• Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell, F. Vitart, and G. Balsamo, 2008: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor. Soc., 134, 13371351, https://doi.org/10.1002/qj.289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMott, C. A., N. P. Klingaman, and S. J. Woolnough, 2015: Atmosphere-ocean coupled processes in the Madden-Julian oscillation. Rev. Geophys., 53, 10991154, https://doi.org/10.1002/2014RG000478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flatau, M., P. J. Flatau, P. Phoebus, and P. P. Niler, 1997: The feedback between equatorial convection and local radiative and evaporative processes: The implications for intraseasonal oscillations. J. Atmos. Sci., 54, 23732386, https://doi.org/10.1175/1520-0469(1997)054<2373:TFBECA>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., and H. L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520533, https://doi.org/10.1175/WAF-D-10-05038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inness, P. M., J. M. Slingo, S. J. Woolnough, R. B. Neale, and V. D. Pope, 2001: Organization of tropical convection in a GCM with varying vertical resolution: Implications for the simulation of the Madden–Julian oscillation. Climate Dyn., 17, 777793, https://doi.org/10.1007/s003820000148.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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
  • Kim, H. M., 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
  • Kumar, A., and M. P. Hoerling, 1995: Prospects and limitations of seasonal atmospheric GCM predictions. Bull. Amer. Meteor. Soc., 76, 335345, https://doi.org/10.1175/1520-0477(1995)076<0335:PALOSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, A., and M. P. Hoerling, 2000: Analysis of a conceptual model of seasonal climate variability and implications for seasonal prediction. Bull. Amer. Meteor. Soc., 81, 255264, https://doi.org/10.1175/1520-0477(2000)081<0255:AOACMO>2.3.CO;2.

    • 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. 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
  • Liu, X., and et al. , 2017: MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Climate Dyn., 48, 32833307, https://doi.org/10.1007/s00382-016-3264-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H.-Y., and et al. , 2014: On the correspondence between mean forecast errors and climate errors in CMIP5 models. J. Climate, 27, 17811798, https://doi.org/10.1175/JCLI-D-13-00474.1.

    • 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
  • Maloney, E. D., and A. H. Sobel, 2004: Surface fluxes and ocean coupling in the tropical intraseasonal oscillation. J. Climate, 17, 43684386, https://doi.org/10.1175/JCLI-3212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002, https://doi.org/10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moorthi, S., and M. J. Suarez, 1999: Documentation of version 2 of relaxed Arakawa–Schubert cumulus parameterization with convective downdrafts. NOAA Office Note 99-01, 44 pp.

  • National Academies of Sciences, Engineering, and Medicine, 2016: Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. National Academies Press, 350 pp., https://doi.org/10.17226/21873.

    • Crossref
    • Export Citation
  • National Research Council, 2010: Assessment of Intraseasonal to Interannual Climate Prediction and Predictability. National Academies Press, 192 pp.

  • Neena, J., 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
  • Pan, H.-L., and W.-S. Wu, 1995: Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC Office Note 409, 43 pp., https://www2.mmm.ucar.edu/wrf/users/phys_refs/CU_PHYS/Old_SAS.pdf.

  • Pegion, K., and B. 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
  • 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
  • Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11, 109120, https://doi.org/10.1175/1520-0442(1998)011<0109:APSPWA>2.0.CO;2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, K. H., W. Q. Wang, J. Gottschalck, Q. Zhang, J. K. E. Schemm, W. R. Higgins, and A. Kumar, 2009: Evaluation of MJO forecast skill from several statistical and dynamical forecast models. J. Climate, 22, 23722388, https://doi.org/10.1175/2008JCLI2421.1.

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

  • Vitart, F., S. Woolnough, M. A. Balmaseda, and A. M. Tompkins, 2007: Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon. Wea. Rev., 135, 27002715, https://doi.org/10.1175/MWR3415.1.

    • 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 et al. , 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., 2011: Predictability and forecasting. Intraseasonal Variability of the Atmosphere–Ocean Climate System, W. K. M. Lau and D. E. Waliser, Eds., 2nd ed. Springer, 433–476.

    • Crossref
    • Export Citation
  • Waliser, D. E., K. M. Lau, and J.-H. Kim, 1999: The influence of coupled SSTs on the Madden–Julian oscillation: A model perturbation experiment. J. Atmos. Sci., 56, 333358, https://doi.org/10.1175/1520-0469(1999)056<0333:TIOCSS>2.0.CO;2.

    • 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
  • Wang, B., and et al. , 2018: Dynamics-oriented diagnostics for the Madden–Julian oscillation. J. Climate, 31, 31173135, https://doi.org/10.1175/JCLI-D-17-0332.1.

