• Agudelo, P. A., J. A. Curry, C. D. Hoyos, and P. J. Webster, 2006: Transition between suppressed and active phases of intraseasonal oscillations in the Indo-Pacific warm pool. J. Climate, 19, 55195530, https://doi.org/10.1175/JCLI3924.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ajayamohan, R. S., H. Annamalai, J.-J. Luo, J. Hafner, and T. Yamagata, 2011: Poleward propagation of boreal summer intraseasonal oscillations in a coupled model: Role of internal processes. Climate Dyn., 37, 851867, https://doi.org/10.1007/s00382-010-0839-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., and T. J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. Science, 294, 581584, https://doi.org/10.1126/science.1063315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., D. B. Stephenson, D. W. J. Thompson, T. J. Dunkerton, A. J. Charlton, and A. O’Neill, 2003: Stratospheric memory and skill of extended-range weather forecasts. Science, 301, 636640, https://doi.org/10.1126/science.1087143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellon, G., and A. Sobel, 2008a: Poleward-propagating intraseasonal monsoon disturbances in an intermediate-complexity axisymmetric model. J. Atmos. Sci., 65, 470489, https://doi.org/10.1175/2007JAS2339.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellon, G., and A. Sobel, 2008b: Instability of the axisymmetric monsoon flow and intraseasonal oscillation. J. Geophys. Res., 113, D07108, https://doi.org/10.1029/2007JD009291.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., and D. A. Randall, 2007: Observed characteristics of the MJO relative to maximum rainfall. J. Atmos. Sci., 64, 23322354, https://doi.org/10.1175/JAS3968.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, C.-P., and H. Lim, 1988: Kelvin wave-CISK: A possible mechanism for the 30–50 day oscillations. J. Atmos. Sci., 45, 17091720, https://doi.org/10.1175/1520-0469(1988)045<1709:KWCAPM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T.-C., M.-C. Yen, and S.-P. Weng, 2000: Interaction between the summer monsoons in East Asia and the South China Sea: Intraseasonal monsoon modes. J. Atmos. Sci., 57, 13731392, https://doi.org/10.1175/1520-0469(2000)057<1373:IBTSMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, C., and Y.-C. Hsueh, 2010: Mechanisms of northward-propagating intraseasonal oscillation—A comparison between the Indian Ocean and the western North Pacific. J. Climate, 23, 66246640, https://doi.org/10.1175/2010JCLI3596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boisséson, E., M. A. Balmaseda, F. Vitart, and K. Mogensen, 2012: Impact of the sea surface temperature forcing on hindcasts of Madden–Julian oscillation events using the ECMWF model. Ocean Sci., 8, 10711084, https://doi.org/10.5194/os-8-1071-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, and D. A. Randall, 2013: Northward propagation mechanisms of the boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J. Climate, 26, 19731992, https://doi.org/10.1175/JCLI-D-12-00191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, D. A. Randall, and M. D. Branson, 2014: Intraseasonal variability in coupled GCMs: The roles of ocean feedbacks and model physics. J. Climate, 27, 49704995, https://doi.org/10.1175/JCLI-D-13-00760.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., J. J. Benedict, N. P. Klingaman, S. J. Woolnough, and D. A. Randall, 2016: Diagnosing ocean feedbacks to the MJO: SST-modulated surface fluxes and the moist static energy budget. J. Geophys. Res. Atmos., 121, 83508373, https://doi.org/10.1002/2016JD025098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drbohlav, H.-K. L., and B. Wang, 2005: Mechanism of the northward-propagating intraseasonal oscillation: Insights from a zonally symmetric model. J. Climate, 18, 952972, https://doi.org/10.1175/JCLI3306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1987: An air–sea interaction model of intraseasonal oscillations in the tropics. J. Atmos. Sci., 44, 23242340, https://doi.org/10.1175/1520-0469(1987)044<2324:AASIMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2004a: Differences of boreal summer intraseasonal oscillations simulated in an atmosphere–ocean coupled model and an atmosphere-only model. J. Climate, 17, 12631271, https://doi.org/10.1175/1520-0442(2004)017<1263:DOBSIO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2004b: The boreal-summer intraseasonal oscillations simulated in a hybrid coupled atmosphere–ocean model. Mon. Wea. Rev., 132, 26282649, https://doi.org/10.1175/MWR2811.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Wang, T. Li, and J. P. McCreary, 2003: Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci., 60, 17331753, https://doi.org/10.1175/1520-0469(2003)060<1733:CBNIOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Wang, and L. Tao, 2006: Satellite data reveal the 3-D moisture structure of tropical intraseasonal oscillation and its coupling with underlying ocean. Geophys. Res. Lett., 33, L3705, https://doi.org/10.1029/2005GL025074.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Yang, Q. Bao, and B. Wang, 2008: Sea surface temperature feedback extends the predictability of tropical intraseasonal oscillation. Mon. Wea. Rev., 136, 577597, https://doi.org/10.1175/2007MWR2172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gadgil, S., and J. Srinivasan, 1990: Low frequency variation of tropical convergence zones. Meteor. Atmos. Phys., 44, 119132, https://doi.org/10.1007/BF01026814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gadgil, S., P. V. Joseph, and N. V. Joshi, 1984: Ocean–atmosphere coupling over monsoon regions. Nature, 312, 141143, https://doi.org/10.1038/312141a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, X. H., and J. L. Stanford, 1987: Low-frequency oscillations of the large-scale stratospheric temperature field. J. Atmos. Sci., 44, 19912000, https://doi.org/10.1175/1520-0469(1987)044<1991:LFOOTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., P.-C. Hsu, and H.-H. Hsu, 2016: Assessments of surface latent heat flux associated with the Madden–Julian oscillation in reanalyses. Climate Dyn., 47, 17551774, https://doi.org/10.1007/s00382-015-2931-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerber, E. P., and Coauthors, 2012: Assessing and understanding the impact of stratospheric dynamics and variability on the Earth system. Bull. Amer. Meteor. Soc., 93, 845859, https://doi.org/10.1175/BAMS-D-11-00145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayashi, Y., 1970: A theory of large-scale equatorial waves generated by condensation heat and accelerating. J. Meteor. Soc. Japan, 48, 140160, https://doi.org/10.2151/jmsj1965.48.2_140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., 2012: Air–sea interaction. Intraseasonal Variability in the Atmosphere–Ocean Climate System, 2nd ed., W. K. M. Lau and D. E. Waliser, Eds., Springer, 247–270.

    • Crossref
    • Export Citation
  • Hendon, H. H., and J. Glick, 1997: Intraseasonal air–sea interaction in the tropical Indian and Pacific Oceans. J. Climate, 10, 647661, https://doi.org/10.1175/1520-0442(1997)010<0647:IASIIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, W., and X. Ren, 2013: Persistent heavy rainfall over South China during May–August: Subseasonal anomalies of circulation and sea surface temperature. Acta Meteor. Sin., 27, 769787, https://doi.org/10.1007/s13351-013-0607-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., and C.-H. Weng, 2001: Northwestward propagation of the intraseasonal oscillation in the western North Pacific during the boreal summer: Structure and mechanism. J. Climate, 14, 38343850, https://doi.org/10.1175/1520-0442(2001)014<3834:NPOTIO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., C.-H. Weng, and C.-H. Wu, 2004: Contrasting characteristics between the northward and eastward propagation of the intraseasonal oscillation during the boreal summer. J. Climate, 17, 727743, https://doi.org/10.1175/1520-0442(2004)017<0727:CCBTNA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., and T. Li, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part II: Apparent heat and moisture sources and eddy momentum transport. J. Climate, 24, 942961, https://doi.org/10.1175/2010JCLI3834.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 49144931, https://doi.org/10.1175/JCLI-D-11-00310.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., T. Li, and C.-H. Tsou, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part I: Energetics diagnosis. J. Climate, 24, 927941, https://doi.org/10.1175/2010JCLI3833.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 10221039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., L.-L. Pan, and M. Watanabe, 2006: Dynamics of synoptic eddy and low-frequency flow interaction. Part II: A theory for low-frequency modes. J. Atmos. Sci., 63, 16951708, https://doi.org/10.1175/JAS3716.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., L. M. V. Carvalho, R. W. Higgins, D. E. Waliser, and J.-K. E. Schemm, 2004: Climatology of tropical intraseasonal convective anomalies: 1979–2002. J. Climate, 17, 523539, https://doi.org/10.1175/1520-0442(2004)017<0523:COTICA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Julian, P. R., and R. A. Madden, 1981: Comments on a paper by T. Yasunari, A quasi-stationary appearance of 30 to 40-day period in the cloudiness fluctuations during the summer monsoon over India. J. Meteor. Soc. Japan, 59, 435437, https://doi.org/10.2151/jmsj1965.59.3_435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanemaru, K., and H. Masunaga, 2014: The potential roles of background surface wind in the SST variability associated with intraseasonal oscillations. J. Climate, 27, 70537068, https://doi.org/10.1175/JCLI-D-13-00774.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., and B. Wang, 2001: Equatorial waves and air–sea interaction in the boreal summer intraseasonal oscillation. J. Climate, 14, 29232942, https://doi.org/10.1175/1520-0442(2001)014<2923:EWAASI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., and B. C. Weare, 2001: The onset of convection in the Madden–Julian oscillation. J. Climate, 14, 780793, https://doi.org/10.1175/1520-0442(2001)014<0780:TOOCIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., B. Wang, and X. Fu, 2002: Simulation of the intraseasonal oscillation in the ECHAM-4 model: The impact of coupling with an ocean model. J. Atmos. Sci., 59, 14331453, https://doi.org/10.1175/1520-0469(2002)059<1433:SOTIOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., and Y. N. Takayabu, 2004: The development of organized convection associated with the MJO during TOGA COARE IOP: Trimodal characteristics. Geophys. Res. Lett., 31, L10101, https://doi.org/10.1029/2004GL019601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klingaman, N. P., H. Weller, J. M. Slingo, and P. M. Inness, 2008: The intraseasonal variability of the Indian summer monsoon using TMI sea surface temperatures and ECMWF reanalysis. J. Climate, 21, 25192539, https://doi.org/10.1175/2007JCLI1850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., D. K. Oosterhof, and A. V. Mehta, 1988: Air–sea interaction on the time scale of 30 to 50 days. J. Atmos. Sci., 45, 13041322, https://doi.org/10.1175/1520-0469(1988)045<1304:AIOTTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and P. H. Chan, 1986: Aspects of the 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and L. Peng, 1987: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part I: Basic theory. J. Atmos. Sci., 44, 950972, https://doi.org/10.1175/1520-0469(1987)044<0950:OOLFOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., G. J. Yang, and S. H. Shen, 1988: Seasonal and intraseasonal climatology of summer monsoon rainfall over East Asia. Mon. Wea. Rev., 116, 1837, https://doi.org/10.1175/1520-0493(1988)116<0018:SAICOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and P. J. Webster, 2002: The boreal summer intraseasonal oscillation: Relationship between northward and eastward movement of convection. J. Atmos. Sci., 59, 15931606, https://doi.org/10.1175/1520-0469(2002)059<1593:TBSIOR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J.-Y., B. Wang, M. C. Wheeler, X. Fu, D. E. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493509, https://doi.org/10.1007/s00382-012-1544-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, K., W. Yu, T. Li, V. S. N. Murty, S. Khokiattiwong, T. R. Adi, and S. Budi, 2013: Structures and mechanisms of the first-branch northward-propagating intraseasonal oscillation over the tropical Indian Ocean. Climate Dyn., 40, 17071720, https://doi.org/10.1007/s00382-012-1492-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

