Abstract
The role of the El Niño-Southern Oscillation (ENSO) in modulating the mean sea surface temperature (SST) in the tropical eastern Pacific is investigated. A strategy is developed to separate the observational record from 1958 to 2010 into two groups, ENSO and non-ENSO periods. A simple analytical framework is constructed to quantitatively delineate the contributions of oceanic dynamic heating (ODH) and surface heat fluxes to rectifying the mean SST. It is found that the differences in the mean SST between the two periods are evident, though minor, despite distinctive interannual SST variabilities. Both linear and nonlinear ODH, as well as surface heat fluxes, contribute to this slight mean SST difference. Idealized oceanic model experiments in the presence and absence of ENSO are conducted. The experiments confirm that ENSO moderately impacts the mean SST in the equatorial eastern Pacific. Although the amplitude of the linear ODH associated with ENSO is large, its impact on the long-term mean SST is small because the sign of its contribution changes between El Niño and La Niña phases. The nonlinear ODH, on the other hand, has the same sign during the warm and cold episodes. However, its accumulated effect on the mean SST is small due to its weak amplitude.
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Data and materials availability
Ocean reanalysis data used for the current study is from ECMWF Ocean Reanalysis System 5 (ORAS 5) and is openly available from Asia–Pacific Data-Research Center of the IPRC in the School of Ocean and Earth Science and Technology at the University of Hawaii at Manoa at APDRC LAS7 for public (hawaii.edu). Another ocean reanalysis data set SODA (Carton and Giese 2008), version 2.2.4 products (SODAv2.2.4) is also available at APDRC LAS7 for public (hawaii.edu). And the new fifth-generation atmospheric reanalysis ERA5 is available on the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) at https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset. Surface atmospheric data is provided by Japanese 55-year Atmospheric Reanalysis (JRA-55) products, which are available from the Research Data Archive (RDA) that is managed by the Data Engineering and Curation Section (DECS) of the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research, at CISL RDA: JRA-55: Japanese 55-year Reanalysis, Monthly Means and Variances (ucar.edu). And Surface heat flux products are from are from NOAA-CIRES 20th Century Reanalysis (20CRv2) at NOAA-CIRES 20th Century Reanalysis (V2)—Datasets—NOAA Data Catalog. Observational SST is from the Extended Reconstructed Sea Surface Temperature version 5 (ERSST V5) from the US National Climate Data Center (NCDC) at https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html. And HadISST from the Met Office Hadley Centre Sea Ice and SST dataset at https://www.metoffice.gov.uk/hadobs/hadisst/. The numerical model simulations upon which this study is based are too large to archive. Instead, we provide all the information needed to replicate the simulations. The model used for these experiments is the ocean component of the Community Earth System Model 2.1 (CESM2.1), Parallel Ocean Program version 2 (POP2). Interested readers can contact Manrui Xue (mxue@hawaii.edu) for the namelist settings and access more information at CESM2 Ocean Model—POP2 & MOM6 (ucar.edu).
