Sperber, K. R. and T. Palmer, 1995: AMIP diagnostic subproject 6: Monsoons and tropical rainfall predictability. Abstracts of the First International AMIP Scientific Conference, Monterey, California, 65.

The interannual variability and potential predictability of rainfall over the Indian subcontinent, the Sahel and the Nordeste region of Brazil have been evaluated from the suite of AMIP simulations. Variations over the Nordeste region are most readily captured owing to the intimate link between the rainfall and the Pacific and Atlantic SSTs. The precipitation variations over the Indian subcontinent and the Sahel are relatively less well captured by the models respectively. Additionally, an Indian monsoon windshear index (akin to that constructed by Webster and Yang, 1992) was calculated for each model. The models are generally more adept at simulating the variability of the windshear index than that associated with the rainfall over this region, indicating that the models exhibit greater fidelity at capturing the large-scale dynamical fluctuations. For each region improved skill scores and enhanced potential predictability result for those models that qualitatively simulate the observed rainfall/SST correlation pattern which is dominated by an ENSO teleconnection in the Pacific Ocean. Accordingly for this subset of the models, the enhancement in skill and potential predictability occurred mainly during years of strong El Niño or La Niña conditions.

A suite of six ECMWF AMIP runs (differing only in their initial conditions) have also been examined. The Indian monsoon rainfall exhibits a consistent response during 1987 and 1988, while during other years differences are simply not very predictable, possibly because of internal chaotic dynamics that are associated with intraseasonal monsoon fluctuations. In this case the potential predictability is poor (less than 1) indicating that the average intramodel spread is greater than the temporal variability of the ensemble mean. For the Sahel and the Nordeste the potential predictability increases to 2.4 and 9.0 respectively indicating a robust response to the boundary conditions for this model.