CCA Forecast for Sahel Rainfall in Jul-Aug-Sep 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. The African Desk, established at the Climate Prediction Center (CPC) of the NWS/NCEP, has been experimenting with African seasonal forecasting in collaboration with the CPC. 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 the Sahel region of northern tropical Africa (10-25N, 20W-45E) for the Jul-Aug-Sep 1999 period. The boreal summer months comprise the climato-logically rainy season in the Sahel, as this is when the highest temperatures and atmospheric moisture content (i.e. the ITCZ) occur at these tropical regions of the Northern Hemisphere.

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. While additional fields such as upper air geopotential height, tropical low-level wind or outgoing longwave radiation might well enhance skill farther, data sets of these fields do not extend far enough into the past to satisfy the CCA's need for a long-term (e.g. at least 25-year) data record from which to identify the dominant relationships. The predictor and predictand data sets used here begin in 1955. For the 1999 Sahel rainfall prediction shown below, the predictor data are the global SST anomaly field over the four 3-month periods of Jun-Jul-Aug 1998, Sep-Oct-Nov 1998, Dec-Jan-Feb 1998-99, and Mar-Apr-May 1999. Using data from 1955-96, relationships between the prior year's SST anomaly evolution and the target year's Jul-Aug-Sep Sahel 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 boreal summer developed. Here the lead time is 1 month, because the latest predictor data used are those of May 1999, 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 Sahel rainfall data come from the gridded global rainfall data set developed by M. Hulme (Hulme 1994), at 2.5 by 3.75 resolution, resulting in 72 points in the Sahel. 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 the Sahel 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 Jul-Aug-Sep precipitation at 1-month lead, with average region wide correlation skill of 0.30, and 0.60 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. As shown in Thiaw et al. (1996) and Barnston et al. (1996), the expected area-average skill in forecasting the Sahel decreases very slowly from the low 0.30s as the lead time is increased from 0 to 1 month, more rapidly from 4 to 7 months lead, and then remains near 0.2 for 7 months out to a year lead. This has favorable implications for moderately long lead forecasts such as that made here, and even for 1 year forecasts, albeit with modest skill.

The spatial loading patterns of the leading CCA modes suggest two major sources of skill in the SST field, each expressed as a separate CCA mode. The first is an interdecadal trend toward warmer SST in the Indian Ocean, the extratropical South Atlantic and the eastern tropical and Southern Hemisphere Pacific. Warming of the SST in those regions has been associated with a decrease in the Jul-Aug-Sep Sahel precipitation, especially between the early 1970s and the mid-1980s. The central north Pacific SST has tended to cool during this same period. The participation of the eastern tropical Pacific and oppositely-signed central North Pacific SST in this predictor pattern implies a presence of ENSO in the relationship with Sahel rainfall: during warm (cold) episodes rainfall is lighter (heavier). However, ENSO does not appear to be the dominating aspect of the relationship. During the past year we transitioned rapidly from a very strong El Niño event to a moderate la Niña episode. Since June 1998, the global SSTs featured a moderately cold ENSO event. In addition, warmer than normal SSTs prevailed over the tropical Atlantic and over the central and eastern Indian Ocean. Over the past three months, the tropical North Atlantic exhibited slightly cooler than normal SSTs. This is helping to set up an Atlantic dipole pattern, with colder than normal SSTs over the northern tropical Atlantic and warmer than normal SSTs from the Gulf of Guinea southward. This Atlantic dipole combined with the ongoing la Niña episode provide conflicting signals for the prediction of the Jul-Sep Sahel rainfall at one month lead.

The resulting forecast for Jul-Aug-Sep 1999 at one month lead time is shown in Fig. 1. The predictions are expressed in terms of departures from the climatological probabilities of 3 equi-probable categories of below, near, and above normal rainfall. The climatological probability of each category is 0.333. Positive (negative) departures from the climatological probability indicates a shift toward wet (dry) extremes. For instance a departure of -0.06 from the climatological probability indicates probabilities of 0.39 and 0.27 for below and above normal rainfall, respectively. For the sake of simplicity, the probability for the normal category remains at 0.33. When skill is low, climatology (denoted with "0") is suggested. Slightly higher than climatological probabilities for above normal rainfall over the eastern and western edges of the domain, and over parts of central Sahel, including Burkina Faso, southeastern Chad, and western Sudan are expected. However, slightly higher than climatological probabilities for below normal rainfall are expected across much of central Sahel.

The field significance of the skill map (not shown), computed using Monte Carlo randomizations (indicating the probability that this skill 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. Where the skill is estimated at 0.50 or higher, confidence in this forecast can be regarded as at least moderate. Where skill is below 0.50 but at least 0.30, confidence is modest but the forecast is still usable. Where below normal rainfall is forecast, there is a shift in the probability distribution against having a wet 1999 rainy season. The lower the expected skill, the more the CCA tends to damp forecast amplitudes toward the mean.

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.

Figures:

Fig. 1. The CCA-based rainfall probability anomaly forecast for the Sahel region of northern tropical Africa for Jul-Aug-Sep 1999. Probability anomalies (X100) are with respect to the "above normal" rainfall tercile: "2" indicates probabilities of .313, .333, .353 for the below, near and above normal terciles, respectively; "-6" indicates .393, .333, .273; and "0" indicates .333, .333, .333 (i.e., climatological probabilities, or no useful forecast information available).