Ocean Dynamics

, Volume 68, Issue 10, pp 1273–1284 | Cite as

Can intra-seasonal wind stress forcing strongly affect spring predictability barrier for ENSO in Zebiak–Cane model?

  • Yue-hua PengEmail author
  • Chong-wei Zheng
  • Tao Lian
  • Jie Xiang


The influence of the uncertainties of intra-seasonal wind stress forcing on Spring Predictability Barrier (SPB) in El Niño–Southern Oscillation (ENSO) prediction is studied with the Zebiak–Cane model and observational wind data which are analyzed with Continuous Wavelet Transform (CWT) and utilized to extract intra-seasonal wind stress signals as external forcing. The observational intra-seasonal wind stress forcing are joined into Zebiak–Cane model to get the Zebiak–Cane-add model and subsequently with the Conditional Nonlinear Optimal Perturbation (CNOP) method, the evolutions of the optimal initial errors (i.e., CNOPs), model errors caused by intra-seasonal wind stress uncertainties, and their joint errors based on ENSO events are calculated. By investigating their error growth rates and prediction errors of Niño-3 indices, the effect of observational intra-seasonal wind stress forcing on seasonal error growth of ENSO is explored and the impact of initial error and model error on ENSO predictability is compared quantitatively. The results show that the model errors led by observational intra-seasonal wind stress forcing could scarcely cause a significant SPB whereas the initial errors and their joint errors can do; hence, the initial errors are most likely the main error source of SPB. In fact, this result emphasizes the primary influence of initial errors on ENSO predictability and lays the basis of adaptive data assimilation for ENSO forecast.


El Niño–Southern Oscillation (ENSO) Intra-seasonal wind stress Spring predictability barrier (SPB) Zebiak–Cane model Conditional nonlinear optimal perturbation (CNOP) 



We would like to acknowledge the financial support by the National Natural Science Foundation of China (no. 41405062). Thanks to two anonymous reviewers for their careful and responsible comments.


