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While the importance of El Niño to the Indian summer monsoon ( ISM) rainfall variability has been widely recognized, the role of tropical Indian Ocean ( TIO) is still unclear. Present results based on coupled model sensitivity experiments revealed that the TIO air-sea interaction trims down suppressed rainfall locally over the ISM region by opposing the influence of tropical Pacific Ocean ( TPO) during El Niño developing summers. Besides, El Niño-induced TIO basin-wide warming supports positive rainfall anomalies over the ISM region by maintaining local convection. TIO local air-sea interaction opposes the Pacific impact even in the absence of Indian Ocean Dipole. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, precipitation outputs from retrospective seasonal forecasts made by nine General Circulation Models ( GCMs) are used to investigate historical Indian summer monsoon seasonal rainfall variability and predictability over India. The observed data is taken from the India Meteorological Department whereas GCMs are obtained from the International Research Institute for Climate and Society, Columbia University, the National Center for Environmental Prediction, and the Japan Agency for Marine Earth Science and Technology. The study focusses on June- September precipitation hindcasts initialized from the 1 May. First, the mean climatology, variance of interannual variability ( IAV), and long-term trends for the nine GCMs were evaluated. Then Empirical Orthogonal Function ( EOF) is used to extract major climate modes and spectral analyses method is used to investigate the temporal properties of the leading principal components. It is found that the models are able to reproduce the climatology and IAV to varying degrees. The EOF and spectral analyses of models hindcast reveal that these models are capable of predicting the observed precipitation variability to some extent. In order to study the remote response, the correlation co-efficient between model predicted rainfall and sea surface temperature ( SST) have been calculated. The results suggest that the models show exaggerated remote response to ENSO SST forcing and the Indian Ocean Dipole Mode index has less predictive skill compared to ENSO. The correlation values between the model predicted Monsoon Hadley Index ( MHI) and observed MHI reveals that only a few of them could exhibit large scale circulation features well. [ABSTRACT FROM AUTHOR]

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982-2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low. [ABSTRACT FROM AUTHOR]