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Malaria epidemics and the influence of the tropical South Atlantic on the Indian monsoon

Nature Climate Change volume 3, pages 502507 (2013) | Download Citation

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The existence of predictability in the climate system beyond the relatively short timescales of synoptic weather1,2 has provided significant impetus to investigate climate variability and its consequences for society. In particular, relationships between the relatively slow changes in sea surface temperature (SST) and climate variability at widely removed points across the globe provide a basis for statistical and dynamical efforts to predict numerous phenomena, from rainfall to disease incidence, at seasonal to decadal timescales. We describe here a remote influence, identified through observational analysis and supported through numerical experiments with a coupled atmosphere–ocean model, of the tropical South Atlantic (TSA) on both monsoon rainfall and malaria epidemics in arid northwest India. Moreover, SST in the TSA is shown to provide the basis for an early warning of anomalous hydrological conditions conducive to malaria epidemics four months later, therefore at longer lead times than those afforded by rainfall. We find that the TSA is not only significant as a modulator of the relationship between the monsoon and the El Niño/Southern Oscillation, as has been suggested by previous work3,4, but for certain regions and temporal lags is in fact a dominant driver of rainfall variability and hence malaria outbreaks.

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Change history

  • 19 March 2013

    In the version of this Letter originally published online, the address given for J. Ballester should have been 'Institut Català de Ciències del Clima (IC3), 08005 Barcelona, Catalunya, Spain'. This error has now been corrected in all versions of the Letter.


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We thank the Director of the National Institute for Malaria Research (NIMR), New Delhi, for support, and the office of the Joint Director, National Vector Borne Diseases, Rajasthan and Gujarat, and District Malaria Officers, for supplying the malaria data. Support for this work was provided by NOAA NA08NOS4730321 (Oceans and Human Health Initiative), the Graham Environmental Sustainability Institute (GESI) at the University of Michigan, NSF ATM-0830068, NOAA NA09OAR4310058 and NASA NNX09AN50G. X.R. benefited from support from the QweCI EUFP7 project. We would like to acknowledge high-performance computing support provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation.

Author information


  1. Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland 20705, USA

    • B. A. Cash
  2. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys, 08010 Barcelona, Catalunya, Spain

    • X. Rodó
  3. Institut Català de Ciències del Clima (IC3), 08005 Barcelona, Catalunya, Spain

    • X. Rodó
    •  & J. Ballester
  4. London School of Hygiene and Tropical Medicine, University of London, London WC1 E7HT, UK

    • M. J. Bouma
  5. Department of Ecology and Evolutionary Biology University of Michigan, Ann Arbor, Michigan 48109-1048, USA

    • A. Baeza
    •  & M. Pascual
  6. National Institute of Malaria Research (ICMR), New Delhi 110077, India

    • R. Dhiman
  7. Howard Hughes Medical Institute, Chevy Chase, Maryland 20815-6789, USA

    • M. Pascual


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M.P., M.J.B. and R.D. conceived the malaria project of which this study is part; B.A.C. and X.R. conceived the climate analyses; B.A.C., X.R. and J.B. designed and implemented the numerical climate experiments; B.A.C. and M.P. conducted the statistical analyses and drafted the paper; A.B. contributed to the analysis of remote-sensing data; R.D. participated in the data collection; and R.D. and M.B. provided malaria expertise. All authors contributed to the writing of the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to M. Pascual.

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