Climate Dynamics

, Volume 48, Issue 3–4, pp 1249–1276 | Cite as

Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

  • Desislava PetrovaEmail author
  • Siem Jan Koopman
  • Joan Ballester
  • Xavier Rodó


El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996–2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted “Spring Barrier”. The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.


El Niño Southern Oscillation Prediction Predictability Subsurface dynamics Time series 



J.B. gratefully acknowledges funding from the European Commission through a Marie Curie International Outgoing Fellowship (Project MEMENTO from the FP7-PEOPLE-2011-IOF call), and from the European Commission and the Catalan Government through a Marie Curie—Beatriu de Pinós Fellowship (Project 00068 from the BP-DGR-2014-B call). X.R. gratefully acknowledges funding from the Ministry of Science and Innovation, Spain (Project PANDORA CGL 2007-63053).

Supplementary material

382_2016_3139_MOESM1_ESM.pdf (251 kb)
Supplementary material 1 (pdf 251 KB)


  1. An SI, Choi J (2009) Seasonal locking of the ENSO asymmetry and its influence on the seasonal cycle of the tropical eastern Pacific sea surface temperature. Atmos Res 94:3–9CrossRefGoogle Scholar
  2. An SI, Jin FF (2004) Nonlinearity and asymmetry of ENSO. J Clim 17:2399–2412CrossRefGoogle Scholar
  3. Ballester J, Bordoni S, Petrova D, Rodo X (2015) On the dynamical mechanism explaining the western pacific subsurface temperature buildup leading to ENSO events. Geophys Res Lett 42:2961–2967CrossRefGoogle Scholar
  4. Ballester J, Bordoni S, Petrova D, Rodo X (2016) Heat advection processes leading to El Niño events as depicted by an ensemble of ocean assmiliation products. Submitted for reviewGoogle Scholar
  5. Ballester J, Rodriguez-Arias MA, Rodo X (2011) A new extratropical tracer describing the role of the Western Pacific in the onset of El Nñio: Implications for ENSO understanding and forecasting. J Clim 24:1425–1437CrossRefGoogle Scholar
  6. Barnston A, Chelliah M, Goldenberg S (1997) Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmos Ocean 35:367–383CrossRefGoogle Scholar
  7. Barnston A, Ropelewski C (1992) Prediction of ENSO episodes using canonical correlation analysis. J Clim 5:1316–1345CrossRefGoogle Scholar
  8. Barnston A, Tippett M, L’Heureux M, Li S, DeWitt D (2012) Skill of real-time seasonal ENSO model predictions during 2002–11. Is our capability increasing? Am Meteorol Soc 93:631–651CrossRefGoogle Scholar
  9. Bjerknes J (1969) Atmospheric teleconnections from the equatorial Pacific. Mon Weather Rev 97:163–172CrossRefGoogle Scholar
  10. Brown J, Fedorov A (2010) How much energy is transferred from the winds to the thermocline on ENSO time scales? J Clim 23:1563–1580CrossRefGoogle Scholar
  11. Chen D, Cane M, Kaplan A, Zebiak S, Huang D (2004) Predictability of El Niño over the past 148 years. Nature 428:15CrossRefGoogle Scholar
  12. Chen D, Lian T, Fu C, Cane M, Tang Y, Murtugudde R, Song X, Wu Q, Zhou L (2015) Strong influence of westerly wind bursts on El Niño diversity. Nat Geosci 8:339–345CrossRefGoogle Scholar
  13. de Jong P (1991) The diffuse Kalman Filter. Ann Stat 19:1073–1083CrossRefGoogle Scholar
  14. Doornik JA (2013) Object-Oriented Matrix Programming using Ox 7.0. Timberlake Consultants Ltd, London. See
  15. Durbin J, Koopman SJ (2012) Time series analysis by state space methods, 2nd edn. Oxford University Press, OxfordCrossRefGoogle Scholar
  16. Eisenman I, Yu L, Tziperman E (2005) Westerly wind bursts: ENSO’s tail rather than the dog? J Clim 18:5224–5238CrossRefGoogle Scholar
