Climate Dynamics

, Volume 43, Issue 7–8, pp 1791–1810 | Cite as

ENSO, the IOD and the intraseasonal prediction of heat extremes across Australia using POAMA-2

  • Christopher J. WhiteEmail author
  • Debra Hudson
  • Oscar Alves


The simulation and prediction of extreme heat over Australia on intraseasonal timescales in association with the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) is assessed using the Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia (POAMA). The analysis is based on hindcasts over 1981–2010 and focuses on weeks 2 and 3 of the forecasts, i.e. beyond a typical weather forecast. POAMA simulates the observed increased probabilities of extreme heat during El Niño events, focussed over south eastern and southern Australia in SON and over northern Australia in DJF, and the decreased probabilities of extreme heat during La Niña events, although the magnitude of these relationships is smaller than observed. POAMA also captures the signal of increased probabilities of extreme heat during positive phases of the IOD across southern Australia in SON and over Western Australia in JJA, but again underestimates the strength of the relationship. Shortcomings in the simulation of extreme heat in association with ENSO and the IOD over southern Australia may be linked to deficiencies in the teleconnection with Indian Ocean SSTs. Forecast skill for intraseasonal episodes of extreme heat is assessed using the Symmetric Extremal Dependence Index. Skill is highest over northern Australia in MAM and JJA and over south-eastern and eastern Australia in JJA and SON, whereas skill is generally poor over south-west Western Australia. Results show there are windows of forecast opportunity related to the state of ENSO and the IOD, where the skill in predicting extreme temperatures over certain regions is increased.


Intraseasonal forecasts Predictability El Niño–Southern Oscillation Indian Ocean Dipole Extreme events Heat waves 



This work was supported by the Managing Climate Variability Program of the Grains Research and Development Corporation (GRDC). The authors would like to thank our colleagues Andrew Marshall, Harry Hendon, Matthew Wheeler and Beth Ebert, as well as two anonymous reviewers, for their insightful comments and advice in the preparation of this manuscript.


