• Ashok, K., and T. Yamagata, 2009: Climate change: The El Niño with a difference. Nature, 461, 481484, doi:10.1038/461481a.

  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi:10.1029/2006JC003798.

    • Search Google Scholar
    • Export Citation
  • Battisti, D. S., and A. C. Hirst, 1989: Interannual variability in the tropical atmosphere/ocean system: Influence of the basic state, ocean geometry and nonlinearity. J. Atmos. Sci., 46, 16871712, doi:10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 18, 820829, doi:10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Busalacchi, A., and J. J. O’Brien, 1981: Interannual variability of the equatorial Pacific in the 1960s. J. Geophys. Res., 86, 10 90110 907, doi:10.1029/JC086iC11p10901.

    • Search Google Scholar
    • Export Citation
  • Cane, M. A., S. E. Zebiak, and S. C. Dolan, 1986: Experimental forecasts of El Niño. Nature, 321, 827832, doi:10.1038/321827a0.

  • Clarke, A. J., and S. Van Gorder, 2001: ENSO prediction using an ENSO trigger and a proxy for western equatorial pacific warm pool movement. Geophys. Res. Lett., 28, 579582, doi:10.1029/2000GL012201.

    • Search Google Scholar
    • Export Citation
  • Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander, and D. J. Vimont, 2010: Central Pacific El Niño and decadal climate change in the North Pacific Ocean. Nat. Geosci., 3, 762765, doi:10.1038/ngeo984.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., 2009: Multi-scale analysis of teleconnection indices: Climate noise and nonlinear trends analysis. Nonlinear Processes Geophys., 16, 6576, doi:10.5194/npg-16-65-2009.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., 2010: Long-range dependence and climate noise characteristics of Antarctic temperature data. J. Climate, 23, 60746081, doi:10.1175/2010JCLI3654.1.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., and T. Woollings, 2011: On the persistence and predictability properties of north Atlantic climate variability. J. Climate, 24, 466472, doi:10.1175/2010JCLI3739.1.

    • Search Google Scholar
    • Export Citation
  • Fu, C., and J. Fletcher, 1985: Two patterns of equatorial warming associated with El Niño. Chin. Sci. Bull., 30, 13601364.

  • Fu, C., H. F. Diaz, and J. O. Fletcher, 1986: Characteristics of the response of sea surface temperature in the central Pacific associated with warm episodes of the Southern Oscillation. Mon. Wea. Rev., 114, 17161739, doi:10.1175/1520-0493(1986)114<1716:COTROS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fu, C., C. Qian, and Z. Wu, 2011: Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach. Sci. China: Earth Sci., 54, 14001406, doi:10.1007/s11430-011-4235-9.

    • Search Google Scholar
    • Export Citation
  • Galanti, E., and E. Tziperman, 2000: ENSO’s phase locking to the seasonal cycle in the fast SST, fast-wave, and mixed-mode regimes. J. Atmos. Sci., 57, 29362950, doi:10.1175/1520-0469(2000)057<2936:ESPLTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hu, Z.-Z., B. Huang, J. L. Kinter III, Z. Wu, and A. Kumar, 2012a: Connection of the stratospheric QBO with global atmospheric general circulation and tropical SST. Part II: Interdecadal variations. Climate Dyn., 38, 2543, doi:10.1007/s00382-011-1073-6.

    • Search Google Scholar
    • Export Citation
  • Hu, Z.-Z., A. Kumar, B. Jha, W. Wang, B. Huang, and B. Huang, 2012b: An analysis of warm pool and cold tongue El Niños: Air–sea coupling processes, global influences, and recent trends. Climate Dyn., 38, 20172035, doi:10.1007/s00382-011-1224-9.

    • Search Google Scholar
    • Export Citation
  • Huang, B., Z.-Z. Hu, J. L. Kinter III, Z. Wu, and A. Kumar, 2012a: Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: Methodology and composite life cycle. Climate Dyn., 38, 123, doi:10.1007/s00382-011-1250-7.

    • Search Google Scholar
    • Export Citation
  • Huang, B., Z.-Z. Hu, E. K. Schneider, Z. Wu, Y. Xue, and B. Klinger, 2012b: Influences of subtropical air–sea interaction on the multidecadal AMOC variability in the NCEP climate forecast system. Climate Dyn., 39, 531555, doi:10.1007/s00382-011-1258-z.

    • Search Google Scholar
    • Export Citation
  • Huang, N. E., and Z. Wu, 2008: A review on Hilbert–Huang transform: Method and its applications on geophysical studies. Rev. Geophys.,46, RG2006, doi:10.1029/2007RG000228.

  • Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung, and H. H. Liu, 1998: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London, 454, 903995, doi:10.1098/rspa.1998.0193.

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

    • Search Google Scholar
    • Export Citation
  • Jin, F. F., 1997b: An equatorial ocean recharge paradigm for ENSO. Part II: A stripped-down coupled model. J. Atmos. Sci., 54, 830847, doi:10.1175/1520-0469(1997)054<0830:AEORPF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kao, H. Y., and J. Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615632, doi:10.1175/2008JCLI2309.1.

    • Search Google Scholar
    • Export Citation
  • Lee, T., and M. J. McPhaden, 2010: Increasing intensity of El Niño in the central-equatorial Pacific. Geophys. Res. Lett., 37, L14603, doi:10.1029/2010GL044007.

    • Search Google Scholar
    • Export Citation
  • Lengaigne, M. E., J. P. Boulanger, C. Menkes, P. Delecluse, P. Inness, J. Cole, and J. Slingo, 2004: Triggering of El Niño by westerly wind events in a coupled general circulation model. Climate Dyn., 23, 601620, doi:10.1007/s00382-004-0457-2.

