• Adames, A. F., and J. M. Wallace, 2014: Three-dimensional structure and evolution of the vertical velocity and divergence fields in the MJO. J. Atmos. Sci., 71, 46614681, https://doi.org/10.1175/JAS-D-14-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adames, A. F., and D. Kim, 2016: The MJO as a dispersive, convectively coupled moisture wave: Theory and observations. J. Atmos. Sci., 73, 913941, https://doi.org/10.1175/JAS-D-15-0170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adames, A. F., D. Kim, S. K. Clark, Y. Ming, and K. Inoue, 2019: Scale analysis of moist thermodynamics in a simple model and the relationship between moisture modes and gravity waves. J. Atmos. Sci., 76, 38633881, https://doi.org/10.1175/JAS-D-19-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., 2020: Diversity of the global teleconnections associated with the Madden–Julian oscillation. J. Climate, 34, 397414, https://doi.org/10.1175/JCLI-D-20-0357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collimore, C. C., D. W. Martin, M. H. Hitchman, A. Huesmann, and D. E. Waliser, 2003: On the relationship between the QBO and tropical deep convection. J. Climate, 16, 25522568, https://doi.org/10.1175/1520-0442(2003)016<2552:OTRBTQ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., B. O. Wolding, E. D. Maloney, and D. A. Randall, 2018: Atmospheric mechanisms for MJO decay over the Maritime Continent. J. Geophys. Res., 123, 51885204, https://doi.org/10.1029/2017JD026979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Densmore, C. R., E. R. Sanabia, and B. S. Barrett, 2019: QBO influence on MJO amplitude over the Maritime Continent: Physical mechanisms and seasonality. Mon. Wea. Rev., 147, 389406, https://doi.org/10.1175/MWR-D-18-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, J., T. Li, and W. Zhu, 2015: Propagating and nonpropagating MJO events over the Maritime Continent. J. Climate, 28, 84308449, https://doi.org/10.1175/JCLI-D-15-0085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonzalez, A. O., and X. Jiang, 2019: Distinct propagation characteristics of intraseasonal variability over the tropical west Pacific. J. Geophys. Res., 124, 53325351, https://doi.org/10.1029/2018JD029884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., J. D. Sheaffer, and J. A. Knaff, 1992: Influence of the stratospheric QBO on ENSO variability. J. Meteor. Soc. Japan, 70, 975995, https://doi.org/10.2151/jmsj1965.70.5_975.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., and S. Abhik, 2018: Differences in vertical structure of the Madden–Julian oscillation associated with the quasi-biennial oscillation. Geophys. Res. Lett., 45, 44194428, https://doi.org/10.1029/2018GL077207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., C. Zhang, and J. D. Glick, 1999: Interannual variation of the Madden–Julian oscillation during austral summer. J. Climate, 12, 25382550, https://doi.org/10.1175/1520-0442(1999)012<2538:IVOTMJ>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1175/JCLI4003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., and J. D. Neelin, 2007: The convective cold top and quasi equilibrium. J. Atmos. Sci., 64, 14671487, https://doi.org/10.1175/JAS3907.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., and M.-Y. Lee, 2005: Topographic effects on the eastward propagation and initiation of the Madden–Julian oscillation. J. Climate, 18, 795809, https://doi.org/10.1175/JCLI-3292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 81798205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inness, P. M., and J. M. Slingo, 2006: The interaction of the Madden–Julian oscillation with the Maritime Continent in a GCM. Quart. J. Roy. Meteor. Soc., 132, 16451667, https://doi.org/10.1256/qj.05.102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaufman, L., and P. J. Rousseeuw, 2009: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, 368 pp.

  • Kiladis, G. N., M. C. Wheeler, P. T. Haertel, K. H. Straub, and P. E. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, https://doi.org/10.1029/2008RG000266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., J. S. Kug, and A. H. Sobel, 2014: Propagating versus nonpropagating Madden–Julian oscillation events. J. Climate, 27, 111125, https://doi.org/10.1175/JCLI-D-13-00084.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-K., and K.-H. Seo, 2016: Cluster analysis of tropical cyclone tracks over the western North Pacific using a self-organizing map. J. Climate, 29, 37313751, https://doi.org/10.1175/JCLI-D-15-0380.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, W. K.-M., and D. E. Waliser, 2011: Intraseasonal Variability in the Atmosphere–Ocean Climate System. 2nd ed. Springer, 614 pp.

  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, http://www.jstor.org/stable/26233278.

    • Search Google Scholar
    • Export Citation
  • Liess, S., and M. A. Geller, 2012: On the relationship between QBO and distribution of tropical deep convection. J. Geophys. Res., 117, D03108, https://doi.org/10.1029/2011JD016317.

    • Crossref
    • 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, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majda, A. J., and J. A. Biello, 2004: A multiscale model for tropical intraseasonal oscillations. Proc. Natl. Acad. Sci. USA, 101, 47364741, https://doi.org/10.1073/pnas.0401034101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majda, A. J., and S. N. Stechmann, 2009: The skeleton of tropical intraseasonal oscillations. Proc. Natl. Acad. Sci. USA, 106, 84178422, https://doi.org/10.1073/pnas.0903367106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., H. H. Hendon, and G. Wang, 2016: On the role of anomalous ocean surface temperatures for promoting the record Madden–Julian oscillation in March 2015. Geophys. Res. Lett., 43, 472481, https://doi.org/10.1002/2015GL066984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., H. H. Hendon, S.-W. Son, and Y. Lim, 2017: Impact of the quasi-biennial oscillation on predictability of the Madden–Julian oscillation. Climate Dyn., 49, 13651377, https://doi.org/10.1007/s00382-016-3392-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neale, R., and J. Slingo, 2003: The Maritime Continent and its role in the global climate: A GCM study. J. Climate, 16, 834848, https://doi.org/10.1175/1520-0442(2003)016<0834:TMCAIR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, J., and A. H. Sobel, 2015: Responses of tropical deep convection to the QBO: Cloud-resolving simulations. J. Atmos. Sci., 72, 36253638. https://doi.org/10.1175/JAS-D-15-0035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nishimoto, E., and S. Yoden, 2017: Influence of the stratospheric quasi-biennial oscillation on the Madden–Julian oscillation during austral summer. J. Atmos. Sci., 74, 11051125, https://doi.org/10.1175/JAS-D-16-0205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oh, J.-H., K.-Y. Kim, and G.-H. Lim, 2012: Impact of MJO on the diurnal cycle of rainfall over the western Maritime Continent in the austral summer. Climate Dyn., 38, 11671180, https://doi.org/10.1007/s00382-011-1237-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oh, J.-H., B.-M. Kim, K.-Y. Kim, H.-J. Song, and G.-H. Lim, 2013: The impact of the diurnal cycle on the MJO over the Maritime Continent: A modeling study assimilating TRMM rain rate into global analysis. Climate Dyn., 40, 893911, https://doi.org/10.1007/s00382-012-1419-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A., and E. Maloney, 2012: An idealized semi-empirical framework for modeling the Madden–Julian oscillation. J. Atmos. Sci., 69, 16911705, https://doi.org/10.1175/JAS-D-11-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A., and E. Maloney, 2013: Moisture modes and the eastward propagation of the MJO. J. Atmos. Sci., 70, 187192, https://doi.org/10.1175/JAS-D-12-0189.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Son, S.-W., Y. Lim, C. Yoo, H. H. Hendon, and J. Kim, 2017: Stratospheric control of the Madden–Julian oscillation. J. Climate, 30, 19091922, https://doi.org/10.1175/JCLI-D-16-0620.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, L., H. Wang, and F. Liu, 2019: Combined effect of the QBO and ENSO on the MJO. Atmos. Oceanic Sci. Lett., 12, 170176, https://doi.org/10.1080/16742834.2019.1588064.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thual, S., and A. J. Majda, 2016: A skeleton model for the MJO with refined vertical structure. Climate Dyn., 46, 27732786, https://doi.org/10.1007/s00382-015-2731-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thual, S., A. J. Majda, and S. N. Stechmann, 2014: A stochastic skeleton model for the MJO. J. Atmos. Sci., 71, 697715, https://doi.org/10.1175/JAS-D-13-0186.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and T. Li, 1994: Convective interaction with boundary-layer dynamics in the development of a tropical intraseasonal system. J. Atmos. Sci., 51, 13861400, https://doi.org/10.1175/1520-0469(1994)051<1386:CIWBLD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., F. Liu, and G. Chen, 2016: A trio-interaction theory for Madden–Julian oscillation. Geosci. Lett., 3, 34, https://doi.org/10.1186/s40562-016-0066-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., G. Chen, and F. Liu, 2019: Diversity of the Madden-Julian oscillation. Sci. Adv., 5, eaax0220, https://doi.org/10.1126/sciadv.aax0220.

  • Wang, L., T. Li, E. Maloney, and B. Wang, 2017: Fundamental causes of propagating and non-propagating MJOS in MJOTF/GASS models. J. Climate, 30, 37433769, https://doi.org/10.1175/JCLI-D-16-0765.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, T., and T. Li, 2021: Factors controlling the diversities of MJO propagation and intensity. J. Climate, 34, 65496563, https://doi.org/10.1175/JCLI-D-20-0859.1.

    • Search Google Scholar
    • Export Citation
  • Weickmann, K. M., 1983: Intraseasonal circulation and outgoing longwave radiation modes during Northern Hemisphere winter. Mon. Wea. Rev., 111, 18381858, https://doi.org/10.1175/1520-0493(1983)111<1838:ICAOLR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C.-H., and H.-H. Hsu, 2009: Topographic influence on the MJO in the Maritime Continent. J. Climate, 22, 54335448, https://doi.org/10.1175/2009JCLI2825.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B., and Coauthors, 2022: S2S prediction in GFDL SPEAR: MJO diversity and teleconnections. Bull. Amer. Meteor. Soc., 103, E463E484, https://doi.org/10.1175/BAMS-D-21-0124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yadav, P., and D. M. Straus, 2017: Circulation response to fast and slow MJO episodes. Mon. Wea. Rev., 145, 15771596, https://doi.org/10.1175/MWR-D-16-0352.1.

    • 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, D., 2021: A shallow water model for convective self-aggregation. J. Atmos. Sci., 78, 571582, https://doi.org/10.1175/JAS-D-20-0031.1.

