• Abatzoglou, J. T., , and T. J. Brown, 2009: Influence of the Madden–Julian oscillation on summertime cloud-to-ground lightning activity over the continental United States. Mon. Wea. Rev., 137, 35963601.

    • Search Google Scholar
    • Export Citation
  • Alaka, G. J., , and E. D. Maloney, 2012: The Madden–Julian oscillation influence on upstream African easterly wave precursor disturbances during boreal summer. J. Climate, 25, 32193236.

    • Search Google Scholar
    • Export Citation
  • Aldrian, E., 2008: Dominant factors of Jakarta's three largest floods. J. Hidrosfir Indones., 3, 105112.

  • Anyamba, E., , E. Williams, , J. Susskind, , A. Fraser-Smith, , and M. Fullekrug, 2000: The manifestation of the Madden–Julian oscillation in global deep convection and in the Schumann resonance intensity. J. Atmos. Sci., 57, 10291044.

    • Search Google Scholar
    • Export Citation
  • Arief, D., , and S. P. Murray, 1996: Low-frequency fluctuations in the Indonesian throughflow through Lombok Strait. J. Geophys. Res., 101 (C5), 12 45512 464.

    • Search Google Scholar
    • Export Citation
  • Barlow, M., 2012: Africa and west Asia. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 477496.

    • Search Google Scholar
    • Export Citation
  • Barlow, M., , M. Wheeler, , B. Lyon, , and H. Cullen, 2005: Modulation of daily precipitation over Southwest Asia by the Madden–Julian oscillation. Mon. Wea. Rev., 133, 35793594.

    • Search Google Scholar
    • Export Citation
  • Barrett, B. S., , J. F. Carrasco, , and A. P. Testino, 2012: Madden–Julian oscillation (MJO) modulation of atmospheric circulation and Chilean winter precipitation. J. Climate, 25, 16781688.

    • Search Google Scholar
    • Export Citation
  • Becker, E. J., , E. Hugo Berbery, , and R. W. Higgins, 2011: Modulation of cold-season U.S. daily precipitation by the Madden–Julian oscillation. J. Climate, 24, 51575166.

    • Search Google Scholar
    • Export Citation
  • Belanger, J. I., , J. A. Curry, , and P. J. Webster, 2010: Predictability of North Atlantic tropical cyclone activity on intraseasonal time scales. Mon. Wea. Rev., 138, 43624374.

    • Search Google Scholar
    • Export Citation
  • Bessafi, M., , and M. C. Wheeler, 2006: Modulation of south Indian Ocean tropical cyclones by the Madden–Julian oscillation and convectively coupled equatorial waves. Mon. Wea. Rev., 134, 638656.

    • Search Google Scholar
    • Export Citation
  • Bond, N. A., , and G. A. Vecchi, 2003: The influence of the Madden–Julian oscillation on precipitation in Oregon and Washington. Wea. Forecasting, 18, 600613.

    • Search Google Scholar
    • Export Citation
  • Bray, N. A., , S. E. Wijffels, , J. C. Chong, , M. Fieux, , S. Hautala, , G. Meyers, , and W. M. L. Morawitz, 1997: Characteristics of the Indo-Pacific throughflow in the eastern Indian Ocean. Geophys. Res. Lett., 24, 25692572.

    • Search Google Scholar
    • Export Citation
  • Brown, A., , S. Milton, , M. Cullen, , B. Golding, , J. Mitchell, , and A. Shelly, 2012: Unified modeling and prediction of weather and climate: A 25-year journey. Bull. Amer. Meteor. Soc., 93, 18651877.

    • Search Google Scholar
    • Export Citation
  • Brunet, G., and Coauthors, 2010: Collaboration of the Weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Amer. Meteor. Soc., 91, 13971406.

    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., , M. C. Wheeler, , and A. H. Sobel, 2009: Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J. Atmos. Sci., 66, 30613074.

    • Search Google Scholar
    • Export Citation
  • Carvalho, L. M. V., , C. Jones, , and B. Liebmann, 2004: The South Atlantic convergence zone: Intensity, form, persistence, and relationships with intraseasonal to interannual activity and extreme rainfall. J. Climate, 17, 88108.

    • Search Google Scholar
    • Export Citation
  • Carvalho, L. M. V., , and C. Jones, , and T. Ambrizzi, 2005: Opposite phases of the Antarctic Oscillation and relationships with intraseasonal to interannual activity in the tropics during the austral summer. J. Climate, 18, 702718.

    • Search Google Scholar
    • Export Citation
  • Cassou, C., 2008: Intraseasonal interaction between the Madden-Julian oscillation and the North Atlantic Oscillation. Nature, 455, 523527.

    • Search Google Scholar
    • Export Citation
  • Chang, C.-P., , P. A. Harr, , and H.-J. Chen, 2005: Synoptic disturbances over the equatorial South China Sea and western maritime continent during boreal winter. Mon. Wea. Rev., 133, 489503.

    • Search Google Scholar
    • Export Citation
  • Chang, C.-P., , N.-C. Lau, , R. H. Johnson, , and M. Jiao, 2011: Bridging weather and climate in research and forecasts of the global monsoon system. Bull. Amer. Meteor. Soc., 92, 369373.

    • Search Google Scholar
    • Export Citation
  • Chao, B. F., , and D. A. Salstein, 2012: Mass, momentum, and geodynamics. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 271296.

    • Search Google Scholar
    • Export Citation
  • Charney, J. G., , and J. Shukla, 1977: Predictability of monsoons. Proc. Joint IUTAM/IUGG Symp. on Monsoon Dynamics, New Delhi, India.

  • Cherry, N. J., 2002: Schumann resonances, a plausible biophysical mechanism for the human health effects of solar/geomagnetic activity. Nat. Hazards, 26, 279331.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., , and S. A. Rutledge, 1998a: The vertical structure of TOGA COARE convection. Part I: Radar echo distributions. J. Atmos. Sci., 55, 27302747.

    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., , and S. A. Rutledge, 1998b: The vertical structure of TOGA COARE convection. Part II: Modulating influences and implications for diabatic heating. J. Atmos. Sci., 55, 27482762.

    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2011: Climate change, atmospheric rivers and floods in California—A multimodel analysis of storm frequency and magnitude changes. J. Amer. Water Resour. Assoc., 47, 514523, doi:10.1111/j.1752-1688.2011.00546.x.

    • Search Google Scholar
    • Export Citation
  • DeWitt, H. L., , D. J. Coffman, , K. J. Schulz, , W. A. Brewer, , T. S. Bates, , and P. K. Quinn, 2013: Atmospheric aerosol properties over the equatorial Indian Ocean and the impact of the Madden-Julian oscillation. J. Geophys. Res., 118, 57365749, doi:10.1002/jgrd.50419.

    • Search Google Scholar
    • Export Citation
  • Dole, R. M., 2008: Linking weather and climate. Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting: A Tribute to Fred Sanders, Meteor. Monogr., No. 33, Amer. Meteor. Soc., 297348.

    • Search Google Scholar
    • Export Citation
  • Donald, A., , H. Meinke, , B. Power, , A. de H. N. Maia, , M. C. Wheeler, , N. White, , R. C. Stone, , and J. Ribbe, 2006: Near-global impact of the Madden-Julian Oscillation on rainfall, Geophys. Res. Lett., 33, L09704, doi:10.1029/2005GL025155.

    • Search Google Scholar
    • Export Citation
  • Duvel, J. P., 2012: Oceans and air-sea interaction. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 513536.

    • Search Google Scholar
    • Export Citation
  • Eisenman, I., , L. Yu, , and E. Tziperman, 2005: Westerly wind bursts: ENSO's tail rather than the dog? J. Climate, 18, 52245238.

  • Ferranti, L., , T. N. Palmer, , F. Molteni, , and K. Klinker, 1990: Tropical–extratropical interaction associated with the 30–60-day oscillation and its impact on medium and extended range prediction. J. Atmos. Sci., 47, 21772199.

    • Search Google Scholar
    • Export Citation
  • Ferreira, R. N., , W. H. Schubert, , and J. J. Hack, 1996: Dynamical aspects of twin tropical cyclones associated with the Madden–Julian oscillation. J. Atmos. Sci., 53, 929945.

    • Search Google Scholar
    • Export Citation
  • Ffield, A., , and A. L. Gordon, 1996: Tidal mixing signatures in the Indonesian seas. J. Phys. Oceanogr., 26, 19241937.

  • Frank, W. M., , and P. E. Roundy, 2006: The role of tropical waves in tropical cyclogenesis. Mon. Wea. Rev., 134, 23972417.

  • Fullekrug, M., , and A. C. Fraser-Smith, 1996: Further evidence for a global correlation of the Earth–ionosphere cavity resonances. Geophys. Res. Lett., 23, 27732776.

    • Search Google Scholar
    • Export Citation
  • Gebbie, G., , and E. Tziperman, 2009: Predictability of SST-modulated westerly wind bursts. J. Climate, 22, 38943909.

  • Godfrey, J. S., 1996: The effect of the Indonesian throughflow on ocean circulation and heat exchange with the atmosphere: A review. J. Geophys. Res., 101 (C5), 12 21712 237.

    • Search Google Scholar
    • Export Citation
  • Goswami, B. N., 2012: South Asian monsoon. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 2172.

    • Search Google Scholar
    • Export Citation
  • Gottschalck, J., and Coauthors, 2010: A framework for assessing operational Madden–Julian oscillation forecasts: A CLIVAR MJO Working Group project. Bull. Amer. Meteor. Soc., 91, 12471258.

    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., , and P. L. Silva Dias, 1995: Analysis of tropical–extratropical interactions with influence functions of a barotropic model. J. Atmos. Sci., 52, 35383555.