    • Search Google Scholar
    • Export Citation
  • Luther, D. S., D. E. Harrison, and R. A. Knox, 1983: Zonal winds in the central equatorial Pacific and El Niño. Science, 222, 327330, doi:10.1126/science.222.4621.327.

    • Search Google Scholar
    • Export Citation
  • McCreary, J. P., 1976: Eastern tropical ocean response to changing wind systems with application to El Niño. J. Phys. Oceanogr., 6, 632645, doi:10.1175/1520-0485(1976)006<0632:ETORTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., H. P. Freitag, S. P. Hayes, B. A. Taft, Z. Chien, and K. Wyrtki, 1988: The response of the equatorial Pacific Ocean to a westerly wind burst in May 1986. J. Geophys. Res., 93 (C9), 10 58910 603, doi:10.1029/JC093iC09p10589.

    • Search Google Scholar
    • Export Citation
  • Misra, V., H. Li, Z. Wu, and S. DiNapoli, 2013: Global seasonal climate predictability in a two tiered forecast system: Part I: Boreal summer and fall seasons. Climate Dyn., 42, 1425–1448, doi:10.1007/s00382-013-1812-y.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., 2004: Our Affair with El Niño: How We Transformed an Enchanting Peruvian Current into a Global Climate Hazard. 1st ed. Princeton University Press, 296 pp.

    • Search Google Scholar
    • Export Citation
  • Qian, C., C. Fu, Z. Wu, and Z. Yan, 2009: On the secular change of spring onset in Stockholm. Geophys. Res. Lett.,36, L12706, doi:10.1029/2009GL038617.

  • Qian, C., Z. Wu, C. Fu, and T. J. Zhou, 2010: On multi-timescale variability of temperature in China in modulated annual cycle reference frame. Adv. Atmos. Sci., 27, 11691182, doi:10.1007/s00376-009-9121-4.

    • Search Google Scholar
    • Export Citation
  • Qian, C., C. Fu, and Z. Wu, 2011: Changes in the amplitude of the temperature annual cycle in China and their implication for climate change research. J. Climate, 24, 52925302, doi:10.1175/JCLI-D-11-00006.1.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in the tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384, doi:10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ruzmaikin, A., and J. Feynman, 2009: Search for climate trends in satellite data. Adv. Adapt. Data Anal., 1, 667679, doi:10.1142/S1793536909000266.

    • Search Google Scholar
    • Export Citation
  • Schopf, P. S., and M. J. Suarez, 1988: Vacillations in a coupled ocean–atmosphere model. J. Atmos. Sci., 45, 549566, doi:10.1175/1520-0469(1988)045<0549:VIACOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis. J. Climate, 21, 22832296, doi:10.1175/2007JCLI2100.1.

    • Search Google Scholar
    • Export Citation
  • Suarez, M. J., and P. S. Schopf, 1988: A delayed action oscillator for ENSO. J. Atmos. Sci., 45, 32833287, doi:10.1175/1520-0469(1988)045<3283:ADAOFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 27712777, doi:10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., Z. Kuang, P. Huybers, L. Yu, and G. Gebbie, 2007: On the predictability and dynamics of westerly wind bursts in the equatorial Pacific. Eos, Trans. Amer. Geophys. Union,88, (Fall Meeting. Suppl.), Abstract GC51A-0166.

  • Vecchio, A., and V. Carbone, 2010: Amplitude–frequency fluctuations of the seasonal cycle, temperature anomalies, and long-range persistence of climate records. Phys. Rev.,82E, 066101, doi:10.1103/PhysRevE.82.066101.

  • Vimont, D. J., D. S. Battisti, and A. C. Hirst, 2001: Footprinting: A seasonal connection between the tropics and mid-latitudes. Geophys. Res. Lett., 28, 39233926, doi:10.1029/2001GL013435.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., J. M. Wallace, and D. S. Battisti, 2003: The seasonal footprinting mechanism in the Pacific: Implications for ENSO. J. Climate, 16, 26682675, doi:10.1175/1520-0442(2003)016<2668:TSFMIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., E. M. Rasmusson, T. P. Mitchell, V. E. Kousky, and E. S. Sarachik, 1998: Variability in the tropical Pacific: Lessons from TOGA. J. Geophys. Res., 103 (C7), 14 24114 259, doi:10.1029/97JC02905.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., and N. E. Huang, 2009: Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal., 1, 141, doi:10.1142/S1793536909000047.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., N. E. Huang, S. R. Long, and C. K. Peng, 2007: On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci. USA, 104, 14 88914 894, doi:10.1073/pnas.0701020104.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., E. K. Schneider, B. P. Kirtman, E. S. Sarachik, N. E. Huang, and C. J. Tucker, 2008: The modulated annual cycle: An alternative reference frame for climate anomalies. Climate Dyn., 31, 823841, doi:10.1007/s00382-008-0437-z.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., N. E. Huang, and X. Chen, 2009: The multi-dimensional ensemble empirical mode decomposition method. Adv. Adapt. Data Anal., 1, 339372, doi:10.1142/S1793536909000187.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., N. E. Huang, J. M. Wallace, B. V. Smoliak, and X. Chen, 2011: On the time-varying trend in global-mean surface temperature. Climate Dyn., 37, 759773, doi:10.1007/s00382-011-1128-8.

    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1975: El Niño—The dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J. Phys. Oceanogr., 5, 572584, doi:10.1175/1520-0485(1975)005<0572:ENTDRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yeh, S. W., J. S. Kug, B. Dewitte, M. H. Kwon, B. P. Kirtman, and F. F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511514, doi:10.1038/nature08316.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., and H.-Y. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958–2001. J. Geophys. Res., 112, D13106, doi:10.1029/2006JD007654.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., H.-Y. Kao, and T. Lee, 2010: Subtropics-related interannual sea surface temperature variability in the equatorial central Pacific. J. Climate, 23, 28692884, doi:10.1175/2010JCLI3171.1.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., Y. Zou, S. T. Kim, and T. Lee, 2012: The changing impact of El Niño on US winter temperatures. Geophys. Res. Lett., 39, L15702, doi:10.1029/2012GL052483.

