One objective of the European PROVOST experiment (PRediction Of climate Variations On Seasonal and interannual Timescales) is to estimate potential dynamical seasonal predictability given ideal surface boundary conditions on a global scale. To this end, four European models (the UKMO Unified Model at climate resolution, the ECMWF T63 model and the ARPEGE model run at T42 by Mätäo-France and T63 by EDF) have been integrated in 9-member ensembles initialized at 24-hour intervals for four months for each season over 15 years from 1979 to 1993 (climatologies are calculated over the same period). Common initial conditions and verifying analyses obtained from the ECMWF reanalysis as well as common SST anomalies from the UKMO GISST and Reynold's OI data sets were used in all experiments. All initializations were at 0000Z finishing on the day prior to the start of the season. The north-eastern region of South America has been identified through these PROVOST experiments using the UKMO model as an area of relatively high predictability and in this paper a real-time seasonal forecast at one month lead is provided for the region based on these assessments.

The area with higher predictability broadly stretches through much of eastern Brazil, the Amazon Basin, French Guiana, Surinam and Guyana, even covering the southeastern parts of the Caribbean. Within this area correlations over the 15 years between ensemble-mean rainfall anomalies and anomaly values obtained from the gridded observed land-surface rainfall data set of Hulme (1994) exceed 0.5, with correlations in excess of 0.8 in the vicinity of the Nordeste region of Brazil (Fig. 1). The observed data set is gridded to the same resolution as the model (2.5° x 3.75°), but in order to reduce noise 4° x 4° blocks have been joined together to produce Figure 1; only blocks with adequate data were retained. Time series of en-semble mean rainfall and the Hulme data for selected regions illustrate the fact that the dynamical model, although able to capture the interannual variability reasonably well, has insufficient variability (Fig. 2). Hence a variance inflation has been calculated using en-semble means for each gridded area and applied to the forecasts from both the members and the ensemble means given below. Also shown in Figure 2 are correlations between ensemble mean predictions and some of the common rainfall indices for the Nordeste (see Colman et al. 1997 for index details); again the model has a high level of predictability (correlations approach 0.9) but requires variance inflation (here done against the FQ index, but results for other indices are almost identical). Note that observed anomalies frequently lie within the ranges of the inflated ensembles or are close outliers to those ranges.

Forecasts as produced for the 1997 March to May season are derived from nine-member ensemble runs, but with two major design differences from the predictability experiments outlined above. First, initialization for the predictions is from late January rather than late February as in the PROVOST runs. Secondly, the real-time experiments use persisted SST anomalies (from January) throughout. Neither is thought likely to have significant negative impacts on the model's ability to provide real-time predictions. Skill over the region as deduced from the PROVOST runs remains high throughout the year, whether for months 1 to 3 or 2 to 4 of the simulations. Indeed, equivalent levels of skill tend to be present on a monthly time scale, although with some drop-off into the fourth month. Hence the shift in the start date is considered highly unlikely to affect potential predictability for the region from that deduced for the "standard" seasons. Concerning use of persisted SST anomalies, experiments for six winter seasons have been carried out to date with persisted SST anomalies. While there is some inevitable loss of predictability associated with the use of persisted anomalies, this appears to be minimal in areas of relatively high predictability such as considered here, and certainly does not eliminate predictability in terms of the levels normally associated with seasonal forecasts (Fig. 3). Use of persisted anomalies fails, of course, during seasons in which there is a substantial readjustment of SST anomalies over ocean areas related to a given region's rainfall; experience has been gained of such failed forecasts for the Nordeste in preliminary work with the model. Currently there is no solution to this problem of rapid intraseasonal SST anomaly distribution changes: the forecasts given below are conditional on the assumption of continuity of the January anomalies.

The ensemble provides a consistent prediction of above-average rainfall during March to May 1997 across the region except over the continental interior (area E - below average in all members) and over the Nordeste (area D), where five members are below-average (Fig. 4 and Table 1). A southward displacement of the ITCZ from its model climatological location is suggested. Inflated ensemble-mean, together with maximum and minimum, rainfall anomalies for each of the gridded areas depicted in Fig. 1, plus for various Nordeste rainfall indices, are listed in Table 1. Note that the gridded prediction for the Nordeste is somewhat less than for the indices, mainly because the block includes signal from the oceanic dry area representing the shift in ITCZ location (Fig. 4).

For the first time since this research began, the dynamical model forecasts for the Nordeste rainfall indices are entirely inconsistent with those from the empirical techniques developed at UKMO (Colman et al. 1997; this issue), techniques with an extended history of high skill. Examination of the empirical methods indicates that most of the information this season is being extracted from the Atlantic dipole, with a sign associated with dry Nordeste conditions. An ENSO predictor is also included in the empirical model, but this is currently weak. Scrutiny of Atlantic SST anomalies in January 1997 reveals a region of warm anomalies extending eastward from the Nordeste/Bahia coastal regions. Brief analysis suggests that anomalies of this sign in this region are typically associated with above-average Nordeste rainfall. It is hypothesized, therefore, that the difference in the empirical and dynamical predictions may result from the dynamical model's ability to respond to SST anomalies off Bahia whereas the UKMO empirical methods are tuned only to basin-scale anomalies and hence do not give much weight to the relatively localized effects from the Bahia region.

Colman, et al., 1997: Multiple regression and discriminant analysis predictions of Mar-Apr-May 1997 rainfall in northeast Brazil. Experimental Long-Lead Forecast Bulletin, Vol. 6, No. 1 (this issue).

Hulme, M., 1994: Validation of large-scale precipitation fields in general circulation models. Global Precipitation and Climate Change, M. Desbois and F. Desalmand, Eds., NATO ASI Series, Vol. 23, Springer-Verlag, 387-406.

Table 1. March to May 1997 seasonal forecast rainfall percentages of normal for the unmodified Ensemble Mean (E Mean - with respect to the model 1979-1993 climate) and inflated Mean (I Mean - with respect to the Hulme data for 1979-1983) and for the highest and lowest (inflated) ensemble members for each of the 6 areas depicted in Figure 1. Also shown are equivalent predictions for three Nordeste rainfall indices (FQ - Fortaleza-Quixeramobim; N -Nobre; H - Hastenrath; see Colman et al., 1997), inflated using the FQ index, estimated from the closest two and four model grid points; the equivalent Hulme area is D. Note that the rainfall indices refer to different periods although all have been predicted using March to May model rainfall (FQ - March-May; Nobre - February-May; Hastenrath - March-April); thus the Nobre prediction is not at long lead.

Fig. 1. Correlations for hindcasts over March to May 1979-93 between ensemble mean rainfall and the Hulme gridded rainfall set over 10 x 15 degree blocks.

Fig. 2. Time series of Ensemble Mean (EM) rainfall (as % of normal) pre- and post-inflation of ensembles created using observed SST and of the Hulme dataset for selected representative areas (see Fig. 1) and for the FQ Nordeste rainfall index. Results for the other indices are similar. Bars indicate inflated range of ensembles and the median. Capture Rate (CR) indicates the number of years out of 15 when the observations lies within the range of the inflated ensemble.

Fig. 3. Ensemble mean January to March one month lead hindcasts as % of normal for Area D (Fig. 1) for ensembles using observed and persisted SSTs from 1989 to 1994. Correlation between observed and persisted SST hindcasts is 0.78. Results for the other regions and the Nordeste indices are similar. Symbols: circle-observed SSTs; triangle-persisted SSTs; square-Hulme observed anomaly.