    • Search Google Scholar
    • Export Citation
  • Lin, A., T. Li, X. Fu, J. Luo, and Y. Masumoto, 2011: Effects of air–sea coupling on the boreal summer intraseasonal oscillations over the tropical Indian Ocean. Climate Dyn., 37, 23032322, https://doi.org/10.1007/s00382-010-0943-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., 1974: Wave-CISK in the tropics. J. Atmos. Sci., 31, 156179, https://doi.org/10.1175/1520-0469(1974)031<0156:WCITT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 24182436, https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., and C. Zhang, 2013: Diabatic heating profiles in recent global reanalyses. J. Climate, 26, 33073325, https://doi.org/10.1175/JCLI-D-12-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, F., and B. Wang, 2014: A mechanism for explaining the maximum intraseasonal oscillation center over the western North Pacific. J. Climate, 27, 958968, https://doi.org/10.1175/JCLI-D-12-00797.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, D. J., and D. L. Hartmann, 2001: Eddy–zonal flow feedback in the Southern Hemisphere. J. Atmos. Sci., 58, 33123327, https://doi.org/10.1175/1520-0469(2001)058<3312:EZFFIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, D., T. Gong, and Y. Diao, 2007: Dynamics of eddy-driven low-frequency dipole modes. Part III: Meridional displacement of westerly jet anomalies during two phases of NAO. J. Atmos. Sci., 64, 32323248, https://doi.org/10.1175/JAS3998.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., 1986: Seasonal variations of the 40–50 day oscillation in the tropics. J. Atmos. Sci., 43, 31383158, https://doi.org/10.1175/1520-0469(1986)043<3138:SVOTDO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and D. L. Hartmann, 1998: Frictional moisture convergence in a composite life cycle of the Madden–Julian oscillation. J. Climate, 11, 23872403, https://doi.org/10.1175/1520-0442(1998)011<2387:FMCIAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J., and J. C. L. Chan, 2005: Intraseasonal variability of the South China Sea summer monsoon. J. Climate, 18, 23882402, https://doi.org/10.1175/JCLI3395.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nanjundiah, R. S., J. Srinivasan, and S. Gadgil, 1992: Intraseasonal variation of the Indian summer monsoon. II: Theoretical aspects. J. Meteor. Soc. Japan, 70, 529550, https://doi.org/10.2151/jmsj1965.70.1B_529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., I. M. Held, and K. H. Cook, 1987: Evaporation–wind feedback and low-frequency variability in the tropical atmosphere. J. Atmos. Sci., 44, 23412348, https://doi.org/10.1175/1520-0469(1987)044<2341:EWFALF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, Y., Y. Zhang, X.-Q. Yang, and G. Chen, 2013: Baroclinic anomalies associated with the Southern Hemisphere annular mode: Roles of synoptic and low-frequency eddies. Geophys. Res. Lett., 40, 23612366, https://doi.org/10.1002/grl.50396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, Y., Y. Zhang, G. Chen, X.-Q. Yang, and D. A. Burrows, 2014: Quantifying barotropic and baroclinic eddy feedbacks in the persistence of the southern annular mode. Geophys. Res. Lett., 41, 86368644, https://doi.org/10.1002/2014GL062210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, Y., Y. Zhang, G. Chen, and X.-Q. Yang, 2016: Delineating the barotropic and baroclinic mechanisms in the midlatitude eddy-driven jet response to lower-tropospheric thermal forcing. J. Atmos. Sci., 73, 429448, https://doi.org/10.1175/JAS-D-15-0090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. J. Meteor. Soc. Japan, 65, 373390, https://doi.org/10.2151/jmsj1965.65.3_373.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pegion, K., and B. P. Kirtman, 2008: The impact of air–sea interactions on the simulation of tropical intraseasonal variability. J. Climate, 21, 66166635, https://doi.org/10.1175/2008JCLI2180.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajendran, K., and A. Kitoh, 2006: Modulation of tropical intraseasonal oscillations by ocean–atmosphere coupling. J. Climate, 19, 366391, https://doi.org/10.1175/JCLI3638.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajendran, K., A. Kitoh, and O. Arakawa, 2004: Monsoon low-frequency intraseasonal oscillation and ocean–atmosphere coupling over the Indian Ocean. Geophys. Res. Lett., 31, L02210, https://doi.org/10.1029/2003GL019031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, X., X.-Q. Yang, and X. Sun, 2013: Zonal oscillation of western Pacific subtropical high and subseasonal SST variations during Yangtze persistent heavy rainfall events. J. Climate, 26, 89298946, https://doi.org/10.1175/JCLI-D-12-00861.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M., and Y. Tanimoto, 2007: Role of SST over the Indian Ocean in influencing the intraseasonal variability of the Indian summer monsoon. J. Meteor. Soc. Japan, 85, 349358, https://doi.org/10.2151/jmsj.85.349.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M., and Y. Tanimoto, 2012: Influence of sea surface temperature on the intraseasonal variability of the South China Sea summer monsoon. Climate Dyn., 39, 12091218, https://doi.org/10.1007/s00382-011-1118-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roxy, M., Y. Tanimoto, B. Preethi, P. Terray, and R. Krishnan, 2013: Intraseasonal SST-precipitation relationship and its spatial variability over the tropical summer monsoon region. Climate Dyn., 41, 4561, https://doi.org/10.1007/s00382-012-1547-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sengupta, D., and M. Ravichandran, 2001: Oscillations of Bay of Bengal sea surface temperature during the 1998 summer monsoon. Geophys. Res. Lett., 28, 20332036, https://doi.org/10.1029/2000GL012548.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sengupta, D., B. N. Goswami, and R. Senan, 2001: Coherent intraseasonal oscillations of ocean and atmosphere during the Asian summer monsoon. Geophys. Res. Lett., 28, 41274130, https://doi.org/10.1029/2001GL013587.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, K.-H., J.-K. E. Schemm, W. Wang, and A. Kumar, 2007: The boreal summer intraseasonal oscillation simulated in the NCEP Climate Forecast System: The effect of sea surface temperature. Mon. Wea. Rev., 135, 18071827, https://doi.org/10.1175/MWR3369.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharmila, S., and Coauthors, 2013: Role of ocean–atmosphere interaction on northward propagation of Indian summer monsoon intra-seasonal oscillations (MISO). Climate Dyn., 41, 16511669, https://doi.org/10.1007/s00382-013-1854-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shinoda, T., H. H. Hendon, and J. Glick, 1998: Intraseasonal variability of surface fluxes and sea surface temperature in the tropical western Pacific and Indian Oceans. J. Climate, 11, 16851702, https://doi.org/10.1175/1520-0442(1998)011<1685:IVOSFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, S., and L. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C.-P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.

  • Torrence, C., and G. P. Compo, 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79, 6178, https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tsou, C.-H., P.-C. Hsu, W.-S. Kau, and H.-H. Hsu, 2005: Northward and northwestward propagation of 30–60 day oscillation in the tropical and extratropical western North Pacific. J. Meteor. Soc. Japan, 83, 711726, https://doi.org/10.2151/jmsj.83.711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and D. E. Harrison, 2002: Monsoon breaks and subseasonal sea surface temperature variability in the Bay of Bengal. J. Climate, 15, 14851493, https://doi.org/10.1175/1520-0442(2002)015<1485:MBASSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and N. E. Graham, 1993: Convective cloud systems and warm-pool sea surface temperatures: Coupled interactions and self-regulation. J. Geophys. Res., 98, 12 88112 893, https://doi.org/10.1029/93JD00872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and H. Rui, 1990: Dynamics of the coupled moist Kelvin–Rossby wave on an equatorial β-plane. J. Atmos. Sci., 47, 397413, https://doi.org/10.1175/1520-0469(1990)047<0397:DOTCMK>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and T. Li, 1993: A simple tropical atmosphere model of relevance to short-term climate variations. J. Atmos. Sci., 50, 260284, https://doi.org/10.1175/1520-0469(1993)050<0260:ASTAMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and X. Xie, 1997: A model for the boreal summer intraseasonal oscillation. J. Atmos. Sci., 54, 7286, https://doi.org/10.1175/1520-0469(1997)054<0072:AMFTBS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and Q. Zhang, 2002: Pacific–East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development. J. Climate, 15, 32523265, https://doi.org/10.1175/1520-0442(2002)015<3252:PEATPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., F. Huang, Z. Wu, J. Yang, X. Fu, and K. Kikuchi, 2009: Multi-scale climate variability of the South China Sea monsoon: A review. Dyn. Atmos. Oceans, 47, 1537, https://doi.org/10.1016/j.dynatmoce.2008.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., M. Chen, and A. Kumar, 2009: Impacts of ocean surface on the northward propagation of the boreal summer intraseasonal oscillation in the NCEP Climate Forecast System. J. Climate, 22, 65616576, https://doi.org/10.1175/2009JCLI3007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woolnough, S. J., J. M. Slingo, and B. J. Hoskins, 2000: The relationship between convection and sea surface temperature on intraseasonal timescales. J. Climate, 13, 20862104, https://doi.org/10.1175/1520-0442(2000)013<2086:TRBCAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, R., and B. Wang, 2001: Multi-stage onset of the summer monsoon over the western North Pacific. Climate Dyn., 17, 277289, https://doi.org/10.1007/s003820000118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yanai, M., S. Esbensen, and J.-H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, B., X. Fu, and B. Wang, 2008: Atmosphere–ocean conditions jointly guide convection of the boreal summer intraseasonal oscillation: Satellite observations. J. Geophys. Res., 113, D11105, https://doi.org/10.1029/2007JD009276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yasunari, T., 1979: Cloudiness fluctuations associated with the Northern Hemisphere summer monsoon. J. Meteor. Soc. Japan, 57, 227242, https://doi.org/10.2151/jmsj1965.57.3_227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yokoi, S., and T. Satomura, 2006: Mechanisms of the northward movement of submonthly scale vortices over the Bay of Bengal during the boreal summer. Mon. Wea. Rev., 134, 22512265, https://doi.org/10.1175/MWR3174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, L., and R. A. Weller, 2007: Objectively analyzed air–sea heat fluxes for the global ice-free oceans (1981–2005). Bull. Amer. Meteor. Soc., 88, 527539, https://doi.org/10.1175/BAMS-88-4-527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, L., X. Jin, and R. A. Weller, 2008: Multidecade global flux datasets from the objectively analyzed air–sea fluxes (OAFlux) project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution OAFlux Project Tech. Rep. OA-2008-01, 64 pp., http://oaflux.whoi.edu/pdfs/OAFlux_TechReport_3rd_release.pdf.

  • Zhang, C., and M. Dong, 2004: Seasonality in the Madden–Julian oscillation. J. Climate, 17, 31693180, https://doi.org/10.1175/1520-0442(2004)017<3169:SITMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and X. Song, 2009: Interaction of deep and shallow convection is key to Madden–Julian oscillation simulation. Geophys. Res. Lett., 36, L09708, https://doi.org/10.1029/2009GL037340.

    • Search Google Scholar
    • Export Citation
  • Zhou, C., and T. Li, 2010: Upscale feedback of tropical synoptic variability to intraseasonal oscillations through the nonlinear rectification of the surface latent heat flux. J. Climate, 23, 57385754, https://doi.org/10.1175/2010JCLI3468.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, W., and J. C. L. Chan, 2005: Intraseasonal oscillations and the South China Sea summer monsoon onset. Int. J. Climatol., 25, 15851609, https://doi.org/10.1002/joc.1209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    Climatological standard deviations of 10–90-day bandpass filtered daily OLR anomalies (shaded) during boreal summer (JJA) for 1985–2009. The black vectors indicate the climatological mean 1000-hPa horizontal wind flows during the same period. The black dashed box (7.5°–22.5°N, 110°–135°E) indicates the target region in this study with larger OLR variance over the WNP.

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    Occurrence ratio of the wavelet power exceeding 95% confidence level (Student’s t test) in 1985–2009 for daily raw OLR anomalies over the WNP target region.

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    Two leading EOF modes—(a) EOF1 and (b) EOF2—of 30–60-day bandpass filtered daily OLR anomalies over the WNP in JJA for 1985–2009, regressed upon the corresponding standardized principal components. The white dotted box (same as the black dashed box in Fig. 1) indicates the domain for EOF decomposition. Percentage of the variance explained by each mode is displayed on the top-right corner.

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    Autocorrelation of the first principal component (PC1; black dashed line) of EOF decomposition for 30–60-day bandpass filtered daily OLR anomalies over the WNP in JJA for 1985–2009, and the cross-correlation of PC1 and the second principal component (PC2; blue solid line). The ordinate is the correlation coefficient where the red long-dashed lines are boundaries of the 95% confidence interval (Student’s t test). The abscissa is the lead/lag time in days with positive (negative) values indicating that PC1 or PC2 lags (leads) PC1.

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    Composite raw pentad anomalies of (left) OLR (vectors indicate 1000-hPa horizontal wind flows) and (right) SST centered at days −10, −5, 0, +5 and +10, in terms of the BSISO events which are identified with PC1. Day 0 denotes the day when the convection is maximized over the WNP in a BSISO event, and the positive (negative) days indicate the days after (before) day 0. The regions surrounded by the dark contours and the vectors displayed indicate the significance exceeding the 90% confidence level (Student’s t test) for shaded variables and wind flows respectively.

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    Standard deviations of the composite raw SST anomalies in one oscillation cycle (days −20 to +20).

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    Latitude–lead/lag–time diagram of the composite raw pentad anomalies along the section of 110°–135°E for (a) OLR (shaded) and SST (contour, interval: 0.1 K), and (b) SST tendency (shaded) and downward net heat flux (contour; interval: 10 W m−2). The abscissa is the lead/lag time in days with positive (negative) values indicating the days after (before) day 0, as defined in Fig. 5.

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    Temporal evolutions of the composite raw anomalies spatially averaged in the WNP target region, for (a) downward net heat flux (, the black dotted-dashed line) and its four components, namely the upward latent heat flux (, the green line), the upward sensible heat flux (, the yellow line), the upward longwave radiative flux (, the brown line), and the downward shortwave radiative flux (, the red line); (b) upward sensible heat flux (yellow dotted-dashed line) and its three components, i.e., the anomalous sea–air temperature difference–related term (cyan line), the anomalous near-surface wind speed–related term (magenta line), and the nonlinear interaction term (light yellow line); and (c) upward water vapor flux (green dotted-dashed line) and its three components, i.e., the anomalous sea–air humidity difference–related term (cyan line), the anomalous near-surface wind speed–related term (magenta line), and the nonlinear interaction term (light yellow line). For comparison, the corresponding composite SST (blue bars) and OLR (gray areas) anomalies are displayed in all the panels, and the black cross-dashed lines in (b) and (c) denote the vertical diffusion heating and moistening rates in the atmospheric boundary layer (vertically integrated from surface to 850 hPa) respectively. A curve (bar/area) in the figure matches the ordinate with the same color, or with the black if the colored one is not provided.

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    Spatial distribution of composite raw pentad anomalies of downward shortwave radiative flux (shaded) and OLR (contour; interval 5 W m−2) centered at day 0.

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    Composite raw pentad anomalies of (left) the upward water vapor flux and (middle),(right) its two major components, respectively, the anomalous near-surface wind speed–related term (vectors indicate 1000-hPa horizontal wind flows) and the anomalous sea–air humidity difference–related term , centered at days −10, −5, 0, +5 and +10. The regions surrounded by the dark contours and the vectors displayed indicate the significance exceeding the 90% confidence level (Student’s t test) for shaded variables and wind respectively.

  • View in gallery

    Composite raw anomalies along 110°–135°E averaged over (a) days −12 to −8 and (b) days −7 to −3, for OLR (gray areas), SST (blue bars), potential temperature difference between 1000 and 850 hPa (brown dashed lines), upward sensible heat flux (, yellow dotted-dashed lines) and the anomalous sea–air temperature difference–related term (yellow solid lines), upward water vapor flux (, green dotted-dashed lines) and the anomalous sea–air humidity difference related term (green solid lines), plotted as a function of the latitude relative to the convection center (negative extrema of the composite OLR anomalies).

  • View in gallery

    Altitude-relative latitude diagrams of composite raw anomalies along 110°–135°E for deep convective heating rate (shaded) averaged over (a) days −12 to −8 and (b) days −7 to −3. The ordinates are heights in hPa and the abscissas are latitudes relative to the convection centers. The contours indicate vertical pressure velocities (interval: 5 × 10−3 Pa s−1) and the vectors indicate wind fields.

  • View in gallery

    Altitude-relative latitude diagrams of composite raw anomalies along 110°–135°E averaged over days −12 to −8 for (a) vertical diffusion heating rate, (b) vertical diffusion moistening rate, and (c) shallow convective moistening rate.

