• AchutaRao, K., and K. R. Sperber, 2002: Simulation of the El Niño Southern Oscillation: Results from the Coupled Model Intercomparison Project. Climate Dyn., 19 , 191209.

    • Search Google Scholar
    • Export Citation
  • Alves, O., and Coauthors, 2003: POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. Proc. National Drought Forum, Brisbane, Queensland, Australia, Department of Primary Industries, 49–56.

  • Baquero-Bernal, A., M. Latif, and S. Legutke, 2002: On dipole-like variability of sea surface temperature in the tropical Indian Ocean. J. Climate, 15 , 13581368.

    • Search Google Scholar
    • Export Citation
  • Birkett, C., R. Murtugudde, and T. Allan, 1999: Indian Ocean climate event bring floods to East Africa’s lakes and Sudd Marsh. Geophys. Res. Lett., 26 , 10311034.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmosphere teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97 , 163172.

  • Cai, W., H. H. Hendon, and G. Meyers, 2005: Indian Ocean dipole-like variability in the CSIRO Mark 3 coupled climate model. J. Climate, 18 , 14491468.

    • Search Google Scholar
    • Export Citation
  • Chambers, D. P., B. D. Tapley, and R. H. Stewart, 1999: Anomalous warming in the Indian Ocean coincident with El Niño. J. Geophys. Res., 104 , 30353047.

    • Search Google Scholar
    • Export Citation
  • Chen, D., L. M. Rothstein, and A. J. Busalacchi, 1994: A hybrid vertical mixing scheme and its application to tropical ocean models. J. Phys. Oceanogr., 24 , 21562179.

    • Search Google Scholar
    • Export Citation
  • Clarke, A. J., 1991: On the reflection and transmission low-frequency energy at the irregular western Pacific boundary. J. Geophys. Res., 96 , 32893305.

    • Search Google Scholar
    • Export Citation
  • Feng, M., and G. Meyers, 2003: Interannual variability in the tropical Indian Ocean: A two-year time scale of Indian Ocean Dipole. Deep-Sea Res., 50 , 22632284.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., and N. E. Graham, 1999: Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. J. Geophys. Res., 104 , D16,. 1909919116.

    • Search Google Scholar
    • Export Citation
  • Gualdi, S., E. Guilyardi, A. Navarra, S. Masina, and P. Delecluse, 2002: The interannual variability in the tropical Indian Ocean as simulated by a coupled GCM. Climate Dyn., 20 , 567582.

    • Search Google Scholar
    • Export Citation
  • Hackert, E. C., and S. Hastenrath, 1986: Mechanisms of Java rainfall anomalies. Mon. Wea. Rev., 114 , 745757.

  • Harrison, D. E., and G. A. Vecchi, 1999: On the termination of El Niño. Geophys. Res. Lett., 26 , 15931596.

  • Hastenrath, S., 2002: Dipoles, temperature gradients, and tropical climate anomalies. Bull. Amer. Meteor. Soc., 83 , 735738.

  • Hasternrath, S., and P. J. Lamb, 1979: Surface Climate and Circulation. Vol. 1, Climatic Atlas of the Indian Ocean, University of Wisconsin Press, 104 pp.

    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., A. Nicklis, and L. Greischar, 1993: Atmospheric–hydrospheric mechanisms of climate anomalies in the western equatorial Indian Ocean. J. Geophys. Res., 98 , 2021920235.

    • Search Google Scholar
    • Export Citation
  • Haylock, M., and J. McBride, 2001: Spatial coherence and predictability of Indonesian wet season rainfall. J. Climate, 14 , 38823887.

  • Hendon, H. H., 2003: Indonesian rainfall variability: Impacts of ENSO and local air–sea interaction. J. Climate, 16 , 17751790.

  • Hirst, A. C., and J. S. Godfrey, 1994: The response to a sudden change in Indonesian Throughflow in a global ocean GCM. J. Phys. Oceanogr., 24 , 18951910.

    • Search Google Scholar
    • Export Citation
  • Huang, B., and J. L. Kinter III, 2002: Interannual variability in the tropical Indian Ocean. J. Geophys. Res., 107 .3199, doi:10.1029/2001JC001278.

    • Search Google Scholar
    • Export Citation
  • Jin, F. F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54 , 811829.

  • Kessler, W. S., 2002: Is ENSO a cycle or a series of events? Geophys. Res. Lett., 29 .2125, doi:10.1029/2002GL015924.

  • Kiladis, G. N., and H. F. Diaz, 1989: Global climatic anomalies associated with extremes in the Southern Oscillation. J. Climate, 2 , 10691090.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., B. J. Soden, and N. C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12 , 917932.

    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, V., and B. P. Kirtman, 2003: Variability of the Indian Ocean: Relation to monsoon and ENSO. Quart. J. Roy. Meteor. Soc., 129 , 16231646.

    • Search Google Scholar
    • Export Citation
  • Lau, N. C., and M. J. Nath, 2003: Atmosphere–ocean variations in the Indo-Pacific sector during ENSO episode. J. Climate, 16 , 320.

  • Lau, N. C., and M. J. Nath, 2004: Coupled GCM simulation of atmosphere–ocean variability associated with zonally asymmetric SST changes in the tropical Indian Ocean. J. Climate, 17 , 245265.

    • Search Google Scholar
    • Export Citation
  • Levitus, S., 1982: Climatological Atlas of the World Ocean. U.S. Government Printing Office, 173 pp.

  • Li, T., Y. Zhang, E. Lu, and D. Wang, 2002: Relative role of dynamic and thermodynamic processes in the development of the Indian Ocean dipole: An OGCM diagnosis. Geophys. Res. Lett., 29 .2110, doi:10.1029/2002GL015789.

    • Search Google Scholar
    • Export Citation
  • Mechoso, C. R., and Coauthors, 1995: The seasonal cycle over the tropical Pacific in coupled ocean–atmosphere general circulation models. Mon. Wea. Rev., 123 , 28252838.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., P. R. Gent, J. M. Arblaster, B. L. Otto Bliesner, E. C. Brady, and A. Craig, 2001: Factors that affect the amplitude of El Niño in global coupled climate models. Climate Dyn., 17 , 515526.

    • Search Google Scholar
    • Export Citation
  • Meinen, C. S., and M. J. McPhaden, 2000: Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Niña. J. Climate, 13 , 35513559.

    • Search Google Scholar
    • Export Citation
  • Meyers, G., 1996: Variation of Indonesian throughflow and the El Niño–Southern Oscillation. J. Geophys. Res., 101 , 1225512263.

  • Murtugudde, R., and A. J. Busalacchi, 1999: Interannual variability in the Indian Ocean. J. Climate, 12 , 23002326.

  • Murtugudde, R., J. P. McCreary, and A. J. Busalacchi, 2000: Oceanic processes associated with anomalous events in the Indian Ocean with relevance to 1997–1998. J. Geophys. Res., 105 , 32953306.

    • Search Google Scholar
    • Export Citation
  • Nicholls, N., 1984: The Southern Oscillation and Indonesian sea surface temperature. Mon. Wea. Rev., 112 , 424432.

  • Pacanowski, R. C., 1995: MOM2 documentation user’s guide and reference manual, version 1.0. GFDL Tech. Rep. 3, 232 pp.

  • Potemra, J. T., 2001: The potential role of equatorial Pacific winds on southern tropical Indian Ocean Rossby waves. J. Geophys. Res., 106 , 24072422.

    • Search Google Scholar
    • Export Citation
  • Rao, S. A., S. K. Behera, Y. Masumoto, and T. Yamagata, 2002: Interannual subsurface variability in the tropical Indian Ocean with a special emphasis on the Indian Ocean Dipole. Deep-Sea Res., 49 , 15491572.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110 , 354384.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperatures, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108 .4407, doi:10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., and T. Yamagata, 2003: Structure of SST and surface wind variability during Indian Ocean Dipole Mode events: COADS observations. J. Climate, 16 , 27352751.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401 , 360363.

    • Search Google Scholar
    • Export Citation
  • Schiller, A., J. S. Godfrey, P. C. McIntosh, G. Meyers, and S. E. Wijffels, 1998: Seasonal near-surface dynamics and thermodynamics of the Indian Ocean and Indonesian Throughflow in a global ocean general circulation model. J. Phys. Oceanogr., 28 , 22882312.

    • Search Google Scholar
    • Export Citation
  • Schiller, A., J. S. Godfrey, P. C. McIntonsh, G. Meyers, and R. Fiedler, 2000: The interannual dynamics and thermodynamics of the Indo-Pacific Oceans. J. Phys. Oceanogr., 30 , 9871012.

    • Search Google Scholar
    • Export Citation
  • Schiller, A., J. S. Godfrey, P. C. McIntosh, G. Meyers, N. R. Smith, O. Alves, G. Wang, and R. Fiedler, 2002: A new version of the Australian Community Ocean Model for seasonal climate prediction. CSIRO Marine Laboratories Rep. 240, 82 pp.

  • Semtner, A. J., 1976: A model for the thermodynamic growth of sea ice in numerical investigations of climate. J. Phys. Oceanogr., 6 , 379389.

    • Search Google Scholar
    • Export Citation
  • Shinoda, T., M. A. Alexander, and H. H. Hendon, 2004a: Remote response of the Indian Ocean to interannual SST variations in the tropical Pacific. J. Climate, 17 , 362372.