  • Yang, D., and A. P. Ingersoll, 2011: Testing the hypothesis that the MJO is a mixed Rossby–gravity wave packet. J. Atmos. Sci., 68, 226239, https://doi.org/10.1175/2010JAS3563.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and A. P. Ingersoll, 2013: Triggered convection, gravity waves, and the MJO: A shallow-water model. J. Atmos. Sci., 70, 24762486, https://doi.org/10.1175/JAS-D-12-0255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and A. P. Ingersoll, 2014: A theory of the MJO horizontal scale. Geophys. Res. Lett., 41, 10591064, https://doi.org/10.1002/2013GL058542.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and S. D. Seidel, 2020: The incredible lightness of water vapor. J. Climate, 33, 28412851, https://doi.org/10.1175/JCLI-D-19-0260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoo, C., and S. Son, 2016: Modulation of the boreal wintertime Madden–Julian oscillation by the stratospheric quasi-biennial oscillation. Geophys. Res. Lett., 43, 13921398, https://doi.org/10.1002/2016GL067762.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden-Julian oscillation. Rev. Geophys., 43, 136, https://doi.org/10.1029/2004RG000158.

  • Zhang, C., 2013: Madden–Julian oscillation: Bridging weather and climate. Bull. Amer, Meteor. Soc., 94, 18491870, https://doi.org/10.1175/BAMS-D-12-00026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and H. H. Hendon, 1997: Propagating and standing components of the intraseasonal oscillation in tropical convection. J. Atmos. Sci., 54, 741752, https://doi.org/10.1175/1520-0469(1997)054<0741:PASCOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and J. Ling, 2017: Barrier effect of the Indo-Pacific Maritime Continent on the MJO: Perspectives from tracking MJO precipitation. J. Climate, 30, 34393459, https://doi.org/10.1175/JCLI-D-16-0614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and B. Zhang, 2018: QBO–MJO connection. J. Geophys. Res., 123, 29572967, https://doi.org/10.1002/2017JD028171.

  • Zhang, C., A. Adames, B. Khouider, B. Wang, and D. Yang, 2020: Four theories of the Madden–Julian oscillation. Rev. Geophys., 58, e2019RG000685, https://doi.org/10.1029/2019RG000685.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery
    Fig. 1.

    Centroids by k-means cluster analysis on OLR Hovmöller diagrams of 104 boreal winter MJO events during 1979–2013, using the same data and method as in Wang et al. (2019). Hovmöller diagrams are obtained by averaging the 20–70-day bandpass-filtered daily OLR between 10°S and 10°N. The reference day (day 0) is defined as when the 20–70-day bandpass-filtered box-mean (75°–95°E, 10°S–10°N) OLR time series reaches its local minimum during each MJO lifespan. The cases with silhouette scores lower than 0.06 are ruled out of the corresponding centroids after the cluster analysis. Numbers of MJO cases in four clusters after (before) the ruling out are indicated by the numbers before (after) the slash. Details of the data and methodology can be found in Wang et al. (2019).

  • View in gallery
    Fig. 2.

    Evolution of intraseasonal (20–70 days) OLR (shadings) and wind field at 850 hPa (vectors) from day −10 to day 20 for four types of MJO in tropics. Only the composited OLR and wind components above 95% confidence level are drawn.

  • View in gallery
    Fig. 3.

    As in Fig. 2, but for the composited vertical sections (10°S−10°N) of specific humidity (shadings) and vertical circulation (vectors) from day −10 to day 10. The wet (dry) anomalies indicating the potential WPWs for stand and jump MJO are marked out by thick green (red) arrow lines, respectively.

  • View in gallery
    Fig. 4.

    As in Fig. 1, but for the equatorial (10°S−10°N) intraseasonal OLR (lines) and column-integrated (from 100 hPa to the surface) specific humidity (shadings). The specific humidity anomalies over the 95% significance level are stippled.

  • View in gallery
    Fig. 5.

    Composited maps for the westward-filtered intraseasonal wind at 850 hPa (vectors) and the column-integrated specific humidity (shadings) over the central-western Pacific from day −5 to day 5 for (a) stand and (b) jump MJO. The specific humidity anomalies over the 95% significance level are stippled, and the wind vectors over the 95% significance level are represented by thick black arrow lines.

  • View in gallery
    Fig. 6.

    Composited MJO OLR (lines) and column-integrated (100–1000 hPa; shading) moisture tendency over the eastern Indian Ocean and MC region at day 0 for (a) NP MJO and (b) EP MJO. OLR intervals are 10 W m−2 from −40 to 40 W m−2 with positive OLR represented by dashed yellow lines and negative OLR by solid green lines. Only the composited OLR results exceeding the 95% confidence level are drawn, and the composited moisture tendency exceeding 95% confidence level is stippled.

  • View in gallery
    Fig. 7.

    Composited intraseasonal column-integrated moisture budget terms over the eastern Indian Ocean and MC (25°S–25°N, 60°–150°E) at day 0 for (a) NP and (b) EP MJO. The moisture tendency term is represented by lines with dashed yellow lines as drying and solid green lines as moistening. Zonal advection, meridional advection, and column terms are shown by shadings in the corresponding rows with values exceeding 95% confidence level stippled.

  • View in gallery
    Fig. 8.

    Box-mean values of the moisture budget analysis terms over the southern sea surface of the MC (20°–10°S, 100°–140°E) for (a) EP and (b) NP MJO.

  • View in gallery
    Fig. 9.

    Box-mean of the decomposed meridional moisture advection terms with different time scales over the southern sea surface of the MC (20°–10°S, 100°–140°E) for (a) EP and (b) NP MJO.

  • View in gallery
    Fig. 10.

    (top) Climatology of boreal winter column-integrated (from the surface to 100 hPa) specific humidity as well as (bottom) the differences between the background moisture for NP and EP MJO and the climatology (shading). The composited MJO-scale horizontal wind components at 850 hPa (vectors in the bottom two rows) are superposed. The region for the box-mean as in Figs. 8 and 9 is marked by dashed red rectangles on the map. The climatology state of boreal winter humidity is computed by averaging November–April monthly fields from 1979 to 2013. The background state for each MJO event is obtained by applying the 3-month running mean of specific humidity field centered with the calendar month of day 0. In the bottom row, only the MJO-scale horizontal wind components exceeding 95% confidence level are displayed.

  • View in gallery
    Fig. 11.

    Phase diagram of QBO and ENSO for MJO events. The x axis refers to the QBO index defined as global mean of equatorial (5°S–5°N) monthly zonal wind; the y axis refers to the ENSO index using monthly Niño-3.4 index. Solid lines represent the climatology mean of two indices. Dashed gray lines parallel to the x axis represent 1°C below and above the climatology mean of monthly Niño-3.4. Dashed gray lines parallel to the y axis represent 1/2 standard deviation above and below the climatology mean of monthly QBO index. Each dot represents one MJO event with different colors indicating the corresponding MJO type. Four stars tell the mean values of the QBO and ENSO indices for four types of MJO. Also, numbers of MJO events lying in each quadruplet of the phase diagram are labeled, respectively.

  • View in gallery
    Fig. 12.

    Composited equatorial (10°S–10°N) vertical sections of MJO induced temperature anomalies (shading) and MJO vertical circulations (vectors) from day −5 to day 5. Two dashed horizontal lines in each subplot indicate the 100- and 200-hPa levels, respectively. Only the temperature anomalies and circulations exceeding 95% confidence level are shown.

  • View in gallery
    Fig. 13.

    Composited Hovmöller diagrams of the intraseasonal equatorial (10°S–10°N) OLR (shading) and tropopause stability (T100 minus T200; lines) for MJO events in Fig. 11 lying in the (a) QBOE and (b) QBOW phases. (c) The covariance between OLR and tropopause stability as a function of the longitude in (a) and (b). Noted that the covariances are normalized by the variance of the box-mean 20–70-day bandpass-filtered OLR time series over the Indian Ocean (10°S–10°N, 75°–95°E) from day −25 to day 25 for MJO events in QBOE and QBOW phases, respectively.

  • View in gallery
    Fig. 14.

    Maps for (a) westward-filtered intraseasonal OLR variance and (b) the composited differences of the westward-filtered intraseasonal OLR variance between QBOE and QBOW phase. (c) As in (b), but between different ENSO phases, all for boreal winter seasons. Differences exceeding 95% confidence level are stippled.

  • View in gallery
    Fig. 15.

    Composited Hovmöller diagrams (10°S–10°N average) of tropopause instability (lines) and premoistening (shadings) for four types of MJO. The tropopause instability is measured by the temperature difference at 100 and 200 hPa with negative (positive) difference indicating instability (stability) and represented by dashed green (solid yellow) lines. The premoistening is measured by the column-integrated (from surface to 100 hPa) moisture tendency. The dashed vertical lines indicate the longitude of 150°E. Each MJO type’s QBO and ENSO preferences are labeled on the top right of each subplot. Only the temperature difference exceeding 95% confidence level is drawn, and the column-integrated moisture tendencies exceeding 95% confidence level are stippled.

  • View in gallery
    Fig. A1.

    Number of MJO cases passing the silhouette test with different cluster numbers defined for the k-means cluster analysis. Dotted lines with different colors represent results with different thresholds (σ) for the silhouette test ranging from 0.04 to 0.08 with an increment of 0.01.

All Time Past Year Past 30 Days
Abstract Views 60 60 0
Full Text Views 543 537 28
PDF Downloads 512 489 31

The Roles of Westward-Propagating Waves and the QBO in Limiting MJO Propagation

Kai HuangaDepartment of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

Search for other papers by Kai Huang in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-7342-6780
and
Kathleen PegionaDepartment of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

Search for other papers by Kathleen Pegion in
Current site
Google Scholar
PubMed
Close
Open access

Abstract

A recent study categorized the Madden–Julian oscillation (MJO) during boreal winter season into four types called stand, jump, slow, and fast MJO. This study focuses on the stand and jump MJO. Based on whether their convection penetrates the Maritime Continent (MC), stand and jump MJOs are seen as non-penetrating (NP) MJOs, while the other two are seen as eastward-penetrating (EP) MJOs. Results reveal the relative roles of the westward-propagating wave (WPW), as well as the QBO and ENSO, in limiting MJO propagation. Lack of the premoistening over the southern sea surface of the MC stops NP MJO from penetrating the MC. The active convection of the WPWs hinders the descending branch of the NP MJO circulation and therefore leads to the insufficient meridional advective moistening over the southern sea surface of the MC. The independent convection over the Pacific for jump MJOs is influenced by a combined effect of the QBO and ENSO. The tropopause instability induced by the MJO is found to significantly decouple from its convection over the Pacific in westerly QBO (QBOW) winters more than in easterly QBO (QBOE) winters. For jump MJOs, the independent convection over the central Pacific comes from local WPWs whose amplification and further development into deep convection are correlated to jump the MJOs’ decoupled tropopause instability. For stand MJOs, however, the seasonal-mean La Niña–like cool SST anomalies weaken the WPW activity over the central Pacific and confine WPWs within the western Pacific. Therefore, the decoupled tropopause instability of stand MJOs is out phase of WPWs and fails to induce an independent convection over the central Pacific.