    • Search Google Scholar
    • Export Citation
  • Guan, B., , D. E. Waliser, , N. P. Molotch, , E. J. Fetzer, , and P. J. Neiman, 2012: Does the Madden–Julian oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Mon. Wea. Rev., 140, 325342.

    • Search Google Scholar
    • Export Citation
  • Hall, J. D., , A. J. Matthews, , and D. J. Karoly, 2001: The modulation of tropical cyclone activity in the Australian region by the Madden–Julian oscillation. Mon. Wea. Rev., 129, 29702982.

    • Search Google Scholar
    • Export Citation
  • Han, W., 2005: Origins and dynamics of the 90-day and 30–60-day variations in the equatorial Indian Ocean. J. Phys. Oceanogr., 35, 708728.

    • Search Google Scholar
    • Export Citation
  • Han, W., , P. Webster, , R. Lukas, , P. Hacker, , and A. Hu, 2004: Impact of atmospheric intraseasonal variability in the Indian Ocean: Low-frequency rectification in equatorial surface current and transport. J. Phys. Oceanogr., 34, 13501372.

    • Search Google Scholar
    • Export Citation
  • Hastenrath, S., , A. Nicklis, , and L. Greischar, 1993: Atmospheric–hydrospheric mechanisms of climate anomalies in the western equatorial Indian Ocean. J. Geophys. Res., 98 (C11), 20 21920 235.

    • Search Google Scholar
    • Export Citation
  • He, J., , H. Lin, , and Z. Wu, 2011: Another look at influences of the Madden-Julian oscillation on the wintertime East Asian weather. J. Geophys. Res., 116, D03109, doi:10.1029/2010JD014787.

    • Search Google Scholar
    • Export Citation
  • Heinlein, R., 1973: Time Enough for Love. G.P. Putnam's Sons, 605 pp.

  • Hendon, H. H., 2012: Air-sea interaction. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 247270.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., , M. C. Wheeler, , and C. Zhang, 2007: Seasonal dependence of the MJO–ENSO relationship. J. Climate, 20, 531543.

  • Herbertson, A. J., 1901: Outlines of Physiography: An Introduction to the Study of the Earth. Edward Arnold, 312 pp.

  • Higgins, R. W., , A. Leetmaa, , Y. Xue, , and A. Barnston, 2000: Dominant factors influencing the seasonal predictability of U.S. precipitation and surface air temperature. J. Climate, 13, 39944017.

    • Search Google Scholar
    • Export Citation
  • Ho, C.-H., , J.-H. Kim, , J.-H. Jeong, , H.-S. Kim, , and D. Chen, 2006: Variation of tropical cyclone activity in the south Indian Ocean: El Niño–Southern Oscillation and Madden-Julian Oscillation effects. J. Geophys. Res., 111, D22101, doi:10.1029/2006JD007289.

    • Search Google Scholar
    • Export Citation
  • Hong, C.-C., , and T. Li, 2009: The extreme cold anomaly over Southeast Asia in February 2008: Roles of ISO and ENSO. J. Climate, 22, 37863801.

    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., 2012: East Asian monsoon. Intraseasonal Variability of the Atmosphere–Ocean Climate System. 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 73110.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., , Y. Kushnir, , G. Ottersen, , and M. Visbeck, 2003: An overview of the North Atlantic Oscillation. North Atlantic Oscillation: Climate Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 135.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., , G. A. Meehl, , D. Bader, , T. Delworth, , B. Kirtman, , and B. Wielicki, 2009: A unified modeling approach to climate system prediction. Bull. Amer. Meteor. Soc., 90, 18191832.

    • Search Google Scholar
    • Export Citation
  • Janicot, S., , F. Mounier, , N. M. J. Hall, , S. Leroux, , B. Sultan, , and G. N. Kiladis, 2009: Dynamics of the West African monsoon. Part IV: Analysis of 25–90-day variability of convection and the role of the Indian monsoon. J. Climate, 22, 15411565.

    • Search Google Scholar
    • Export Citation
  • Janicot, S., and Coauthors, 2011: Intraseasonal variability of the West African monsoon. Atmos. Sci. Lett., 12, 5866.

  • Jeong, J.-H., , C.-H. Ho, , B.-M. Kim, , and W.-T. Kwon, 2005: Influence of the Madden-Julian Oscillation on wintertime surface air temperature and cold surges in East Asia. J. Geophys. Res., 110, D11104, doi:10.1029/2004JD005408.

    • Search Google Scholar
    • Export Citation
  • Jeong, J.-H., , B.-M. Kim, , C.-H. Ho, , and Y.-H. Noh, 2008: Systematic variation in wintertime precipitation in East Asia by MJO-induced extratropical vertical motion. J. Climate, 21, 788801.

    • Search Google Scholar
    • Export Citation
  • Jia, X., , L. J. Chen, , F. M. Ren, , and C. Y. Li, 2011: Impacts of the MJO on winter rainfall and circulation in China. Adv. Atmos. Sci., 28, 521533, doi:10.1007/s00376-010-9118-z.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., , M. Zhao, , and D. E. Waliser, 2012: Modulation of tropical cyclones over the eastern Pacific by the intraseasonal variability simulated in an AGCM. J. Climate, 25, 65246538.

    • Search Google Scholar
    • Export Citation
  • Jin, D., , D. E. Waliser, , C. Jones, , and R. Murtugudde, 2013: Modulation of tropical ocean surface chlorophyll by the Madden–Julian oscillation. Climate Dyn., 40, 3958.

    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., , L. Lin, , A. Timmermann, , and J. Zhao, 2007: Ensemble-mean dynamics of the ENSO recharge oscillator under state-dependent stochastic forcing. Geophys. Res. Lett., 34, L03807, doi:10.1029/2006GL027372.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., , and S. B. Feldstein, 2010: The continuum of North Pacific sea level pressure patterns: Intraseasonal, interannual, and interdecadal variability. J. Climate, 23, 851867.

    • Search Google Scholar
    • Export Citation
  • Jones, C., , and L. M. V. Carvalho, 2012: Spatial–intensity variations in extreme precipitation in the contiguous United States and the Madden–Julian oscillation. J. Climate, 25, 48494913.

    • Search Google Scholar
    • Export Citation
  • Jones, C., , D. E. Waliser, , K. M. Lau, , and W. Stern, 2004: Global occurrences of extreme precipitation events and the Madden–Julian oscillation: Observations and predictability. J. Climate, 17, 45754589.

    • Search Google Scholar
    • Export Citation
  • Jones, C., , J. Gottschalck, , L. M. V. Carvalho, , and W. R. Higgins, 2011a: Influence of the Madden–Julian oscillation on forecasts of extreme precipitation in the contiguous United States. Mon. Wea. Rev., 139, 332350.

    • Search Google Scholar
    • Export Citation
  • Jones, C., , L. M. V. Carvalho, , J. Gottschalck, , and W. Higgins, 2011b: The Madden–Julian oscillation and the relative value of deterministic forecasts of extreme precipitation in the contiguous United States. J. Climate, 24, 24212428.

    • Search Google Scholar
    • Export Citation
  • Juliá, C., , D. A. Rahn, , and J. A. Rutllant, 2012: Assessing the influence of the MJO on strong precipitation events in subtropical, semi-arid north-central Chile (30°S). J. Climate, 25, 70037013.

    • Search Google Scholar
    • Export Citation
  • Kapur, A., , and C. Zhang, 2012: Multiplicative MJO forcing of ENSO. J. Climate, 25, 81328147.

  • Kapur, A., , C. Zhang, , J. Zavala-Garay, , and H. H. Hendon, 2011: Role of stochastic forcing in ENSO in observations and a coupled GCM. Climate Dyn., 38, 87107.

    • Search Google Scholar
    • Export Citation
  • Kashino, Y., , H. Watanabe, , B. Herunadi, , M. Aoyama, , and D. Hartoyo, 1999: Current variability at the Pacific entrance of the Indonesian Throughflow. J. Geophys. Res., 104 (C5), 11 02111 035.

    • Search Google Scholar
    • Export Citation
  • Keen, R. A., 1982: The role of cross-equatorial tropical cyclone pairs in the Southern Oscillation. Mon. Wea. Rev., 110, 14051416.

  • Kessler, W. S., 2012: The oceans. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 199246.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., , K. H. Straub, , G. C. Reid, , and K. S. Gage, 2001: Aspects of interannual and intraseasonal variability of the tropopause and lower stratosphere. Quart. J. Roy. Meteor. Soc., 127, 19611983.

    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282293.

  • Knox, R. A., 1976: On a long series of measurements of Indian Ocean equatorial currents near Addu Atoll. Deep-Sea Res., 23, 211221.

  • Kodama, Y.-M., , M. Tokuda, , and F. Murata, 2006: Convective activity over the Indonesian maritime continent during CPEA-I as evaluated by lightning activity and Q1 and Q2 profiles. J. Meteor. Soc. Japan, 84, 133149.

    • Search Google Scholar
    • Export Citation
  • Laing, A. G., , R. E. Carbone, , and V. Levizzani, 2011: Cycles and propagation of deep convection over equatorial Africa. Mon. Wea. Rev., 139, 28322853.

    • Search Google Scholar
    • Export Citation
  • Lau, W. K. M., 2012: El–Niño Southern Oscillation connection. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 297334.

    • Search Google Scholar
    • Export Citation
  • Lau, W. K. M., , and D. E. Waliser, Eds., 2012: Intraseasonal Variability of the Atmosphere–Ocean Climate System. 2nd ed. Springer, 613 pp.

    • 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.