    • Search Google Scholar
    • Export Citation
  • Zebiak, S. E., and M. A. Cane, 1987: A model El Niño–Southern Oscillation. Mon. Wea. Rev., 115, 22622278, doi:10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., P. Chang, and L. Ji, 2009: Linking the Pacific meridional mode to ENSO: Coupled model analysis. J. Climate, 22, 34883505, doi:10.1175/2008JCLI2473.1.

    • Search Google Scholar
    • Export Citation
  • Zhu, J., B. Huang, and Z. Wu, 2012: The role of ocean dynamics in the interaction between the Atlantic meridional and equatorial modes. J. Climate, 25, 35833598, doi:10.1175/JCLI-D-11-00364.1.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Stacked plot of the SST (°C) averaged over the Niño-3.4 region (brown) and its EEMD components (blue). The thin black lines are the zero references.

  • View in gallery

    The alternative index (blue) and the conventional Niño-3.4 index from NOAA’s Climate Prediction Center (brown). Red lines indicate the threshold of ±0.5°C.

  • View in gallery

    SSTA evolution of the MEEMD interannual component on a 2–3-yr time scale over the equatorial Pacific of the 1957/58 EP ENSO warm event. The colorbar indicates the value of the SSTA (°C).

  • View in gallery

    As in Fig. 3, but for the development stage of the 1991/92 WCP ENSO warm event. Point A is at 20°N, 120°W, point B is at 0°, 180°, and point C is at 0°, 80°W. The black dashed line depicts a path of warming signal propagation from A to B to C.

  • View in gallery

    Hovmöller diagrams of the SSTA evolution of the interannual component on a 2–3-yr time scale. In total, 81 grid points are selected; the x axis denotes the ith (i = 1, 2, …, 81) grid point following the path from A to B to C shown in Fig. 4. The colorbar indicates the value of the SSTA (°C).

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 122 73 5
PDF Downloads 92 57 3

Sea Surface Temperature Anomalies off Baja California: A Possible Precursor of ENSO

View More View Less
  • 1 Department of Earth, Ocean and Atmospheric Science, and Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida
  • | 2 Department of Earth, Ocean and Atmospheric Science, The Florida State University, Tallahassee, Florida
© Get Permissions
Full access

Abstract

Many recent studies have shown that observed El Niño–Southern Oscillation (ENSO) events are spatially and temporally diverse and that they have undergone changes in characteristics. To quantitatively capture these features, multidimensional ensemble empirical mode decomposition (MEEMD) is employed to isolate the temporal–spatial evolution of the sea surface temperature anomalies (SSTAs) on naturally separated time scales. An alternative Niño-3.4 index is also defined to reflect more on the interannual variability of equatorial Pacific SSTAs. Using this alternative index, 27 ENSO warm events are identified and the spatial–temporal evolution of each event is examined. It is found that a patch of SSTAs off Baja California appears to extend southwestward and reach the equatorial region near the international date line in about 1 year. This warm signal then amplifies and extends eastward, developing into an ENSO warm event. This type of development has been dominant in recent decades. For this type of ENSO warm event, it appears that SSTAs off Baja California are instrumental to ENSO development, possibly serving as a precursor of an ENSO event.

Corresponding author address: Zhaohua Wu, Meteorology, Rm. 404 Love Bldg., The Florida State University, 1017 Academic Way, Tallahassee, FL 32306-4520. E-mail: zwu@fsu.edu

Abstract

Many recent studies have shown that observed El Niño–Southern Oscillation (ENSO) events are spatially and temporally diverse and that they have undergone changes in characteristics. To quantitatively capture these features, multidimensional ensemble empirical mode decomposition (MEEMD) is employed to isolate the temporal–spatial evolution of the sea surface temperature anomalies (SSTAs) on naturally separated time scales. An alternative Niño-3.4 index is also defined to reflect more on the interannual variability of equatorial Pacific SSTAs. Using this alternative index, 27 ENSO warm events are identified and the spatial–temporal evolution of each event is examined. It is found that a patch of SSTAs off Baja California appears to extend southwestward and reach the equatorial region near the international date line in about 1 year. This warm signal then amplifies and extends eastward, developing into an ENSO warm event. This type of development has been dominant in recent decades. For this type of ENSO warm event, it appears that SSTAs off Baja California are instrumental to ENSO development, possibly serving as a precursor of an ENSO event.

Corresponding author address: Zhaohua Wu, Meteorology, Rm. 404 Love Bldg., The Florida State University, 1017 Academic Way, Tallahassee, FL 32306-4520. E-mail: zwu@fsu.edu
Keywords: El Nino; ENSO

1. Introduction

The El Niño–Southern Oscillation (ENSO) is a quasi-periodic climate phenomenon occurring on an interannual time scale in the tropical Pacific. Bjerknes (1969) was the first to suggest that a coupling between the atmosphere and the ocean could lead to the amplification of warm/cold sea surface temperature anomalies (SSTAs). This type of coupling was integrated into a deterministic coupled ocean–atmosphere model of intermediate complexity (Cane et al. 1986; Zebiak and Cane 1987). This model was also used in the successful prediction of ENSO (Cane et al. 1986). Bjerknes’s work did not address the transition between ENSO warm and cold phases. Equatorial wave dynamics, the vertical thermal structure in the ocean, and the interaction between midlatitude ocean and tropical ocean were later added to the Bjerknes positive feedback mechanism to form more complete theories of oscillatory ENSO evolution. These theories include the delayed-oscillator model (Schopf and Suarez 1988; Suarez and Schopf 1988; Battisti and Hirst 1989), the recharge–discharge oscillatory model (Jin 1997a,b), and their reconciliation (Galanti and Tziperman 2000).

