• Adames, Á. 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
  • Adcroft, A. , and Coauthors, 2019: The GFDL global ocean and sea ice model OM4.0: Model description and simulation features. J. Adv. Model. Earth Syst., 11, 31673211, https://doi.org/10.1029/2019MS001726.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bladé, I. , and D. L. Hartmann , 1993: Tropical intraseasonal oscillations in a simple nonlinear model. J. Atmos. Sci., 50, 29222939, https://doi.org/10.1175/1520-0469(1993)050<2922:TIOIAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bushuk, M. , and Coauthors, 2021: Seasonal prediction and predictability of regional Antarctic sea ice. J. Climate, 34, 62076233, https://doi.org/10.1175/JCLI-D-20-0965.1.

    • Search Google Scholar
    • Export Citation
  • C3S, 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store, https://cds.climate.copernicus.eu/cdsapp#!/home.

    • Search Google Scholar
    • Export Citation
  • Cassou, C. , 2008: Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation. Nature, 455, 523527, https://doi.org/10.1038/nature07286.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G. , 2021: 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
  • Chen, G. , and B. Wang , 2020: Circulation factors determining the propagation speed of the Madden–Julian oscillation. J. Climate, 33, 33673380, https://doi.org/10.1175/JCLI-D-19-0661.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeFlorio, M. J. , D. E. Waliser , B. Guan , D. A. Lavers , F. M. Ralph , and F. Vitart , 2018: Global assessment of atmospheric river prediction skill. J. Hydrometeor., 19, 409426, https://doi.org/10.1175/JHM-D-17-0135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L. , and Coauthors, 2020: SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. J. Adv. Model. Earth Syst., 12, e2019MS001895, https://doi.org/10.1029/2019MS001895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, W. , M. Zhao , Y. Ming , and V. Ramaswamy , 2021: Representation of tropical mesoscale convective systems in a general circulation model: Climatology and response to global warming. J. Climate, 34, 56575671, https://doi.org/10.1175/JCLI-D-20-0535.1.

    • Search Google Scholar
    • Export Citation
  • Ferranti, L. , T. N. Palmer , F. Molteni , and E. Klinker , 1990: Tropical-extratropical interaction associated with the 30–60 day oscillation and its impact on medium and extended range prediction. J. Atmos. Sci., 47, 21772199, https://doi.org/10.1175/1520-0469(1990)047<2177:TEIAWT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, X. , J.-Y. Lee , P.-C. Hsu , H. Taniguchi , B. Wang , W. Wang , and S. Weaver , 2013: Multi-model MJO forecasting during DYNAMO/CINDY period. Climate Dyn., 41, 10671081, https://doi.org/10.1007/s00382-013-1859-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R. , and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haertel, P. , 2021: Kelvin/Rossby wave partition of Madden-Julian oscillation circulations. Climate, 9, 2, https://doi.org/10.3390/cli9010002.

  • Harris, L. , and Coauthors, 2020: GFDL SHiELD: A unified system for weather-to-seasonal prediction. J. Adv. Model. Earth Syst., 12, e2020MS002223, https://doi.org/10.1029/2020MS002223.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M. , and Coauthors, 2019: Structure and performance of GFDL’s CM4.0 climate model. J. Adv. Model. Earth Syst., 11, 36913727, https://doi.org/10.1029/2019MS001829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson, S. A. , E. D. Maloney , and S.-W. Son , 2017: Madden–Julian oscillation Pacific teleconnections: The impact of the basic state and MJO representation in general circulation models. J. Climate, 30, 45674587, https://doi.org/10.1175/JCLI-D-16-0789.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X. , B. Xiang , M. Zhao , T. Li , S.-J. Lin , Z. Wang , and J.-H. Chen , 2018: Intraseasonal tropical cyclogenesis prediction in a global coupled model system. J. Climate, 31, 62096227, https://doi.org/10.1175/JCLI-D-17-0454.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X. , and Coauthors, 2020: Fifty years of research on the Madden-Julian oscillation: Recent progress, challenges, and perspectives. J. Geophys. Res. Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911.

    • 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, 342 pp.

  • Kerns, B. W. , and S. S. Chen , 2020: A 20-year climatology of Madden-Julian oscillation convection: Large-scale precipitation tracking from TRMM-GPM rainfall. J. Geophys. Res. Atmos., 125, e2019JD032142, https://doi.org/10.1029/2019JD032142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N. , K. H. Straub , and P. T. Haertel , 2005: Zonal and vertical structure of the Madden–Julian oscillation. J. Atmos. Sci., 62, 27902809, https://doi.org/10.1175/JAS3520.1.