  1. Alexander LV, Arblaster JM (2009) Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. Int J Climatol 29:417–435CrossRefGoogle Scholar
  2. Alexander MA, Blade I, Newman M, Lazante JR, Lau NC, Scott JD (2002) The atmospheric bridge: the influence of ENSO teleconnections on air–sea interaction over the global oceans. J Clim 15:2205–2231CrossRefGoogle Scholar
  3. Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Ambenje P, Rupa Kumar K, Revadekar J, Griffiths G (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos 111:D05109. doi: 10.1029/2005JD006290 CrossRefGoogle Scholar
  4. Ansell T, Reason CJC, Meyers G (2000) Variability in the tropical southeast Indian Ocean and links with southeast Australian winter rainfall. Geophys Res Lett 27:3977–3980CrossRefGoogle Scholar
  5. Arblaster JM, Alexander LV (2012) The impact of the El Niño–Southern Oscillation on maximum temperature extremes. Geophys Res Lett 39:L20702. doi: 10.1029/2012GL053409 CrossRefGoogle Scholar
  6. Becker EJ, van den Dool H, Peńa M (2013) Short-term climate extremes: prediction skill and predictability. J Clim 26:512–531CrossRefGoogle Scholar
  7. Bretherton CS, Widmann M, Dymnikov V, Wallace J, Bladé I (1999) The effective number of spatial degrees of freedom of a time-varying field. J Clim 12:1990–2009CrossRefGoogle Scholar
  8. Cai W, Hendon HH, Meyers G (2005) Indian Ocean dipole like variability in the CSIRO Mark3 climate model. J Clim 18:1449–1468CrossRefGoogle Scholar
  9. Cai W, Jones DA, Harle K, Cowan T, Power S, Smith I, Arblaster J, Abbs D (2007) Chapter 2: past climate change, climate change in Australia. CSIRO technical report, CSIRO, AustraliaGoogle Scholar
  10. Cai W, van Rensch P, Cowan T, Hendon HH (2011) Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. J Clim 24:3910–3923CrossRefGoogle Scholar
  11. Casati B, Wilson LJ, Stephenson DB, Nurmi P, Ghelli A, Pocernich M, Damrath U, Ebert EE, Brown BG, Mason S (2008) Forecast verification: current status and future directions. Meteorol Appl 15:3–18CrossRefGoogle Scholar
  12. Chambers LE, Griffiths GM (2008) The changing nature of temperature extremes in Australia and New Zealand. Aust Meteorol Mag 57:13–35Google Scholar
  13. CliMag (2009) Multi-week forecasts will help bridge the gap. In: CliMag (Managing Climate Variability Newsletter) 18: December. Available from the Grains Research and Development Corporation, AustraliaGoogle Scholar
  14. Ferro CAT, Stephenson DB (2011) Extremal dependence indices: improved verification measures for deterministic forecasts of rare binary events. Weather Forecast 26:699–713CrossRefGoogle Scholar
  15. Ferro CAT, Stephenson DB (2012) Deterministic forecasts of extreme events and warnings. In: Jolliffe IT, Stephenson DB (eds) Forecast verification: a practitioner’s guide in atmospheric science, 2nd edn. Wiley, ChichesterGoogle Scholar
  16. Fischer AS, Terray P, Guilyardi E, Gualdi S, Delecluse P (2005) Two independent triggers for the Indian Ocean dipole/zonal mode in a coupled GCM. J Clim 18:3349–3428CrossRefGoogle Scholar
  17. Fisher RA (1915) Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10:507–521Google Scholar
  18. Hamilton E, Eade R, Graham RJ, Scaife AA, Smith DM, Maidens A, MacLachlan C (2012) Forecasting the number of extreme daily events on seasonal timescales. J Geophys Res Atmos 117:D03114. doi: 10.1029/2011JD016541 CrossRefGoogle Scholar
  19. Hogan RJ, Mason IB (2012) Deterministic forecasts of binary events. In: Jolliffe IT, Stephenson DB (eds) Forecast verification: a practitioner’s guide in atmospheric science, 2nd edn. Wiley, ChichesterGoogle Scholar
  20. Hudson D, Marshall AG, Alves O (2011a) Intraseasonal forecasting of the 2009 summer and winter Australian heat waves using POAMA. Weather Forecast 26:257–279CrossRefGoogle Scholar
  21. Hudson D, Alves O, Hendon HH, Marshall AG (2011b) Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia. Q J R Meteor Soc 137:673–689CrossRefGoogle Scholar
  22. Hudson D, Alves O, Hendon HH, Wang G (2011c) The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Clim Dyn 36:1155–1171CrossRefGoogle Scholar
  23. Hudson D, Marshall AG, Yin Y, Alves O, Hendon HH (2013) Improving intraseasonal prediction with a new ensemble generation strategy. Mon Weather Rev. doi: 10.1175/MWR-D-13-00059.1 Google Scholar
  24. Jewson S, Caballero R (2003) The use of weather forecasts in the pricing of weather derivatives. Meteorol Appl 10:377–389CrossRefGoogle Scholar
  25. Jones DA, Trewin BC (2000) On the relationships between the El Niño–Southern Oscillation and Australian land surface temperature. Int J Climatol 20:697–719CrossRefGoogle Scholar