  17. Fedorov A, Harper S, Philander SG, Winter B, Wittenberg A (2003) How predictable is El Niño? Bull Am Meteorol Soc 84:911–919CrossRefGoogle Scholar
  18. Fedorov A, Philander SG (2001) A stability analysis of tropical ocean-atmosphere interactions: bridging measurements and theory for El Niño. J Clim 14:3086–3101CrossRefGoogle Scholar
  19. Gebbie G, Tziperman E (2009) Predictability of SST-modulated westerly wind bursts. J Clim 22:3894–3909CrossRefGoogle Scholar
  20. Ghil M, Allen MR, Dettinger MD, Ide K, Kondrashov D, Mann ME, Robertson AW, Saunders A, Tian Y, Varadi F, Yiou P (2002) Advanced spectral methods for climatic time series. Rev Geophys 40:3.1–3.41CrossRefGoogle Scholar
  21. Gill A (1985) Elements of coupled ocean-atmosphere models for the tropics. Elsevier Oceanogr Ser 40:303–327CrossRefGoogle Scholar
  22. Glantz MH (2015) Shades of chaos: lessons learned about lessons learned about forecasting El Niño and its impacts. Int J Disaster Risk Sci 6:94–103CrossRefGoogle Scholar
  23. Good SA, Martin MJ, Rayner NA (2013) EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J Geophys Res Oceans 118:6704–6716CrossRefGoogle Scholar
  24. Goswami BN, Shukla J (1991) Predictability of a coupled ocean-atmosphere model. J Clim 4:3–22CrossRefGoogle Scholar
  25. Hare S, Mantua N (2000) Empirical evidence for North Pacific regime shifts in 1977 and 1989. Prog Oceanogr 47:103–145CrossRefGoogle Scholar
  26. Harvey A (1989) Forecasting, structural time series models and the Kalman filter. Cambridge University Press, CambridgeGoogle Scholar
  27. Harvey A, Koopman SJ (2000) Signal extraction and the formulation of unobserved components models. Econom J 3:84–107CrossRefGoogle Scholar
  28. Harvey A, Koopman SJ, Penzer J (1998) Messy time series. In: Fomby T, Hill RC (eds) Advances in econometrics, vol 13. JAI Press, New YorkGoogle Scholar
  29. Harvey A, Shephard N (1993) Structural time series models. Handbook of statistics, 11. Elsevier, AmsterdamGoogle Scholar
  30. Ishii M, Shouji A, Sugimoto S, Matsumoto T (2005) Objective analyses of SST and marine meteorological variables for the 20th century using COADS and the Kobe collection. Int J Climatol 25:865–879CrossRefGoogle Scholar
  31. Izumo T, Lengaigne M, Vialard J, Luo J, Yamagata T, Madec G (2014) Influence of Indian Ocean Dipole and Pacific recharge on following year’s El Niño: interdecadal robustness. J Clim Dyn 42:291–310CrossRefGoogle Scholar
  32. Jiang N, Neelin D, Ghill M (1993) Quasi-quadrennial and quasibiennial variability in COADS equatorial Pacific sea surface temperature and zonal wind. In: Proceedings of the 17th Climate Diagnostics Workshop. Climate Analysis Center, NOAA, pp 348–353Google Scholar
  33. Jiang N, Neelin D, Ghill M (1995) Quasi-quadrennial and quasi-biennial variability in the equatorial Pacific. J Clim Dyn 12:291–310Google Scholar
  34. Jin FF (1997a) An equatorial ocean recharge paradigm for ENSO. Part I: conceptual model. J Atmos Sci 54:811–829CrossRefGoogle Scholar
  35. Jin FF (1997b) An equatorial ocean recharge paradigm for ENSO. Part II: a stripped down coupled model. J Atmos Sci 54:830–847CrossRefGoogle Scholar
  36. Jin FF, Kug JS, An S, Kang IS (2003) A near-annual coupled ocean-atmosphere mode in the equatorial Pacific. Geophys Res Lett 30Google Scholar
  37. Jin FF, Neelin JD, Ghil M (1994) El Niño on the devil’s staircase—annual subharmonic steps to chaos. Science 264:70–72CrossRefGoogle Scholar
  38. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng Trans ASMA Ser D 82:35–45CrossRefGoogle Scholar
  39. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  40. Kang IS, Kug JS (2002) El Niño and La Niña sea surface temperature anomalies: asymmetry characteristics associated with their wind stress anomalies. J Geophys Res 107:4372CrossRefGoogle Scholar