Although these models and theories have captured many features of ENSO, they cannot explain the strong amplitude modulation of ENSO (at times, the disappearance of ENSO could last for a decade) in the observations. To initiate a new ENSO cycle, a trigger may be necessary. Wyrtki (1975) suggested that the onset of SST warming is tied to an abrupt change in zonal wind stress, which was supported by several model simulations (McCreary 1976; Busalacchi and O’Brien 1981). This hypothesis of triggering was later confirmed by observational data (Luther et al. 1983; McPhaden et al. 1988) and elaborated in various types of models that emphasize the role of westerly wind bursts (Clarke and Van Gorder 2001; Lengaigne et al. 2004; Tziperman et al. 2007).

The studies referenced above have enhanced our understanding of the physics behind ENSO, especially those episodes summarized in the composites of early ENSO events (Rasmusson and Carpenter 1982; Wallace et al. 1998), which are characterized by the westward propagation of SSTAs from the eastern equatorial Pacific to the central and the western equatorial Pacific. Since 1985, a new type of ENSO has been observed in which the SSTAs are mostly centered in the central Pacific, whereas the propagation of SSTAs is not as evident (Fu and Fletcher 1985; Fu et al. 1986; Yu and Kao 2007; Ashok et al. 2007; Kao and Yu 2009; Ashok and Yamagata 2009). The spatial structure of this new type of ENSO, obtained by empirical orthogonal function (EOF) analysis or a regression approach (with a concept of footprinting), shows SSTAs extending from the central Pacific to Baja California (Vimont et al. 2001, 2003; Ashok et al. 2007; Kao and Yu 2009; Ashok and Yamagata 2009). The lagged correlation between SSTAs at the central Pacific and off Baja California implies that the SSTAs off Baja California may serve as a trigger for the development of central Pacific ENSO (Yu et al. 2010). Some model simulations further established the linkages between tropical and subtropical/high-latitude climate variability on decadal time scale and revealed the change of ENSO characteristics in response to global warming (Vimont et al. 2001, 2003; Zhang et al. 2009; Yeh et al. 2009; Di Lorenzo et al. 2010). Categorizing the central Pacific ENSO adds to our understanding of its phenomenological complexity; however, this categorization also induces challenges for understanding the physics of its origin and dynamics.

In this study, we take advantage of a recently developed analysis method—multidimensional ensemble empirical mode decomposition (MEEMD) (Wu et al. 2009)—to isolate the evolution of tropical Pacific SSTAs on naturally separated time scales, focusing on ENSO evolution, especially its developing stage, over the last 130 years. Previous studies using the aforementioned footprint methods cannot reveal the sequential development of ENSO events. Also, footprint methods often focus on the information accumulated over the whole temporal domain of data, which may lead to misinterpreting characteristics of a large-amplitude, temporally local event as general properties of the data. With MEEMD, we can address these two drawbacks. In addition, the extracted spatial–temporal evolution of SSTAs on different time scales helps to reveal the changes in ENSO characteristics over the last 130 years.

2. Data and methods

In this study, the monthly extended reconstructed sea surface temperature version 3b dataset (ERSST.v3b) (Smith et al. 2008) over the tropical Pacific Ocean (30°S–30°N, 120°E–74°W) is analyzed. To ensure stable reconstruction with sparse data, ERSST.v3b is generated by combining in situ SST data based on the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) release 2.4 with statistical methods. The dataset has 2° × 2° spatial resolution. The monthly SSTA of a particular month is calculated by removing the mean of SST of all months over the period 1880–2009.

In this study, MEEMD is used to extract the evolution of tropical Pacific SSTAs on naturally separated time scales. MEEMD, based on ensemble empirical mode decomposition (EEMD) (Huang et al. 1998; Huang and Wu 2008; Wu and Huang 2009), was developed to analyze multidimensional spatial–temporal data. EEMD and MEEMD have already been widely adopted in climate studies (Franzke 2009; Qian et al. 2009; Ruzmaikin and Feynman 2009; Franzke 2010; Qian et al. 2010; Vecchio and Carbone 2010; Franzke and Woollings 2011; Fu et al. 2011; Wu et al. 2011; Hu et al. 2012a; Huang et al. 2012a,b; Zhu et al. 2012; Misra et al. 2013).

In MEEMD, each time series at a grid point is decomposed using EEMD; that is, a time series x(t) is expressed as
e1
where cj(t) is an adaptively obtained amplitude–frequency-modulated oscillatory component and rn(t) is the residual of data x(t), which is either monotonic or contains only one extremum from which no additional oscillatory components can be extracted. EEMD is a sparse decomposition method: for any time series of length N, the decomposition results in fewer than log2N number of components. An example of this method of decomposition is displayed in Fig. 1 in which the spatially averaged SST over the Niño-3.4 region (with their temporal mean subtracted) is decomposed. In the decomposition process, the extrema of the input data (brown line) are identified and used to determine the highest-frequency riding wave over a changing lower-frequency reference. With such an identification, this riding wave (the blue line immediately below the original data) is extracted from the original data to form the first component of the original data. The remaining lower-frequency reference still contains oscillations of many different frequencies. Therefore, the decomposition process continues by taking the remaining reference time series as the new input. In this way, the riding waves of different frequencies are extracted level by level until the remaining reference time series becomes a secular trend that is either monotonic or contains only one extremum (Wu et al. 2007). All the components and the secular trend are displayed as the blue lines from the top to the bottom in Fig. 1, with a later component having a lower frequency than its predecessor. More details of the decomposition method can be found in Wu and Huang (2009).
Fig. 1.
Fig. 1.

Stacked plot of the SST (°C) averaged over the Niño-3.4 region (brown) and its EEMD components (blue). The thin black lines are the zero references.