  26. Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Aust Meteorol Oceanogr J 58:233–248Google Scholar
  27. Kharin VV, Zwiers FW, Zhang X, Hegerl GC (2007) Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim 20:1419–1444CrossRefGoogle Scholar
  28. Klein SA, Soden BJ, Lau NC (1999) Remote sea surface temperature variations during ENSO: evidence for a tropical atmospheric bridge. J Clim 12:917–932CrossRefGoogle Scholar
  29. Luo JJ, Mason S, Behera SK, Yamagata T (2008) Extended ENSO prediction using a fully coupled ocean–atmosphere model. J Clim 21:84–93CrossRefGoogle Scholar
  30. Manabe S, Holloway J (1975) The seasonal variation of the hydrological cycle as simulated by a global model of the atmosphere. J Geophys Res 80:1617–1649. doi: 10.1029/JC080i012p01617 CrossRefGoogle Scholar
  31. Marshall AG, Hudson D, Wheeler MC, Hendon HH, Alves O (2011a) Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system. Clim Dyn 37:2129–2141CrossRefGoogle Scholar
  32. Marshall AG, Hudson D, Wheeler MC, Hendon HH, Alves O (2011b) Simulation and prediction of the Southern Annular Mode and its influence on Australian intra-seasonal climate in POAMA. Clim Dyn 38:2483–2502CrossRefGoogle Scholar
  33. Marshall AG, Hudson D, Wheeler M, Alves O, Hendon HH, Pook MJ, Risbey JS (2013) Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2. Clim Dyn. doi: 10.1007/s00382-013-2016-1
  34. Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation. Q J R Meteor Soc 128:2145–2166CrossRefGoogle Scholar
  35. Mason K, Nairn J, Herbst J, Felgate P (2010) Heatwave—the Adelaide experience. In: Proceedings of the 20th international symposium on the forensic sciences (ANZFSS), 5–9 September, Sydney, AustraliaGoogle Scholar
  36. Matsueda M (2011) Predictability of Euro-Russian blocking in summer of 2010. Geophys Res Lett 38:L06801. doi: 10.1029/2010GL046557 Google Scholar
  37. Meyers G, McIntosh P, Pigot L, Pook M (2007) The years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean. J Clim 20:2872–2880CrossRefGoogle Scholar
  38. Min S-K, Cai W, Whetton P (2013) Influence of climate variability on seasonal extremes over Australia. J Geophys Res Atmos 118:643–654. doi: 10.1002/jgrd.50164 CrossRefGoogle Scholar
  39. Nairn J, Fawcett R, Ray D (2009) ‘Defining and predicting excessive heat events, a national system’. In: Proceedings of the CAWCR modelling workshop: understanding high impact weather, 30 November–2 December 2009, Melbourne, Australia, pp 83–86Google Scholar
  40. Nicholls N, Uotila P, Alexander L (2010) Synoptic influences on seasonal, interannual and decadal temperature variations in Melbourne, Australia. Int J Climatol 30:1372–1381Google Scholar
  41. Price Waterhouse Coopers (2011) Protecting human health and safety during severe and extreme heat events: a national framework. Commonwealth Government Report, AustraliaGoogle Scholar
  42. Rashid HA, Hendon HH, Wheeler MC, Alves O (2010) Predictability of the Madden–Julian Oscillation in the POAMA dynamical seasonal prediction system. Clim Dyn 36:649–661CrossRefGoogle Scholar
  43. Risbey JS, Pook MJ, McIntosh PC, Wheeler MC, Hendon HH (2009) On the remote drivers of rainfall variability in Australia. Mon Weather Rev 137:3233–3253CrossRefGoogle Scholar
  44. Roulston MS, Kaplan DT, Hardenberg J, Smith LA (2003) Using medium-range weather forecasts to improve the value of wind energy production. Renew Energy 28:585–602CrossRefGoogle Scholar
  45. Saji NH, Yamagata T (2003) Possible impacts of Indian Ocean dipole mode events on global climate. Clim Res 25:151–169CrossRefGoogle Scholar
  46. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363Google Scholar
  47. Saji NH, Xie S, Yamagata T (2006) Tropical Indian Ocean Variability in the IPCC twentieth-century climate simulations. J Clim 19:4397–4417CrossRefGoogle Scholar
  48. Samuel JM, Verdon DC, Sivapalan M, Franks SW (2006) Influence of Indian Ocean sea surface temperature variability on southwest Western Australian winter rainfall. Water Resour Res 42:W08402Google Scholar
  49. Sankarasubramanian A, Lall U, Devineni N, Espinueva S (2009) The role of monthly updated climate forecasts in improving intraseasonal water allocation. J Appl Meteorol Clim 48:1464–1482CrossRefGoogle Scholar
  50. Schiller A, Godfrey J, McIntosh P, Meyers G (1997) A global ocean general circulation model climate variability studies. CSIRO marine research report no. 227, CSIRO, AustraliaGoogle Scholar
  51. Schiller A, Godfrey J, McIntosh P, Meyers G, Smith N, Alves O, Wang O, Fiedler R (2002) A new version of the Australian community ocean model for seasonal climate prediction. CSIRO marine research report no. 240, CSIRO, AustraliaGoogle Scholar
