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

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Abstract

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.

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References

  • Alexander LV, Arblaster JM (2009) Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. Int J Climatol 29:417–435

    Article  Google Scholar 

  • 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 15:2205–2231

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ansell T, Reason CJC, Meyers G (2000) Variability in the tropical southeast Indian Ocean and links with southeast Australian winter rainfall. Geophys Res Lett 27:3977–3980

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Becker EJ, van den Dool H, Peńa M (2013) Short-term climate extremes: prediction skill and predictability. J Clim 26:512–531

    Article  Google Scholar 

  • 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 12:1990–2009

    Article  Google Scholar 

  • Cai W, Hendon HH, Meyers G (2005) Indian Ocean dipole like variability in the CSIRO Mark3 climate model. J Clim 18:1449–1468

    Article  Google Scholar 

  • 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, Australia

  • 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 24:3910–3923

    Article  Google Scholar 

  • 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 15:3–18

    Article  Google Scholar 

  • Chambers LE, Griffiths GM (2008) The changing nature of temperature extremes in Australia and New Zealand. Aust Meteorol Mag 57:13–35

    Google Scholar 

  • 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, Australia

  • Ferro CAT, Stephenson DB (2011) Extremal dependence indices: improved verification measures for deterministic forecasts of rare binary events. Weather Forecast 26:699–713

    Article  Google Scholar 

  • 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, Chichester

    Google Scholar 

  • 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 18:3349–3428

    Article  Google Scholar 

  • Fisher RA (1915) Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10:507–521

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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, Chichester

    Google Scholar 

  • Hudson D, Marshall AG, Alves O (2011a) Intraseasonal forecasting of the 2009 summer and winter Australian heat waves using POAMA. Weather Forecast 26:257–279

    Article  Google Scholar 

  • 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 137:673–689

    Article  Google Scholar 

  • Hudson D, Alves O, Hendon HH, Wang G (2011c) The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Clim Dyn 36:1155–1171

    Article  Google Scholar 

  • 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 

  • Jewson S, Caballero R (2003) The use of weather forecasts in the pricing of weather derivatives. Meteorol Appl 10:377–389

    Article  Google Scholar 

  • Jones DA, Trewin BC (2000) On the relationships between the El Niño–Southern Oscillation and Australian land surface temperature. Int J Climatol 20:697–719

    Article  Google Scholar 

  • Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Aust Meteorol Oceanogr J 58:233–248

    Google Scholar 

  • 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 20:1419–1444

    Article  Google Scholar 

  • Klein SA, Soden BJ, Lau NC (1999) Remote sea surface temperature variations during ENSO: evidence for a tropical atmospheric bridge. J Clim 12:917–932

    Article  Google Scholar 

  • Luo JJ, Mason S, Behera SK, Yamagata T (2008) Extended ENSO prediction using a fully coupled ocean–atmosphere model. J Clim 21:84–93

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 37:2129–2141

    Article  Google Scholar 

  • 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 38:2483–2502

    Article  Google Scholar 

  • 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

  • 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 128:2145–2166

    Article  Google Scholar 

  • 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, Australia

  • Matsueda M (2011) Predictability of Euro-Russian blocking in summer of 2010. Geophys Res Lett 38:L06801. doi:10.1029/2010GL046557

    Google Scholar 

  • 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 20:2872–2880

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 83–86

  • Nicholls N, Uotila P, Alexander L (2010) Synoptic influences on seasonal, interannual and decadal temperature variations in Melbourne, Australia. Int J Climatol 30:1372–1381

    Google Scholar 

  • Price Waterhouse Coopers (2011) Protecting human health and safety during severe and extreme heat events: a national framework. Commonwealth Government Report, Australia

    Google Scholar 

  • Rashid HA, Hendon HH, Wheeler MC, Alves O (2010) Predictability of the Madden–Julian Oscillation in the POAMA dynamical seasonal prediction system. Clim Dyn 36:649–661

    Article  Google Scholar 

  • Risbey JS, Pook MJ, McIntosh PC, Wheeler MC, Hendon HH (2009) On the remote drivers of rainfall variability in Australia. Mon Weather Rev 137:3233–3253