Citation: Journal of the Atmospheric Sciences 71, 5; 10.1175/JAS-D-13-0397.1

On all naturally separated time scales, EEMD can catch small local differences in the temporal domain in neighboring grids [e.g., see Fig. 8 in Huang and Wu (2008)]; therefore, piecing together EEMD components on similar time scales at all grids tends to give the temporal evolution of the spatially coherent structure on naturally separated scales. This is the essence of MEEMD.

3. Results

a. Typical spatial–temporal evolution of ENSO

To identify an ENSO event in the historical record, we follow Wu et al. (2008) and Qian et al. (2011) to define a Niño-3.4 SSTA index that contains only interannual variability [the sum of the three interannual components (the third, fourth, and fifth blue lines from the top) displayed in Fig. 1]. An ENSO warm or cold episode is identified when the index exceeds ±0.5°C for at least 5 consecutive months (Trenberth 1997). Our new index avoids the difficulty of determining an appropriate reference for a climatological annual cycle for SSTA and the effect of global scale climate change on the ENSO index. The ENSO events identified with our index are mostly consistent with those identified with the traditional Niño-3.4 index from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center, as shown in Fig. 2, with the two indices having a correlation coefficient of 0.894.

Fig. 2.
Fig. 2.

The alternative index (blue) and the conventional Niño-3.4 index from NOAA’s Climate Prediction Center (brown). Red lines indicate the threshold of ±0.5°C.

Citation: Journal of the Atmospheric Sciences 71, 5; 10.1175/JAS-D-13-0397.1

The spatial–temporal evolution of interannual variability of tropical Pacific SSTAs over the entire temporal domain is extracted. Through further analysis of 1) the locations (e.g., Niño-1+2 region, date line equatorial region, etc.) of initial SSTAs (>0.2°C), 2) their amplification and zonal propagation, and 3) maximum SSTA strength over the Niño-3.4 region in the following months, we find four typical types of ENSO evolution:

  1. Eastern Pacific ENSO (EP): For this type of ENSO, its spatial–temporal evolution resembles the development of SSTA summarized by Rasmusson and Carpenter (1982). The noticeable (greater than 0.2°C) initial SSTA on a 2–3-yr time scale first appears in the Niño-1+2 region; it amplifies, propagates westward to the international date line, and develops into a mature ENSO warm event in about 1 year, as the 1957/58 event shown in Fig. 3.

  2. Eastern central Pacific ENSO (ECP): For this type of ENSO, the noticeable initial SSTA on a 2–3-yr time scale first appears in the eastern central Pacific (110°–150°W). This anomaly amplifies, propagates both eastward and westward, and develops into a mature ENSO warm event in the following months (figure not shown).

  3. Western central Pacific ENSO (WCP): For this type of ENSO, the noticeable initial SSTA on a 2–3-yr time scale first appears in the western central Pacific (west of 150°W). This anomaly amplifies, propagates eastward, and develops into a mature ENSO warm event in the following months, as the 1991/92 warm event displayed in Fig. 4. However, the development of such types of events is much more intriguing. About 1 year before the appearance of noticeable initial SSTA near the equatorial international date line, a small patch of warming signal first appears off Baja California. In the subsequent months, this warming signal extends southwestward to the equatorial date line, then strengthens after reaching the equatorial central Pacific. The spatial structure of the warming in the winter months of 1990/91 closely resembles the spatial structure of the central Pacific ENSO (Yu and Kao 2007; Ashok et al. 2007; Kao and Yu 2009; Ashok and Yamagata 2009).

  4. Mixed ENSO (MIX): For this type of ENSO, the noticeable initial SSTAs on a 2–3-yr time scale first appear independently in the Niño-1+2 region and western and/or eastern central Pacific regions, and subsequently develop and merge into a mature ENSO event (figure not shown).

Fig. 3.
Fig. 3.

SSTA evolution of the MEEMD interannual component on a 2–3-yr time scale over the equatorial Pacific of the 1957/58 EP ENSO warm event. The colorbar indicates the value of the SSTA (°C).

Citation: Journal of the Atmospheric Sciences 71, 5; 10.1175/JAS-D-13-0397.1

Fig. 4.
Fig. 4.

As in Fig. 3, but for the development stage of the 1991/92 WCP ENSO warm event. Point A is at 20°N, 120°W, point B is at 0°, 180°, and point C is at 0°, 80°W. The black dashed line depicts a path of warming signal propagation from A to B to C.

Citation: Journal of the Atmospheric Sciences 71, 5; 10.1175/JAS-D-13-0397.1

Table 1 presents the classification of ENSO warm events from 1880 to 2006 (with the 2009 event not listed, for its evolution is not finished by the end of the data we analyzed).

Table 1.

Classification of ENSO events. The first column is the order of events, the second column lists the year when an ENSO warm event occurred, the third column is the classification based on Yu et al. (2012), the fourth column is our new classification, the fifth column gives initial longitudes of SSTAs for different types of ENSO, and the last column indicates whether the SSTAs off Baja California serve as a precursor for an ENSO event. It is noted that the fifth column cells corresponding to MIX ENSOs contain multiple longitude numbers, for they start with multiple SSTAs centers.

Table 1.

b. A precursor of WCP and MIX ENSOs

The spatial–temporal evolution of interannual SSTAs displayed in Fig. 4 shows that the SSTAs off Baja California can serve as a precursor of the development of a WCP ENSO and some MIX ENSOs that have initial SSTAs near the international date line. For 11 ENSO events that have initial SSTAs near the date line equatorial region, 9 of them show that these initial SSTAs originate off Baja California. (see Table 1). Hovmöller diagrams of the SSTA evolution on a 2–3-yr time scale show the robustness of this result (Fig. 5). The spatial axis of these diagrams starts off Baja California (point A in Fig. 4), extends to the intersect point of the equator and the international date line (point B), and then further extends along the equator to the western coast of Central America (point C). The temporal axis covers the period from January 1980 to December 2009. This precursor appears to be robust (Fig. 5 and Table 1).