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    (a) Composite raw anomalies along 110°–135°E averaged over days −12 to −8 for moisture budget terms in the low-level atmosphere (1000–700 hPa) over the regions with relative latitudes of −2.5° to 2.5° (purple bars), 5°–10° (green bars), and 10°–15° (yellow bars). From left to right, local moisture tendency, horizontal moisture advection, vertical moisture advection, and apparent moisture sink divided by . (b) As in (a), but for (from left to right) zonal advection of climatological moisture fields by anomalous wind flow, meridional advection of anomalous moisture fields by climatological wind flow, vertical diffusion moistening provided by the CFSR products, and anomalous sea–air humidity difference induced upward surface water vapor flux calculated with the OAFlux products. (c) As in (a), but for (from left to right) horizontal divergence of climatological moisture fields by anomalous wind flow, vertical moisture flux, deep convective moistening provided by the CFSR products, and sum of the left three terms in (c).

  • View in gallery

    (a) Composite raw anomalies along 110°–135°E averaged over days −12 to −8 for horizontal convergence in the boundary layer over the regions of −2.5° to 2.5° relative to the convection center. From left to right, diagnosed free-atmospheric wave dynamics–induced term , diagnosed SST anomaly gradient induced term , diagnosed total anomaly , and the observed anomaly . The purple and yellow bars indicate the boundary layer depths of 1000–850 hPa and 1000–700 hPa respectively. (b),(c) As in (a), but for regions with relative latitudes of 5°–10° and 10°–15°, respectively.

  • View in gallery

    Composite raw anomalies along 110°–135°E averaged over days −12 to −8 for moisture budget in the low-level atmosphere (1000–700 hPa) over the regions with relative latitudes of 5°–10° (green bars) and 10°–15° (yellow bars). From left to right, the SST anomaly gradient induced horizontal moisture convergence , the net oceanic effect contributed local moisture tendency , the net atmospheric effect contributed local moisture tendency , the sum of the net atmospheric and oceanic effects , and the observed local moisture tendency .

  • View in gallery

    As in Fig. 13, but for the average over days −7 to −3.

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    As in Fig. 11, but for the average over (a) days −2 to +2, (b) days +3 to +7, and (c) days +8 to +12. The abscissa represents the latitudes relative to the positive extremums of the anomalous OLR.

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    Schematic diagram of the air–sea interaction in the BSISO over the WNP, for (a) an active convection-induced warm SST anomaly in the northern side of the convection, and (b) the feedback of the warm SST anomaly on the convection, which provides an alternative mechanism for the northward propagation of the convection.

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Role of Air–Sea Interaction in the 30–60-Day Boreal Summer Intraseasonal Oscillation over the Western North Pacific

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  • 1 China Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, and Jiangsu Collaborative Innovation Center of Climate Change, School of Atmospheric Sciences, Nanjing University, Nanjing, China
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Abstract

This study investigates the role of air–sea interaction in the 30–60-day boreal summer intraseasonal oscillation (BSISO) over the western North Pacific with daily outgoing longwave radiation (OLR), CFSR, and OAFlux datasets for 1985–2009. The BSISO events are identified with the first principal component of 30–60-day bandpass filtered OLR anomalies. Composite analysis of these events reveals that during the northward migration of BSISO, the convection can interact with underlying sea surface temperature (SST). A near-quadrature phase relationship exists between the convection and SST anomalies. An active (a suppressed) convection tends to induce a cold (warm) underlying SST anomaly by reducing (increasing) downward solar radiation but a warm SST anomaly in its northern (southern) portion by reducing near-surface wind and upward latent and sensible heat fluxes, resulting in a 10-day delayed maximized warm SST anomaly ahead of the active convection. In turn, this warm SST anomaly tends to increase upward surface sensible and latent heat fluxes via amplifying sea–air temperature and humidity differences. This oceanic feedback acts to heat, moisten, and destabilize the low-level atmosphere, favoring the trigger of shallow convection, which can further develop into deep convection. The maximum warm SST anomaly lies in the southern (northern) portion of the convectively suppressed (enhanced) area, which weakens the anomalous descending motion in the southern portion of convectively suppressed area and preconditions the boundary layer to promote convection development in the northern portion of convectively enhanced area. Such a spatial and temporal phase relationship between the convection and SST anomalies suggest that air–sea interaction can play a delayed negative feedback role in the BSISO cycle and provide an alternative mechanism responsible for its northward propagation.

Denotes content that is immediately available upon publication as open access.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Xiu-Qun Yang, xqyang@nju.edu.cn

Abstract

This study investigates the role of air–sea interaction in the 30–60-day boreal summer intraseasonal oscillation (BSISO) over the western North Pacific with daily outgoing longwave radiation (OLR), CFSR, and OAFlux datasets for 1985–2009. The BSISO events are identified with the first principal component of 30–60-day bandpass filtered OLR anomalies. Composite analysis of these events reveals that during the northward migration of BSISO, the convection can interact with underlying sea surface temperature (SST). A near-quadrature phase relationship exists between the convection and SST anomalies. An active (a suppressed) convection tends to induce a cold (warm) underlying SST anomaly by reducing (increasing) downward solar radiation but a warm SST anomaly in its northern (southern) portion by reducing near-surface wind and upward latent and sensible heat fluxes, resulting in a 10-day delayed maximized warm SST anomaly ahead of the active convection. In turn, this warm SST anomaly tends to increase upward surface sensible and latent heat fluxes via amplifying sea–air temperature and humidity differences. This oceanic feedback acts to heat, moisten, and destabilize the low-level atmosphere, favoring the trigger of shallow convection, which can further develop into deep convection. The maximum warm SST anomaly lies in the southern (northern) portion of the convectively suppressed (enhanced) area, which weakens the anomalous descending motion in the southern portion of convectively suppressed area and preconditions the boundary layer to promote convection development in the northern portion of convectively enhanced area. Such a spatial and temporal phase relationship between the convection and SST anomalies suggest that air–sea interaction can play a delayed negative feedback role in the BSISO cycle and provide an alternative mechanism responsible for its northward propagation.

Denotes content that is immediately available upon publication as open access.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Xiu-Qun Yang, xqyang@nju.edu.cn

1. Introduction

The tropical intraseasonal oscillation (ISO) exhibits pronounced seasonality (Madden 1986; Zhang and Dong 2004; Jones et al. 2004). The concept of boreal summer intraseasonal oscillation (BSISO) has been suggested (e.g., Wang and Xie 1997; Lawrence and Webster 2002; Lee et al. 2013) in order to differentiate it from the equatorial trapped eastward-propagating convective variability that prevails in boreal winter (also known as the MJO; Madden and Julian 1994). The BSISO involves off-equatorial activities, and has complicated propagation patterns and multiple periods. Lee et al. (2013) defined two real-time BSISO indices (BSISO1 and BSISO2) capable of capturing a large fraction of the intraseasonal variance in the Asian summer monsoon (ASM) region where the BSISO is the most intense. BSISO1 represents the canonical northward/northeastward-propagating mode over the ASM region in conjunction with the eastward-propagating MJO, the life cycle of which is about 30–60 days. BSISO2 is active primarily during the premonsoon and monsoon-onset season with a relatively shorter period and will not be discussed in this work. The BSISO is the dominant source of the intraseasonal variability of the ASM, and the East Asian summer monsoon (Tao and Chen 1987) is more affected by the northward-propagating BSISO over the western North Pacific (WNP) as revealed by previous studies (e.g., Lau and Chan 1986; Lau et al. 1988; Chen et al. 2000; Wu and Wang 2001; Zhou and Chan 2005; Mao and Chan 2005; B. Wang et al. 2009; Hong and Ren 2013). Given the importance of the BSISO for its impact on the ASM, understanding the associated dynamical processes is especially important.

The ISO is generally attributed to the atmospheric internal dynamical processes. In the extratropics, the source of the ISO is associated with the eddy–zonal flow interaction (e.g., Lorenz and Hartmann 2001; Jin et al. 2006; Luo et al. 2007; Nie et al. 2013, 2014, 2016) or the troposphere–stratosphere coupling (e.g., Gao and Stanford 1987; Baldwin and Dunkerton 2001; Baldwin et al. 2003; Gerber et al. 2012). In the tropics, the eastward-propagating MJO is considered to originate from the wave-CISK (conditional instability of the second kind) mechanism that emphasizes the convection–moisture feedback (Hayashi 1970; Lindzen 1974; Lau and Peng 1987; Chang and Lim 1988; Wang and Rui 1990) and the wind-induced surface heat exchange (WISHE) mechanism, which takes into account of the tropical mean easterly (Emanuel 1987; Neelin et al. 1987). Some early studies considered the BSISO as the northward extension of the MJO (Yasunari 1979; Julian and Madden 1981; Madden 1986). Further investigations suggested that the BSISO could originate from the Rossby wave emanation from the equatorial Kelvin–Rossby wave packet, in which the frictional convergence is responsible for its northward propagation (Wang and Xie 1997; Maloney and Hartmann 1998; Kemball-Cook and Wang 2001; Lawrence and Webster 2002). The upscale feedback of the synoptic variability also exists in the BSISO (Zhou and Li 2010; Hsu et al. 2011; Hsu and Li 2011).

The northward propagation of the BSISO is also tied to the thermodynamics and circulation fields of the ASM. The poleward gradients of the low-level atmospheric temperature/humidity (Gadgil and Srinivasan 1990; Nanjundiah et al. 1992; Jiang et al. 2004; Li et al. 2013) as well as the boundary layer moisture advection due to the background southwesterly flow (Nitta 1987; Hsu and Weng 2001; Chou and Hsueh 2010; DeMott et al. 2013) over the ASM region favor the northward migration of the convection. The interaction between the convection-generated circulation and the vertical shear of the background flow is another important factor responsible for the northward propagation of the BSISO (Jiang et al. 2004; Drbohlav and Wang 2005; Tsou et al. 2005; Yokoi and Satomura 2006; Bellon and Sobel 2008a,b).

Besides the atmospheric internal dynamical processes, the coherent variation of the underlying sea surface temperature (SST) associated with the tropical ISO has early been noticed and attracted a great deal of attention (e.g., Gadgil et al. 1984; Krishnamurti et al. 1988; Waliser and Graham 1993). As summarized in Hendon (2012), the intraseasonal SST fluctuations are mainly driven by the surface heat flux anomalies, and the solar radiation and surface evaporation are the primary contributors. The enhanced evaporation lags the enhanced MJO convection by about 1–2 weeks (Hendon and Glick 1997; Shinoda et al. 1998; Woolnough et al. 2000) or generally follows the BSISO convection (Kemball-Cook and Wang 2001; Sengupta et al. 2001; Hsu and Weng 2001; Klingaman et al. 2008), while the solar radiation is maximized (minimized) over the convectively enhanced (suppressed) area. Therefore an active convection is followed by (or nearly in phase with) a maximized upward net surface heat flux anomaly, and a subsequent cold SST anomaly is observed with a lag time of about 1–2 weeks relative to the active convection (and vice versa).

Other studies (e.g., Vecchi and Harrison 2002; Roxy and Tanimoto 2012) show that a warm (cold) SST anomaly appears before the enhanced (suppressed) convection according to their lead–lag relationship, and suggest that the SST anomalies have an impact on convective activity. To what degree the oceanic feedbacks can contribute to the BSISO and which specific processes drive these feedbacks, however, are still open questions. Previous observational studies revealed that, in general, the SST anomaly in the tropical ISO directly heats/cools the near-surface air and modulates the surface evaporation via influencing the vertical specific humidity gradient, and is able to further alter the atmospheric instability or moist static energy in the low-level atmosphere (Kemball-Cook and Wang 2001; Rajendran et al. 2004; Agudelo et al. 2006; Roxy and Tanimoto 2007, 2012; Ren et al. 2013). While a good agreement exists among studies evaluating the response of the atmospheric instability to the SST anomaly, the oceanic modulation on the low-level moisture fields requires more investigation. For instance, the satellite observed boundary layer moistening before the ISO convection is much weaker or undetectable in traditional reanalyses (Fu et al. 2006; Yang et al. 2008), and so is the smooth transition from boundary layer moistening to shallow convection and then to deep convection, in which the ocean could play a positive feedback role (Yang et al. 2008). Because of the short observational period of the satellite data, the adoption of modern reanalysis is essential to obtain reliable climatological features of the BSISO. The moisture budget diagnosis for the BSISO over the WNP is also required in order to quantitatively calculate the relative contributions of the atmospheric and oceanic effects in the low-level moistening processes, as previous studies diagnosed for MJO (Hsu and Li 2012; DeMott et al. 2014, 2016). In addition, the SST anomaly gradient-induced surface wind response (Lindzen and Nigam 1987) contributes to the low-level convergence on intraseasonal time scales (Lin et al. 2011; Hsu and Li 2012) and is supposed to be one of the northward propagating mechanisms of the BSISO in cooperation with the background zonal flow of the ASM (Vecchi and Harrison 2002; Roxy and Tanimoto 2007, 2012). However, the BSISO-related air–sea interaction processes vary with regions (Hsu et al. 2004; Chou and Hsueh 2010; DeMott et al. 2013; Roxy et al. 2013), and for the WNP area where the meridional component is nonnegligible in the low-level background flow, the northward propagating mechanisms related to the SST anomaly gradient and background circulation remain questionable.

There are some modeling studies that capture the existence of the oceanic feedback in the BSISO (Kemball-Cook et al. 2002; Fu et al. 2003; Fu and Wang 2004a,b; Rajendran et al. 2004; Rajendran and Kitoh 2006; Seo et al. 2007; W. Wang et al. 2009; Sharmila et al. 2013). In the coupled runs of these studies, the BSISO characteristics are closer to the observations than in the uncoupled runs, and the modulations of the ocean on the atmosphere are mainly through influencing surface heat fluxes, atmospheric instability, and surface moisture convergence, which agrees well with the observations. However, some uncertainties due to model dependence still remain. For instance, an active ocean is the necessary condition for the (continuous) northward propagation in many modeling studies (e.g., Kemball-Cook et al. 2002; W. Wang et al. 2009; Sharmila et al. 2013), but in some other cases the atmosphere-only model is capable of producing the northward propagation, although the amplitude of the BSISO convection is weaker than in observations (Fu et al. 2003; Fu and Wang 2004a). The propagating component of the simulated BSISO could even be not necessarily sensitive to local air–sea interaction (e.g., Ajayamohan et al. 2011). Although the previous observational studies suggest that air–sea interaction may not be the main driver of the tropical ISO through quantitative diagnoses (e.g., Hsu and Li 2012; DeMott et al. 2013), the disagreements between the modeling studies indicate that the role of the air–sea interaction in the BSISO has not been fully understood, and the uncertainties induced by the model dependence make it more necessary to conduct further observational studies in order to understand the role of air–sea interaction in the BSISO.