© 2022 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: Kai Huang, khuang8@gmu.edu

Abstract

A recent study categorized the Madden–Julian oscillation (MJO) during boreal winter season into four types called stand, jump, slow, and fast MJO. This study focuses on the stand and jump MJO. Based on whether their convection penetrates the Maritime Continent (MC), stand and jump MJOs are seen as non-penetrating (NP) MJOs, while the other two are seen as eastward-penetrating (EP) MJOs. Results reveal the relative roles of the westward-propagating wave (WPW), as well as the QBO and ENSO, in limiting MJO propagation. Lack of the premoistening over the southern sea surface of the MC stops NP MJO from penetrating the MC. The active convection of the WPWs hinders the descending branch of the NP MJO circulation and therefore leads to the insufficient meridional advective moistening over the southern sea surface of the MC. The independent convection over the Pacific for jump MJOs is influenced by a combined effect of the QBO and ENSO. The tropopause instability induced by the MJO is found to significantly decouple from its convection over the Pacific in westerly QBO (QBOW) winters more than in easterly QBO (QBOE) winters. For jump MJOs, the independent convection over the central Pacific comes from local WPWs whose amplification and further development into deep convection are correlated to jump the MJOs’ decoupled tropopause instability. For stand MJOs, however, the seasonal-mean La Niña–like cool SST anomalies weaken the WPW activity over the central Pacific and confine WPWs within the western Pacific. Therefore, the decoupled tropopause instability of stand MJOs is out phase of WPWs and fails to induce an independent convection over the central Pacific.

© 2022 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: Kai Huang, khuang8@gmu.edu

1. Introduction

The Madden–Julian oscillation (MJO), named after its discoverers (Madden and Julian 1971, 1972), refers to a large-scale organized convective envelope coupled with a baroclinic circulation. Statistically, the MJO is characterized by an intraseasonal time scale (20–100 days) and a planetary scale with zonal wavenumber of 1–3 (Kiladis et al. 2009). The MJO is typically initiated over the Indian Ocean, propagating eastward with a relatively slow phase speed (5 m s−1). After crossing the date line, MJO convection dies while its higher-troposphere circulation keeps propagating eastward around the global tropical belt at a faster speed (Lau and Waliser 2011; Zhang 2005). As the dominant tropical intraseasonal signal in the atmosphere, the MJO provides predictability for the subseasonal to seasonal (S2S) time scale (Zhang 2013). It also affects the weather and climate systems around the globe, such as the outbreak and withdrawal of the Asian monsoon system (Lau and Waliser 2011), the initiation of El Niño events (Zhang 2013), the genesis of tropical cyclones (Kim and Seo 2016), and the evolution of extratropical circulations through teleconnections (Adames and Wallace 2014; Weickmann 1983).

There is a growing body of theoretical work to understand the core dynamics of MJO propagation. Among this, four main schools of MJO theory are summarized in Zhang et al. (2020), namely the skeleton and multiscale theory (Majda and Biello 2004; Majda and Stechmann 2009; Thual and Majda 2016; Thual et al. 2014), the moisture mode theory (Adames and Kim 2016; Sobel and Maloney 2012, 2013), the convective–dynamic–moisture three-way (“trio”) interaction theory (Wang et al. 2016), and the gravity wave theory (Yang 2021; Yang and Ingersoll 2011, 2013, 2014). The gravity wave theory views MJO as a dry wave where its eastward propagation is a result of the gravity wave’s faster eastward phase speed compared with its westward phase speed (Yang and Seidel 2020), while the other three theories all emphasize the essential role of moisture in the MJO dynamics. In the other three theories, the so-called premoistening in the lower troposphere leading the MJO deep convection system is fundamental for its eastward propagation (Majda and Stechmann 2009; Adames and Kim 2016; Wang et al. 2016). However, they consider different physics responsible for this leading premoistening. The skeleton and multiscale theory argues that the premoistening is a result of the leading small-scale convection organized by equatorial wave activities. By contrast, in the moisture mode theory, both the horizontal and vertical moisture advections above the planetary boundary layer (PBL) induced by the MJO anomalous winds provide the dominant contribution to the leading premoistening. In the convective–dynamic–moisture three-way interaction theory, the frictional moisture convergence within the PBL caused by the Kelvin wave component ahead of the MJO deep convection is essential for the premoistening in the lower troposphere.

Recent studies demonstrate the impacts of the Maritime Continent (MC) on MJO propagation. MJO convection always decays and sometimes is even terminated while crossing the MC, referred to as the MC barrier effect (Feng et al. 2015; Zhang 2005; Zhang and Ling 2017). Some studies argue that the unique topography of the MC including high mountains blocks the moisture advection and causes the MC barrier effect (Hsu and Lee 2005; Wu and Hsu 2009; Inness and Slingo 2006). The strong diurnal cycle of precipitation over the MC is found to compete with the MJO for moisture and it is considered part of the reason for the MC barrier effect (Neale and Slingo 2003; Oh et al. 2012, 2013; Wang and Li 1994; Zhang and Hendon 1997). Recently, DeMott et al. (2018) demonstrated that some westward-propagating dry precursors may interact with the MJO over the MC and cause the demise of the MJO convection. Gonzalez and Jiang (2019) also found a competition over the Pacific Ocean between the eastward propagation of the MJO and the westward propagation of a west Pacific intraseasonal mode (WPIM). Moreover, scale analysis using a simple model by Adames et al. (2019) suggests that the westward-propagating large-scale intraseasonal wave component in the tropics is probably a moisture mode like the MJO. Although it is not clear whether the westward-propagating dry precursors in DeMott et al. (2018) and the WPIM in Gonzalez and Jiang (2019) refer to the same phenomenon, both DeMott et al. (2018) and Gonzalez and Jiang (2019) argued that the amplitude of these westward-propagating waves (WPWs) is related to the seasonal mean background changes due to El Niño–Southern Oscillation (ENSO). However, changes in the seasonal mean background also leave a direct impact on MJO propagation as described in the next paragraph. Therefore, it is unknown which influence is more important for MJO propagation, the direct impacts from the seasonal mean background or the interaction with these WPWs.

MJO propagation also exhibits year-to-year variations due to the influences of interannual variability like ENSO and the quasi-biennial oscillation (QBO). The interannual variation of the MJO was initially attributed to ENSO influences (Hendon et al. 1999, 2007; Marshall et al. 2016). The MJO is found to propagate farther eastward across the date line during El Niño winters than during neutral or La Niña winters. The probability of a MJO crossing the MC, however, does not change significantly in different ENSO phases (Hendon et al. 1999; Son et al. 2017). More recent studies found that less than 10% of the variance in MJO interannual activity can be attributed to ENSO (Hendon and Abhik 2018; Son et al. 2017). In contrast, 40%–50% of the interannual variability of boreal winter MJO activity can be attributed to the QBO (Marshall et al. 2017; Son et al. 2017). During the QBO westerly (QBOW) phase, MJO activity is decreased whereas during the QBO easterly (QBOE) phase it is enhanced (Densmore et al. 2019; Marshall et al. 2017; Nishimoto and Yoden 2017; Son et al. 2017; Yoo and Son 2016). Moreover, MJO shows a more continuous eastward propagation across the MC during QBOE whereas it is more confined west of the MC during QBOW (Nishimoto and Yoden 2017; Son et al. 2017; Wang et al. 2019; Zhang and Zhang 2018). However, the argument that MJO events are stronger during QBOE than QBOW remains controversial. Zhang and Zhang (2018) demonstrate that the stronger MJO during QBOE is due to more MJO days as a result of more frequently initiated events with longer duration, rather than due to stronger individual MJO events. The physical mechanisms for the QBO–MJO connection are not completely understood. It is generally thought to occur through the QBO-related changes in the upper-tropospheric static stability and the vertical zonal wind shear across the tropopause (Nishimoto and Yoden 2017; Son et al. 2017; Yoo and Son 2016). Hendon and Abhik (2018) suggested that the positive temperature anomalies in the upper troposphere and cold anomalies near the tropopause at 100 hPa are stronger and more in phase with the MJO convection during QBOE, leading to reduced tropopause stability, which enhances the MJO convection and extends MJO propagation farther eastward across MC. The strong wind shear of the QBO could also disrupt the coherent structure of deep convective plumes, thus influencing MJO activity (Collimore et al. 2003; Gray et al. 1992; Nie and Sobel 2015). Also, like ENSO, the QBO could lead to seasonal mean state changes (Zhang and Zhang 2018; Collimore et al. 2003; Liess and Geller 2012). Sun et al. (2019) found a combined effect of ENSO and QBO on MJO propagation due to changes in the zonal gradients of the seasonal mean background. The possible combined effects of the QBO and ENSO on MJO propagation needs further investigation. It is noteworthy that both the interannual variability and the intraseasonal WPWs could have impacts on MJO propagation over the MC, but it is unknown which impacts are more important.

The categorization of the MJOs into propagating and non-propagating types was first proposed by Kim et al. (2014) where the non-propagating MJO refers to the ones failing to propagate from the Indian Ocean across the MC and into the western Pacific. Kim et al. (2014) suggest that the strongly suppressed convection to the east of MJO active convection is the key for the MJO’s eastward propagation. The negative heating anomaly associated with the strongly suppressed phase of outgoing longwave radiation (OLR) would induce a local low-level anticyclonic Rossby gyre, which advects the moisture to the east of MJO convection. This moisture advection promotes the eastward propagation of the MJO. However, Wang et al. (2017) argue that such positive moisture tendency to the east of MJO convection comes from the vertical advection of the mean moisture induced by an anomalous intraseasonal ascending motion instead of the horizontal moisture advection. Such vertical moisture advection is disrupted by a dry Rossby-wave-like signal for non-propagating MJOs. Recently, Wang et al. (2019) further categorizes boreal-winter MJO cases into four types according to their propagation. By applying a k-means cluster analysis on their OLR Hovmöller diagrams, four types of MJO propagation are found, namely stand, jump, slow, and fast MJO (Fig. 1). The slow and fast MJO cases propagate eastward continuously from the Indian Ocean into the Pacific with different phase speeds. They are similar to the propagating MJO in Kim et al. (2014) and Wang et al. (2017). The stand and jump MJO cases are more like the non-propagating MJO with standing convection over the Indian Ocean. Jump cases have an independent convection that initiates and develops over the Pacific while the convection over the Indian Ocean decays. Therefore, the jump MJO propagates in a jumping-like behavior. Wang and Li (2021) investigated the diversity in MJO intensity and phase speed and found that the sea surface temperature anomaly (SSTA) may influence the MJO diversity through tuning the background seasonal-mean moisture as well as the leading boundary layer moistening processes for the MJO. Xiang et al. (2022) also investigated MJO diversity in a subseasonal-to-seasonal prediction system and found different QBO phase backgrounds as well as different predictability for the four MJO types. Chen (2020) demonstrates that the four types of MJO found in Wang et al. (2019) can excite significantly different extratropical teleconnections, and thus result in diverse global responses. Different extratropical teleconnections induced by slow and fast MJO are also noted in Yadav and Straus (2017).

