• An, S.-I., 2008: Interannual variations of the tropical ocean instability wave and ENSO. J. Climate, 21, 36803686, https://doi.org/10.1175/2008JCLI1701.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • An, S.-I., and F.-F. Jin, 2004: Nonlinearity and asymmetry of ENSO. J. Climate, 17, 23992412, https://doi.org/10.1175/1520-0442(2004)017<2399:NAAOE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • An, S.-I., E. Tziperman, Y. Okumura, and T. Li, 2020: ENSO irregularity and asymmetry. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 153–172, https://doi.org/10.1002/9781119548164.ch7.

    • Crossref
    • Export Citation
  • Ballester, J., D. Petrova, S. Bordoni, B. Cash, M. García-Díez, and X. Rodó, 2016: Sensitivity of El Niño intensity and timing to preceding subsurface heat magnitude. Sci. Rep., 6, 36344, https://doi.org/10.1038/srep36344.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., M. K. Tippett, M. Ranganathan, and M. L. L’Heureux, 2019: Deterministic skill of ENSO predictions from the North American multimodel ensemble. Climate Dyn., 53, 72157234, https://doi.org/10.1007/s00382-017-3603-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bayr, T., C. Wengel, M. Latif, D. Dommenget, J. Lübbecke, and W. Park, 2019: Error compensation of ENSO atmospheric feedbacks in climate models and its influence on simulated ENSO dynamics. Climate Dyn., 53, 155172, https://doi.org/10.1007/s00382-018-4575-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellenger, H., E. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in climate models: From CMIP3 to CMIP5. Climate Dyn., 42, 19992018, https://doi.org/10.1007/s00382-013-1783-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bertrand, A., M. Lengaigne, K. Takahashi, A. Avadí, F. Poulain, C. Harrod, 2020: El Niño Southern Oscillation (ENSO) effects on fisheries and aquaculture. FAO Fisheries and Aquaculture Tech. Paper 660, 264 pp., https://doi.org/10.4060/ca8348en.

    • Crossref
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blanke, B., and P. Delecluse, 1993: Variability of the tropical Atlantic Ocean simulated by a general circulation model with two different mixed-layer physics. J. Phys. Oceanogr., 23, 13631388, https://doi.org/10.1175/1520-0485(1993)023<1363:VOTTAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bougeault, P., 1985: A simple parameterization of the large-scale effects of cumulus convection. Mon. Wea. Rev., 113, 21082121, https://doi.org/10.1175/1520-0493(1985)113<2108:ASPOTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiodi, A. M., and D. E. Harrison, 2015: Equatorial Pacific easterly wind surges and the onset of La Niña events. J. Climate, 28, 776792, https://doi.org/10.1175/JCLI-D-14-00227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiodi, A. M., D. E. Harrison, and G. A. Vecchi, 2014: Subseasonal atmospheric variability and El Niño waveguide warming: Observed effects of the Madden–Julian oscillation and westerly wind events. J. Climate, 27, 36193642, https://doi.org/10.1175/JCLI-D-13-00547.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, K., G. A. Vecchi, and A. T. Wittenberg, 2013: ENSO transition, duration, and amplitude asymmetries: Role of the nonlinear wind stress coupling in a conceptual model. J. Climate, 26, 94629476, https://doi.org/10.1175/JCLI-D-13-00045.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Déqué, M., C. Dreveton, A. Braun, and D. Cariolle, 1994: The ARPEGE/IFS atmosphere model: A contribution to the French community climate modelling. Climate Dyn., 10, 249266, https://doi.org/10.1007/BF00208992.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DiNezio, P. N., and et al. , 2017a: A 2 year forecast for a 60–80% chance of La Niña in 2017–18. Geophys. Res. Lett., 44, 11 62411 635, https://doi.org/10.1002/2017GL074904.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DiNezio, P. N., C. Deser, Y. M. Okumura, and A. Karspeck, 2017b: Predictability of 2 year La Niña events in a coupled general circulation model. Climate Dyn., 49, 42374261, https://doi.org/10.1007/s00382-017-3575-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dommenget, D., T. Bayr, and C. Frauen, 2013: Analysis of the non-linearity in the pattern and time evolution of El Niño Southern Oscillation. Climate Dyn., 40, 28252847, https://doi.org/10.1007/s00382-012-1475-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eisenman, I., L. Yu, and E. Tziperman, 2005: Westerly wind bursts: ENSO’s tail rather than the dog? J. Climate, 18, 52245238, https://doi.org/10.1175/JCLI3588.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frauen, C., and D. Dommenget, 2010: El Niño and La Niña amplitude asymmetry caused by atmospheric feedbacks. Geophys. Res. Lett., 37, L18801, https://doi.org/10.1029/2010GL044444.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gadgil, S., P. V. Joseph, and N. V. Joshi, 1984: Ocean–atmosphere coupling over monsoon regions. Nature, 312, 141143, https://doi.org/10.1038/312141a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebbie, G., I. Eisenman, A. T. Wittenberg, and E. Tziperman, 2007: Modulation of westerly wind bursts by sea surface temperature: A semistochastic feedback for ENSO. J. Atmos. Sci., 64, 32813295, https://doi.org/10.1175/JAS4029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goddard, L., and A. Gershunov, 2020: Impact of El Niño on weather and climate extremes. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 361–375, https://doi.org/10.1002/9781119548164.ch16.