    • 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, D. , H. Kim , and M.-I. Lee , 2017: Why does the MJO detour the Maritime Continent during austral summer? Geophys. Res. Lett., 44, 25792587, https://doi.org/10.1002/2017GL072643.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. , F. Vitart , and D. E. Waliser , 2018: Prediction of the Madden–Julian oscillation: A review. J. Climate, 31, 94259443, https://doi.org/10.1175/JCLI-D-18-0210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H. , M. A. Janiga , and K. Pegion , 2019: MJO propagation processes and mean biases in the SubX and S2S reforecasts. J. Geophys. Res. Atmos., 124, 93149331, https://doi.org/10.1029/2019JD031139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P. , and Coauthors, 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. , S. Abhik , H. H. Hendon , M. Bell , C. Lucas , A. G. Marshall , and E. C. J. Oliver , 2019: On the emerging relationship between the stratospheric quasi-biennial oscillation and the Madden-Julian oscillation. Sci. Rep., 9, 2981, https://doi.org/10.1038/s41598-019-40034-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, C.-Y. , S. J. Camargo , F. Vitart , A. H. Sobel , and M. K. Tippett , 2018: Subseasonal tropical cyclone genesis prediction and MJO in the S2S dataset. Wea. Forecasting, 33, 967988, https://doi.org/10.1175/WAF-D-17-0165.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, C.-Y. , and Coauthors, 2020: Subseasonal predictions of tropical cyclone occurrence and ACE in the S2S dataset. Wea. Forecasting, 35, 921938, https://doi.org/10.1175/WAF-D-19-0217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, T. , L. Wang , M. Peng , B. Wang , C. Zhang , W. Lau , and H.-C. Kuo , 2018: A paper on the tropical intraseasonal oscillation published in 1963 in a Chinese journal. Bull. Amer. Meteor. Soc., 99, 17651779, https://doi.org/10.1175/BAMS-D-17-0216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B. , and C. A. Smith , 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, https://doi.org/10.1175/1520-0477-77.6.1274.

    • Search Google Scholar
    • Export Citation
  • Lim, Y. , S.-W. Son , and D. Kim , 2018: MJO prediction skill of the subseasonal-to-seasonal prediction models. J. Climate, 31, 40754094, https://doi.org/10.1175/JCLI-D-17-0545.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, Y. , S.-W. Son , A. G. Marshall , H. H. Hendon , and K.-H. Seo , 2019: Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models. Climate Dyn., 53, 16811695, https://doi.org/10.1007/s00382-019-04719-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H. , G. Brunet , and J. Derome , 2008: Forecast skill of the Madden–Julian oscillation in two Canadian atmospheric models. Mon. Wea. Rev., 136, 41304149, https://doi.org/10.1175/2008MWR2459.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H. , G. Brunet , and J. Derome , 2009: An observed connection between the North Atlantic oscillation and the Madden–Julian oscillation. J. Climate, 22, 364380, https://doi.org/10.1175/2008JCLI2515.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, H. , G. Brunet , and S. Fontecilla Juan , 2010: Impact of the Madden-Julian Oscillation on the intraseasonal forecast skill of the North Atlantic Oscillation. Geophys. Res. Lett., 37, L19803, https://doi.org/10.1029/2010GL044315.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, S.-J. , 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C. , B. Tian , K.-F. Li , G. L. Manney , N. J. Livesey , Y. L. Yung , and D. E. Waliser , 2014: Northern Hemisphere mid-winter vortex-displacement and vortex-split stratospheric sudden warmings: Influence of the Madden-Julian Oscillation and Quasi-Biennial Oscillation. J. Geophys. Res. Atmos., 119, 1259912620, https://doi.org/10.1002/2014JD021876.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J. , Y. Da , T. Li , and F. Hu , 2020: Impact of ENSO on MJO pattern evolution over the maritime continent. J. Meteor. Res., 34, 11511166, https://doi.org/10.1007/s13351-020-0046-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, F. , and Coauthors, 2020: GFDL’s SPEAR seasonal prediction system: initialization and ocean tendency adjustment (OTA) for coupled model predictions. J. Adv. Model. Earth Syst., 12, e2020MS002149, https://doi.org/10.1029/2020MS002149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyu, M. , X. Jiang , Z. Wu , D. Kim , and Á. F. Adames , 2021: Zonal-scale of the Madden-Julian Oscillation and its propagation speed on the interannual time-scale. Geophys. Res. Lett., 48, e2020GL091239, https://doi.org/10.1029/2020GL091239.

