Seasonal prediction of global sea level anomalies using an ocean-atmosphere dynamical model

Advanced warning of extreme sea level events is an invaluable tool for coastal communities, allowing the implementation of management policies and strategies to minimise loss of life and infrastructure damage. This study is an initial attempt to apply a dynamical coupled ocean-atmosphere model to the prediction of seasonal sea level anomalies (SLA) globally for up to 7 months in advance. We assess the ability of the Australian Bureau of Meteorology's operational seasonal dynamical forecast system, the Predictive Ocean Atmosphere Model for Australia (POAMA), to predict seasonal SLA, using gridded satellite altimeter observation-based analyses over the period 1993-2010 and model reanalysis over 1981-2010. Hindcasts from POAMA are based on a 33-member ensemble of seasonal forecasts that are initialised once per month for the period 1981-2010. Our results show POAMA demonstrates high skill in the equatorial Pacific basin and consistently exhibits more skill globally than a forecast based on persistence. Model predictability estimates indicate there is scope for improvement in the higher latitudes and in the Atlantic and Southern Oceans. Most characteristics of the asymmetric SLA fields generated by El-Nino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.