    Article  Google Scholar 

  • Roulston MS, Kaplan DT, Hardenberg J, Smith LA (2003) Using medium-range weather forecasts to improve the value of wind energy production. Renew Energy 28:585–602

    Article  Google Scholar 

  • Saji NH, Yamagata T (2003) Possible impacts of Indian Ocean dipole mode events on global climate. Clim Res 25:151–169

    Article  Google Scholar 

  • Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363

    Google Scholar 

  • Saji NH, Xie S, Yamagata T (2006) Tropical Indian Ocean Variability in the IPCC twentieth-century climate simulations. J Clim 19:4397–4417

    Article  Google Scholar 

  • 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:W08402

    Google Scholar 

  • 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 48:1464–1482

    Article  Google Scholar 

  • 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, Australia

  • 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, Australia

  • 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 109–230

  • Spiegel MR (1961) Schaum’s outline of theory and problems of Statistics. Schaum Publishing Company, New York

    Google Scholar 

  • State of Victoria (2009) January 2009 Heatwave in Victoria: an assessment of health impacts. Victoria health technical report, Australia

  • Stephenson DB, Casati B, Ferro CAT, Wilson CA (2008) The extreme dependency score: a non-vanishing measure for forecasts of rare events. Meteorol Appl 15:41–50

    Article  Google Scholar 

  • Stockdale TN (1997) Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon Weather Rev 125:809–818

    Article  Google Scholar 

  • Stockdale TN, Anderson DLT, Alves JOS, Balmaseda MA (1998) Global seasonal rainfall forecasts using a coupled ocean–atmosphere model. Nature 392:370–373

    Article  Google Scholar 

  • Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19:57–70

    Article  Google Scholar 

  • 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 79:185–211

    Article  Google Scholar 

  • 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, Australia

  • Trewin B, Vermont H (2010) Changes in the frequency of record temperatures in Australia, 1957–2009. Aust Meteorol Oceanogr J 60:113–119

    Google Scholar 

  • Valcke S, Terray L, Piacentini A (2000) Oasis 2.4, Ocean atmosphere sea ice soil: user’s guide. TR/CMGC/00/10, CERFACS, Toulouse, France

  • Verdon DC, Franks SW (2005) Indian Ocean sea surface temperature variability and winter rainfall: Eastern Australia. Water Resour Res 41:W09413

    Google Scholar 

  • Vitart F (2005) Monthly forecast and the summer 2003 heat wave over Europe: a case study. Atmos Sci Lett 6:112–117

    Article  Google Scholar 

  • Wajsowicz RC (2007) Seasonal-to-interannual forecasting of tropical Indian Ocean sea surface temperature anomalies: potential predictability and barriers. J Clim 20:3320–3343

    Article  Google Scholar 

  • 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 6:40–46

    Google Scholar 

  • Webster PJ, Moore AM, Loschnigg JP, Leben RR (1999) Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401:356–360

    Article  Google Scholar 

  • 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 41:3145–3165

    Google Scholar 

  • Wilks D (2006) Statistical methods in atmospheric sciences, 2nd edn. Academic Press, Burlington

    Google Scholar 

  • 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 25:6905–6929

    Article  Google Scholar 

  • Yin Y, Alves O, Oke PR (2011a) An ensemble ocean data assimilation system for seasonal prediction. Mon Weather Rev 139:786–808

    Article  Google Scholar 

  • 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, Australia

  • Zeng L (2000) Weather derivatives and weather insurance: concept, application, and analysis. Bull Am Meteorol Soc 81:2075–2082

    Article  Google Scholar 

  • Zhao M, Hendon HH (2009) Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model. Q J R Meteor Soc 135:337–352

    Article  Google Scholar 

  • 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, Australia

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Acknowledgments

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.

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White, C.J., Hudson, D. & Alves, O. ENSO, the IOD and the intraseasonal prediction of heat extremes across Australia using POAMA-2. Clim Dyn 43, 1791–1810 (2014). https://doi.org/10.1007/s00382-013-2007-2

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  • DOI: https://doi.org/10.1007/s00382-013-2007-2

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