Fig. 5.
Fig. 5.

Hovmöller diagrams of the SSTA evolution of the interannual component on a 2–3-yr time scale. In total, 81 grid points are selected; the x axis denotes the ith (i = 1, 2, …, 81) grid point following the path from A to B to C shown in Fig. 4. The colorbar indicates the value of the SSTA (°C).

Citation: Journal of the Atmospheric Sciences 71, 5; 10.1175/JAS-D-13-0397.1

c. Changing ENSO

In sections 3a and 3b, we discussed the developments of four types of ENSO. Whether these four types of ENSO occurred sporadically over the whole temporal span of data or a particular type of ENSO was dominant in one lengthy subperiod is further analyzed. As mentioned earlier, the traditional footprint type of analysis cannot answer this question. Using spatially and temporally local decomposition, we can count the occurrences of each type of ENSO for the data period.

Table 1 summarizes the ENSO warm events since 1880 and their types, identified by examining the interannual evolution of SSTAs over the equatorial Pacific. For comparison, the classification of ENSO types in Yu et al. (2012) is also listed. It appears that the type of ENSO that has an initial SSTA of 2–3-yr variability near date line equatorial regions is dominant in the recent decades (after 1980), which is consistent with what was found in models (e.g., Yeh et al. 2009). In addition, the possible precursor of Baja California SSTAs can be found in almost all WCP and MIX ENSO events. The minor difference in the classification of ENSO types in Yu et al. (2012) and in this study might be due to 1) adopting different definitions of ENSO—their definition was based on total anomaly with respect to an arbitrary climatological annual cycle, whereas ours is based only on interannual variability—and 2) using different criteria in defining different types of events—their criteria emphasize the synchronized variability (corresponding to principle components of leading modes or their equivalents) over either eastern equatorial Pacific or central equatorial Pacific, whereas ours emphasize the evolution in the developing stage of individual events in different regions.

4. Summary and discussion

To examine the spatial–temporal evolution of ENSO, an adaptive and spatially and temporally local decomposition method (i.e., MEEMD) is employed to analyze SSTAs in the equatorial Pacific spanning the period 1880–2009. Our results show that there are four types of evolution for ENSO warm events: EP resembles the canonical ENSO evolution, with interannual SSTAs starting in the Niño-1+2 region and then amplifying and propagating westward; WCP starts with isolated interannual SSTAs off Baja California and then extends southwestward to the central Pacific prior to the development of a central Pacific ENSO; ECP starts with isolated interannual SSTAs in the eastern central Pacific and then amplifies and propagates both eastward and westward; and MIX is a mixed version of the previous three types. EP is dominant before 1910, ECP occurs most often between 1930 and 1970, WCP occurs after 1980, and MIX dominates between 1910 and 1930, as well as after 1970. The spatial structure of the mature stage in WCP and some of MIX evolutions resembles that of the central Pacific ENSO obtained in previous studies (Yu and Kao 2007; Ashok et al. 2007; Kao and Yu 2009; Ashok and Yamagata 2009). However, our new approach significantly improves our understanding of the early stages of ENSO by revealing how the central Pacific ENSO develops.

We also found that a patch of SSTAs often occurs off Baja California 1 year before it reaches the central Pacific region. This patch of SSTAs may serve as a precursor of the developments of both WCP and some MIX ENSOs and therefore bears significant prediction value, especially in the later decades when these types of ENSO events become much more frequent among all ENSO events.

The important dynamics associated with the initiation of WCP and some MIX ENSOs and subsequent development remains unclear. The more frequent occurrence of WCP and MIX in recent decades raises a question as to why there was a shift from conventional ENSO dominance to central Pacific ENSO dominance around 1980. It should also be noted that our classification of ENSO reconciles some differences in earlier ENSO classification studies (Philander 2004; Ashok et al. 2007; Yeh et al. 2009; Lee and McPhaden 2010; Hu et al. 2012b; Yu et al. 2012). Further studies are needed to understand the origin and the development of the central Pacific ENSO.

Acknowledgments

This work was supported by the Chinese Ministry of Science and Technology (Feng and Zou) under 973 Project 2010CB951600 and the U.S. National Science Foundation Grant AGS-1139479 (Feng and Wu).

REFERENCES

  • Ashok, K., and T. Yamagata, 2009: Climate change: The El Niño with a difference. Nature, 461, 481484, doi:10.1038/461481a.

  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi:10.1029/2006JC003798.

    • Search Google Scholar
    • Export Citation
  • Battisti, D. S., and A. C. Hirst, 1989: Interannual variability in the tropical atmosphere/ocean system: Influence of the basic state, ocean geometry and nonlinearity. J. Atmos. Sci., 46, 16871712, doi:10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 18, 820829, doi:10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Busalacchi, A., and J. J. O’Brien, 1981: Interannual variability of the equatorial Pacific in the 1960s. J. Geophys. Res., 86, 10 90110 907, doi:10.1029/JC086iC11p10901.

    • Search Google Scholar
    • Export Citation
  • Cane, M. A., S. E. Zebiak, and S. C. Dolan, 1986: Experimental forecasts of El Niño. Nature, 321, 827832, doi:10.1038/321827a0.

  • Clarke, A. J., and S. Van Gorder, 2001: ENSO prediction using an ENSO trigger and a proxy for western equatorial pacific warm pool movement. Geophys. Res. Lett., 28, 579582, doi:10.1029/2000GL012201.