    • Crossref
    • Export Citation
  • Graham, N. E., and T. P. Barnett, 1987: Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science, 238, 657659, https://doi.org/10.1126/science.238.4827.657.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, C., M. J. McPhaden, F. Wang, and S. Hu, 2019: Quantifying the role of oceanic feedbacks on ENSO asymmetry. Geophys. Res. Lett., 46, 21402148, https://doi.org/10.1029/2018GL081332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guilyardi, E., P. Braconnot, F.-F. Jin, S. T. Kim, M. Kolasinski, T. Li, and I. Musat, 2009: Atmosphere feedbacks during ENSO in a coupled GCM with a modified atmospheric convection scheme. J. Climate, 22, 56985718, https://doi.org/10.1175/2009JCLI2815.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hewitt, H. T., D. Copsey, I. D. Culverwell, C. M. Harris, R. S. R. Hill, A. B. Keen, A. J. McLaren, and E. C. Hunke, 2011: Design and implementation of the infrastructure of HadGEM3: The next-generation Met Office climate modelling system. Geosci. Model Dev., 4, 223253, https://doi.org/10.5194/gmd-4-223-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holbrook, N. J., and et al. , 2020: ENSO-driven ocean extremes and their ecosystem impacts. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 409–428, https://doi.org/10.1002/9781119548164.ch18.

    • Crossref
    • Export Citation
  • Holmes, R. M., S. McGregor, A. Santoso, and M. H. England, 2019: Contribution of tropical instability waves to ENSO irregularity. Climate Dyn., 52, 18371855, https://doi.org/10.1007/s00382-018-4217-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Im, S.-H., S.-I. An, S. T. Kim, and F.-F. Jin, 2015: Feedback processes responsible for El Niño–La Niña amplitude asymmetry. Geophys. Res. Lett., 42, 55565563, https://doi.org/10.1002/2015GL064853.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Izumo, T., M. Lengaigne, J. Vialard, I. Suresh, and Y. Planton, 2019: On the physical interpretation of the lead relation between warm water volume and the El Niño Southern Oscillation. Climate Dyn., 52, 29232942, https://doi.org/10.1007/s00382-018-4313-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811829, https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., and S.-I. An, 1999: Thermocline and zonal advective feedbacks within the equatorial ocean recharge oscillator model for ENSO. Geophys. Res. Lett., 26, 29892992, https://doi.org/10.1029/1999GL002297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys. Res. Lett., 33, L23708, https://doi.org/10.1029/2006GL027221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., L. Lin, A. Timmermann, and J. Zhao, 2007: Ensemble-mean dynamics of the ENSO recharge oscillator under state-dependent stochastic forcing. Geophys. Res. Lett., 34, L03807, https://doi.org/10.1029/2006GL027372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleeman, R., 2002: Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci., 59, 20572072, https://doi.org/10.1175/1520-0469(2002)059<2057:MDPUUR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kokoska, S., and D. Zwillinger, 2000: CRC Standard Probability and Statistics Tables and Formulae: Student Edition. CRC Press, 200 pp., https://doi.org/10.1201/b16923.

