To evaluate the accuracy of model forecasts and provide a measure of the skill of POAMA (Predictive Ocean Atmosphere Model for Australia), hindcasts of sea surface temperature anomalies (SSTA) are compared to observed SSTA for the same period. SSTA are calculated for both the model forecasts and observed values as the difference between SST values and the relevant climatology. The climatology is the monthly mean sea surafce temperature (SST) over the period 1982-2006, computed relative to start month and lead-time for the model, and removing this from SST values reduces the effects of any model bias (Stockdale 1997). Skill is calculated by correlating model anomalies with observed anomalies in both space and time. The correlation coefficient (r) is defined as the ratio of the covariance of the sample populations to the product of their standard deviations, with a skill value of 1.0 indicating a perfect fit between model and observed values. For more information see Spillman and Alves (2009).
The first plot shows the spatial skill of the model ensemble mean SSTA forecasts for the target three month season January-February-March (JFM) for up to 5 months prior to the forecast season (1982-2006). The forecasts exhibit useful skill for lead-times 0-2 months. The higher skill in the northern parts compared to the southern area is likely due to the larger influence of tropical variability, principally ENSO.
The plot below shows regional skill of the model ensemble mean GBR Index, persistence and potential predictability for all lead-times for (a) forecasts starting all months, and for (b) target season JFM 1982-2006 starting at different lead-times. Model skill exceeds 0.5 for lead-times of 0-2 months and is an improvement over persistence forecasts at greater lead-times (Spillman and Alves 2009).
Potential predictability is the upper level of skill that can be achieved for a model forecast given a perfect model and initial conditions (Griffies and Bryan 1997). It is calculated by using one ensemble member as a reference and calculating the skill of the mean of the remaining ensemble members in predicting it. This is repeated using different ensemble members as the reference ensemble member. Skill is never perfect as the chaotic component in the system leads to ensemble spread, which in turn limits predictability.
For further information visit POAMA group.
Please be aware that all POAMA forecasts are subject to the Bureau of Meteorology's copyright and disclaimer.