Climate Dynamics

, Volume 53, Issue 1–2, pp 155–172 | Cite as

Error compensation of ENSO atmospheric feedbacks in climate models and its influence on simulated ENSO dynamics

  • Tobias BayrEmail author
  • Christian Wengel
  • Mojib Latif
  • Dietmar Dommenget
  • Joke Lübbecke
  • Wonsun Park


Common problems in state-of-the-art climate models are a cold sea surface temperature (SST) bias in the equatorial Pacific and the underestimation of the two most important atmospheric feedbacks operating in the El Niño/Southern Oscillation (ENSO): the positive, i.e. amplifying wind-SST feedback and the negative, i.e. damping heat flux-SST feedback. To a large extent, the underestimation of those feedbacks can be explained by the cold equatorial SST bias, which shifts the rising branch of the Pacific Walker Circulation (PWC) too far to the west by up to 30°, resulting in an erroneous convective response during ENSO events. Based on simulations from the Kiel Climate Model (KCM) and the 5th phase of Coupled Model Intercomparison Project (CMIP5), we investigate how well ENSO dynamics are simulated in case of underestimated ENSO atmospheric feedbacks (EAF), with a special focus on ocean–atmosphere coupling over the equatorial Pacific. While models featuring realistic atmospheric feedbacks simulate ENSO dynamics close to observations, models with underestimated EAF exhibit fundamental biases in ENSO dynamics. In models with too weak feedbacks, ENSO is not predominantly wind-driven as observed; instead ENSO is driven significantly by a positive shortwave radiation feedback. Thus, although these models simulate ENSO, which in terms of simple indices is consistent with observations, it originates from very different dynamics. A too weak oceanic forcing on the SST via the positive thermocline, the Ekman and the zonal advection feedback is compensated by weaker atmospheric heat flux damping. The latter is mainly caused by a biased shortwave-SST feedback that erroneously is positive in most climate models. In the most biased models, the shortwave-SST feedback contributes to the SST anomaly growth to a similar degree as the ocean circulation. Our results suggest that a broad continuum of ENSO dynamics can exist in climate models and explain why climate models with less than a half of the observed EAF strength can still depict realistic ENSO amplitude.


El Niño/Southern Oscillationm ENSO atmospheric feedbacks ENSO dynamics Equatorial Pacific cold SST bias CMIP5 Perturbed physics ensemble 



The authors would like to thank the anonymous reviewers for their constructive comments. We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, the individual modeling groups of the Climate Model Intercomparison Project (CMIP5), the UK Met Office and ECMWF for providing the data sets. The climate model integrations of the KCM and ECHAM5 were performed at the Computing Centre of Kiel University and the North-German Supercomputing Alliance (HLRN). This work was supported by the SFB 754 “Climate-Biochemistry Interactions in the tropical Ocean”, the European Union’s InterDec project, the ARC Centre of Excellence for Climate System Science (Grant CE110001028), the ARC project ‘‘Beyond the linear dynamics of the El Niño Southern Oscillation’’ (Grant DP120101442). This is a contribution to the Cluster of Excellence “The Future Ocean” at the University of Kiel.