This study aims at exploring through which specific processes the atmosphere and the ocean affect each other, and what role the air–sea interaction plays in the BSISO over the WNP with observational data. Section 2 describes the data and methods. Extraction of the BSISO signature and its basic features are presented in section 3. Section 4 analyzes the atmospheric forcing and oceanic feedback processes associated with the BSISO. The last section is devoted to conclusions and discussion.

2. Data and methods

The Climate Forecast System Reanalysis (CFSR) data provided by the National Centers for Environmental Prediction (NCEP) is used in this study (Saha et al. 2010). The daily mean, low-resolution (2.5° × 2.5°) pressure level (up to 37 levels) reanalysis, and model output diabatic heating products of CFSR are used to describe the circulation and three-dimensional diabatic heating/moistening of the atmosphere. The following variables of diabatic heating/moistening are applied in the study: the deep convective heating/moistening rate (CNVHR/CNVMR), the shallow convective moistening rate (SHAMR), the large-scale moistening rate (LRGMR), and the vertical diffusion heating/moistening rate (VDFHR/VDFMR).

The other datasets include the National Oceanic and Atmospheric Administration (NOAA) outgoing longwave radiation (OLR) daily data at 2.5° spatial resolution (Liebmann and Smith 1996) and the Woods Hole Oceanographic Institution Objectively Analyzed Air–Sea Fluxes (OAFlux) daily data at 1° spatial resolution (Yu and Weller 2007). The downward net heat flux in the OAFlux dataset is the sum of the downward shortwave and upward longwave radiative fluxes and the upward sensible and latent heat fluxes. The period of 1985–2009 for all the datasets is chosen in order to cover the time range of the OAFlux dataset. All the data were preprocessed by first subtracting the climatological annual cycle and then removing the yearly seasonal mean (i.e., removing the interannual signals).

The sensible and latent heat fluxes are influenced by the atmospheric and oceanic states simultaneously. In the OAFlux dataset, they are calculated with the bulk algorithm and proportional to and respectively, where is the near-surface wind speed, and and are the temperature and specific humidity differences between the sea surface and near-surface air, respectively. To isolate and quantitatively diagnose the contributions of the atmosphere and ocean to the sensible or latent heat flux anomaly, the term or is decomposed into three components (where a prime denotes the anomalous component and an overbar denotes the climatological component): or , which is affected by the atmospheric near-surface wind anomaly; or , which is affected by both oceanic and atmospheric anomalies; and or , the anomaly of the nonlinear interaction term. The detailed derivations are shown in appendix A. The near-surface wind speed together with the temperature and humidity information of the sea surface/near-surface air is also provided in the OAFlux dataset and adopted in the calculation.

As discussed in the introduction, the moisture budget diagnosis is a useful approach for understanding the air–sea interaction processes. A new method is introduced in appendix B for low-level moisture budget diagnosis with the help of a slab boundary layer model (Wang and Li 1993), the surface heat fluxes, and the CFSR diabatic heating products. With the momentum budget equation of the slab boundary layer model, the SST anomaly gradient-induced low-level convergence can be diagnosed, and then the related moisture convergence anomaly can be calculated. In addition, the moistening induced by the sea surface evaporation is diagnosed by decomposing the latent heat flux anomaly with the bulk algorithm. While these two major processes are attributed to oceanic effects and the rest are attributed to atmospheric effects, the relative contributions of the atmosphere and ocean to the low-level moisture budget can be quantitatively diagnosed.

3. Extraction of the BSISO signature and the atmosphere–ocean relation

Figure 1 presents the climatological standard deviations of 10–90-day bandpass filtered daily OLR anomalies during boreal summer over the ASM region. The low-frequency convections are particularly active over the western North Pacific, and the highest variabilities roughly fall in the region of 7.5°–22.5°N, 110°–135°E, which is the target region in this study. The climatological horizontal winds at 1000 hPa denoted by the vectors in Fig. 1 depict the characteristics of low-level flows of the ASM. The South China Sea summer monsoon trough, which is crucial to the existence of the maximum intraseasonal variability center (Liu and Wang 2014), is located just inside in the target region.

Fig. 1.
Fig. 1.

Climatological standard deviations of 10–90-day bandpass filtered daily OLR anomalies (shaded) during boreal summer (JJA) for 1985–2009. The black vectors indicate the climatological mean 1000-hPa horizontal wind flows during the same period. The black dashed box (7.5°–22.5°N, 110°–135°E) indicates the target region in this study with larger OLR variance over the WNP.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

To extract the local BSISO signals, a wavelet analysis (Torrence and Compo 1998) is applied to the area-mean daily OLR anomalies over the WNP target region. The climatological annual cycle of the time–frequency characteristics is shown in Fig. 2, in which the occurrence ratio of the wavelet power exceeding 95% confidence level (Student’s t test) in all analyzed years for raw anomalies (without filtering) of daily OLR is plotted. The analysis is applied to the occurrence ratio instead of the wavelet power itself because the former is not affected by the bias introduced by the great contrast of the oscillation amplitudes between different frequency bands. This figure demonstrates that the OLR fluctuations over the WNP target region have a wide frequency band in the second half of the year, and hence a bandpass filter is required to extract the BSISO signal. There are two separable centers with different typical periods and active seasons in the 30–60-day band. One with relatively shorter period in June–August (JJA) is selected since the other is mainly active in boreal autumn.

Fig. 2.
Fig. 2.

Occurrence ratio of the wavelet power exceeding 95% confidence level (Student’s t test) in 1985–2009 for daily raw OLR anomalies over the WNP target region.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Figure 3 shows two leading EOF (empirical orthogonal function) modes of 30–60-day bandpass filtered daily OLR anomalies over the WNP during JJA. These two modes with triple and dipole structures explain more than two-thirds of the total variance (EOF1, 45%; EOF2, 23%). The autocorrelation of the first principal component (PC1; the black dashed line in Fig. 4) is minimized when the lag step is about ±20 days, and the cross-correlation between PC1 and the second principal component (PC2; the blue solid line in Fig. 4) is minimized (maximized) when PC2 lags (leads) PC1 by about 10 days. These features suggest that the first two EOF modes capture a northward-propagating mode of the convection anomalies of the BSISO with a principal period of roughly 40 days. This mode originates from the equatorial western Pacific, strengthens at around 15°N, and dissipates over subtropical East Asia. In this study the BSISO events over the WNP are identified simply with the PC1. A total of 39 stronger events with the local maximums of PC1 exceeding one standard deviation are identified, with which the time-lead/lag composites are constructed. Hereinafter day 0 (the simultaneous composite) refers to as the time when PC1 reaches its local maximum, i.e., the mature phase of the anomalous convection over the WNP in a BSISO event, and the positive (negative) days indicate the time after (before) day 0.

Fig. 3.
Fig. 3.

Two leading EOF modes—(a) EOF1 and (b) EOF2—of 30–60-day bandpass filtered daily OLR anomalies over the WNP in JJA for 1985–2009, regressed upon the corresponding standardized principal components. The white dotted box (same as the black dashed box in Fig. 1) indicates the domain for EOF decomposition. Percentage of the variance explained by each mode is displayed on the top-right corner.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Fig. 4.
Fig. 4.

Autocorrelation of the first principal component (PC1; black dashed line) of EOF decomposition for 30–60-day bandpass filtered daily OLR anomalies over the WNP in JJA for 1985–2009, and the cross-correlation of PC1 and the second principal component (PC2; blue solid line). The ordinate is the correlation coefficient where the red long-dashed lines are boundaries of the 95% confidence interval (Student’s t test). The abscissa is the lead/lag time in days with positive (negative) values indicating that PC1 or PC2 lags (leads) PC1.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The composite raw pentad anomalies of OLR and SST during the convectively active phases of the BSISO over the WNP (centered at days −10, −5, 0, +5 and +10) are shown in Fig. 5, and the convectively suppressed phases (days −30 to −10 or days +10 to +30) have similar features but with opposite signs. In the left panels of Fig. 5, the continuous northward propagation of the anomalous convection from the equatorial western Pacific to the subtropical East Asia and its significant enhancement over the WNP are found, which agrees well with the characteristics depicted by the first two EOF modes. The active convection is accompanied by the cyclonic low-level wind anomalies, and the anomalous cyclonic circulation has a slight northward/northwestward shift relative to the well-developed convection center. Hence the strong westerly/southwesterly anomalies prevail over the entire southern portion of the convection-enhanced area (see vectors in the left panels). In the right panels, the associated SST anomalies over the WNP are significant. Compared to the left panels, the warm SST anomalies appear before the enhanced convection and the cold SST anomalies appear after the enhanced convection. The standard deviation of the composite raw SST anomalies in one oscillation cycle (Fig. 6; days −20 to +20 are selected as one oscillation cycle) shows that the most prominent SST anomalies associated with the BSISO convection are located in the WNP target region, with higher values over the South China Sea. The latitude–lead/lag–time diagram of the raw OLR and SST pentad anomalies along the section of 110°–135°E (Fig. 7a) and their temporal evolutions averaged over the WNP target region (Fig. 8) reveal some features as follows: 1) organized oscillations of the OLR and SST anomalies propagate northward over the WNP, and the SST fluctuations are more confined to the off-equatorial latitudes; 2) the temporal evolutions and the meridional spatial distributions of these two variables are in near-quadrature, in which the enhanced (suppressed) convection leads the cold (warm) SST and the cold (warm) SST leads the suppressed (enhanced) convection, both by about a quarter of cycle; and 3) these northward-propagating signals are enhanced over the WNP.

Fig. 5.
Fig. 5.

Composite raw pentad anomalies of (left) OLR (vectors indicate 1000-hPa horizontal wind flows) and (right) SST centered at days −10, −5, 0, +5 and +10, in terms of the BSISO events which are identified with PC1. Day 0 denotes the day when the convection is maximized over the WNP in a BSISO event, and the positive (negative) days indicate the days after (before) day 0. The regions surrounded by the dark contours and the vectors displayed indicate the significance exceeding the 90% confidence level (Student’s t test) for shaded variables and wind flows respectively.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Fig. 6.
Fig. 6.

Standard deviations of the composite raw SST anomalies in one oscillation cycle (days −20 to +20).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Fig. 7.
Fig. 7.

Latitude–lead/lag–time diagram of the composite raw pentad anomalies along the section of 110°–135°E for (a) OLR (shaded) and SST (contour, interval: 0.1 K), and (b) SST tendency (shaded) and downward net heat flux (contour; interval: 10 W m−2). The abscissa is the lead/lag time in days with positive (negative) values indicating the days after (before) day 0, as defined in Fig. 5.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Fig. 8.
Fig. 8.

Temporal evolutions of the composite raw anomalies spatially averaged in the WNP target region, for (a) downward net heat flux (, the black dotted-dashed line) and its four components, namely the upward latent heat flux (, the green line), the upward sensible heat flux (, the yellow line), the upward longwave radiative flux (, the brown line), and the downward shortwave radiative flux (, the red line); (b) upward sensible heat flux (yellow dotted-dashed line) and its three components, i.e., the anomalous sea–air temperature difference–related term (cyan line), the anomalous near-surface wind speed–related term (magenta line), and the nonlinear interaction term (light yellow line); and (c) upward water vapor flux (green dotted-dashed line) and its three components, i.e., the anomalous sea–air humidity difference–related term (cyan line), the anomalous near-surface wind speed–related term (magenta line), and the nonlinear interaction term (light yellow line). For comparison, the corresponding composite SST (blue bars) and OLR (gray areas) anomalies are displayed in all the panels, and the black cross-dashed lines in (b) and (c) denote the vertical diffusion heating and moistening rates in the atmospheric boundary layer (vertically integrated from surface to 850 hPa) respectively. A curve (bar/area) in the figure matches the ordinate with the same color, or with the black if the colored one is not provided.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The near-quadrature phase relationship between the OLR and SST anomalies in the BSISO over the WNP implies that the atmosphere and the ocean may influence each other, and the WNP target region where both the OLR anomalies and the associated SST fluctuations are the largest should be the key region of the air–sea coupling. Previous studies with focus on the BSISO over the Indian Ocean region presented similar temporal or spatial lead/lag relations between OLR and SST anomalies with observational data (e.g., Sengupta and Ravichandran 2001; Vecchi and Harrison 2002; Roxy and Tanimoto 2007; Klingaman et al. 2008) or with numerical experiments (e.g., Fu et al. 2003; Fu and Wang 2004a; Rajendran et al. 2004; W. Wang et al. 2009; Sharmila et al. 2013). However, the background circulations over the WNP are different from those over Indian Ocean. Hence, the propagation properties of BSISO may be region-dependent. As discussed in the introduction, it is important to examine the role of oceanic feedback in the transition from boundary layer moistening to shallow convection, and further to deep convection with recent reanalysis data, and a quantitative moisture budget diagnosis is necessary as one of the approaches to understand in what degree the air–sea interaction can contribute to the BSISO. The following section will explore these issues by analyzing the specific air–sea interaction processes associated with the BSISO over the WNP.

4. The air–sea interactions in the BSISO

a. Atmospheric forcing on the ocean

Figure 7b shows the latitude–lead/lag–time diagram of the composite raw pentad anomalies of SST tendency and downward net surface heat flux. The anomalous SST tendency is characterized by a pattern similar to the OLR anomalies in Fig. 7a, especially over the off-equatorial areas, which is determined by the near-quadrature phase relationship between the OLR and SST anomalies, as discussed before. Besides, the anomalous downward net surface heat flux matches well with the SST tendency anomalies, in which an enhanced (suppressed) convection is accompanied with anomalous negative (positive) downward net surface heat flux and decrease (increase) of the underlying SST anomaly. Such relationships suggest that the surface heat budget via flux exchange is one of the primary causes of the SST variations, and the surface heat flux could be highly related to the convective activities due to their spatial and temporal consistencies.

