5.3 Predictability of Indian Monsoon Rainfall in the Context of the Recent Global Warming

Tuesday, 9 January 2018: 3:15 PM
Ballroom G (ACC) (Austin, Texas)
Bin Wang, Univ. of Hawaii, Honolulu, HI; and J. Li

Prediction of Indian summer monsoon rainfall (ISMR) is at the heart of the monsoon prediction. Despite century-long efforts and enormous progress made since 1886, the operational forecasts of All Indian Rainfall Index (AIRI) during recent decades (1989-2012) have little skill. Using data from 1871-20014 and with a physical-empirical (P-E) prediction model and couple climate model’s numerical experiments, we show that the recent failure is largely due to the dynamical models’ inability to capture new predictability sources emerging during the recent global warming. The P-E model for AIRI that captures three new predictors foreshadowing the development of the central-Pacific El Nino-Southern Oscillation (CP-ENSO), the strengthening of the North and South Pacific Highs and the Asian Low, can produce an independent forecast correlation skill of 0.51 for 1989-2012 and a 92-yr retrospective forecast skill of 0.64 for 1921-2012.

While most studies so far have focused on predicting the AIRI, it is more useful and challenging to forecast seasonal rainfall anomaly pattern across India. The 46-year (1960-2005) hindcast made by the five ENSEMBLE coupled models’ multi-model ensemble (MME) yields a temporal correlation coefficient (TCC) skill of 0.43 for predicting AIRI, but only 0.16 for predicting anomaly pattern. Here we developed the Predictable Mode Analysis (PMA) method and a suite of P-E models to predict ISMR anomaly pattern. We show that the first three observed empirical orthogonal function (EOF) patterns of the ISMR have their distinct dynamical origins rooted in the eastern Pacific (EP)-type ENSO, central Pacific (CP)-type ENSO, and Nino4 SST anomalies, respectively. The dynamical models’ skill for predicting ISMR distribution primarily comes from these three potentially predictable modes, which account for about 51% of the total observed variance. Based on understanding the lead-lag relationships between the lower boundary anomalies and the predictable modes, a set of P-E models is established to predict the ISMR anomaly pattern by using the sum of the predictable modes. The cross-validated TCC skill of the P-E models is more than doubled that of ENSEMBLE models’ MME hindcast, suggesting a large room for improvement of the current dynamical prediction. The methodology proposed here can be applied to a wide range of climate prediction and predictability studies. The limitation and future improvement are also discussed.

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