  1. Bayr T, Latif M, Dommenget D, Wengel C, Harlaß J, Park W (2018) Mean-state dependence of ENSO atmospheric feedbacks in climate models. Clim Dyn 50:3171–3194. CrossRefGoogle Scholar
  2. Bellenger H, Guilyardi E, Leloup J, Lengaigne M, Vialard J (2014) ENSO representation in climate models: from CMIP3 to CMIP5. Clim Dyn 42:1999–2018. CrossRefGoogle Scholar
  3. Bjerknes J (1969) Atmospheric teleconnections from the Equatorial Pacific. Mon Weather Rev 97:163–172CrossRefGoogle Scholar
  4. Carton JA, Giese BS (2008) A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon Weather Rev 136:2999–3017. CrossRefGoogle Scholar
  5. Chen L, Li T, Yu Y, Behera SK (2017) A possible explanation for the divergent projection of ENSO amplitude change under global warming. Clim Dyn 49:3799–3811. CrossRefGoogle Scholar
  6. Davey M, Huddleston M, Sperber K, Braconnot P, Bryan F, Chen D, Colman R, Cooper C, Cubasch U, Delecluse P, DeWitt D, Fairhead L, Flato G, Gordon C, Hogan T, Ji M, Kimoto M, Kitoh A, Knutson T, Latif M, Le Treut H, Li T, Manabe S, Mechoso C, Meehl G, Power S, Roeckner E, Terray L, Vintzileos A, Voss R, Wang B, Washington W, Yoshikawa I, Yu J, Yukimoto S, Zebiak S (2002) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420. CrossRefGoogle Scholar
  7. Dommenget D (2010) The slab ocean El Niño. Geophys Res Lett 37:L20701. CrossRefGoogle Scholar
  8. Dommenget D (2016) A simple model perturbed physics study of the simulated climate sensitivity uncertainty and its relation to control climate biases. Clim Dyn 46:427–447. CrossRefGoogle Scholar
  9. Dommenget D, Rezny M (2018) A caveat note on tuning in the development of coupled climate model. J Adv Model Earth Syst 10:335–356, CrossRefGoogle Scholar
  10. Dommenget D, Haase S, Bayr T, Frauen C (2014) Analysis of the Slab Ocean El Nino atmospheric feedbacks in observed and simulated ENSO dynamics. Clim Dyn 42:3187–3205. CrossRefGoogle Scholar
  11. Drews A, Greatbatch RJ (2016) Atlantic multidecadal variability in a model with an improved North Atlantic Current. Geophys Res Lett 43:8199–8206. CrossRefGoogle Scholar
  12. Ferrett S, Collins M (2016) ENSO feedbacks and their relationships with the mean state in a flux adjusted ensemble. Climate Dyn. Google Scholar
  13. Ferrett S, Collins M, Ren HL (2017a) Diagnosing relationships between mean state biases and El Niño shortwave feedback in CMIP5 models. J Clim 31:1315–1335. CrossRefGoogle Scholar
  14. Ferrett S, Collins M, Ren HL (2017b) Understanding bias in the evaporative damping of El Niño Southern Oscillation events in CMIP5 models. J Clim 30:6351–6370. CrossRefGoogle Scholar
  15. 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 Ocean 118:6704–6716. CrossRefGoogle Scholar
  16. Graham FS, Brown JN, Langlais C, Marsland SJ, Wittenberg AT, Holbrook NJ (2014) Effectiveness of the Bjerknes stability index in representing ocean dynamics. Clim Dyn 43:1–16, CrossRefGoogle Scholar
  17. Guan C, McPhaden MJ (2016) Ocean processes affecting the twenty-first-century shift in ENSO SST variability. J Clim 29:6861–6879. CrossRefGoogle Scholar
  18. Guilyardi E, Braconnot P, Jin FF, Kim ST, Kolasinski M, Li T, Musat I (2009a) Atmosphere feedbacks during ENSO in a coupled GCM with a modified atmospheric convection scheme. J Clim 22:5698–5718. CrossRefGoogle Scholar
  19. Guilyardi A, Wittenberg A, Fedorov M, Collins C, Wang A, Capotondi GJ, van Oldenborgh, Stockdale T (2009b) Understanding El Niño in ocean-atmosphere general circulation models: progress and challenges. Bull Am Meteorol Soc 90:325–340. CrossRefGoogle Scholar
  20. Jin FF, Kim ST, Bejarano L (2006) A coupled-stability index for ENSO. Geophys Res Lett 33:2–5. Google Scholar
  21. Karamperidou C, Jin FF, Conroy JL (2017) The importance of ENSO nonlinearities in tropical pacific response to external forcing. Clim Dyn 49:2695–2704. CrossRefGoogle Scholar
  22. Kim ST, Cai W, Jin F-F, Yu J-Y (2014a) ENSO stability in coupled climate models and its association with mean state. Clim Dyn 42:3313–3321. CrossRefGoogle Scholar
  23. Levitus S, Boyer TP, Conkright ME, O’Brien T, Antonov J, Stephens C, Stathoplos L, Johnson D, Gelfeld R (1998) World ocean data base 1998. In: Introduction. NOAA Atlas NESDIS 18, vol 1. U.S. Government Printing Office, Washington, DC, p 346Google Scholar
  24. Kim ST, Cai W, Jin FF, Yu JY (2014b) ENSO stability in coupled climate models and its association with mean state. Clim Dyn 42:3313–3321. CrossRefGoogle Scholar
  25. Li L, Wang B, Zhang GJ, Li L, Wang B, Zhang GJ (2015) The role of moist processes in shortwave radiative feedback during ENSO in the CMIP5 models. J Clim 28:9892–9908. CrossRefGoogle Scholar
  26. Li Y, Li J, Zhang W, Chen Q, Feng J, Zheng F, Wang W, Zhou X (2017) Impacts of the Tropical Pacific cold tongue mode on ENSO diversity under global warming. J Geophys Res Ocean 122:8524–8542. CrossRefGoogle Scholar