The temporal evolutions of the composite raw anomalies of the surface heat flux components associated with the BSISO over the WNP target region are shown in Fig. 8a. The downward shortwave radiative flux (the red line) and upward sensible/latent heat flux (the yellow/green line) anomalies are generally in the same direction with the downward net heat flux (the black dotted-dashed line) and contribute to most of the latter’s fluctuations (the sensible heat flux has relatively smaller magnitude). These components could possibly be influenced by the convective activities since they are highly correlated with the OLR anomaly. For the shortwave radiation, an active convection tends to increase the cumulus cloud cover and reflect/absorb a great portion of solar radiation, thus reducing the amount of the shortwave radiation reaching the sea surface; and an inactive convection has the opposite effect. Such processes explain the temporal in-phase relationship between the downward shortwave radiative flux anomaly and OLR anomaly shown in Fig. 8a. The spatial correspondence between these two variables can be identified in Fig. 9, illustrating the mature phase of the active convection over the WNP.

Fig. 9.
Fig. 9.

Spatial distribution of composite raw pentad anomalies of downward shortwave radiative flux (shaded) and OLR (contour; interval 5 W m−2) centered at day 0.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

As discussed in section 2 and derived in appendix A, the anomalous sensible heat and water vapor (or latent heat) fluxes have several components that need to be examined individually. The temporal evolutions of the composite raw anomalies of these variables over the WNP target region are shown in Figs. 8b and 8c. First, we notice the out-of-phase relationships between the anomalous near-surface wind speed–related terms ( and , the magenta lines) and OLR, which suggests that an active convection tends to increase surface sensible/latent heat flux through increasing the total near-surface wind speed; and vice versa as stated above. Second, the anomalous sea–air temperature and humidity difference–related terms ( and , the cyan lines) generally follow the SST anomalies, indicating that the ocean appears to play the leading role in determining the sea–air temperature or humidity difference anomalies. The effects of nonlinear interactions between the anomalous near-surface wind speed and anomalous sea–air temperature/humidity difference [ and , the light yellow lines] are negligible.

Figure 10 shows the spatial distributions of the total water vapor flux anomalies and their two major components, centered at days −10, −5, 0, +5, and +10. The sensible heat flux has similar features and will not be displayed and discussed alone. Since the cyclonic low-level wind anomalies accompanied by the active convection tend to increase the total near-surface wind speed in the existence of the seasonal mean monsoon trough (and vice versa; Wang and Zhang 2002; Liu and Wang 2014), the anomalous near-surface wind speed–induced upward (downward) water vapor flux anomalies (the middle column of Fig. 10) are found to mostly appear over the convectively enhanced (suppressed) regions shown in Fig. 5. The total upward water vapor flux anomalies are dominated by the anomalous near-surface wind speed–related term at the mature phase of the active convection over the WNP (around day 0), and the largest anomalies appear in the southern portion of the convectively enhanced area where both anomalous and background southwesterly flows are the strongest; the anomalous cross-equatorial flow to the south of the active convection also reinforces the low-level background flow (Fig. 1). With such near-surface wind speed anomaly distributions, an active convection results in more sensible/latent heat loss of the underlying ocean surface, with the peak southward shifted; and vice versa. It is also worth noticing in Fig. 10 that the anomalous sea–air humidity difference–related term (the right column) plays important roles, and it will be analyzed in the next subsection.

Fig. 10.
Fig. 10.

Composite raw pentad anomalies of (left) the upward water vapor flux and (middle),(right) its two major components, respectively, the anomalous near-surface wind speed–related term (vectors indicate 1000-hPa horizontal wind flows) and the anomalous sea–air humidity difference–related term , centered at days −10, −5, 0, +5 and +10. The regions surrounded by the dark contours and the vectors displayed indicate the significance exceeding the 90% confidence level (Student’s t test) for shaded variables and wind respectively.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The results of the analysis above suggest that the net heat flux anomaly is the principal factor influencing the SST variation over the WNP in the BSISO cycle, and the shortwave radiation flux and surface sensible/latent heat flux anomalies are the major contributors of the net heat flux variation. An active (a suppressed) convection tends to induce a cold (warm) underlying SST anomaly by reducing (increasing) downward solar radiation but a warm SST anomaly in its northern (southern) portion by reducing near-surface wind speed and upward latent/sensible heat fluxes, as well as a cold SST anomaly in its southern (northern) portion by increasing near-surface wind speed and upward latent/sensible heat fluxes. Consequently, a 10-day delayed maximized warm (cold) SST anomaly appears just ahead of (behind) the active convection.

b. Feedback of the ocean on the atmosphere

As shown in Figs. 8b,c and 10, the ocean influences the variations of the sensible heat and water vapor fluxes in the BSISO cycle by modifying sea–air temperature and humidity differences, which would further influence the low-level atmospheric states. The black cross-dashed lines in Figs. 8b and 8c represent the vertical diffusion heating and moistening rate (VDFHR and VDFMR) anomalies in the atmospheric boundary layer, respectively. Good agreements between the VDFHR/VDFMR anomalies and the upward sensible heat/water vapor flux anomalies are found, suggesting that the surface fluxes are the major sources of the vertical diffusion heating and moistening in the atmospheric boundary layer. To explore the specific processes of the oceanic feedback on the atmosphere, in the following figures (from Fig. 11) the composite raw anomalies along 110°–135°E are plotted as a function of the latitude relative to the anomalous convection center. Here a relative latitude coordinate whose zero coincides with the anomalous convection center (negative or positive extremum of the composite OLR anomalies) is introduced to substitute for the real latitude. With the relative latitude, the northward-propagating convection center is located at the same latitude in those diagrams for the temporal average, as if the average were done by spatially tracking the anomalous convection.

Fig. 11.
Fig. 11.

Composite raw anomalies along 110°–135°E averaged over (a) days −12 to −8 and (b) days −7 to −3, for OLR (gray areas), SST (blue bars), potential temperature difference between 1000 and 850 hPa (brown dashed lines), upward sensible heat flux (, yellow dotted-dashed lines) and the anomalous sea–air temperature difference–related term (yellow solid lines), upward water vapor flux (, green dotted-dashed lines) and the anomalous sea–air humidity difference related term (green solid lines), plotted as a function of the latitude relative to the convection center (negative extrema of the composite OLR anomalies).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The warm SST anomalies over the WNP are maximized at around day −10 (Figs. 7 and 8) as the response to the preexisting convectively suppressed phase and the active convection on the southern side, and the ocean appears to exert effects on the atmosphere at this moment. Spatially, the warm SST anomalies precede the active convection by about a quarter of cycle (Figs. 5 and 11a). To the north of the deep convection center, the convection-generated low-level northeasterly anomalies (Fig. 12a) that oppose the climatological southwesterly tend to decrease the total near-surface wind speed. This fact can also be found in Fig. 10 (the middle column, day −10) over the northern South China Sea/Philippine Sea where the anomalous convection is suppressed. However, in the presence of the warm SST anomalies, the anomalous sea–air temperature and humidity difference–related terms ( and , the yellow and green solid lines in Fig. 11a, and the right column of Fig. 10 at around day −10 for the latter) contribute to the total upward sensible heat and water vapor flux anomalies (the yellow and green dotted-dashed lines in Fig. 11a, and the left column of Fig. 10 at around day −10 for the latter) to the north of the convection center. These two terms are extremely crucial over the WNP where the seasonal mean monsoon trough is located and the background near-surface wind speed is higher than that over other areas (Fig. 1). As a result, the positive low-level (below 850 hPa) VDFHR and VDFMR anomalies extend to the north of the deep convective area (Figs. 13a,b).

Fig. 12.
Fig. 12.

Altitude-relative latitude diagrams of composite raw anomalies along 110°–135°E for deep convective heating rate (shaded) averaged over (a) days −12 to −8 and (b) days −7 to −3. The ordinates are heights in hPa and the abscissas are latitudes relative to the convection centers. The contours indicate vertical pressure velocities (interval: 5 × 10−3 Pa s−1) and the vectors indicate wind fields.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Fig. 13.
Fig. 13.

Altitude-relative latitude diagrams of composite raw anomalies along 110°–135°E averaged over days −12 to −8 for (a) vertical diffusion heating rate, (b) vertical diffusion moistening rate, and (c) shallow convective moistening rate.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

Although the total upward sensible heat/water vapor (or latent heat) flux anomalies are relatively small and did not fully cover the entire convectively suppressed region at around day −10 (Figs. 10 and 11a), this could be an indication of the key time point for the oceanic feedback to exert effect. Around day −10, the SST anomalies begin to dominate in the sensible/latent heat flux anomalies, and reverse the direction of the preexisting downward total anomalies caused by the negative near-surface wind speed anomalies. This time point is also crucial because the SST anomalies are maximized and appear to have the most intense effect on the atmosphere. However, the small magnitudes of the total sensible heat/water vapor flux anomalies indicate the existence of the atmospheric effects that are nonnegligible. The quantitative analysis of the relative contributions of the atmospheric and oceanic effects at this time point is necessary. For this purpose, a low-level moisture budget diagnosis with the method introduced in section 2 (derived in appendix B) is applied. In the existence of the spatial near-quadrature distribution of the convection and SST anomalies at around day −10 (Figs. 5 and 11a), three regions along 110°–135°E with different latitudes relative to the convection center are chosen for the diagnosis, which are −2.5° to 2.5° (the active convection center), 5° to 10° (the convectively enhanced area with underlying warm SST anomalies), and 10° to 15° (the convectively suppressed area with underlying warm SST anomalies).

The composite raw anomalies of the moisture budget terms and their major components in the low-level atmosphere (below 700 hPa) over the three regions at around day −10 are shown in Fig. 14. Figure 14a exhibits the anomalous moisture budget terms in Eq. (11) of appendix B and the following major features are revealed. First, the anomalous local moisture tendencies are close to the horizontal moisture advection anomalies , with negative values over the active convection center and positive values over the warm SST anomalies. Such anomalous local moisture tendency distributions favor the enhancement or development of the anomalous convection to the north of the active convection center, but do not favor the maintaining of the strongest convection over its original position, therefore forming a tendency for the northward propagating of convection. Second, the vertical moisture advection anomalies are positive (negative) over the convectively enhanced (suppressed) areas, but almost canceled by the anomalous apparent moisture sink–related moisture loss (gain), and these two terms have the largest magnitudes among the moisture budget terms in Eq. (11).

Fig. 14.
Fig. 14.

(a) Composite raw anomalies along 110°–135°E averaged over days −12 to −8 for moisture budget terms in the low-level atmosphere (1000–700 hPa) over the regions with relative latitudes of −2.5° to 2.5° (purple bars), 5°–10° (green bars), and 10°–15° (yellow bars). From left to right, local moisture tendency, horizontal moisture advection, vertical moisture advection, and apparent moisture sink divided by . (b) As in (a), but for (from left to right) zonal advection of climatological moisture fields by anomalous wind flow, meridional advection of anomalous moisture fields by climatological wind flow, vertical diffusion moistening provided by the CFSR products, and anomalous sea–air humidity difference induced upward surface water vapor flux calculated with the OAFlux products. (c) As in (a), but for (from left to right) horizontal divergence of climatological moisture fields by anomalous wind flow, vertical moisture flux, deep convective moistening provided by the CFSR products, and sum of the left three terms in (c).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The climatological meridional flow and anomalous moisture fields induced term (shown in Fig. 14b) is the largest among the horizontal moisture advection terms and determines the signs of the total horizontal moisture advection anomalies in all three regions. It provides positive moistening tendency anomalies in front of the active convection center. This finding contradicts the results from some of the previous studies that emphasized the importance of the interaction between anomalous circulation and moisture fields (e.g., Hsu and Weng 2001; DeMott et al. 2013), or the conclusion that the moisture advection by the mean horizontal flow does not favor the northward propagation (Chou and Hsueh 2010). A possible reason is that at this time point around day −10 there exists anomalous low-level northerly associated with the vertically overturning circulation as shown in Fig. 12, and the atmosphere over the deep convection center is highly moistened. Therefore, the anomalous circulation does not, but the climatological southwesterly flow does transport water vapor to the north of the convection center.

The vertical diffusion moistening (VDFMR) anomalies in the low-level atmosphere induced by the surface water vapor flux anomalies are of comparable magnitudes to the horizontal advection terms (Figs. 14a,b). The largest VDFMR anomaly is found over the active convection center where the anomalous sea–air humidity difference–related component is near zero, and hence the atmospheric process (the near-surface wind speed anomaly) should dominate the upward surface water vapor flux anomaly over this area. The positive VDFMR anomalies become smaller to the north of the active convection center while the terms become larger, which indicates that the warm SST anomalies tend to play a more and more important role in contributing to the upward total anomalies. Over the region with relative latitudes of 10°–15° (yellow bars) where the anomalous convection is suppressed and the underlying SST anomalies are warm, there exist large and a small VDFMR anomaly and both are positive, suggesting that the oceanic effect is the key factor to reverse the negative near-surface wind speed anomaly-induced lack of evaporation, and maintains the total upward surface water vapor flux anomaly. Therefore, with the warm SST anomalies, the positive low-level VDFMR anomalies extend to the north of the deep convective area and provide a favorable environment for convection developing, as found in Fig. 13b.

Low-level moisture converge anomalies dominate over the convectively enhanced area and divergence anomalies over the suppressed area (Fig. 14c), dominated by the climatological moisture fields and anomalous wind flow–induced term , and the other components are negligible and not shown. The vertical moisture flux anomalies and the deep convective moistening (CNVMR; the other condensational moistening terms are negligible and not shown) anomalies are both negative (positive) over the convectively enhanced (suppressed) area. These three vertical motion-related terms have rather large magnitudes and spatial differences; however, their sum (denoted by “Sum_vert” in Fig. 14c) obtains quite small values. Therefore they should all be taken into account in the moisture budget; otherwise the atmospheric (or oceanic) effects could be highly overestimated (or underestimated).

