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.