Fig. 1.
Fig. 1.

Centroids by k-means cluster analysis on OLR Hovmöller diagrams of 104 boreal winter MJO events during 1979–2013, using the same data and method as in Wang et al. (2019). Hovmöller diagrams are obtained by averaging the 20–70-day bandpass-filtered daily OLR between 10°S and 10°N. The reference day (day 0) is defined as when the 20–70-day bandpass-filtered box-mean (75°–95°E, 10°S–10°N) OLR time series reaches its local minimum during each MJO lifespan. The cases with silhouette scores lower than 0.06 are ruled out of the corresponding centroids after the cluster analysis. Numbers of MJO cases in four clusters after (before) the ruling out are indicated by the numbers before (after) the slash. Details of the data and methodology can be found in Wang et al. (2019).

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

Based on the four types of MJO propagation identified in Wang et al. (2019), this paper will attempt to understand the physical processes involved in limiting the propagation of the MJO from the perspective of the moisture mode theory by investigating the relative role of the intraseasonal WPWs in MJO propagation. Also, the potential QBO and ENSO phase preference among these four MJO types as well as their influences will be investigated. This work will also address the debate whether the horizontal or vertical moisture advection is more important for the propagation of the MJO through the MC.

2. Data

Three datasets are used in this work. The daily averaged OLR data with a resolution of 2.5° × 2.5° from the National Centers for Environmental Prediction (NCEP)–National Oceanic and Atmospheric Administration (NOAA)-interpolated OLR dataset (Liebmann and Smith 1996) are used to identify and categorize MJO cases. The daily wind components, temperature, geopotential, and specific humidity on 37 vertical levels (10–1000 hPa) from the ERA-Interim reanalysis dataset (Dee et al. 2011) provide the insight on MJO horizontal and vertical structure. Also, the monthly sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) dataset (Huang et al. 2017) are used to diagnose the seasonal SST background. The temporal range of the three datasets is the same, covering 1979–2013. The original resolution of the ERA-Interim dataset is 0.75° × 0.75°. The original resolution of the ERSSTv5 dataset is 1.0° × 1.0°. They are both interpolated onto the same spatial grids as OLR before the analysis.

3. Methodology

a. MJO identification and categorization

Methods for MJO identification and categorization in this work follow that in Wang et al. (2019). They are briefly illustrated here. Details can be found in Wang et al. (2019).

To identify MJO events, the daily OLR anomalies are obtained by subtracting the daily climatology and its first three harmonics. Then, a 20–70-day bandpass Lanczos filtering is applied on OLR anomalies to extract its intraseasonal variations. An MJO event is identified when the box-mean bandpass-filtered OLR time series over the Indian Ocean (10°S–10°N, 75°–95°E) is below its −1 standard deviation for at least 5 successive days. The reference day, or day 0, for each MJO event is defined as the date when the time series reaches its local minimum. A total of 104 MJO events are identified within the boreal winter seasons (November–April) of 1979–2013.

A k-means cluster analysis (Kaufman and Rousseeuw 2009) is applied to 104 Hovmöller diagrams of the intraseasonal OLR anomalies (10°S–10°N) to categorize them into four types. Four clusters are chosen because these MJO events can be optimally fitted into four clusters (Wang et al. 2019). The cluster analysis domain in the Hovmöller diagram covers 30 days from day −10 to day 20 and a zonal range of 60°E–180° after applying a zonal three-point running mean and setting OLR > −5 W m−2 to zero on diagrams. To determine how well each MJO event fits into its assigned cluster, a silhouette test (Kaufman and Rousseeuw 2009) is applied to each cluster member of the k-means cluster analysis. The silhouette score for each member ranges from −1 to 1. With a higher silhouette score, the member is more similar to the centroid of its assigned cluster than the other cluster centroids (Kaufman and Rousseeuw 2009). MJO events with silhouette scores lower than 0.06 are identified as outliers not clearly belonging to any of the four clusters and excluded from the corresponding clusters after the k-means cluster analysis. Ninety MJO events remain in the four clusters after removing 14 outliers as shown in Fig. 1.

To address the robustness of the k-means cluster analysis, a series of sensitivity experiments are conducted by changing the defined cluster number for k-means cluster analysis and the threshold for the silhouette test after the analysis, respectively (shown in the appendix). Results show that a cluster number of 4 is the best fit for this method, and the threshold for silhouette test set as 0.06 is reasonable.

The four types of MJO propagation, namely stand, jump, fast, and slow, are also seen in visual inspection of individual MJO events (not shown).

b. QBO and ENSO indices

The monthly global mean of the equatorial (5°S–5°N) zonal wind at 50 hPa from ERA-Interim reanalysis is used as the QBO index to diagnose the QBO phase. Following Son et al. (2017), the easterly and westerly phases of the QBO are defined when the monthly global mean of the zonal wind is below and above one-half of its standard deviation, respectively.

The widely used Niño-3.4 index is used to diagnose the ENSO phase, which is computed by averaging the monthly SST anomalies from ERSSTv5 within the central-eastern Pacific (5°S–5°N, 170°–60°W).

c. Composite analysis

Composite analysis is used to diagnose the MJO vertical structure and the seasonal mean background state of the four types of MJO with the reference day defined in section 2a. The composite analysis of MJO-related structures is performed using 20–70-day bandpass-filtered variables. Composite analysis of the seasonal mean background states uses monthly, 3-month running mean, and seasonal mean (April–November) variables. Student’s t test is conducted to validate that these composite results are significantly different from zero.

4. Results

a. Horizontal evolution

By definition, the four types of MJO exhibit distinctly different propagating features during their lifespans as illustrated in Fig. 1. More detailed evolution of their convection and the coupled lower-tropospheric horizontal circulations are given in Fig. 2. For EP (slow and fast) MJO events, their convection and circulation highly resemble that of the canonical MJO with a continuous eastward propagation (Zhang 2005, 2013; Yadav and Straus 2017). At day −10, weak convection can already be seen over the central-western Indian Ocean with leading lower-troposphere easterlies. The organized convection gets amplified from day −10 to day 0. At day 0, the strong MJO convective envelope over the Indian Ocean is accompanied by suppressed convection over the MC. The leading equatorial easterlies and lagging westerlies are well organized. The off-equatorial cyclones in both hemispheres are also evident in the coupled circulation pattern, indicating the well-developed Rossby wave gyres of the MJO. With the help of leading easterlies as well as the off-equatorial meridional winds, EP MJO events penetrate the MC with a southward detouring over the sea surface between the MC and Australia, consistent with previous studies on MJO propagation. During their propagation through the MC, the convection over the Indian Ocean turns from an active to a suppressed phase at day 10.

Fig. 2.
Fig. 2.

Evolution of intraseasonal (20–70 days) OLR (shadings) and wind field at 850 hPa (vectors) from day −10 to day 20 for four types of MJO in tropics. Only the composited OLR and wind components above 95% confidence level are drawn.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

For NP MJO events, the convection and circulation evolution are different than that of EP MJO events. From day −5 to day 5, when the MJO convection is over the eastern Indian Ocean, the suppressed convection over the MC is very weak or even missing (Fig. 2). The leading easterlies of NP MJO are disrupted. For stand MJO events, such disruption can be seen over the middle-eastern MC in the Southern Hemisphere at day 0, and it is coupled with a smaller-scale convective dipole mode over the same region. Also, the lower-troposphere leading easterlies vanish from day 5, while the extremely strong equatorial westerlies related to the off-equatorial Rossby gyres in the west of stand MJO convection are evident. Such a circulation pattern indicates a strong Rossby wave component and weak Kelvin wave component for stand MJO events. For jump MJO, the circulation is much less organized. Both the equatorial Kelvin wave component and off-equatorial Rossby wave component are not evident. The leading easterlies almost vanish over the MC and western Pacific. It is unique for jump MJOs that an independent convection develops over the central western Pacific from day 0 when its stalled convection over the Indian Ocean decays. The independent convection over the Pacific Ocean also shows a westward propagation, similar to the convective dipole mode for stand MJO events. From day 5, the independent convection reaches the MC with lower-troposphere westerlies over the western Pacific. The convection over the Indian Ocean decays from day 0 and turns to a suppressed phase at day 10. The independent convection over the western Pacific also starts to decay from day 10 and turns to the suppressed phase at day 20.

Although the convective envelope of NP MJO events highly resembles that of EP MJO events at day 0, their disrupted leading easterlies at the lower troposphere over the MC indicate a different coupled circulation than that for EP MJO events. Also, the convective dipole mode over the western Pacific for stand MJO events and the independent convection over the central Pacific for jump MJO events both show a westward propagation, suggesting a potential role of WPWs in limiting the NP MJO propagation over the MC.

b. Westward-propagating waves for NP MJO

As suggested in DeMott et al. (2018) and Feng et al. (2015), the WPWs over the western Pacific are more clearly evident in the longitude–height cross section of intraseasonal moisture anomalies. To confirm the potential WPWs during the NP MJO lifespan, the vertical structure of MJO convection and circulation is diagnosed.

The composited height–longitude section of equatorial specific humidity and wind circulation of the four MJO types are shown in Fig. 3. For EP MJO, its westward-tilted convection is coupled by a well-constructed baroclinic circulation with a lower-troposphere convergence and a higher-troposphere divergence. The descending motion ahead of EP MJO convection and the dry anomalies in the middle troposphere are strong and broad in the zonal range. The convection–circulation coupled structure of the EP MJO events propagate eastward with a slow phase speed. NP MJO events, however, show a smaller zonal scale in their convection and circulation. Their dry anomalies and descending motions are relatively weak. For stand MJO events, the convection and circulation become almost vertically stacked from day 0, consistent with previous studies indicating that the westward-tilted convection is important to the eastward propagation of the MJO (Majda and Stechmann 2009; Adames and Kim 2016; Wang et al. 2016). The leading lower-troposphere easterlies for NP MJO events are also disrupted from day 5.

Fig. 3.
Fig. 3.