As discussed in section 2 and appendix B, both atmospheric internal dynamics and SST anomalies could account for the anomalous boundary layer convergence. Following Hsu and Li (2012), these processes are quantitatively diagnosed with Eqs. (14) and (15), and the results are shown in Fig. 15. To test the sensitivity of the result to the boundary layer depth, two different depths, 1000–850 hPa (purple bars) and 1000–700 hPa (yellow bars), are applied. The observed anomalies are provided for comparison (denoted by , the rightmost column of Fig. 15). Despite unavoidable biases, the diagnosed boundary layer convergence anomalies, which are the sums of the atmospheric and oceanic forcings (denoted by ), have the same signs as observation and capture the observed magnitudes in most cases. Over the convectively enhanced regions (Figs. 15a,b), there exist positive convergence anomalies in the boundary layer; nevertheless, the contributions from the free atmospheric wave effect (denoted by ) and the SST anomaly gradient effect (denoted by ) are different over the active convection center (Fig. 15a) and over the warm SST anomalies (Figs. 15b,c). The atmospheric effect dominates the positive boundary layer convergence anomalies over the active convection center. Although it might be under- or overestimated, it is clear that the oceanic effect provides negative convergence anomalies and tends to offset the atmospheric effect, which is not favorable for the enhancement or maintaining of the active convection over its central area (Fig. 15a). Over the convectively enhanced area with underlying warm SST anomalies (Fig. 15b), both atmospheric and oceanic effects result in the positive convergence anomalies, and the latter accounts for more than half of the total anomalies. Over the convectively suppressed area with underlying warm SST anomalies (Fig. 15c), in contrast, the atmospheric effect dominates the total divergence anomalies, the oceanic effect appears to offset such divergence anomalies, and the accompanied low-level moisture dissipates.

Fig. 15.
Fig. 15.

(a) Composite raw anomalies along 110°–135°E averaged over days −12 to −8 for horizontal convergence in the boundary layer over the regions of −2.5° to 2.5° relative to the convection center. From left to right, diagnosed free-atmospheric wave dynamics–induced term , diagnosed SST anomaly gradient induced term , diagnosed total anomaly , and the observed anomaly . The purple and yellow bars indicate the boundary layer depths of 1000–850 hPa and 1000–700 hPa respectively. (b),(c) As in (a), but for regions with relative latitudes of 5°–10° and 10°–15°, respectively.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

In appendix B, a method that is able to quantitatively diagnose the “net” effects of the atmospheric and oceanic forcing on the low-level moisture budget is introduced, as seen in Eqs. (16)(24). In this method, the low-level horizontal moisture convergence anomaly is mainly attributed to the term , in which can be calculated with the two linear forcing terms, while the term is replaced by the sum of the total condensational moistening anomaly and surface upward water vapor flux anomaly. The net oceanic forcing to the low-level moisture budget is composed of the SST anomaly–related components of the horizontal moisture convergence and surface upward water vapor flux anomalies, while the remaining terms are attributed to the atmospheric effects. Thus, the moisture tendency anomaly can be quantitatively diagnosed with the sum of the net atmospheric and oceanic effects as shown in Eq. (24).

The diagnosed results of Eq. (24) for the low-level atmosphere (1000–700 hPa) are shown in Fig. 16. Over the areas of the warm SST anomalies, the net atmospheric effect (denoted by ) is negative while the net oceanic effect (denoted by ) is positive for the anomalous low-level moisture budget. Since the latter has larger magnitude, the sum of them (denoted by ) is positive. Although uncertainty of the diagnosed result is unavoidable as discussed in appendix B, the term is quite close to the observed local moisture tendency anomaly (denoted by ), which indicates that the diagnosis is reasonable in this case. As shown in Figs. 14 and 15, the anomalous vertical transport and consumption by condensation of the water vapor are responsible for the negative anomalies of the net atmospheric effect over the convectively enhanced area, while the low-level divergence anomaly is the principal cause over the convectively suppressed area. For the net oceanic effect, both the components (shown in Fig. 16) and (shown in Fig. 14b) are positive over the warm SST anomalies, and the term is slightly larger. Therefore, through increasing the upward water vapor fluxes and enhancing the low-level convergence, the feedback of the warm SST anomalies contributes to the positive low-level moistening anomalies in front of the active convection.

Fig. 16.
Fig. 16.

Composite raw anomalies along 110°–135°E averaged over days −12 to −8 for moisture budget in the low-level atmosphere (1000–700 hPa) over the regions with relative latitudes of 5°–10° (green bars) and 10°–15° (yellow bars). From left to right, the SST anomaly gradient induced horizontal moisture convergence , the net oceanic effect contributed local moisture tendency , the net atmospheric effect contributed local moisture tendency , the sum of the net atmospheric and oceanic effects , and the observed local moisture tendency .

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The above analyses based on surface heat flux analysis and low-level moisture budget diagnosis at the time point of the maximized warm SST anomalies suggest that the oceanic feedback on the atmosphere in the BSISO over the WNP can be significantly detected and varies with regions. First, the warm SST anomalies contribute to the total upward sensible heat/water vapor flux anomalies by modifying the sea–air temperature/humidity difference in front of the active convection center where the near-surface wind speed anomalies are negative (Figs. 10 and 11). Second, for the warm SST anomalies that spatially precede the active convection by about a quarter of cycle, the SST anomaly gradient tends to decrease the positive low-level convergence anomalies over the active convection center, contributes to more than half of the positive low-level convergence anomalies over the northern portion of the convectively enhanced area, and appears to offset the negative low-level convergence anomalies over the southern portion of the convectively suppressed area (Fig. 15). In addition, the anomalous potential temperature difference between 1000 and 850 hPa (the brown dashed lines in Fig. 11) generally follows the SST anomalies, indicating the existence of low-level instability anomalies in front of the convection center, and the near-surface air directly heated by the warm SST anomalies could be the principal cause.

The combination of the oceanic feedback effects discussed above acts to suppress the anomalous descending motions over the convectively suppressed area as well as the subsequent active convection over its central area. However, it favors the development of the convection in front of the active convection center, and then finally promotes the northward propagation of the active convection. As a visible evidence of such a preconditioning, the anomalous shallow convection (characterized by its moistening rate) is triggered in the northern (southern) portion of the convectively enhanced (suppressed) area where the warm SST anomalies are maximized (Fig. 13c). The shallow convection is the first stage or an indication of the deep convection developing in the tropical ISO, since it transports moisture upward and the deep convection is then triggered when the midtropospheric moisture reaches its maximum (Kemball-Cook and Weare 2001; Kikuchi and Takayabu 2004; Benedict and Randall 2007; Zhang and Song 2009).

At the following pentad centered at day −5 (Figs. 10, 11b, 12b, and 17), the warm SST anomalies to the north of the active convection keep contributing to the upward sensible/latent heat flux anomalies and the higher instabilities (Fig. 11b). Hence the northward extending of the vertical diffusion heating/moistening anomalies together with the anomalous shallow convection still exist (Fig. 17), indicating that the warmer sea surface is able to maintain the shallow convection and continually preconditioning for the deep convection. On the other hand, the convectively suppressed area shrinks and the anomalous descending motions are weakened (Fig. 12b), and the warm SST anomalies should be partially responsible for it. At the same time, the active convection is enhanced and the area expands northward in the cyclonic background flow of the seasonal mean monsoon trough where the moisture convergence is increased (Liu and Wang 2014), and the warm SST anomalies are weakened in response to the atmospheric forcing (Figs. 11b and 12b).

Fig. 17.
Fig. 17.

As in Fig. 13, but for the average over days −7 to −3.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

The active convection over the WNP weakens after day 0 when it is mostly enhanced, and cold SST anomalies emerge that tend to promote the dissipation of the active convection (Figs. 5 and 18). As discussed before, the intense oceanic latent heat loss with a southward-shifted peak over the active convection area could be found at around day 0, and hence the cold SST anomalies appear over the southern portion of the convection first, and then extend northward with growing amplitudes (Figs. 18a,b and 5). The colder sea surface brings the increased low-level atmospheric stabilities, together with the negative sea–air temperature/humidity difference anomalies, which contribute to the decreasing of the upward sensible/latent heat fluxes (Figs. 10 and 18). The combination of these factors favors the decaying of the active convection (Figs. 18b,c). The cold SST anomalies are maximized at around day +10 and lie to the north of the positive OLR anomalies (Fig. 18c), which act as favorable conditions for the following convectively suppressed phase in the same ways described before.

Fig. 18.
Fig. 18.

As in Fig. 11, but for the average over (a) days −2 to +2, (b) days +3 to +7, and (c) days +8 to +12. The abscissa represents the latitudes relative to the positive extremums of the anomalous OLR.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

In the oceanic feedback processes, an enhanced (suppressed) convection-generated cold (warm) SST anomaly tends to suppress this enhanced (suppressed) convection over its southern portion and act as favorable conditions for the northern portion of the suppressed (enhanced) convection in the wake. Therefore the oceanic feedback process provides an alternative mechanism responsible for the northward propagation of the BSISO-associated convective activities. On the other hand, in the existence of the near-quadrature phase relationship between anomalous convection and SST, there is a time lag for the SST anomaly to reverse its status under the current atmospheric forcing. In this case, the suppression of the current convective anomaly, which indicates the negative feedback effect of the ocean on the atmosphere, is always delayed.

5. Conclusions and discussion

a. Conclusions

This study investigates the detailed air–sea interaction processes in the 30–60-day northward-propagating BSISO over the western North Pacific (WNP; with a target region of 7.5°–22.5°N, 110°–135°E), with daily OLR, NCEP/CFSR reanalysis, and OAFlux datasets for 1985–2009. Two leading EOF modes of 30–60-day bandpass filtered OLR fluctuations over the WNP capture a northward-propagating mode of the convection anomalies of BSISO with a principal period of 40 days. This mode originates from the equatorial western Pacific, strengthens at around 15°N, and dissipates over the subtropical East Asia. The BSISO events are defined in terms of the first principal component of the EOF decomposition, and a total of strong events is identified. The lead–lag relationship between the convection and SST anomalies in these events is examined with the composite analysis.

Corresponding to the northward migration of the anomalous convection in association with the BSISO, significant air–sea interaction processes are found, as shown in a schematic diagram of Fig. 19. There is a near-quadrature phase relationship such that the enhanced (suppressed) convection leads the cold (warm) SST and the cold (warm) SST leads the suppressed (enhanced) convection, both by about a quarter of cycle. As illustrated in Fig. 19a, an enhanced convection appears to increase the cumulus cloud cover and reflect/absorb a great portion of solar radiation, thus reducing the amount of the shortwave radiation reaching the sea surface. Meanwhile, the enhanced convection with a cyclonic low-level wind anomaly appears to decelerate (accelerate) the near-surface wind speed in the northern (southern) portion of the convection where the background (South China Sea–tropical western Pacific summer monsoon) southwesterly prevails, thus reducing (enhancing) the surface evaporation (latent heat flux) and the sensible heat flux. Through the above two major processes, an active convection induces a maximum warm (cold) SST anomaly in the northern (southern) portion of the convection in about 10 days, while an inactive convection has the opposite results.

Fig. 19.
Fig. 19.

Schematic diagram of the air–sea interaction in the BSISO over the WNP, for (a) an active convection-induced warm SST anomaly in the northern side of the convection, and (b) the feedback of the warm SST anomaly on the convection, which provides an alternative mechanism for the northward propagation of the convection.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0109.1

On the other hand, it is found that the convection-generated SST anomalies can have a delayed feedback effect on the convection (Fig. 19b). The warm SST anomaly that is formed in the northern (southern) portion of the active (suppressed) convection directly increases the low-level atmospheric instability, as pointed out by most of the previous works (e.g., Roxy and Tanimoto 2012; Ren et al. 2013). Meanwhile, the warm SST anomaly increases the upward surface sensible and latent heat fluxes via amplifying the sea–air temperature and humidity differences. This process could be highly effective in the WNP region where the seasonal mean monsoon trough locates and the background wind speed is the largest (Kanemaru and Masunaga 2014). As a result, significant vertical diffusion heating and moistening anomalies are observed in the low-level atmosphere. At the same time, our diagnosis with the momentum budget equation of the slab boundary layer model by Wang and Li (1993) reveals that the meridional SST anomaly gradient tends to offset the positive low-level convergence anomalies over the active convection center, but to promote the positive low-level convergence anomalies over the warm SST anomalies, which spatially precede the active convection by about a quarter of cycle. Further, a low-level moisture budget analysis shows that during the transition phase of the BSISO the net oceanic effect is positive while the net atmospheric effect is negative for the anomalous low-level moisture budget over the warm SST anomalies, and the former contributes to the positive local moistening tendency anomalies. These conditions created by the warm SST anomaly act to suppress the active convection over its central area and the anomalous descending motions to the north of the convectively enhanced area, triggering the shallow convection that is the first stage or an indication of the deep convection developing over the warm SST anomalies. Therefore, the warm SST anomaly acts to weaken the convectively suppressed phase and favor the northward propagation of the subsequent convectively enhanced phase, and vice versa. Such an air–sea interaction process tends to play a delayed negative feedback role in the BSISO cycle and provide an alternative mechanism responsible for its northward propagation.

b. Discussion

A striking feature of the air–sea interaction in the BSISO cycle over the WNP is the near-quadrature phase relationship between anomalous convection and SST. Temporally, an enhanced (suppressed) convection results in a cold (warm) SST anomaly a quarter period later, which in turn weakens the enhanced (suppressed) convection. In this case, a negative feedback is offered by the air–sea interaction. Because of the large heat capacity of the ocean mixed layer, it takes time for the SST anomaly to change sign. Therefore, such a negative feedback is always delayed, and it is a crucial characteristic of an oscillation system. It is thus speculated that the air–sea interaction is capable of providing an extra source of the low-frequency oscillation in which the persistency of the SST anomaly could affect the time scale of the oscillation, in addition to the atmospheric internal dynamics. Spatially, the maximum warm SST anomaly lies to the south (north) of the convectively suppressed (enhanced) area, which weakens the anomalous descending motion or preconditions the boundary layer to promote the convection development. Such a spatial phase relationship between convection and SST anomalies suggests that the air–sea interaction could be a candidate mechanism for the northward propagation of the BSISO.