    • Search Google Scholar
    • Export Citation
  • Shinoda, T., H. H. Hendon, and M. A. Alexander, 2004b: Surface and subsurface dipole variability in the Indian Ocean and its relation with ENSO. Deep-Sea Res., 51 , 619635.

    • Search Google Scholar
    • Export Citation
  • Valcke, S., L. Terray, and A. Piacentine, 2000: OASIS 2.4 ocean atmosphere sea ice soil user’s guide, version 2.4. CERFACS Tech. Rep. CERFACS TR.CMGC.00-10, 85 pp.

  • Venzke, S., M. Latif, and A. Villwock, 2000: The coupled GCM ECHO-2. Part II: Indian Ocean response to ENSO. J. Climate, 13 , 13711383.

    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and T. Li, 2003: Atmosphere–warm ocean interaction and its impacts on Asian–Australian monsoon variations. J. Climate, 16 , 11951211.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., A. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401 , 356360.

    • Search Google Scholar
    • Export Citation
  • Wilson, S. G., 2000: How ocean vertical mixing and accumulation of warm surface water influence the “sharpness” of the equatorial thermocline. J. Climate, 13 , 36383656.

    • Search Google Scholar
    • Export Citation
  • Xie, S. P., H. Annamalai, F. A. Schott, and J. P. McCreary, 2002: Structure and mechanism of south Indian Ocean climate variability. J. Climate, 15 , 864878.

    • Search Google Scholar
    • Export Citation
  • Zhong, A., R. Colman, N. Smith, M. Naughton, L. Rikus, K. Puri, and F. Tseitkin, 2001: Ten-year AMIP1 climatologies from versions of the BMRC Atmospheric Model. BMRC Research Rep. 83, 33 pp.

  • Zhong, A., O. Alves, A. Schiller, F. Tseitkin, and N. Smith, 2004: Results from a preliminary version of ACOM2/BAM coupled seasonal forecast model. BMRC Research Rep. 95, 32 pp.

  • View in gallery

    Annual mean ocean temperature in the upper 400 m along the equator from (a) observations (Levitus 1982) and (b) the coupled simulation. Contour interval is 1°C.

  • View in gallery

    Simulated mean precipitation (contours) and surface wind (vectors) in the tropical Indian Ocean for (a) DJF, (b) MAM, (c) JJA, and (d) SON. The vector scale is shown at the middle top. The contour interval is 2 mm day−1, with zero contours suppressed. Shading denotes rainfall exceeding 8 mm day−1.

  • View in gallery

    Mean annual cycle of SST (°C) with annual mean removed along (top) 0°–10°S in the Indian Ocean and (bottom) 2°S–2°N in the Pacific Ocean. (left) Observations from 1949 to 1998 (HadISST) and (right) the coupled simulation. Contour interval is 0.5°C. Negative values are shaded (dashed curve).

  • View in gallery

    Standard deviation of monthly SST anomalies for (a) observations from 1949 to 1998 (HadISST) and (b) coupled simulation. Contour interval is 0.1°C for values greater than 0.45°C and 0.05°C for values less than 0.45°C. Values greater than 0.45°C are shaded.

  • View in gallery

    First EOF of tropical Indo-Pacific SST monthly anomalies (SST1) for (a) observations from 1949 to 1998 (HadISST) and (b) the coupled simulation. Explained variance is indicated above each panel. The EOFs have been scaled for a one standard deviation anomaly of their respective principal components. Contour interval is 0.1°C with negative values shaded (dashed curve). (c) Mean annual cycle of the variance of PC1. (d) Lag correlation of SST1 and Niño-3.4 from observations (solid curve) and from the coupled simulation (dashed curve). Positive lag means SST1 leads Niño-3.4. A correlation of 0.2 is judged to be significant at the 95% level assuming 100 degrees of freedom (i.e., each year is independent).

  • View in gallery

    (a) EOF1 and (b) –2 of simulated Indo-Pacific heat content (HC) anomalies (i.e., mean temperature above 300 m). The EOFs have been scaled for a one standard deviation anomaly of their principal components. Contour interval is 0.1°C with negative values shaded (dashed curve). Explained variance is shown above each panel. (c) Mean annual cycle of the variance of HC1 and HC2. (d) Lag correlation of SST1 and HC1 (solid curve) and HC1 and HC2 (dashed curve). Positive lag means the first time series leads the second time series.

  • View in gallery

    Lag regression of surface wind stress (vectors, scale at top of panel) and HC anomalies (contours) onto SST1 in SON. Regression coefficients are scaled for a one standard anomaly of SST1. Lag is in seasons and positive lag mean SST1 leads. Contour interval is 0.1°C. Thick (thin) solid curves indicate positive (negative) values. Zero contours are suppressed. Heavy (light) shading denotes positive (negative) regression coefficients that are significant at the 95% level, assuming 100 degrees of freedom.

  • View in gallery

    As in Fig. 7, but for sea surface temperature regressed onto SST1 in SON. Contour interval is 0.1°C. Dotted–dashed curve indicates negative values.

  • View in gallery

    As in Fig. 8, but for rainfall regressed onto SST1 in SON. Contour interval is 0.5 mm day−1.

  • View in gallery

    (a) ENSO composite (warm–cold) of Niño-3.4 SST index (solid curve) and SST (dashed curve) and zonal wind stress (dotted curve) averaged over southeastern Indian Ocean (0°–10°S, 80°–100°E). The scale for Niño-3.4 and SST is degrees Celsius. The units for wind stress are 0.025 Nm−2. (b) Same as (a), except for the subsurface dipole composite. (c) ENSO composite of net surface heat flux (solid curve), latent heat flux (dotted curve), shortwave radiative flux (dashed curve), and total advection of heat (dotted–dashed curve) in eastern Indian Ocean (0°–10°S, 80°–100°E). (d) Same as (c), except for the subsurface dipole composite. Units in (c) and (d) are degrees Celsius per month.

  • View in gallery

    (a) The first EOF of heat content anomaly in SON for the tropical Indian Ocean. The explained variance is given on the top of the panel. The EOF has been scaled for a one standard deviation anomaly of the principal component. Contour interval is 0.1°C with negative values shaded (dashed curve). (b) Principal component (solid curve) and Niño-3.4 SST index (dashed curve). Both time series have been standardized.

  • View in gallery

    Lag regression of surface wind stress (vectors, scale at top of panel) and HC anomalies (contours) onto EOF1 of heat content in the Indian Ocean in SON for (a) lag −1 (JJA), (b) lag 0 (SON), and (c) lag +1 (DJF). The plotting convention is as in Fig. 7.

  • View in gallery

    Regression of SST and rainfall in SON onto EOF1 of heat content in the Indian Ocean in SON. Plotting convection is as in Figs. 8c and 9c.

  • View in gallery

    Correlation by month of UIOEq with Niño-3.4 SST index (dotted curve), UIOeq with SSTIOe (solid curve), and SSTIOe with HCIOe (long dashed curve). Thin dashed curves indicate significant correlations at the 95% significance level assuming 100 degrees of freedom (each year is independent). For clarity 1 ¼ annual cycle is shown beginning in Apr.

  • View in gallery

    Regression of sea level pressure (SLP) in JJA (lag −1) onto (a) HC1 and (b) SST1 in SON. Contour interval is 0.2 hPa with negative values shaded (dashed curve). Zero contours are suppressed. Significant regression coefficients are indicated as in Fig. 7.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 168 78 5
PDF Downloads 92 47 4

Indian Ocean Variability and Its Association with ENSO in a Global Coupled Model

View More View Less
  • 1 Bureau of Meteorology Research Centre, Melbourne, Australia
Full access

Abstract

The evolution of the Indian Ocean during El Niño–Southern Oscillation is investigated in a 100-yr integration of an Australian Bureau of Meteorology coupled seasonal forecast model. During El Niño, easterly anomalies are induced across the eastern equatorial Indian Ocean. These act to suppress the equatorial thermocline to the west and elevate it to the east and initially cool (warm) the sea surface temperature (SST) in the east (west). Subsequently, the entire Indian Ocean basin warms, mainly in response to the reduced latent heat flux and enhanced shortwave radiation that is associated with suppressed rainfall. This evolution can be partially explained by the excitation of an intrinsic coupled mode that involves a feedback between anomalous equatorial easterlies and zonal gradients in SST and rainfall. This positive feedback develops in the boreal summer and autumn seasons when the mean thermocline is shallow in the eastern equatorial Indian Ocean in response to trade southeasterlies. This positive feedback diminishes once the climatological surface winds become westerly at the onset of the Australian summer monsoon.

ENSO is the leading mechanism that excites this coupled mode, but not all ENSO events are efficient at exciting it. During the typical El Niño (La Niña) event, easterly (westerly) anomalies are not induced until after boreal autumn, which is too late in the annual cycle to instigate strong dynamical coupling. Only those ENSO events that develop early (i.e., before boreal summer) instigate a strong coupled response in the Indian Ocean. The coupled mode can also be initiated in early boreal summer by an equatorward shift of the subtropical ridge in the southern Indian Ocean, which stems from uncoupled extratropical variability.