    • Crossref
    • Export Citation
  • Lai, A. W., M. Herzog, and H. Graf, 2015: Two key parameters for the El Niño continuum: Zonal wind anomalies and western Pacific subsurface potential temperature. Climate Dyn., 45, 34613480, https://doi.org/10.1007/s00382-015-2550-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, S. M., and B. P. Kirtman, 2019: Linking preconditioning to extreme ENSO events and reduced ensemble spread. Climate Dyn., 52, 74177433, https://doi.org/10.1007/s00382-017-3791-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, S. M., and K. Pegion, 2020: Do asymmetries in ENSO predictability arise from different recharged states? Climate Dyn., 54, 15071522, https://doi.org/10.1007/s00382-019-05069-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lengaigne, M., J.-P. Boulanger, C. Menkes, P. Delecluse, and J. Slingo, 2004: Westerly wind events in the tropical Pacific and their influence on the coupled ocean–atmosphere system: A review. Earth’s Climate: The Ocean–Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 49–69, https://doi.org/10.1029/147GM03.

    • Crossref
    • Export Citation
  • Levine, A. F. Z., F.-F. Jin, and M. J. McPhaden, 2016: Extreme noise–extreme El Niño: How state-dependent noise forcing creates El Niño–La Niña asymmetry. J. Climate, 29, 54835499, https://doi.org/10.1175/JCLI-D-16-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Heureux, M. L., A. F. Z. Levine, M. Newman, C. Ganter, J.-J. Luo, M. K. Tippett, and T. N. Stockdale, 2020: ENSO prediction. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 227–246, https://doi.org/10.1002/9781119548164.ch10.

    • Crossref
    • Export Citation
  • Lin, I.-I., and et al. , 2020: ENSO and tropical cyclones. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 377–408, https://doi.org/10.1002/9781119548164.ch17.

    • Crossref
    • Export Citation
  • Lloyd, J., E. Guilyardi, H. Weller, and J. Slingo, 2009: The role of atmosphere feedbacks during ENSO in the CMIP3 models. Atmos. Sci. Lett., 10, 170176, https://doi.org/10.1002/asl.227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lloyd, J., E. Guilyardi, and H. Weller, 2012: The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part III: The shortwave flux feedback. J. Climate, 25, 42754293, https://doi.org/10.1175/JCLI-D-11-00178.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopez, H., and B. P. Kirtman, 2014: WWBs, ENSO predictability, the spring barrier and extreme events. J. Geophys. Res. Atmos., 119, 10 11410, https://doi.org/10.1002/2014JD021908.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madec, G., and et al. , 2017: NEMO ocean engine (version v3.6-patch). Note du pôle de modélisation de L’Institut Pierre-Simon Laplace (IPSL) No. 27, 402 pp., https://doi.org/10.5281/zenodo.3248739.

    • Crossref
    • Export Citation
  • Martinez-Villalobos, C., M. Newman, D. J. Vimont, C. Penland, and J. D. Neelin, 2019: Observed El Niño–La Niña asymmetry in a linear model. Geophys. Res. Lett., 46, 99099919, https://doi.org/10.1029/2019GL082922.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGregor, S., A. Timmermann, F.-F. Jin, and W. S. Kessler, 2016: Charging El Niño with off-equatorial westerly wind events. Climate Dyn., 47, 11111125, https://doi.org/10.1007/s00382-015-2891-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., 2015: Playing hide and seek with El Niño. Nat. Climate Change, 5, 791795, https://doi.org/10.1038/nclimate2775.

  • McPhaden, M. J., A. Santoso, and W. Cai, 2020: El Niño Southern Oscillation in a Changing Climate. Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 528 pp., https://doi.org/10.1002/9781119548164.

    • Crossref
    • Export Citation
  • Meinen, C. S., and M. J. McPhaden, 2000: Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Nina. J. Climate, 13, 35513559, https://doi.org/10.1175/1520-0442(2000)013<3551:OOWWVC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neske, S., and S. McGregor, 2018: Understanding the warm water volume precursor of ENSO events and its interdecadal variation. Geophys. Res. Lett., 45, 15771585, https://doi.org/10.1002/2017GL076439.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petrova, D., S. Koopman, J. Ballester, and X. Rodó, 2017: Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model. Climate Dyn., 48, 12491276, https://doi.org/10.1007/s00382-016-3139-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Planton, Y. Y., 2020: CNRM-CM5_for_ENSO_predictability. Mendeley Data, V1, accessed 2020, https://doi.org/10.17632/bsnd8md962.1.