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

    • Search Google Scholar
    • Export Citation
  • Leroy, A., , and M. C. Wheeler, 2008: Statistical prediction of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 136, 36373654.

    • Search Google Scholar
    • Export Citation
  • L'Heureux, M. L., , and R. W. Higgins, 2008: Boreal winter links between the Madden–Julian oscillation and the Arctic Oscillation. J. Climate, 21, 30403050.

    • Search Google Scholar
    • Export Citation
  • Li, C. Y., , W. Zhou, , J. C. L. Chan, , and P. Huang, 2012: Asymmetric modulation of the western North Pacific cyclogenesis by the Madden–Julian oscillation under ENSO conditions. J. Climate, 25, 53745385.

    • Search Google Scholar
    • Export Citation
  • Li, K.-F., , B. Tian, , D. E. Waliser, , and Y. L. Yung, 2010: Tropical mid-tropospheric CO2 variability driven by the Madden–Julian oscillation. Proc. Natl. Acad. Sci. USA, 107, 19 17119 175.

    • Search Google Scholar
    • Export Citation
  • Li, K.-F., , B. Tian, , D. E. Waliser, , M. J. Schwartz, , J. L. Neu, , J. R. Worden, , and Y. L. Yung, 2012: Vertical structure of MJO-related subtropical ozone variations from MLS, TES, and SHADOZ data. Atmos. Chem. Phys., 12, 425436.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , H. H. Hendon, , and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J. Meteor. Soc. Japan, 72, 401412.

    • Search Google Scholar
    • Export Citation
  • Lin, H., , and G. Brunet, 2009: The influence of the Madden–Julian oscillation on Canadian wintertime surface air temperature. Mon. Wea. Rev., 137, 22502262.

    • Search Google Scholar
    • Export Citation
  • Lin, H., , G. Brunet, , and J. Derome, 2009: An observed connection between the North Atlantic Oscillation and the Madden–Julian oscillation. J. Climate, 22, 364380.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1975: Climatic predictability. The Physical Basis of Climate and Climate Modeling, GARP Publication Series, Vol. 16, World Meteorological Organization, 132136.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40-50 day period. J. Atmos. Sci., 29, 11091123.

    • Search Google Scholar
    • Export Citation
  • Majda, A. J., , and S. N. Stechmann, 2012: Multiscale theories for the MJO. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau & and D. E. Waliser , Eds., Springer, 549565.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2000a: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Climate, 13, 14511460.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2000b: Modulation of hurricane activity in the Gulf of Mexico by the Madden–Julian oscillation. Science, 287, 20022004.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: Observations. J. Atmos. Sci., 58, 25452558.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and J. Shaman, 2008: Intraseasonal variability of the West African monsoon and Atlantic ITCZ. J. Climate, 21, 28982918.

    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., , D. Hudson, , M. C. Wheeler, , H. H. Hendon, , and O. Alves, 2010: Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system. Climate Dyn., 37, 21292141, doi:10.1007/s00382-010-0948-2.

    • Search Google Scholar
    • Export Citation
  • Masumoto, Y., , H. Hase, , Y. Kuroda, , H. Matsuura, , and K. Takeuchi, 2005: Intraseasonal variability in the upper layer currents observed in the eastern equatorial Indian Ocean. Geophys. Res. Lett., 32, L02607, doi:10.1029/2004GL021896.

    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., 2004: Intraseasonal variability over tropical Africa during northern summer. J. Climate, 17, 24272440.

  • Matthews, A. J., , and M. P. Meredith, 2004: Variability of Antarctic circumpolar transport and the southern annular mode associated with the Madden–Julian oscillation. Geophys. Res. Lett., 31, L24312, doi:10.1029/2004GL021666.

    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., 1982: Variability in the central equatorial Indian Ocean. Part I: Ocean dynamics. J. Mar. Res., 40, 157176.

  • McPhaden, M. J., 1999: Genesis and evolution of the 1997–98 El Niño. Science, 283, 950954.

  • McPhaden, M. J., 2004: Evolution of the 2002/03 El Niño. Bull. Amer. Meteor. Soc., 85, 677695.

  • McPhaden, M. J., 2008: Evolution of the 2006-07 El Niño: The role of intraseasonal to interannual time scale dynamics. Adv. Geosci., 14, 219230.

    • Search Google Scholar
    • Export Citation
  • Mitsutakea, G., , K. Otsukaa, , M. Hayakawab, , M. Sekiguchib, , G. Cornélissenc, , and F. Halberg, 2005: Does Schumann resonance affect our blood pressure? Biomed. Pharmacother., 59 (Suppl.), S10S14.

    • Search Google Scholar
    • Export Citation
  • Mo, K. C., 2000: The association between intraseasonal oscillations and tropical storms in the Atlantic basin. Mon. Wea. Rev., 128, 40974107.

    • Search Google Scholar
    • Export Citation
  • Mo, K. C., , C. Jones, , and J. N. Paegle, 2012: Pan-America. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 111146.

    • Search Google Scholar
    • Export Citation
  • Molcard, R., , M. Fieux, , and A. G. Ilahude, 1996: The Indo–Pacific throughflow in the Timor Passage. J. Geophys. Res., 101 (C5), 12 41112 420.

    • Search Google Scholar
    • Export Citation
  • Moncrieff, M. W., , D. E. Waliser, , M. J. Miller, , M. E. Shapiro, , G. Asrar, , and J. Caughey, 2012: Multiscale convective organization and the YOTC Virtual Global Field Campaign. Bull. Amer. Meteor. Soc., 93, 11711187.

    • Search Google Scholar
    • Export Citation
  • Moon, J.-Y., , B. Wang, , and K.-J. Ha, 2011: ENSO regulation of MJO teleconnection. Climate Dyn., 37, 11331149.

  • Moon, J.-Y., , B. Wang, , and K.-J. Ha, 2012: MJO modulation on 2009/10 winter snowstorms in the United States. J. Climate, 25, 978991.

  • Mori, M., , and M. Watanabe, 2008: The growth and triggering mechanisms of the PNA: A MJO-PNA coherence. J. Meteor. Soc. Japan, 86, 213236.

    • Search Google Scholar
    • Export Citation
  • Morita, J., , Y. N. Takayabu, , S. Shige, , and Y. Kodama, 2006: Analysis of rainfall characteristics of the Madden-Julian oscillation using TRMM satellite data. Dyn. Atmos. Oceans, 42, 107126.

    • Search Google Scholar
    • Export Citation
  • Mote, P. W., , H. L. Clark, , T. J. Dunkerton, , R. S. Harwood, , and H. C. Pumphrey, 2000: Intraseasonal variations of water vapor in the tropical upper troposphere and tropopause region. J. Geophys. Res., 105 (D13), 17 45717 470.

    • Search Google Scholar
    • Export Citation
  • Mu, M., and Coauthors, 2011: Daily and hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide. J. Geophys. Res., 116, D24303, doi:10.1029/2011JD016245.

    • Search Google Scholar
    • Export Citation
  • Mysak, L. A., , and G. J. Mertz, 1984: A 40- to 60-day oscillation in the source region of the Somali Current during 1976. J. Geophys. Res., 89 (C1), 711715.

    • Search Google Scholar
    • Export Citation
  • Nagura, M., , and M. J. McPhaden, 2010: Dynamics of zonal current variations associated with the Indian Ocean dipole. J. Geophys. Res., 115, C11026, doi:10.1029/2010JC006423.

    • Search Google Scholar
    • Export Citation
  • Nagura, M., , and M. J. McPhaden, 2012: The dynamics of wind-driven intraseasonal variability in the equatorial Indian Ocean. J. Geophys. Res., 117, C02001, doi:10.1029/2011JC007405.

    • Search Google Scholar
    • Export Citation
  • Naumann, G., , and W. M. Vargas, 2010: Joint diagnostic of the surface air temperature in southern South America and the Madden–Julian oscillation. Wea. Forecasting, 25, 12751280.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , J. H. Richter, , and M. Jochum, 2008: The impact of convection on ENSO: From a delayed oscillator to a series of events. J. Climate, 21, 59045924.

    • Search Google Scholar
    • Export Citation
  • Ohta, K., , N. Watanabe, , and M. Hayakawa, 2006: Survey of anomalous Schumann resonance phenomena observed in Japan in possible association with earthquakes in Taiwan. Phys. Chem. Earth, 31, 397402.

    • Search Google Scholar
    • Export Citation
  • Oliver, E. C. J., , and K. R. Thompson, 2010: Madden-Julian oscillation and sea level: Local and remote forcing. J. Geophys. Res., 115, C01003, doi:10.1029/2009JC005337.

    • Search Google Scholar
    • Export Citation
  • Park, T.-W., , C.-H. Ho, , S. Yang, , and J.-H. Jeong, 2010: Influences of Arctic Oscillation and Madden-Julian oscillation on cold surges and heavy snowfalls over Korea: A case study for the winter of 2009–2010. J. Geophys. Res., 115, D23122, doi:10.1029/2010JD014794.

    • Search Google Scholar
    • Export Citation
  • Perez, C. L., , A. M. Moore, , J. Zavala-Garay, , and R. Kleeman, 2005: A comparison of the influence of additive and multiplicative stochastic forcing on a coupled model of ENSO. J. Climate, 18, 50665085.

    • Search Google Scholar
    • Export Citation
  • Pohl, B., , and P. Camberlin, 2006a: Influence of the Madden–Julian Oscillation on East African rainfall. Part I: Intraseasonal variability and regional dependency. Quart. J. Roy. Meteor. Soc., 132, 25212539.

    • Search Google Scholar
    • Export Citation
  • Pohl, B., , and P. Camberlin, 2006b: Influence of the Madden–Julian Oscillation on East African rainfall. II: March–May season extremes and interannual variability. Quart. J. Roy. Meteor. Soc., 132, 25412559.