As in Fig. 2, but for the composited vertical sections (10°S−10°N) of specific humidity (shadings) and vertical circulation (vectors) from day −10 to day 10. The wet (dry) anomalies indicating the potential WPWs for stand and jump MJO are marked out by thick green (red) arrow lines, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

The WPWs during NP MJO lifespans are confirmed in Fig. 3 with their potential existence indicated by thick arrow lines. Dry anomalies are evident over the western and central Pacific for stand and jump MJO events, respectively. These dry anomalies are considered different than those for the EP MJO, which occupies almost the whole central-western Pacific, and are the result of the descending branch of EP MJO circulation. The dry anomalies for NP MJO over the Pacific, however, are not induced by the descents of NP MJO circulation. At day −10, the descending branch of the NP MJO circulation is still over the Indian Ocean centered around 90°E and these descents already induce some dry anomalies there. The dry anomalies marked by the red arrows over the Pacific at day −10 are separated from those induced by the NP MJO descending branch. At day −5, the dry anomalies over the Pacific for NP MJO propagate westward and intercept NP MJO convection over the MC. At day 0, the westward-propagating suppressed convection is replaced by organized shallow convection, indicated by wet anomalies in the lower troposphere over the Pacific for NP MJO. The shallow convection also propagates westward from the Pacific to the MC. For jump MJO, the new shallow convection over the central Pacific gets amplified from day 0. It is noteworthy that the descending motions to the east of NP MJO convection are still evident at day −5, but they are replaced by shallow ascents at day 0.

The WPWs for NP MJO can be more clearly seen in Fig. 4, where they are represented by equatorial specific humidity anomalies propagating westward from the central-western Pacific to the MC. They are independent from the NP MJO since some westward-propagating dry anomalies are evident as early as from day −10 over the Pacific, separated from the suppressed-convection MJO phase over the Indian Ocean. The WPWs for stand MJO are weaker than that for jump MJO. Also, the WPWs are mainly over the western Pacific for stand MJO while those for jump MJO occupy the whole central-western Pacific during their propagation.

Fig. 4.
Fig. 4.

As in Fig. 1, but for the equatorial (10°S−10°N) intraseasonal OLR (lines) and column-integrated (from 100 hPa to the surface) specific humidity (shadings). The specific humidity anomalies over the 95% significance level are stippled.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

The lower-tropospheric wind and the column-integrated specific humidity related to the WPWs for NP MJO are shown by Fig. 5. These WPW horizontal structures are obtained by applying the westward-filtering on the intraseasonal anomalies. Note that such filtering may also include the signals from any standing mode. The WPWs for NP MJO have a dipole mode with wet anomalies over the equatorial Pacific and dry anomalies to the west in the Southern (Northern) Hemisphere for stand (jump) MJO at day −5. The westward propagation of the dipole mode is led by lower-tropospheric westerlies to the west of wet anomalies, which is the equatorial part of the coupled cyclonic gyres in both hemispheres symmetric about the equator. These westerlies induced by WPWs over the eastern MC disrupt the leading easterlies there for stand and jump MJO (Fig. 2). As the WPWs reach the MC, they propagate to higher latitudes as shown by the wet anomalies in the Southern Hemisphere at day 5 for stand MJO and the dry anomalies over the northeastern MC from day 0 to day 5 for jump MJO, respectively.

Fig. 5.
Fig. 5.

Composited maps for the westward-filtered intraseasonal wind at 850 hPa (vectors) and the column-integrated specific humidity (shadings) over the central-western Pacific from day −5 to day 5 for (a) stand and (b) jump MJO. The specific humidity anomalies over the 95% significance level are stippled, and the wind vectors over the 95% significance level are represented by thick black arrow lines.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

c. Moisture budget analysis of MJO propagation

The lower-troposphere premoistening to the east of the MJO convection is considered as the key for the MJO propagation through the MC (Majda and Stechmann 2009; Adames and Kim 2016; Wang et al. 2016). It is still controversial whether the horizontal moisture advection or the vertical advection is responsible for such leading moistening. As suggested in Kim et al. (2014), the strong descending motion of the MJO to the east of the MC excites dry Rossby waves over the MC. The lower-troposphere off-equatorial meridional wind anomalies of the Rossby wave, in turn, generate positive moisture tendencies by advecting the moisture from the equatorial region to the southern sea surface of the MC. However, Feng et al. (2015) emphasize that the vertical motion induced by the shallow convection over the MC generates vertical moisture advection and leads to the positive moisture tendencies there.

The WPWs have potential impacts on both the horizontal and vertical moisture advection over the MC. The descending motion induced by the suppressed convection of the WPWs could directly weaken the shallow convection over the MC, and the local vertical moisture advection will be weakened as a result. The ascending motion of the WPWs may weaken the MJO descending motion to the east of the MC, so that the dry Rossby wave over the MC is also weakened. As a result, the horizontal moisture advection over the MC induced by the Rossby wave wind anomalies would be insufficient.

Therefore, to address the question how MJO propagation over the MC is influenced by the intraseasonal WPWs, it is essential to investigate how the moisture tendencies vary over the MC among NP and EP MJO and the roles of the horizontal and vertical moisture advection play in that variability. The moisture budget analysis (Yanai et al. 1973) is conducted over the eastern Indian Ocean and the MC to answer these questions. The moisture budget equation is
qt˜=uqx˜+υqy˜+ωqp˜P˜+E˜.

The left-hand term in Eq. (1) is the moisture tendency term. The four terms on the right-hand side of Eq. (1) are the zonal advection, meridional advection, column process, and the surface evaporation term, respectively. We combine the vertical advection and precipitation together as the column process term here because the vertical moisture advection naturally has greater amplitude due to the great background vertical moisture gradient. And the vertical moisture advection is largely canceled out by the condensation induced by precipitation within the atmosphere column. The tildes represent the 20–70-day bandpass-filtering, and the angle brackets indicate the column integration from the surface to 100 hPa.

The moisture tendencies over the MC for NP MJO events are different from that for EP MJO events. Figure 6 shows the composited maps of the intraseasonal OLR and column-integrated moisture tendency over the eastern Indian Ocean and MC at day 0. For EP MJO events, the MJO convection over the eastern Indian Ocean is led by a large-scale moistening centered over the southern sea surface of MC, which allows the MJO convection to penetrate the MC through a southward detouring as in Fig. 2. For NP MJO, the moisture tendencies are characterized by negative anomalies lagging the MJO convection over the Indian Ocean, and the leading moistening is almost missing over the southern sea surface of the MC. Although there is some moistening over the equatorial eastern MC, the anomalies are weak and disconnected from the MJO convection. Also, these leading moistening anomalies are mainly over the eastern islands of the MC. Moistening anomalies around 90°E in both hemispheres symmetric about the equator are evident for NP MJO, possibly related to the strong Rossby gyres as noted in Fig. 2.

Fig. 6.
Fig. 6.

Composited MJO OLR (lines) and column-integrated (100–1000 hPa; shading) moisture tendency over the eastern Indian Ocean and MC region at day 0 for (a) NP MJO and (b) EP MJO. OLR intervals are 10 W m−2 from −40 to 40 W m−2 with positive OLR represented by dashed yellow lines and negative OLR by solid green lines. Only the composited OLR results exceeding the 95% confidence level are drawn, and the composited moisture tendency exceeding 95% confidence level is stippled.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

The meridional moisture advection is responsible for EP MJOs’ successful propagation across the MC. Figure 7 shows the maps of the right-hand-side terms at day 0. For both NP and EP MJO, the drying anomalies to the west of the MJO convection over the Indian Ocean are induced by the zonal advection term. For EP MJO, the leading moistening tendencies over the southern sea surface of the MC mainly come from the meridional advection term. The moisture tendency caused by the vertical motion is largely canceled out by the drying induced by the water phase change term. As a result, the column process term (Vertical adv − Precip) is out of phase with the moisture tendency term over the MC. The contributions of right-hand-side terms over the southern sea surface of the MC are quantified by the box-mean of these terms over that region (20°–10°S, 100°–140°E). The box-mean values are given in Fig. 8. For EP MJO events that penetrate the MC, the meridional moisture advection is the main contributor to the leading premoistening processes. For NP MJO events, the missing premoistening is represented by the small value of the moisture tendency term, and it is due to the sharp decrease in the meridional moisture advection term. The contribution from the column term is even slightly negative for both EP and NP MJO over the southern sea surface of the MC. Therefore, it is the meridional moisture advection that mainly influences the MJO’s propagation across the MC.

Fig. 7.
Fig. 7.

Composited intraseasonal column-integrated moisture budget terms over the eastern Indian Ocean and MC (25°S–25°N, 60°–150°E) at day 0 for (a) NP and (b) EP MJO. The moisture tendency term is represented by lines with dashed yellow lines as drying and solid green lines as moistening. Zonal advection, meridional advection, and column terms are shown by shadings in the corresponding rows with values exceeding 95% confidence level stippled.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

Fig. 8.
Fig. 8.

Box-mean values of the moisture budget analysis terms over the southern sea surface of the MC (20°–10°S, 100°–140°E) for (a) EP and (b) NP MJO.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

d. Roles of the WPWs in limiting MJO propagation

To investigate how the WPWs influence the NP MJO’s propagation across the MC, the meridional wind component and specific humidity in the meridional moisture advection terms of Eq. (1) are decomposed into three different time scales including the seasonal (>70 days), intraseasonal (20–70 days), and higher-frequency (2–20 days) time scales. The seasonal variations are indicated by the subscript m, as in υm and qm. They are obtained by applying a low-pass-filtering on the raw data that includes the annual cycle and its first three harmonics. The intraseasonal variations are indicated by the variables with primes (e.g., υ′ and q′). They are obtained by applying a bandpass-filtering on the anomalies excluding the annual cycle and its first three harmonics. The higher-frequency variables are indicated by the subscript h, as in υh and qh. They are obtained by applying a high-pass filtering on the anomalies excluding the annual cycle and its first three harmonics. Since the temporal resolution of the dataset is daily, the time scale of 2–20 days is retained after the high-pass filtering. The horizontal advection term is therefore decomposed into nine terms: −υmqm/∂y, −υ′∂qm/∂y, −υhqm/∂y, −υmq′/∂y, −υ′q′/∂y, −υhq′/∂y, −υmqh/∂y, −υ′∂qh/∂y, and −υhqh/∂y. These nine terms are then vertically integrated and bandpass-filtered to extract their intraseasonal variation.

