Advertisement

The impact of stochastic physics on the El Niño Southern Oscillation in the EC-Earth coupled model

  • Chunxue YangEmail author
  • Hannah M. Christensen
  • Susanna Corti
  • Jost von Hardenberg
  • Paolo Davini
Article

Abstract

The impact of stochastic physics on El Niño Southern Oscillation (ENSO) is investigated in the EC-Earth coupled climate model. By comparing an ensemble of three members of control historical simulations with three ensemble members that include stochastics physics in the atmosphere, we find that in EC-Earth the implementation of stochastic physics improves the excessively weak representation of ENSO. Specifically, the amplitude of both El Niño and, to a lesser extent, La Niña increases. Stochastic physics also ameliorates the temporal variability of ENSO at interannual time scales, demonstrated by the emergence of peaks in the power spectrum with periods of 5–7 years and 3–4 years. Based on the analogy with the behaviour of an idealized delayed oscillator model (DO) with stochastic noise, we find that when the atmosphere–ocean coupling is small (large) the amplitude of ENSO increases (decreases) following an amplification of the noise amplitude. The underestimated ENSO variability in the EC-Earth control runs and the associated amplification due to stochastic physics could be therefore consistent with an excessively weak atmosphere–ocean coupling. The activation of stochastic physics in the atmosphere increases westerly wind burst (WWB) occurrences (i.e. amplification of noise amplitude) that could trigger more and stronger El Niño events (i.e. increase of ENSO oscillation) in the coupled EC-Earth model. Further analysis of the mean state bias of EC-Earth suggests that a cold sea surface temperature (SST) and dry precipitation bias in the central tropical Pacific together with a warm SST and wet precipitation bias in the western tropical Pacific are responsible for the coupled feedback bias (weak coupling) in the tropical Pacific that is related to the weak ENSO simulation. The same analysis of the ENSO behaviour is carried out in a future scenario experiment (RCP8.5 forcing), highlighting that in a coupled model with an extreme warm SST, characterized by a strong coupling, the effect of stochastic physics on the ENSO representation is opposite. This corroborates the hypothesis that the mean state bias of the tropical Pacific region is the main reason for the ENSO representation deficiency in EC-Earth.

Notes

Acknowledgements

Free data accessibility to the climate user community is granted through a dedicated THREDDS Web Server hosted by CINECA (https://sphinx.hpc.cineca.it/thredds/sphinx.html), where it is possible to browse and directly download all of the Climate SPHINX data used in the present work. Further details on the data accessibility and on the Climate SPHINX project itself are available on the official website of the project (http://www.to.isac.cnr.it/sphinx/). The authors acknowledge computing resources provided by LRZ and CINECA in the framework of Climate SPHINX and Climate SPHINX reloaded PRACE projects. Jost von Hardenberg acknowledges support from the European Union’s Horizon 2020 research and innovation programme under Grant agreement 641816 (CRESCENDO). Hannah Christensen acknowledges support from the Natural Environment Research Council under Grant agreement NE/P018238/1. Susanna Corti and Chunxue Yang acknowledge support by the PRIMAVERA project, funded by the European Commission under Grant agreement 641727 of the Horizon 2020 research programme. We acknowledge the contribution of the C3S 34a Lot 2 Copernicus Climate Change Service project, funded by the European Union, to the development of software tools used in this work. The National Center for Atmospheric Research is sponsored by the National Science Foundation.

