CCA Forecast for Southern Africa Rainfall in Jan-Feb-Mar 1999

contributed by Wassila Thiaw and Anthony Barnston

Climate Prediction Center, NOAA, Camp Springs, Maryland

Severe and recurrent rainfall deficits across the African continent during the past two to three decades have been detrimental to the economy of the African nations. Thus, policy makers and funding agencies often face tough challenges to make relief plans. There clearly is a need for forecasts of short-term climate fluctuations, such as for seasonal total rainfall one or more seasons in advance. While numerical approaches are being considered, work so far has focused more on statistical methods. Here we apply canonical correlation analysis (CCA) to produce an experimental forecast for rainfall anomalies in southern Africa (35-10S,10-50E) for the Jan-Feb-Mar 1999 period.

The CCA method is a multivariate regression that relates patterns in predictor fields to patterns in the predictand field. The prediction design used here is the same as that of the CCA used as one of the tools for operational climate prediction in the U.S. (Barnston, 1994), based on earlier work of Barnett and Preisendorfer (1987). Four consecutive 3-month predictor periods are followed by a lead time and then a single 3-month predictand, or target, period. Forecast skill experiments have indicated that the global SST field serves best as a predictor. The predictor and predictand data sets used here begin in 1955. For the 1999 southern Africa rainfall prediction shown below, the predictor data are the global SST anomaly field over the four 3-month periods of Dec-Jan-Feb 1997-98, Mar-Apr-May 1998, Jun-Jul-Aug 1998, and Sep-Oct-Nov 1998. Using data from 1955-96, relationships between the prior year's SST anomaly evolution and the target year's Jan-Feb-Mar Southern Africa rainfall anomaly patterns are linearly modeled by the CCA. The predictor SST data for the current forecast are then projected onto the preferred relationships derived from the past years, and a forecast for 1999 austral summer developed. Here the lead time is 1 month, because the latest predictor data used are those of November 1998, preceding the beginning of the target period by 1 month.

The predictor SST data were derived from a combination of the COADS data (Slutz et al. 1985) and more recent OI data (Smith et al. 1996). The predictand Southern Africa rainfall data come from the gridded global rainfall data set developed by M. Hulme (Hulme, 1994), at 2.5 by 3.75 lat-lon resolution, resulting in 65 points in southern Africa. A rainfall data set consisting of individual stations has also been tested, with results shown in Thiaw et al. (1996) and Barnston et al. (1996). While skill results are roughly similar between the two rainfall data sets for southern Africa because of the sufficient station data density, the gridded data tend to show higher skill in parts of Africa having sparser data. This may be because the gridded data are developed using stations that have major gaps during some periods, while the station data set completely excludes such stations.

The diagnostic data produced by CCA indicate that expected skill is modest to moderate in predicting Jan-Feb-Mar precipitation at 1 month lead, with average region-wide correlation skill of 0.25, and 0.50 or higher at some locations. A cross-validation design is used in obtaining these skill estimates, where each year is held out of the developmental data set in turn, and then used as the forecast target.

The spatial loading patterns of the leading CCA mode suggest that the tropical pacific SST anomaly, representing the ENSO state, is an important determinant of the current forecast. A cool tropical Pacific SST tends to be associated with positive rainfall anomalies in most of southern Africa. In the most recent several months, the ENSO state has been moderately cool and conducive to enhanced to near normal rainfall in much of southern Africa.

The forecast presented below is expressed in terms of probability anomalies with respect to climatological probabilities for three equiprobable categories: below, near, and above normal (B, N, and A, respectively). The climatological probability of each category is 1/3. The forecast provides the estimated deviation from these probabilities for Jan-Feb-Mar 1999. For example, a 0.15 probability anomaly for above normal rainfall implies probabilities of below, near, and above normal categories of 0.183, 0.333, and 0.483 compared to the climatological probabilities of 0.333, 0.333, 0.333. In some cases the probability of the near normal category may be elevated at the expense of the two outer categories. Expressing the forecasts probabilistically enables uncertainty to be conveyed in the forecasts. It assumes that the users have some idea of the rainfall amount intervals defined by the bottom, middle and top thirds of the distribution. In a rough sense, these categories may often be equated to dryness, normal, or plentiful rainfall cases.

The probability forecast for southern Africa for Jan-Feb-Mar 1999 is shown in Fig. 1. The predictand grid points are shown by the locations of the numbers on the map. These numbers are probability anomalies for the above (A), near (N), and below normal (B) categories (X100). Higher than climatological probabilities for above normal rainfall across much of southern Africa are expected. The probabilities are even higher for northeastern southern Africa, including northern Mozambique and Malawi. Climatology is depicted in much of Angola, western Zambia, and southern Zimbabwe. The field significance of the collective skill over all 65 grid points, computed using Monte Carlo randomizations (indicating the probability that the skill level of this forecast map could have occurred by chance), is 0.000; i.e. there is virtually no chance that these levels of skill over this region are accidental.

The probability anomalies shown in Fig. 1 are moderately strong to fairly weak, with only a few points depicting anomalies of 0.10 or higher. This is because of the modest skill at most of the locations. Furthermore, the anomalies in the predictor SSTs are only moderately indicative of enhanced southern African rainfall by virtue of the moderately cold ENSO episode

References

Barnston, A.G., W. Thiao and V. Kumar, 1996: Long-lead forecasts of seasonal precipitation in Africa using CCA. Wea. Forecasting, 11, 506-520.

Hulme, M., 1994: Validation of large-scale precipitation fields in general circulation models. In Global Precipitation and Climate Change, M. Desbois and F. Desalmand, Ed., NATO ASI Series, Springer-Verlag, Berlin, 466 pp.

Slutz, R., S.J. Lubler, J.D. Hiscox, S.D. Woodruff, R.J. Jenne, D.H. Joseph, P.M. Steurer, and J.D. Elius, 1985: Comprehensive Ocean Atmosphere Data Set. NOAA, Boulder, CO, 268 pp. [Available from Climate Research Program, ERL, R/E/AR6, 325 Broadway, Boulder, CO 80303.]

Smith, T.M., R.W. Reynolds, and C.F. Ropelewski, 1994: Optimal averaging of seasonal sea surface temperatures and associated confidence intervals (1860-1989). J. Climate, 7, 949-964.

Thiaw, W., A.G. Barnston and V. Kumar, 1996: Teleconnections and seasonal rainfall prediction in Africa. Proceedings of the 20th Annual Climate Diagnostics Workshop, Seattle, Washington, October 23-27, 1995, 413-416.

Figure 1.The CCA-based rainfall probability anomaly forecast for southern Africa for Jan-Feb-Mar 1999. Probability anomalies (X100) are with respect to the "above normal" rainfall tercile: "15" indicates probabilities of .183, .333, .483 for the below, near and above normal terciles, respectively; "-5" indicates .383, .333, .283; and "0" indicates .333, .333, .333 (i.e., climatological probabilities, or no useful forecast information available).