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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Charney, J. G. & Shukla, J. Predictability of Monsoons 99–109 (Cambridge Univ. Press, 1981).
Shukla, J. & Paolino, D. A. The southern oscillation and long range forecasting of the summer monsoon rainfall over India. Mon. Weath. Rev. 111, 1830–1837 (1983).
Kucharski, F., Bracco, A., Yoo, J. H. & Molteni, F. Low-frequency variability of the Indian monsoon–ENSO relationship and the tropical Atlantic: The ‘weakening’ of the 1980s and 1990s. J. Clim. 20, 4244–4266 (2007).
Kucharski, F., Bracco, A., Yoo, J. H. & Molteni, F. Atlantic forced component of the Indian monsoon interannual variability. Geophys. Res. Lett. 35, http://dx.doi.org/10.1029/2007GL033037 (2008).
Thomson, M. C. et al. Malaria early-warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439, 576–579 (2006).
Hay, S. I. et al. Forecasting, warning, and detection of malaria epidemics: A case study. Lancet 361, 1705–1706 (2003).
Laneri, K. et al. Forcing versus feedback: Epidemic malaria and monsoon rains in north-west India. PLoS Comput. Biol. 6, e1000898 (2010).
Baeza, A. et al. Climate forcing and desert malaria: The effect of irrigation. Malaria J. 10, 190 (2011).
Webster, P. & Yang, S. J. Monsoon and ENSO: Selectively Interactive Systems. Q. J. R. Meteorol. Soc. 118, 877–926 (1992).
Nigam, S. On the dynamical basis for the Asian summer monsoon rainfall-El Niño relationship. J. Clim. 7, 1750–1771 (1944).
Terray, P., Delecluse, P., Labattu, S. & Terray, L. Sea surface temperature associations with the late Indian summer monsoon. Clim. Dynam. 21, 593–618 (2003).
Terray, P., Chauvin, F. & Douville, H. Impact of southeast Indian Ocean sea surface temperature anomalies on monsoon-ENSO-dipole variability in a coupled ocean-atmosphere model. Clim. Dynam. 28, 553–580 (2007).
Kucharski, F. et al. A Gill-Matsuno-type mechanism explains the tropical Atlantic influence on African and Indian monsoon rainfall. Q. J. R. Meteorol. Soc. 135, 569–579 (2009).
Connor, S. J., Thomson, M. C., Flasse, S. P. & Perryman, A. H. Environmental information systems in malaria risk mapping and epidemic forecasting. Disasters 22, 29–56 (1998).
Bouma, M. J. & van der Kaay, H. J. The El Niño-Southern Oscillation and the historic malaria epidemics on the Indian subcontinent and Sri Lanka: An early warning system for future epidemics? Trop. Med. Int. Health 1, 89–96 (1996).
Pascual, M., Rodó, X., Ellner, S. P., Colwell, R. & Bouma, M. J. Cholera dynamics and El Niño-Southern oscillation. Science 289, 1766–1769 (2000).
Rodó, X., Pascual, M., Fuchs, G. & Faruque, A. S. G. ENSO and cholera: A nonstationary link related to climate change? Proc. Natl Acad. Sci. USA 99, 12901–12906 (2002).
Cash, B. A., Rodó, X. & Kinter III, J. L. Links between Tropical Pacific SST and the regional climate of Bangladesh: Role of the eastern and Central Tropical Pacific. J. Clim. 21, 4647–4663 (2008).
Cash, B. A., Rodó, X., Kinter III, J. L. & Yunus, M. Disentangling the impact of ENSO and Indian Ocean variability on the regional climate of Bangladesh: Implications for cholera risk. J. Clim. 23, 2817–2831 (2010).
Chen, M., Xie, P. & Janowiak, J. E. Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeorol. 3, 249–266 (2002).
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, http://dx.doi.org/10.1029/2002JD002670 (2003).
Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sensing 26, 4485–4498 (2005).
Descloux, E. et al. Climate-based models for understanding and forecasting Dengue epidemics. PLoS Negl. Trop. 6, e1470 (2012).
Fawcett, T. ROC Graphs: Notes and Practical Considerations for Researchers (Kluwer Academic, 2004).
Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. & Xavier, P. K. Increasing trend of extreme rain events over India in a warming environment. Science 314, 1442–1445 (2006).
Losada, T. et al. Tropical response to the Atlantic Equatorial mode: AGCM multimodel approach. Clim. Dynam. 35, 45–52 (2010).
Haarsma, R. J. & Hazeleger, W. Extra-tropical atmospheric response to equatorial Atlantic cold tongue anomalies. J. Clim. 4, 2076–2091 (2007).
Ding, H., Keenlyside, N. S. & Latif, M. Impact of the Equatorial Atlantic on the El Niño Southern Oscillation. Clim. Dynam. 38, 1965–1972 (2012).
Kumar, K. K. On the weakening relationship between the Indian Monsoon and ENSO. Science 284, 2156–2159 (1999).
Team, T. G. G. A. M. D. The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations. J. Clim. 17, 4641–4673 (2004).
Zebiak, S. E. Air–sea interaction in the equatorial Atlantic region. J. Clim. 6, 1567–1586 (1993).
Huang, B., Schopf, P. S. & Shukla, J. Intrinsic ocean–atmosphere variability in the tropical Atlantic Ocean. J. Clim. 17, 2058–2077 (2004).
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.
The authors declare no competing financial interests.
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Cash, B., Rodó, X., Ballester, J. et al. Malaria epidemics and the influence of the tropical South Atlantic on the Indian monsoon. Nature Clim Change 3, 502–507 (2013). https://doi.org/10.1038/nclimate1834
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