* Current affiliation: National Meteorological and Oceanographic Centre, Melbourne, Australia

Corresponding author address: Harry H. Hendon, BMRC, GPO Box 1289K, Melbourne, VIC 3001, Australia. Email: h.hendon@bom.gov.au

Abstract

The evolution of the Indian Ocean during El Niño–Southern Oscillation is investigated in a 100-yr integration of an Australian Bureau of Meteorology coupled seasonal forecast model. During El Niño, easterly anomalies are induced across the eastern equatorial Indian Ocean. These act to suppress the equatorial thermocline to the west and elevate it to the east and initially cool (warm) the sea surface temperature (SST) in the east (west). Subsequently, the entire Indian Ocean basin warms, mainly in response to the reduced latent heat flux and enhanced shortwave radiation that is associated with suppressed rainfall. This evolution can be partially explained by the excitation of an intrinsic coupled mode that involves a feedback between anomalous equatorial easterlies and zonal gradients in SST and rainfall. This positive feedback develops in the boreal summer and autumn seasons when the mean thermocline is shallow in the eastern equatorial Indian Ocean in response to trade southeasterlies. This positive feedback diminishes once the climatological surface winds become westerly at the onset of the Australian summer monsoon.

ENSO is the leading mechanism that excites this coupled mode, but not all ENSO events are efficient at exciting it. During the typical El Niño (La Niña) event, easterly (westerly) anomalies are not induced until after boreal autumn, which is too late in the annual cycle to instigate strong dynamical coupling. Only those ENSO events that develop early (i.e., before boreal summer) instigate a strong coupled response in the Indian Ocean. The coupled mode can also be initiated in early boreal summer by an equatorward shift of the subtropical ridge in the southern Indian Ocean, which stems from uncoupled extratropical variability.

* Current affiliation: National Meteorological and Oceanographic Centre, Melbourne, Australia

Corresponding author address: Harry H. Hendon, BMRC, GPO Box 1289K, Melbourne, VIC 3001, Australia. Email: h.hendon@bom.gov.au

1. Introduction

The most prominent interannual variation of sea surface temperature (SST) in the tropical Indian Ocean is associated with El Niño–Southern Oscillation (ENSO). It is traditionally described as a basinwide warming that lags the warming in the eastern Pacific by a few months (e.g., Klein et al. 1999). The delayed basinwide warming, though primarily driven by surface heat flux anomalies that are remotely forced by SST anomalies in the equatorial eastern Pacific (Venzke et al. 2000; Lau and Nath 2003; Shinoda et al. 2004a), does drive rainfall variability around the Indian Ocean basin (e.g., Goddard and Graham 1999; Lau and Nath 2003).

The Indian Ocean does not just simply warm up in unison during ENSO (e.g., Rasmusson and Carpenter 1982; Nicholls 1984; Kiladis and Diaz 1989; Hastenrath et al. 1993; Huang and Kinter 2002; Hendon 2003; Krishnamurthy and Kirtman 2003; Wang et al. 2003). Rather, at the onset stages of El Niño (June–August), the far eastern equatorial Indian Ocean typically is anomalously cold. The western tropical Indian Ocean then begins to warm. Subsequently, the cold anomaly in the eastern Indian Ocean rapidly changes sign as El Niño matures in December–January, hence, resulting in a basin-scale warm anomaly that peaks in boreal spring as El Niño decays in the Pacific. This evolution of SST anomalies helps explain the rainfall anomalies in western Indonesia and eastern Africa during ENSO (e.g., Nicholls 1984; Hackert and Hastenrath 1986; Hastenrath et al. 1993; Haylock and McBride 2001; Hendon 2003).

The anomalous zonal SST gradient in the equatorial Indian Ocean that initially develops during ENSO is similar to the “Indian Ocean dipole” that has recently been described by Saji et al. (1999) and Webster et al. (1999). They envision this east–west dipole to be an expression of a coupled mode of variability in much the same manner as ENSO: near-equatorial easterly anomalies elevate the thermocline in the eastern Indian Ocean and drive off-equatorial downwelling that is communicated westward by Rossby waves (e.g., Murtugudde and Busalacchi 1999; Huang and Kinter 2002; Rao et al. 2002; Xie et al. 2002). Alongshore anticyclonic southerlies further promote upwelling and cooling along the Sumatra–Java coast. The anomalous zonal SST gradient that develops promotes reduced rainfall in the east and enhanced rainfall in the western portion of the basin (e.g., Birkett et al. 1999; Saji et al. 1999), thereby feeding back onto the equatorial easterly anomalies. The generation of oceanic Rossby waves provides some memory to the system (e.g., Xie et al. 2002). They may play a role, via reflection at the African coast into downwelling Kelvin waves that travel eastward onto the Java–Sumatra coast, in the demise or turnaround of a “dipole” event (Webster et al. 1999; Feng and Meyers 2003).

A vigorous debate has ensued concerning the existence, and independence from ENSO, of a coupled dipole mode (e.g., Hastenrath 2002; Saji and Yamagata 2003). This debate is of more than just academic interest because it bears on the ability to make long-range forecasts of climate variability in the Indian Ocean region. For instance, if interannual variability in the Indian Ocean basin is essentially completely dependant on ENSO, then the skill of the extended-range forecasts will depend primarily on the ability to forecast ENSO and to simulate the regional response. On the other hand, if local air–sea coupling in the Indian Ocean generates interannual variability via mechanisms that are independent of ENSO, then an improved understanding of its dynamics will be required in order to assess and improve its predictability.

Improved understanding of the role of air–sea coupling in the Indian Ocean and its role in global climate variability is hindered by the lack of comprehensive observations in the region and the occurrence of a trend in recent time series of SST (e.g., Saji and Yamagata 2003). As a step forward, analyses of coupled global climate models has provided insight into the mechanisms of air–sea coupling in the Indian Ocean with and without the external triggering by ENSO (e.g., Baquero-Bernal et al. 2002; Gualdi et al. 2002; Lau and Nath 2004; Shinoda et al. 2004a; Cai et al. 2005). The approach in the current study will be to carefully examine simulated variability in the Indian Ocean that is associated with ENSO, with intent to understand whether, and how, this variability may be attributed to the excitation of a coupled response. We will further try to isolate any intrinsic coupled variability in the Indian Ocean. The coupled model that is used here realistically simulates the amplitude and phase locking to the annual cycle of ENSO, and the mean state and seasonal cycle of the Indian Ocean. Hence, it is an appropriate tool to look at the Indian Ocean variability that is associated with ENSO.

The remainder of the paper is organized as follows: the coupled model is briefly described in section 2. In section 3, the capability of the model to simulate the mean and annual cycle of upper-ocean temperature, surface wind, and rainfall, which are indicative of the model’s ability to simulate realistic coupled behavior in the Tropics, is briefly assessed. Section 4 describes the simulated interannual variability in the Indian Ocean that is associated with ENSO. Coupled variability in the Indian Ocean is described in section 5. Finally, conclusions are provided in section 6.

2. Model details and experimental design

The coupled global model is based on the first version of the Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast model, which is used for routine seasonal forecasting by the Australian Bureau of Meteorology. The performance of the model in seasonal forecast mode is described by Alves et al. (2003) and Zhong et al. (2004). The key focus in the development of this model was the simulation of interannual variability in the tropical oceans and atmosphere.

a. Atmosphere model

The atmospheric component is a recent version of the Bureau of Meteorology Research Centre (BMRC) Atmospheric Model (BAM; version 3.1), and employs spectral horizontal truncation at T47 with 17 vertical levels. BAM is a unified climate–numerical weather prediction model and is used routinely for weather forecasting by the Bureau of Meteorology. Model details are given in Zhong et al. (2001).

b. Ocean model

The ocean component is the Australian Community Ocean Model version 2 (ACOM2) that was developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Marine Research (Schiller et al. 2002). It is based on the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model 2 (MOM2) code (Pacanowski 1995). Meridional spacing is 0.5° within 8° of the equator, increasing gradually to 1.5° near the poles. The zonal grid spacing is uniformly 2°. It has 25 vertical levels, with 12 of these in the top 185 m. Vertical mixing is based on Chen et al. (1994).

Schiller et al. (1998) put some effort into improving the simulation in the Indonesian Throughflow region. They modified the model’s topography and mixing in that area, allowing for a transport of water masses through the Lombok Strait and the Timor Sea. Because water flows from the Pacific to the Indian Ocean, tidally induced vertical mixing in the Indonesian archipelago is able to change the water mass structure of the Indian Ocean significantly. To simulate this observed feature, Schiller et al. gradually increased the vertical mixing coefficients (diffusion and viscosity) in the Indonesian region to a maximum of 2 × 10−4 m2 s−1 in the Banda Sea. The additional mixing is independent of time. That is, no attempt is made to resolve the time scales that are associated with its physical origin. This is legitimate as long as one is only concerned with its larger-time-scale effects on SST.

c. Sea ice and coupling

The sea ice model component is a version of the “zero layer” thermodynamic model of Semtner (1976). The atmospheric component (BAM3), the oceanic component (ACOM2), and the sea ice submodel are coupled through the Ocean Atmosphere Sea Ice Soil (OASIS) coupler (Valcke et al. 2000). Coupling occurs once daily.

d. Long coupled integration

The primary purpose of the extended integration was to investigate the inherent interannual variability in the tropical Pacific and Indian Oceans. The coupled model was run freely, with no flux correction, for 110 yr, starting with initial conditions that are representative of the state of the ocean–atmosphere system in early 1982. The atmospheric initial conditions were taken from an Atmospheric Model Intercomparision Project (AMIP)-style integration of the atmosphere model. The ocean initial conditions were taken from the POAMA ocean data assimilation scheme (Alves et al. 2003). Because the model is initialized with realistic initial conditions, it will drift. Most of the drift in the Tropics occurs in the first 3–4 yr. For this reason we exclude the first 10 yr and remove the weak remaining trend [e.g., ∼1 K (100 yr)−1 subsurface warming] prior to our analyses.