    • 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
  • 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
  • Martin, Z. , and Coauthors, 2021: The influence of the quasi-biennial oscillation on the Madden–Julian oscillation. Nat. Rev. Earth Environ., 2, 477489, https://doi.org/10.1038/s43017-021-00173-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthews, A. J. , 2008: Primary and successive events in the Madden–Julian Oscillation. Quart. J. Roy. Meteor. Soc., 134, 439453, https://doi.org/10.1002/qj.224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mundhenk, B. D. , E. A. Barnes , E. D. Maloney , and C. F. Baggett , 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian oscillation and quasi-biennial oscillation. npj Climate Atmos. Sci., 1, 20177, https://doi.org/10.1038/s41612-017-0008-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H. , T. L. Delworth , W. F. Cooke , M. Zhao , B. Xiang , and P.-C. Hsu , 2020: Detected climatic change in global distribution of tropical cyclones. Proc. Natl. Acad. Sci. USA, 117, 1070610714, https://doi.org/10.1073/pnas.1922500117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nardi, K. M. , C. F. Baggett , E. A. Barnes , E. D. Maloney , D. S. Harnos , and L. M. Ciasto , 2020: Skillful all-season S2S prediction of U.S. precipitation using the MJO and QBO. Wea. Forecasting, 35, 21792198, https://doi.org/10.1175/WAF-D-19-0232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neena, J. M. , J. Y. Lee , D. Waliser , B. Wang , and X. Jiang , 2014: Predictability of the Madden–Julian oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE). J. Climate, 27, 45314543, https://doi.org/10.1175/JCLI-D-13-00624.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rashid, H. A. , H. H. Hendon , M. C. Wheeler , and O. Alves , 2011: Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Climate Dyn., 36, 649661, https://doi.org/10.1007/s00382-010-0754-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, H.-L. , J. Wu , C.-B. Zhao , Y.-J. Cheng , and X.-W. Liu , 2016: MJO ensemble prediction in BCC-CSM1.1(m) using different initialization schemes. Atmos. Ocean. Sci. Lett., 9, 6065, https://doi.org/10.1080/16742834.2015.1116217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W. , T. M. Smith , C. Liu , D. B. Chelton , K. S. Casey , and M. G. Schlax , 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496, https://doi.org/10.1175/2007JCLI1824.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, J. C. , 1981: The North Pacific Oscillation. J. Climatol., 1, 3957, https://doi.org/10.1002/joc.3370010106.

  • Stan, C. , D. M. Straus , J. S. Frederiksen , H. Lin , E. D. Maloney , and C. Schumacher , 2017: Review of tropical-extratropical teleconnections on intraseasonal time scales. Rev. Geophys., 55, 902937, https://doi.org/10.1002/2016RG000538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tseng, K.-C. , E. Maloney , and E. Barnes , 2019: The consistency of MJO teleconnection patterns: An explanation using linear Rossby wave theory. J. Climate, 32, 531548, https://doi.org/10.1175/JCLI-D-18-0211.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F. , 2017: Madden–Julian Oscillation prediction and teleconnections in the S2S database. Quart. J. Roy. Meteor. Soc., 143, 22102220, https://doi.org/10.1002/qj.3079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B. , 1988: Dynamics of tropical low-frequency waves: An analysis of the moist Kelvin wave. J. Atmos. Sci., 45, 20512065, https://doi.org/10.1175/1520-0469(1988)045<2051:DOTLFW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B. , and H. Rui , 1990: Synoptic climatology of transient tropical intraseasonal convection anomalies: 1975–1985. Meteor. Atmos. Phys., 44, 4361, https://doi.org/10.1007/BF01026810.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B. , and S.-S. Lee , 2017: MJO propagation shaped by zonal asymmetric structures: Results from 24 GCM simulations. J. Climate, 30, 79337952, https://doi.org/10.1175/JCLI-D-16-0873.1.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J. , H. Kim , D. Kim , S. A. Henderson , C. Stan , and E. D. Maloney , 2020: MJO teleconnections over the PNA region in climate models. Part II: Impacts of the MJO and basic state. J. Climate, 33, 50815101, https://doi.org/10.1175/JCLI-D-19-0865.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S. , M. K. Tippett , A. H. Sobel , Z. K. Martin , and F. Vitart , 2019: Impact of the QBO on prediction and predictability of the MJO convection. J. Geophys. Res. Atmos., 124, 1176611782, https://doi.org/10.1029/2019JD030575.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weaver, S. J. , W. Wang , M. Chen , and A. Kumar , 2011: Representation of MJO variability in the NCEP climate forecast system. J. Climate, 24, 46764694, https://doi.org/10.1175/2011JCLI4188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, Y. , and H.-L. Ren , 2019: Modulation of ENSO on fast and slow MJO modes during boreal winter. J. Climate, 32, 74837506, https://doi.org/10.1175/JCLI-D-19-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C. , and H. H. Hendon , 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, J. , H.-L. Ren , B. Lu , P. Zhang , C. Zhao , and X. Liu , 2020: Effects of moisture initialization on MJO and its teleconnection prediction in BCC subseasonal coupled model. J. Geophys. Res. Atmos., 125, e2019JD031537, https://doi.org/10.1029/2019JD031537.

