ENSO Asymmetry in the CAMS-CSM

  • Lijuan HuaEmail author
  • Lin Chen
Original Article


This study presents an overview of El Niño-Southern Oscillation (ENSO) asymmetry using the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). We discover that the coupled run of the CAMS-CSM has an obvious bias of ENSO opposite-sign asymmetry compared to observation, mainly in the eastern Pacific. Further analysis shows that the spatial distributions of sea surface temperature anomalies (SSTA) during both phases of the ENSO present individual biases, consisting of a warmer field during the warm phase and a colder field during the cold phase, in comparison with observation. The bias of ENSO asymmetry during both phases is partly due to the unrealistic simulation of shortwave (SW) radiation flux and the corresponding total cloud cover (TCC). The Atmospheric Model Intercomparison Project (AMIP) run demonstrates that biases of the SW radiation flux and the associated TCC originate in the atmospheric component of the model, which could be attributed to its unrealistic cloud microphysical scheme. Through air-sea interaction, these biases are amplified significantly during both ENSO phases of the coupled run. Moreover, another cause for the bias of ENSO asymmetry during the warm phase is the relatively slow decay of the ENSO in the simulation, with the thermocline anomalies propagating eastward more slowly. The bias of ENSO asymmetry in the cold phase is attributed to oceanic internal dynamic advection, mainly associated with zonal and meridional terms. Further analysis also highlights the essential role of reasonably representing the climatological mean state in ENSO model simulation.


ENSO asymmetry Shortwave radiation flux Oceanic internal dynamic advection 



This work was funded by the National Natural Science Foundation of China (No. 41606011), the National Key Research and Development Program of China (No. 2016YFE0102400 and No. 2016YFA0600602), and the Basic Scientific Research and Operation Foundation of CAMS (No. 2017Y007), the Major Program of National Natural Science Foundation of China (No. 91637210), the Startup Foundation for Introducing Talent of NUIST, the open fund of State Key Laboratory of Loess and Quartary Geology (SKLLQG1802), and the LASG open project.


