7.5W to 33.75E, 10N to 12.5N), and for an area extending further south to the coast (region 4, approximately 7.5W to 7.5E, 5N to 10N). The four regions are labeled in figure 1a.

The statistical forecasting techniques are based on March and April sea surface temperature (SST) anomaly patterns. Further details can be found in Folland et al, 1991. Several forecasts have been made using different versions of each technique, and they have been averaged together with dynamical forecasts produced using the Met Office Atmosphere Global Circulation Model (AGCM) and persistence forecasts (observed rainfall for last year's season) to obtain the forecasts shown below in figure 1.

The forecast period for regions 2-4 is July-September. For region 1 annual rainfall is predicted, though most of the rain in this region falls during July- September. For forecasting purposes, the predicted rainfall indices are categorized into quints which are equi-probable over 1961-1990. The 5 quints are referred to as Very Dry, Dry, Average, Wet and Very Wet. In table 1 the quints are defined as percentages of 1961-1990 average.

Table 1 Quint Boundaries

Region	Very-Dry/Dry	Dry/Average	Average/Wet	Wet/ Very Wet
1	75	97	109	121
2	81	93	102	117
3	88	99	104	112
4	82	94	106	115

Sea Surface Temperature Anomalies

The SST indices used to predict rainfall in north Africa represent regional and global scale anomaly patterns. Most important are tropical Pacific and Atlantic anomalies, and interhemispheric differences in anomalies.

Warm SST in the Tropical NW Pacific and Eastern Mediterranean favors above average rainfall in regions 2 and 3 and to lesser extent in region 1 this year. Warm SST in the South Atlantic near the African coast favors above average rainfall in region 4 and

below average rainfall in region 1. The inter- hemispheric contrast in SST anomaly is small this year favoring average rainfall in regions 1 2 and 3.

The Prediction System

The forecasts are weighted combinations (table 3) of statistical forecasts, dynamical forecasts and persistence (last year's observed seasonal rainfall). The statistical best estimate forecasts are produced by linear regression with SST indices as predictors. Statistical probability forecasts are calculated from the same SST indices using linear discriminant analysis.

The following predictors are used to create the forecasts.

Table 1: Statistical Forecast Predictors

Predictors	Training period	Reference
a. Time indices of 3 Global Scale SST EOFS	1901-2000, 1951- 2000	Folland et al. 1991
b. Time indices of 2 EOFs of South Atlantic SST and 1 EOF of Pacific SST	1901-2000, 1951-2000
c. Time indices of 3 Global Scale EOFs of filtered SST	1901-2000, 1951-2000	Folland et al. 1999
d. May-June indices of Global filtered EOFs calculated using predicted SST	1901-2000, 1951-2000
e. Time index of correlation field between March-April SST and rainfall with correlations not significant at 5% level set to 0	1981-2000

In previous years, only predictors (a) and (b) were used. Adding predictors (c), (d) and (e) was found to improve trial forecast skill over 1951-2000 (Table 2). In particular adding predictor (e) improved skill in predicting region 1 and 2 rainfall variability between 1981-2000. Predictors (a) and (b) are poor at predicting variability over this period. The trial forecasts referred to in table 2 were produced using the jackknife method; i.e. data for the forecast year and the next two subsequent years were excluded when calculating prediction equations.

Table 2: Performance of trial forecasts using combinations of Predictors, 1951-2000 Measured Using Correlation Between Forecast and Observed.

Predictors	Region 1	Region 2	Region 3	Region 4
a+b (as in previous years)	0.48	0.47	0.49	0.40
a+b+c	0.59	0.53	0.48	0.40
a+b+c+e	0.69	0.62	0.49	0.43
a+b+c+d+e	0.71	0.63	0.52	0.42
a+b+c+d+e+ dynamical	0.73	0.67	O.57	0.56
a+b-c+d+e+ dynamical + persistence	0.74	0.66	0.57	N/A

The dynamical forecast was produced using the 19 level HADAM3 version of the Met Office AGCM. The forecast is based on an ensemble of 9 AGCM runs each initialised with slightly different atmospheric conditions observed over the period May 3rd-4th and forced using SST anomalies observed in April which are assumed to persist throughout the forecast period. The AGCM ensemble was run to 5 months ahead (up to 30th September). Further information about dynamical ensemble forecasts at the Met Office can be found on our website at http://www.metoffice.com/research/seasonal.

