Climate Dynamics

, Volume 38, Issue 11–12, pp 2483–2502 | Cite as

Simulation and prediction of the Southern Annular Mode and its influence on Australian intra-seasonal climate in POAMA

  • Andrew G. Marshall
  • Debra Hudson
  • Matthew C. Wheeler
  • Harry H. Hendon
  • Oscar Alves


We assess the ability of the Predictive Ocean Atmosphere Model for Australia (POAMA) to simulate and predict the Southern Annular Mode (SAM) and its influence on Australian intra-seasonal climate using a 27-year hindcast dataset. The analysis consists of three stages: (1) prediction of the SAM, (2) simulation of SAM climate anomalies over Australia, and (3) prediction of Australian climate anomalies in association with the SAM. POAMA achieves skilful prediction of the SAM index for lead times out to about 2 weeks with little skill seen beyond 3 weeks when calculated over all hindcast start months; the inherent strong persistence of the SAM appears to be a key factor for its extended-range predictability in a dynamical forecast model. POAMA also simulates SAM climate anomalies over Australia reasonably well despite notable biases in its representation of the SAM to the south and east of the continent. The model reproduces Australian rainfall anomalies most effectively throughout June–November, and least effectively throughout March–May. Skilful prediction of the SAM index, together with realistic simulation of SAM climate anomalies over Australia, translates into more skilful forecasts of rainfall and maximum temperature at intra-seasonal timescales during austral winter and spring. When the SAM is strong in the initial conditions, there is higher skill in forecasting rainfall anomalies over eastern Australia and maximum temperature anomalies over most of the continent during June–November at lead times of 2–3 weeks, compared with when the SAM is weak. The SAM thus contributes to intra-seasonal prediction skill in the Australian region in POAMA.


Empirical Orthogonal Function Ensemble Member Rainfall Anomaly Southern Annular Mode Predictive Skill 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Managing Climate Variability Program of Grains Research and Development Corporation. We would like to thank Drs. Eun-Pa Lim, James Risbey, Li Shi and Peter McIntosh for reviewing an earlier version of the manuscript and providing useful comments. Thanks also to Drs. Mike Pook, Jaclyn Brown and Gary Meyers for useful discussions throughout the course of this work, and to the two anonymous reviewers for suggested revisions to the manuscript. Robert Fawcett and Andrew Charles provided the NCC rainfall data used in this study. The NNR1 dataset was provided by the NOAA/OAR/ESRL PSD in Boulder, Colorado, USA, from their web site at The ERA-40 data was provided by the ECMWF in Reading, UK, from their website at


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Andrew G. Marshall
    • 1
  • Debra Hudson
    • 2
  • Matthew C. Wheeler
    • 2
  • Harry H. Hendon
    • 2
  • Oscar Alves
    • 2
  1. 1.Centre for Australian Weather and Climate ResearchHobartAustralia
  2. 2.Centre for Australian Weather and Climate ResearchMelbourneAustralia

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