Rethinking Indian monsoon rainfall prediction in the context of recent global warming

Bin Wang (), Baoqiang Xiang, Juan Li, Peter J. Webster, Madhavan N. Rajeevan, Jian Liu () and Kyung-Ja Ha
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Bin Wang: University of Hawaii at Manoa
Baoqiang Xiang: NOAA/Geophysical Fluid Dynamics Laboratory
Juan Li: University of Hawaii at Manoa
Peter J. Webster: Earth and Atmospheric Sciences, Georgia Institute of Technology
Madhavan N. Rajeevan: Earth System Science Organization, Ministry of Earth Sciences
Jian Liu: Key Laboratories for Virtual Geographic Environment and Numerical Simulation of Large Scale Complex System, School of Geography Science, Nanjing Normal University
Kyung-Ja Ha: Pusan National University

Nature Communications, 2015, vol. 6, issue 1, 1-9

Abstract: Abstract Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction. Despite enormous progress having been made in predicting ISMR since 1886, the operational forecasts during recent decades (1989–2012) have little skill. Here we show, with both dynamical and physical–empirical models, that this recent failure is largely due to the models’ inability to capture new predictability sources emerging during recent global warming, that is, the development of the central-Pacific El Nino-Southern Oscillation (CP–ENSO), the rapid deepening of the Asian Low and the strengthening of North and South Pacific Highs during boreal spring. A physical–empirical model that captures these new predictors can produce an independent forecast skill of 0.51 for 1989–2012 and a 92-year retrospective forecast skill of 0.64 for 1921–2012. The recent low skills of the dynamical models are attributed to deficiencies in capturing the developing CP–ENSO and anomalous Asian Low. The results reveal a considerable gap between ISMR prediction skill and predictability.

Date: 2015
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DOI: 10.1038/ncomms8154

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