An assessment of the value of

HEADLINE: OUR OVERALL BEST ESTIMATE IS FOR THE DRY QUINT CATEGORY

INTRODUCTION

The Met Office use SST based statistical methods and dynamical models of the global ocean-atmosphere system to make seasonal predictions of tropical rainfall. Forecasts have been made for tropical East Africa October-December rainfall (the 'short rains') since1994 and appear in previous September issues of this bulletin.

A set of statistical forecasts and a set of dynamical forecasts are presented here for the whole East Africa Region (5N-15S, 30E to Indian Ocean Coast referred to henceforth as the “whole region” ) and for 2.5^olatitude x 3.75^olongitude boxes shown in figures 1 and 2..

FORECAST METHODS

The statistical forecasts are made by using linear regression and discriminant analysis techniques, with three indices of global sea surface temperature (SST) anomaly patterns and 2 indices of mean SST anomaly for 2 box regions (all predictors are defined in the Appendix) known to be linked to E African rainfall. The forecast model is derived from historical rainfall and SST information.

The dynamical forecasts are presented in figure 2 and produced using GloSea, the Met Office GLObal SEAsonal forecast model which is a version of the HadCM3 Coupled Ocean-Atmosphere model modified for seasonal forecasting. GloSea is run as a 40-member ensemble of predictions initialised with atmosphere and oceanic conditions observed in early September. GloSea hindcasts produced as part of the DEMETER project (see www.ecmwf.int/research/demeter for more about DEMETER) were used to correct the GLOSEA forecast output for model bias and convert the forecast into probability format.

For “Whole region” forecasts, the historical rainfall record is divided into 5 equi-probable categories.

Based on 1961-1990 rainfall, the category boundaries (as percentages of mean rainfall) are:

Grid box forecasts are expressed as probabilities of terciles which are climatologically equi-probable over 1961-1990. This is in order to make the forecasts compatible with GHACOF forecasts which are expressed in the same format.

Grid box forecasts are also presented as probabilities of a wetter season than last year (OND 2003) and as probabilities of an extreme season defined as being wetter or drier than any of the past 10 years (1994-2003) ,

FORECAST SKILL (FOR THE WHOLE REGION)

PERFORMANCE OF TRIAL FORECASTS FOR 50 PAST YEARS

The statistical and dynamical forecasts were tested using trial forecasts over the period 1948 to 1997 and 1959 to 2000 respectively. The assessment measure used is correlation. Statistical linear regression forecasts were assessed using a method where a trial forecast is made for each year using a regression equation calculated using data for the remaining years. This assessment provides a good measure of forecast skill from minimal data.

Statistical forecast skill correlation=0.53

GloSea model forecast skill correlation=0.38

These correlations are statistically significant at the 5% level.

Note: The GloSea assessments were of trial forecasts initialised in early August whilst the GloSea forecast presented here was initialised in early September. Hence the skill of the dynamical forecast presented here is probably slightly higher than these GloSea assessments indicate as more recent precursor information is used.

PERFORMANCE OF REAL TIME STATISTCAL FORECASTS (USING INFORMATION UP TO LATE SEPTEMBER)

Forecasts have been produced for this region since 1994. The forecasts for 1994 and 1995 were strongly influenced by above and below average SST in the NW Pacific respectively and the forecasts for 1997-2000 where influenced by the 1997 El Nino and the 1998-1999 La Nina events.
The observed rainfalls for 2000 and 2001 were quite close to the dry/average and very dry/dry boundaries respectively so the forecasts for these seasons were more accurate than apparent from the table.

Year

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Forecast Category

Very

Wet

Dry

Average

Wet

Dry or

Average

Dry

Average

Dry or

VeryDry

Dry

Observed

Category

Average

Dry

Very

Dry

Very

Wet

Very

Dry

Very Dry

Wet

Very

Dry

Note: The categories used for the 1994-1998 forecasts are based on a 1951-1980 climatology. For the 1999 and later forecasts, categories based on the 1961-1990 climatology are used as 1961-1990 is the accepted WMO standard climatology period and is used by most forecasters. The 1961-1990 rainfall average is 104% of the 1951-1980 average.

FORECASTS FOR THE 2004 SEASON

STATISTICAL FORECAST FOR WHOLE REGION

Below average SST in the NW Pacific near Japan and in the tropical E Pacific near the Peruvian coast are favouring below average rainfall in E Africa this year. The regression forecast is 93% of the 1961-1990 average and is in the DRY category.