    • Search Google Scholar
    • Export Citation
  • Wang, W., and M. Schlesinger, 1999: The dependence on convection parameterization of the tropical intraseasonal oscillation simulated by the UIUC 11-layer atmospheric GCM. J. Climate, 12, 14231457, https://doi.org/10.1175/1520-0442(1999)012<1423:TDOCPO>2.0.CO;2.

    • 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
  • Wang, W., A. Kumar, J. X. Fu, and M.-P. Hung, 2015: What is the role of the sea surface temperature uncertainty in the prediction of tropical convection associated with the MJO? Mon. Wea. Rev., 143, 31563175, https://doi.org/10.1175/MWR-D-14-00385.1.

    • 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
  • Williams, K. D., and et al. , 2013: The Transpose-AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models. J. Climate, 26, 32583274, https://doi.org/10.1175/JCLI-D-12-00429.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., H.-Y. Ma, J. S. Boyle, S. A. Klein, and Y. Zhang, 2012: On the correspondence between short- and long-time-scale systematic errors in CAM4/CAM5 for the Year of Tropical Convection. J. Climate, 25, 79377955, https://doi.org/10.1175/JCLI-D-12-00134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., M. Dong, S. Gualdi, H. H. Hendon, E. D. Maloney, A. Marshall, K. R. Sperber, and W. Wang, 2006: Simulations of the Madden–Julian oscillation in four pairs of coupled and uncoupled global models. Climate Dyn., 27, 573592, https://doi.org/10.1007/s00382-006-0148-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and M. Mu, 2005: Simulation of the Madden–Julian Oscillation in the NCAR CCM3 using a revised Zhang–McFarlane convection parameterization scheme. J. Climate, 18, 40464064, https://doi.org/10.1175/JCLI3508.1.

    • 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, W. Wang, Z.-Z. Hu, B. Huang, and M. A. Balmaseda, 2017a: Importance of convective parameterization in ENSO predictions. Geophys. Res. Lett., 44, 63346342, https://doi.org/10.1002/2017GL073669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J., W. Wang, and A. Kumar, 2017b: Simulations of MJO propagation across the maritime continent: Impacts of SST feedback. J. Climate, 30, 16891704, https://doi.org/10.1175/JCLI-D-16-0367.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J., A. Kumar, H.-C. Lee, and H. Wang, 2017c: Seasonal predictions using a simple ocean initialization scheme. Climate Dyn., 49, 39894007, https://doi.org/10.1007/s00382-017-3556-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Dependence of MJO Predictability on Convective Parameterizations

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  • 1 NOAA/NWS/NCEP/Climate Prediction Center, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland
  • | 2 NOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland
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Abstract

This study revisits MJO predictability based on the “perfect model” approach with a contemporary model. Experiments are performed to address the reasons for substantial uncertainties in current estimates of MJO predictability, with a focus on the influence of atmospheric convection parameterization. Specifically, two atmospheric convection schemes are applied for experiments with the NOAA Climate Forecast System, version 2 (CFSv2). MJO potential predictability and prediction skill are assessed, with MJO indices taken as the first two principal components of the combined fields of near-equatorially averaged 200-hPa zonal wind, 850-hPa zonal wind, and outgoing longwave radiation at the top of the atmosphere. Analyses indicate that the convection scheme alone can have substantial influence on the estimate of MJO predictability, with estimates differing by as much as 15 days. Further diagnostics suggest that the shorter predictability with one convection scheme is mainly caused by too weak of an MJO signal. The choice of atmospheric convection scheme also exerts effects on the phase dependency of MJO predictability, and the “Maritime Continent prediction barrier” is identified to be more evident with one convection scheme than with the other.

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

Corresponding author: Jieshun Zhu, jieshun.zhu@noaa.gov

Abstract

This study revisits MJO predictability based on the “perfect model” approach with a contemporary model. Experiments are performed to address the reasons for substantial uncertainties in current estimates of MJO predictability, with a focus on the influence of atmospheric convection parameterization. Specifically, two atmospheric convection schemes are applied for experiments with the NOAA Climate Forecast System, version 2 (CFSv2). MJO potential predictability and prediction skill are assessed, with MJO indices taken as the first two principal components of the combined fields of near-equatorially averaged 200-hPa zonal wind, 850-hPa zonal wind, and outgoing longwave radiation at the top of the atmosphere. Analyses indicate that the convection scheme alone can have substantial influence on the estimate of MJO predictability, with estimates differing by as much as 15 days. Further diagnostics suggest that the shorter predictability with one convection scheme is mainly caused by too weak of an MJO signal. The choice of atmospheric convection scheme also exerts effects on the phase dependency of MJO predictability, and the “Maritime Continent prediction barrier” is identified to be more evident with one convection scheme than with the other.

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

Corresponding author: Jieshun Zhu, jieshun.zhu@noaa.gov
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