3. Simulation of mean and annual cycle

Simulating realistic mean atmospheric and oceanic states, as well as their annual cycle, in a nonflux-adjusted coupled model is a challenging task (e.g., Mechoso et al. 1995). Before analyzing the details of the interannual variability that are associated with ENSO, we first discuss briefly the main features of the mean state of the tropical atmosphere and oceans that bear on the model’s ability to realistically simulate ENSO and associated coupled variability in the Indian Ocean. These include the thermocline structure in the equatorial Indian and Pacific Oceans, the annual cycle of SST in the eastern portions of these basins, and the Australian–Asian monsoon.

Typical of many coupled models (e.g., Mechoso et al. 1995; Meehl et al. 2001), the simulated equatorial Pacific cold tongue is too narrow and extends too far west (not shown). In addition, a warm SST bias off of the coast of Peru results from a lack of upwelling and stratus cloud, which leads to an overestimation of downward solar radiation reaching the ocean surface. On the other hand, the zonal gradient of SST and stratification in the equatorial Pacific, which are important for the simulation of ENSO (e.g., Wilson 2000; Meehl et al. 2001), are reasonably simulated (Fig. 1). Comparison is made with observations from Levitus (1982). In response to weak mean westerly winds in the equatorial Indian Ocean, the model properly simulates a thermocline that slopes slightly upward to the west. The slope is slightly less and the thermocline is more diffuse than observed, which may result from weaker-than-observed westerlies during the Australian summer monsoon (see below).

The major features of the Australian–Asian monsoon are reasonably simulated (Fig. 2). Especially relevant to coupled behavior in the Indian Ocean are the strong southeastelies along the Java–Sumatra coast during June–July–August (JJA) and September–October–November (SON), which act to promote coastal upwelling during these seasons, and the aforementioned weak equatorial westerlies that drive an eastward Wyrtki jet and shut off upwelling along the Java–Sumatra coast during December–January–February (DJF). The strong southwesterly Somali jet during JJA also induces oceanic upwelling off of the African and Arabian coasts, producing cold SSTs and suppressed rainfall. A model deficiency is the lack of westerlies north of the equator in the transition seasons [March–April–May (MAM) and SON; e.g., Hastenrath and Lamb 1979]. This deficiency, which is reflected in the weaker-than-observed slope of the equatorial thermocline, presumably stems from the lack of oceanic rainfall to the north of the equator in the eastern Indian Ocean at these times.

Figures 3a,b show the annual cycle of SST averaged about 5°S in the Indian Ocean, which is the latitude where the strongest SST anomalies in the east are observed to occur (e.g., Saji et al. 1999), and where strong ocean–atmosphere coupling is likely to occur. The model is compared to observations that are derived from the Hadley Center–developed Global Sea Ice Coverage and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003) for the 50-yr period of 1949–98. The amplitude (order 1 K) and phasing of the annual cycle are well simulated. The coldest part of the year (June–October) coincides with maximum southeasterly trade winds, and the warmest part of the year (January–April) coincides with monsoonal westerly surface winds.

The annual cycle of SST in the equatorial Pacific, which is often of the wrong phase and is too weak in non-flux-corrected coupled climate models (e.g., AchutaRao and Sperber 2002), is reasonably simulated (Figs. 3c,d). In particular, the dominance of the annual harmonic, and its amplitude (order 2 K), phasing (coldest SST in the eastern Pacific in September), and westward phase progression are well captured. However, the annual cycle is too late in the east, which may delay the initial development of El Niño, because El Niño can be viewed as an amplification of the annual cycle (e.g., Rasmusson and Carpenter 1982). And, the annual cycle is too strong in the western Pacific, which is related to the westward extension of the cold tongue. Overall, however, this coupled model appears to simulate critical aspects of the mean state and its annual cycle that bear on the ability of the model to generate realistic interannual coupled behavior.

4. Indo-Pacific variability associated with ENSO

a. Spatial distribution of variance

The standard deviation of monthly SST, from the coupled model and from observations (HadISST), is displayed in Fig. 4. Maximum variability is simulated in the equatorial eastern Pacific, where ENSO’s influence is greatest. Compared to the observations, Pacific variability in the model is slightly reduced (maximum ∼1.0 K compared to observed ∼1.5 K), too equatorially confined, and extends along the equator too far into the western Pacific. These features are typical defects that are associated with coupled model simulations of ENSO (e.g., AchutaRao and Sperber 2002). On the other hand, maximum variability is simulated to occur in the eastern Pacific (∼120°W), which is not far west of the observed maximum but is farther east than some other coupled simulations.

In the tropical Indian Ocean three distinct features of the observations are simulated. Maximum variability occurs off of the Java–Sumatra coast and in the western Arabian Sea, which are regions of coastal upwelling during the Asian summer monsoon season. And, maximum variability occurs along the west Australian coast, which is a region dominated by ENSO variations in the Pacific that propagate through the Indonesian Throughflow as coastal trapped Kelvin waves (e.g., Meyers 1996). The magnitude of the SST variability also agrees well with observations (note that the amplitude is about 1/3 to 1/2 that in the equatorial eastern Pacific). The model also generates enhanced SST variability between the equator and 10°S that extends across much of the Indian Ocean basin. This may be associated with westward-propagating Rossby waves that interact with a region of mean open-ocean upwelling and shallow thermocline west of 80°E (e.g., Xie et al. 2002). The absence of a stronger maximum west of 80°E along ∼10°S in the model probably stems from weaker-than-observed mean equatorial westerlies, which results in reduced mean upwelling south of the equator and the mean thermocline in the equatorial Indian Ocean not tilting upward to the west as steeply as observed (Fig. 1).

b. Dominant modes of SST variability

Empirical orthogonal function (EOF) analysis is conducted to objectively identify the ENSO mode in the tropical Indo-Pacific region (30°S–30°N, 40°E–80°W). Model results that are based on monthly mean data are compared with EOFs derived from the HadISST anomalies for the 50-yr period of 1949–98. The leading mode of SST variability (EOF1) accounts for 25% of the variance in the model and 39% of the variance in the observations. None of the higher-order modes in both observations and model data individually explain more than 9% of the respective variability.

EOF1 from the model and observations depicts mature El Niño conditions (Figs. 5a,b), with maximum loading in the equatorial eastern Pacific surrounded by weaker, oppositely signed anomalies extending into the North and South Pacific. As expected from the spatial distribution of SST variance (Fig. 4), EOF1 from the model has stronger loading in the west Pacific than does that based on observations. The association of EOF1 with ENSO in both the observations and model is confirmed by the strong simultaneous correlation (greater than 0.95) of the principal component (PC) of EOF1 with the Niño-3.4 index (SST averaged 5°N–5°S, 170°–120°W; Fig. 5d).

The seasonality of the ENSO mode is diagnosed by computing the annual cycle of explained variance by EOF1 (Fig. 5c). The model realistically simulates the tendency for El Niño to peak at the end of the calendar year, though the model peaks slightly earlier than observed. The realistic annual phase locking of El Niño presumably reflects on the ability of the model to simulate a realistic annual cycle in SST in the Pacific (Fig. 3), because ENSO can be considered to be an amplification of the annual cycle in the eastern Pacific (e.g., Rasmusson and Carpenter 1982). However, the annual variation of the ENSO mode is stronger than observed, and its life cycle is shorter and overly biennial, as can be inferred from the autocorrelation of PC1 (Fig. 5d).

In the Indian Ocean, EOF1 from observations exhibits mostly positive loadings in the west and weaker negative loadings in the seas to the north of Australia. This structure typically precedes the basinwide warming that lags mature El Niño conditions in the Pacific by 3–4 months (e.g., Klein et al. 1999). The model exhibits a similar structure in the western Indian Ocean. In the east, the negative loading is stronger along the central Australian coast than observed. This strong negative perhaps reflects a model defect that is associated with excessive penetration of Pacific Rossby wave energy through the Indonesian Throughflow and onto the coast as a trapped Kelvin wave. This feature is discussed in more detail below.

c. Modes of subsurface variability

EOF analysis of the upper-ocean heat content (mean temperature above 300 m) is employed to explore the connection between SST and subsurface thermal variations. The first two EOFs of heat content from the model explain 27% and 14%, respectively, of the variance in the Indo-Pacific. The first mode (Fig. 6a) exhibits a zonal dipole structure in the equatorial Pacific, with maximum loading centered on the equator in the eastern Pacific and at ∼5° latitude in the western Pacific. This structure in the Pacific is consistent with that of a downwelling Kelvin wave that has propagated into the eastern Pacific and an upwellling Rossby wave that has just reached the western Pacific boundary. This EOF is similar to the leading EOF of the observed heat content (Meinen and McPhaden 2000), and is indicative of mature El Niño conditions. It exhibits a peak correlation (>0.9) with PC1 from the SST analysis (or, equivalently, with Niño-3.4) at zero lag (Fig. 6d). This first mode also exhibits a zonal dipole structure in the equatorial Indian Ocean, but with opposite tilt to that in the Pacific.

