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SCIENCE CHINA Earth Sciences, Volume 60, Issue 9: 1572-1588(2017) https://doi.org/10.1007/s11430-016-0106-9

Evolution of the 2015/16 El Niño and historical perspective since 1979

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  • ReceivedJul 21, 2016
  • AcceptedSep 18, 2016
  • PublishedDec 22, 2016

Abstract

The 2015/16 El Niño developed from weak warm conditions in late 2014 and NINO3.4 reached 3oC in November 2015. We describe the characteristics of the evolution of the 2015/16 El Niño using various data sets including SST, surface winds, outgoing longwave radiation and subsurface temperature from an ensemble operational ocean reanalyses, and place this event in the context of historical ENSO events since 1979. One salient feature about the 2015/16 El Niño was a large number of westerly wind bursts and downwelling oceanic Kelvin waves (DWKVs). Four DWKVs were observed in April-November 2015 that initiated and enhanced the eastern-central Pacific warming. Eastward zonal current anomalies associated with DWKVs advected the warm pool water eastward in spring/summer. An upwelling Kelvin wave (UWKV) emerged in early November 2015 leading to a rapid decline of the event. Another outstanding feature was that NINO4 reached a historical high (1.7oC), which was 1oC (0.8oC) higher than that of the 1982/83 (1997/98) El Niño. Although NINO3 was comparable to that of the 1982/83 and 1997/98 El Niño, NINO1+2 was much weaker. Consistently, enhanced convection was displaced 20 degree westward, and the maximum D20 anomaly was about 1/3−1/2 of that in 1997 and 1982 near the west coast of South America.


Acknowledgment

We would like to thank Dr. Dake Chen for the invitation and suggestion for the paper. We also thank Dr. Caihong Wen and Dr. Emily Becker for their constructive comments and suggestions at the internal review. The scientific results and conclusions, as well as any view or opinions expressed herein, are those of the author(s) and do not necessarily reflect the views of NWS, NOAA, or the Department of Commerce.


Open access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.


References

[1] Ando K, Matsumoto T, Nagahama T, Ueki I, Takatsuki Y, Kuroda Y. Drift characteristics of a moored conductivity-temperature-depth sensor and correction of salinity data. J Atmos Ocean Technol, 2005, 22: 282-291 CrossRef ADS Google Scholar

[2] Amaya D J, Xie S P, Miller A J, McPhaden M J. Seasonality of tropical Pacific decadal trends associated with the 21st century global warming hiatus. J Geophys Res Oceans, 2015, 120: 6782-6798 CrossRef ADS Google Scholar

[3] Balmaseda M A, Mogensen K, Weaver A T. 2013. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc, 131: 1132–1161. Google Scholar

[4] Balmaseda M A, Co-authors. 2015. The ocean reanalyses intercomparison project (ORA-IP). J Oper Oceanogr, 7: 81–99. Google Scholar

[5] Behringer D W, Ji M, Leetmaa A. 1998. An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon Weather Rev, 126: 1013–1021. Google Scholar

[6] Behringer D W, Xue Y. 2004. Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. In: Eighth Symposium on Integrated Observing and Assimilation System for Atmosphere, Ocean, and Land Surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center. Seattle. 11–15. Google Scholar

[7] Cravatte S, Delcroix T, Zhang D, McPhaden M, Leloup J. Observed freshening and warming of the western Pacific Warm Pool. Clim Dyn, 2009, 33: 565-589 CrossRef ADS Google Scholar

[8] Chen D, Lian T, Fu C, Cane M A, Tang Y, Murtugudde R, Song X, Wu Q, Zhou L. Strong influence of westerly wind bursts on El Niño diversity. Nat Geosci, 2015, 8: 339-345 CrossRef ADS Google Scholar

[9] Chen S, Wu R, Chen W, Yu B, Cao X. Genesis of westerly wind bursts over the equatorial western Pacific during the onset of the strong 2015–2016 El Niño. Atmos Sci Lett, 2016, 17: 384-391 CrossRef Google Scholar

[10] Fedorov A V, Hu S, Lengaigne M, Guilyardi E. The impact of westerly wind bursts and ocean initial state on the development, and diversity of El Niño events. Clim Dyn, 2015, 44: 1381-1401 CrossRef ADS Google Scholar

[11] Gasparin F, Roemmich D. The strong freshwater anomaly during the onset of the 2015/2016 El Niño. Geophys Res Lett, 2016, 43: 6452-6460 CrossRef ADS Google Scholar

[12] Hu S, Fedorov A V. Exceptionally strong easterly wind burst stalling El Niño of 2014. Proc Natl Acad Sci USA, 2016, 113: 2005-2010 CrossRef PubMed ADS Google Scholar

