MARK R. JURY
Geography Dept, Univ Zululand, KwaDlangezwa, 3 886
Background
The climate of southern Africa fluctuates widely, putting pressure on water resources and food security. At present over 20% of the population are under-nourished. Within a decade - 80 million people will face water scarcity (Petersen 1997). Rain-fed crops use - 70% of the available water and govern the economy of most southern African countries (Hulme et al 1996).
Fluctuations of seasonal rains have been linked with regional sea surface temperatures (SST) and the global El Niiio Southern Oscillation (ENSO). A reduction in societal stress could be affected by the reliable prediction of seasonal rains, underpinned by knowledge of ocean-atmosphere coupling processes (Hastenrath et al, 1993; Mason et al, 1996). Analysis of climatic data has highlighted the relationship between certain ENS0 warm events and southern African drought (Ropelewski and Halpert, 1987; Janowiak, 1988). As the drought begins, the tropical east Atlantic cools and upper westerly flow increases across Africa. In the tropical eastern Pacific and western Indian Ocean warming occurs (Cadet, 1985; Jury and Pathack, 1993) causing the Indian monsoon trough to consume the moisture intended for southern Africa (Jury, 1992; Jury et al 1994). A conceptual model of the regional circulation during El Nifio will be presented.
Predicting vear-to-year variability of resources
For development of statistical long-range models for southern Africa, historical data are required on key climatic signals in the preceding spring and fluctuations in resources available at the end of the rainy season. Following extensive research, a number of predictors have been assembled:
the Southern Oscillation Index, Quasi-biennial Oscillation; tropical Atlantic upper winds; sea surface temperature (SST) pattern time series; and air pressure and marine winds over the surrounding oceans. Statistical tests suggest that the total number of candidate predictors should be limited to half the training period.
‘Target’ indices are formulated for seasonal rainfall and maximum temperature, crop yields for maize and sugar, streamflows for major rivers, and other economic indices with application to hydrology and agricultural sectors. Spatial and temporal averaging is guided by the research of Nicholson et al (I 988), Pathack (1993), Makarau (1995) and Levey and Jury (1996). Although users may prefer small targets, larger areas are more predictable. Requirements for distinct predictions of early versus late summer rainfall, representing potential shifts in crop planting activities, are considered.
Multivariate equations are formulated by insertion of only two or three predictors. This improves the subsequent ‘skill’ of the model in application. Each predictor should be unique and uncorrelated. Predictive models are expected to explain over 50% of the variance. The accompanying figure and table describes the predictive model for South Africa’s agricultural income adjusted for inflation. At least one of the variables is closely associated with the global ENS0 phase. Skill validation tests indicate that reliable predictions for southern Africa’s
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resources are probable - 70% of the time 4 - 6 months in advance. Farmers and other decision makers can take mitigating action on the basis of consensus predictions from climate specialists using statistical and numerical models. In addition to long-range forecasts, current conditions can be monitored using satellite colour imagery (NDVI). Examples of past drought distribution and intensity will be provided.
Tarqet Model equation (1971-l 992) S A Agric Income +.30(ATpc3)+.33(W-SindP)+.24(SocnU)
variance expl. DW stat 58 % 2.33
Predictors in order of appearance: SST difference between the northern and southern tropical Atlantic, Pressure difference between the western and southern Indian Ocean, Zonal winds over the ocean south of Africa.
2 -’
-2-, , 1 . . . r . . . I I. I I I. I I
1870 1974 1978 1982 1986 1998 1994
P-YEARS
Figure - Hindcast fit of South African agricultural income (inflation adjusted) with a multi-variate model based on climate predictors at 4-6 month lead time.
Products and summary
In the regional forum for climate prediction (SARCOF), the needs of user groups are evaluated.
From user feedback, the skill of the forecast is the highest priority. Miss rates exceeding one-in- four years are of reduced value, because mitigating actions during a ‘bad’ year may be twice as costly as potential agricultural profits during a ‘good’ year.
Models to predict climate impacts in southern Africa have been formulated, tested and implemented by Climate Impact Predictions, Univ. Zululand. Predictor inputs are tracked via Internet from NCEP, USA. In the August to October season, long-range outlooks are provided on an Internet web site to assist in reducing drought risk in the coming summer season:
http://weather.iafrica.com/forecasts/cip~seasonal~outlook.html
The predictability of our limited food and water resources has been assessed, and reliable statistical models have been developed for a number of targets. Southern African governments and resource managers can anticipate drought and plan mitigation efforts aimed at improving economic security. It is expected that much of Africa can find similar tools to alleviate climate impacts in the near future.
References
Cadet, D.L. 1985: The Southern Oscillation over the Indian Ocean. Journal of Climate 5, 189-212.
Hastenrath, S., Nicklis, A. and Greischar, I,. 1993: Atmospheric-hydrospheric mechanisms of climate anomalies in the Western Equatorial Indian Ocean. Journal of Geophysical Research 98,202 19-20235.
Hulme, M., Arntzen, J., Downing, T., Leemans, R., Malcolm, J., Reynard, N., Ringrose, S., Rogers, D., Chiziya, E., Conway, D., Joyce, A., Jain, P., Magadza, C., Markham and Mulenga, H. 1996: Climatic Change and Southern Africa: an Exploration of some Potentiul Impacts and Implications in the SADC Region, Report to WWF International, 104~~.
Janowiak, J.E. 1988: An investigation of interannual rainfall variability in Africa. Journal of Climate 1, 240-255.
Jury M.R. 1992: A climatic dipole governing the interannual variability of convection over the SW Indian Ocean and SE Africa region. Trends in Geophysicul Research 1, 165-l 72.
Jury, M.R. and Pathack, B.M.R. 1993: Composite climatic patterns associated with extreme modes of summer rainfall over southern Africa: 1975-l 984. Theoretical and Applied Climatology 47, 137-l 45.
Jury, M.R., McQueen, C.A. and Levey, KM. 1994: SOI and QBO signals in the African region.
Theoretical and Applied Climatology 50, 103-l 15.
Levey, K.M. and Jury, M.R., 1996: Composite intraseasonal oscillations of convection over southern Africa.
J. Climate, 9, 1910-1920.
Makarau, A, 1995, Intra-seasonal oscillatory mods of the southern African summer circulation, PhD Thesis, Oceanogr dept, Univ Cape Town, 2 13 pp.
Mason, S.J., Joubert, A.M. Cosijn, C. and Crimp, S.J. 1996: Review of the current state of seasonal forecasting techniques with applicability to southern Africa. WaterSA 22,203-209.
Nicholson, S E, Kim, J and Hoopingamer, 1988, Atlas of African rainfall and its interannual variability, Meteor dept, Florida State Univ, 237 pp.
Pathack, B M, 1993, Modulation of South African summer rainfall by global climatic processes, PhD Thesis, Oceanogr dept, Univ Cape Town, 2 14 pp.
Petersen, M S, 1997, African water resources development and human needs, Hydor Aulos, 1,2, l-8 Ropelewski, CF. and Halpert, M.S. 1987: Precipitation patterns associated with El NifiolSouthem Oscillation. Monthly Weather Review I 15, 1606-l 626.
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