    • Search Google Scholar
    • Export Citation
  • Di Lorenzo, E., K. M. Cobb, J. C. Furtado, N. Schneider, B. T. Anderson, A. Bracco, M. A. Alexander, and D. J. Vimont, 2010: Central Pacific El Niño and decadal climate change in the North Pacific Ocean. Nat. Geosci., 3, 762765, doi:10.1038/ngeo984.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., 2009: Multi-scale analysis of teleconnection indices: Climate noise and nonlinear trends analysis. Nonlinear Processes Geophys., 16, 6576, doi:10.5194/npg-16-65-2009.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., 2010: Long-range dependence and climate noise characteristics of Antarctic temperature data. J. Climate, 23, 60746081, doi:10.1175/2010JCLI3654.1.

    • Search Google Scholar
    • Export Citation
  • Franzke, C., and T. Woollings, 2011: On the persistence and predictability properties of north Atlantic climate variability. J. Climate, 24, 466472, doi:10.1175/2010JCLI3739.1.

    • Search Google Scholar
    • Export Citation
  • Fu, C., and J. Fletcher, 1985: Two patterns of equatorial warming associated with El Niño. Chin. Sci. Bull., 30, 13601364.

  • Fu, C., H. F. Diaz, and J. O. Fletcher, 1986: Characteristics of the response of sea surface temperature in the central Pacific associated with warm episodes of the Southern Oscillation. Mon. Wea. Rev., 114, 17161739, doi:10.1175/1520-0493(1986)114<1716:COTROS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fu, C., C. Qian, and Z. Wu, 2011: Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach. Sci. China: Earth Sci., 54, 14001406, doi:10.1007/s11430-011-4235-9.

    • Search Google Scholar
    • Export Citation
  • Galanti, E., and E. Tziperman, 2000: ENSO’s phase locking to the seasonal cycle in the fast SST, fast-wave, and mixed-mode regimes. J. Atmos. Sci., 57, 29362950, doi:10.1175/1520-0469(2000)057<2936:ESPLTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hu, Z.-Z., B. Huang, J. L. Kinter III, Z. Wu, and A. Kumar, 2012a: Connection of the stratospheric QBO with global atmospheric general circulation and tropical SST. Part II: Interdecadal variations. Climate Dyn., 38, 2543, doi:10.1007/s00382-011-1073-6.

    • Search Google Scholar
    • Export Citation
  • Hu, Z.-Z., A. Kumar, B. Jha, W. Wang, B. Huang, and B. Huang, 2012b: An analysis of warm pool and cold tongue El Niños: Air–sea coupling processes, global influences, and recent trends. Climate Dyn., 38, 20172035, doi:10.1007/s00382-011-1224-9.

    • Search Google Scholar
    • Export Citation
  • Huang, B., Z.-Z. Hu, J. L. Kinter III, Z. Wu, and A. Kumar, 2012a: Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: Methodology and composite life cycle. Climate Dyn., 38, 123, doi:10.1007/s00382-011-1250-7.

    • Search Google Scholar
    • Export Citation
  • Huang, B., Z.-Z. Hu, E. K. Schneider, Z. Wu, Y. Xue, and B. Klinger, 2012b: Influences of subtropical air–sea interaction on the multidecadal AMOC variability in the NCEP climate forecast system. Climate Dyn., 39, 531555, doi:10.1007/s00382-011-1258-z.

    • Search Google Scholar
    • Export Citation
  • Huang, N. E., and Z. Wu, 2008: A review on Hilbert–Huang transform: Method and its applications on geophysical studies. Rev. Geophys.,46, RG2006, doi:10.1029/2007RG000228.

  • Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung, and H. H. Liu, 1998: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London, 454, 903995, doi:10.1098/rspa.1998.0193.

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

    • Search Google Scholar
    • Export Citation
  • Jin, F. F., 1997b: An equatorial ocean recharge paradigm for ENSO. Part II: A stripped-down coupled model. J. Atmos. Sci., 54, 830847, doi:10.1175/1520-0469(1997)054<0830:AEORPF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kao, H. Y., and J. Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615632, doi:10.1175/2008JCLI2309.1.

    • Search Google Scholar
    • Export Citation
  • Lee, T., and M. J. McPhaden, 2010: Increasing intensity of El Niño in the central-equatorial Pacific. Geophys. Res. Lett., 37, L14603, doi:10.1029/2010GL044007.

    • Search Google Scholar
    • Export Citation
  • Lengaigne, M. E., J. P. Boulanger, C. Menkes, P. Delecluse, P. Inness, J. Cole, and J. Slingo, 2004: Triggering of El Niño by westerly wind events in a coupled general circulation model. Climate Dyn., 23, 601620, doi:10.1007/s00382-004-0457-2.

    • Search Google Scholar
    • Export Citation
  • Luther, D. S., D. E. Harrison, and R. A. Knox, 1983: Zonal winds in the central equatorial Pacific and El Niño. Science, 222, 327330, doi:10.1126/science.222.4621.327.

    • Search Google Scholar
    • Export Citation
  • McCreary, J. P., 1976: Eastern tropical ocean response to changing wind systems with application to El Niño. J. Phys. Oceanogr., 6, 632645, doi:10.1175/1520-0485(1976)006<0632:ETORTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., H. P. Freitag, S. P. Hayes, B. A. Taft, Z. Chien, and K. Wyrtki, 1988: The response of the equatorial Pacific Ocean to a westerly wind burst in May 1986. J. Geophys. Res., 93 (C9), 10 58910 603, doi:10.1029/JC093iC09p10589.

    • Search Google Scholar
    • Export Citation
  • Misra, V., H. Li, Z. Wu, and S. DiNapoli, 2013: Global seasonal climate predictability in a two tiered forecast system: Part I: Boreal summer and fall seasons. Climate Dyn., 42, 1425–1448, doi:10.1007/s00382-013-1812-y.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., 2004: Our Affair with El Niño: How We Transformed an Enchanting Peruvian Current into a Global Climate Hazard. 1st ed. Princeton University Press, 296 pp.

