Advances in Atmospheric Sciences

, Volume 34, Issue 6, pp 737–756 | Cite as

Nonlinearity modulating intensities and spatial structures of central Pacific and eastern Pacific El Niño events

Original Paper

Abstract

This paper compares data from linearized and nonlinear Zebiak–Cane model, as constrained by observed sea surface temperature anomaly (SSTA), in simulating central Pacific (CP) and eastern Pacific (EP) El Ni˜no. The difference between the temperature advections (determined by subtracting those of the linearized model from those of the nonlinear model), referred to here as the nonlinearly induced temperature advection change (NTA), is analyzed. The results demonstrate that the NTA records warming in the central equatorial Pacific during CP El Ni˜no and makes fewer contributions to the structural distinctions of the CP El Ni˜no, whereas it records warming in the eastern equatorial Pacific during EP El Ni˜no, and thus significantly promotes EP El Ni˜no during El Ni˜no–type selection. The NTA for CP and EP El Ni˜no varies in its amplitude, and is smaller in CP El Ni˜no than it is in EP El Ni˜no. These results demonstrate that CP El Ni˜no are weakly modulated by small intensities of NTA, and may be controlled by weak nonlinearity; whereas, EP El Ni˜no are significantly enhanced by large amplitudes of NTA, and are therefore likely to be modulated by relatively strong nonlinearity. These data could explain why CP El Ni˜no are weaker than EP El Ni˜no. Because the NTA for CP and EP El Ni˜no differs in spatial structures and intensities, as well as their roles within different El Ni˜no modes, the diversity of El Ni˜no may be closely related to changes in the nonlinear characteristics of the tropical Pacific.

Key words

El Niño diversity nonlinearity intensity spatial structures nonlinear temperature advection 

