Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

Hyerim Kim Ulsan National Institute of Science and Technology, Ulsan, South Korea

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Myong-In Lee Ulsan National Institute of Science and Technology, Ulsan, South Korea

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Daehyun Kim University of Washington, Seattle, Washington

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Hyun-Suk Kang Climate Research Laboratory, National Institute of Meteorological Research, Jeju, South Korea

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Yu-Kyung Hyun Climate Research Laboratory, National Institute of Meteorological Research, Jeju, South Korea

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Abstract

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

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

Corresponding author: Dr. Myong-In Lee, milee@unist.ac.kr

Abstract

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

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

Corresponding author: Dr. Myong-In Lee, milee@unist.ac.kr
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  • Adames, Á. F., and J. M. Wallace, 2014: Three-dimensional structure and evolution of the MJO and its relation to the mean flow. J. Atmos. Sci., 71, 20072026, https://doi.org/10.1175/JAS-D-13-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baxter, S., S. Weaver, J. Gottschalck, and Y. Xue, 2014: Pentad evolution of wintertime impacts of the Madden–Julian oscillation over the contiguous United States. J. Climate, 27, 73567367, https://doi.org/10.1175/JCLI-D-14-00105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bergman, J. W., H. H. Hendon, and K. M. Weickmann, 2001: Intraseasonal air–sea interactions at the onset of El Niño. J. Climate, 14, 17021719, https://doi.org/10.1175/1520-0442(2001)014<1702:IASIAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description—Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677699, https://doi.org/10.5194/gmd-4-677-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and A. Shelly, 2012: Unified modeling and prediction of weather and climate: A 25-year journey. Bull. Amer. Meteor. Soc., 93, 18651877, https://doi.org/10.1175/BAMS-D-12-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottschalck, J., and Coauthors, 2010: A framework for assessing operational Madden–Julian oscillation forecasts: A CLIVAR MJO Working Group Project. Bull. Amer. Meteor. Soc., 91, 12471258, https://doi.org/10.1175/2010BAMS2816.1.

    • 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
  • Hewitt, H. T., D. Copsey, I. D. Culverwell, C. M. Harris, R. S. R. Hill, A. B. Keen, A. J. McLaren, and E. C. Hunke, 2011: Design and implementation of the infrastructure of HadGEM3: The next-generation Met Office climate modelling system. Geosci. Model Dev., 4, 223253, https://doi.org/10.5194/gmd-4-223-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and D. J. Karoly, 1981: The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci., 38, 11791196, https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and T. Ambrizzi, 1993: Rossby wave propagation on a realistic longitudinally varying flow. J. Atmos. Sci., 50, 16611671, https://doi.org/10.1175/1520-0469(1993)050<1661:RWPOAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos Sea Ice Model documentation and software user’s manual, version 4.1. Los Alamos National Laboratory Tech. Rep. LA-CC-06-012, 76 pp., https://csdms.colorado.edu/w/images/CICE_documentation_and_software_user%27s_manual.pdf.

  • Jeong, J.-H., B.-M. Kim, C.-H. Ho, and Y.-H. Noh, 2008: Systematic variation in wintertime precipitation in East Asia by MJO-induced extratropical vertical motion. J. Climate, 21, 788801, https://doi.org/10.1175/2007JCLI1801.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, X., C. Lijuan, R. Fumin, and L. Chongyin, 2011: Impacts of the MJO on winter rainfall and circulation in China. Adv. Atmos. Sci., 28, 521533, https://doi.org/10.1007/s00376-010-9118-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., D. C. Collins, S. B. Feldstein, M. L. L’Heureux, and E. E. Riddle, 2014: Skillful wintertime North American temperature forecasts out to 4 weeks based on the state of ENSO and the MJO. Wea. Forecasting, 29, 2338, https://doi.org/10.1175/WAF-D-13-00102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W., and R. Kleeman, 2000: Rectification of the Madden–Julian oscillation into the ENSO cycle. J. Climate, 13, 35603575, https://doi.org/10.1175/1520-0442(2000)013<3560:ROTMJO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., M.-I. Lee, H.-M. Kim, S. D. Schubert, and J. H. Yoo, 2014: The modulation of tropical storm activity in the western North Pacific by the Madden–Julian oscillation in GEOS-5 AGCM experiments. Atmos. Sci. Lett., 15, 335341, https://doi.org/10.1002/asl2.509.