  41. Kleeman R, McCreary J, Klinger B (1999) A mechanism for generating ENSO decadal variability. Geophys Res Lett 26:1743–1746CrossRefGoogle Scholar
  42. Kondrashov D, Kravtsov S, Robertson A, Ghil M (2005) A hierarchy of data-based ENSO models. J Clim 18:4425–4444CrossRefGoogle Scholar
  43. Koopman SJ, Harvey AC, Doornik JA, Shephard N (2010) STAMP 8.3: structural time series analyser, modeller and predictor. Timberlake Consultants, LondonGoogle Scholar
  44. Koopman SJ, Shephard N, Doornik JA (2008) Statistical algorithms for models in state space form: SsfPack 3.0. Timberlake Consultants Press, LondonGoogle Scholar
  45. Krishnamurthy L, Vecchi G, Msadek R, Wittenberg A, Delworth T, Zeng F (2015) The seasonality of the great plains low-level jet and ENSO relationship. J Clim 28:4525–4544CrossRefGoogle Scholar
  46. Lau K, Shen P (1988) Annual cycle, quasi-biennial oscillation and Southern Oscillation in global precipitation. J Geophys Res 93:10975–10988CrossRefGoogle Scholar
  47. Mantua N, Battisti D (1995) Aperiodic variability in the Zebiak-Cane coupled ocean-atmosphere model: air-sea interactions in the western equatorial Pacific. J Clim 8:2897–2927CrossRefGoogle Scholar
  48. McGregor S, Timmermann A, Shneider N, Stuecker M, England M (2012) The effect of the South Pacific Convergence Zone on the termination of El Niño events and the meridional asymmetry of ENSO. J Clim 25:5566–5586CrossRefGoogle Scholar
  49. McPhaden M (2004) Evolution of the 2002/2003 El Niño. Am Meteorol Soc 85:677–695CrossRefGoogle Scholar
  50. McPhaden M, Timmermann A, Widlansky M, Balmaseda M, Stockdale T (2014) The curious case of the El Niño that never happened: a perspective from 40 years of progress in climate research and forecasting. Bull Am Meteorol SocGoogle Scholar
  51. McPhaden M, Yu X (1999) Equatorial waves and the 1997/98 El Niño. Geophys Res Lett 26:2961–2964CrossRefGoogle Scholar
  52. Moron V, Plaut G (2003) The impact of El Niño-Southern Oscillation upon weather regimes over Europe and the North Atlantic during boreal winter. Int J Climatol 23:363–379CrossRefGoogle Scholar
  53. Neelin J (1990) A hybrid coupled general circulation model for El Niño studies. J Atmos Sci 47:677–695CrossRefGoogle Scholar
  54. Neelin J (1991) The slow surface temperature mode and the fast-wave limit: analytic theory for tropical interannual oscillations and experiments in a hybrid coupled model. J Atmos Sci 48:584–606CrossRefGoogle Scholar
  55. Penland C (1996) A stochastic model of Indo-Pacific sea surface temperature anomalies. Phys D 98:534–558CrossRefGoogle Scholar
  56. Penland C, Magorian T (1993) Prediction of Niño3 sea surface temperatures using linear inverse modeling. J Clim 6:1067–1076CrossRefGoogle Scholar
  57. Philander S (1989) El Niño and La Niña. J Atmos Sci 77:451–459Google Scholar
  58. Philander S, Yamagata T, Pacanowski R (1984) Unstable air-sea interactions in the tropics. J Atmos Sci 41:604–613CrossRefGoogle Scholar
  59. Ramesh N, Murtugudde R (2013) All flavours of El Niño have similar early subsurface origins. Nat Clim Change 3:42–46CrossRefGoogle Scholar
  60. Rasmusson E, Carpenter T (1982) Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon Weather Rev 110:354–384CrossRefGoogle Scholar
  61. Rasmusson E, Wang X, Ropelewski C (1990) The biennial component of ENSO variability. J Mar Syst 1:71–96CrossRefGoogle Scholar
  62. Sarachik E, Cane M (2010) The El Niño Southern Oscillation Phenomenon. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  63. Stein K, Timmermann A, Shcneider N (2011) Phase synchronization of El Niño–Southern Oscillation with the annual cycle. Phys Rev Lett 107Google Scholar