    • Search Google Scholar
    • Export Citation
  • Pyne, S. J., , P. L. Andrews, , and R. D. Laven, 1996: Introduction to Wildland Fire. John Wiley and Sons, 769 pp.

  • Qiu, B., , M. Mao, , and Y. Kashino, 1999: Intraseasonal variability in the Indo-Pacific Throughflow and the regions surrounding the Indonesian Seas. J. Phys. Oceanogr., 29, 15991618.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., , P. J. Neiman, , G. N. Kiladis, , K. Weickmann, , and D. W. Reynolds, 2011: A multiscale observational case study of a Pacific Atmospheric river exhibiting tropical–extratropical connections and a mesoscale frontal wave. Mon. Wea. Rev., 139, 11691189.

    • Search Google Scholar
    • Export Citation
  • Rao, S. A., , and T. Yamagata, 2004: Abrupt termination of Indian Ocean dipole events in response to intraseasonal disturbances. Geophys. Res. Lett., 31, L19306, doi:10.1029/2004GL020842.

    • Search Google Scholar
    • Export Citation
  • Rao, S. A., , S. Masson, , J. J. Luo, , S. K. Behera, , and T. Yamagata, 2007: Termination of Indian Ocean dipole events in a coupled general circulation model. J. Climate, 20, 30183035.

    • Search Google Scholar
    • Export Citation
  • Rao, S. A., , J.-J. Luo, , S. K. Behera, , and T. Yamagata, 2008: Generation and termination of Indian Ocean dipole events in 2003, 2006 and 2007. Climate Dyn., 33, 751767, doi:10.1007/s00382-008-0498-z.

    • Search Google Scholar
    • Export Citation
  • Reid, J. S., and Coauthors, 2012: Multi-scale meteorological conceptual analysis of observed active fire hotspot activity and smoke optical depth in the Maritime Continent. Atmos. Chem. Phys., 12, 21172147, doi:10.5194/acp-12-2117-2012.

    • Search Google Scholar
    • Export Citation
  • Resplandy, L., , J. Vialard, , M. Lévy, , O. Aumont, , and Y. Dandonneau, 2009: Seasonal and intraseasonal biogeochemical variability in the thermocline ridge of the southern tropical Indian Ocean. J. Geophys. Res., 114, C07024, doi:10.1029/2008JC005246.

    • Search Google Scholar
    • Export Citation
  • Roundy, P. E., 2012: Tropical-extratropical interactions. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 497512.

    • Search Google Scholar
    • Export Citation
  • Roundy, P. E., , K. MacRitchie, , J. Asuma, , and T. Melino, 2010: Modulation of the global atmospheric circulation by combined activity in the Madden–Julian oscillation and the El Niño–Southern Oscillation during boreal winter. J. Climate, 23, 40454059.

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

    • Search Google Scholar
    • Export Citation
  • Sassi, F., , M. Salby, , H. C. Pumphrey, , and W. G. Read, 2002: Influence of the Madden-Julian oscillation on upper tropospheric humidity. J. Geophys. Res., 107, 4681, doi:10.1029/2001JD001331.

    • Search Google Scholar
    • Export Citation
  • Schott, F., , and J. P. McCreary, 2001: The monsoon circulation of the Indian Ocean. Prog. Oceanogr., 51, 1123.

  • Schwartz, M. J., , D. E. Waliser, , B. Tian, , D. L. Wu, , J. H. Jiang, , and W. G. Read, 2008: Characterization of MJO-related upper tropospheric hydrological processes using MLS. Geophys. Res. Lett., 35, L08812, doi:10.1029/2008GL033675.

    • Search Google Scholar
    • Export Citation
  • Seiki, A., , and Y. N. Takayabu, 2007a: Westerly wind bursts and their relationship with intraseasonal variations and ENSO. Part I: Statistics. Mon. Wea. Rev., 135, 33253345.

    • Search Google Scholar
    • Export Citation
  • Seiki, A., , and Y. N. Takayabu, 2007b: Westerly wind bursts and their relationship with intraseasonal variations and ENSO. Part II: Energetics over the western and central Pacific. Mon. Wea. Rev., 135, 33463361.

    • Search Google Scholar
    • Export Citation
  • Seiki, A., , Y. N. Takayabu, , T. Yasuda, , N. Sato, , C. Takahashi, , K. Yoneyama, , and R. Shirooka, 2011: Westerly wind bursts and their relationship with ENSO in CMIP3 models. J. Geophys. Res., 116, D03303, doi:10.1029/2010JD015039.

    • Search Google Scholar
    • Export Citation
  • Senan, R., , D. Sengupta, , and B. N. Goswami, 2003: Intraseasonal “monsoon jets” in the equatorial Indian Ocean. Geophys. Res. Lett., 30, 1750, doi:10.1029/2003GL017583.

    • Search Google Scholar
    • Export Citation
  • Sengupta, D., , R. Senan, , B. N. Goswami, , and J. Vialard, 2007: Intraseasonal variability of equatorial Indian Ocean zonal currents. J. Climate, 20, 30363055.

    • Search Google Scholar
    • Export Citation
  • Shapiro, M., and Coauthors, 2010: An Earth-System prediction initiative for the twenty-first century. Bull. Amer. Meteor. Soc., 91, 13771388.

    • Search Google Scholar
    • Export Citation
  • Shi, L., , O. Alves, , H. H. Hendon, , G. Wang, , and D. Anderson, 2009: The role of stochastic forcing in ensemble forecasts of the 1997/98 El Nino. J. Climate, 22, 25262540.

    • Search Google Scholar
    • Export Citation
  • Shibagaki, Y., and Coauthors, 2006: Multi-scale convective systems associated with intraseasonal variation over the Indonesian maritime continent. Mon. Wea. Rev., 134, 16821696.

    • Search Google Scholar
    • Export Citation
  • Slade, S. A., , and E. D. Maloney, 2013: An intraseasonal prediction model of Atlantic and east Pacific tropical cyclone genesis. Mon. Wea. Rev., 141, 19251942.

    • Search Google Scholar
    • Export Citation
  • Son, S.-W., , and S. Lee, 2007: Intraseasonal variability of the zonal-mean extratropical tropopause height. J. Atmos. Sci., 64, 26952708.

    • Search Google Scholar
    • Export Citation
  • Souza, E. B., , and T. Ambrizzi, 2006: Modulation of the intraseasonal rainfall over tropical Brazil by the Madden–Julian oscillation. Int. J. Climatol., 26, 17591776.

    • Search Google Scholar
    • Export Citation
  • Sperber, K. R., , J. M. Slingo, , and P. M. Inness, 2012: Modeling intraseasonal variability. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 399432.

    • Search Google Scholar
    • Export Citation
  • Stan, C., , M. Khairoutdinov, , C. A. DeMott, , V. Krishnamurthy, , D. M. Straus, , D. A. Randall, , J. L. Kinter III, , and J. Shukla, 2010: An ocean-atmosphere climate simulation with an embedded cloud resolving model. Geophys. Res. Lett., 37, L01702, doi:10.1029/2009GL040822.

    • Search Google Scholar
    • Export Citation
  • Tangang, F. T., , L. Juneng, , E. Salimun, , P. N. Vinayachandran, , Y. K. Seng, , C. J. C. Reason, , S. K. Behera, , and T. Yasunari, 2008: On the roles of the northeast cold surge, the Borneo vortex, the Madden-Julian oscillation, and the Indian Ocean dipole during the extreme 2006/2007 flood in southern peninsular Malaysia. Geophys. Res. Lett., 35, L14S07, doi:10.1029/2008GL033429.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. B., , and P. E. Rounday, 2013: The relationship between the Madden–Julian oscillation and U.S. violent tornado outbreaks in the spring. Mon. Wea. Rev., 141, 20872095.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., , and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., , and J. M. Wallace, 2001: Regional climate impacts of the Northern Hemisphere annular mode. Science, 293, 8589.

  • Tian, B., , and W. E. Waliser, 2012: Chemical and biological impacts. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 569586.

    • Search Google Scholar
    • Export Citation
  • Tian, B., , C. O. Ao, , D. E. Waliser, , E. J. Fetzer, , A. J. Mannucci, , and J. Teixeira, 2012: Intraseasonal temperature variability in the upper troposphere and lower stratosphere from the GPS radio occultation measurements. J. Geophys. Res., 117, D15110, doi:10.1029/2012JD017715.

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., , and N. A. Bond, 2004: The Madden-Julian oscillation (MJO) and northern high latitude winter-time surface air temperatures. Geophys. Res. Lett., 31, L04104, doi:10.1029/2003GL018645.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and P. E. Roundy, 2011: The Madden–Julian oscillation's influence on African easterly waves and downstream tropical cyclogenesis. Mon. Wea. Rev., 139, 27042722.

    • Search Google Scholar
    • Export Citation
  • Virts, K. S., , and J. M. Wallace, 2010: Annual, interannual, and intraseasonal variability of tropical tropopause transition layer cirrus. J. Atmos. Sci., 67, 30973112.

    • Search Google Scholar
    • Export Citation
  • Virts, K. S., , J. A. Thornton, , J. M. Wallace, , M. L. Hutchins, , R. H. Holzworth, , and A. R. Jacobson, 2011: Daily and intraseasonal relationships between lightning and NO2 over the Maritime Continent. Geophys. Res. Lett., 38, L19803, doi:10.1029/2011GL048578.

    • Search Google Scholar
    • Export Citation
  • Vitart, F., 2009: Impact of the Madden–Julian oscillation on tropical storms and risk of landfall in the ECMWF forecast system. Geophys. Res. Lett., 36, L15802, doi:10.1029/2009GL039089.

