The project was a collaborative effort of the coordinators and scientists from the different modelling groups across the country. All the runs were made at the Centre for Development of Advanced Computing (CDAC) at Bangalore on the PARAM Padma supercomputing system.

Two sets of simulations were made for this purpose. In the first set, the AGCMs were forced by the observed sea surface temperature (SST) for May– September during 1985–2004. In the second set, runs were made for 1987, 1988, 1994, 1997 and 2002 forced by SST which was obtained by assuming that the April anomalies persist during May–September. The results of the first set of runs show, as expected from earlier studies, that none of the models were able to simulate the correct sign of the anomaly of the Indian summer monsoon rainfall for all the years.

However, among the five models, one simulated the correct sign in the largest number of years and the second model showed maximum skill in the simulation of the extremes (i.e. droughts or excess rainfall years). The first set of runs showed some common bias which could arise either from an excessive sensitivity of the models to El Niño Southern Oscillation (ENSO) or an inability of the models to simulate the link of the Indian monsoon rainfall to Equatorial Indian Ocean Oscillation (EQUINOO), or both.

Analysis of the second set of runs showed that with a weaker ENSO forcing, some models could simulate the link with EQUINOO, suggesting that the errors in the monsoon simulations with observed SST by these models could be attributed to unrealistically high sensitivity to ENSO.

The basic results presented in the study have yielded some insight into the ‘how and why’ of the simulation of the interannual variation of monsoon rainfall by these models. Such an understanding is a necessary prerequisite for improvement of the models. For generating predictions of the rainfall during the summer monsoon using such models, it is necessary to predict SST for the forthcoming season. Clearly, predictions have to be eventually generated with models of the coupled atmosphere– ocean system using initial conditions for the ocean as well as the atmosphere. 

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