The second EOF of heat content has a more zonally symmetric structure in the Pacific (Fig. 6b), with anomalies centered at 10°–15° latitude flanking an opposite-signed anomaly along the equator. This mode also is similar to the second EOF of observed heat content and corresponds to the discharge (or recharge) phase of ENSO (e.g., Jin 1997). These first two EOFs of heat content also exhibit strong seasonal variation in amplitude (Fig. 6c), with EOF1 peaking in November and EOF2 peaking in June.

In the Indian Ocean EOF2 has a similar zonal dipole structure as EOF1, but now loadings along the central Australian coast are stronger and loadings west of Sumatra are weaker. The large loading along the west Australian coast is similar to that simulated by Schiller et al. (2000), using an ocean model forced with observed surface fluxes, and by Hirst and Godfrey (1994) 9–18 months after the Indonesian Throughflow was opened in their model.

The association of these first two EOFs of heat content with the systematic evolution of ENSO in the Pacific is confirmed by the cross correlation of their respective PCs (Fig. 6d). Maximum positive correlation (r = 0.69) occurs when the second mode lags the first by 5 months (discharge of heat along the equator in the Pacific), and most negative correlation (r = −0.46) occurs when the second mode leads the first by 4 months (recharge of heat along the equator). This asymmetry about zero lag suggests that a discharge (or recharge) of heat following an El Niño (La Niña) event is more systematic than is a recharge (discharge) of heat prior to an El Niño (La Niña) event. Kessler (2002) has reported a similar asymmetry based on observations.

The lag correlation between the leading two EOFs of the Indo-Pacific heat content also suggests a systematic evolution of heat content in the Indian Ocean during ENSO. At the mature phase of ENSO (Fig. 6a), heat content anomalies in the Indian Ocean are mostly confined to the equatorial region and exhibit a zonally out-of-phase behavior, which is opposite to that in the Pacific. Five months later (Fig. 6b) the eastern Indian Ocean anomaly weakens, and, because heat discharges off of the equator in the Pacific, the anomaly on the central Australian coast strengthens. This strengthening is consistent with the notion that the Rossby wave impinging on the western Pacific boundary leaks through the Indonesian Throughflow and southward onto the Australian coast as a coastal Kelvin wave (e.g., Clarke 1991; Meyers 1996; Potemra 2001).

d. Evolution associated with ENSO

A detailed description of the evolution of the Indian Ocean during ENSO is developed using lag regression with respect to SST EOF1 (or, equivalently, Niño-3.4) for the SON season, which is just prior to the peak of El Niño in the model (Fig. 5c). Regression coefficients are scaled for a one standard anomaly of EOF1. Regressions are shown for heat content and surface wind (Fig. 7), SST (Fig. 8), and rainfall (Fig. 9) for the JJA season (lag −1 season) through to the following MAM season (lag +2 seasons).

Because ENSO has a strong biennial component in the model, the previous MAM season (lag −2 season, not shown) depicts conditions at the demise of the previous La Niña event, and is similar to the opposite of conditions in the following MAM season (lag + 2 seasons; Figs. 7d, 8d and 9d). Hence, in the MAM season prior to the onset of El Niño, equatorial heat content in the equatorial Pacific is high and SST is below normal in the equatorial central Pacific and across much of the Indian Ocean. El Niño then evolves, consistent with a positive Bjerknes (1969) feedback in the equatorial Pacific and according to the delayed oscillator paradigm. Warm SST in the central and eastern Pacific in JJA (Fig. 8a) is associated with enhanced rainfall (Fig. 9a) and surface westerly anomalies, which act to suppress the equatorial thermocline (Fig. 7a) and warm the SST in the east. Off-equatorial upwelling Rossby waves, in response to the equatorial westerly anomalies in the central Pacific, impinge on the western Pacific boundary at lag 0 (SON, Fig. 7b). By lag +1 (DJF), these Rossby waves appear to reflect into upwelling eastward-propagating Kelvin waves, effectively discharging heat in the equatorial Pacific and bringing to an end the El Niño event in the MAM (Figs. 7d and 8d).

A close inspection of Fig. 7 also reveals that the equatorial surface westerly anomalies near the date line suddenly shift south in DJF (lag +1) prior to the demise of the SST anomaly (Fig. 8c), thereby yielding weaker westerly or even easterly anomalies in the western equatorial Pacific waveguide. This southward shift of the westerly anomalies effectively results in anomalous easterly stress forcing in the waveguide, thereby promoting an upwelling Kelvin wave that is apparent one season later (MAM; Fig. 7d). Such behavior has been identified in the observations (e.g., Harrison and Vecchi 1999) and has been postulated to explain the observed phase locking of the termination of El Niño. The southward shift of the westerly anomaly, even though the SST anomaly remains strong along the equator, apparently stems from the annual cycle of SST in the central Pacific: The warmest total SST shifts south of the equator in DJF, which, thus, provides an off-equatorial focus for anomalous convection (and westerlies) even in the presence of an equatorially symmetric SST anomaly.

In the Indian Ocean during the initial stages of El Niño (JJA, lag −1), little wind (Fig. 7a), and SST anomalies (Fig. 8a) are evident. However, rainfall is realistically suppressed in the Indian monsoon (Fig. 9a), and monsoonal winds in the southern Arabian Sea are anomalously weak. As El Niño intensifies and expands westward in the Pacific, easterly anomalies develop along the equator in the Indian Ocean in SON (lag 0, Fig. 7b). In response to these easterly anomalies, the equatorial thermocline in the Indian Ocean elevates to the east and deepens to the west (Figs. 7b,c). In conjunction, SSTs warm symmetrically about the equator in the western Indian Ocean and cool primarily south of the equator in the east (Figs. 8b,c). Rainfall anomalies then develop with a similar structure as the SST anomalies (Figs. 9b,c). In the southeast equatorial Indian Ocean where SST is cool and rainfall is decreased, the surface circulation is realistically anticyclonic (Figs. 7b,c; e.g., Wang et al. 2003), which promotes alongshore southerlies off the Java–Sumatra coast. This anticyclonic structure is consistent with the steady response of the atmosphere to a heat sink (reduced rainfall) that is displaced south in the equatorial eastern Indian Ocean and is further promoted by enhanced rainfall in the western Indian Ocean (e.g., Huang and Kinter 2002; Lau and Nath 2003; Shinoda et al. 2004a).

The anomalous zonal gradient of SST and heat content across the equatorial Indian Ocean peaks in DJF (lag +1; Figs. 7c and 8c), with the downwelling Rossby wave now appearing to impinge on the African coast. Also evident is the arrival of the upwelling Rossby wave from the Pacific, via the Indonesian Throughflow, onto the western Australian coast in the form of a coastally trapped Kelvin wave.

At lag +2 (MAM), the easterly anomalies in the Indian Ocean have weakened in conjunction with the weakening of the warm SST and westerly wind anomalies in the Pacific (Fig. 7d). The downwelling Rossby wave at the African coast now appears to reflect as a downwelling Kelvin wave, which is just apparent back on the Sumatra coast. Its arrival coincides with the decay of the cold SST anomaly in the east (Fig. 8d). Hence, at the decaying stages of El Niño, the entire tropical Indian Ocean is warm (except for the cold SST anomaly on the central west Australian coast that is associated with the aforementioned coastally trapped, upwelling Kelvin wave).

e. Comparison with observed ENSO variations

The simulated evolution of the Indian Ocean during ENSO bears many similarities to the observed behavior and suggests an active role for the Indian Ocean during ENSO. The simulated subsurface variations depicted in Fig. 7 are similar to those described by Chambers et al. (1999), who used satellite observations of sea level and SST to describe ENSO variations during the 1990s. The coupled model realistically simulates coldest SST anomaly in the eastern Indian Ocean, and, hence, most negative anomalous zonal SST gradient across the Indian Ocean, to occur late in the calendar year as El Niño matures in the Pacific (Figs. 8b,c). SSTs then rapidly warm up in the eastern Indian Ocean, realistically yielding a basinwide warm anomaly that peaks some 4–6 months after El Niño peaks (e.g., Klein et al. 1999; Huang and Kinter 2002). However, the cold anomaly in the eastern Indian Ocean in the JJA and SON seasons is weaker, less spatially extensive, and less anchored to the Java–Sumatra coast than observed (e.g., Hendon 2003). The rapid demise of the anomalous negative zonal SST gradient stems from the realistic rapid warming of the eastern Indian Ocean beginning in DJF (e.g., Hendon 2003), but the simulated eastern warming is slightly delayed compared to observations.

Analysis of the heat budget of the upper-30-m layer in the eastern equatorial Indian Ocean (0°–10°S, 80°–100°E) during the course of ENSO (Figs. 10a,c) confirms the realistic behavior of the model. The initial cooling in JJA results primarily from enhanced latent loss in conjunction with enhanced easterlies, which is consistent with the observed behavior (e.g., Li et al. 2002; Hendon 2003). Subsequently, because the easterlies strengthen in SON, oceanic advection (primarily vertical and zonal advection) acts to further cool the east (e.g., Huang and Kinter 2002; Murtugudde et al. 2000). Despite the continual strengthening of the easterly anomaly into DJF, the latent heat flux changes sign (becomes negative), and the advective tendency goes to zero (or becomes weakly positive). Strong enhanced shortwave radiation then results in rapid warming.

