Can improved SSTA prediction be translated into better seasonal rainfall forecast?

Abstract:

Seasonal rainfall forecasts are predicted throughout Australia based on concurrent sea surface temperature anomalies (SSTAs) fields coming from the main dynamical Australian model, the Predictive Ocean-Atmosphere Model for Australia (POAMA). In this study, we derive SSTA fields using a multi-model combination approach by including information from five additional models. The combination takes into account the cross-dependence between all the models while combining individual SSTA fields. The resulting SSTA fields are then used to derive SSTA indices to issue seasonal rainfall forecasts rainfall over a 2.5 degree grid using a Bayesian Model Averaging approach currently in use operationally. These forecasts are compared with those derived from a single SSTA model in three settings: (i) 2.5 degree gridded rainfall over Australia; (ii) only grid cells where one of the models (multi-model or single model) had skill (measured in terms of mean squared error relative to climatology) ; and (iii) only grid cells where both of the models had skill. The results indicate that the forecasts derived using multi-model based SSTA indices offer clear improvements over the case where a single SSTA model is used in three seasons, ASO (August, September and October), FMA (February, March and April), and MJJ (May, June and July). A further assessment over grid locations where predictability is significantly better than climatology indicates consistent improvements when the multi-model SSTA fields are used.