References

  1. Alder RF, Huffman GJ, Chang A, Ferraro R, Xie P-P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–Present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
  2. An S, Ham Y, Kug J-S, Timmermann A, Choi J, Kang I-S (2010) The inverse effect of annual mean state and annual cycle changes on ENSO. J Clim 23:1095–1110CrossRefGoogle Scholar
  3. Battisti DS, Hirst AC (1989) Interannual variability in the tropical atmosphere/ocean system: influence of the basic state, ocean geometry and nonlinearity. J Atmos Sci 6:1687–1712CrossRefGoogle Scholar
  4. Berner J, Doblas-Reyes FJ, Palmer TN, Shutts G, Weisheimer A (2008) Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. Philos Trans R Soc Lond 366A:2561–2579,  https://doi.org/10.1098/rsta.2008.0033 Google Scholar
  5. Berner J, Jung T, Palmer TN (2012) Systematic model error: the impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations. J Clim 25:4946–4962.  https://doi.org/10.1175/JCLI-D-11-00297.1 CrossRefGoogle Scholar
  6. Berner J, Achatz U, Batte L, De La Camara A, Crommelin D, Christensen H, Colangeli M, Dolaptchiev S, Franzke CLE, Friederichs P, Imkeller P, Jarvinen H, Juricke S, Kitsios V, Lott F, Lucarini V, Mahajan S, Palmer TN, Penland C, Von Storch J-S, Sakradzija M, Weniger M, Weisheimer A, Williams PD, Yano J-I (2017) Stochastic parameterization: Towards a new view of weather and climate models. Bull Am Meteorol Soc.  https://doi.org/10.1175/BAMS-D-15-00268.1 Google Scholar
  7. Blanke B, Nedin JD, Gutzler D (1997) Estimating the effects of stochastic wind stress forcing on ENSO irregularity. J Clim 10:1473–1486CrossRefGoogle Scholar
  8. Bouttier F, Vié B, Nuissier O, Raynaud L (2012) Impact of stochastic physics in a convection-permitting ensemble. Mon Weather Rev 140:3706–3721.  https://doi.org/10.1175/MWR-D-12-00031.1 CrossRefGoogle Scholar
  9. Bove MC, Elsner JB, Landsea CW, Niu X, Brien JJO (1998) Effect of El Niño on U.S. landfalling hurricanes, revisited. Bull Am Meteorol Soc 79:2477–2482.  https://doi.org/10.1175/1520-0477(1998)079,2477:EOENOO.2.0.CO;2 CrossRefGoogle Scholar
  10. Buizza R, Miller M, Palmer TN (1999) Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q J R Meteorol Soc 125:2887–2908.  https://doi.org/10.1002/qj.49712556006 CrossRefGoogle Scholar
  11. Cane M, Zebiak SE (1985) The theory of El Niño and the Southern Oscillation. Science 228:1085–1087CrossRefGoogle Scholar
  12. Cane MA, Minnich M, Zebiak SE (1990) A study of self-excited oscillations of the tropical ocean–atmosphere system. Part I: Linear analysis. J Atmos Sci 47:1562–1577CrossRefGoogle Scholar
  13. Chang P, Ji L, Li H, Fliigel M (1996) Chaotic dynamics versus stochastic processes in El Niño-Southern Oscillation in coupled ocean–atmosphere models. Physica D 98:301–320CrossRefGoogle Scholar
  14. Christensen HM, Berner J, Coleman D, Palmer TN (2017) Stochastic parameterization and the El Niño–Southern Oscillation. J Clim 30, 17–38,  https://doi.org/10.1175/JCLI-D-16-0122.1 CrossRefGoogle Scholar
  15. Collins M, An S-I, Cai W, Ganachaud A, Guilyardi E, Jin F-F, Jochum M, Lengaigne M, Power S, Timmermann A, Vecchi G, Wittenberg A (2010) The impact of global warming on the tropical Pacific Ocean and El Niño. Nat Geosci 3:391–397.  https://doi.org/10.1038/ngeo868 CrossRefGoogle Scholar
  16. Davini P, von Hardenberg J, Corti S, Christensen HM, Juricke S, Subramanian A, Watson PAG, Weisheimer A, Palmer TN, 2017, Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in climate simulations (submitted) Google Scholar
  17. Eisenman I, Yu LS, Tziperman E (2005) Westerly wind bursts: ENSO’s tail rather than the dog? J Clim 18:5224–5238CrossRefGoogle Scholar
  18. Fedorov AV (2002) The response of the coupled tropical ocean–atmosphere to westerly wind bursts. Q J R Meteorol Soc 128(579):1–23CrossRefGoogle Scholar