    • Search Google Scholar
    • Export Citation
  • Kim, H.-M., and I.-S. Kang, 2008: The impact of ocean–atmosphere coupling on the predictability of boreal summer intraseasonal oscillation. Climate Dyn., 31, 859870, https://doi.org/10.1007/s00382-008-0409-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems. J. Climate, 27, 53645378, https://doi.org/10.1175/JCLI-D-13-00480.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282293, https://doi.org/10.1175/2009JCLI2978.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, M.-I., H.-S. Kang, D. Kim, D. Kim, H. Kim, and D. Kang, 2014: Validation of the experimental hindcasts produced by the GloSea5 seasonal prediction system. Asia-Pac. J. Atmos. Sci., 50, 307326, https://doi.org/10.1007/s13143-014-0019-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, Y., S. 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
  • 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
  • MacLachlan, C., and Coauthors, 2015: Global Seasonal Forecast System version 5 (GloSea5): A high-resolution seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 10721084, https://doi.org/10.1002/qj.2396.

    • 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
  • Madec, G., and Coauthors, 2016: NEMO Ocean Engine, version 3.6. Note du Pôle de Modelisation 27, Institut Pierre-Simon Laplace, Paris, France, 386 pp., https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf.

  • Maloney, E. D., and D. L. Hartmann, 2000: Modulation of eastern North Pacific hurricanes by Madden–Julian oscillation. J. Climate, 13, 14511460, https://doi.org/10.1175/1520-0442(2000)013<1451:MOENPH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and D. L. Hartmann, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: Observations. J. Atmos. Sci., 58, 25452558, https://doi.org/10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., B. J. Hoskins, and M. Masutani, 2004: The global response to tropical heating in the Madden–Julian oscillation during the northern winter. Quart. J. Roy. Meteor. Soc., 130, 19912011, https://doi.org/10.1256/qj.02.123.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., 1999: Genesis and evolution of the 1997–1998 El Niño. Science, 283, 950954, https://doi.org/10.1126/science.283.5404.950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., 2004: Evolution of the 2002/03 El Niño. Bull. Amer. Meteor. Soc., 85, 677695, https://doi.org/10.1175/BAMS-85-5-677.

  • Megann, A. P., and Coauthors, 2014: GO 5.0: The joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications. Geosci. Model Dev., 7, 10691092, https://doi.org/10.5194/gmd-7-1069-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moon, J.-Y., B. Wang, and K.-J. Ha, 2011: ENSO regulation of MJO teleconnection. Climate Dyn., 37, 11331149, https://doi.org/10.1007/s00382-010-0902-3.

    • 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
  • Pegion, K., and B. P. Kirtman, 2008: The impact of air–sea interactions on the predictability of the tropical intraseasonal oscillation. J. Climate, 21, 58705886, https://doi.org/10.1175/2008JCLI2209.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rae, J. G. L., H. T. Hewitt, A. B. Keen, J. K. Ridley, A. E. West, C. M. Harris, E. C. Hunke, and D. N. Walters, 2015: Development of global sea ice 6.0 CICE configuration for the Met Office global coupled model. Geosci. Model Dev., 8, 22212230, https://doi.org/10.5194/gmdd-8-2529-2015.

    • 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
  • Reichler, T., and J. O. Roads, 2005: Long-range predictability in the tropics. Part II: 30–60-day variability. J. Climate, 18, 634650, https://doi.org/10.1175/JCLI-3295.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roundy, P. E., 2014: Some aspects of Western Hemisphere circulation and the Madden–Julian oscillation. J. Atmos. Sci., 71, 20272039, https://doi.org/10.1175/JAS-D-13-0210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rui, H., and B. Wang, 1990: Development characteristics and dynamic structure of tropical intraseasonal convection anomalies. J. Atmos. Sci., 47, 357379, https://doi.org/10.1175/1520-0469(1990)047<0357:DCADSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci., 45, 12281251, https://doi.org/10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schreck, C. J., and J. Molinari, 2011: Tropical cyclogenesis associated with Kelvin waves and the Madden–Julian oscillation. Mon. Wea. Rev., 139, 27232734, https://doi.org/10.1175/MWR-D-10-05060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, K.-H., 2009: Statistical-dynamical prediction of the Madden–Julian oscillation using NCEP Climate Forecast System (CFS). Int. J. Climatol., 29, 21462155, https://doi.org/10.1002/joc.1845.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, K.-H., and Y. Xue, 2005: MJO-related oceanic Kelvin waves and the ENSO cycle: A study with the NCEP Global Ocean Data Assimilation. Geophys. Res. Lett., 32, L07712, https://doi.org/10.1029/2005GL022511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, K.-H., and H. Lee, 2017: Mechanisms for a PNA-like teleconnection pattern in response to the MJO. J. Atmos. Sci., 74, 17671781, https://doi.org/10.1175/JAS-D-16-0343.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 30793102, https://doi.org/10.1256/qj.04.106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takayabu, Y. N., T. Iguchi, M. Kachi, A. Shibata, and H. Kanzawa, 1999: Abrupt termination of the 1997–98 El Niño in response to a Madden–Julian oscillation. Nature, 402, 279282, https://doi.org/10.1038/46254.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tennant, W. J., G. J. Shutts, A. Arribas, and S. A. Thompson, 2011: Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic prediction skill. Mon. Wea. Rev., 139, 11901206, https://doi.org/10.1175/2010MWR3430.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Valcke, S., 2013: The OASIS3 coupler: A European climate modelling community software. Geosci. Model Dev., 6, 373388, https://doi.org/10.5194/gmd-6-373-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., 2014: Evolution of ECMWF sub-seasonal prediction skill scores. Quart. J. Roy. Meteor. Soc., 140, 18891899, https://doi.org/10.1002/qj.2256.