To quantify the contribution of each decomposed term to the leading premoistening processes over the southern sea surface of the MC, the box-mean values of these nine terms over that region (20°–10°S, 100°–140°E) at day 0 are calculated for EP and NP MJO events, respectively (Fig. 9). For EP MJO events, the meridional moisture advection responsible for the premoistening over the southern sea surface of the MC is mainly contributed by the seasonal mean moisture advection induced by the intraseasonal meridional wind anomalies. For NP MJO events, this term is largely reduced as shown in Fig. 9b. It is found that such reduction for NP MJOs is due to the missing intraseasonal off-equatorial meridional wind anomalies over that region as displayed in Fig. 10. This indicates that the dry Rossby wave induced by the MJO descending motion to the east of MC is not well developed. This is probably related to the active convection of the WPWs as it disrupts the MJO descending motions starting from day 0 (Figs. 3a,b). The lower-tropospheric leading easterlies over the eastern MC for NP MJO are also disrupted as displayed in Fig. 10b. This is probably due to the involvement of the lower-tropospheric westerlies induced by WPWs over the same region as shown in Fig. 5. As for the seasonal-mean moisture background, there is not much difference between NP and EP MJO. They both show some weak dry anomalies over northern Australia. For the NP MJO, there is also some broad wetting over the Northern Hemisphere and some drying over the Indian Ocean in the Southern Hemisphere. But again, the amplitude of such deviations from the reference state is very small.

Fig. 9.
Fig. 9.

Box-mean of the decomposed meridional moisture advection terms with different time scales over the southern sea surface of the MC (20°–10°S, 100°–140°E) for (a) EP and (b) NP MJO.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

Fig. 10.
Fig. 10.

(top) Climatology of boreal winter column-integrated (from the surface to 100 hPa) specific humidity as well as (bottom) the differences between the background moisture for NP and EP MJO and the climatology (shading). The composited MJO-scale horizontal wind components at 850 hPa (vectors in the bottom two rows) are superposed. The region for the box-mean as in Figs. 8 and 9 is marked by dashed red rectangles on the map. The climatology state of boreal winter humidity is computed by averaging November–April monthly fields from 1979 to 2013. The background state for each MJO event is obtained by applying the 3-month running mean of specific humidity field centered with the calendar month of day 0. In the bottom row, only the MJO-scale horizontal wind components exceeding 95% confidence level are displayed.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

e. QBO and ENSO phase preferences

Apart from the intraseasonal WPWs presented above, the MJO propagation over the MC is also influenced by ENSO and QBO phases. Wang et al. (2019) already found that the composited seasonal mean SST anomalies for stand MJO events show a La Niña–like pattern, while those for fast MJO events show an El Niño–like pattern. The QBO’s influence on MJO propagation is found to be more dominant than that of ENSO (e.g., Son et al. 2017). Xiang et al. (2022) found that stand MJO shows a QBOW phase background while slow MJO shows a QBOE phase background. However, it is meaningful to investigate whether different QBO phase preferences still stand in our analysis since we focus on a different and longer time period, from 1979 to 2013, which provides more MJO cases for study.

There are indeed certain phase preferences among MJO types as shown by the QBO–ENSO phase diagram for MJO events (Fig. 11). NP MJOs including stand and jump MJO events show a QBOW preference. Among all the NP MJO events, 19 of them occur during the QBOW phase, whereas only six of them happen during the QBOE phase. Such QBOW phase preference is not seen for EP MJO events, consistent with the conclusions of previous studies that during the QBOW phase MJO is less active over the Pacific, with more events failing to penetrate the MC (Nishimoto and Yoden 2017; Son et al. 2017; Wang et al. 2019; Zhang and Zhang 2018). For the ENSO phase preference, which is already revealed in Wang et al. (2019), stand MJO shows a La Niña phase preference while fast MJO shows an El Niño phase preference. It is worth noting that the ENSO phase preferences are statistically significant as the composited 3-month running mean of monthly SST anomalies over the central-eastern Pacific exceeds the 95% confidence level [shown by Fig. 5 in Wang et al. (2019)]. However, the composited 3-month running mean of monthly zonal wind at 50 hPa fails to pass the 95% confidence level (not shown). This is probably due to both the limited number of cases for NP MJO and the fact that a few NP MJO events happen when the stratospheric zonal wind anomalies at 50 hPa are extremely strong easterlies (six NP MJOs in the QBOE phase indicated in Fig. 11). Both factors increase the standard deviation for the samples and make the composited background stratospheric zonal wind not significant. Therefore, the possible QBOW phase preference for MJO propagation is not entirely clear.

Fig. 11.
Fig. 11.

Phase diagram of QBO and ENSO for MJO events. The x axis refers to the QBO index defined as global mean of equatorial (5°S–5°N) monthly zonal wind; the y axis refers to the ENSO index using monthly Niño-3.4 index. Solid lines represent the climatology mean of two indices. Dashed gray lines parallel to the x axis represent 1°C below and above the climatology mean of monthly Niño-3.4. Dashed gray lines parallel to the y axis represent 1/2 standard deviation above and below the climatology mean of monthly QBO index. Each dot represents one MJO event with different colors indicating the corresponding MJO type. Four stars tell the mean values of the QBO and ENSO indices for four types of MJO. Also, numbers of MJO events lying in each quadruplet of the phase diagram are labeled, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

Our findings of the QBO phase preferences among MJO types are different from those in Xiang et al. (2022), where they found a QBOW background state for stand MJO and a QBOE background state for slow MJO. This is possibly due to the decadal variability of the MJO–QBO connection since we use a different time period for our analysis. We find that there are changes of the MJO case distribution among four types before and after the 1990s. In the 1980s, the case number of EP MJOs is more than the twice that for NP MJOs. However, beginning with the 1990s, the case number of NP MJOs increases so that it is almost about the same as that for EP MJOs (not shown here). The time period of our analysis is not long enough to confirm that the decadal variability is robust. Therefore, possible decadal variation needs further investigation.

f. A combined effect of the QBO and ENSO on MJO propagation

The jump MJO events are also characterized by an independent convection developing over the central Pacific from day 0 (Fig. 2b). Both the longitude–height cross section (Fig. 2b) and the Hovmöller diagrams of the jump MJO convection (Fig. 4b) indicate that the independent convection over the Pacific shows a westward propagation. Does this independent convection for jump MJO events develop from the WPW? Hendon and Abhik (2018) demonstrate that during the QBOW phase, the tropopause instability induced by the MJO is less in phase with its convection over the Pacific. Is it possible that the less coupled tropopause instability tends to enhance other convective systems over the Pacific during the QBOW phase? Also, the phase diagrams of the QBO and ENSO for MJO events (Fig. 11) show that besides the QBOW phase preference shared by both stand and jump MJO events, stand MJO events also have a La Niña phase preference, which is not seen for jump MJO events. Does this particular La Niña phase preference prevent the development of an independent convection over the Pacific for stand MJOs?

The temperature anomalies induced by the MJO convection vary among MJO types. Figure 12 shows the longitude–height section of MJO-induced heating and circulation. For EP MJO, the MJO-induced heating is well confined around the MJO convection with the maximum heating centered in the middle troposphere and a westward-tilted vertical structure in the lower-middle troposphere. In the upper-middle troposphere, MJO-induced heating exhibits an eastward-tilted vertical structure. Such heating is overlain by an anomalous cooling at around 100 hPa, which is a result of adiabatic adjustment to maintain hydrostatic balance in response to the diabatic warming of the troposphere induced by enhanced convection (Holloway and Neelin 2007). The anomalous heating below anomalous cooling around the tropopause leads to the tropopause instability (Hendon and Abhik 2018), which favors the development for convective systems. For EP MJO events, the tropopause instability is coupled with MJO convection and propagates eastward coherently with it. For NP MJO events, the tropopause instability is still evident but with reduced heating anomalies below the level of 200 hPa. It is noteworthy that although the NP MJO convection does not propagate over the Indian Ocean, its corresponding tropopause instability shows an eastward propagation. For jump MJO events, the propagating tropopause instability becomes in phase with the shallow convection over the central Pacific, as indicated by the shallow ascending motions in the lower troposphere at day 0. The shallow convection then develops into a deep convective system with the help of the tropopause instability at day 5. The decoupling between the MJO convection and its induced tropopause instability is probably due to the preferred QBOW phase for stand and jump MJOs.

Fig. 12.
Fig. 12.

Composited equatorial (10°S–10°N) vertical sections of MJO induced temperature anomalies (shading) and MJO vertical circulations (vectors) from day −5 to day 5. Two dashed horizontal lines in each subplot indicate the 100- and 200-hPa levels, respectively. Only the temperature anomalies and circulations exceeding 95% confidence level are shown.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

To better investigate the possible changes in the tropopause instability and MJO convection coupling during different QBO phases, we composited the equatorial OLR and the tropopause stability Hovmöller diagrams during QBOE and QBOW boreal winters for MJO cases in Fig. 13. During QBOE boreal winters the MJO tends to propagate farther east into the Pacific Ocean, whereas during QBOW winters the MJO OLR is very weak over the Pacific. Also, the tropopause instability generated by the MJO in QBOW boreal winter is decoupled with its convection over the Pacific as it maintains a continuous eastward propagation but the MJO convection stalls over the MC. Such decoupling is then quantified by the covariance between the OLR and tropopause stability. The covariance is then normalized by the variance of the box-mean intraseasonal bandpass-filtered OLR time series over the Indian Ocean (10°S–10°N, 75°–95°E) from day −25 to day 25 for MJO events in QBOE and QBOW phases, respectively. The normalization is applied to remove the influence of the stronger MJO magnitude in QBOE phase than in QBOW phase. As shown by Fig. 13c, the normalized covariance between MJO convection and its tropopause instability is very strong over the Indian Ocean in both QBOE and QBOW boreal winters. During QBOE phases, such normalized covariance remains strong over the Pacific Ocean although with slight decreases over the MC, and it gradually decreases with longitude, reaching to zero at the date line. However, during the QBOW phase, the normalized covariance shows a rapid drop to below zero over the MC and remains weak over the whole Pacific. Such a contrast in the normalized covariance over the Pacific between the QBOE and QBOW phases again indicates that the coupling between the MJO tropopause instability and its convection is highly related to and possibly influenced by the QBO phase.

Fig. 13.
Fig. 13.

Composited Hovmöller diagrams of the intraseasonal equatorial (10°S–10°N) OLR (shading) and tropopause stability (T100 minus T200; lines) for MJO events in Fig. 11 lying in the (a) QBOE and (b) QBOW phases. (c) The covariance between OLR and tropopause stability as a function of the longitude in (a) and (b). Noted that the covariances are normalized by the variance of the box-mean 20–70-day bandpass-filtered OLR time series over the Indian Ocean (10°S–10°N, 75°–95°E) from day −25 to day 25 for MJO events in QBOE and QBOW phases, respectively.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

The lack of independent convection over the central Pacific for stand MJO events can be attributed to their La Niña phase preference. As displayed in Fig. 14, the WPW activity is largely influenced by the ENSO while the QBO has very slight influence. In the boreal winters with El Niño conditions, the WPW is stronger over the equatorial central-western Pacific than in the boreal winter season with La Niña conditions. With preferred La Niña conditions, the WPWs for stand MJO are more confined over the western Pacific and the MC as shown in Fig. 15. For stand MJO events, the corresponding WPWs occur mainly over the western Pacific whereas the WPW activities for jump MJO occupy the whole central-western Pacific. Therefore, the eastward-propagating tropopause instability of stand MJO events fails to induce an independent convection due to the weak WPWs over the central Pacific, which is a result of the preferred La Niña phase. It is noteworthy that the WPWs over the western Pacific encounter the decoupled tropopause instability around day 0, as indicated by Fig. 15a, which amplifies the shallow convection of the WPW around the MC. Such amplification can be seen from the deepening of the ascents over the MC for stand MJOs as in Fig. 3a from day 0 to day 5 as well as the accumulation of the wet anomalies in the lower troposphere there. However, as the moisture tendency over the MC soon turns to drying anomalies after day 5 as in Fig. 15a for stand MJO events, such amplification of WPW convection over the MC is disrupted and the wet anomalies over the MC vanish after day 10 (Fig. 3a).

