Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia

Abstract

AbstractThis study examines the potential use of the Predictive Ocean Atmosphere Model for Australia (POAMA), the Bureau of Meteorology's dynamical seasonal forecast system, as an intraseasonal prediction tool for Australia. This would fill the current prediction capability gap between weather forecasts and seasonal outlooks for Australia. The intraseasonal forecast skill of a 27‐year hindcast dataset is investigated, focusing on precipitation and minimum and maximum temperatures over Australia in the second fortnight (average days 15–28 of the forecast). Most of the skill for forecasting precipitation and maximum temperature in the second fortnight is focused over eastern Australia, during austral winter and spring for precipitation and during spring for maximum temperature. For this region and seasons the forecast of the second fortnight performs generally better than using climatology, persistence of observed, or persistence of the forecast for the first fortnight (average days 1–14). The model has generally poor skill in predicting minimum temperatures. The role of key drivers of Australian climate variability for providing predictability at intraseasonal time‐scales is investigated. This is done for the austral winter and spring seasons, when POAMA's skill for predicting precipitation is highest. Forecast skill is found to be increased during extremes of the El Niño Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode. The regions of impact of these modes of climate variability on forecast skill are similar to those regions identified in observed studies as being influenced by the respective drivers. In contrast, there is no significant relationship between intraseasonal forecast skill for precipitation and the amplitude of the Madden Julian Oscillation (MJO) in winter and spring, although the analysis does not distinguish between the phases of the MJO. The results indicate that the use of POAMA for intraseasonal forecasting is promising. Copyright © 2011 Royal Meteorological Society