Climate Dynamics

, Volume 42, Issue 1–2, pp 253–270 | Cite as

Intrinsic modulation of ENSO predictability viewed through a local Lyapunov lens

  • Christina KaramperidouEmail author
  • Mark A. Cane
  • Upmanu Lall
  • Andrew T. Wittenberg


The presence of rich ENSO variability in the long unforced simulation of GFDL’s CM2.1 motivates the use of tools from dynamical systems theory to study variability in ENSO predictability, and its connections to ENSO magnitude, frequency, and physical evolution. Local Lyapunov exponents (LLEs) estimated from the monthly NINO3 SSTa model output are used to characterize periods of increased or decreased predictability. The LLEs describe the growth of infinitesimal perturbations due to internal variability, and are a measure of the immediate predictive uncertainty at any given point in the system phase-space. The LLE-derived predictability estimates are compared with those obtained from the error growth in a set of re-forecast experiments with CM2.1. It is shown that the LLEs underestimate the error growth for short forecast lead times (less than 8 months), while they overestimate it for longer lead times. The departure of LLE-derived error growth rates from the re-forecast rates is a linear function of forecast lead time, and is also sensitive to the length of the time series used for the LLE calculation. The LLE-derived error growth rate is closer to that estimated from the re-forecasts for a lead time of 4 months. In the 2,000-year long simulation, the LLE-derived predictability at the 4-month lead time varies (multi)decadally only by 9–18 %. Active ENSO periods are more predictable than inactive ones, while epochs with regular periodicity and moderate magnitude are classified as the most predictable by the LLEs. Events with a deeper thermocline in the west Pacific up to five years prior to their peak, along with an earlier deepening of the thermocline in the east Pacific in the months preceding the peak, are classified as more predictable. Also, the GCM is found to be less predictable than nature under this measure of predictability.


ENSO Predictability Local Lyapunov exponents 



We thank Dr. Ken Takahashi, and an anonymous reviewer for their constructive comments that significantly improved this manuscript. We acknowledge the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA for dissemination of the NOAA ERSST.v3 datasets. CK was partially supported by the Alexander S. Onassis Public Benefit Foundation Scholarship Program. MAC was supported by grants NOAA-CICAR NA08OAR4320912, DOE DE-SC0005108 and the Office of Naval Research MURI (N00014-12-1-0911).


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Copyright information

© Springer-Verlag (outside the USA) 2013

Authors and Affiliations

  • Christina Karamperidou
    • 1
    • 4
    Email author
  • Mark A. Cane
    • 2
  • Upmanu Lall
    • 1
  • Andrew T. Wittenberg
    • 3
  1. 1.Earth and Environmental Engineering DepartmentColumbia UniversityNew YorkUSA
  2. 2.Lamont-Doherty Earth ObservatoryPalisadesUSA
  3. 3.US DOC/NOAA/GFDLPrincetonUSA
  4. 4.Department of MeteorologySchool of Ocean and Earth Sciences and Technology, University of HawaiiHonoluluUSA

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