  19. Fedorov AV, Hu S, Lengaigne M, Guilyardi E (2015) The impact of westerly wind bursts and ocean initial state on the development and diversity of El Niño events. Clim Dyn 44:1381–1401CrossRefGoogle Scholar
  20. Flato G et al (2013) Evaluation of climate models. In: Stocker TF et al (eds) Climate change 2013: the physical science basis. Cambridge University Press, Cambridge, pp 741–866Google Scholar
  21. Flügel M, Chang P, Penland C (2004) The role of stochastic forcing in modulating ENSO predictability. J Clim 17: 3125–3140,  https://doi.org/10.1175/1520-0442(2004)017,3125:TROSFI.2.0.CO;2 CrossRefGoogle Scholar
  22. Gebbie G, Eisenman I, Wittenberg A, Tziperman E (2007) Modulation of westerly wind bursts by sea surface temperature: a semi-stochastic feedback of ENSO. J Atmos Sci 64:3281–3295CrossRefGoogle Scholar
  23. Guilyardi E, Wittenberg A, Fedorov A, Collins M, Wang C, Capotondi A, van Oldenborgh GJ, Stockdale T (2009) Understanding El Niño in ocean–atmosphere general circulation models: progress and challenges. Bull Am Meteorol Soc 90:325–340.  https://doi.org/10.1175/2008BAMS2387.1 CrossRefGoogle Scholar
  24. Ham Y-G, Kug J-S (2014) Effects of Pacific intertropical convection zone precipitation bias on ENSO phase transition. Environ Res Lett 9:064008 (8 pp)CrossRefGoogle Scholar
  25. Jankov I, Berner J, Beck J, Jiang H, Olson JB, Grell G, Smirnova TG, Benjamin SG, Brown JM (2017) A performance comparison between multiphysics and stochastic approaches within a North American RAP ensemble. Mon Weather Rev 145:1161–1179.  https://doi.org/10.1175/MWR-D-16-0160.1 CrossRefGoogle Scholar
  26. Jin F-F (1996) Tropical ocean–atmosphere interaction, the Pacific cold tongue, and the El Niño-southern oscillation. Science 274:76–78CrossRefGoogle Scholar
  27. Jin F-F (1997a) An equatorial recharge paradigm for ENSO, I, Conceptual model. J Atmos Sci 54:811–829CrossRefGoogle Scholar
  28. Jin F-F (1997b) An equatorial recharge paradigm for ENSO, II, A stripped-down coupled model. J Atmos Sci 54:830–845CrossRefGoogle Scholar
  29. Kessler WS, Kleeman R (2000) Rectification of the Madden-Julian oscillation into the ENSO cycle. J Clim 13:3560–3575CrossRefGoogle Scholar
  30. Kim ST, Cai W, Jin F-F, Yu J-Y (2014) ENSO variability in coupled climate models and its association with mean state. Clim Dyn 42:3313–3321CrossRefGoogle Scholar
  31. Lengaigne M, Guilyardi E, Boulanger JP, Menkes C, Delecluse P, Inness P, Cole J, Slingo J (2004) Triggering El Niño by westerly wind events in a coupled general circulation model. Clim Dyn 23:601–620CrossRefGoogle Scholar
  32. Levine AFZ, Jin F-F, McPhaden MJ (2016) Extreme noise–extreme El Niño: how state-dependent noise forcing creates El Niño–La Niña asymmetry. J Clim 29:5483–5499.  https://doi.org/10.1175/JCLID-16-0091.1 CrossRefGoogle Scholar
  33. Lin JW-B, Neelin JD (2000) Influence of a stochastic moist convective parametrization on tropical climate variability. Geophys Res Lett, 27, 3691–3694,  https://doi.org/10.1029/2000GL011964 CrossRefGoogle Scholar
  34. Lin J, Neelin JD (2003) Towards stochastic deep convective parameterization in general circulation models. Geophys Res Lett 30:1162.  https://doi.org/10.1029/2002GL016203 Google Scholar
  35. Lloyd J, Guilyardi E, Weller J (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 37:1271–1292CrossRefGoogle Scholar
  36. Luo J-J, Masson S, Behera S, Shingu S, Yamagata T (2005) Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J Clim 18:4474–4497CrossRefGoogle Scholar
  37. Madec G (2008) NEMO ocean engine, 2008. In: Technical report, Institute Pierre-Simon Laplace (IPSL)Google Scholar
  38. Münnich M, Cane MA, Zebiak SE (1991) A study of self- excited oscillations of the tropical ocean–atmosphere system. Part II: Nonlinear case. J Atmos Sci 48: 1238–1248.  https://doi.org/10.1175/1520-0469(1991)048,1238:ASOSEO.2.0.CO;2 CrossRefGoogle Scholar