    • Search Google Scholar
    • Export Citation
  • Vitart, F., , and F. Molteni, 2010: Simulation of the Madden–Julian oscillation and its teleconnections in the ECMWF forecast system. Quart. J. Roy. Meteor. Soc., 136, 842855.

    • Search Google Scholar
    • Export Citation
  • Vitart, F., , A. Leroy, , and M. C. Wheeler, 2010: A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 138, 36713682.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., 2012: Predictability and forecasting. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 433476.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., , K. M. Lau, , W. Stern, , and C. Jones, 2003a: Potential predictability of the Madden–Julian oscillation. Bull. Amer. Meteor. Soc., 84, 3350.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., , R. Murtugudde, , and L. E. Lucas, 2003b: Indo- Pacific Ocean response to atmospheric intraseasonal variability: 1. Austral summer and the Madden–Julian Oscillation. J. Geophys. Res., 108, 3160, doi:10.1029/2002JC001620.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and Coauthors, 2006: The Experimental MJO Prediction Project. Bull. Amer. Meteor. Soc., 87, 425431.

  • Waliser, D. E., , B. Tian, , X. Xie, , W. T. Liu, , M. J. Schwartz, , and E. J. Fetzer, 2009: How well can satellite data characterize the water cycle of the Madden-Julian Oscillation? Geophys. Res. Lett., 36, L21803, doi:10.1029/2009GL040005.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., , and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812.

    • Search Google Scholar
    • Export Citation
  • Wang, B., 2012: Theories. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 335398.

    • Search Google Scholar
    • Export Citation
  • Wang, C.-C., , and G. Magnusdottir, 2006: The ITCZ in the central and eastern Pacific on synoptic time scales. Mon. Wea. Rev., 134, 14051421.

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

    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., , and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., , and J. L. McBride, 2012: Australian monsoon. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 147198.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., , H. H. Hendon, , S. Cleland, , H. Meinke, , and A. Donald, 2009: Impacts of the MJO on Australian rainfall and circulation. J. Climate, 22, 14821497.

    • Search Google Scholar
    • Export Citation
  • Wong, S., , and A. E. Dessler, 2007: Regulation of H2O and CO in tropical tropopause layer by the Madden–Julian oscillation. J. Geophys. Res., 112, D14305, doi:10.1029/2006JD007940.

    • Search Google Scholar
    • Export Citation
  • Wu, X., , L. Deng, , X. Song, , G. Vettoretti, , W. R. Peltier, , and G. J. Zhang, 2007: Impact of a modified convective scheme on the Madden-Julian oscillation and El Niño–Southern Oscillation in a coupled climate model. Geophys. Res. Lett., 34, L16823, doi:10.1029/2007GL030637.

    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1973: An equatorial jet in the Indian Ocean. Science, 181, 262264.

  • Wyrtki, K., 1987: Indonesian throughflow and the associated pressure gradient. J. Geophys. Res., 92 (C12), 12 94112 946.

  • Yamakawa, S., , and R. Suppiah, 2009: Extreme climatic events in recent years and their links to large-scale atmospheric circulation features. Global Environ. Res., 13, 6978.

    • Search Google Scholar
    • Export Citation
  • Yoneyama, K., , C. Zhang, , and C. Long, 2013: Tracking pulses of the Madden–Julian oscillation. Bull. Amer. Meteor. Soc., 94, 18711891.

    • Search Google Scholar
    • Export Citation
  • Yoo, C., , S. Feldstein, , and S. Lee, 2011: The impact of the Madden-Julian oscillation trend on the Arctic amplification of surface air temperature during the 1979–2008 boreal winter. Geophys. Res. Lett., 38, L24804, doi:10.1029/2011GL049881.

    • Search Google Scholar
    • Export Citation
  • Yoo, C., , S. Lee, , and S. Feldstein, 2012: Mechanisms of arctic surface air temperature change in response to the Madden–Julian oscillation. J. Climate, 25, 57775790.

    • Search Google Scholar
    • Export Citation
  • Zavala-Garay, J., , C. Zhang, , A. M. Moore, , A. Wittenberg, , M. Harrison, , A. Rosati, , A. T. Weaver, , and J. Vialard, 2008: Sensitivity of hybrid ENSO models to uncoupled atmospheric variability. J. Climate, 21, 37043721.

    • Search Google Scholar
    • Export Citation
  • Zhan, Z., , J. Li, , and A. Gettelman, 2006: Intraseasonal variations of upper tropospheric water vapor in Asian monsoon region. Atmos. Chem. Phys. Discuss., 6, 80698095.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden-Julian Oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.

  • Zhang, C., 2012: Vertical structure from recent observations. Intraseasonal Variability of the Atmosphere–Ocean Climate System, 2nd ed. W. K.-M. Lau, and D. E. Waliser, Eds., Springer, 537548.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., , and J. Gottschalck, 2002: SST anomalies of ENSO and the Madden–Julian oscillation in the equatorial Pacific. J. Climate, 15, 24292445.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., , B. Wang, , and Q. Zeng, 2009: Impact of the Madden–Julian oscillation on summer rainfall in southeast China. J. Climate, 22, 201216.

    • Search Google Scholar
    • Export Citation
  • Zhou, L., , and R. Murtugudde, 2010: Influences of Madden–Julian oscillations on the eastern Indian Ocean and the maritime continent. Dyn. Atmos. Oceans, 2, 257274, doi:10.1016/j.dynatmoce.2009.12.003.

    • Search Google Scholar
    • Export Citation
  • Zhou, L., , R. Murtugudde, , and M. Jochum, 2008: Dynamics of the Intraseasonal oscillations in the Indian Ocean South Equatorial Current. J. Phys. Oceanogr., 38, 121132.

    • Search Google Scholar
    • Export Citation
  • Zhou, S., , M. L'Heureux, , S. Weaver, , and A. Kumar, 2012: A composite study of the MJO influence on the surface air temperature and precipitation over the continental United States. Climate Dyn., 38, 14591471, doi:10.1007/s00382-011-1001-9.

    • Search Google Scholar
    • Export Citation
  • Zhou, X., , and J. R. Holton, 2002: Intraseasonal variations of tropical cold-point tropopause temperatures. J. Climate, 15, 14601473.

  • Zhu, C., , T. Nakazawa, , J. Li, , and L. Chen, 2003: The 30–60 day intraseasonal oscillation over the western North Pacific Ocean and its impacts on summer flooding in China during 1998. Geophys. Res. Lett., 30, 1952, doi:10.1029/2003GL017817.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (above). Phase diagram of the RMM index. Each point represents a day. Eight phases and corresponding approximate locations of enhanced convective signals of the MJO are labeled. Points within the circle represent weak or no MJO (from Wheeler and Hendon 2004).

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    (right). Composites of intraseasonal (30– 90 days) anomalies in TRMM precipitation (mm day−1) during November–April of 1998–2012 based on the RMM index.

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    Rainfall anomalies measured by surface rain gauges during MJO phases (a) 2, (b) 4, (c) 6, and (d) 8 for austral winter. Anomalies are expressed as maximum vertical distance between the unconditional cumulative distribution function (CDF) and the corresponding conditional CDF for a particular MJO phase (vertical differences are measured at the point of maximum divergence in dimensionless units of “percent change in probability”). Positive (negative) distances indicate evidence of enhanced (suppressed) rainfall during the respective phase (from Donald et al. 2006).

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    TC tracks (1975–2011) and precipitation anomalies (1998–2011) in MJO phases 2 and 3, 4 and 5, 6 and 7, and 8 and 1 when the amplitude of the RMM index is greater than one. The total number of days of TCs in each phase group is listed. The TC tracks are from the International Best Track Archive for Climate Stewardship (IBTrACS) v03r04. Precipitation data are from Tropical Rainfall Measuring Mission (TRMM) 3B43 v7.

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    Locations of large floods during 1985–2010 based on the Dartmouth Flood Observatory Global Archive of Large Flood Events at University of Colorado (http://floodobservatory.colorado.edu/Archives/index.html). Red boxes mark regions where probability of total flood days and/or events are significantly affected by the MJO. Blue and green boxes are examples shown in Figs. 4 and 5, respectively.

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    MJO influences of large floods of the West Coast of North America (blue box in Fig. 3). (a) Monthly probability of total flood days during MJO episodes (black bars) and when there is no MJO (gray). (b) Probability of flood events in each MJO phase (total number of flood events starting in each phase divided by the total number of days in that phase). Vertical error bars denote ranges of the 95% confidence level.

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    MJO influences of large floods of the Philippines (green box in Fig. 3). (a) Monthly probability of total flood days during MJO episodes (black bars) and when there is no MJO (gray). (b) Probability of total flood days in each MJO phase (total number of flood days in each phase divided by the total number of days in that phase). (c) Probability of flood events as functions of MJO phases. Vertical error bars denote ranges of the 95% confidence level.

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    Ratio of fire detections for MJO phases 5–8 over 1–4 in June–November (from Reid et al. 2012).

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    Total fire carbon emission (g C m2 day−1) based on data of Mu et al. (2011). Red boxes indicate regions where fire emission fluctuates substantially with MJO phases.

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    Summer (June–September) lightning frequency rank-order anomalies (z scores) stratified by RMM phases (denoted in lower-right corner). Red (blue) shading denotes areas of enhanced (suppressed) lighting activity for each RMM phase exceeding the 95% confidence interval (from Abatzoglou and Brown 2009).

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    December–February composites of surface air temperature anomalies (°C) for each MJO phase (from Zhou et al. 2012).