    • Crossref
    • Export Citation
  • Planton, Y. Y., J. Vialard, E. Guilyardi, M. Lengaigne, and T. Izumo, 2018: Western Pacific oceanic heat content: A better predictor of La Niña than of El Niño. Geophys. Res. Lett., 45, 98249833, https://doi.org/10.1029/2018GL079341.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Planton, Y. Y., and et al. , 2020: Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bull. Amer. Meteor. Soc, 102, E193E217, https://doi.org/10.1175/BAMS-D-19-0337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Praveen Kumar, B., J. Vialard, M. Lengaigne, V. S. N. Murty, and M. J. McPhaden, 2012: TropFlux: Air–sea fluxes for the global tropical oceans—Description and evaluation. Climate Dyn., 38, 15211543, https://doi.org/10.1007/s00382-011-1115-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Praveen Kumar, B., J. Vialard, M. Lengaigne, V. S. N. Murty, M. J. McPhaden, M. F. Cronin, F. Pinsard, and K. Gopala Reddy, 2013: TropFlux wind stresses over the tropical oceans: Evaluation and comparison with other products. Climate Dyn., 40, 20492071, https://doi.org/10.1007/s00382-012-1455-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Puy, M., 2016: L’influence des coups de vent d’ouest dans le Pacifique équatorial sur El Niño: Origines atmosphériques et impacts océaniques. Doctoral dissertation, Université Pierre et Marie Curie–Paris VI, 224 pp., https://tel.archives-ouvertes.fr/tel-01360580/document.

  • Puy, M., J. Vialard, M. Lengaigne, and E. Guilyardi, 2016: Modulation of equatorial Pacific westerly/easterly wind events by the Madden–Julian oscillation and convectively-coupled Rossby waves. Climate Dyn., 46, 21552178, https://doi.org/10.1007/s00382-015-2695-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Puy, M., and et al. , 2019: Influence of westerly wind events stochasticity on El Niño amplitude: The case of 2014 vs. 2015. Climate Dyn., 52, 74357454, https://doi.org/10.1007/s00382-017-3938-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramesh, N., and R. Murtugudde, 2013: All flavours of El Niño have similar early subsurface origins. Nat. Climate Change, 3, 4246, https://doi.org/10.1038/nclimate1600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2006: The NCEP Climate Forecast System. J. Climate, 19, 34833517, https://doi.org/10.1175/JCLI3812.1.

  • Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116, 435460, https://doi.org/10.1002/qj.49711649210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, J., R. Zhang, T. Li, X. 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, 605617, https://doi.org/10.1175/2009JCLI2894.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takahashi, K., and B. Dewitte, 2016: Strong and moderate nonlinear El Niño regimes. Climate Dyn., 46, 16271645, https://doi.org/10.1007/s00382-015-2665-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taschetto, A. S., C. C. Ummenhofer, M. F. Stuecker, D. Dommenget, K. Ashok, R. R. Rodrigues, and S. W. Yeh, 2020: ENSO atmospheric teleconnections. El Niño Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 309–335, https://doi.org/10.1002/9781119548164.ch14.

    • Crossref
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Timmermann, A., and et al. , 2018: El Niño–Southern Oscillation complexity. Nature, 559, 535545, https://doi.org/10.1038/s41586-018-0252-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., M. L. L’Heureux, E. J. Becker, A. Kumar, 2020: Excessive momentum and false alarms in late-spring ENSO forecasts. Geophys. Res. Lett., 47, e2020GL087008, https://doi.org/10.1029/2020GL087008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Valcke, S., 2013: The OASIS3 coupler: A European climate modelling community software. Geosci. Model Dev., 6, 373388, https://doi.org/10.5194/gmd-6-373-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vialard, J., C. Menkes, J.-P. Boulanger, P. Delecluse, E. Guilyardi, M. J. McPhaden, and G. Madec, 2001: A model study of oceanic mechanisms affecting equatorial Pacific sea surface temperature during the 1997–98 El Niño. J. Phys. Oceanogr., 31, 16491675, https://doi.org/10.1175/1520-0485(2001)031<1649:AMSOOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Voldoire, A., and et al. , 2013: The CNRM-CM5.1 global climate model: Description and basic evaluation. Climate Dyn., 40, 20912121, https://doi.org/10.1007/s00382-011-1259-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., and M. J. McPhaden, 2000: The surface layer heat balance in the equatorial Pacific Ocean, Part II: Interannual variability. J. Phys. Oceanogr., 30, 29893008, https://doi.org/10.1175/1520-0485(2001)031<2989:TSLHBI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., and M. J. McPhaden, 2001: Surface layer heat balance in the equatorial Pacific Ocean during the 1997-98 El Niño and the 1998-99 La Niña. J. Climate, 14, 33933407, https://doi.org/10.1175/1520-0442(2001)014<3393:SLTBIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., and S. Yang, 1992: Monsoon and ENSO: Selectively interactive systems. Quart. J. Roy. Meteor. Soc., 118, 877926, https://doi.org/10.1002/qj.49711850705.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1985: Water displacements in the Pacific and the genesis of El Nino cycles. J. Geophys. Res., 90, 71297132, https://doi.org/10.1029/JC090iC04p07129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, S., and A. V. Fedorov, 2020: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in climate models. Geophys. Res. Lett., 47, e2020GL088381, https://doi.org/10.1029/2020GL088381.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 113 113 62
Full Text Views 39 39 24
PDF Downloads 52 52 33