  27. Lloyd J, Guilyardi E, Weller H, Slingo J (2009) The role of atmosphere feedbacks during ENSO in the CMIP3 models. Atmos Sci Lett 10:170–176. CrossRefGoogle Scholar
  28. Lloyd J, Guilyardi E, Weller H (2011) The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the heat flux feedback mechanisms. Clim Dyn 37:1271–1292. CrossRefGoogle Scholar
  29. Lloyd J, Guilyardi E, Weller H (2012) The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part III: the shortwave flux feedback. J Clim 25:4275–4293. CrossRefGoogle Scholar
  30. Lübbecke JF, Mcphaden MJ (2014) Assessing the twenty-first-century shift in enso variability in terms of the bjerknes stability index. J Clim 27:2577–2587. CrossRefGoogle Scholar
  31. Mauritsen T, Stevens B, Roeckner E, Crueger T, Esch M, Giorgetta M, Haak H, Jungclaus J, Klocke D, Matei D, Mikolajewicz U, Notz D, Pincus R, Schmidt H, Tomassini L (2012) Tuning the climate of a global model. J Adv Model Earth Syst. Google Scholar
  32. Madec G, Delecluse P, Imbard M, Lévy C (1998) OPA 8.1 Ocean General Circulation Model reference manual. Note du Pole de modélisation 11, Institut Pierre-Simon Laplace, p 91Google Scholar
  33. Madec G (2008) NEMO ocean engine. Note du Pole modélisation 27, Institut Pierre-Simon Laplace, p 193Google Scholar
  34. McPhaden MJ (1999) The child prodigy of 1997–1998. Nature 398:559–562CrossRefGoogle Scholar
  35. Park W, Keenlyside NS, Latif M, Ströh A, Redler R, Roeckner E, Madec G (2009) Tropical Pacific climate and its response to global warming in the Kiel climate model. J Clim 22:71–92. CrossRefGoogle Scholar
  36. Philander S (1990) El Niño, La Niña, and the southern oscillation. Academic Press, San Diego, 293Google Scholar
  37. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407CrossRefGoogle Scholar
  38. Roeckner E, Baeuml G, Bonventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Tompkins A (2003) The atmospheric general circulation model ECHAM5. PART I: model description, Report 349. Max Planck Institute for Meteorology, Hamburg, p 140Google Scholar
  39. Simmons A, Uppala S, Dee D, Kobayashi S (2007) ERA-Interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsl 110:25–35Google Scholar
  40. Stocker T, Qin D, Plattner G, Tignor M, Allen S (2013) IPCC 2013: climate change 2013: the physical science basis. contribution of working group I to the Fifth Assessment Report of the Intergovernmental Panel on climate change. Cambridge University Press, New York, 1535Google Scholar
  41. Taylor KE, Stouffer RJ, Meehl Ga (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. CrossRefGoogle Scholar
  42. Timmermann A, An SI, Kug JS, Jin FF, Cai W, Cobb K, Lengaigne M, McPhaden MJ, Stuecker MF, Stein K, Wittenberg AT, Yun KS, Bayr T, Chen HC, Chikamoto Y, Dewitte B, Dommenget D, Grothe P, Ham YG, Hayashi M, Ineson S, Kang D, Kim W, Lee JY, Li T, Luo JJ, McGregor S, Power S, Rashid H, Ren HL, Santoso A, Takahashi K, Todd A, Wang G, Wang G, Xie R, Yang WH, Yeh W, Yoon J, Zeller E, Zhang X (2018) El Niño-Southern oscillation complexity. Nature 559:1–25, CrossRefGoogle Scholar
  43. Trenberth KE (1997) The definition of El Niño. Bull Am Meteorol Soc 78:2771–2778CrossRefGoogle Scholar
  44. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, Bechtold VD, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Berg LV, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM,  Jenne R, Mcnally AP, Mahfouf JF, Morcrette JJ, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012. CrossRefGoogle Scholar
  45. Vannière B, Guilyardi E, Madec G, Doblas-Reyes FJ, Woolnough S (2013) Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO. Clim Dyn 40:963–981. CrossRefGoogle Scholar
  46. Vijayeta A, Dommenget D (2018) An evaluation of ENSO dynamics in CMIP simulations in the framework of the recharge oscillator model. Clim Dyn 51:1–19. CrossRefGoogle Scholar
  47. Wengel C, Dommenget D, Latif M, Bayr T, Vijayeta A (2018a) What controls ENSO-amplitude diversity in climate models? Geophys Res Lett 45:1–8. CrossRefGoogle Scholar
  48. Wengel C, Latif M, Park W, Harlaß J, Bayr T (2018b) Seasonal ENSO phase locking in the Kiel Climate Model: the importance of the equatorial cold sea surface temperature bias. Clim Dyn. Google Scholar
  49. Zhai X, Greatbatch RJ (2007) Wind work in a model of the northwest Atlantic Ocean. Geophys Res Lett 34:1–4. Google Scholar
  50. Zheng XT, Xie SP, Lv LH, Zhou ZQ (2016) Intermodel uncertainty in ENSO amplitude change tied to Pacific Ocean warming pattern. J Clim 29:7265–7279. CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.GEOMAR Helmholtz Centre for Ocean Research KielKielGermany
  2. 2.Cluster of Excellence “The Future Ocean”University of KielKielGermany
  3. 3.School of Mathematical SciencesMonash UniversityClaytonAustralia

Personalised recommendations