Fig. 14.
Fig. 14.

Maps for (a) westward-filtered intraseasonal OLR variance and (b) the composited differences of the westward-filtered intraseasonal OLR variance between QBOE and QBOW phase. (c) As in (b), but between different ENSO phases, all for boreal winter seasons. Differences exceeding 95% confidence level are stippled.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

Fig. 15.
Fig. 15.

Composited Hovmöller diagrams (10°S–10°N average) of tropopause instability (lines) and premoistening (shadings) for four types of MJO. The tropopause instability is measured by the temperature difference at 100 and 200 hPa with negative (positive) difference indicating instability (stability) and represented by dashed green (solid yellow) lines. The premoistening is measured by the column-integrated (from surface to 100 hPa) moisture tendency. The dashed vertical lines indicate the longitude of 150°E. Each MJO type’s QBO and ENSO preferences are labeled on the top right of each subplot. Only the temperature difference exceeding 95% confidence level is drawn, and the column-integrated moisture tendencies exceeding 95% confidence level are stippled.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

5. Summary and discussion

Inspired by the four types of MJO propagation identified in Wang et al. (2019), this paper investigates what limits the propagation of MJO events across the MC. We diagnose the roles of WPWs on the failure of NP MJO events to propagate across the MC. Results show that these events are accompanied by intraseasonal WPWs over the central-western Pacific, which interfere with MJO convection and propagation. Different QBO and ENSO phase preferences are also found to contribute to the diverse evolution of the convective systems over the Pacific among four MJO types through the tropopause instability structure and the WPW activity, respectively. This work suggests that both the low-level premoistening processes and the high-level tropopause instability structure are important for the diversity in MJO propagation.

The moisture budget analysis reveals that the intraseasonal WPWs are able to influence the MJO propagation across the MC mainly through suppressing the meridional moisture advection over the southern sea surface of the MC, which is responsible for the insufficient premoistening there. For EP MJO events, the meridional moisture advection over the southern sea surface of the MC is related to local off-equatorial meridional winds that are part of the dry Rossby gyre over the MC induced by the descending motion of the MJO circulation (Kim et al. 2014). For NP events, the active convection of the WPWs weakens this descending motion and the meridional moisture advection is therefore insufficient for them to propagate. While this mechanism is identified in composite analysis, it is also evident in individual events (see the online supplemental information for details). Note that although the column process term differences between EP and NP MJO are small over the southern sea surface of the MC, they are large over the equatorial MC (Fig. 7). For EP MJO, the column process term contributes to the moistening tendencies around the equator (Fig. 6b) and helps parts of the EP MJO convection to penetrate MC over the islands (Fig. 2). We also found that for jump MJO events, the WPWs induce intraseasonal dry anomalies over the northeastern MC and these dry anomalies are advected by local seasonal-mean northeasterlies into the MC, responsible for the overall drying tendencies over the MC for jump MJO (see the supplemental information for details). Some differences in the seasonal-mean moisture background among four MJO types are also found. This is probably related to the background ENSO state as presented in the supplemental information.

The different evolutions of convection over the central Pacific for stand and jump MJO events can be explained by the combined influence of the QBO and ENSO. The tropopause instability induced by the MJO is less coupled with its convection during QBOW than QBOE (Hendon and Abhik 2018). The NP MJO events, which have a QBOW phase preference, induce the eastward-propagating tropopause instability decoupled from the stalled convection. The WPWs over the central Pacific are amplified by the tropopause instability and develop into an independent convective envelope for jump MJO events. This independent convection does not develop for stand MJO events because of the La Niña–like cool seasonal mean SST anomalies over the central Pacific. These cool anomalies confine the WPWs over the western Pacific, which are out of phase with the tropopause instability.

The influences of the WPWs on NP MJOs are supported by the findings in previous studies on MJO propagation like those of DeMott et al. (2018) and Gonzalez and Jiang (2019). This demonstrates the essential role of the premoistening for MJO propagation, as supported by four schools of MJO theory (Zhang et al. 2020). This paper links the physical mechanism for the influences of WPWs on horizontal moisture advection to their active convection through hindering the vertical descending motion of MJO circulation, rather than focusing on the role of the intraseasonal dry anomalies of WPWs such as dry precursors as in DeMott et al. (2018).

Previous studies have emphasized the tropopause instability as the potential mechanism by which the QBO impacts the MJO (Hendon and Abhik 2018). In this study, we confirmed the decoupling of the tropopause instability with MJO convection during the QBOW phase from an event-based view. We also quantified such decoupling by the normalized covariance between the tropopause instability and the MJO OLR (Fig. 13). We found that the decoupled tropopause instability for NP MJOs will enhance the intraseasonal WPWs over the central Pacific, complicating the local intraseasonal variability. The combined effects of the QBO and ENSO were also proposed by Sun et al. (2019). However, they explain such effects solely as a result of changes in the seasonal zonal mean gradients of moisture and vertical velocity in the equatorial region. We identify another mechanism for the combined effects through the changes in the tropopause instability influenced by the QBO as well as the WPW activity influenced by the ENSO-related SST anomalies (Fig. 14).

The QBO phase preferences found in this paper are somewhat different from those found in Xiang et al. (2022). This is probably due to the different time periods used [1979–2013 for our study and 2000–19 in Xiang et al. (2022)]. This indicates a possible decadal variation for the MJO–QBO connection. We investigated the case number distribution of these four MJO types as a function of decade and found an apparent change in the ratio of NP to EP MJO case number before and after the 1990s (not shown). During the 1980s, the number of EP MJO events is more than twice of that for NP MJO events. However, from the 1990s, the case number of EP MJO events is about the same as that for NP MJO events. Due to the limited time period of 35 years in our study, robust conclusions about this decadal variability will require further investigation.

A complete understanding of the relationship between the QBO and MJO remains elusive from the results of this study. The tropopause instability occurs at a very high level of the atmosphere. How such high-level instability amplifies the shallow convection, such as for jump MJOs over the central Pacific and for stand MJOs around the MC, remains an open question. However, our study suggests that these two processes are highly correlated. The cloud-radiative feedback is another very potential mechanism for the QBO–MJO connection (Son et al. 2017; Zhang and Zhang 2018). We investigated this mechanism through the linear regression coefficients between the net radiative heating rate and precipitation rate over the Indo-Pacific (not shown). But the result does not show much difference among four MJO types. Due to the limited sample size available in the reanalysis dataset used, the proposed mechanism for the QBO–MJO connection remains elusive. In particular, there are only 14 jump MJO events and whether the QBOW phase preference is robust for these types of events is not clear. Future work will explore this relationship in model simulations and sensitivity experiments.

Acknowledgments.

The authors would like to thank Dr. Jadwiga Richter and Prof. Hyemi Kim for their valuable advice on this research. This research is supported by the U.S. Department of Energy (DE-SC0019433).

Data availability statement

The data generated by the identification and classification of MJO events are available at https://github.com/KaiHuang94/MJO_Diversity/tree/master/outputs and from the corresponding author.

APPENDIX

Robustness of the k-Means Cluster Analysis

The robustness of the k-means cluster analysis is examined in sensitivity experiments by varying the predefined number of clusters, as well as the threshold value (σ) of the silhouette score. A higher silhouette score indicates that an MJO event is well matched to its assigned cluster. The goal is to find an optimal value such that the number of clusters is robust when varied and the threshold chosen allows the most MJO events to be successfully classified into a cluster. Figure A1 demonstrates that the MJO cases can be best fit into four clusters using the k-means cluster analysis. The number of cases passing the silhouette test is always the highest for four clusters with a threshold from 0.05 to 0.07.

Fig. A1.
Fig. A1.

Number of MJO cases passing the silhouette test with different cluster numbers defined for the k-means cluster analysis. Dotted lines with different colors represent results with different thresholds (σ) for the silhouette test ranging from 0.04 to 0.08 with an increment of 0.01.

Citation: Journal of Climate 35, 18; 10.1175/JCLI-D-21-0691.1

REFERENCES

  • Adames, A. F., and J. M. Wallace, 2014: Three-dimensional structure and evolution of the vertical velocity and divergence fields in the MJO. J. Atmos. Sci., 71, 46614681, https://doi.org/10.1175/JAS-D-14-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adames, A. F., and D. Kim, 2016: The MJO as a dispersive, convectively coupled moisture wave: Theory and observations. J. Atmos. Sci., 73, 913941, https://doi.org/10.1175/JAS-D-15-0170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adames, A. F., D. Kim, S. K. Clark, Y. Ming, and K. Inoue, 2019: Scale analysis of moist thermodynamics in a simple model and the relationship between moisture modes and gravity waves. J. Atmos. Sci., 76, 38633881, https://doi.org/10.1175/JAS-D-19-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., 2020: Diversity of the global teleconnections associated with the Madden–Julian oscillation. J. Climate, 34, 397414, https://doi.org/10.1175/JCLI-D-20-0357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collimore, C. C., D. W. Martin, M. H. Hitchman, A. Huesmann, and D. E. Waliser, 2003: On the relationship between the QBO and tropical deep convection. J. Climate, 16, 25522568, https://doi.org/10.1175/1520-0442(2003)016<2552:OTRBTQ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., B. O. Wolding, E. D. Maloney, and D. A. Randall, 2018: Atmospheric mechanisms for MJO decay over the Maritime Continent. J. Geophys. Res., 123, 51885204, https://doi.org/10.1029/2017JD026979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Densmore, C. R., E. R. Sanabia, and B. S. Barrett, 2019: QBO influence on MJO amplitude over the Maritime Continent: Physical mechanisms and seasonality. Mon. Wea. Rev., 147, 389406, https://doi.org/10.1175/MWR-D-18-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, J., T. Li, and W. Zhu, 2015: Propagating and nonpropagating MJO events over the Maritime Continent. J. Climate, 28, 84308449, https://doi.org/10.1175/JCLI-D-15-0085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonzalez, A. O., and X. Jiang, 2019: Distinct propagation characteristics of intraseasonal variability over the tropical west Pacific. J. Geophys. Res., 124, 53325351, https://doi.org/10.1029/2018JD029884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., J. D. Sheaffer, and J. A. Knaff, 1992: Influence of the stratospheric QBO on ENSO variability. J. Meteor. Soc. Japan, 70, 975995, https://doi.org/10.2151/jmsj1965.70.5_975.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., and S. Abhik, 2018: Differences in vertical structure of the Madden–Julian oscillation associated with the quasi-biennial oscillation. Geophys. Res. Lett., 45, 44194428, https://doi.org/10.1029/2018GL077207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., C. Zhang, and J. D. Glick, 1999: Interannual variation of the Madden–Julian oscillation during austral summer. J. Climate, 12, 25382550, https://doi.org/10.1175/1520-0442(1999)012<2538:IVOTMJ>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1175/JCLI4003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., and J. D. Neelin, 2007: The convective cold top and quasi equilibrium. J. Atmos. Sci., 64, 14671487, https://doi.org/10.1175/JAS3907.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, H.-H., and M.-Y. Lee, 2005: Topographic effects on the eastward propagation and initiation of the Madden–Julian oscillation. J. Climate, 18, 795809, https://doi.org/10.1175/JCLI-3292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 81798205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inness, P. M., and J. M. Slingo, 2006: The interaction of the Madden–Julian oscillation with the Maritime Continent in a GCM. Quart. J. Roy. Meteor. Soc., 132, 16451667, https://doi.org/10.1256/qj.05.102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaufman, L., and P. J. Rousseeuw, 2009: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, 368 pp.