[13] Huang B, Xue Y, Zhang D, Kumar A, McPhaden M J. The NCEP GODAS ocean analysis of the tropical Pacific mixed layer heat budget on seasonal to interannual time scales. J Clim, 2010, 23: 4901-4925 CrossRef Google Scholar

[14] Huang B, Thorne P W, Smith T M, Liu W, Lawrimore J, Banzon V F, Zhang H M, Peterson T C, Menne M. Further exploring and quantifying uncertainties for extended reconstructed sea surface temperature (ERSST) version 4. J Clim, 2016, 29: 3119-3142 CrossRef ADS Google Scholar

[15] Jacox M G, Hazen E L, Zaba K D, Rudnick D L, Edwards C A, Moore A M, Bograd S J. Impacts of the 2015−2016 El Niño on the California current system: Early assessment and comparison to past events. Geophys Res Lett, 2016, 43: 7072-7080 CrossRef ADS Google Scholar

[16] Ji M, Behringer D W, Leetmaa A. 1998. An improved coupled model for ENSO prediction and implications for ocean initialization. Part II: The coupled model. Mon Weather Rev, 126: 1022–1034. Google Scholar

[17] Jin F F. 1997. An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J Atmos Sci, 54: 811–829. Google Scholar

[18] Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Jenne R, Joseph D. The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteorol Soc, 1996, 77: 437-471 CrossRef Google Scholar

[19] Kumar A, Hu Z Z. Interannual and interdecadal variability of ocean temperature along the equatorial Pacific in conjunction with ENSO. Clim Dyn, 2014, 42: 1243-1258 CrossRef ADS Google Scholar

[20] Levine A, Jin F F, McPhaden M J. Extreme noise-extreme El Niño: How state-dependent noise forcing creates El Niño-La Niña asymmetry. J Clim, 2016, 29: 5483-5499 CrossRef ADS Google Scholar

[21] Lee T, McPhaden M J. Increasing intensity of El Niño in the central-equatorial Pacific. Geophys Res Lett, 2010, 37: L14603 CrossRef ADS Google Scholar

[22] Lukas R, Lindstrom E. The mixed layer of the western equatorial Pacific Ocean. J Geophys Res, 1991, 96: 3343-3357 CrossRef ADS Google Scholar

[23] McPhaden M J. Ji M, Julian P, Meyers G, Mitchum G T. 1998. The tropical ocean-global atmosphere (TOGA) observing system: A decade of progress. J Geophys Res, 103: 169–14,240. Google Scholar

[24] McPhaden M J. Genesis and evolution of the 1997–98 El Niño. Science, 1999, 283: 950-954 CrossRef Google Scholar

[25] McPhaden M J. A 21st century shift in the relationship between ENSO SST and warm water volume anomalies. Geophys Res Lett, 2012, 39: L09706 CrossRef ADS Google Scholar

[26] McPhaden M J. Playing hide and seek with El Niño. Nat Clim Change, 2015, 5: 791-795 CrossRef ADS Google Scholar

[27] Meinen C S, McPhaden M J. Observations of warm water volume changes in the Equatorial Pacific and their relationship to El Niño and La Niña. J Clim, 2000, 13: 3551-3559 CrossRef Google Scholar

[28] Picaut J, Ioualalen M, Menkes C, Delcroix T, McPhaden M J. Mechanism of the zonal displacements of the Pacific Warm Pool: Implications for ENSO. Science, 1996, 274: 1486-1489 CrossRef ADS Google Scholar

[29] Reynolds R W, Rayner N A, Smith T M, Stokes D C, Wang W. An improved in situ and satellite SST analysis for climate. J Clim, 2002, 15: 1609-1625 CrossRef Google Scholar

[30] Shi L, Alves O, Wedd R, Balmaseda M A, Chang Y, Chepurin G, Ferry N, Fujii Y, Gaillard F, Good S A, Guinehut S, Haines K, Hernandez F, Lee T, Palmer M, Peterson K A, Masuda S, Storto A, Toyoda T, Valdivieso M, Vernieres G, Wang X, Yin Y. An assessment of upper ocean salinity content from the ocean reanalyses inter-comparison project (ORA-IP). Clim Dyn, 2015, : doi: 10.1007/s00382-015-2868-7 CrossRef ADS Google Scholar

[31] Stockdale T N, Anderson D L T, Balmaseda M A, Doblas-Reyes F, Ferranti L, Mogensen K, Palmer T N, Molteni F, Vitart F. ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn, 2011, 37: 455-471 CrossRef ADS Google Scholar

[32] Takahashi K, Martinez R, Montecinos A, Dewitte B, Gutiérrez D, Rodriguez-Rubio E. 2014. Regional applications of observations in the eastern Pacific. In: Report of the Tropical Pacific Observing System 2020 Workshop (TPOS 2020). Vol II. Scripps Institution of Oceanography, GCOS-184/GOOS-206/WCRP-6/2014. San Diego: United States Publication. 171–205. Google Scholar