The discriminant analysis technique gives the following probabilities for the 5

(1961-1990 based) categories:

HIGHER RESOLUTION GRID BOX FORECASTS

STATISTICAL TERCILE PROBABILITY FORECASTS (FIGURE 1a)

Figure 1a consists of 6 statistical forecast probability maps. The upper row of 3 maps show probabilities for the 3 terciles for all grid boxes for which there are data. The second row of 3 maps (labelled skill mask) is a repeat of the first row but only probabilities for grid boxes where there is significant correlation skill according to an independent test are shown. To be included in the skill mask map, linear regression hindcasts for the box must pass at least 1 of these 2 tests:

Correlation skill over 1949-1998 is significant at the 5% level
Correlation skill calculated over the subset of forecasts predicting the same tercile as predicted for this year is significant at the 5% level

The average or dry tercile is favoured for most boxes south of the equator. Further north, probabilities are generally close to chance (33.3%).

STATISTICAL FORECASTS OF EXTREMES AND CHANGE FROM LAST YEAR (Figure 1b)

Figure 1b is similar to figure 1a but probabilities of a wetter season than last year and of 10-year extremes are presented. The probability of 2004 being wetter than 2003 is very high over most of the region as 2003 was very dry. Probabilities for an extreme dry or an extreme wet season are generally close to or lower than expected from climatology (<10%) except in South East Tanzania where probabilities for an extreme wet season are slightly elevated and in NE Kenya where probabilities for an extreme dry season are slightly elevated.

GLOSEA TERCILE FORECAST PROBABILITIES (Fig 2a)

The GloSea forecast (fig. 2a) probabilities are closer to chance than the statistical forecasts but the dry and average categories are still favoured south of the equator.

GLOSEA FORECASTS OF EXTREMES AND CHANGE FROM LAST YEAR (Figure 2b)

A wetter year than last year is generally favoured. Probabilities for extreme seasons are generally close to or lower than expected from climatology (<10%) except in Southern Tanzania where probabilities for an extreme wet season are slightly elevated and in NE Kenya where probabilities for an extreme dry season are slightly elevated The locations of these elevated extreme probabilities correspond well with the statistical forecasts (fig.1b).

OVERALL BEST ESTIMATE:

(a) Whole Region:

This year, the dynamical and the statistical forecasts are both indicating that below-average precipitation is likely though the statistical forecast is drier than the dynamical. The DRY quint category has the highest probability according to both the statistical and dynamical forecasts and is located near the middle of the probability distribution in each case, hence the DRY quint category is our “best estimate” for the region as a whole.

(b) Higher resolution grid boxes:

Consistent with the large area forecast, the dry tercile is generally most probable, particularly in the statistical forecasts. The highest probabilities for the DRY tercile are over southern Kenya and most of Tanzania. There is a slightly elevated risk of an extreme wet season in Southern Tanzania and a slightly elevated risk of an extreme dry season near the NE Kenyan coast.

NOTE: These forecasts are experimental and should be used with caution.

REFERENCE:

Mutai, C.C., Ward, M.N and Colman, A.W. Prediction of East Africa seasonal "short rainfall" rooted in evidence for widespread SST-forced variability during October-December. I.J.Climatol.18 975-997 (1998).

FIGURE 1a: PROBABILITY FORECASTS BY STATISTICAL METHOD FOR TERCILES

FIGURE 1b: PROBABILITY FORECASTS BY STATISTICAL METHOD FOR A WETTER SEASON THAN LAST YEAR AND FOR 0 YEAR EXTREME SEASONS

FIGURE 2a: PROBABILITY FORECASTS FROM GLOSEA FORECAST FOR TERCILES

FIGURE 2b: PROBABILITY FORECASTS BY GLOSEA FOR A WETTER SEASON THAN LAST YEAR AND FOR 0 YEAR EXTREME SEASONS

APPENDIX:

The figures show the rotated global SST EOF patterns used as predictors for the statistical forecast. For all 3 patterns, positive SST anomalies in regions with positive weights and negative SST anomalies in regions with negative weights favour above average rainfall and vice-versa.

The 2 box regions are

1) “NE Indian ocean” region 0-10N 50-60E + 0-10S,40-60E

2) “Pacific” region 0-20N, 150E-150W

The discriminant and regression forecasts are the average of 3 predictions from the following 3 sets of predictors:

1) Rotated EOF2, EOF 4 and EOF 5

2) Rotated EOF2, EOF 4 and EOF 5 + NE Indian Ocean region

3) Rotated EOF2, EOF 4 and EOF 5 + NE Indian Ocean region + Pacific region

The predictors are weighted as follows

Rotated EOF2, EOF4 and EOF 5 all 25% , NE Indian Ocean region 16.7%, Pacific region 8.3%

The pattern shown in figure A3 (rotated EOF 5) is the most important predictor contributing to over 50% of the forecast variance.

Figure A1:

Figure A2:

Figure A3:

Very Dry/Dry	Dry/Average	Average/Wet	Wet /Very Wet
74%	86%	102%	124%

Seasonal Forecasting Group, Ocean Applications, Met Office, Exeter, UK