5. Coupled Indian Ocean variability

The evolution of the Indian Ocean during ENSO is suggestive of a coupled response, at least from September through December when anomalous equatorial easterlies coexist with anomalous zonal gradients of SST and rainfall. The question is, thus, raised as to whether the response during ENSO can be at least partially attributed to the excitation of a coupled mode that is intrinsic to the Indian Ocean. Furthermore, even though ENSO dominates the overall interannual variability in the Indian Ocean, ENSO accounts for less than 10% of the SST variance in the eastern Indian Ocean. This begs the question as to what other mechanisms drive variability in the eastern Indian Ocean and whether they result from coupling. Put another way, is there coupled variability in the Indian Ocean that operates independently from ENSO?

a. Coupled mode

To explore the nature of the variability in the Indian Ocean that may be coupled and not directly associated with ENSO, EOF analysis is performed on heat content in the tropical Indian Ocean (25°S–25°N). We focus on the SON season, because this is when dynamical coupling appears to be strongest and when anomalous zonal gradients in SST, heat content, and rainfall are most prominent (e.g., Figs. 7 –9). The first EOF of Indian Ocean heat content in SON (Fig. 11a) explains more than 1/2 of the heat content variance and has a pronounced zonal dipole structure. Off-equatorial maxima to the west have a horizontal structure that is similar to a Rossby wave, and the opposite-signed anomaly to the east is reminiscent of a Kelvin wave. Such a structure is similar to that which is simulated at the mature phase of El Niño (Figs. 7b,c), when easterly anomalies are well developed across the central equatorial Indian Ocean. However, EOF1 is only modestly correlated with Niño-3.4 (r = 0.4; Fig. 11b), which means that more than 80% of the variance of this mode is unaccounted by ENSO. Still, many El Niño events are evident in its PC time series (e.g., during years 55–70 and 85–95; Fig. 11b), but occasionally large El Niños are not evident in the PC time series (years 34 and 38). And, large excursions sometimes occur in the absence of El Niño (e.g., years 44 and 88). Thus, while this zonal dipole in heat content typically develops during ENSO, it also develops in the absence of ENSO. It is also interesting to note that the modest correlation of this leading EOF of heat content in the Indian Ocean with Niño-3.4 (∼0.4) is similar to that which is observed (e.g., Shinoda et al. 2004b).

The evolution of heat content and surface winds that are associated with this subsurface zonal dipole is depicted by lag correlation with respect to the PC of EOF1 (Fig. 12). The evolution is similar to that associated with the canonical El Niño (Fig. 7), but some notable discrepancies are apparent. As during El Niño, a coupled feedback in the equatorial Indian Ocean is suggested that drives the development of the zonal dipole in heat content in the JJA and SON seasons. But, in contrast to the typical El Niño event, development of a strong dipole is associated with easterly anomalies that are already present by JJA (Fig. 12a, cf. Fig. 7a; see also Saji and Yamagata 2003 and Shinoda et al. 2004b). The easterly anomalies in JJA and SON act to depress the thermocline to the west with the structure of a Rossby wave and elevate the thermocline to the east with the structure of a Kelvin wave. Southerly winds along the Java–Sumatra coast, associated with the anticyclonic surface circulation and suppressed rainfall over the cold SST in the eastern Indian Ocean in SON (Fig. 13), further act to promote upwelling and help cool the SST in the eastern Indian Ocean. However, in SON when the subsurface zonal dipole is most developed, the accompanying cold SST anomaly in the eastern Indian Ocean is now clearly anchored on the Java–Sumatra coast and is stronger and of a greater zonal extent than typically occurs during El Niño (cf. Fig. 8b). As during El Niño, a rapid loss of the positive feedback between zonal winds and SST gradient is apparent once the Australian summer monsoon commences in DJF.

b. Seasonal cycle and relation to ENSO

The correlation between the equatorial surface zonal wind in the central Indian Ocean (UIOEq; averaged 5°S–5°N, 70°–90°E) and SST in the eastern Indian Ocean (SSTIOE; averaged 0°–10°S, 80°–100°E) provides an indication of coupling of zonal wind with SST (Fig. 14). Outside of the Australian summer monsoon season (December–April) SSTIOE is strongly correlated with UIOEq, which is suggestive of a positive feedback. Colder SSTIOE is associated with stronger easterly anomalies, which then drive SSTIOE to be more negative by the mechanisms discussed in section 4. The seasonality of this feedback (weak during the Australian summer monsoon) can be understood by the correlation between the SSTIOE and heat content anomaly in the eastern Indian Ocean (HCIOE; averaged in same region as SSTIOE). The connection of the eastern thermocline variations (and UIOEq variations, because of their tight coupling) with the surface only occurs outside of the period of the Australian summer monsoon, that is, during May–December. This is when the trade southeasterlies prevail, resulting in mean upwelling and an elevated mean thermocline in the eastern Indian Ocean (Fig. 2).

The strongest impact of El Niño on the surface easterlies (negative correlation between UIOEq and Niño-3.4) occurs in January–February (Fig. 14), but this is when the correlation of SSTIOE with HCIOE is near zero. And, UIOeq and Niño-3.4 are uncorrelated from about May–August, which is when local coupling between the winds, thermocline, and SST in the Indian Ocean is peaking. Thus, the greatest influence of ENSO on the zonal wind in the Indian Ocean occurs well after the time of the year when local coupling is strong. This suggests both that model’s ENSO typically develops too late in the calendar year to fully tap into dynamical air–sea coupling in the eastern Indian Ocean and that local coupling earlier in the year (June–November) in the Indian Ocean is operating independent of ENSO.

The strong control by the seasonal cycle in the Indian Ocean on air–sea coupling is highlighted by examination of the upper-layer heat budget in the eastern Indian Ocean during strong subsurface dipole events (Figs. 10b,d). The initial (i.e., June) cooling in the east is driven by enhanced latent heat flux associated with anomalous easterlies. But, advective cooling rapidly increases, with the surface heat flux then becoming positive and acting to damp the cold anomaly. Thus, strong subsurface dipole events in the Indian Ocean are associated with strong dynamical cooling of SST in the east associated with equatorial easterly anomalies and a surface anticyclone in the southeast that is already developed by JJA. During the canonical El Niño, the easterlies do not commence until after September. While a subsurface zonal dipole is often associated with El Niño (Fig. 11b), it is not typically associated with a strong zonal SST gradient. Apparently it is only those El Niños that develop early (prior to JJA) that are able to initiate a strong anomalous zonal gradient in SST. The canonical El Niño does not generate easterly anomalies and an associated subsurface dipole until after September, which is too late to generate a strong coupled feedback.

c. Initiation of Indian Ocean coupled events

Development of a strong subsurface zonal dipole and associated anomalous zonal SST gradient in SON is associated with an antecedent surface anticyclone in the southeast Indian Ocean that is already well established in JJA −1 (Fig. 15a). During the typical El Niño, the negative swing of the Southern Oscillation also drives a surface anticyclone (Fig. 15b) and accompanying equatorial easterlies. But, during the typical El Niño event, the Southern Oscillation is only just beginning to swing negatively in JJA. Establishment of the antecedent anticyclonic easterly anomalies in the absence of El Niño appears to be associated with an equatorward shift of the subtropical jet/ridge over the southern Indian Ocean and a decrease in surface pressure along about 35°–40°S (not shown). Lau and Nath (2004) also report that development of a strong surface zonal SST dipole in another coupled model is preceded by a meridional shift in the subtropical jet/ridge. Here, however, the predecessor is an equatorward shift, while Lau and Nath report a poleward shift. Nonetheless, association of the onset of the subsurface/surface zonal dipole with a meridional shift in the large-scale extratropical circulation suggests that sustained large-scale forcing by the atmosphere can trigger a coupled response in the equatorial Indian Ocean.

6. Conclusions

Variability that is associated with ENSO dominates the tropical Indian Ocean in the BMRC coupled climate model. The El Niño signal is evident beginning in late boreal summer with cooling in the east and warming in the west, instigated by remotely forced surface easterly winds associated with the eastward displacement of the Walker circulation. Anomalous equatorially surface easterlies drive the thermocline down to the west and up to the east. Anomalous anticyclonic southeasterlies along the Java–Sumatra coast act to promote coastal upwelling and enhance latent heat flux. SST is further cooled in the east, thereby promoting reduced rainfall in the east and enhanced rainfall to the west and stronger equatorial easterlies. This positive feedback in the southeast Indian Ocean diminishes once the mean surface winds become westerly at the onset of the Australian summer monsoon. Then, the thermocline seasonally deepens and mean upwelling ceases in the east, thereby eliminating any communication of subsurface anomalies with the surface. Ultimately, the tropical Indian Ocean warms after El Niño matures in boreal winter. This evolution of the Indian Ocean, though largely explained by remotely forced surface heat flux variations, has the hallmark of a locally coupled response during boreal summer and autumn.

The Indian Ocean variability during El Niño also involves remotely driven ocean dynamics. The upwelling Rossby wave that impinges on the western Pacific boundary at the height of El Niño leaks through the Indonesian Throughflow, leading to the elevation of the thermocline and cooling of SST on the west Australian coast.