  1. Chen D, Zebiak SE, Busalacchi AJ, Cane MA (1995) An improved procedure for El Niño forecasting: implications for predictability. Science 269:1699–1702CrossRefGoogle Scholar
  2. Chen D, Cane MA, Kaplan A, Zebiak SE, Huang DJ (2004) Predictability of El Niño over the past 148 years. Nature 428:733–736CrossRefGoogle Scholar
  3. 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
  4. Chiodi AM, Harrison DE, Vecchi GA (2014) Subseasonal atmospheric variability and El Niño waveguide warming: observed effects of the madden-Julian oscillation and westerly wind events. J Clim 27:3619–3642. CrossRefGoogle Scholar
  5. Duan WS, Mu M (2006) Investigating decadal variability of El Nific Southern Oscillation asymmetry by conditional nonlinear optimal perturbation. Journal of Geophysical Research Oceans, 111:
  6. Duan W, Wei C (2013) The "spring predictability barrier" for ENSO predictions and its possible mechanism: results from a fully coupled model. Int J Climatol 33:1280–1292. CrossRefGoogle Scholar
  7. Duan WS, Mu M, Wang B (2004) Conditional nonlinear optimal perturbation as the optimal precursors for ENSO events. J Geophys Res 109:
  8. Duan WS, Liu XC, ZHu KY, Mu M (2009a) Exploring the initial errors that cause a significant “spring predictability barrier” for El Nino event. J Geophys Res 114(C04022).
  9. Duan WS, Xue F, Mu M (2009b) Investigating a nonlinear characteristic of El Niño events by conditional nonlinear optimal perturbation. Atmos Res 94(1):8.
  10. Gebbie G, Tziperman E (2009) Incorporating a semi-stochastic model of ocean-modulated westerly wind bursts into an ENSO prediction model. Theor Appl Climatol 97(1–2):65–73CrossRefGoogle Scholar
  11. Hendon HH, Wheeler MC, Zhang C (2007) Seasonal dependence of the MJO-ENSO 722 relationship. J Clim 20:531–543CrossRefGoogle Scholar
  12. Kleeman R, Moore AM (1997) A theory for the limitation of ENSO predictability due to stochastic atmospheric transients. J Atmos Sci 54:753–767CrossRefGoogle Scholar
  13. Larson, S. M., & Kirtman, B. P. (2016): Drivers of coupled model ENSO error dynamics and the spring predictability barrier. Climate dynamics, 1–14. DOI:
  14. Levine AFZ, Jin FF (2010) Noise-induced instability in the ENSO recharge oscillator. J Atmos Sci 67:529–542CrossRefGoogle Scholar
  15. Levine AFZ, McPhaden MJ (2015) The annual cycle in ENSO growth rate as a cause of the spring predictability barrier. Geophys Res Lett 42:5034–5041. CrossRefGoogle Scholar
  16. Lizumi T et al (2014) Impacts of El Niño southern oscillation on the global yields of major crops. Nat Commun 5:3712. CrossRefGoogle Scholar
  17. Lopez H, Kirtman BP (2014) WWBS, ENSO predictability, the spring barrier and extreme events. J Geophys Res Atmos 119:10,114–10,138. CrossRefGoogle Scholar
  18. Luo JJ, Masson S, Behera S, Yamagata T (2008) Extended ENSO predictions using a fully coupled ocean–atmosphere model. J Clim 21:9. Google Scholar
  19. Madden, R.A., and P. R. Julian (1972). Description of global-scale circulation cells in the tropics with a 40–50 day period, J. Atmos. Sci., 29, 1109–1123. doi:<1109:DOGS CC>2.0.CO;2
  20. McPhaden MJ (1999) Climate oscillations: genesis and evolution of the 1997–98 El Niño. Science 283:950–954CrossRefGoogle Scholar
  21. Moore AM, Kleeman R (1999) Stochastic forcing of ENSO by the intraseasonal oscillation. J Clim 12:1199–1220CrossRefGoogle Scholar
  22. Mu M, Duan WS (2003) A new approach to studying ENSO predictability: conditional nonlinear optimal perturbation. Chin Sci Bull 48:1045–1047. CrossRefGoogle Scholar
  23. Mu M, Jiang Z (2008) A new approach to the generation of initial perturbations for ensemble prediction: conditional nonlinear optimal perturbation. Chinese Sci Bull 53(113):6–2068. Google Scholar
  24. Mu M, Sun L, Henk DA (2004) The sensitivity and stability of the ocean’s thermocline circulation to finite amplitude freshwater perturbation. J Phys Oceanogr 34:10–2315.<2305:TSASOT>2.0.CO;2 CrossRefGoogle Scholar
  25. Mu M, Xu H, Wansuo D (2007a) A kind of initial errors related to “spring predictability barrier” for El Niño events in Zebiak–Cane model. Geophys Res Lett 34.
  26. Mu M, Wansuo D, Wang B (2007b) Season-dependent dynamics of nonlinear optimal error growth and El Niño–Southern Oscillation predictability in a theoretical model. J Geophys Res 112.
  27. Mu M, Zhou F, Wang H (2009) A method to identify the sensitive areas n targeting for tropical cyclone prediction: conditional nonlinear optimal perturbation. Mon Weather Rev 137:16–1639. CrossRefGoogle Scholar
  28. Neelin D, Battisti D, Hirst A, Jin FF, Wakata Y, Yamagata T, Zebiak S (1998) ENSO theory. J Geophys Res 104:14262–14,290Google Scholar
  29. Peng YH, Duan WS, Xiang J (2011) Effect of stochastic MJO forcing on ENSO predictability. Adv Atmos Sci 28(6):11–1290. CrossRefGoogle Scholar
  30. Peng YH, Duan WS, Xiang J (2012) Can the uncertainties of Madden Jullian oscillation cause a significant “spring predictability barrier” for ENSO events. Acta Meteor Sin 26(5):12–578. CrossRefGoogle Scholar
  31. Peng YH, Song JQ, Xiang J, Sun CZ (2015) Impact of observational MJO forcing on ENSO predictability in the Zebiak–Cane model: part I. Effect on the maximum prediction error. Acta Oceanol Sin 34(5):7. CrossRefGoogle Scholar
  32. Philander S G H. (1990). El Niño, La Niña, and the Southern Oscillation. Academic Press, 293 ppGoogle Scholar
  33. Roulston MS, Neelin JD (2000) The response of an ENSO model to climate noise, weather noise and intra seasonal forcing. Geophys Res Lett 27:3723–3726CrossRefGoogle Scholar
  34. Tang Y, Deng Z, Zhou X, Cheng Y (2008) Interdecadal variation of ENSO predictability in multiple models. J Clim 21:22–4833. CrossRefGoogle Scholar
  35. Tippett MK, Barnston AG, Li S (2012) Performance of recent multimodel ENSO forecasts. J Appl Meteor Climatol 51:637–654CrossRefGoogle Scholar
  36. Vecchi GA, Harrison DE (2000) Tropical Pacific Sea surface temperature anomalies, El Niño, and equatorial westerly wind events. J Clim 13:1814–1830CrossRefGoogle Scholar
  37. Wang B, Fang Z (1996) Chaotic oscillation of tropical climate: a dynamic system theory for ENSO. J Atmos Sci 53:<2786:COOTCA>2.0.CO;2
  38. Webster PJ, Yang S (1992) Monsoon and ENSO: selectively interactive systems. Quart. J. Roy. Meteor. Soc. 118:877–926. CrossRefGoogle Scholar
  39. Yu YS, Duan WS, Xu H et al (2009) Dynamics of nonlinear error growth and season-dependent predictability of El Niño events in the Zebiak–Cane model. Quart J Roy Meteor Soc 135:2146–2160. CrossRefGoogle Scholar
  40. Yu YS, Mu M, Duan WS (2012) Does model parameter error cause a significant “spring predictability barrier” for El Niño events in the Zebiak–Cane model? J. Climate 25:1263–1277CrossRefGoogle Scholar
  41. Zavala-Garay J, Moore AM, Kleeman R (2004) Influence of stochastic forcing on ENSO prediction. J Geophys Res 109:C11007. CrossRefGoogle Scholar
  42. Zebiak SE (1989) On the 30–60 day oscillation and the prediction of El Nino. J Clim 15:7Google Scholar
  43. Zebiak SE, Cane MA (1987) A model El Nino southern oscillation. Mon Weather Rev 115:16CrossRefGoogle Scholar
  44. Zhang RH, Zhou GQ, Chao JP (2003) On ENSO dynamics and its prediction. Chin J Atmos Sci 27:14. Google Scholar
  45. Zheng F, Zhu J (2010) Spring predictability barrier of ENSO events from the perspective of an ensemble prediction system. Glob Planet Chang 72:108–117CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Dalian Naval AcademyDalianChina
  2. 2.Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Satellite Ocean Environment DynamicsSecond Institute of OceanographyHangzhou, 310012China
  4. 4.College of Meteorology and OceanographyNational University of Defence TechnologyNanjingChina

Personalised recommendations