  39. Neale RB, Richter JH, Jochum M (2008) The impact of convection on ENSO: from a delayed oscillator to a series of events. J Clim 21: 5904–5924,  https://doi.org/10.1175/2008JCLI2244.1 CrossRefGoogle Scholar
  40. Neelin JD (1990) A hybrid coupled general circulation model for El Niño studies. J Atmos Sci J7:674–693CrossRefGoogle Scholar
  41. Palmer TN, Buizza R, Doblas-Reyes F, Jung T, Leutbecher M, Shutts GJ, Steinheimer M, Weisheimer A (2009) Stochastic parametrization and model uncertainty. ECMWF tech rep 38. J Clim 30(598):1–44. http://www.ecmwf.int/sites/default/files/elibrary/2009/11577-stochastic-parametrization-and-model-uncertainty.pdf
  42. Penland C, Sardeshmukh PD (1995) The optimal growth of tropical sea surface temperature anomalies. J Clim 8:1999–2024CrossRefGoogle Scholar
  43. Philander SGH (1990) El Niño, La Niña, and the Southern Oscillation. Academic Press, New YorkGoogle Scholar
  44. Philip SY, Collins M, van Oldenborgh GJ, van den Hurk BJJM (2010) The role of atmosphere and ocean physical processes in ENSO in a perturbed physics coupled climate model. Ocean Sci 6:441–459CrossRefGoogle Scholar
  45. Rasmusson EM, Carpenter TH (1982) Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon Weather Rev 110: 354–384.  https://doi.org/10.1175/1520-0493(1982)110,0354:VITSST.2.0.CO;2 CrossRefGoogle Scholar
  46. 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(D14):4407.  https://doi.org/10.1029/2002JD002670 CrossRefGoogle Scholar
  47. Ropelewski CF, Halpert MS (1996) Quantifying Southern Oscillation—precipitation relationships. J Clim 9:1043–1059.  https://doi.org/10.1175/1520-0442(1996)009,1043:QSOPR.2.0.CO2 CrossRefGoogle Scholar
  48. Sanchez C, Williams KD, Collins M (2016) Improved stochastic physics schemes for global weather and climate models. Q J R Meteorol Soc 142:147–159.  https://doi.org/10.1002/qj.2640 CrossRefGoogle Scholar
  49. Sarachik ES, Cane MA (2010) The El Niño–Southern Oscillation phenomenon. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  50. Sardeshmukh PD, Penland C, Newman M (2001) Rossby waves in a stochastically fluctuating medium. In: Imkeller P, von Storch J-S (eds) Stochastic climate models. Birkhaueser, Basel, pp 359–384Google Scholar
  51. Sardeshmukh PD, Penland C, Newman M (2003) Drifts induced by multiplicative red noise with application to climate. Europhys Lett 63:498–504.  https://doi.org/10.1209/epl/i2003-00550-y CrossRefGoogle Scholar
  52. Sriver RL, Timmermann A, Mann ME, Keller K, Goose H (2014) Improved representation of tropical Pacific Ocean—atmosphere dynamics in an intermediate complexity climate model. J Clim 27:168–185CrossRefGoogle Scholar
  53. Stevenson SL (2012) Significant changes to ENSO strength and impacts in the twenty-first century: results from CMIP5. Geophys Res Lett 39:L17703.  https://doi.org/10.1029/2012GL052759 CrossRefGoogle Scholar
  54. Stone L, Saparin PI, Huppert H, Price C (1998) El Niño chaos: the role of noise and stochastic resonance on the ENSO cycle. Geophys Res Lett 25: 175–178.  https://doi.org/10.1029/97GL53639 CrossRefGoogle Scholar
  55. Suarez MJ, Schopf PS (1988) A delayed action oscillator for ENSO. J Atmos Sci 45:3283–3287.  https://doi.org/10.1175/1520-0469(1988)045,3283:ADAOFE.2.0.CO;2 CrossRefGoogle Scholar
  56. Sun Y, Sun DZ, Wu L et al (2013) Western Pacific warm pool and ENSO asymmetry in CMIP3 models. Adv Atmos Sci 30:940–953.  https://doi.org/10.1007/s00376-012-2161-1 CrossRefGoogle Scholar
  57. Taylor K, Stouffer R, Meehl G (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485CrossRefGoogle Scholar
  58. Titchner HA, Rayner NA (2014) The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2:1. Sea ice concentrations. J Geophys Res Atmos 119:2864–2889.  https://doi.org/10.1002/2013JD020316 CrossRefGoogle Scholar