References
An SI, Jin FF (2000) An eigen analysis of the interdecadal changes in the structure and frequency of ENSO mode. Geophys Res Lett 27:1573–1576
An SI, Jin FF (2001) collective role of thermocline and zonal advective feedbacks in the ENSO mode*. J Clim 14:3421–3432. https://doi.org/10.1175/1520-0442(2001)014%3c3421:CROTAZ%3e2.0.CO;2
An SI, Kim JW (2018) ENSO Transition asymmetry: Internal and external causes and intermodel diversity. Geophys Res Lett 45:5095–5104. https://doi.org/10.1029/2018GL078476
An SI, Wang B (2000) Interdecadal change of the structure of the ENSO mode and its impact on the ENSO frequency. J Clim 13(12):2044–2055. https://doi.org/10.1175/1520-0442(2000)013%3c2044:ICOTSO%3e2.0.CO;2
An SI, Tziperman E, Okumura YM, Li T (2020) Chapter 7: ENSO irregularity and asymmetry. El Nino/Southern oscillation in a changing climate. Wiley, pp 153–172
Battisti DS, Hirst AC (1989) Interannual variability in a tropical atmosphere–ocean model: Influence of the basic state, ocean geometry and nonlinearity. J Atmos Sci 15 46(12):1687–712. https://doi.org/10.1175/1520-0469(1989)046%3C1687:IVIATA%3E2.0.CO;2
Bjerknes J (1969) Atmospheric teleconnections from the equatorial Pacific. Mon Weather Rev 97(3):163–172
Carton JA, Giese BS (2008) A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon Wea Rev 136:2999–3017. https://doi.org/10.1175/2007MWR1978.1
Chang P, Wang B, Li T, Ji L (1994) Interactions between the seasonal cycle and the Southern Oscillation-Frequency entrainment and chaos in a coupled ocean-atmosphere model. Geophys Res Lett 21:2817–2820. https://doi.org/10.1029/94GL02759
Chen M, Li T, Shen X, Wu B (2016) Relative roles of dynamic and thermodynamic processes in causing evolution asymmetry between El Niño and La Niña*. J Clim 29:2201–2220. https://doi.org/10.1175/JCLI-D-15-0547.1
Choi J, An SI, Yeh SW (2012) Decadal amplitude modulation of two types of ENSO and its relationship with the mean state. Clim Dyn 38:2631–2644. https://doi.org/10.1007/s00382-011-1186-y
Choi KY, Vecchi GA, Wittenberg AT (2013) ENSO transition, duration, and amplitude asymmetries: role of the nonlinear wind stress coupling in a conceptual model. J Clim 26:9462–9476. https://doi.org/10.1175/JCLI-D-13-00045.1
Chung PH, Li T (2013) Interdecadal relationship between the mean state and El Niño types. J Clim 26(2):361–379. https://doi.org/10.1175/JCLI-D-12-00106.1
Compo GP et al (2011) The twentieth century reanalysis project: the twentieth century reanalysis project. Q.J.R. Meteorol Soc 137:1–28. https://doi.org/10.1002/qj.776
Danabasoglu G, Bates SC, Briegleb BP, Jayne SR, Jochum M, Large WG, Peacock S, Yeager SG (2012) The CCSM4 ocean component. J Clim 25:1361–1389. https://doi.org/10.1175/JCLI-D-11-00091.1
DiNezio PN, Deser C (2014) Nonlinear controls on the persistence of La Niña. J Clim 27:7335–7355. https://doi.org/10.1175/JCLI-D-14-00033.1
Fedorov AV, Philander SG (2000) Is El Niño changing? Science 288:1997–2002. https://doi.org/10.1126/science.288.5473.1997
Fedorov AV, Philander SG (2001) A stability analysis of tropical ocean–atmosphere interactions: bridging measurements and theory for El Niño. J Clim 14:3086–3101. https://doi.org/10.1175/1520-0442(2001)014,3086:ASAOTO.2.0.CO;2
Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH (2011) The community climate system model version 4. J Clim 24:4973–4991. https://doi.org/10.1175/2011JCLI4083.1
Guan C, McPhaden MJ (2016) Ocean processes affecting the twenty-first-century shift in ENSO SST variability. J Clim 29(19):6861–6879. https://doi.org/10.1175/JCLI-D-15-0870.1
Hersbach H et al (2018) ERA5 hourly data on single levels from 1959 to present. Copernic Climate Change Serv C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.adbb2d47
