EGU2020-2235, updated on 13 Sep 2023
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

Jiale Lou1,2, Terence O'Kane3, and Neil Holbrook1,4
Jiale Lou et al.
  • 1Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
  • 2ARC Centre of Excellence for Climate System Science, University of Tasmania, Hobart, Australia (
  • 3CSIRO Oceans and Atmosphere, Hobart, Australia (terence.o'
  • 4ARC Centre of Excellence for Climate Extremes, University of Tasmania, Hobart, Australia (

A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extra-tropical sea surface temperature, and South Pacific Ocean vertically-averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble the El-Niño Southern Oscillation (ENSO) and the South Pacific Decadal Oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the mid-latitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the atmospheric high-frequency variability of the Pacific South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in Mar-May (MAM) but nevertheless displays significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June-September (JAS), indicating remote but delayed influences from the Tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability, and further that by characterizing the upper ocean temperature contribution in the LIM the seasonal predictability of both ENSO and the SPDO variability is increased.

How to cite: Lou, J., O'Kane, T., and Holbrook, N.: A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2235,, 2020.


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