Abstract
Prospects for El Niño–Southern Oscillation (ENSO) predictability at long lead-times lie in the subsurface oceanic memory along the equatorial Pacific. Long considered a reliable precursor to ENSO, the oceanic heat content in springtime, often referred to as the recharge-discharge, is considered the most promising indicator of an ENSO event to come. In this study, we utilize January initialized hindcasts from the North American Multi-model Ensemble (NMME) over 1982–2010 to confront the hypothesis that the springtime recharge is a skillful predictor of ENSO the following winter. We find that the NMME ensemble mean predictions for the springtime recharge are highly skilled, even at a 10-months lead. Overall, as an independent predictor of ENSO, the springtime recharge-discharge tips the scale towards like-sign ENSO, but the spread of ENSO outcomes remains large. In both observations and the NMME predictions, recharged (discharged) states rarely evolve into La Niña (El Niño) events, yet an ENSO-neutral state is as likely to occur after a preconditioned state as is a like-sign ENSO event, particularly in observations. However, more often than in observations, the initialized predictions follow springtime recharged, neutral, and discharged states with El Niño, ENSO-neutral, and La Niña events, respectively, indicating that the NMME underestimates the uncertainty in nature. Predictions from initially recharged and discharged states also produce comparable signal-to-noise ratios in December ENSO predictions over the hindcast period. Therefore, in the realistic forecast setting considered, neither a recharged nor a discharged state produces a more predictable ENSO outcome, which is at odds with conclusions from recent predictability studies.
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References
Ballester J, Petrova D, Bordoni S, Ben Cash M, García-Díez, Rodó X (2016) Sensitivity of El Niño intensity and timing to preceding subsurface heat magnitude. Sci Rep. https://doi.org/10.1038/srep36344
Barnston AG, Tippett MK, Ranganathan M, L’Heureux ML (2017) Deterministic skill of ENSO predictions from the North American Multimodel Ensemble. Clim Dyn. https://doi.org/10.1007/s00382-017-3603-3
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. https://doi.org/10.1007/s00382-013-1783-z
Busalacchi AJ, Takeuchi K, O’Brien JJ (1983) Interannual variability of the equatorial Pacific—Revisited. J Geophys Res Oceans 88:7551–7562. https://doi.org/10.1029/JC088iC12p07551
Cane MA, Zebiak SE, Dolan SC (1986) Experimental forecasts of El Niño. Nature 321:827–832. https://doi.org/10.1038/321827a0
Capotondi A (2013) ENSO diversity in the NCAR CCSM4 climate model. J Geophys Res Oceans 118:4755–4770. https://doi.org/10.1002/jgrc.20335
Carton JA, Giese BS (2008) A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon Weather Rev 136:2999–3017. https://doi.org/10.1175/2007MWR1978.1
Compo GP et al (2011) The Twentieth Century reanalysis project. Q J R Meteorol Soc 137:1–28. https://doi.org/10.1002/qj.776
DiNezio PN, Deser C, Okumura Y, Karspeck A (2017) Predictability of 2-year La Niña events in a coupled general circulation model. Clim Dyn 49:4237–4261. https://doi.org/10.1007/s00382-017-3575-3
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 44:1381–1401. https://doi.org/10.1007/s00382-014-2126-4
Giese BS, Ray S (2011) El Niño variability in simple ocean data assimilation (SODA), 1871–2008. J Geophys Res Oceans. https://doi.org/10.1029/2010JC006695
Ham Y-G, Kug J-S (2012) How well do current climate models simulate two types of El Niño? Clim Dyn 39:383–398. https://doi.org/10.1007/s00382-011-1157-3
Hu S, Fedorov AV (2016) Exceptionally strong easterly wind burst stalling El Niño of 2014. Proc Natl Acad Sci 113:2005. https://doi.org/10.1073/pnas.1514182113
Hu S, Fedorov AV (2017) The extreme El Niño of 2015–2016: the role of westerly and easterly wind bursts, and preconditioning by the failed 2014 event. Clim Dyn. https://doi.org/10.1007/s00382-017-3531-2