  52. Seneviratne SI, Nicholls N, Easterling D, Goodess CM, Kanae S, Kossin J, Luo Y, Marengo J, McInnes K, Rahimi M, Reichstein M, Sorteberg A, Vera C, Zhang X (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach J, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change (IPCC). Cambridge University Press, Cambridge, NY, pp 109–230Google Scholar
  53. Spiegel MR (1961) Schaum’s outline of theory and problems of Statistics. Schaum Publishing Company, New YorkGoogle Scholar
  54. State of Victoria (2009) January 2009 Heatwave in Victoria: an assessment of health impacts. Victoria health technical report, AustraliaGoogle Scholar
  55. Stephenson DB, Casati B, Ferro CAT, Wilson CA (2008) The extreme dependency score: a non-vanishing measure for forecasts of rare events. Meteorol Appl 15:41–50CrossRefGoogle Scholar
  56. Stockdale TN (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125:809–818CrossRefGoogle Scholar
  57. Stockdale TN, Anderson DLT, Alves JOS, Balmaseda MA (1998) Global seasonal rainfall forecasts using a coupled ocean–atmosphere model. Nature 392:370–373CrossRefGoogle Scholar
  58. Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19:57–70CrossRefGoogle Scholar
  59. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79:185–211CrossRefGoogle Scholar
  60. Trewin BC (2009) A new index for monitoring changes in heatwaves and extended cold spells. In: Proceedings of the 9th international conference on southern hemisphere meteorology and oceanography, 6–8 February 2009, Melbourne, AustraliaGoogle Scholar
  61. Trewin B, Vermont H (2010) Changes in the frequency of record temperatures in Australia, 1957–2009. Aust Meteorol Oceanogr J 60:113–119Google Scholar
  62. Valcke S, Terray L, Piacentini A (2000) Oasis 2.4, Ocean atmosphere sea ice soil: user’s guide. TR/CMGC/00/10, CERFACS, Toulouse, FranceGoogle Scholar
  63. Verdon DC, Franks SW (2005) Indian Ocean sea surface temperature variability and winter rainfall: Eastern Australia. Water Resour Res 41:W09413Google Scholar
  64. Vitart F (2005) Monthly forecast and the summer 2003 heat wave over Europe: a case study. Atmos Sci Lett 6:112–117CrossRefGoogle Scholar
  65. Wajsowicz RC (2007) Seasonal-to-interannual forecasting of tropical Indian Ocean sea surface temperature anomalies: potential predictability and barriers. J Clim 20:3320–3343CrossRefGoogle Scholar
  66. Wang G, Hudson D, Yin Y, Alves O, Hendon H, Langford S, Liu G, Tseitkin F (2011) POAMA-2 SST skill assessment and beyond. CAWCR Res Lett 6:40–46Google Scholar
  67. Webster PJ, Moore AM, Loschnigg JP, Leben RR (1999) Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401:356–360CrossRefGoogle Scholar
  68. White CJ, McInnes KL, Cechet RP, Corney SP, Grose MR, Holz G, Katzfey JJ, Bindoff NL (2013) On regional dynamical downscaling for the assessment and projection of future temperature and precipitation extremes across Tasmania, Australia. Clim Dyn 41:3145–3165Google Scholar
  69. Wilks D (2006) Statistical methods in atmospheric sciences, 2nd edn. Academic Press, BurlingtonGoogle Scholar
  70. Xue Y, Balmaseda MA, Boyer T, Ferry N, Good S, Ishikawa I, Kumar A, Rienecker M, Rosati T, Yin Y (2012) A comparative analysis of upper-ocean heat content variability from an ensemble of operational ocean reanalyses. J Clim 25:6905–6929CrossRefGoogle Scholar
  71. Yin Y, Alves O, Oke PR (2011a) An ensemble ocean data assimilation system for seasonal prediction. Mon Weather Rev 139:786–808CrossRefGoogle Scholar
  72. Yin Y, Alves O, Hudson D (2011b) Coupled ensemble initialization for a new intraseasonal forecast system using POAMA at the Bureau of Meteorology. In: Proceedings of the international union of geodesy and geophysics conference (IUGG), 28 June–7 July, Melbourne, AustraliaGoogle Scholar
  73. Zeng L (2000) Weather derivatives and weather insurance: concept, application, and analysis. Bull Am Meteorol Soc 81:2075–2082CrossRefGoogle Scholar
  74. Zhao M, Hendon HH (2009) Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model. Q J R Meteor Soc 135:337–352CrossRefGoogle Scholar
  75. Zhong A, Alves O, Hendon H, Rikus L (2006) On aspects of the mean climatology and tropical interannual variability in the BMRC Atmospheric Model (BAM 3.0). BMRC research report no. 121, Bureau of Meteorology, AustraliaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christopher J. White
    • 1
    Email author
  • Debra Hudson
    • 2
  • Oscar Alves
    • 2
  1. 1.Centre for Australian Weather and Climate Research (CAWCR)Bureau of MeteorologyHobartAustralia
  2. 2.Centre for Australian Weather and Climate Research (CAWCR)Bureau of MeteorologyMelbourneAustralia

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