Climate Dynamics

, Volume 52, Issue 9–10, pp 6279–6300 | Cite as

The El Niño–Southern Oscillation’s effect on summer heatwave development mechanisms in Australia

  • Tammas Francis LoughranEmail author
  • Andrew J. Pitman
  • Sarah E. Perkins-Kirkpatrick


We investigate how the El Niño–Southern Oscillation (ENSO) affects the mechanisms and development of heatwaves in Australia. There are three fundamental mechanisms through which heat can accumulate in the atmosphere to generate temperatures high enough, and long-lasting enough to cause a heatwave. First, heat is advected, usually from lower latitudes, via a slow moving synoptic high pressure system; second, via diabatic heating of the boundary layer by the land surface; and third, via the subsidence of high potential temperature air which warms adiabatically as it approaches the heatwave affected region. Using an atmospheric model, we examine how ENSO affects these three mechanisms using prescribed sea surface temperatures characteristic of El Niño and La Niña conditions. By generating multiple ensembles of the same ENSO conditions, we can generate many ENSO realisations and examine how this large-scale mode of variability influences Australian heatwaves. We find that heatwave frequency and duration in the north and northeast are primarily affected by ENSO through land surface processes, soil moisture and changes in the surface energy balance. The importance of atmosphere–land coupling in ENSO related heatwave variability may help explain El Niño events with unusually few heatwaves and improve seasonal heatwave predictions. Other heatwave development mechanisms, such as the advection of heat and the subsidence of adiabatically warming air, are more important for the southern regions of Australia, but the influence of ENSO is weaker. The southeast tends to receive little influence from ENSO.


Heatwaves El Niño Variability 



This work was supported and funded by the Australian Postgraduate Award, the Australian Research Councils (ARC) Discovery Early Career Researcher Award grant DE140100952, the ARC Centre of Excellence for Climate System Science Grant CE110001028 and the ARC Centre of Excellence for Climate Extremes Grant CE170100023. The modelling was undertaken with the assistance of resources provided at the NCI National Facility systems at the Australian National University through the National Computational Merit Allocation Scheme supported by the Australian Government. The HadISST data was provided the Met Office ( Thanks to Nicholas Herold at the University of New South Wales for developing the composite SST fields used to force the model. Lastly, we thank the University of Melbourne for providing access to the Traj3d Lagrangian tracking scheme.


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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Climate Change Research Centre, Faculty of ScienceUniversity of New South WalesSydneyAustralia
  2. 2.Australian Research Council Centre of Excellence for Climate System ScienceSydneyAustralia
  3. 3.Australian Research Council Centre of Excellence for Climate ExtremesSydneyAustralia

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