  59. Timmermann A, An S-I, Kug J-S, Jin F-F, Cai W, Capotondi A, Cobb K, Lengaigne M, McPhaden MJ, Stuecker MF, Stein K, Wittenberg AT, Yun K-S, Bayr T, Chen H-C, Chikamoto Y, Dewitte B, Dommenger D, Grothe P, Guilyard E, Ham Y-G, Hayashi M, Ineson S, Kang D, Kim S, Kim W, Santoso A, Takahashi K, Todd A, Wang G, Wang G, Xie R, Yang W-H, Yeh S-W, Hoon J, Zeller E, Zhang X (2018) El Niño–Southern Oscillation complexity. Nature 559:535–545CrossRefGoogle Scholar
  60. Valcke S (2013) The OASIS3 coupler: a European climate modelling community software. Geosci Model Dev 6:373CrossRefGoogle Scholar
  61. Vancoppenolle M, Bouillon S, Fichefet T, Goosse H, Lecomte O, Morales Maqueda M, Madec G (2012) LIM, the Louvain-la-Neuve sea ice model, notes du pôle de modélisationGoogle Scholar
  62. Vecchi G, Harrison DE (2000) Tropical Pacific sea surface temperature anomalies, El Niño, and equatorial westerly wind events. J Clim 13:1814–1830CrossRefGoogle Scholar
  63. Vitart F, Molteni F (2010) Simulation of the Madden–Julian oscillation and its teleconnections in the ECMWF forecast system. Q J R Meteorol Soc 136:842–855CrossRefGoogle Scholar
  64. Vitart FMA, Balmaseda L, Ferranti, Anderson D (2003) Westerly wind events and the 1997/98 El Niño event in the ECMWF seasonal forecasting system: a case study. J Clim 16:3153–3170CrossRefGoogle Scholar
  65. Watanabe M, Wittenberg AT (2012) A method for disentangling El Niño-mean state interaction. Geophys Res Lett 39:L14702CrossRefGoogle Scholar
  66. Watanabe M, Kug J-S, Jin F-F, Collins M, Ohba M, Wittenberg A (2012) Uncertainty in the ENSO amplitude change from the past to the future. Geophys Res Lett 39:L20703CrossRefGoogle Scholar
  67. Weisheimer A, Corti S, Palmer TN, Vitart F (2014) Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system. Philos Trans R Soc Lond 372A:20130290.  https://doi.org/10.1098/rsta.2013.0290 CrossRefGoogle Scholar
  68. Wieners CE, de Ruijter WPM, Ridderinkhof W, von Der Heydt AS, Dijkstra HA (2016) Coherent tropical indo-pacific interannual climate variability. J Clim 29:4269–4291.  https://doi.org/10.1175/JCLI-D-15-0262.1 CrossRefGoogle Scholar
  69. Williams PD (2012) Climatic impacts of stochastic fluctuations in air–sea fluxes. Geophys Res Lett 39:L10705.  https://doi.org/10.1029/2012GL051813 Google Scholar
  70. Wyrtki K (1985) Water displacement in the Pacific and the genesis of El Niño cycles. J Geophys Res 90:7129–7132CrossRefGoogle Scholar
  71. Xiang B, Wang B, Ding Q, Jin F-F, Fu X, Kim H-J (2012) Reduction of the thermocline feedback associated with mean SST bias in ENSO simulation. Clim Dyn 39(6):1413–1430.  https://doi.org/10.1007/s00382-011-1164-4 CrossRefGoogle Scholar
  72. Yang C, Giese BS (2013) El Niño Southern Oscillation in an ensemble ocean reanalysis and coupled climate models. J Geophys Res Oceans 118:4052–4071.  https://doi.org/10.1002/jgrc.20284 CrossRefGoogle Scholar
  73. Yeh S-W, Kirtman B (2006) Origin of decadal El Niño–Southern Oscillation-like variability in a coupled general circulation model. J Geophys Res 111:C01009.  https://doi.org/10.1029/2005JC002985 CrossRefGoogle Scholar
  74. Yonehara H, Ujiie M (2011) A stochastic physics scheme for model uncertainties in the JMA one-week ensemble prediction system, CAS/JSC WGNE. Res Activ Atmos Ocean Model 41:69–10. http://www.wcrp-climate.org/WGNE/BlueBook/2011/documents/author-list.html
  75. Zebiak SE (1989) On the 30–60 day oscillation and the prediction of El Niño. J Clim 2:1381–1387CrossRefGoogle Scholar
  76. Zhang T, Sun D-Z (2014) ENSO asymmetry in CMIP5. J Clim 27:4070–4093.  https://doi.org/10.1175/JCLI-D-13-00454.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Istituto di Scienze dell’Atmosfera e del ClimaConsiglio Nazionale delle Ricerche (ISAC-CNR)BolognaItaly
  2. 2.Atmospheric, Oceanic and Planetary PhysicsUniversity of OxfordOxfordUK
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Istituto di Scienze dell’Atmosfera e del ClimaConsiglio Nazionale delle Ricerche (ISAC-CNR)TorinoItaly
  5. 5.Istituto di Scienze MarineConsiglio Nazionale delle Ricerche (ISMAR-CNR)RomeItaly

Personalised recommendations