The low-level moisture budget diagnosis for the northward-propagating BSISO over the WNP in this work reveals some different aspects in comparison with the eastward-propagating MJO during its peak phase over the eastern Indian Ocean (Hsu and Li 2012). To the east of the MJO convection center where the mean westerly flow prevails, the total upward latent heat flux anomaly is negative due to the intraseasonal easterly anomaly. The lack of evaporation-resulted warm SST anomaly plays a supporting role in the low-level moisture convergence through induced hydrostatic effect on the sea level pressure (Lindzen and Nigam 1987), and the relative contribution of it is small (about 10%–25%). For the northward-propagating BSISO over the WNP, the warmed SST to the north of the active convection acts to reverse the sign of the pre-existing downward sensible and latent heat flux anomaly caused by the lower near-surface wind speed, and contributes to positive low-level convergence anomaly at the same time. On the other hand, the free-atmospheric wave effect accounts for less than half of the total convergence anomaly over the convectively enhanced area with underlying warm SST anomalies, but leads to divergence anomaly over the convectively suppressed area to the north of the active convection.

Our further calculation with Eq. (17) points out that during the oceanic feedback process, the net atmospheric effect is negative while the net oceanic effect is positive for the anomalous boundary layer moistening in front of the active convection center, and the net oceanic effect plays the dominant role so that the total moistening anomaly is positive, which agrees with the observation. Since many of the previous studies with observational data diagnoses (e.g., Chou and Hsueh 2010; Hsu and Li 2012; DeMott et al. 2013) concluded that the SST-linked mechanisms play a secondary role in the tropical ISO, the finding from this study provides a new angle of view, at least on the low-level moisture budget process. The disagreement mainly results from the lack of consideration of the apparent moisture sink–related moisture loss or gain in the previous studies. In the current study, this term is calculated with the diabatic heating and surface flux products, and the associated atmospheric and oceanic contributions are separated. Therefore the net atmospheric and oceanic effects on the low-level moisture budget can be quantitatively diagnosed accordingly, and the results manifest that the oceanic feedback is a nonnegligible process in preconditioning and promoting the northward migration of the BSISO convection over the WNP.

There are some other points of view in understanding the role of the SST anomaly gradient-induced surface circulation (Lindzen and Nigam 1987) in the BSISO, besides its contribution to the anomalous low-level convergence. Some previous studies hypothesized that the interaction between the background and SST anomaly-induced surface flow enhances the northward propagation (e.g., Vecchi and Harrison 2002; Roxy and Tanimoto 2012). For instance, when superimposed on the background southwesterly flow over the South China Sea, a warm SST anomaly tends to induce a lower surface pressure and warm up (cool down) the sea surface to the north (south) with the anomalous northeasterly (southwesterly) flow, resulting an enhancement of the northward propagation of the SST and surface pressure anomalies (Roxy and Tanimoto 2012). Obviously, such a hypothesis does not emphasize the observed near-quadrature phase relationship between anomalous convection (circulation) and SST anomaly, as well as the complicated response processes of the boundary layer to the convection-generated SST anomaly. Thus it is inconsistent with our conclusion that central to the alternative northward propagating mechanism generated by the air–sea interaction is the delayed negative feedback by convection-generated SST anomaly.

All results in this study are based on the data analysis. There are some further issues to be addressed. For example, some key findings related to the oceanic feedback, such as the vertical diffusion heating and/or moistening in the boundary layer and the triggering of the shallow convection, are based on model-output diabatic heating products. Although CFSR’s model-forecasted diabatic heating profiles show the reliable quality (e.g., Ling and Zhang 2013), more solid evidence is required from direct observational data. The quantitative diagnosis of the low-level moisture budget reveals that the atmospheric and oceanic effects vary with regions, and the positive local moistening tendency anomalies over the warm SST anomalies are dominated by the oceanic effects. These findings would be helpful for further investigations on how to separate the two sources of the BSISO (air–sea interaction vs atmospheric internal dynamics), and to what degree the air–sea interaction can modulate the BSISO. In some previous modeling studies with long-term integration experiments (e.g., Fu et al. 2003; Fu and Wang 2004a; Pegion and Kirtman 2008; Sharmila et al. 2013), the atmospheric general circulation models forced with the daily observed SST or with the SST output from the coupled run are unable to reproduce the near-quadrature relation between anomalous convection (circulation) and SST anomaly, and the BSISO-related convective activities are significantly weaker in these experiments. We argue that in the absence of the near-quadrature phase relationship, the lack of the extra low-frequency oscillation source provided by the air–sea interaction could be a crucial reason for the unrealistic BSISO in the models, as the previous studies found in the MJO or BSISO hindcasts (e.g., Fu et al. 2008; de Boisséson et al. 2012). To clearly identify the role of the air–sea interaction in the BSISO cycle, more improved numerical experiments with atmospheric general circulation model coupled and uncoupled to the oceanic model are required, and reproducing the near-quadrature relation between anomalous convection (circulation) and the SST anomaly seems to be a key.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (41330420 and 41621005) and the China Meteorological Special Project (GYHY200806004 and GYHY201406022). The CFSR data are provided by Research Data Archive at the National Center for Atmospheric Research and available at http://dx.doi.org/10.5065/D69K487J. Interpolated OLR data are provided by the NOAA/OAR/ESRL at http://www.esrl.noaa.gov/psd/. The authors acknowledge the WHOI OAFlux project for providing the OAFlux products. The OAFlux data are downloaded from ftp://ftp.whoi.edu/pub/science/oaflux/data_v3/.

APPENDIX A

Decomposition of the Sensible and Latent Heat Fluxes with Bulk Algorithm

In the OAFlux dataset, the downward net heat flux has four components, namely the downward shortwave and upward longwave radiative fluxes, and the upward sensible and latent heat fluxes:
e1
According to Yu and Weller (2007) and Yu et al. (2008), the radiative fluxes are based on satellite observations while the surface sensible and latent heat fluxes are calculated using the bulk algorithm:
e2
e3
where is the density of the air, the specific heat capacity, and the latent heat of evaporation/condensation. The turbulent exchange coefficients for sensible and latent heat fluxes are denoted by and , respectively. Also, is the wind speed near the sea surface; and are the temperature and specific humidity differences between the sea surface and near-surface air, respectively. To calculate the upward water vapor flux , should be divided by :
e4
The terms and in the sensible and latent heat fluxes are determined by atmospheric and oceanic states simultaneously and the influences of these two factors need to be isolated. For the latent heat flux, we have
e5
and can be decomposed into
e6
where an overbar denotes the climatological component and a prime denotes the anomalous component. With the bulk formula Eq. (3), can be derived as follows:
e7
where
e8
is the anomalous component of the nonlinear interaction between and . The decomposition of the sensible heat flux is in the same way. Thus we have
e9
Apparently, the sensible or latent heat flux anomaly has three components: the anomalous sea–air temperature or humidity difference–related term or , which is affected by both oceanic and atmospheric anomalies; the anomalous near-surface wind speed–related term or , which is only affected by the atmospheric anomaly; and the anomaly of the nonlinear interaction term or .

The turbulent exchange coefficients and are calculated with the remaining terms in the bulk formulas [Eqs. (2) and (3)] at every time step of the raw OAFlux dataset. Since previous studies showed that the intraseasonal latent heat flux in CFSR was overestimated compared against the OAFlux dataset and in situ buoy observations (Gao et al. 2016), here the surface fluxes and the terms , , and are directly obtained from or calculated with the variables provided in the OAFlux dataset. The climatological values of and are used in decomposing the and with Eqs. (7) and (9). We have assessed the bias introduced by neglecting the temporal variations of and , and the result shows that it can be ignored.

APPENDIX B

Formulations for Low-Level Moisture Budget Diagnosis

The low-level moisture budget diagnosis is applied in this work in order to quantitatively calculate the atmospheric and oceanic contributions to the low-level moistening. Following Yanai et al. (1973), the moisture budget equation at a constant pressure level can be written as
e10
where is the specific humidity, the time, the horizontal wind vector, the horizontal gradient operator, the pressure, the vertical pressure velocity, the atmospheric apparent moisture sink, and the latent heat of evaporation/condensation. The first two terms on the right-hand side of Eq. (10) represent the horizontal and vertical moisture advections, and the third term indicates the moisture gain/loss through other physical processes such as condensation and vertical diffusion. The vertical moisture advection can be further decomposed into the horizontal moisture convergence and the vertical flux . Note that the latter is calculated with grid-scale vertical motion and has three-dimensional distributions, but is not the surface water vapor flux . The anomalous form (denoted by a prime) of Eq. (10) can be derived as the following:
e11
and the anomalous components of the horizontal moisture advection and convergence terms can be decomposed in the same way as deriving the sensible/latent heat flux anomaly introduced in appendix A:
e12
e13
The low-level horizontal moisture convergence was suggested to be one of the principal moisture sources in the tropical ISO (e.g., Benedict and Randall 2007; Hsu and Li 2012). Theoretically, in addition to the atmospheric internal dynamics, the SST anomaly gradient-induced pressure gradient force contributes to the anomalous boundary layer convergence (Lindzen and Nigam 1987). Such an effect has been detected in previous modeling studies of the BSISO (Kemball-Cook et al. 2002; Fu and Wang 2004b) or observational diagnosis of the MJO (Hsu and Li 2012). Following Hsu and Li (2012), the relative contributions to the anomalous low-level convergence induced by the free-atmospheric dynamics and SST anomaly can be quantitatively diagnosed with a slab boundary layer model developed by Wang and Li (1993), whose momentum budget equation is
e14
where the prime indicates the anomalous component, is the Coriolis parameter, the vertical unit vector, the vertically averaged horizontal wind in the boundary layer, the friction coefficient equal to , the horizontal gradient operator, the geopotential at the top of the boundary layer, the gas constant of air, and the pressures at the bottom and top of the boundary layer, respectively, and the surface temperature. The first term on the right-hand side of Eq. (14) represents the free atmospheric wave effect, and the second term represents the SST anomaly forcing effect. With either of these two linear forcing terms, the anomalous low-level horizontal convergence can be solved:
e15
where indicates the vertically averaged horizontal convergence anomaly in the boundary layer, with subscripts “atm” and “ssta” representing the free-atmospheric wave dynamics and SST anomaly gradient induced components, respectively; the total anomaly diagnosed with Eq. (14) is the sum of these two forcing terms.
In the current study, the vertically integrated moisture budget in the boundary layer is diagnosed at the moment when the BSISO convection transits from the suppression to an active phase. The result shows that is the principal component in the horizontal moisture convergence anomalies while the other terms can be neglected (see section 4b of the main text and Fig. 14):
e16
where is the acceleration of gravity. If we apply the boundary layer momentum budget equation of Wang and Li (1993) in which the low-level convergence anomaly is determined by the two linear forcing terms as shown in Eq. (15), the right-hand side of Eq. (16) has the following form:
e17
where is equivalent to the vertically integrated climatological water vapor field in the slab boundary layer model, it can be obtained with the rest terms in Eq. (17) since they are all calculable. Thus the net atmospheric and oceanic effects induced low-level moisture convergence anomalies can be represented by and , respectively.
In the CFSR diabatic heating products, the moistening equivalent to the term includes the condensational moistening (LHRMR) and vertical diffusion moistening (VDFMR):
e18
in which the term LHRMR is the sum of the deep convective, shallow convective, and large-scale condensational moistening. Since vertical diffusion moistening at a certain layer is proportional to the vertical convergence of the upward water vapor flux there, the vertical integration of VDFMR in the boundary layer is supposed to be equal to the surface upward water vapor flux (i.e., the major source of the VDFMR):
e19
where indicates the upward water vapor flux anomaly at level , and its value at the top of the boundary layer is nearly zero. The surface flux can be further decomposed with the bulk formula as discussed in appendix A:
e20
where the nonlinear interaction term is quite small relative to the other components in the current study (see section 4a of the main text and Fig. 8c) and has been neglected in Eq. (20).
As long as the low-level moisture convergence anomaly can be diagnosed with Eqs. (16) and (17), and the term is replaced by the sum of the total condensational moistening anomaly and surface upward water vapor flux anomaly with the help of Eqs. (18)(20), the vertically integrated moisture tendency anomaly in the boundary layer can be written as
e21
All the components in Eq. (21) can be calculated with or directly obtained from the reanalysis, the OAFlux dataset, and the diabatic heating products. The terms and in Eq. (21) are attributed to the oceanic forcing, and the other terms on the right-hand side represent the atmospheric effects. In this case, the relative contributions of the atmosphere and ocean to the low-level moisture budget (denoted by and respectively) can be quantitatively diagnosed:
e22
e23
and their sum
e24
is the diagnosed total moisture tendency anomaly induced by the atmospheric and oceanic forcings.

It is necessary to point out that uncertainties exist in the diagnostic result of Eq. (21), and there are three major sources. First, the smaller terms in the low-level moisture convergence anomaly and the water vapor flux anomaly are neglected in the derivation [Eqs. (16) and (20)]. Second, the replacement of the vertical integration of the VDFMR with the surface water vapor flux [Eq. (19)] is reasonable only if the bottom and top for the vertical integration are equal to those of the actual boundary layer. Third, three different datasets are used for calculating Eq. (21), and the inconsistency between them is unavoidable, as is the error introduced by the model-forecast diabatic heating products since they are not the observational results [Eq. (18)].

