Climate Dynamics

, Volume 44, Issue 9–10, pp 2623–2635 | Cite as

The ENSO-Australian rainfall teleconnection in reanalysis and CMIP5

  • Andrew D. KingEmail author
  • Markus G. Donat
  • Lisa V. Alexander
  • David J. Karoly


Australian rainfall is strongly influenced by El Niño-southern oscillation (ENSO). The relationship between ENSO and rainfall in eastern Australia is non-linear; the magnitude of La Niña events has a greater effect on rainfall than does the magnitude of El Niño events, and the cause of the non-linearity is unclear from previous work. The twentieth century reanalysis succeeds in capturing the asymmetric ENSO-rainfall relationship. In the reanalysis the asymmetry is strongly related to moisture availability in the south-west Pacific whereas wind flow is of less importance. Some global climate models (GCMs) in the coupled model intercomparison project (CMIP5) archive capture the asymmetric nature of the ENSO-rainfall relationship whilst others do not. Differences in thermodynamic processes and their relationships with ENSO are the primary cause of variability in model performance. Analysis of an atmosphere-only run of a GCM which fails to capture the non-linear ENSO-rainfall relationship is also conducted. The atmospheric run forced by observed sea surface temperatures shows no significant improvement in the ENSO-rainfall relationship over the corresponding coupled model run in the CMIP5 archive. This result suggests that some models are failing to capture the atmospheric teleconnection between the tropical Pacific and Australia, and both this and a realistic representation of oceanic ENSO characteristics are required for coupled models to accurately capture the ENSO-rainfall teleconnection. These findings have implications for the study of rainfall projections in the region.


Twentieth century reanalysis Precipitation Dynamics Thermodynamics Asymmetry El Niño-southern oscillation 



We thank Steve Woolnough and an anonymous reviewer for their useful feedback on this paper. We also thank Jaclyn Brown for useful discussions. Funding for this project was provided by Australian Research Council grant CE110001028. We thank the Bureau of Meteorology, the Bureau of Rural Sciences, and CSIRO for providing the Australian Water Availability Project data. Twentieth Century Reanalysis data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. Support for the Twentieth Century Reanalysis Project is provided by the U.S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. HadISST SSTs were provided by the U.K. Met Office. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Supplementary material

382_2014_2159_MOESM1_ESM.eps (8.1 mb)
Supplementary material 1 (EPS 8311 kb) (a-b) Maps of Spearman’s rank correlation coefficients between the Oct-Mar Niño-3.4 index and precipitation in AWAP in (a) seasons where the Niño-3.4 index is negative only and (b) seasons where the Niño-3.4 index is positive only. Stippling indicates correlations significant at the 5 % level