The dynamical forecast output is expressed as both deterministic forecasts and probability forecasts for the 5 quint categories. The deterministic forecasts are produced by correcting the ensemble mean forecast for model bias as observed in model simulations for 1961-1990. The probabilities indicate the distribution of observed quint categories obtained for years when the model simulated the same rainfall category as that forecast for 2001.

Table 3 Forecast Weights

	Deterministic Forecasts	Probability Forecasts
Region	Statistical |Dynamical | Persistence	Stastistical|Dynamical|Persistence
1	0.52 |0.23 | 0.25	0.56 |0.19 |0.25
2	0.50 |0.25 | 0.25	0.55 |0.20 |0.25
3	0.49 |0.26 | 0.25	0.53 |0.22 |0.25
4	0.60 |0.40 | 0.00	0.63 |0.37 |0.00

The forecasts are weighted to reflect the reliability of the different inputs. The ratio of weights for the statistical forecast/dynamical forecast/persistence is about 2:1:1 for each of regions 1,2 and 3. Persistence is not used for the region 4 forecast, as persistence skill is negligible for this region. For region 4 the statistical /dynamical weighting ratio is about the same as for the other regions, (i.e. 2:1)

Last Year

Last year, the DRY category was observed in regions 1 and 3 and the VERY DRY category was observed in regions 2 and 4.

Forecast Summary

Forecasts for regions 1-4 are shown in figure 1. Weighted average deterministic forecasts are shown as percentages of the 1961-1990 average in figure 1a. In Figure 1b, the forecasts are expressed as percentage standardized units (e.g. standardized values of +100 indicate rainfalls one standard deviation above average) relative to 1961-1990 (NB. 1901-1980 for region 1 for compatibility with previous publications by the Met Office and Nicholson (1984). Quint categories are indicated in figure 1c. The skill of these weighted forecasts is indicated in fig. 1d by the trial forecast correlations with observed rainfall in the period 1951-2000. The correlations are well above the 5% significance level for all 4 regions. Probability forecasts for the 5 quint categories are shown in figure1f-j respectively. The Relative Operating Characteristic (ROC) skill in figure 1e is a measure of the performance of these probability forecasts over the period 1951-2000. ROC scores above 60% are considered to indicate significant (5% level) skill.

There are considerable differences between the forecasts for 2001 provided by the different methods. Persistence favors the Dry or Very Dry category in regions 1,2 and 3. Statistical methods favor the DRY category in region 1 and Wet or Very Wet categories in regions 2,3 and 4. The dynamical forecasts favor Average or Wet categories in regions 1 2 and 3 and the DRY category in region 4. Due to this disagreement between forecast methods, confidence is Low.

Our best estimate forecasts are:

Region 1: Wet

Region 2: Wet

Region 3: Wet

Region 4: Average

Hence, region 1 is expected to have a similar rainfall amount to last year whilst it is expected to be wetter in regions 2 and 3 than in 2000 but less wet than in 1999. Region 4 is expected to have more rain than last year but a similar amount to 1999. There is an above chance probability of a "Very Wet" category rainfall season in regions 1,2 and 3 (fig 1j).

Impact or SST Changes Between 1st May and 10th June.

SST anomalies associated with north African rainfall have not changed since April in a way that would significantly alter the forecast for N African rainfall. Hence our forecast remains the same as issued in May except for the change to the region 1 deterministic forecast referred to in note * below. An updated forecast will be issued to relevant National Met services and published on our website in July.

*Note

This forecast is the same as the forecast issued in May except that more discussion of statistical predictors is included here and the forecast for Region 1 has been revised from Dry to Wet due to a computational error affecting the original forecast.

References:

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

Folland, C.K., Parker, D.E., Colman, A.W. and Washington, 1999: Large scale modes of Ocean Surface Temperature since the late nineteenth century. In Beyond El Niño decadal and Interdecadal variability. Ed. A. Navarra, Springer pp 75-102.

Nicholson, S.E. 1985: Sub-Saharan rainfall 1981-84. J. Clim. Appl. Met., 24, pp 1388-1391.

Figure 1: Predictions for 2001 and Prediction Skill for 4 North African Regions. Probabilities, Skill and Regression (Standardized Units) Forecasts are Percentages.