Nature, 439, 576-579
 

Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

Madeleine C. Thomson
International Research Institute for Climate Prediction (IRI), The Earth Institute at Columbia University
New York, USA

Francisco J. Doblas-Reyes
ECMWF, Shinfield Park
RG2 9AX, Reading, UK

Simon J. Mason
International Research Institute for Climate Prediction (IRI), The Earth Institute at Columbia University New York, USA

Renate Hagedorn
ECMWF, Shinfield Park
RG2 9AX, Reading, UK

Stephen J. Connor
International Research Institute for Climate Prediction (IRI), The Earth Institute at Columbia University New York, USA

Thandi Phindela
National Malaria Control Unit, Ministry of Health
Gaborone, Botswana

Andrew P. Morse
Department of Physics, University of Liverpool
Liverpool, L69 7ZT, U.K.

Tim N. Palmer
ECMWF, Shinfield Park
RG2 9AX, Reading, UK


Epidemic malaria control is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate fluctuations are a major determinant of epidemics in parts of Africa, where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes in epidemic risk. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically-based seasonal-timescale multi-model ensemble predictions of climate using the leading global coupled ocean-atmosphere climate models developed in Europe. This pioneering forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over forecasts issued with observed precipitation with, at the same time, a comparably high level of probabilistic prediction skill. In years where the forecast probability distribution is different from that of climatology, the information can be used by malaria decision makers for improved resource allocation.



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