  1. Alexander LV, Arblaster JM (2009) Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. Int J Climatol 29:417–435. doi: 10.1002/joc.1730 CrossRefGoogle Scholar
  2. Ashcroft L, Karoly DJ, Gergis J (2013) Southeastern Australian climate variability 1860–2009: a multivariate analysis. Int J Climatol. doi: 10.1002/joc.3812 Google Scholar
  3. Bellenger H, Guilyardi E, Leloup J, Lengaigne M, Vialard J (2013) ENSO representation in climate models: from CMIP3 to CMIP5. Clim Dyn. doi: 10.1007/s00382-013-1783-z Google Scholar
  4. Brown JR, Moise AF, Colman RA (2012) The south pacific convergence zone in CMIP5 simulations of historical and future climate. Clim Dyn. doi: 10.1007/s00382-012-1591-x Google Scholar
  5. Cai W, van Rensch P (2012) The 2011 southeast Queensland extreme summer rainfall: a confirmation of a negative pacific decadal oscillation phase? Geophys Res Lett 39:L08702. doi: 10.1029/2011GL050820 CrossRefGoogle Scholar
  6. Cai W, van Rensch P, Cowan T, Sullivan A (2010) Asymmetry in ENSO teleconnection with regional rainfall, its multidecadal variability and impact. J Clim 23:4944–4955CrossRefGoogle Scholar
  7. Compo et al (2011) The twentieth century reanalysis project. Q J R Meteor Soc 137:1–28. doi: 10.1002/qj.776 CrossRefGoogle Scholar
  8. Doblas-Reyes FJ, Pavan V, Stephenson DB (2003) The skill of multi-model seasonal forecasts of the wintertime north Atlantic oscillation. Clim Dyn 21:501–514. doi: 10.1007/s00382-003-0350-4 CrossRefGoogle Scholar
  9. Donat MG, Leckebusch GC, Wild S, Ulbrich U (2010) Benefits and limitations of regional multi-model ensembles for storm loss estimations. Clim Res 44:211–225. doi: 10.3354/cr00891 CrossRefGoogle Scholar
  10. Folland C, Kinter JL III (2002) The climate of the twentieth century project. CLIVAR Exch 7(2):37–39Google Scholar
  11. Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting-1 basic concept. Tellus A 57:219–233. doi: 10.1111/j.1600-0870.2005.00103.x CrossRefGoogle Scholar
  12. Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Aust Meteor Ocean J 58:233–248Google Scholar
  13. King AD, Lewis SC, Perkins SE, Alexander LV, Donat MG, Karoly DJ, Black MT (2013a) Limited Evidence of Anthropogenic Influence on the 2011–2012 Extreme Rainfall over Southeast Australia [in “Explaining Extreme Events of 2012 from a Climate Perspective”] Bull Am Meteor Soc 94(9):S55–S58Google Scholar
  14. King AD, Alexander LV, Donat MG (2013b) Asymmetry in the response of Eastern Australia extreme rainfall to low-frequency Pacific variability. Geophys Res Lett 40:2271–2277. doi: 10.1002/grl.50427 CrossRefGoogle Scholar
  15. King AD, Alexander LV, Donat MG (2013c) The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia. Int J Climatol 33:2376–2387. doi: 10.1002/joc.3588 CrossRefGoogle Scholar
  16. King AD, Klingaman NP, Alexander LV, Donat MG, Jourdain NC, Maher P (2014) Extreme rainfall variability in Australia: Patterns, drivers, and predictability. J Clim (submitted)Google Scholar
  17. Klingaman NP, Woolnough SJ, Syktus J (2013) On the drivers of inter-annual and decadal rainfall variability in Queensland, Australia. Int J Climatol 33:2413–2430. doi: 10.1002/joc.3593 CrossRefGoogle Scholar
  18. Nicholls N, Drosdowsky W, Lavery B (1997) Australian rainfall variability and change. Weather 52:66–71CrossRefGoogle Scholar
  19. Power S, Haylock M, Colman R, Wang X (2006) The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. J Clim 19:4755–4771CrossRefGoogle Scholar
  20. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res (Atmos) 108:4407. doi: 10.1029/2002JD002670 CrossRefGoogle Scholar
  21. 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
  22. Scaife et al (2009) The CLIVAR C20C project: selected twentieth century climate events. Clim Dyn 33:603–614. doi: 10.1007/s00382-008-0451-1 CrossRefGoogle Scholar
  23. Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res (Atmos) 118:1716–1733. doi: 10.1002/jgrd.50203 CrossRefGoogle Scholar
  24. Taschetto AS, Sen Gupta A, Jourdain NC, Santoso A, Ummenhofer CC, England MH (2014) Cold tongue and warm pool ENSO events in CMIP5: mean state and future projections. J Clim 27:2861–2885. doi: 10.1175/JCLI-D-13-00437.1 CrossRefGoogle Scholar
  25. Taylor KE, Stouffer RJ, Meehl GA (2012) An Overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  26. Vincent EM, Lengaigne M, Menkes CE, Jourdain NC, Marchesiello P, Madec G (2011) Interannual variability of the South Pacific convergence zone and implications for tropical cyclone genesis. Clim Dyn 36:1881–1896. doi: 10.1007/s00382-009-0716-3 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew D. King
    • 1
    Email author
  • Markus G. Donat
    • 1
  • Lisa V. Alexander
    • 1
  • David J. Karoly
    • 2
  1. 1.ARC Centre of Excellence for Climate System Science and Climate Change Research Centre, Level 4, Mathews BuildingUniversity of New South WalesSydneyAustralia
  2. 2.ARC Centre of Excellence for Climate System Science and School of Earth SciencesUniversity of MelbourneMelbourneAustralia

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