  64. Thompson CJ, Battisti DS (2000) A linear stochastic dynamical model of ENSO. Part I: model development. J Clim 13:2818–2832CrossRefGoogle Scholar
  65. Thompson CJ, Battisti DS (2001) A linear stochastic dynamical model of ENSO. Part II: analysis. J Clim 14:445–466CrossRefGoogle Scholar
  66. Tong H (1990) Non-linear time series. Oxford University Press, OxfordGoogle Scholar
  67. Torrence C, Webster PJ (1998) The annual cycle of persistence in the El Niño–Southern Oscillation. Q J R Meteorol Soc 124:1985–2004Google Scholar
  68. Trenberth K (1976) Spatial and temporal variations in the Southern Oscillation. Q J R Meteorol Soc 102:639–653CrossRefGoogle Scholar
  69. Tziperman E, Stone L, Cane M, Jarosh H (1994) El Niño chaos: overlapping of resonances between the seasonal cycle and the Pacific ocean-atmosphere oscillator. Science 264:72–74CrossRefGoogle Scholar
  70. Tziperman E, Yu L (2007) Quantifying the dependence of westerly wind bursts on the large-scale tropical Pacific SST. J Clim 20:2760–2768CrossRefGoogle Scholar
  71. Tziperman E, Zebiak S, Cane M (1997) Mechanisms of seasonal—ENSO interaction. J Atmos Sci 54:61–71CrossRefGoogle Scholar
  72. Wyrtki K (1975) El Niño—the dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J Phys Oceanogr 5:572–584CrossRefGoogle Scholar
  73. Wyrtki K (1985) Water displacements in the Pacific and the genesis of El Niño cycles. J Geophys Res 90:7129–7132CrossRefGoogle Scholar
  74. Xue Y, Cane M, Zebiak S, Blumenthal M (1994) On the prediction of ENSO: a study with a low-order Markov model. Tellus A 46:512–528CrossRefGoogle Scholar
  75. Yasunari T (1989) A possible link of the QBO’s between the stratosphere, troposphere and the surface temperature in the tropics. J Meteorol Soc Jpn 67Google Scholar
  76. Yeh S, Kirtman B (2004) The decadal ENSO variability in a hybrid coupled. J Am Meteorol Soc 17:1225–1238Google Scholar
  77. Yeh S, Kirtman B (2005) Tropical Pacific decadal variability and ENSO amplitude modulation. Geophys Res Lett 32Google Scholar
  78. Yu J, Kao H, Lee T (2011) Subsurface ocean temperature indices for central-Pacific and eastern-Pacific types of El Niño and La Niña events. Theor Appl Climatol 103:337–344CrossRefGoogle Scholar
  79. Yu J, Mechoso C (2001) A coupled atmosphere-ocean GCM study of the ENSO cycle. J Clim 14:2329–2350CrossRefGoogle Scholar
  80. Yu X, McPhaden M (1999) Seasonal variability in the equatorial Pacific. J Phys Oceanogr 29:925–947CrossRefGoogle Scholar
  81. Zebiak S (1985) Tropical atmosphere-ocean interaction and the El Niño/Southern Oscillation phenomenon. PhD Thesis, Massachusetts Institute of TechnologyGoogle Scholar
  82. Zebiak S (1989) Oceanic heat content variability and El Niño cycles. J Phys Oceanogr 19:475–486CrossRefGoogle Scholar
  83. Zebiak SE, Cane MA (1987) A model El Niño-Southern Oscillation. Mon Weather Rev 115:2262–2278CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Desislava Petrova
    • 1
    • 2
    Email author
  • Siem Jan Koopman
    • 3
  • Joan Ballester
    • 1
    • 4
  • Xavier Rodó
    • 1
    • 5
  1. 1.Climate Dynamics and Impacts UnitCatalan Institute of Climate Sciences (IC3)BarcelonaSpain
  2. 2.Department of PhysicsUniversity of Barcelona (UB)BarcelonaSpain
  3. 3.Department of EconometricsVrije Universiteit AmsterdamAmsterdamThe Netherlands
  4. 4.California Institute of Technology (Caltech)PasadenaUSA
  5. 5.Institució Catalana de Recerca i Estudis Avancats (ICREA)BarcelonaSpain

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