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.
Highlights
Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of tropical climate prediction
The Indian Meteorological Department (IMD) measures the Indian summer monsoon rainfall (ISMR) using the AllIndia Rainfall Index (AIRI), the total amount of summer June-to-September (JJAS) rainfall averaged over the entire Indian subcontinent[1], which represents very well the leading principal mode of the ISMR2 and the like-signed severe rainfall anomalies occurring across India[3]
We show that the recent failure is largely due to the models’ inability to capture new predictability sources emerging during the recent global warming
Summary
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. 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. 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. We show that the recent failure is largely due to the models’ inability to capture new predictability sources emerging during the recent global warming
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