[33] Toyoda T, Fujii Y, Yasuda T, Usui N, Iwao T, Kuragano T, Kamachi M. 2013. Improved analysis of the seasonal-interannual fields by a global ocean data assimilation system. Theoret Appl Mech Jpn, 61: 31–48. Google Scholar

[34] Trenberth K E, Branstator G W, Karoly D, Kumar A, Lau N C, Ropelewski C. Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J Geophys Res, 1998, 103: 14291-14324 CrossRef ADS Google Scholar

[35] Vernieres G, Keppenne C, Rienecker M M, Jacob J, Kovach R. 2012. The GEOS-ODAS, description and evaluation. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2012-104606, Vol. 30. Google Scholar

[36] Vialard J, Delecluse P. 1998. An OGCM study for the TOGA decade. Part II: Barrier-layer formation and variability. J Phys Oceanogr, 28: 1089–1106. Google Scholar

[37] Weisberg R H, Wang C. A Western Pacific oscillator paradigm for the El Niño-Southern Oscillation. Geophys Res Lett, 1997, 24: 779-782 CrossRef ADS Google Scholar

[38] Wen C, Kumar A, Xue Y, McPhaden M J. Changes in tropical Pacific thermocline depth and their relationship to ENSO after 1999. J Clim, 2014, 27: 7230-7249 CrossRef ADS Google Scholar

[39] Xie P, Boyer T, Bayler E, Xue Y, Byrne D, Reagan J, Locarnini R, Sun F, Joyce R, Kumar A. An in situ-satellite blended analysis of global sea surface salinity. J Geophys Res Oceans, 2014, 119: 6140-6160 CrossRef ADS Google Scholar

[40] Xue Y, Leetmaa A, Ji M. ENSO prediction with Markov Models: The impact of sea level. J Clim, 2000, 13: 849-871 CrossRef Google Scholar

[41] Xue Y, Huang B, Hu Z Z, Kumar A, Wen C, Behringer D, Nadiga S. An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn, 2011, 37: 2511-2539 CrossRef ADS Google Scholar

[42] Yin Y, Alves O, Oke P R. An ensemble ocean data assimilation system for seasonal prediction. Mon Weather Rev, 2011, 139: 786-808 CrossRef Google Scholar

[43] Zebiak S E. Oceanic heat content variability and El Niño cycles. J Phys Oceanogr, 1989, 19: 475-486 CrossRef Google Scholar

[44] Zhang S, Harrison M J, Rosati A, Wittenberg A. System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev, 2007, 135: 3541-3564 CrossRef ADS Google Scholar

[45] Zheng F, Zhang R H. Effects of interannual salinity variability and freshwater flux forcing on the development of the 2007/08 La Niña event diagnosed from Argo and satellite data. Dyn Atmos Oceans, 2012, 57: 45-57 CrossRef ADS Google Scholar

[46] Zhu J, Kumar A, Huang B, Balmaseda M A, Hu Z Z, Marx L, Kinter III J L. The role of off-equatorial surface temperature anomalies in the 2014 El Niño prediction. Sci Rep, 2016, 6: 19677 CrossRef PubMed ADS Google Scholar

  • Figure 1

    Time series of number of daily temperature profiles per month accumulated in the tropical Pacific within 8oS–8oN from TAO/TRITON (red line), Argo (green line), XBT (blue line) and TAO/TRITON/Argo/XBT together (black line) from January 1982 to March 2015 (a). Number of daily temperature profiles accumulated in 2012 (b), 2013 (c), 2014 (d) and 2015 (e).

  • Figure 2

    Longitude-time plot of (a) ensemble mean, (b) ensemble spread, and (c) absolute value of ensemble mean divided by ensemble spread of D20 anomaly (m) averaged over the 2oS–2oN band in the equatorial Pacific. The quantities shown are 3-month-running mean.

  • Figure 3

    Longitude-time plot of (a) zonal wind at 850 mb (u850), (b) sea surface temperature (SST) averaged in 2oS–2oN, and (c) outgoing longwave radiation (OLR) averaged in 5oS–5oN in the equatorial Pacific. The quantities shown are 3-pentad-running mean. The green line in (c) shows the position of 29oC water.

  • Figure 4

    Longitude-time plot of (a) SST anomaly, (b) zonal wind anomaly at 850 mb (u850), and (c) depth of 20oC anomaly (D20) averaged in 2oS–2oN in the equatorial Pacific. The quantities shown are 3 pentad-running mean.