    • Search Google Scholar
    • Export Citation
  • Qian, C., C. Fu, Z. Wu, and Z. Yan, 2009: On the secular change of spring onset in Stockholm. Geophys. Res. Lett.,36, L12706, doi:10.1029/2009GL038617.

  • Qian, C., Z. Wu, C. Fu, and T. J. Zhou, 2010: On multi-timescale variability of temperature in China in modulated annual cycle reference frame. Adv. Atmos. Sci., 27, 11691182, doi:10.1007/s00376-009-9121-4.

    • Search Google Scholar
    • Export Citation
  • Qian, C., C. Fu, and Z. Wu, 2011: Changes in the amplitude of the temperature annual cycle in China and their implication for climate change research. J. Climate, 24, 52925302, doi:10.1175/JCLI-D-11-00006.1.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in the tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384, doi:10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ruzmaikin, A., and J. Feynman, 2009: Search for climate trends in satellite data. Adv. Adapt. Data Anal., 1, 667679, doi:10.1142/S1793536909000266.

    • Search Google Scholar
    • Export Citation
  • Schopf, P. S., and M. J. Suarez, 1988: Vacillations in a coupled ocean–atmosphere model. J. Atmos. Sci., 45, 549566, doi:10.1175/1520-0469(1988)045<0549:VIACOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis. J. Climate, 21, 22832296, doi:10.1175/2007JCLI2100.1.

    • Search Google Scholar
    • Export Citation
  • Suarez, M. J., and P. S. Schopf, 1988: A delayed action oscillator for ENSO. J. Atmos. Sci., 45, 32833287, doi:10.1175/1520-0469(1988)045<3283:ADAOFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 27712777, doi:10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., Z. Kuang, P. Huybers, L. Yu, and G. Gebbie, 2007: On the predictability and dynamics of westerly wind bursts in the equatorial Pacific. Eos, Trans. Amer. Geophys. Union,88, (Fall Meeting. Suppl.), Abstract GC51A-0166.

  • Vecchio, A., and V. Carbone, 2010: Amplitude–frequency fluctuations of the seasonal cycle, temperature anomalies, and long-range persistence of climate records. Phys. Rev.,82E, 066101, doi:10.1103/PhysRevE.82.066101.

  • Vimont, D. J., D. S. Battisti, and A. C. Hirst, 2001: Footprinting: A seasonal connection between the tropics and mid-latitudes. Geophys. Res. Lett., 28, 39233926, doi:10.1029/2001GL013435.

    • Search Google Scholar
    • Export Citation
  • Vimont, D. J., J. M. Wallace, and D. S. Battisti, 2003: The seasonal footprinting mechanism in the Pacific: Implications for ENSO. J. Climate, 16, 26682675, doi:10.1175/1520-0442(2003)016<2668:TSFMIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., E. M. Rasmusson, T. P. Mitchell, V. E. Kousky, and E. S. Sarachik, 1998: Variability in the tropical Pacific: Lessons from TOGA. J. Geophys. Res., 103 (C7), 14 24114 259, doi:10.1029/97JC02905.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., and N. E. Huang, 2009: Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal., 1, 141, doi:10.1142/S1793536909000047.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., N. E. Huang, S. R. Long, and C. K. Peng, 2007: On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci. USA, 104, 14 88914 894, doi:10.1073/pnas.0701020104.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., E. K. Schneider, B. P. Kirtman, E. S. Sarachik, N. E. Huang, and C. J. Tucker, 2008: The modulated annual cycle: An alternative reference frame for climate anomalies. Climate Dyn., 31, 823841, doi:10.1007/s00382-008-0437-z.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., N. E. Huang, and X. Chen, 2009: The multi-dimensional ensemble empirical mode decomposition method. Adv. Adapt. Data Anal., 1, 339372, doi:10.1142/S1793536909000187.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., N. E. Huang, J. M. Wallace, B. V. Smoliak, and X. Chen, 2011: On the time-varying trend in global-mean surface temperature. Climate Dyn., 37, 759773, doi:10.1007/s00382-011-1128-8.

    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1975: El Niño—The dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J. Phys. Oceanogr., 5, 572584, doi:10.1175/1520-0485(1975)005<0572:ENTDRO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yeh, S. W., J. S. Kug, B. Dewitte, M. H. Kwon, B. P. Kirtman, and F. F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511514, doi:10.1038/nature08316.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., and H.-Y. Kao, 2007: Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958–2001. J. Geophys. Res., 112, D13106, doi:10.1029/2006JD007654.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., H.-Y. Kao, and T. Lee, 2010: Subtropics-related interannual sea surface temperature variability in the equatorial central Pacific. J. Climate, 23, 28692884, doi:10.1175/2010JCLI3171.1.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., Y. Zou, S. T. Kim, and T. Lee, 2012: The changing impact of El Niño on US winter temperatures. Geophys. Res. Lett., 39, L15702, doi:10.1029/2012GL052483.

    • Search Google Scholar
    • Export Citation
  • Zebiak, S. E., and M. A. Cane, 1987: A model El Niño–Southern Oscillation. Mon. Wea. Rev., 115, 22622278, doi:10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., P. Chang, and L. Ji, 2009: Linking the Pacific meridional mode to ENSO: Coupled model analysis. J. Climate, 22, 34883505, doi:10.1175/2008JCLI2473.1.

    • Search Google Scholar
    • Export Citation
  • Zhu, J., B. Huang, and Z. Wu, 2012: The role of ocean dynamics in the interaction between the Atlantic meridional and equatorial modes. J. Climate, 25, 35833598, doi:10.1175/JCLI-D-11-00364.1.

    • Search Google Scholar
    • Export Citation
Save