Hirst AC (1986) Unstable and damped equatorial modes in simple coupled ocean–atmosphere models. J Atmos Sci 43:606–630. https://doi.org/10.1175/1520-0469(1986)043%3c0606:UADEMI%3e2.0.CO;2
Hirst AC (1988) Slow instabilities in tropical ocean basin–global atmosphere models. J Atmos Sci 45:830–852. https://doi.org/10.1175/1520-0469(1988)045%3c0830:SIITOB%3e2.0.CO;2
Hua L, Yu Y, Sun DZ (2015) A further study of ENSO rectification: results from an OGCM with a seasonal Cycle*. J Clim 28:1362–1382. https://doi.org/10.1175/JCLI-D-14-00404.1
Huang B et al (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 30(20):8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1
Jin FF (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%3c0811:AEORPF%3e2.0.CO;2
Kessler WS, Kleeman R (2000) Rectification of the madden–Julian oscillation into the ENSO Cycle. J Clim 13:3560–3575. https://doi.org/10.1175/1520-0442(2000)013%3c3560:ROTMJO%3e2.0.CO;2
Kobayashi S et al (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn 93:5–48. https://doi.org/10.2151/jmsj.2015-001
Li T (1997a) Air-sea interactions of relevance to the ITCZ: analysis of coupled instabilities and experiments in a hybrid Coupled GCM. J Atmos Sci 54:134–147. https://doi.org/10.1175/1520-0469(1997)054%3c0134:ASIORT%3e2.0.CO;2
Li T (1997b) Phase transition of the El Niño-Southern oscillation: a stationary SST mode. J Atmos Sci 54:2872–2887. https://doi.org/10.1175/1520-0469(1997)054%3c2872:PTOTEN%3e2.0.CO;2
Li T, Hsu P-C (2018) Fundamentals of tropical climate dynamics. Springer, Cham
Li T, Philander GH (1996) On the annual cycle in the eastern equatorial Pacific. J Clim 9:2986–2998. https://doi.org/10.1175/1520-0442(1996)009%3c2986:OTACOT%3e2.0.CO;2
Liang J, Yang XQ, Sun DZ (2012) The Effect of ENSO events on the tropical pacific mean climate: insights from an analytical model. J Clim 25:7590–7606. https://doi.org/10.1175/JCLI-D-11-00490.1
Lucas DD, Klein R, Tannahill J, Ivanova D, Brandon S, Domyancic D, Zhang Y (2013) Failure analysis of parameter-induced simulation crashes in climate models. Geosci Model Dev 6:1157–1171. https://doi.org/10.5194/gmd-6-1157-2013
Ogata T, Xie SP, Wittenberg A, Sun DZ (2013) Interdecadal amplitude modulation of El Niño-Southern Oscillation and its impact on tropical Pacific decadal variability. J Clim 26(18):7280–7297. https://doi.org/10.1175/JCLI-D-12-00415.1
Ohba M, Ueda H (2007) An impact of SST anomalies in the Indian Ocean in acceleration of the El Niño to La Niña transition. J Meteorol Soc Jpn 85:335–348. https://doi.org/10.2151/jmsj.85.335
Okumura YM, Deser C (2010) Asymmetry in the duration of El Niño and La Niña. J Clim 23:5826–5843. https://doi.org/10.1175/2010JCLI3592.1
Philander SG (1990) El Niño , La Niña , and the Southern Oscillation. Academic Press, San Diego, CA, 1989. x, 293 pp., illus. $59.50. International geophysics series, vol. 46. Science 248(4957):904–905
Philander SG, Yamagada T, Pacanowski RC (1984) Unstable air-sea interactions in the tropics. J Atmos Sci 41:604–613. https://doi.org/10.1175/1520-0469(1984)041%3c0604:UASIIT%3e2.0.CO;2
Philander SG, Gu D, Lambert G, Li T, Halpern D, Lau NC, Pacanowski RC (1996) Why the ITCZ is mostly north of the equator. J Clim 9:2958–2972. https://doi.org/10.1175/1520-0442(1996)009<2958:WTIIMN>2.0.CO;2
Power S et al (2021) Decadal climate variability in the tropical Pacific: characteristics, causes, predictability, and prospects. Science 374(6563):eaay9165. https://doi.org/10.1126/science.aay9165
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(5):354–384. https://doi.org/10.1175/1520-0493(1982)110%3c0354:VITSST%3e2.0.CO;2
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
