The development of a dynamical model seasonal prediction service for island nations in the tropical South Pacific is described. The forecast model is the Australian Bureau of Meteorology's Predictive Ocean-Atmosphere Model for Australia (POAMA), a dynamical seasonal forecast system. Using a hindcast set for the period 1982-2006, POAMA is shown to provide skillful forecasts of El Niño and La Niña many months in advance and, because the model faithfully simulates the spatial and temporal variability of rainfall associated with displacements of the southern Pacific convergence zone (SPCZ) and ITCZ during La Niña and El Niño, it also provides good predictions of rainfall throughout the tropical Pacific region. The availability of seasonal forecasts from POAMA should be beneficial to Pacific island countries for the production of regional climate outlooks across the region.

Increases in the frequency of extreme weather and climate events and the severity of their impacts on the natural environment and society have been observed across the globe in recent decades. In addition to natural climate variability and greenhouse-induced climate change, extreme weather and climate events produce the most pronounced impacts. In this paper, the climate of three island countries in the Western Pacific: Fiji, Samoa and Tuvalu, has been analysed. 804quences of the observed increase in anthropogenic greenhouse gas concentration and will likely have even stronger negative impacts on the natural environment and society in the future. This should be taken into consideration by authorities of Pacific Island Countries and aid donors when developing strategies to adapt to the increasing risk of climate extremes. Here we demonstrate that the modern science of seasonal climate prediction is well developed, with current dynamical climate models being able to provide skilful predictions of regional rainfall two-three months in advance. The dynamic climate model-based forecast products are now disseminated to the National Meteorological Services of 15 island countries in the Western Pacific through a range of web-based information tools. We conclude with confidence that seasonal climate prediction is an effective solution at the regional level to provide governments and local communities of island nations in the Western Pacific with valuable assistance for informed decision making for adaptation to climate variability and change.

Seasonal predictions based on coupled atmosphere-ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Largescale fields from the Predictive Ocean-Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.

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