  • Figure 5

    Average 850 mb zonal wind (black dash line) and SST anomaly (shading) in the (a) NINO4 and (b) NINO3 region. (c), (d) are the same as (a), (b) except for OLR (black dash line) and SST (shading) anomaly. The quantities shown are 3 month-running mean.

  • Figure 6

    NINO3.4 SST (black line) is overlaid with D20 anomaly (shading) for average in (a) (120oE–80oW, 5oS–5oN), (b) (120oE–155oW, 5oS–5oN), and (c) (155o–80oW, 5oS–5oN). The quantities shown are 3 month-running mean.

  • Figure 7

    From the top to bottom, seasonal mean anomaly for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS), October-November-December (OND) of 2015 and JFM 2016. (left panel) SST anomaly (oC, shading) overlaid with anomalous 850 mb wind vector (m/s, see label on the top right of each plot), (right panel) D20 anomaly (m, shading) overlaid with OLR anomaly (dash contours are for −70, −50, −30 −10, and solid contours for 10, 30, 50, 70).

  • Figure 8

    From the top to bottom, seasonal mean anomaly for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS), October-November-December (OND) of 1997 and JFM 1998. (left panel) SST anomaly (oC, shading) overlaid with anomalous 850 mb wind vector (m/s, see label on the top right of each plot), (right panel) D20 anomaly (m, shading) overlaid with OLR anomaly (dash contours are for −70, −50, −30 −10, and solid contours for 10, 30, 50, 70).

  • Figure 9

    From the top to bottom, seasonal mean anomaly for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS), October-November-December (OND) of the difference between the 2015/16 and 1997/98 El Niño. (left panel) SST anomaly (oC, shading) overlaid with anomalous 850 mb wind vector (m/s, see label on the top right of each plot), (right panel) D20 anomaly (m, shading) overlaid with OLR anomaly (dash contours are for −70, −50, −30 −10, and solid contours for 10, 30, 50, 70).

  • Figure 10

    From the top to bottom, seasonal mean anomaly for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS), October-November-December (OND) of 1982/83 El Niño. (left panel) SST anomaly (oC, shading) overlaid with anomalous 850 mb wind vector (m/s, see label on the top right of each plot), (right panel) D20 anomaly (m, shading) overlaid with OLR anomaly (dash contours are for −70, −50, −30 −10, and solid contours for 10, 30, 50, 70).

  • Figure 11

    From the top to bottom, seasonal mean anomaly for January-February−March (JFM), April-May-June (AMJ), July-August-September (JAS), October-November-December (OND) of the difference between the 2015/16 and 1982/83 El Niño. (left panel) SST anomaly (oC, shading) overlaid with anomalous 850 mb wind vector (m/s, see label on the top right of each plot), (right panel) D20 anomaly (m, shading) overlaid with OLR anomaly (dash contours are for −70, −50, −30 −10, and solid contours for 10, 30, 50, 70).

  • Table 1   List of ocean reanalysis products used in the studya)

    Product

    Forcing

    Ocean model

    Data assimilation method

    Ocean observations

    Analysis period

    NCEP

    (GODAS)

    NCEP-R2

    1°×1/3° MOM3

    3DVAR

    T/SST

    1979−present

    Behringer and Xue, 2004

    GFDL

    (ECDA)

    Coupled DA

    1o×1/3° MOM4

    EnKF

    T/S/SST

    1979−present

    Zhang et al., 2007

    BOM

    (PEODAS)

    ERA40 to 2002; NCEP-R2 thereafter

    1°×2°

    MOM2

    EnKF

    T/S/SST

    1970−present

    Yin et al., 2011

    ECMWF

    (ORAS4)

    ERA40 to 1988; ERAi thereafter

    1°×1/3° NEMO3

    3DVAR

    SLA/T/S/SST/SIC

    1979−present

    Balmaseda et al., 2013

    JMA

    (MOVE-G2)

    JRA55 corr +

    CORE Bulk

    1o×0.5° MRI.COM3

    3DVAR

    SLA/T/S/SST/SIC

    1979−present

    Toyoda et al., 2013

    NASA

    (MERRA Ocean)

    MERRA +

    Bulk

    0.5°×1/4° MOM4

    EnOI

    SLA/T/S/SST/SIC

    1979−present

    Vernieres et al., 2012

    a)The data assimilation column lists the observation types used for their estimation (T/S for temperature and salinity; SLA: altimeter-derived sea level anomalies; SST: sea surface temperature, SIC: sea-ice concentration), as well as assimilation techniques used for reanalysis: Ensemble Optimal Interpolation (EnOI), Ensemble Kalman Filter (EnKF), Variational methods (3DVar). The atmospheric surface forcing is usually provided by atmospheric reanalyses, using either direct daily fluxes, or different bulk formulations. There are also systems that use fluxes from coupled data assimilation systems (Coupled DA).

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