    • 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
  • Vitart, F., and F. Molteni, 2010: Simulation of the Madden–Julian oscillation and its teleconnections in the ECMWF forecast system. Quart. J. Roy. Meteor. Soc., 136, 842855, https://doi.org/10.1002/qj.623.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., A. Leroy, and M. C. Wheeler, 2010: A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 138, 36713682, https://doi.org/10.1175/2010MWR3343.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and Coauthors, 2017: The Subseasonal to Seasonal (S2S) Prediction Project database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., K. M. Lau, W. Stern, and C. Jones, 2003: Potential predictability of the Madden–Julian oscillation. Bull. Amer. Meteor. Soc., 84, 3350, https://doi.org/10.1175/BAMS-84-1-33.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and Coauthors, 2006: The Experimental MJO Prediction Project. Bull. Amer. Meteor. Soc., 87, 425431, https://doi.org/10.1175/BAMS-87-4-421.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D. N., and Coauthors, 2011: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations. Geosci. Model Dev., 4, 919941, https://doi.org/10.5194/gmd-4-919-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D. N., and Coauthors, 2017: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci. Model Dev., 10, 14871520, https://doi.org/10.5194/gmd-10-1487-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., M.-P. Hung, S. J. Weaver, A. Kumar, and X. Fu, 2014: MJO prediction in the NCEP Climate Forecast System version 2. Climate Dyn., 42, 25092520, https://doi.org/10.1007/s00382-013-1806-9.

    • 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
  • Wheeler, M. C., H. H. Hendon, S. Cleland, H. Meinke, and A. Donald, 2009: Impacts of the Madden–Julian oscillation on Australian rainfall and circulation. J. Climate, 22, 14821498, https://doi.org/10.1175/2008JCLI2595.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, K. D., and Coauthors, 2015: The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev., 8, 15091524, https://doi.org/10.5194/gmd-8-1509-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, J., H. L. Ren, J. Zuo, C. Zhao, L. Chen, and Q. Li, 2016: MJO prediction skill, predictability, and teleconnection impacts in the Beijing Climate Center Atmospheric General Circulation Model. Dyn. Atmos. Oceans, 75, 7890, https://doi.org/10.1016/j.dynatmoce.2016.06.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B., M. Zhao, X. Jiang, S.-J. Lin, T. Li, X. Fu, and G. Vecchi, 2015: 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
  • Yasunari, T., 1979: Cloudiness fluctuations associated with the Northern Hemisphere summer monsoon. J. Meteor. Soc. Japan, 57, 227242, https://doi.org/10.2151/jmsj1965.57.3_227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yoo, C., S. Park, D. Kim, J. Yoon, and H. Kim, 2015: Boreal winter MJO teleconnection in the Community Atmosphere Model version 5 with the Unified Convection parameterization. J. Climate, 28, 81358150, https://doi.org/10.1175/JCLI-D-15-0022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., and J. Gottschalck, 2002: SST anomalies of ENSO and the Madden–Julian oscillation in the equatorial Pacific. J. Climate, 15, 24292445, https://doi.org/10.1175/1520-0442(2002)015<2429:SAOEAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Q., and H. van den Dool, 2012: Relative merit of model improvement versus availability of retrospective forecasts: The case of Climate Forecast System MJO prediction. Wea. Forecasting, 27, 10451051, https://doi.org/10.1175/WAF-D-11-00133.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
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