Izumo T, Lengaigne M, Vialard J, Suresh I, Planton Y (2018) On the physical interpretation of the lead relation between Warm Water Volume and the El Niño Southern Oscillation. Clim Dyn. https://doi.org/10.1007/s00382-018-4313-1
Jin EK et al (2008) Current status of ENSO prediction skill in coupled ocean–atmosphere models. Clim Dyn 31:647–664. https://doi.org/10.1007/s00382-008-0397-3
Jin F-F (1997) An equatorial ocean recharge paradigm for ENSO. Part I: conceptual model. J Atmos Sci 54:811–829. https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2
Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP–DOE AMIP-II Reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1644. https://doi.org/10.1175/BAMS-83-11-1631
Kirtman BP, Shukla J, Balmaseda M, Graham N, Penland C, Xue Y, Zebiak S (2002) A report to the climate variability and predictability (CLIVAR) Numerical Experimentation Group (NEG), vol 14
Kirtman BP et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601. https://doi.org/10.1175/BAMS-D-12-00050.1
Kug J-S, Jin F-F, An S-I (2009) Two types of El Niño events: cold tongue El Niño and warm pool El Niño. J Clim 22:1499–1515. https://doi.org/10.1175/2008JCLI2624.1
Kumar A, Peng P, Chen M (2014) Is there a relationship between potential and actual skill? Mon Weather Rev 142:2220–2227. https://doi.org/10.1175/MWR-D-13-00287.1
Larson SM, Kirtman BP (2014) The Pacific merIDional mode as an ENSO precursor and predictor in the North American multimodel ensemble. J Clim 27:7018–7032. https://doi.org/10.1175/JCLI-D-14-00055.1
Larson SM, Kirtman BP (2015) An alternate approach to ensemble ENSO forecast spread: Application to the 2014 forecast: ENSEMBLE ENSO FORECAST SPREAD. Geophys Res Lett 42:9411–9415. https://doi.org/10.1002/2015GL066173
Larson SM, Kirtman BP (2017) Drivers of coupled model ENSO error dynamics and the spring predictability barrier. Clim Dyn 48:3631–3644. https://doi.org/10.1007/s00382-016-3290-5
Larson SM, Kirtman BP (2019) Linking preconditioning to extreme ENSO events and reduced ensemble spread. Clim Dyn 52:7417–7433. https://doi.org/10.1007/s00382-017-3791-x
Latif M, Barnett TP, Cane MA, Flügel M, Graham NE, von Storch H, Xu J-S, Zebiak SE (1994) A review of ENSO prediction studies. Clim Dyn 9:167–179. https://doi.org/10.1007/BF00208250
Lengaigne M, Guilyardi E, Boulanger J-P, Menkes C, Delecluse P, Inness P, Cole J, Slingo J (2004) Triggering of El Niño by westerly wind events in a coupled general circulation model. Clim Dyn 23:601–620. https://doi.org/10.1007/s00382-004-0457-2
Levine AFZ, McPhaden MJ (2015) The annual cycle in ENSO growth rate as a cause of the spring predictability barrier: ENSO SPRING PREDICTABILITY BARRIER. Geophys Res Lett 42:5034–5041. https://doi.org/10.1002/2015GL064309
Levine AFZ, McPhaden MJ (2016) How the July 2014 easterly wind burst gave the 2015–2016 El Niño a head start. Geophys Res Lett 43:6503–6510. https://doi.org/10.1002/2016GL069204
Lopez H, Kirtman BP (2014) WWBs, ENSO predictability, the spring barrier and extreme events. J Geophys Res Atmos 119:10,114–10,138, https://doi.org/10.1002/2014JD021908
Lopez H, Kirtman BP, Tziperman E, Gebbie G (2013) Impact of interactive westerly wind bursts on CCSM3. Dyn Atmos Oceans 59:24–51. https://doi.org/10.1016/j.dynatmoce.2012.11.001
McCreary JP (1983) A model of tropical ocean-atmosphere interaction. Mon Weather Rev 111:370–387. https://doi.org/10.1175/1520-0493(1983)111<0370:AMOTOA>2.0.CO;2
McGregor S, Timmermann A, Jin F-F, Kessler WS (2016) Charging El Niño with off-equatorial westerly wind events. Clim Dyn 47:1111–1125. https://doi.org/10.1007/s00382-015-2891-8
McPhaden MJ (2003) Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophys Res Lett. https://doi.org/10.1029/2003GL016872
McPhaden MJ (2015) Playing hide and seek with El Niño. Nat Clim Change 5:791
Meinen CS, McPhaden MJ (2000) Observations of warm water volume changes in the equatorial pacific and their relationship to El Niño and La Niña. J Clim 13:3551–3559. https://doi.org/10.1175/1520-0442(2000)013<3551:OOWWVC>2.0.CO;2