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Madden–Julian Oscillation: Bridging Weather and Climate

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  • 1 RSMAS, University of Miami, Miami, Florida
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The Madden–Julian oscillation exerts broad influences on global weather and climate as its center of convection moves from the tropical Indian Ocean into the Pacific. Weather events under the influence of the MJO include precipitation, surface temperature, tropical cyclones, tornadoes, flood, wildfire, and lightning, among others. Several climate phenomena are also affected by the MJO. They are the monsoons, El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific and North American pattern, the Arctic and Antarctic Oscillations or northern and southern annual modes, the Indian Ocean dipole, the Wyrtki jets, and the Indonesian Through-flow. This article provides a brief summary of the connections between the MJO and these weather and climate phenomena. These connections demonstrate the critical role of the MJO in the weather–climate continuum and its prediction.

CORRESPONDING AUTHOR: Chidong Zhang, RSMAS/MPO, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33133, E-mail: czhang@rsmas.miami.edu

The Madden–Julian oscillation exerts broad influences on global weather and climate as its center of convection moves from the tropical Indian Ocean into the Pacific. Weather events under the influence of the MJO include precipitation, surface temperature, tropical cyclones, tornadoes, flood, wildfire, and lightning, among others. Several climate phenomena are also affected by the MJO. They are the monsoons, El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific and North American pattern, the Arctic and Antarctic Oscillations or northern and southern annual modes, the Indian Ocean dipole, the Wyrtki jets, and the Indonesian Through-flow. This article provides a brief summary of the connections between the MJO and these weather and climate phenomena. These connections demonstrate the critical role of the MJO in the weather–climate continuum and its prediction.

CORRESPONDING AUTHOR: Chidong Zhang, RSMAS/MPO, University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33133, E-mail: czhang@rsmas.miami.edu

As a prime example of intraseasonal variability, the Madden–Julian Oscillation affects— and is pivotal to predicting—both weather and climate.

The conceptual separation of weather and climate is deeply rooted in our daily experience, as Herbertson (1901) put it: “Climate is what on an average we may expect, weather is what actually we get.”1 Translated into a scientific language, weather is a state of the atmosphere at a particular instance and climate is a set of statistics of an ensemble of many different states (Lorenz 1975). The weather–climate separation had its scientific basis in numerical prediction. It has been perceived that weather predictability comes from initiation conditions, while climate predictability from boundary conditions (Charney and Shukla 1977). This distinction would cease to exist in the modern practice of “seamless prediction” for weather and climate using “unified prediction models” (Hurrell et al. 2009; Brown et al. 2012). In such models, all components of the Earth system are coupled to each other, the only boundary condition needed is at the top of the atmosphere, and the source of predictability comes from, in addition to initiation conditions, the “memory” of slowly varying subsystems (the ocean, soil moisture, land, and sea ice), quasiperiodic phenomena, and known external forcing (Lorenz 1975). Yet, the weather–climate separation has penetrated so deep in our thinking that their traditional definitions are still often used in scientific and official documents, leaving a gaping vacancy in between. This vacancy is occupied by intraseasonal (20–90 days) variability.

Intraseasonal variability is by no means merely red noise filling the gap between synoptic and seasonal variability. Intraseasonal phenomena are distinct from higher- and lower-frequency variability by their significant spectral peaks and coherent spatial patterns. The Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972) is the best example. Its large-scale signals in the atmospheric circulation, deep convection, and other variables propagating eastward slowly (~5 m s−1) from the Indian to Pacific Oceans are the dominant component of the tropical intraseasonal variability. They are so robust that they can be discerned from raw data without statistical manipulation (Zhang 2005).

The MJO plays a critical role in connecting or bridging weather and climate. This bridging role can be appreciated from different perspectives. The MJO affects many weather and climate phenomena. Its effects on weather depend on the state or phase of certain climate phenomena (e.g., ENSO), and their combined effects may lead to extreme weather events. Climate modes under the influence of the MJO in turn modulate weather in many parts of the world. The MJO is involved in scale interactions across a wide range of spectrum from the diurnal cycle to interannual variability (Moncrieff et al. 2012). Forecast of the Earth system to serve the society requires seamless prediction that covers daily, intraseasonal, seasonal, interannual, and longer variabilities (Dole 2008; Brunet et al. 2010; Shapiro et al. 2010; Chang et al. 2011). Improved MJO forecasting benefits prediction of tropical cyclones (Vitart 2009; Vitart et al. 2010), extratropical weather regimes (Marshall et al. 2010; Vitart and Molteni 2010), and ENSO (Shi et al. 2009); serves users from many sectors of the society (Gottschalck et al. 2010); and helps close the gap between traditional weather and short-term climate prediction (Waliser et al. 2006).

This article provides a brief summary of MJO effects on certain types of weather and climate events. The author hopes to convince its readers that weather and climate must be treated as a continuum by including the MJO and intraseasonal variability in general and reinforce the notion that the societal need for weather and climate prediction must be met with improved understanding and forecast of the MJO (Waliser et al. 2003a).

PRECIPITATION.

The sidebar “Detecting MJO influences on weather and climate” (Fig. SB2) illustrates rainfall variability in the tropics associated with the MJO during boreal winter (November–April). MJO influences on precipitation are not limited to the tropics and this season. A global map of precipitation anomalies associated with MJO in austral winter is given in Fig. 1. Anomalies in precipitation change signs between MJO phases in many places of the world.

DETECTING MJO INFLUENCES ON WEATHER AND CLIMATE

When discussing possible effects of the MJO on a particular type of weather or climate events, we must be mindful that all MJO episodes do not cause those events and all those events are not related to the MJO. The issue is whether and how the MJO may modulate the chances of occurrence, strengths, or spatial patterns/distributions of those events, as illustrated by examples given in this article.

MJO influences on weather events are commonly described as how those events vary with its phases. MJO phases can be defined in terms of the timing and locations of its center of convection (maximum rainfall anomalies) and associated wind fields. Most commonly used MJO phases are based on the real-time multivariate MJO (RMM) index of Wheeler and Hendon (2004). The RMM index is derived from a combined EOF analysis of daily anomalies in upper-and lower-level zonal wind and outgoing longwave radiation (OLR). MJO phases are defined by the principle components of the first two leading EOFs, normalized by their standard deviation (Fig. SB1). Each day, represented by a dot on the phase diagram, belongs to a particular phase. The distance of the dot from the center measures the amplitude of the MJO on that day. Composites of rainfall or any other field for each phase illustrate the canonical behavior of the MJO. In the boreal winter composite (Fig. SB2), the convection center of the MJO, represented by the maximum of positive anomalies in rainfall, starts over the Indian Ocean in phases 1–3, passes through the Maritime Continent in phases 4 and 5 and into the western Pacific in phases 6 and 7, and may continue their circumnavigating journey into the western hemisphere in phases 8 and 1 and thus complete its full cycle. During boreal summer, the zonal movement of the MJO convection center is accompanied by an additional northward movement associated with the Asian summer monsoon (Lawrence and Webster 2002). When the amplitude is less than 1 (within the circle on the phase diagram), the MJO is considered very weak or not existing (no MJO) and can be assigned as phase 0.

Fig. SB1
Fig. SB1

(above). Phase diagram of the RMM index. Each point represents a day. Eight phases and corresponding approximate locations of enhanced convective signals of the MJO are labeled. Points within the circle represent weak or no MJO (from Wheeler and Hendon 2004).

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

Fig. SB2
Fig. SB2

(right). Composites of intraseasonal (30– 90 days) anomalies in TRMM precipitation (mm day−1) during November–April of 1998–2012 based on the RMM index.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

MJO influences on climate may also depend on its phases. Some climate events are more likely to start, amplify, or change sign in certain MJO phases than others. Some other climate phenomena are related to activities of a group of MJO events over a period (e.g., a season), instead of phases of individual MJO events. There can be a time lag between the group MJO activities and the climate phenomena they affect.

Fig. 1.
Fig. 1.

Rainfall anomalies measured by surface rain gauges during MJO phases (a) 2, (b) 4, (c) 6, and (d) 8 for austral winter. Anomalies are expressed as maximum vertical distance between the unconditional cumulative distribution function (CDF) and the corresponding conditional CDF for a particular MJO phase (vertical differences are measured at the point of maximum divergence in dimensionless units of “percent change in probability”). Positive (negative) distances indicate evidence of enhanced (suppressed) rainfall during the respective phase (from Donald et al. 2006).

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

All monsoon systems undergo intraseasonal fluctuations (Lau and Waliser 2012). The MJO is a prominent source of the monsoon intraseasonal fluctuations. It affects the Asian summer monsoon mainly through, in addition to its eastward propagation, its northward propagation, which is unique in boreal summer. The onset of the South Asian monsoon is more likely to occur when MJO convection just starts over the Indian Ocean or in MJO phases 2 and 3 than in other phases. There are typically three or four major northward-propagating MJO events during a monsoon season, each inducing a local intraseasonal spike in rainfall. About 50%–80% of the total intraseasonal variance in the Asian summer monsoon rainfall is related to the MJO. On top of that, rainfall from synoptic monsoon lows and depressions enhanced by the MJO increases the chance of floods. Goswami (2012) and Hsu (2012) provided detailed descriptions of the role of the MJO in the Asian summer monsoon.

The MJO affects the Australian monsoon as its convection center propagates eastward, passing over the northern part of Australia (Wheeler et al. 2009). MJO accounts for more than 80% of onset dates of the Australian monsoon. Heavy rain (weekly rainfall in the top quintile of the December–February season) varies from 130 mm near the coast to 10 mm in central Australia. The probability of heavy rain at a given location depends on the longitudes of the MJO convection center. A detailed review on the role of the MJO in the Australia monsoon is given by Wheeler and McBride (2012).