  • Kiladis, G. N., M. C. Wheeler, P. T. Haertel, K. H. Straub, and P. E. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, https://doi.org/10.1029/2008RG000266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., J. S. Kug, and A. H. Sobel, 2014: Propagating versus nonpropagating Madden–Julian oscillation events. J. Climate, 27, 111125, https://doi.org/10.1175/JCLI-D-13-00084.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-K., and K.-H. Seo, 2016: Cluster analysis of tropical cyclone tracks over the western North Pacific using a self-organizing map. J. Climate, 29, 37313751, https://doi.org/10.1175/JCLI-D-15-0380.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, W. K.-M., and D. E. Waliser, 2011: Intraseasonal Variability in the Atmosphere–Ocean Climate System. 2nd ed. Springer, 614 pp.

  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, http://www.jstor.org/stable/26233278.

    • Search Google Scholar
    • Export Citation
  • Liess, S., and M. A. Geller, 2012: On the relationship between QBO and distribution of tropical deep convection. J. Geophys. Res., 117, D03108, https://doi.org/10.1029/2011JD016317.

    • Crossref
    • 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, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majda, A. J., and J. A. Biello, 2004: A multiscale model for tropical intraseasonal oscillations. Proc. Natl. Acad. Sci. USA, 101, 47364741, https://doi.org/10.1073/pnas.0401034101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majda, A. J., and S. N. Stechmann, 2009: The skeleton of tropical intraseasonal oscillations. Proc. Natl. Acad. Sci. USA, 106, 84178422, https://doi.org/10.1073/pnas.0903367106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., H. H. Hendon, and G. Wang, 2016: On the role of anomalous ocean surface temperatures for promoting the record Madden–Julian oscillation in March 2015. Geophys. Res. Lett., 43, 472481, https://doi.org/10.1002/2015GL066984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., H. H. Hendon, S.-W. Son, and Y. Lim, 2017: Impact of the quasi-biennial oscillation on predictability of the Madden–Julian oscillation. Climate Dyn., 49, 13651377, https://doi.org/10.1007/s00382-016-3392-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neale, R., and J. Slingo, 2003: The Maritime Continent and its role in the global climate: A GCM study. J. Climate, 16, 834848, https://doi.org/10.1175/1520-0442(2003)016<0834:TMCAIR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, J., and A. H. Sobel, 2015: Responses of tropical deep convection to the QBO: Cloud-resolving simulations. J. Atmos. Sci., 72, 36253638. https://doi.org/10.1175/JAS-D-15-0035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nishimoto, E., and S. Yoden, 2017: Influence of the stratospheric quasi-biennial oscillation on the Madden–Julian oscillation during austral summer. J. Atmos. Sci., 74, 11051125, https://doi.org/10.1175/JAS-D-16-0205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oh, J.-H., K.-Y. Kim, and G.-H. Lim, 2012: Impact of MJO on the diurnal cycle of rainfall over the western Maritime Continent in the austral summer. Climate Dyn., 38, 11671180, https://doi.org/10.1007/s00382-011-1237-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oh, J.-H., B.-M. Kim, K.-Y. Kim, H.-J. Song, and G.-H. Lim, 2013: The impact of the diurnal cycle on the MJO over the Maritime Continent: A modeling study assimilating TRMM rain rate into global analysis. Climate Dyn., 40, 893911, https://doi.org/10.1007/s00382-012-1419-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A., and E. Maloney, 2012: An idealized semi-empirical framework for modeling the Madden–Julian oscillation. J. Atmos. Sci., 69, 16911705, https://doi.org/10.1175/JAS-D-11-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A., and E. Maloney, 2013: Moisture modes and the eastward propagation of the MJO. J. Atmos. Sci., 70, 187192, https://doi.org/10.1175/JAS-D-12-0189.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Son, S.-W., Y. Lim, C. Yoo, H. H. Hendon, and J. Kim, 2017: Stratospheric control of the Madden–Julian oscillation. J. Climate, 30, 19091922, https://doi.org/10.1175/JCLI-D-16-0620.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, L., H. Wang, and F. Liu, 2019: Combined effect of the QBO and ENSO on the MJO. Atmos. Oceanic Sci. Lett., 12, 170176, https://doi.org/10.1080/16742834.2019.1588064.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thual, S., and A. J. Majda, 2016: A skeleton model for the MJO with refined vertical structure. Climate Dyn., 46, 27732786, https://doi.org/10.1007/s00382-015-2731-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thual, S., A. J. Majda, and S. N. Stechmann, 2014: A stochastic skeleton model for the MJO. J. Atmos. Sci., 71, 697715, https://doi.org/10.1175/JAS-D-13-0186.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and T. Li, 1994: Convective interaction with boundary-layer dynamics in the development of a tropical intraseasonal system. J. Atmos. Sci., 51, 13861400, https://doi.org/10.1175/1520-0469(1994)051<1386:CIWBLD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., F. Liu, and G. Chen, 2016: A trio-interaction theory for Madden–Julian oscillation. Geosci. Lett., 3, 34, https://doi.org/10.1186/s40562-016-0066-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., G. Chen, and F. Liu, 2019: Diversity of the Madden-Julian oscillation. Sci. Adv., 5, eaax0220, https://doi.org/10.1126/sciadv.aax0220.

  • Wang, L., T. Li, E. Maloney, and B. Wang, 2017: Fundamental causes of propagating and non-propagating MJOS in MJOTF/GASS models. J. Climate, 30, 37433769, https://doi.org/10.1175/JCLI-D-16-0765.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, T., and T. Li, 2021: Factors controlling the diversities of MJO propagation and intensity. J. Climate, 34, 65496563, https://doi.org/10.1175/JCLI-D-20-0859.1.

    • Search Google Scholar
    • Export Citation
  • Weickmann, K. M., 1983: Intraseasonal circulation and outgoing longwave radiation modes during Northern Hemisphere winter. Mon. Wea. Rev., 111, 18381858, https://doi.org/10.1175/1520-0493(1983)111<1838:ICAOLR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C.-H., and H.-H. Hsu, 2009: Topographic influence on the MJO in the Maritime Continent. J. Climate, 22, 54335448, https://doi.org/10.1175/2009JCLI2825.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B., and Coauthors, 2022: S2S prediction in GFDL SPEAR: MJO diversity and teleconnections. Bull. Amer. Meteor. Soc., 103, E463E484, https://doi.org/10.1175/BAMS-D-21-0124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yadav, P., and D. M. Straus, 2017: Circulation response to fast and slow MJO episodes. Mon. Wea. Rev., 145, 15771596, https://doi.org/10.1175/MWR-D-16-0352.1.

    • 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, D., 2021: A shallow water model for convective self-aggregation. J. Atmos. Sci., 78, 571582, https://doi.org/10.1175/JAS-D-20-0031.1.

  • Yang, D., and A. P. Ingersoll, 2011: Testing the hypothesis that the MJO is a mixed Rossby–gravity wave packet. J. Atmos. Sci., 68, 226239, https://doi.org/10.1175/2010JAS3563.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and A. P. Ingersoll, 2013: Triggered convection, gravity waves, and the MJO: A shallow-water model. J. Atmos. Sci., 70, 24762486, https://doi.org/10.1175/JAS-D-12-0255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and A. P. Ingersoll, 2014: A theory of the MJO horizontal scale. Geophys. Res. Lett., 41, 10591064, https://doi.org/10.1002/2013GL058542.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., and S. D. Seidel, 2020: The incredible lightness of water vapor. J. Climate, 33, 28412851, https://doi.org/10.1175/JCLI-D-19-0260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoo, C., and S. Son, 2016: Modulation of the boreal wintertime Madden–Julian oscillation by the stratospheric quasi-biennial oscillation. Geophys. Res. Lett., 43, 13921398, https://doi.org/10.1002/2016GL067762.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden-Julian oscillation. Rev. Geophys., 43, 136, https://doi.org/10.1029/2004RG000158.

  • Zhang, C., 2013: Madden–Julian oscillation: Bridging weather and climate. Bull. Amer, Meteor. Soc., 94, 18491870, https://doi.org/10.1175/BAMS-D-12-00026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and H. H. Hendon, 1997: Propagating and standing components of the intraseasonal oscillation in tropical convection. J. Atmos. Sci., 54, 741752, https://doi.org/10.1175/1520-0469(1997)054<0741:PASCOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and J. Ling, 2017: Barrier effect of the Indo-Pacific Maritime Continent on the MJO: Perspectives from tracking MJO precipitation. J. Climate, 30, 34393459, https://doi.org/10.1175/JCLI-D-16-0614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and B. Zhang, 2018: QBO–MJO connection. J. Geophys. Res., 123, 29572967, https://doi.org/10.1002/2017JD028171.

  • Zhang, C., A. Adames, B. Khouider, B. Wang, and D. Yang, 2020: Four theories of the Madden–Julian oscillation. Rev. Geophys., 58, e2019RG000685, https://doi.org/10.1029/2019RG000685.

    • Crossref
    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save