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References

  1. An, S.-I., and F. F. Jin, 2004: Nonlinearity and asymmetry of ENSO. J. Climate, 17, 2399–2412.CrossRefGoogle Scholar
  2. Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, doi: 10.1029/2006JC003798.Google Scholar
  3. Barkmeijer, J., T. Iversen, and T. N. Palmer, 2003: Forcing singular vectors and other sensitive model structures. Quart. J. Roy. Meteor. Soc., 129, 2401–2423.CrossRefGoogle Scholar
  4. Battisti, D. S., 1988: Dynamics and thermodynamics of a warming event in a coupled tropical atmosphere-ocean model. J. Atmos. Sci., 45, 2889–2919.CrossRefGoogle Scholar
  5. Bellenger, H., E. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in climate models: From CMIP3 to CMIP5. Climate Dyn., 42, 1999–2018, doi: 10.1007/s000382-013-1783-z.CrossRefGoogle Scholar
  6. Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172.CrossRefGoogle Scholar
  7. Chen, D. K., S. E. Zebiak, A. J. Busalacchi, and M. A. Cane, 1995: An improved procedure for EI Niño forecasting: Implications for predictability. Science, 269, 1699–1702.CrossRefGoogle Scholar
  8. Chen, D. K., M. A. Cane, A. Kaplan, S. E. Zebiak, and D. J. Huang, 2004: Predictability of El Niño over the past 148 years. Nature, 428, 733–736.CrossRefGoogle Scholar
  9. Chen, D. K., and Coauthors, 2015: Strong influence of westerly wind bursts on El Niño diversity. Nature Geoscience, 8, 339–345, doi: 10.1038/ngeo2399.CrossRefGoogle Scholar
  10. Chung, P.-H., and T. Li, 2013: Interdecadal relationship between the mean state and El Niño types. J. Climate, 26, 361–379.CrossRefGoogle Scholar
  11. Duan, W. S., and M. Mu, 2006: Investigating decadal variability of El Niño–Southern Oscillation asymmetry by conditional nonlinear optimal perturbation. J. Geophys. Res., 111, doi: 10.1029/2005JC003458.Google Scholar
  12. Duan, W. S., M. Mu, and B. Wang, 2004: Conditional nonlinear optimal perturbations as the optimal precursors for El Niño-Southern Oscillation events. J. Geophys. Res., 109, doi: 10.1029/2004JD004756.Google Scholar
  13. Duan, W. S., H. Xu, and M. Mu, 2008: Decisive role of nonlinear temperature advection in El Niño and La Niña amplitude asymmetry. J. Geophys. Res., 113, doi: 10.1029/2006JC 003974.Google Scholar
  14. Duan, W. S., B. Tian, and H. Xu, 2014: Simulations of two types of El Niño events by an optimal forcing vector approach. Climate Dyn., 43, 1677–1692.CrossRefGoogle Scholar
  15. Feng, F., and W. S. Duan, 2013: The role of constant optimal forcing in correcting forecast models. Science China Earth Sciences, 56, 434–443.CrossRefGoogle Scholar
  16. Feng, J., and J. P. Li, 2011: Influence of El Niño Modoki on spring rainfall over South China. J. Geophys. Res., 116, doi: 10.1029/2010JD015160.Google Scholar
  17. Guan, C., and M. J. McPhaden, 2016: Ocean processes affecting the twenty-first-century shift in ENSO SST variability. J. Climate, 29, doi: 10.1175/JCLI-D-15-0870. 1.Google Scholar
  18. Hendon, H. H., E. Lim, G. M. Wang, O. Alves, and D. Hudson, 2009: Prospects for predicting two flavors of El Niño. Geophys. Res. Lett., 36, L19713, doi: 10.1029/2009GL040100.CrossRefGoogle Scholar
  19. Jin, F.-F., 1997a: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811–829.Google Scholar
  20. Jin, F.-F., 1997b: An equatorial ocean recharge paradigm for ENSO. Part II: A stripped-down coupled model. J. Atmos. Sci., 54, 830–847.Google Scholar
  21. Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615–632.CrossRefGoogle Scholar
  22. Kim, H.-M., P. J. Webster, and J. A. Curry, 2009: Impact of shifting patterns of Pacific Ocean warming on north Atlantic tropical cyclones. Science, 325, 77–80.CrossRefGoogle Scholar
  23. Kim, S. T., J.-Y. Yu, A. Kumar, and H. Wang, 2012: Examination of the two types of ENSO in the NCEP CFS model and its extratropical associations. Mon. Wea. Rev., 140, 1908–1923.CrossRefGoogle Scholar
  24. Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Niño events: Cold tongue El Niño and warm pool El Niño. J. Climate, 22, 1499–1515.CrossRefGoogle Scholar
  25. Kug, J.-S., Y.-G. Ham, J.-Y. Lee, and F.-F. Jin, 2012: Corrigendum: Improved simulation of two types of El Niño in CMIP5 models. Environ. Res. Lett., 7, 034002, doi: 10.1088/1748-9326/7/3/039502.CrossRefGoogle Scholar
  26. Larkin, N. K., and D. E. Harrison, 2005: On the definition of El Niño and associated seasonal average U. S. weather anomalies. Geophys. Res. Let., 32, L13705, doi: 10.1029/2005GL022738.CrossRefGoogle Scholar
  27. Latif, M., and Coauthors, 1998: A review of the predictability and prediction of ENSO. J. Geophys. Res., 103, 14375–14393.CrossRefGoogle Scholar
  28. Lee, T., and M. J. McPhaden, 2010: Increasing intensity of El Niño in the central-equatorial Pacific. Geophys. Res. Lett., 37, doi: 10.1029/2010GL044007.Google Scholar