Large-scale perturbations that are excited by MJO convection and propagate into the Americas can induce intra-seasonal fluctuations in rainfall of the American monsoons. In austral summer, rainfall over southern Brazil is heavier than normal (by up to 15–20 mm day−1; 50%–75% of the mean) when MJO convection moves into the central Pacific, especially east of the date line, or when it starts over the Indian Ocean, but it is lighter than normal when the MJO convection center is near or immediately east of the Maritime Continent. In boreal summer, MJO convective activities along the ITCZ over the northeastern tropical Pacific make it easier for the MJO to influence the North American monsoon. Its rainfall can differ as much as 25%–100% at individual stations between opposite phases of the MJO during July–September. The largest changes are along the Pacific coast, over southern Mexico and Central America, and on the Gulf coast of Mexico. Mo et al. (2012) provided a detailed summary of the role of the MJO in the pan-American monsoons.

Over West Africa, intraseasonal variability accounts about 30% of the total monsoon rainfall. One-third of the intraseasonal variability is related to the MJO or the African MJO mode (Janicot et al. 2011). At certain locations rainfall fluctuates by a factor of 2 between MJO phases. Near Lake Chad, about 50% of the amplitude of intraseasonal convective anomalies is related to the MJO (Alaka and Maloney 2012). From there, rainfall anomalies move westward to the rest of West Africa, presumably related to the Rossby waves generated by MJO convection over the Indian Ocean (Matthews 2004). As these convective systems move westward, some of them become part of African easterly waves. These African easterly waves are enhanced when the MJO convection center is over the Indian Ocean and suppressed when it is over the Maritime Continent and western Pacific (Ventrice et al. 2011). Janicot et al. (2011) and Barlow (2012) described in detail the MJO influences on the West African monsoon.

MJO also influences precipitation outside the monsoon regions and monsoon rainy seasons. Examples can be found in the Middle East and Southwest Asia (Barlow et al. 2005; Barlow 2012), Southeast and East Asia (Jeong et al. 2008; Zhang et al. 2009; He et al. 2011; Jia et at. 2011), equatorial Africa (Pohl and Camberlin 2006a, b; Laing et al. 2011), Brazil (Carvalho et al. 2004; Souza and Ambrizzi 2006), Chile (Barrett et al. 2012), and North America (Bond and Vecchi 2003; Becker et al. 2011).

MJO influences on precipitation extend to extreme rainfall, defined as precipitation breaking the records or within a given top percentile. On a global scale, extreme rainfall events during active MJO periods (phases 1–8) are about 40% higher than in its quiescent periods (phase 0) (Jones et al. 2004). The MJO might have been one of the factors for the heavy snowfalls in the Tokyo metropolitan area on 3 February 2008 (Yamakawa and Suppiah 2009). The record-breaking snow events in the eastern United States in December 2009 and February 2010 was attributed to the MJO with its unusual active convection over the central Pacific during an El Niño year on top of the negative phase of the North Atlantic Oscillation (Moon et al. 2012). Over the highland region of east equatorial Africa, 62% of extreme rainfall events in March–May occur when MJO convection over the Indian Ocean is active, while 72% of extreme rainfall near the coastal region occurs when MJO convection is suppressed over the Indian Ocean and Maritime Continent. There, negative anomalies during weak MJO years often follow the peaks of ENSO warm events (Pohl and Camberlin 2006b). In the subtropical, semiarid north-central coastal area of Chile (30°S), about 80% of the strong precipitation events (normally 3–5 per year) during the fall and winter of rainy years are related to enhanced MJO convection in the central equatorial Pacific (Juliá et al. 2012).

Extreme rainfall (exceeding 90th percentile of frequency distribution in intensity and spatial coverage) in boreal winter occurs twice more frequently over the contiguous United States when the MJO is active (phases 1–8) than inactive (phase 0) and most frequently when the MJO convection center is over the Indian Ocean (Jones and Carvalho 2012). Such MJO influences are stronger during ENSO warm than cold events. Forecast skill for the winter extreme precipitation is higher when MJO convection over the Indian–western Pacific Oceans are suppressed (Jones et al. 2011a,b).

The MJO affects precipitation in remote areas by modifying the strength of meridional overturning circulations (Zhang et al. 2009; He et al. 2011) and moisture transport (Jeong et al. 2008; Jia et al. 2011), exciting Rossby wave trains that emanate from the tropics into the extratropics (Grimm and Silva Dias 1995), and forcing zonally propagating equatorial Rossby and Kelvin waves (Matthews 2004; Janicot et al. 2009). A phenomenon known as the “atmospheric river” acts as a conveyer belt to transport moisture from the tropical central Pacific to the West Coast of the United States, where it may cause torrential rain and floods (Dettinger 2011). MJO may enhance rainfall along the West Coast of the United States through strengthening the atmospheric river (Ralph et al. 2011). As a result, total snow accumulation in the Sierra Nevada significantly increases (reduces) when MJO convection is active over the eastern Indian Ocean (Western Hemisphere); the corresponding magnitude of daily anomalies is about half the daily mean in the cold season (Guan et al. 2012).

TORNADOES.

Violent tornado outbreak days, with six or more tornadoes on the (Enhanced) Fujita [(E)F] scale of at least (E)F2 magnitude reported within a 24-h period, tend to occur in spring over the contiguous United States. Thompson and Roundy (2013) documented that violent tornado outbreak days during March, April, and May are more than twice as frequent during MJO phase 2 as during other phases, including phase 0. Atmospheric conditions favorable for tornado formation can be provided by combined intraseasonal and seasonal anomalous patterns in upper-tropospheric troughs and upper- and lower-tropospheric winds.

TROPICAL CYCLONES.

Favorable large-scale conditions for genesis, intensification, and longevity of tropical cyclones (TCs) can be altered by the MJO. Figure 2 shows TC tracks in different MJO phases and composites of precipitation anomalies of the MJO. The density of the tracks indicates total TC days or the TC occurrence frequency when normalized by the total days of each MJO phase. This figure summarizes the known MJO modulation of TCs that has been documented by many studies—some of which are mentioned below. It shows an obvious eastward shift of the most dense TC tracks along with the positive precipitation anomalies of the MJO from the Indian Ocean to the eastern Pacific.

Fig. 2.
Fig. 2.

TC tracks (1975–2011) and precipitation anomalies (1998–2011) in MJO phases 2 and 3, 4 and 5, 6 and 7, and 8 and 1 when the amplitude of the RMM index is greater than one. The total number of days of TCs in each phase group is listed. The TC tracks are from the International Best Track Archive for Climate Stewardship (IBTrACS) v03r04. Precipitation data are from Tropical Rainfall Measuring Mission (TRMM) 3B43 v7.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

Over the southern Indian Ocean, TCs are more frequent in MJO phases 2 and 3 than in phases 8 and 1. The number of TCs there tend to increase by twofold (Liebmann et al. 1994) or more precisely by a factor of 2.6 (Bessafi and Wheeler 2006), from periods of negative convective anomalies (or low-level easterly anomalies) of the MJO over the Indian Ocean to positive convective anomalies (or westerly anomalies). Between phases 2 and 3 and phases 4 and 5, heavy TC genesis locations shift eastward with the MJO convection center across the Indian Ocean (Ho et al. 2006). TCs occur most frequently in MJO phases 4 and 5 and least frequently in phases 8 and 1 over the northern Indian Ocean and also near the northwestern coast of Australia, with a difference of 4 to 1 (Hall et al. 2001).

Over the tropical southwestern Pacific, TCs appear most frequently in MJO phases 6 and 7 and least frequently in phases 4 and 5. Over the northwestern Pacific, TCs are most frequent in phases 6 and 7 and least frequent in phases 2 and 3. The MJO modulation of TCs over the Indian and western Pacific Oceans sometimes leads to twin tropical cyclones straddling at the equator (Keen 1982; Ferreira et al. 1996).

Over the tropical eastern Pacific, there are more TCs (hurricanes) in MJO phases 8 to 3 than in phases 4 and 5. TCs vary coherently with the low-level zonal wind anomalies of the MJO and there are over four times more hurricane-strength storms during westerly phases of the MJO than its easterly phases (Maloney and Hartmann 2000a). Global models with sufficiently high grid spacing well capture this MJO–TC connection (Jiang et al. 2012).

The MJO can also considerably influence hurricanes in the Gulf of Mexico, Caribbean Sea, and tropical Atlantic (Mo 2000). More hurricanes tend to occur in MJO phases 2 and 3 than in phases 6 and 7. Differences in major hurricane numbers and hurricane days in the main development region (7.5°–22.5°N, 20°–75°W) are a factor of 3 (Klotzbach 2010). Hurricane genesis in these regions is four times more likely to occur in local low-level westerly wind phases of the MJO than in its easterly phases, and strong hurricanes (categories 3–5) have an even greater preference (fivefold) to occur during local westerly phases of the MJO (Maloney and Hartmann 2000b). Numerical prediction skill of Atlantic hurricanes sensitively depends on MJO phases and strength at model initiation time (Belanger et al. 2010).

Globally, more TCs tend to occur near the eastern edge and to the poleward side of the local low-level westerly wind anomalies of the MJO and within the eastern and equatorward portion of its cyclonic vortex gyres (Frank and Roundy 2006). Possible mechanisms for the MJO influences on TCs include reduced vertical wind shear, enhanced low-level convergence, cyclonic relative vorticity, deep convection, midlevel moisture, small eddies, and synoptic disturbances serving as embryos for TCs (Liebmann et al. 1994; Mo 2000; Maloney and Hartmann 2001; Maloney and Shaman 2008; Camargo et al. 2009).