REFERENCES

  • Agudelo, P. A., J. A. Curry, C. D. Hoyos, and P. J. Webster, 2006: Transition between suppressed and active phases of intraseasonal oscillations in the Indo-Pacific warm pool. J. Climate, 19, 55195530, https://doi.org/10.1175/JCLI3924.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ajayamohan, R. S., H. Annamalai, J.-J. Luo, J. Hafner, and T. Yamagata, 2011: Poleward propagation of boreal summer intraseasonal oscillations in a coupled model: Role of internal processes. Climate Dyn., 37, 851867, https://doi.org/10.1007/s00382-010-0839-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., and T. J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. Science, 294, 581584, https://doi.org/10.1126/science.1063315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., D. B. Stephenson, D. W. J. Thompson, T. J. Dunkerton, A. J. Charlton, and A. O’Neill, 2003: Stratospheric memory and skill of extended-range weather forecasts. Science, 301, 636640, https://doi.org/10.1126/science.1087143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellon, G., and A. Sobel, 2008a: Poleward-propagating intraseasonal monsoon disturbances in an intermediate-complexity axisymmetric model. J. Atmos. Sci., 65, 470489, https://doi.org/10.1175/2007JAS2339.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellon, G., and A. Sobel, 2008b: Instability of the axisymmetric monsoon flow and intraseasonal oscillation. J. Geophys. Res., 113, D07108, https://doi.org/10.1029/2007JD009291.

    • Search Google Scholar
    • Export Citation
  • Benedict, J. J., and D. A. Randall, 2007: Observed characteristics of the MJO relative to maximum rainfall. J. Atmos. Sci., 64, 23322354, https://doi.org/10.1175/JAS3968.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, C.-P., and H. Lim, 1988: Kelvin wave-CISK: A possible mechanism for the 30–50 day oscillations. J. Atmos. Sci., 45, 17091720, https://doi.org/10.1175/1520-0469(1988)045<1709:KWCAPM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T.-C., M.-C. Yen, and S.-P. Weng, 2000: Interaction between the summer monsoons in East Asia and the South China Sea: Intraseasonal monsoon modes. J. Atmos. Sci., 57, 13731392, https://doi.org/10.1175/1520-0469(2000)057<1373:IBTSMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, C., and Y.-C. Hsueh, 2010: Mechanisms of northward-propagating intraseasonal oscillation—A comparison between the Indian Ocean and the western North Pacific. J. Climate, 23, 66246640, https://doi.org/10.1175/2010JCLI3596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boisséson, E., M. A. Balmaseda, F. Vitart, and K. Mogensen, 2012: Impact of the sea surface temperature forcing on hindcasts of Madden–Julian oscillation events using the ECMWF model. Ocean Sci., 8, 10711084, https://doi.org/10.5194/os-8-1071-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, and D. A. Randall, 2013: Northward propagation mechanisms of the boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J. Climate, 26, 19731992, https://doi.org/10.1175/JCLI-D-12-00191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., C. Stan, D. A. Randall, and M. D. Branson, 2014: Intraseasonal variability in coupled GCMs: The roles of ocean feedbacks and model physics. J. Climate, 27, 49704995, https://doi.org/10.1175/JCLI-D-13-00760.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., J. J. Benedict, N. P. Klingaman, S. J. Woolnough, and D. A. Randall, 2016: Diagnosing ocean feedbacks to the MJO: SST-modulated surface fluxes and the moist static energy budget. J. Geophys. Res. Atmos., 121, 83508373, https://doi.org/10.1002/2016JD025098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drbohlav, H.-K. L., and B. Wang, 2005: Mechanism of the northward-propagating intraseasonal oscillation: Insights from a zonally symmetric model. J. Climate, 18, 952972, https://doi.org/10.1175/JCLI3306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1987: An air–sea interaction model of intraseasonal oscillations in the tropics. J. Atmos. Sci., 44, 23242340, https://doi.org/10.1175/1520-0469(1987)044<2324:AASIMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2004a: Differences of boreal summer intraseasonal oscillations simulated in an atmosphere–ocean coupled model and an atmosphere-only model. J. Climate, 17, 12631271, https://doi.org/10.1175/1520-0442(2004)017<1263:DOBSIO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2004b: The boreal-summer intraseasonal oscillations simulated in a hybrid coupled atmosphere–ocean model. Mon. Wea. Rev., 132, 26282649, https://doi.org/10.1175/MWR2811.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Wang, T. Li, and J. P. McCreary, 2003: Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci., 60, 17331753, https://doi.org/10.1175/1520-0469(2003)060<1733:CBNIOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Wang, and L. Tao, 2006: Satellite data reveal the 3-D moisture structure of tropical intraseasonal oscillation and its coupling with underlying ocean. Geophys. Res. Lett., 33, L3705, https://doi.org/10.1029/2005GL025074.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Yang, Q. Bao, and B. Wang, 2008: Sea surface temperature feedback extends the predictability of tropical intraseasonal oscillation. Mon. Wea. Rev., 136, 577597, https://doi.org/10.1175/2007MWR2172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gadgil, S., and J. Srinivasan, 1990: Low frequency variation of tropical convergence zones. Meteor. Atmos. Phys., 44, 119132, https://doi.org/10.1007/BF01026814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gadgil, S., P. V. Joseph, and N. V. Joshi, 1984: Ocean–atmosphere coupling over monsoon regions. Nature, 312, 141143, https://doi.org/10.1038/312141a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, X. H., and J. L. Stanford, 1987: Low-frequency oscillations of the large-scale stratospheric temperature field. J. Atmos. Sci., 44, 19912000, https://doi.org/10.1175/1520-0469(1987)044<1991:LFOOTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., P.-C. Hsu, and H.-H. Hsu, 2016: Assessments of surface latent heat flux associated with the Madden–Julian oscillation in reanalyses. Climate Dyn., 47, 17551774, https://doi.org/10.1007/s00382-015-2931-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerber, E. P., and Coauthors, 2012: Assessing and understanding the impact of stratospheric dynamics and variability on the Earth system. Bull. Amer. Meteor. Soc., 93, 845859, https://doi.org/10.1175/BAMS-D-11-00145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayashi, Y., 1970: A theory of large-scale equatorial waves generated by condensation heat and accelerating. J. Meteor. Soc. Japan, 48, 140160, https://doi.org/10.2151/jmsj1965.48.2_140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., 2012: Air–sea interaction. Intraseasonal Variability in the Atmosphere–Ocean Climate System, 2nd ed., W. K. M. Lau and D. E. Waliser, Eds., Springer, 247–270.

    • Crossref
    • Export Citation
  • Hendon, H. H., and J. Glick, 1997: Intraseasonal air–sea interaction in the tropical Indian and Pacific Oceans. J. Climate, 10, 647661, https://doi.org/10.1175/1520-0442(1997)010<0647:IASIIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, W., and X. Ren, 2013: Persistent heavy rainfall over South China during May–August: Subseasonal anomalies of circulation and sea surface temperature. Acta Meteor. Sin., 27, 769787, https://doi.org/10.1007/s13351-013-0607-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., and C.-H. Weng, 2001: Northwestward propagation of the intraseasonal oscillation in the western North Pacific during the boreal summer: Structure and mechanism. J. Climate, 14, 38343850, https://doi.org/10.1175/1520-0442(2001)014<3834:NPOTIO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., C.-H. Weng, and C.-H. Wu, 2004: Contrasting characteristics between the northward and eastward propagation of the intraseasonal oscillation during the boreal summer. J. Climate, 17, 727743, https://doi.org/10.1175/1520-0442(2004)017<0727:CCBTNA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., and T. Li, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part II: Apparent heat and moisture sources and eddy momentum transport. J. Climate, 24, 942961, https://doi.org/10.1175/2010JCLI3834.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 49144931, https://doi.org/10.1175/JCLI-D-11-00310.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., T. Li, and C.-H. Tsou, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part I: Energetics diagnosis. J. Climate, 24, 927941, https://doi.org/10.1175/2010JCLI3833.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J. Climate, 17, 10221039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., L.-L. Pan, and M. Watanabe, 2006: Dynamics of synoptic eddy and low-frequency flow interaction. Part II: A theory for low-frequency modes. J. Atmos. Sci., 63, 16951708, https://doi.org/10.1175/JAS3716.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., L. M. V. Carvalho, R. W. Higgins, D. E. Waliser, and J.-K. E. Schemm, 2004: Climatology of tropical intraseasonal convective anomalies: 1979–2002. J. Climate, 17, 523539, https://doi.org/10.1175/1520-0442(2004)017<0523:COTICA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Julian, P. R., and R. A. Madden, 1981: Comments on a paper by T. Yasunari, A quasi-stationary appearance of 30 to 40-day period in the cloudiness fluctuations during the summer monsoon over India. J. Meteor. Soc. Japan, 59, 435437, https://doi.org/10.2151/jmsj1965.59.3_435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanemaru, K., and H. Masunaga, 2014: The potential roles of background surface wind in the SST variability associated with intraseasonal oscillations. J. Climate, 27, 70537068, https://doi.org/10.1175/JCLI-D-13-00774.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., and B. Wang, 2001: Equatorial waves and air–sea interaction in the boreal summer intraseasonal oscillation. J. Climate, 14, 29232942, https://doi.org/10.1175/1520-0442(2001)014<2923:EWAASI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., and B. C. Weare, 2001: The onset of convection in the Madden–Julian oscillation. J. Climate, 14, 780793, https://doi.org/10.1175/1520-0442(2001)014<0780:TOOCIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., B. Wang, and X. Fu, 2002: Simulation of the intraseasonal oscillation in the ECHAM-4 model: The impact of coupling with an ocean model. J. Atmos. Sci., 59, 14331453, https://doi.org/10.1175/1520-0469(2002)059<1433:SOTIOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., and Y. N. Takayabu, 2004: The development of organized convection associated with the MJO during TOGA COARE IOP: Trimodal characteristics. Geophys. Res. Lett., 31, L10101, https://doi.org/10.1029/2004GL019601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klingaman, N. P., H. Weller, J. M. Slingo, and P. M. Inness, 2008: The intraseasonal variability of the Indian summer monsoon using TMI sea surface temperatures and ECMWF reanalysis. J. Climate, 21, 25192539, https://doi.org/10.1175/2007JCLI1850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., D. K. Oosterhof, and A. V. Mehta, 1988: Air–sea interaction on the time scale of 30 to 50 days. J. Atmos. Sci., 45, 13041322, https://doi.org/10.1175/1520-0469(1988)045<1304:AIOTTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and P. H. Chan, 1986: Aspects of the 40–50 day oscillation during the northern summer as inferred from outgoing longwave radiation. Mon. Wea. Rev., 114, 13541367, https://doi.org/10.1175/1520-0493(1986)114<1354:AOTDOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., and L. Peng, 1987: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part I: Basic theory. J. Atmos. Sci., 44, 950972, https://doi.org/10.1175/1520-0469(1987)044<0950:OOLFOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-M., G. J. Yang, and S. H. Shen, 1988: Seasonal and intraseasonal climatology of summer monsoon rainfall over East Asia. Mon. Wea. Rev., 116, 1837, https://doi.org/10.1175/1520-0493(1988)116<0018:SAICOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and P. J. Webster, 2002: The boreal summer intraseasonal oscillation: Relationship between northward and eastward movement of convection. J. Atmos. Sci., 59, 15931606, https://doi.org/10.1175/1520-0469(2002)059<1593:TBSIOR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J.-Y., B. Wang, M. C. Wheeler, X. Fu, D. E. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493509, https://doi.org/10.1007/s00382-012-1544-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, K., W. Yu, T. Li, V. S. N. Murty, S. Khokiattiwong, T. R. Adi, and S. Budi, 2013: Structures and mechanisms of the first-branch northward-propagating intraseasonal oscillation over the tropical Indian Ocean. Climate Dyn., 40, 17071720, https://doi.org/10.1007/s00382-012-1492-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

    • Search Google Scholar
    • Export Citation
  • Lin, A., T. Li, X. Fu, J. Luo, and Y. Masumoto, 2011: Effects of air–sea coupling on the boreal summer intraseasonal oscillations over the tropical Indian Ocean. Climate Dyn., 37, 23032322, https://doi.org/10.1007/s00382-010-0943-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., 1974: Wave-CISK in the tropics. J. Atmos. Sci., 31, 156179, https://doi.org/10.1175/1520-0469(1974)031<0156:WCITT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 24182436, https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, J., and C. Zhang, 2013: Diabatic heating profiles in recent global reanalyses. J. Climate, 26, 33073325, https://doi.org/10.1175/JCLI-D-12-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, F., and B. Wang, 2014: A mechanism for explaining the maximum intraseasonal oscillation center over the western North Pacific. J. Climate, 27, 958968, https://doi.org/10.1175/JCLI-D-12-00797.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, D. J., and D. L. Hartmann, 2001: Eddy–zonal flow feedback in the Southern Hemisphere. J. Atmos. Sci., 58, 33123327, https://doi.org/10.1175/1520-0469(2001)058<3312:EZFFIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, D., T. Gong, and Y. Diao, 2007: Dynamics of eddy-driven low-frequency dipole modes. Part III: Meridional displacement of westerly jet anomalies during two phases of NAO. J. Atmos. Sci., 64, 32323248, https://doi.org/10.1175/JAS3998.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., 1986: Seasonal variations of the 40–50 day oscillation in the tropics. J. Atmos. Sci., 43, 31383158, https://doi.org/10.1175/1520-0469(1986)043<3138:SVOTDO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122, 814837, https://doi.org/10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and D. L. Hartmann, 1998: Frictional moisture convergence in a composite life cycle of the Madden–Julian oscillation. J. Climate, 11, 23872403, https://doi.org/10.1175/1520-0442(1998)011<2387:FMCIAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J., and J. C. L. Chan, 2005: Intraseasonal variability of the South China Sea summer monsoon. J. Climate, 18, 23882402, https://doi.org/10.1175/JCLI3395.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nanjundiah, R. S., J. Srinivasan, and S. Gadgil, 1992: Intraseasonal variation of the Indian summer monsoon. II: Theoretical aspects. J. Meteor. Soc. Japan, 70, 529550, https://doi.org/10.2151/jmsj1965.70.1B_529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., I. M. Held, and K. H. Cook, 1987: Evaporation–wind feedback and low-frequency variability in the tropical atmosphere. J. Atmos. Sci., 44, 23412348, https://doi.org/10.1175/1520-0469(1987)044<2341:EWFALF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, Y., Y. Zhang, X.-Q. Yang, and G. Chen, 2013: Baroclinic anomalies associated with the Southern Hemisphere annular mode: Roles of synoptic and low-frequency eddies. Geophys. Res. Lett., 40, 2361