On the predictability of SSTA indices from CMIP5 decadal experiments

Abstract

Sea surface temperature anomaly climate indices in the tropical Pacific and Indian Oceans are statistically significant predictors of seasonal rainfall in the Indo-Pacific region. On this basis, this study evaluates the predictability of nine such indices, at interannual timescales, from the decadal hindcast experiments of four general circulation models. A Monte Carlo scheme is applied to define the periods of enhanced predictability for the indices. The effect of a recommended drift correction technique and the models’ capabilities in simulating two specific El Niño and La Niña events are also examined. The results indicate that the improvement from drift correction is noticeable primarily in the full-field initialized models. Models show skillful predictability at timescales up to maximum a year for most indices, with indices in the tropical Pacific and the Western Indian Ocean having longer predictability horizons than other indices. The multi model ensemble mean shows the highest predictability for the Indian Ocean West Pole Index at 25 months. Models simulate the observed peaks during the El Niño and La Niña events in the Niño 3.4 index with limited success beyond timescales of a year, as expected from the predictability horizons. However, our study of a small number of events and models shows full-field initialized models outperforming anomaly initialized ones in simulating these events at annual timescales.

Keywords

• general circulation models
• SSTA indices
• predictability
• Indo-Pacific
• CMIP5 decadal hindcasts