Based on the observed modulation of TCs by the MJO, statistical models for predicting TC genesis or occurrence with lead times beyond one week have been developed (Leroy and Wheeler 2008; Slade and Maloney 2013). Dynamical models that can reproduce the observed relationship between the MJO and TCs attain skills of predicting their landfall (Vitart 2009). Their skill in predicting TCs up to 20 days has been attributed to their skill in predicting the MJO (Vitart et al. 2010).

FLOOD.

Observational studies have suggested that the MJO might have had influences on certain major flood events. Examples include a series of severe floods during the summer of 1998 in eastern China (Zhu et al. 2003), the Afghanistan flood of April 2002 (Barlow et al. 2005), the extreme 2006–07 flood in the southern peninsula of Malaysia (Tangang et al. 2008), and the largest floods on record at Jakarta in 2002, 2007, and 2008 (Aldrian 2008). While relating individual flood events to individual MJO events must face large uncertainties, MJO influences on flood occurrence probability can be quantified with statistical significance. An analysis of a global flood data (see http://floodobservatory.colorado.edu/Archives/index.html) suggested possible effects of the MJO on global “large flood events,” which are defined as extreme flood events with damages that have been reported with intervals of a decade or longer. The locations of such large flood events are shown in Fig. 3. Several flood regions are defined, somewhat arbitrarily, based on known flood vulnerability (e.g., the Philippines) or geographic location (e.g., Australia). In a given region, flood probability is measured as the total number of flood days and flood events,2 normalized by the total number of days in a given MJO phase or calendar month.

Fig. 3.
Fig. 3.

Locations of large floods during 1985–2010 based on the Dartmouth Flood Observatory Global Archive of Large Flood Events at University of Colorado (http://floodobservatory.colorado.edu/Archives/index.html). Red boxes mark regions where probability of total flood days and/or events are significantly affected by the MJO. Blue and green boxes are examples shown in Figs. 4 and 5, respectively.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

Over the West Coast of North America (blue box in Fig. 3), for example, there is a strong seasonal cycle in large floods, with many more flood days in December and January than in June and July (Fig. 4a). Most December and January floods occur when there are MJO events. While early winter (November–December) floods in the northwest United States tend to occur when MJO convection is active over the Indian Ocean (Bond and Vecchi 2003), large flood events are most likely to start in MJO phase 6 when its convection center is located over the western Pacific and least likely in phase 1 (Fig. 4b) when positive precipitation anomalies of the MJO are generally in the Western Hemisphere (Fig. SB2). Differences between these two phases and between them and phase 0 (no MJO) are significant at the 95% confidence level.

Fig. 4.
Fig. 4.

MJO influences of large floods of the West Coast of North America (blue box in Fig. 3). (a) Monthly probability of total flood days during MJO episodes (black bars) and when there is no MJO (gray). (b) Probability of flood events in each MJO phase (total number of flood events starting in each phase divided by the total number of days in that phase). Vertical error bars denote ranges of the 95% confidence level.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

Another example is the Philippines (green box in Fig. 3), which is located on the pathway of MJO propagation. There, the seasonality of large flood is marked by a dip in April (Fig. 5a). Both total flood days (Fig. 5b) and flood events (Fig. 5c) are substantially higher in phases 6 and 7 than in phases 2 and 3, and both are significantly different from those in phase 0 (no MJO). The MJO modifies the occurrence probability of large flood days and/or events also in many other regions (red boxes in Fig. 3).

Fig. 5.
Fig. 5.

MJO influences of large floods of the Philippines (green box in Fig. 3). (a) Monthly probability of total flood days during MJO episodes (black bars) and when there is no MJO (gray). (b) Probability of total flood days in each MJO phase (total number of flood days in each phase divided by the total number of days in that phase). (c) Probability of flood events as functions of MJO phases. Vertical error bars denote ranges of the 95% confidence level.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

FIRE.

Possible MJO effects on fire (biomass burning due both to wildfire and manmade fire in agriculture practice) come obviously from its modulation on rainfall that may help prevent, delay, or terminate fire. An example is the influence of the MJO on fire over the Maritime Continent, where biomass burning is part of the land management practice (Reid et al. 2012). During the dry and burning season (June–November), fire maximum (minimum) occurs during local dry (rainy) phases (5–8 versus 1–4) of the MJO, as expected. The ratio of fire accounts between these phases can be as high as 10 (Fig. 6). The strongest MJO effects tend to occur in regions where convective rainfall is enhanced by orography (e.g., Sumatra, western Borneo). Similar results were obtained during the wet season (December–May) with two exceptions. Fire activity is more delayed after the rainy phases of the MJO because it takes longer for the burning materials to dry. Stronger MJO rainfall leads to much lower fire minimum than during dry seasons, effectively doubling the relative differences between fire maximum and minimum.

Fig. 6.
Fig. 6.

Ratio of fire detections for MJO phases 5–8 over 1–4 in June–November (from Reid et al. 2012).

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

Globally, the MJO affects fire in many regions. A similar analysis method as for flood revealed that carbon fire emission (Mu et al. 2011) in certain parts of the world fluctuates substantially with MJO phases (Fig. 7). For example, over northwestern Canada and Alaska, fire is most likely to occur in MJO phase 1 and is least likely in phase 4. In northeastern China and adjacent Siberia, fire chances are also the lowest in MJO phase 4 but the highest in phase 7. MJO effects on fire in these high-latitude regions must be explained in terms of tropical–extratropical teleconnections.

Fig. 7.
Fig. 7.

Total fire carbon emission (g C m2 day−1) based on data of Mu et al. (2011). Red boxes indicate regions where fire emission fluctuates substantially with MJO phases.

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

LIGHTNING AND GLOBAL ELECTROMAGNETIC FIELD.

Over the eastern tropical Indian Ocean, the maximum flash rate occurs slightly after OLR anomalies of the MJO reach maximum, and the minimum flash rate (about 18% lower than the maximum) slightly after OLR anomalies are minimum (Morita et al. 2006). For the same amount of rain, the flash rate increases when MJO convection is suppressed and rain systems tend to show more continental characteristics with isolated, very deep (>10 km) convective cells but decreases when MJO convection becomes active and rain systems tend to show more oceanic characteristics with wide-spread convection and moderate depth (~7 km). Over large islands of the Maritime Continent (e.g., Sumatra, Kalimantan), lightning frequency tends to reach its maximum (up to 50% of the climatological means) immediately prior to a local convectively active phase of the MJO and reach its minimum during and immediately after an inactive phase (Kodama et al. 2006; Virts et al. 2011). Such fluctuations in lightning activity through the convective life cycle of the MJO are consistent to the substantial amount of “vertically intense” convection observed in the suppressed period of the MJO and a lack of it in the active period over the ocean (Demott and Rutledge 1998a,b) and land (Shibagaki et al. 2006).

Cloud-to-ground lightning strikes can be an ignition mechanism for wildfire under relatively dry conditions, such as summer across vast sections of the western United States (Pyne et al. 1996). There is a strong connection between the MJO and summertime lightning activity across the United States (Fig. 8). Lightning activity tends to emanate northward from the desert southwest into the northern Rocky Mountains through MJO phases 6–8. After that, it quickly spreads eastward to the Great Lakes area and the east coast in phase 1. In addition, lightning frequency increases over 60% across the U.S. Southeast as the MJO convection center moves over the Maritime Continent (phase 6). It is argued that the MJO provides favorable conditions for the northward propagation of widespread lightning activity through the amplification of the upper-level ridge over the western United States and the development of midtropospheric instability (Abatzoglou and Brown 2009).

Fig. 8.
Fig. 8.

Summer (June–September) lightning frequency rank-order anomalies (z scores) stratified by RMM phases (denoted in lower-right corner). Red (blue) shading denotes areas of enhanced (suppressed) lighting activity for each RMM phase exceeding the 95% confidence interval (from Abatzoglou and Brown 2009).

Citation: Bulletin of the American Meteorological Society 94, 12; 10.1175/BAMS-D-12-00026.1

The Schumann resonances (SRs) are electromagnetic waves of zonal wavenumber one trapped in the natural cavity between Earth and the ionosphere. The SRs affect human health, such as cancer, blood pressure, heart attack, brain waves, and the nervous system (Cherry 2002; Mitsutakea et al. 2005). SR anomalies may help early detection of earthquakes (Ohta et al. 2006). Its intensity is largely modulated by fluctuations in the number and intensities of lightning flashes worldwide. There are intraseasonal (20–30 days) fluctuations in SR data (Fullekrug and Fraser-Smith 1996) because of the global connection between the MJO and lightning. As anomalous SR intensity undergoes its intraseasonal cycle, there is a systematic eastward shift of anomalous convective activity from tropical South America to the western Pacific (Anyamba et al. 2000), consistent with the eastward propagation pattern of the MJO. Maximum SR intensity occurs when maximum convection is over Africa. Minimum SR intensity occurs when convection is maximum over the western Pacific and minimum over Africa.

SURFACE TEMPERATURE.

In East Asia, two-thirds of extreme cold surges with temperature reductions greater than two standard deviations occur when MJO convection is over the Indian Ocean (Jeong et al. 2005). Combined effects of the MJO and other factors (e.g., the Arctic Oscillation) might have led to an extreme cold surge with record-breaking snowfall in Korea (Park et al. 2010). In general, the MJO tends to prevent weak cold surges from penetrating southward into the subtropics and tropics (Chang et al. 2005). During a cold year of ENSO, however, an MJO event with its convection center stalled over Sumatra might have resulted in an extreme cold event that broke a 50-yr record of minimum daily temperature and duration of large negative temperature anomalies (>1 standard deviation) over Southea