  1. An, S.-I.: A review of interdecadal changes in the nonlinearity of the El Niño-southern oscillation. Theor. Appl. Climatol. 97, 29–40 (2009)CrossRefGoogle Scholar
  2. An, S.-I., Jin, F.-F.: Nonlinearity and asymmetry of ENSO. J. Clim. 17, 2399–2412 (2004)CrossRefGoogle Scholar
  3. Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5. Clim. Dyn. 42, 1999–2018 (2014)CrossRefGoogle Scholar
  4. Capotondi, A., Wittenberg, A., Masina, S.: Spatial and temporal structure of tropical Pacific interannual variability in 20th century coupled simulations. Ocean Model. 15, 274–298 (2006)CrossRefGoogle Scholar
  5. Chen, L., Yu, Y.Q., Sun, D.-Z.: Cloud and water vapor feedbacks to the El Niño warming: are they still biased in CMIP5 models? J. Clim. 26, 4947–4961 (2013)CrossRefGoogle Scholar
  6. Chen, L., Yu, Y.Q., Zheng, W.P.: Improved ENSO simulation from climate system model FGOALS-g1.0 to FGOALS-g2.0. Clim. Dyn. 47, 2617–2634 (2016)CrossRefGoogle Scholar
  7. Dai, Y.J., Zeng, X., Dickinson, R.E., et al.: The common land model. Bull Amer Meteor Soc. 84, 1013–1023 (2003)CrossRefGoogle Scholar
  8. Dommenget, D., Bayr, T., Frauen, C.: Analysis of the non-linearity in the pattern and time evolution of El Niño southern oscillation. Clim. Dyn. 40, 2825–2847 (2013)CrossRefGoogle Scholar
  9. Giese, B. S., and S. Ray, 2011: El Niño variability in simple ocean data assimilation (SODA), 1871-2008. J. Geophys. Res., 116, C02024Google Scholar
  10. Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2004: A technical guide to MOM4. GFDL Ocean group technical report no. 5. NOAA/Geophysical Fluiddynamics LaboratoryGoogle Scholar
  11. Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C.Z., Capotondi, A., van Oldenborgh, G.J., Stockdale, T.: Understanding El Niño in ocean-atmosphere general circulation models: progress and challenges. Bull Am Meterol Soc. 90, 325–340 (2009)CrossRefGoogle Scholar
  12. Hua, L.J., Yu, Y., Sun, D.-Z.: A further study of ENSO rectification: results from an OGCM with a seasonal cycle. J. Clim. 28, 1362–1382 (2015)CrossRefGoogle Scholar
  13. Hua, L.J., Chen, L., Rong, X.Y., Su, J.Z., Wang, L., Li, T., Yu, Y.Q.: Impact of atmospheric model resolution on simulation of ENSO feedback processes: a coupled model study. Clim. Dyn. 51, 3077–3092 (2018)CrossRefGoogle Scholar
  14. Huang, B., Xue, Y., Wang, H., Wang, W., Kumar, A.: Mixed layer heat budget of the El Niño in NCEP climate forecast system. Clim. Dyn. 39, 1–17 (2012)CrossRefGoogle Scholar
  15. Huffman, G.J., Adler, R.F., Bolvin, D.T., Gu, G.: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett. 36, L17808 (2009)CrossRefGoogle Scholar
  16. Im, S.-H., An, S.I., Kim, S.T., Jin, F.F.: Feedback processes responsible for El Niño-La Niña amplitude asymmetry. Geophys. Res. Lett. 42, 5556–5563 (2015)CrossRefGoogle Scholar
  17. Jin, F.-F., An, S.-I.: Thermocline and zonal advection feedbacks within the equatorial ocean recharge oscillator model for ENSO. Geophys. Res. Lett. 26, 2989–2992 (1999)CrossRefGoogle Scholar
  18. Jin, F.F., An, S.-I., Timmermann, A., Zhao, J.X.: Strong El Niño events and nonlinear dynamical heating. Geophys. Res. Lett. 30, 1120 (2003)CrossRefGoogle Scholar
  19. Kalnay, E., et al.: The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996)CrossRefGoogle Scholar
  20. Kang, I.-S., Kug, J.-S.: El Niño and La Niña Sea surface temperature anomalies: asymmetry characteristics associated with their wind stress anomalies. J. Geophys. Res. 107, 4372 (2002)CrossRefGoogle Scholar
  21. Kug, J.-S., Kang, I.-S.: Interactive feedback between the Indian Ocean and ENSO. J. Clim. 19, 1784–1801 (2006)CrossRefGoogle Scholar
  22. Kug, J.-S., Ham, Y.-G., Lee, J.-Y., Jin, F.-F.: Improved simulation of two types of El Niño in CMIP5 models. Environ. Res. Lett. 7, 039502 (2012)CrossRefGoogle Scholar
  23. Kumar, A., Hu, Z.Z.: Uncertainty in the ocean-atmosphere feedbacks associated with ENSO in the reanalysis products. Clim. Dyn. 39, 575–588 (2012)CrossRefGoogle Scholar
  24. Li, L.J., Wang, B., Zhang, G.J.: The role of nonconvective condensation processes in response of surface shortwave cloud radiative forcing to El Niño warming. J. Clim. 27, 6721–6736 (2014)CrossRefGoogle Scholar
  25. Li, L.J., Wang, B., Zhang, G.J.: The role of moist processes in shortwave radiative feedback during ENSO in the CMIP5 models. J. Clim. 28, 9892–9908 (2015)CrossRefGoogle Scholar
  26. Lloyd, J., Guilyardi, E., Weller, H.: The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part III: the shortwave flux feedback. J. Clim. 25, 4275–4293 (2012)CrossRefGoogle Scholar
  27. McPhaden, M.J., Zebiak, S.E., Glantz, M.H.: ENSO as an integrating concept in earth science. Science. 314, 1740–1745 (2006)CrossRefGoogle Scholar
  28. Ohba: Important factors for long-term changes in ENSO transitivity. Int. J. Climatol. 33, 1495–1509 (2013)CrossRefGoogle Scholar