ENSO accounts for about ¼ of the coupled variability in the tropical Indian Ocean, which implies that the coupling of the equatorial zonal wind and the gradient of SST and rainfall occurs independent of ENSO. Furthermore, not all ENSO events strongly excite this coupled behavior. ENSO-induced equatorial zonal wind anomalies typically develop too late in the annual cycle to initiate strong dynamical coupling in the eastern Indian Ocean. Shinoda et al. (2004b) offer a similar explanation as to what distinguishes observed strong subsurface/surface zonal dipole events from the evolution during a typical El Niño event.

Independent of ENSO, a meridional shift of the subtropical jet/ridge in the southern Indian Ocean can also produce southeasterly wind anomalies in the central and eastern Indian Ocean that trigger a strong coupled response during boreal summer and autumn. This role for extratropical circulation anomalies for instigating coupled behavior in the tropical Indian Ocean and the relatively limited duration of positive air–sea feedback (commences in June and ends abruptly in December) implies that predictability of tropical Indian Ocean climate might be limited, especially in the absence of ENSO, to less than 6 months. Ongoing work is aimed at better understanding coupled behavior in the tropical Indian Ocean and its predictability and role in the global climate variability with model runs where ENSO is artificially suppressed.

Acknowledgments

This work was initiated when A. Zhong was employed in the Ocean and Marine Forecasting Group of BMRC. Discussions with Dr. S. Power, W. Cai, N. Smith, and R. Colman, and constructive reviews by S. Hastenrath and an anonymous reviewer, are appreciated. We are grateful to F. Tseitin and A. Sulaiman for computing support in setting up the coupled model. Support from the BMRC Model Development Group, Climate Dynamics Group, and Climate Forecasting Group is acknowledged.

REFERENCES

  • AchutaRao, K., and K. R. Sperber, 2002: Simulation of the El Niño Southern Oscillation: Results from the Coupled Model Intercomparison Project. Climate Dyn., 19 , 191209.

    • Search Google Scholar
    • Export Citation
  • Alves, O., and Coauthors, 2003: POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. Proc. National Drought Forum, Brisbane, Queensland, Australia, Department of Primary Industries, 49–56.

  • Baquero-Bernal, A., M. Latif, and S. Legutke, 2002: On dipole-like variability of sea surface temperature in the tropical Indian Ocean. J. Climate, 15 , 13581368.

    • Search Google Scholar
    • Export Citation
  • Birkett, C., R. Murtugudde, and T. Allan, 1999: Indian Ocean climate event bring floods to East Africa’s lakes and Sudd Marsh. Geophys. Res. Lett., 26 , 10311034.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmosphere teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97 , 163172.

  • Cai, W., H. H. Hendon, and G. Meyers, 2005: Indian Ocean dipole-like variability in the CSIRO Mark 3 coupled climate model. J. Climate, 18 , 14491468.

    • Search Google Scholar
    • Export Citation
  • Chambers, D. P., B. D. Tapley, and R. H. Stewart, 1999: Anomalous warming in the Indian Ocean coincident with El Niño. J. Geophys. Res., 104 , 30353047.

    • Search Google Scholar
    • Export Citation
  • Chen, D., L. M. Rothstein, and A. J. Busalacchi, 1994: A hybrid vertical mixing scheme and its application to tropical ocean models. J. Phys. Oceanogr., 24 , 21562179.

    • Search Google Scholar
    • Export Citation
  • Clarke, A. J., 1991: On the reflection and transmission low-frequency energy at the irregular western Pacific boundary. J. Geophys. Res., 96 , 32893305.

    • Search Google Scholar
    • Export Citation
  • Feng, M., and G. Meyers, 2003: Interannual variability in the tropical Indian Ocean: A two-year time scale of Indian Ocean Dipole. Deep-Sea Res., 50 , 22632284.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., and N. E. Graham, 1999: Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. J. Geophys. Res., 104 , D16,. 1909919116.

    • Search Google Scholar
    • Export Citation
  • Gualdi, S., E. Guilyardi, A. Navarra, S. Masina, and P. Delecluse, 2002: The interannual variability in the tropical Indian Ocean as simulated by a coupled GCM. Climate Dyn., 20 , 567582.

    • Search Google Scholar
    • Export Citation
  • Hackert, E. C., and S. Hastenrath, 1986: Mechanisms of Java rainfall anomalies. Mon. Wea. Rev., 114 , 745757.

  • Harrison, D. E., and G. A. Vecchi, 1999: On the termination of El Niño. Geophys. Res. Lett., 26 , 15931596.

  • Hastenrath, S., 2002: Dipoles, temperature gradients, and tropical climate anomalies. Bull. Amer. Meteor. Soc., 83 , 735738.

  • Hasternrath, S., and P. J. Lamb, 1979: Surface Climate and Circulation. Vol. 1, Climatic Atlas of the Indian Ocean, University of Wisconsin Press, 104 pp.

    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., A. Nicklis, and L. Greischar, 1993: Atmospheric–hydrospheric mechanisms of climate anomalies in the western equatorial Indian Ocean. J. Geophys. Res., 98 , 2021920235.

    • Search Google Scholar
    • Export Citation
  • Haylock, M., and J. McBride, 2001: Spatial coherence and predictability of Indonesian wet season rainfall. J. Climate, 14 , 38823887.

  • Hendon, H. H., 2003: Indonesian rainfall variability: Impacts of ENSO and local air–sea interaction. J. Climate, 16 , 17751790.

  • Hirst, A. C., and J. S. Godfrey, 1994: The response to a sudden change in Indonesian Throughflow in a global ocean GCM. J. Phys. Oceanogr., 24 , 18951910.

    • Search Google Scholar
    • Export Citation
  • Huang, B., and J. L. Kinter III, 2002: Interannual variability in the tropical Indian Ocean. J. Geophys. Res., 107 .3199, doi:10.1029/2001JC001278.

    • Search Google Scholar
    • Export Citation
  • Jin, F. F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54 , 811829.

  • Kessler, W. S., 2002: Is ENSO a cycle or a series of events? Geophys. Res. Lett., 29 .2125, doi:10.1029/2002GL015924.

  • Kiladis, G. N., and H. F. Diaz, 1989: Global climatic anomalies associated with extremes in the Southern Oscillation. J. Climate, 2 , 10691090.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., B. J. Soden, and N. C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12 , 917932.

    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, V., and B. P. Kirtman, 2003: Variability of the Indian Ocean: Relation to monsoon and ENSO. Quart. J. Roy. Meteor. Soc., 129 , 16231646.

    • Search Google Scholar
    • Export Citation
  • Lau, N. C., and M. J. Nath, 2003: Atmosphere–ocean variations in the Indo-Pacific sector during ENSO episode. J. Climate, 16 , 320.

  • Lau, N. C., and M. J. Nath, 2004: Coupled GCM simulation of atmosphere–ocean variability associated with zonally asymmetric SST changes in the tropical Indian Ocean. J. Climate, 17 , 245265.

    • Search Google Scholar
    • Export Citation
  • Levitus, S., 1982: Climatological Atlas of the World Ocean. U.S. Government Printing Office, 173 pp.

  • Li, T., Y. Zhang, E. Lu, and D. Wang, 2002: Relative role of dynamic and thermodynamic processes in the development of the Indian Ocean dipole: An OGCM diagnosis. Geophys. Res. Lett., 29 .2110, doi:10.1029/2002GL015789.

    • Search Google Scholar
    • Export Citation
  • Mechoso, C. R., and Coauthors, 1995: The seasonal cycle over the tropical Pacific in coupled ocean–atmosphere general circulation models. Mon. Wea. Rev., 123 , 28252838.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., P. R. Gent, J. M. Arblaster, B. L. Otto Bliesner, E. C. Brady, and A. Craig, 2001: Factors that affect the amplitude of El Niño in global coupled climate models. Climate Dyn., 17 , 515526.

    • Search Google Scholar
    • Export Citation
  • Meinen, C. S., and M. J. McPhaden, 2000: Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Niña. J. Climate, 13 , 35513559.

    • Search Google Scholar
    • Export Citation
  • Meyers, G., 1996: Variation of Indonesian throughflow and the El Niño–Southern Oscillation. J. Geophys. Res., 101 , 1225512263.

  • Murtugudde, R., and A. J. Busalacchi, 1999: Interannual variability in the Indian Ocean. J. Climate, 12 , 23002326.

  • Murtugudde, R., J. P. McCreary, and A. J. Busalacchi, 2000: Oceanic processes associated with anomalous events in the Indian Ocean with relevance to 1997–1998. J. Geophys. Res., 105 , 32953306.

    • Search Google Scholar
    • Export Citation
  • Nicholls, N., 1984: The Southern Oscillation and Indonesian sea surface temperature. Mon. Wea. Rev., 112 , 424432.

  • Pacanowski, R. C., 1995: MOM2 documentation user’s guide and reference manual, version 1.0. GFDL Tech. Rep. 3, 232 pp.

  • Potemra, J. T., 2001: The potential role of equatorial Pacific winds on southern tropical Indian Ocean Rossby waves. J. Geophys. Res., 106 , 24072422.

