Climate Dynamics

, Volume 52, Issue 3–4, pp 1837–1855 | Cite as

Contribution of tropical instability waves to ENSO irregularity

  • Ryan M. HolmesEmail author
  • Shayne McGregor
  • Agus Santoso
  • Matthew H. England


Tropical instability waves (TIWs) are a major source of internally-generated oceanic variability in the equatorial Pacific Ocean. These non-linear phenomena play an important role in the sea surface temperature (SST) budget in a region critical for low-frequency modes of variability such as the El Niño–Southern Oscillation (ENSO). However, the direct contribution of TIW-driven stochastic variability to ENSO has received little attention. Here, we investigate the influence of TIWs on ENSO using a \(1/4^\circ\) ocean model coupled to a simple atmosphere. The use of a simple atmosphere removes complex intrinsic atmospheric variability while allowing the dominant mode of air−sea coupling to be represented as a statistical relationship between SST and wind stress anomalies. Using this hybrid coupled model, we perform a suite of coupled ensemble forecast experiments initiated with wind bursts in the western Pacific, where individual ensemble members differ only due to internal oceanic variability. We find that TIWs can induce a spread in the forecast amplitude of the Niño 3 SST anomaly 6-months after a given sequence of WWBs of approximately \(\pm \,45\%\) the size of the ensemble mean anomaly. Further, when various estimates of stochastic atmospheric forcing are added, oceanic internal variability is found to contribute between about \(20\%\) and \(70\%\) of the ensemble forecast spread, with the remainder attributable to the atmospheric variability. While the oceanic contribution to ENSO stochastic forcing requires further quantification beyond the idealized approach used here, our results nevertheless suggest that TIWs may impact ENSO irregularity and predictability. This has implications for ENSO representation in low-resolution coupled models.


Tropical instability waves El Niño–Southern Oscillation Ocean general circulation model Hybrid coupled model Stochastic forcing Predictability 



This study benefited from discussions with Vishal Dixit and comments from two anonymous reviewers. A.S. and M.H.E. are supported by the Earth Science and Climate Change Hub of the Australian Government’s National Environmental Science Programme (NESP) and the Centre for Southern Hemisphere Oceans Research (CSHOR), a joint research centre for Southern Hemisphere oceans between QNLM, CSIRO, UNSW and UTAS. S.M. was supported by the Australian Research Council. The altimeter products were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) ( We thank the TAO Project Office of NOAA/PMEL for providing the TAO data. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.


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

Authors and Affiliations

  1. 1.Climate Change Research Centre and ARC Centre of Excellence for Climate System ScienceUniversity of New South WalesSydneyAustralia
  2. 2.School of Mathematics and StatisticsUniversity of New South WalesSydneyAustralia
  3. 3.School of Earth, Atmosphere and EnvironmentMonash UniversityMelbourneAustralia
  4. 4.Centre for Southern Hemisphere Oceans Research (CSHOR)CSIRO Oceans and AtmosphereHobartAustralia

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