The Asymmetric Influence of Ocean Heat Content on ENSO Predictability in the CNRM-CM5 Coupled General Circulation Model

View More View Less
  • 1 a LOCEAN-IPSL, CNRS-IRD-MNHN-Sorbonne Université, Paris, France
  • | 2 b NCAS-Climate, University of Reading, Reading, United Kingdom
  • | 3 c MARBEC, University of Montpellier, CNRS, IFREMER, IRD, Sète, France
  • | 4 d NOAA/Pacific Marine Environmental Laboratory, Seattle, Washington
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Unusually high western Pacific Ocean oceanic heat content often leads to El Niño about 1 year later, while unusually low heat content leads to La Niña. Here, we investigate if El Niño–Southern Oscillation (ENSO) predictability also depends on the initial state recharge, and we discuss the underlying mechanisms. To that end, we use the CNRM-CM5 model, which has a reasonable representation of the main observed ENSO characteristics, asymmetries, and feedbacks. Observations and a 1007-yr-long CNRM-CM5 simulation indicate that discharged states evolve more systematically into La Niña events than recharged states into neutral states or El Niño events. We ran 70-member ensemble experiments in a perfect-model setting, initialized in boreal autumn from either recharged or discharged western Pacific heat content, sampling the full range of corresponding ENSO phases. Predictability measures based both on spread and signal-to-noise ratio confirm that discharged states yield a more predictable ENSO outcome one year later than recharged states. As expected from recharge oscillator theory, recharged states evolve into positive central Pacific sea surface temperature anomalies in boreal spring, inducing stronger and more variable westerly wind event activity and a fast growth of the ensemble spread during summer and autumn. This also enhances the positive wind stress feedback in autumn, but the effect is offset by changes in thermocline and heat flux feedbacks. The state-dependent component of westerly wind events is thus the most likely cause for the predictability asymmetry in CNRM-CM5, although changes in the low-frequency wind stress feedback may also contribute.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Planton’s current affiliation: NOAA/Pacific Marine Environmental Laboratory, Seattle, Washington.

Corresponding author: Yann Y. Planton, yann.planton@locean-ipsl.upmc.fr

Abstract

Unusually high western Pacific Ocean oceanic heat content often leads to El Niño about 1 year later, while unusually low heat content leads to La Niña. Here, we investigate if El Niño–Southern Oscillation (ENSO) predictability also depends on the initial state recharge, and we discuss the underlying mechanisms. To that end, we use the CNRM-CM5 model, which has a reasonable representation of the main observed ENSO characteristics, asymmetries, and feedbacks. Observations and a 1007-yr-long CNRM-CM5 simulation indicate that discharged states evolve more systematically into La Niña events than recharged states into neutral states or El Niño events. We ran 70-member ensemble experiments in a perfect-model setting, initialized in boreal autumn from either recharged or discharged western Pacific heat content, sampling the full range of corresponding ENSO phases. Predictability measures based both on spread and signal-to-noise ratio confirm that discharged states yield a more predictable ENSO outcome one year later than recharged states. As expected from recharge oscillator theory, recharged states evolve into positive central Pacific sea surface temperature anomalies in boreal spring, inducing stronger and more variable westerly wind event activity and a fast growth of the ensemble spread during summer and autumn. This also enhances the positive wind stress feedback in autumn, but the effect is offset by changes in thermocline and heat flux feedbacks. The state-dependent component of westerly wind events is thus the most likely cause for the predictability asymmetry in CNRM-CM5, although changes in the low-frequency wind stress feedback may also contribute.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Planton’s current affiliation: NOAA/Pacific Marine Environmental Laboratory, Seattle, Washington.

Corresponding author: Yann Y. Planton, yann.planton@locean-ipsl.upmc.fr
Save