Neelin JD, Battisti DS, Hirst AC, Jin F-F, Wakata Y, Yamagata T, Zebiak SE (1998) ENSO theory. J Geophys Res Oceans 103:14261–14290. https://doi.org/10.1029/97JC03424
Neske S, McGregor S (2018) Understanding the warm water volume precursor of ENSO events and its Interdecadal variation. Geophys Res Lett 45:1577–1585. https://doi.org/10.1002/2017GL076439
Pegion K, DelSole T, Becker E, Cicerone T (2017) Assessing the fidelity of predictability estimates. Clim Dyn. https://doi.org/10.1007/s00382-017-3903-7
Planton Y, Vialard J, Guilyardi E, Lengaigne M, Izumo T (2018) Western Pacific Oceanic heat content: a better predictor of La Niña than of El Niño. Geophys Res Lett 45:9824–9833. https://doi.org/10.1029/2018GL079341
Puy M et al (2017) Influence of Westerly wind events stochasticity on El Niño amplitude: the case of 2014 vs. Clim Dyn.https://doi.org/10.1007/s00382-017-3938-9
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 Atmos. https://doi.org/10.1029/2002JD002670
Rebert JP, Donguy JR, Eldin G, Wyrtki K (1985) Relations between sea level, thermocline depth, heat content, and dynamic height in the tropical Pacific Ocean. J Geophys Res Oceans 90:11719–11725. https://doi.org/10.1029/JC090iC06p11719
Saha S et al (2006) The NCEP climate forecast system. J Clim 19:3483–3517. https://doi.org/10.1175/JCLI3812.1
Tang Y, Lin H, Moore AM (2008) Measuring the potential predictability of ensemble climate predictions. J Geophys Res. https://doi.org/10.1029/2007JD008804
Timmermann A et al (2018) El Niño–Southern Oscillation complexity. Nature 559:535–545. https://doi.org/10.1038/s41586-018-0252-6
Tippett MK, Ranganathan M, L’Heureux M, Barnston AG, DelSole T (2017) Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble. Clim Dyn. https://doi.org/10.1007/s00382-017-3721-y
Webster PJ, Yang S (1992) Monsoon and Enso: selectively interactive systems. Q J R Meteorol Soc 118:877–926. https://doi.org/10.1002/qj.49711850705
White WB, Pazan SE, Inoue M (1987) Hindcast/forecast of ENSO events based upon the redistribution of observed and model heat content in the Western Tropical Pacific, 1964–1986. J Phys Oceanogr 17:264–280. https://doi.org/10.1175/1520-0485(1987)017<0264:HOEEBU>2.0.CO;2
Wyrtki K (1975) El Niño—the dynamic response of the equatorial Pacific Oceanto atmospheric forcing. J Phys Oceanogr 5:572–584. https://doi.org/10.1175/1520-0485(1975)005<0572:ENTDRO>2.0.CO;2
Wyrtki K (1985) Water displacements in the Pacific and the genesis of El Niño cycles. J Geophys Res Oceans 90:7129–7132. https://doi.org/10.1029/JC090iC04p07129
Yeh S-W et al (2018) ENSO atmospheric teleconnections and their response to Greenhouse gas forcing. Rev Geophys 56:185–206. https://doi.org/10.1002/2017RG000568
Zebiak SE, Cane MA (1987) A model El Niño–Southern oscillation. Mon Weather Rev 115:2262–2278. https://doi.org/10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2
Acknowledgements
The authors are grateful to J. Vialard and one anonymous reviewer for helpful comments that substantially improved visualization and discussion of the results. SL thanks B. Kirtman for discussion about the NMME SSH predictions and K. Dixon and J. Infanti for data visualization suggestions. The altimeter products were produced by Ssalto/Duacs and distributed by Aviso+, with support from Cnes (https://www.aviso.altimetry.fr). The NMME project and data dissemination is supported by NOAA, NSF, NASA and DOE, with the help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive. GODAS data can be downloaded at ftp://ftp.cdc.noaa.gov/Datasets/godas/.
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Larson, S.M., Pegion, K. Do asymmetries in ENSO predictability arise from different recharged states?. Clim Dyn 54, 1507–1522 (2020). https://doi.org/10.1007/s00382-019-05069-5
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DOI: https://doi.org/10.1007/s00382-019-05069-5