Rodgers KB, Friederichs P, Latif M (2004) Tropical pacific decadal variability and its relation to decadal modulations of ENSO. J Clim 17:3761–3774. https://doi.org/10.1175/1520-0442(2004)017%3c3761:TPDVAI%3e2.0.CO;2
Smith R et al (2010) The Parallel Ocean Program (POP) reference manual, ocean component of the Community Climate System Model (CCSM). Tech. Rep. LAUR-10-01853, Los Alamos Natl Lab 141:1–10
Su J, Zhang R, Li T, Rong X, Kug JS, Hong CC (2010) Causes of the El Niño and La Niña amplitude asymmetry in the equatorial eastern Pacific. J Clim 23:605–617. https://doi.org/10.1175/2009JCLI2894.1
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%3c3283:ADAOFE%3e2.0.CO;2
Sun T, Okumura YM (2020) Impact of ENSO-like tropical Pacific decadal variability on the relative frequency of El Niño and La Niña events. Geophys Res Lett 47(3):e2019GL085832. https://doi.org/10.1029/2019GL085832
Sun F, Yu JY (2009) A 10–15-Yr modulation cycle of ENSO intensity. J Clim 22:1718–1735. https://doi.org/10.1175/2008JCLI2285.1
Sun DZ, Zhang T (2006) A regulatory effect of ENSO on the time-mean thermal stratification of the equatorial upper ocean. Geophys Res Lett 33:L07710. https://doi.org/10.1029/2005GL025296
Sun DZ, Zhang T, Sun Y, Yu Y (2014) Rectification of El Niño-southern oscillation into climate anomalies of decadal and longer time scales: results from forced ocean GCM experiments. J Clim 27:2545–2561. https://doi.org/10.1175/JCLI-D-13-00390.1
Wang B (1995) Interdecadal changes in El Niño onset in the last four decades. J Clim 8(2):267–285. https://doi.org/10.1175/1520-0442(1995)008%3c0267:ICIENO%3e2.0.CO;2
Wang B, An SI (2001) Why the properties of El Niño changed during the late 1970s. Geophys Res Lett 28:3709–3712. https://doi.org/10.1029/2001GL012862
Whitaker JS, Compo GP, Wei X, Hamill TM (2004) Reanalysis without radiosondes using ensemble data assimilation. Mon Wea Rev 132:1190–1200. https://doi.org/10.1175/1520-0493(2004)132%3c1190:RWRUED%3e2.0.CO;2
Xie SP, Deser C, Vecchi GA, Ma J, Teng H, Wittenberg AT (2010) Global warming pattern formation: sea surface temperature and rainfall*. J Clim 23:966–986. https://doi.org/10.1175/2009JCLI3329.1
Yu JY, Kim ST (2011) Reversed spatial asymmetries between El Niño and La Niña and their linkage to decadal ENSO modulation in CMIP3 models. J Clim 24:5423–5434. https://doi.org/10.1175/JCLI-D-11-00024.1
Zhang L, Li T (2014) A simple analytical model for understanding the formation of sea surface temperature patterns under global warming*. J Clim 27:8413–8421. https://doi.org/10.1175/JCLI-D-14-00346.1
Zuo H, Balmaseda MA, Tietsche S, Mogensen K, Mayer M (2019) The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment. Ocean Sci 15:779–808. https://doi.org/10.5194/os-15-779-2019
Acknowledgements
The authors would like to acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. This work was jointly supported by NSFC grant 42088101, NOAA NA18OAR4310298, and NSF AGS-2006553.
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This study was supported by the National Oceanic and Atmospheric Administration (NA18OAR4310298), National Science Foundation (AGS-2006553), and National Social Science Fund of China (42088101).
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M.X. applied statistical, mathematical, and computational techniques to analyze the data and wrote the original draft. T.L. has the funding acquisition and did the supervision, provided the conceptualization and methodology. All authors reviewed and edited the manuscript.
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Xue, M., Li, T. To what extent does ENSO rectify the tropical Pacific mean state?. Clim Dyn 61, 3875–3891 (2023). https://doi.org/10.1007/s00382-023-06750-6
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DOI: https://doi.org/10.1007/s00382-023-06750-6