    • Search Google Scholar
    • Export Citation
  • Xiang, B. , and Coauthors, 2015a: Beyond weather time-scale prediction for Hurricane Sandy and Super Typhoon Haiyan in a global climate model. Mon. Wea. Rev., 143, 524535, https://doi.org/10.1175/MWR-D-14-00227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B. , M. Zhao , X. Jiang , S.-J. Lin , T. Li , X. Fu , and G. Vecchi , 2015b: The 3–4-week MJO prediction skill in a GFDL coupled model. J. Climate, 28, 53515364, https://doi.org/10.1175/JCLI-D-15-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B. , Y. Q. Sun , J.-H. Chen , N. C. Johnson , and X. Jiang , 2020: Subseasonal prediction of land cold extremes in boreal wintertime. J. Geophys. Res. Atmos., 125, e2020JD032670, https://doi.org/10.1029/2020JD032670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, Y.-B. , S.-J. Chen , I.-L. Zhang , and Y.-L. Hung , 1963: A preliminarily statistic and synoptic study about the basic currents over southeastern Asia and the initiation of typhoon (in Chinese). Acta Meteor. Sin., 33, 206217.

    • Search Google Scholar
    • Export Citation
  • Yoo, C. , and S.-W. 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. , and B. Zhang , 2018: QBO-MJO Connection. J. Geophys. Res. Atmos., 123, 29572967, https://doi.org/10.1002/2017JD028171.

  • Zhang, C. , Á. F. 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
  • Zhao, M. , 2020: Simulations of atmospheric rivers, their variability, and response to global warming using GFDL’s new high-resolution general circulation model. J. Climate, 33, 1028710303, https://doi.org/10.1175/JCLI-D-20-0241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M. , and Coauthors, 2018a: The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs. J. Adv. Model. Earth Syst., 10, 691734, https://doi.org/10.1002/2017MS001208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M. , and Coauthors, 2018b: The GFDL global atmosphere and land model AM4.0/LM4.0: 2. Model description, sensitivity studies, and tuning strategies. J. Adv. Model. Earth Syst., 10, 735769, https://doi.org/10.1002/2017MS001209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J. , and A. Kumar , 2019: Role of sea surface salinity feedback in MJO predictability: A study with CFSv2. J. Climate, 32, 57455759, https://doi.org/10.1175/JCLI-D-18-0755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J. , A. Kumar , and W. Wang , 2020: Dependence of MJO predictability on convective parameterizations. J. Climate, 33, 47394750, https://doi.org/10.1175/JCLI-D-18-0552.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

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  • 1 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and University Corporation for Atmospheric Research, Boulder, Colorado;
  • | 2 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey;
  • | 3 International Pacific Research Center, University of Hawai‘i at Mānoa, Honolulu, Hawaii;
  • | 4 Earth System Modeling Center, Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China;
  • | 5 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and University Corporation for Atmospheric Research, Boulder, Colorado;
  • | 6 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and Vulcan Inc., Seattle, Washington;
  • | 7 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and University Corporation for Atmospheric Research, Boulder, Colorado;
  • | 8 NOAA/Geophysical Fluid Dynamics Laboratory, and Cooperative Institute for Modeling the Earth System, Program in Oceanic and Atmospheric Sciences, Princeton, New Jersey;
  • | 9 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and University Corporation for Atmospheric Research, Boulder, Colorado;
  • | 10 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey;
  • | 11 NOAA/Geophysical Fluid Dynamics Laboratory, and Cooperative Institute for Modeling the Earth System, Program in Oceanic and Atmospheric Sciences, Princeton, New Jersey;
  • | 12 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and Science Applications International Corporation, Reston, Virginia;
  • | 13 NOAA/Geophysical Fluid Dynamics Laboratory, and Cooperative Institute for Modeling the Earth System, Program in Oceanic and Atmospheric Sciences, Princeton, New Jersey;
  • | 14 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey;
  • | 15 NOAA/Geophysical Fluid Dynamics Laboratory, and Cooperative Institute for Modeling the Earth System, Program in Oceanic and Atmospheric Sciences, Princeton, New Jersey;
  • | 16 University Corporation for Atmospheric Research, Boulder, Colorado, and Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland
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Abstract

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

© 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: Dr. Baoqiang Xiang, baoqiang.xiang@noaa.gov

Abstract

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

© 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: Dr. Baoqiang Xiang, baoqiang.xiang@noaa.gov

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