    • Search Google Scholar
    • Export Citation
  • Rao, S. A., S. K. Behera, Y. Masumoto, and T. Yamagata, 2002: Interannual subsurface variability in the tropical Indian Ocean with a special emphasis on the Indian Ocean Dipole. Deep-Sea Res., 49 , 15491572.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110 , 354384.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperatures, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108 .4407, doi:10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., and T. Yamagata, 2003: Structure of SST and surface wind variability during Indian Ocean Dipole Mode events: COADS observations. J. Climate, 16 , 27352751.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401 , 360363.

    • Search Google Scholar
    • Export Citation
  • Schiller, A., J. S. Godfrey, P. C. McIntosh, G. Meyers, and S. E. Wijffels, 1998: Seasonal near-surface dynamics and thermodynamics of the Indian Ocean and Indonesian Throughflow in a global ocean general circulation model. J. Phys. Oceanogr., 28 , 22882312.

    • Search Google Scholar
    • Export Citation
  • Schiller, A., J. S. Godfrey, P. C. McIntonsh, G. Meyers, and R. Fiedler, 2000: The interannual dynamics and thermodynamics of the Indo-Pacific Oceans. J. Phys. Oceanogr., 30 , 9871012.

    • Search Google Scholar
    • Export Citation
  • Schiller, A., J. S. Godfrey, P. C. McIntosh, G. Meyers, N. R. Smith, O. Alves, G. Wang, and R. Fiedler, 2002: A new version of the Australian Community Ocean Model for seasonal climate prediction. CSIRO Marine Laboratories Rep. 240, 82 pp.

  • Semtner, A. J., 1976: A model for the thermodynamic growth of sea ice in numerical investigations of climate. J. Phys. Oceanogr., 6 , 379389.

    • Search Google Scholar
    • Export Citation
  • Shinoda, T., M. A. Alexander, and H. H. Hendon, 2004a: Remote response of the Indian Ocean to interannual SST variations in the tropical Pacific. J. Climate, 17 , 362372.

    • Search Google Scholar
    • Export Citation
  • Shinoda, T., H. H. Hendon, and M. A. Alexander, 2004b: Surface and subsurface dipole variability in the Indian Ocean and its relation with ENSO. Deep-Sea Res., 51 , 619635.

    • Search Google Scholar
    • Export Citation
  • Valcke, S., L. Terray, and A. Piacentine, 2000: OASIS 2.4 ocean atmosphere sea ice soil user’s guide, version 2.4. CERFACS Tech. Rep. CERFACS TR.CMGC.00-10, 85 pp.

  • Venzke, S., M. Latif, and A. Villwock, 2000: The coupled GCM ECHO-2. Part II: Indian Ocean response to ENSO. J. Climate, 13 , 13711383.

    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and T. Li, 2003: Atmosphere–warm ocean interaction and its impacts on Asian–Australian monsoon variations. J. Climate, 16 , 11951211.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., A. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401 , 356360.

    • Search Google Scholar
    • Export Citation
  • Wilson, S. G., 2000: How ocean vertical mixing and accumulation of warm surface water influence the “sharpness” of the equatorial thermocline. J. Climate, 13 , 36383656.

    • Search Google Scholar
    • Export Citation
  • Xie, S. P., H. Annamalai, F. A. Schott, and J. P. McCreary, 2002: Structure and mechanism of south Indian Ocean climate variability. J. Climate, 15 , 864878.

    • Search Google Scholar
    • Export Citation
  • Zhong, A., R. Colman, N. Smith, M. Naughton, L. Rikus, K. Puri, and F. Tseitkin, 2001: Ten-year AMIP1 climatologies from versions of the BMRC Atmospheric Model. BMRC Research Rep. 83, 33 pp.

  • Zhong, A., O. Alves, A. Schiller, F. Tseitkin, and N. Smith, 2004: Results from a preliminary version of ACOM2/BAM coupled seasonal forecast model. BMRC Research Rep. 95, 32 pp.

Fig. 1.
Fig. 1.

Annual mean ocean temperature in the upper 400 m along the equator from (a) observations (Levitus 1982) and (b) the coupled simulation. Contour interval is 1°C.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 2.
Fig. 2.

Simulated mean precipitation (contours) and surface wind (vectors) in the tropical Indian Ocean for (a) DJF, (b) MAM, (c) JJA, and (d) SON. The vector scale is shown at the middle top. The contour interval is 2 mm day−1, with zero contours suppressed. Shading denotes rainfall exceeding 8 mm day−1.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 3.
Fig. 3.

Mean annual cycle of SST (°C) with annual mean removed along (top) 0°–10°S in the Indian Ocean and (bottom) 2°S–2°N in the Pacific Ocean. (left) Observations from 1949 to 1998 (HadISST) and (right) the coupled simulation. Contour interval is 0.5°C. Negative values are shaded (dashed curve).

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 4.
Fig. 4.

Standard deviation of monthly SST anomalies for (a) observations from 1949 to 1998 (HadISST) and (b) coupled simulation. Contour interval is 0.1°C for values greater than 0.45°C and 0.05°C for values less than 0.45°C. Values greater than 0.45°C are shaded.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 5.
Fig. 5.

First EOF of tropical Indo-Pacific SST monthly anomalies (SST1) for (a) observations from 1949 to 1998 (HadISST) and (b) the coupled simulation. Explained variance is indicated above each panel. The EOFs have been scaled for a one standard deviation anomaly of their respective principal components. Contour interval is 0.1°C with negative values shaded (dashed curve). (c) Mean annual cycle of the variance of PC1. (d) Lag correlation of SST1 and Niño-3.4 from observations (solid curve) and from the coupled simulation (dashed curve). Positive lag means SST1 leads Niño-3.4. A correlation of 0.2 is judged to be significant at the 95% level assuming 100 degrees of freedom (i.e., each year is independent).

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 6.
Fig. 6.

(a) EOF1 and (b) –2 of simulated Indo-Pacific heat content (HC) anomalies (i.e., mean temperature above 300 m). The EOFs have been scaled for a one standard deviation anomaly of their principal components. Contour interval is 0.1°C with negative values shaded (dashed curve). Explained variance is shown above each panel. (c) Mean annual cycle of the variance of HC1 and HC2. (d) Lag correlation of SST1 and HC1 (solid curve) and HC1 and HC2 (dashed curve). Positive lag means the first time series leads the second time series.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 7.
Fig. 7.

Lag regression of surface wind stress (vectors, scale at top of panel) and HC anomalies (contours) onto SST1 in SON. Regression coefficients are scaled for a one standard anomaly of SST1. Lag is in seasons and positive lag mean SST1 leads. Contour interval is 0.1°C. Thick (thin) solid curves indicate positive (negative) values. Zero contours are suppressed. Heavy (light) shading denotes positive (negative) regression coefficients that are significant at the 95% level, assuming 100 degrees of freedom.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for sea surface temperature regressed onto SST1 in SON. Contour interval is 0.1°C. Dotted–dashed curve indicates negative values.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 9.
Fig. 9.

As in Fig. 8, but for rainfall regressed onto SST1 in SON. Contour interval is 0.5 mm day−1.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 10.
Fig. 10.

(a) ENSO composite (warm–cold) of Niño-3.4 SST index (solid curve) and SST (dashed curve) and zonal wind stress (dotted curve) averaged over southeastern Indian Ocean (0°–10°S, 80°–100°E). The scale for Niño-3.4 and SST is degrees Celsius. The units for wind stress are 0.025 Nm−2. (b) Same as (a), except for the subsurface dipole composite. (c) ENSO composite of net surface heat flux (solid curve), latent heat flux (dotted curve), shortwave radiative flux (dashed curve), and total advection of heat (dotted–dashed curve) in eastern Indian Ocean (0°–10°S, 80°–100°E). (d) Same as (c), except for the subsurface dipole composite. Units in (c) and (d) are degrees Celsius per month.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 11.
Fig. 11.

(a) The first EOF of heat content anomaly in SON for the tropical Indian Ocean. The explained variance is given on the top of the panel. The EOF has been scaled for a one standard deviation anomaly of the principal component. Contour interval is 0.1°C with negative values shaded (dashed curve). (b) Principal component (solid curve) and Niño-3.4 SST index (dashed curve). Both time series have been standardized.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 12.
Fig. 12.

Lag regression of surface wind stress (vectors, scale at top of panel) and HC anomalies (contours) onto EOF1 of heat content in the Indian Ocean in SON for (a) lag −1 (JJA), (b) lag 0 (SON), and (c) lag +1 (DJF). The plotting convention is as in Fig. 7.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 13.
Fig. 13.

Regression of SST and rainfall in SON onto EOF1 of heat content in the Indian Ocean in SON. Plotting convection is as in Figs. 8c and 9c.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 14.
Fig. 14.

Correlation by month of UIOEq with Niño-3.4 SST index (dotted curve), UIOeq with SSTIOe (solid curve), and SSTIOe with HCIOe (long dashed curve). Thin dashed curves indicate significant correlations at the 95% significance level assuming 100 degrees of freedom (each year is independent). For clarity 1 ¼ annual cycle is shown beginning in Apr.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Fig. 15.
Fig. 15.

Regression of sea level pressure (SLP) in JJA (lag −1) onto (a) HC1 and (b) SST1 in SON. Contour interval is 0.2 hPa with negative values shaded (dashed curve). Zero contours are suppressed. Significant regression coefficients are indicated as in Fig. 7.

Citation: Journal of Climate 18, 17; 10.1175/JCLI3493.1

Save