  29. Li, J. P., and Coauthors, 2013: Progress in air–land–sea interactions in Asia and their role in global and Asian climate change. Chinese Journal of Atmospheric Sciences, 37(2), 518–538. (in Chinese)Google Scholar
  30. Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1), 503–528.CrossRefGoogle Scholar
  31. Lopez, H., and B. P. Kirtman, 2014: WWBs, ENSO predictability, the spring barrier and extreme events. J. Geophys. Res., 119, 10114–10138, doi: 10.1002/2014JD021908.CrossRefGoogle Scholar
  32. McPhaden, M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in earth science. Science, 314, 1740–1745.CrossRefGoogle Scholar
  33. Mo, K. C., 2010: Interdecadal modulation of the impact of ENSO on precipitation and temperature over the United States. J. Climate, 23, 3639–3656.CrossRefGoogle Scholar
  34. Na, H., B.-G. Jang, W.-M. Choi, and K.-Y. Kim, 2011: Statistical simulations of the future 50-year statistics of cold-tongue El Niño and warm pool El Niño. Asia-Pacific Journal of Atmospheric Sciences, 47, 223–233, doi: 10.1007/s13143-011-0011-1.CrossRefGoogle Scholar
  35. Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the southern oscillation/El Niño. Mon. Wea. Rev., 110, 354–384.CrossRefGoogle Scholar
  36. Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects of the El Niño/southern oscillation. Science, 222, 1195–1202.CrossRefGoogle Scholar
  37. Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi: 10.1029/2002JD002670.CrossRefGoogle Scholar
  38. Rosati, A., K. Miyakoda, and R. Gudgel, 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev., 125, 754–772.CrossRefGoogle Scholar
  39. Su, J. Z., R. H. Zhang, T. Li, X. Y. Rong, J.-S. Kug, and C.-C. Hong, 2010: Causes of the El Niño and La Niña amplitude asymmetry in the equatorial eastern Pacific. J. Climate, 23, 605–617.CrossRefGoogle Scholar
  40. Su, J. Z., T. Li, and R. H. Zhang, 2014: The initiation and developing mechanisms of central Pacific El Niños. J. Climate, 27, 4473–4485.CrossRefGoogle Scholar
  41. Takahashi, K., A. Montecinos, K. Goubanova, and B. Dewitte, 2011: ENSO regimes: Reinterpreting the canonical and Modoki El Niño. Geophys. Res. Lett., 38, L10704, doi: 10.1029/2011GL047364.CrossRefGoogle Scholar
  42. Tang, Y. M., Z. W. Deng, X. B. Zhou, Y. J. Cheng, and D. K. Chen, 2008: Interdecadal variation of ENSO predictability in multiple models. J. Climate, 21, 4811–4833.CrossRefGoogle Scholar
  43. Taschetto, A. S., A. S. Gupta, N. C. Jourdain, A. Santoso, C. C. Ummenhofer, and M. H. England, 2014: Cold tongue and warm pool ENSO events in CMIP5: Mean state and future projections. J. Climate, 27, 2861–2885.CrossRefGoogle Scholar
  44. Tian, B., and W. S. Duan, 2016: Comparison of the initial errors most likely to cause a spring predictability barrier for two types of El Niño events. Climate Dyn., 47, 779–792.CrossRefGoogle Scholar
  45. Wang, C. Z., 2001: A unified oscillator model for the El Niño-Southern Oscillation. J. Climate, 14, 98–115.CrossRefGoogle Scholar
  46. Wang, C. Z., and J. Picaut, 2004: Understanding ENSO physics— A review. Earth’s Climate: The Ocean-Atmosphere Interaction, C. Wang, S. P. Xie and J. A. Carton, Eds., American Geophysical Union, 21–48.Google Scholar
  47. Wang, C. Z., and X. Wang, 2013: Classifying El Niño Modoki I and II by different impacts on rainfall in southern china and typhoon tracks. J. Climate, 26, 1322–1338.CrossRefGoogle Scholar
  48. Weisberg, R. H., and C. Z. Wang, 1997: A western Pacific oscillator paradigm for the El Niño-Southern Oscillation. Geophys. Res. Lett., 24, 779–782.CrossRefGoogle Scholar
  49. Weng, H. Y., K. Ashok, S. K. Behera, S. A. Rao, and T. Yamagata, 2007: Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer. Climate Dyn., 29, 113–129.CrossRefGoogle Scholar
  50. Xiang, B. Q., B. Wang, and T. Li, 2013: A new paradigm for the predominance of standing Central Pacific Warming after the late 1990s. Climate Dyn., 41, 327–340, doi: 10.1007/s00382-012-1427-8.CrossRefGoogle Scholar
  51. Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511–514.CrossRefGoogle Scholar
  52. Yeh, S.-W., Y.-G. Ham, and J.-Y. Lee, 2012: Changes in the tropical Pacific SST trend from CMIP3 to CMIP5 and its implication of ENSO. J. Climate, 25, 7764–7771.CrossRefGoogle Scholar
  53. Yu, J.-Y., H.-Y. Kao, and T. Lee, 2010: Subtropics-related interannual sea surface temperature variability in the central equatorial Pacific. J. Climate, 23, 2869–2884, doi: 10.1175/2010JCLI3171. 1.CrossRefGoogle Scholar
  54. Zebiak, S. E., and M. A. Cane, 1987: A model El Niño-Southern oscillation. Mon. Wea. Rev., 115, 2262–2278.CrossRefGoogle Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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