These pages provide information on sea surface temperatures for monitoring coral bleaching events in the Great Barrier Reef, Australia.
The Predictive Ocean Atmosphere Model for Australia
POAMA stands for the Predictive Ocean Atmosphere Model for Australia. POAMA is the Bureau of Meteorology's dynamical (physics based) climate model used for multi-week to seasonal through to inter-annual climate outlooks. It is a state of the art long-range forecast system using ocean, atmosphere and land data observations to initiate outlooks up to nine months ahead. POAMA has been running operationally since 2002 and the current version, POAMA-2(M24) since 27 March 2013.
The coupled model in POAMA consists of the Australian Bureau of Meteorology atmospheric model (BAM 3.0) and the CMAR Australian Community Ocean Model V.2 (ACOM2), coupled using the Ocean Atmosphere Sea Ice Soil (OASIS) coupling software. The atmospheric model component has a horizontal spectral resolution of T47(approximately a 250 km grid) and 17 vertical levels. The ocean model grid spacing is 2 degrees in the zonal direction and 0.5-1.5 degrees in the meridional direction, with 25 vertical levels, of which 12 are in the upper 185 m.
The forecasts from POAMA are initialised from observed atmospheric and oceanic states. Atmosphere initial conditions are provided by an Atmosphere Land Initialisation (ALI) scheme, where a BAM3.0 atmosphere-only integration forced with observed SST, is nudged towards the Bureau's real-time global numerical weather prediction analyses (Hudson et al. 2011). The ocean model is initialised using an ocean data assimilation scheme based on the POAMA Ensemble Ocean Data Assimilation System (PEODAS; Yin et al. 2011).
POAMA forecasts are run twice weekly, and consist of 33 scenarios for the coming 9 months. The variability of the results among the 33 runs gives an indication of the uncertainty in the future evolution of the climate system. When many individual forecasts are considered together they are said to comprise an ensemble and the spread in the conditions they forecast can be used to gauge the likelihood of future conditions. For details of how the POAMA forecast ensemble is generated see Hudson et al (2013).
Apart from the real-time forecasts, there is also a large set of retrospective forecasts, also called hindcasts, which are forecasts made for past events. The hind-cast set can be used to measure model performance by assessing the skill of the model in predicting past events. Hindcasts from POAMA were generated 3 times per month (on the 1st, 11th and 21st) for the period 1981-2010.
Lead-time is defined as the time elapsed between the model start date and the forecast date, i.e. if the model start month is January, the forecasts at lead-times 0, 1, 2 and 3 months are for January, February, March and April respectively.
- POAMA-2 Operational ENSO Forecasts
- POAMA-2 Operational Great Barrier Reef Forecasts
- POAMA-2 Experimental Forecasts
- Hudson, D., Alves O., Hendon H.H., and Wang G., 2011: The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Climate Dyn. 36, 1155-1171.
- Hudson D., Marshall A.G., Yin Y., Alves O. and Hendon H.H., 2013: Improving intraseasonal prediction with a new ensemble generation strategy. Monthly Weather Review (accepted).
- Spillman C.M., 2011: Operational real-time seasonal forecasts for coral reef management. J. Oper. Oceanog., 4, 13-22.
- Spillman C.M., 2011: Real-time predictions of coral bleaching risk for the Great Barrier Reef: Summer 2010/2011. CAWCR Res. Lett., 6, 34-39.
- Spillman C. and Alves O., 2009: Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef. Coral Reefs, 28, 197-206.
- Spillman C.M., Alves O., and Hudson D.A., 2010: Real-time seasonal SST predictions for the Great Barrier Reef during the summer of 2009/2010. CAWCR Res. Lett., 4, 11-19.
- Spillman C.M., Alves O., Hudson D.A. and Charles A.N., 2009: POAMA SST predictions for the Great Barrier Reef: Summer 2008/2009. CAWCR Res. Lett., 2, 30-34.
- Yin Y., Alves O. and Oke P. R., 2011: An ensemble ocean data assimilation system for seasonal prediction. Mon. Wea. Rev., 139, 786-808.
For further information visit POAMA group.