  29. Ohba, M., Ueda, H.: Basin-wide warming in the equatorial Indian Ocean associated with El Niño. SOLA. 1, 89–92 (2005)CrossRefGoogle Scholar
  30. Ohba, M., Ueda, H.: An impact of SST anomalies in the Indian Ocean in acceleration of the El Niño to La Niña transition. J Meteor Soc Japan. 85, 335–348 (2007)CrossRefGoogle Scholar
  31. Ohba, M., Ueda, H.: Role of nonlinear atmospheric response to SST on the asymmetric transition process of ENSO. J. Clim. 22, 177–192 (2009a)CrossRefGoogle Scholar
  32. Ohba, M., Ueda, H.: Seasonally different response of the Indian Ocean to the remote forcing of El Niño: linking the dynamics and thermodynamics. SOLA. 5, 176–179 (2009b)CrossRefGoogle Scholar
  33. Ohba, M., Watanabe, M.: Role of the indo-Pacific interbasin coupling in predicting asymmetric ENSO transition and duration. J. Clim. 25, 3321–3335 (2012)CrossRefGoogle Scholar
  34. Ohba, M., Nohara, D., Udea, H.: Simulatio of asymmetric ENSO transition in WCRP CMIP3 multimodel experiments. J. Clim. 23, 6051–6067 (2010)CrossRefGoogle Scholar
  35. Okumura, Y.M., Deser, C.: Asymmetry in the duration of El Niño and La Niña. J. Clim. 23, 5826–5843 (2010)CrossRefGoogle Scholar
  36. Rayner, N.A., Parker, D.E., Horton, E.B., Folland, C.K., Alexander, L.V., Rowell, D.P., Kent, E.C., Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003)CrossRefGoogle Scholar
  37. Rodgers, K.B., Friederichs, P., Latif, M.: Tropical Pacific decadal variability and its relation to decadal modulations of ENSO. J. Clim. 17, 3761–3774 (2004)CrossRefGoogle Scholar
  38. Roeckner, E., G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, and A. Tompkins, 2003: The atmospheric general circulation model ECHAM5, part I: model description. Max-Planck-Institut for Meteorologie, rep. 349, Hamburg, pp 127Google Scholar
  39. Rong et al., 2018: The CAMS climate system model and a basic evaluation of climatology and climate variability simulation. J Meteor Res., Second RevisionGoogle Scholar
  40. Su, J.Z., Zhang, R.H., Li, T., Rong, X., Kug, J.S., Hong, C.C.: Causes of the El Niño and La Niña amplitude asymmetry in the equatorial eastern Pacific. J. Clim. 23, 605–617 (2010)CrossRefGoogle Scholar
  41. Sun, Y., Sun, D.-Z., Wu, L.X., Wang, F.: Western Pacific warm pool and ENSO asymmetry in CMIP3 models. Adv Atmos Soi. 30, 940–953 (2013)CrossRefGoogle Scholar
  42. Sun, D.-Z., Zhang, T., Yan, S., Yu, Y.: Rectification of El Niño-southern oscillation into climate anomalies of longer time-scales: results from forced ocean GCM experiments. J. Clim. 27, 2545–2561 (2014)CrossRefGoogle Scholar
  43. Sun, Y., Wang, F., Sun, D.-Z.: Weak ENSO asymmetry due to weak nonlinear Air-Sea interaction in CMIP5 climate models. Adv Atmos Soi. 33, 352–364 (2016)CrossRefGoogle Scholar
  44. Timmermann, A., Jin, F.-F.: Phytoplankton influences on tropical climate. Geophys. Res. Lett. 29, 2104 (2002)Google Scholar
  45. Uppala, S.M., KÅllberg, P.W., Simmons, A.J., Andrae, U., Bechtold, V.D.C., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Berg, L.V.D., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., Mcnally, A.P., Mahfouf, J.F., Morcrette, J.J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P., Woollen, J.: The ERA-40 re-analysis. Q. J. R. Meteorol. Soc. 131, 2961–3012 (2005)CrossRefGoogle Scholar
  46. Vialard, J., Menkes, C., Boulanger, J.-P., Delecluse, P., Guilyardi, E., McPhaden, M.J., Madec, G.: A model study of oceanic mechanisms affecting equatorial Pacific Sea surface temperature during the 1997-98 El Niño. J. Phys. Oceanogr. 31, 1649–1675 (2001)CrossRefGoogle Scholar
  47. Vila, D., Hernandez, C., Ferraro, R., Semunegus, H.: The performance of hydrological monthly products using SSM/I-SSMI/S sensors. J. Hydrometeorol. 14, 266–274 (2013)CrossRefGoogle Scholar
  48. Winton, M.: A reformulated three-layer sea ice model. J. Atmos. Ocean. Technol. 17, 525–531 (2000)CrossRefGoogle Scholar
  49. Zhang, T., Sun, D.-Z.: ENSO asymmetry in CMIP5 models. J. Clim. 27, 4070–4093 (2014)CrossRefGoogle Scholar
  50. Zhang, Y., Rossow, W.B., Lacis, A.A., Oinas, V., Mishchenko, M.I.: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: refinements of the radiative transfer model and input data. J. Geophys. Res. 109, D19105 (2004)CrossRefGoogle Scholar
  51. Zhou, T.J., Wu, B., Dong, L.: Advances in research of ENSO changes and the associated impacts on Asian-Pacific climate. Ais-Pac J Atmos Sci. 50, 405–422 (2014)CrossRefGoogle Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological SciencesBeijingChina
  2. 2.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME) / Joint International Research Laboratory of Climate and Environmental Change (ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  3. 3.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  4. 4.State Key Laboratory of Loess and Quaternary Geology (SKLLQG), Institute of Earth EnvironmentChinese Academy of SciencesXi’anChina

Personalised recommendations