Global versus Local MJO Forecast Skill of the ECMWF Model during DYNAMO

Jian Ling The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, and Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, and European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Peter Bauer European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Peter Bechtold European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Anton Beljaars European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Richard Forbes European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Frederic Vitart European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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Marcela Ulate Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

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Chidong Zhang Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

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Abstract

This study introduces a concept of global versus local forecast skill of the Madden–Julian oscillation (MJO). The global skill, measured by a commonly used MJO index [the Real-time Multivariate MJO (RMM)], evaluates the model’s capability of forecasting global patterns of the MJO, with an emphasis on the zonal wind fields. The local skill is measured by a method of tracking the eastward propagation of MJO precipitation. It provides quantitative information of the strength, propagation speed, and timing of MJO precipitation in a given region, such as the Indian Ocean. Both global and local MJO forecast skills are assessed for ECMWF forecasts of three MJO events during the 2011–12 Dynamics of the MJO (DYNAMO) field campaign. Characteristics of error growth differ substantially between global and local MJO forecast skills, and between the three MJO quantities (strength, speed, and timing) of the local skill measure. They all vary considerably among the three MJO events. Deterioration in global forecast skill for these three events appears to be related to poor local skill in forecasting the propagation speed of MJO precipitation. The global and local MJO forecast skill measures are also applied to evaluate numerical experiments of observation denial, humidity relaxation, and forcing by daily perturbations in sea surface temperature (SST). The results suggest that forecast skill or errors of convective initiation of the three MJO events have global origins. Effects of local (Indian Ocean) factors, such as enhanced observations in the initial conditions, variability of tropospheric humidity and tropical SST, on forecasts of MJO initiation and propagation are limited.

Corresponding author address: Dr. Jian Ling, LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China. E-mail: lingjian@lasg.iap.ac.cn

This article is included in the DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation special collection.

Abstract

This study introduces a concept of global versus local forecast skill of the Madden–Julian oscillation (MJO). The global skill, measured by a commonly used MJO index [the Real-time Multivariate MJO (RMM)], evaluates the model’s capability of forecasting global patterns of the MJO, with an emphasis on the zonal wind fields. The local skill is measured by a method of tracking the eastward propagation of MJO precipitation. It provides quantitative information of the strength, propagation speed, and timing of MJO precipitation in a given region, such as the Indian Ocean. Both global and local MJO forecast skills are assessed for ECMWF forecasts of three MJO events during the 2011–12 Dynamics of the MJO (DYNAMO) field campaign. Characteristics of error growth differ substantially between global and local MJO forecast skills, and between the three MJO quantities (strength, speed, and timing) of the local skill measure. They all vary considerably among the three MJO events. Deterioration in global forecast skill for these three events appears to be related to poor local skill in forecasting the propagation speed of MJO precipitation. The global and local MJO forecast skill measures are also applied to evaluate numerical experiments of observation denial, humidity relaxation, and forcing by daily perturbations in sea surface temperature (SST). The results suggest that forecast skill or errors of convective initiation of the three MJO events have global origins. Effects of local (Indian Ocean) factors, such as enhanced observations in the initial conditions, variability of tropospheric humidity and tropical SST, on forecasts of MJO initiation and propagation are limited.

Corresponding author address: Dr. Jian Ling, LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China. E-mail: lingjian@lasg.iap.ac.cn

This article is included in the DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation special collection.

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  • Bechtold, P., and Coauthors, 2012: Progress in predicting tropical systems: The role of convection. Tech. Memo. 686, ECMWF Tech. Rep., 61 pp.

  • Buizza, R., and T. N. Palmer, 1995: The singular-vector structure of the atmospheric general circulation. J. Atmos. Sci., 52, 14341456, doi:10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 28872908, doi:10.1002/qj.49712556006.

    • Search Google Scholar
    • Export Citation
  • de Boisséson, E., M. A. Balmaseda, F. Vitart, and K. Mogensen, 2012: Impact of the sea surface temperature forcing on hindcasts of Madden-Julian Oscillation events using the ECMWF model. Ocean Sci., 8, 10711084, doi:10.5194/os-8-1071-2012.

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

    • 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, doi:10.1175/2010BAMS2816.1.

    • Search Google Scholar
    • Export Citation
  • Gottschalck, J., P. E. Roundy, C. J. Schreck III, A. Vintzileos, and C. Zhang, 2013: Large-scale atmospheric and oceanic conditions during the 2011–12 DYNAMO field campaign. Mon. Wea. Rev., 141, 4173–4196, doi:10.1175/MWR-D-13-00022.1.

    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., and P. L. Silva Dias, 1995: Analysis of tropical–extratropical interactions with influence functions of a barotropic model. J. Atmos. Sci., 52, 35383555, doi:10.1175/1520-0469(1995)052<3538:AOTIWI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., L. R. Leung, and J. Dudhia, 2011: Thermodynamics of the Madden–Julian oscillation in a regional model with constrained moisture. J. Atmos. Sci., 68, 19741989, doi:10.1175/2011JAS3592.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., B. Wang, and X. H. 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, doi:10.1175/1520-0469(2002)059<1433:SOTIOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., and K. M. Weickmann, 1992: Circulation anomalies associated with tropical convection during northern winter. Mon. Wea. Rev., 120, 19001923, doi:10.1175/1520-0493(1992)120<1900:CAAWTC>2.0.CO;2.

    • 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, doi:10.1175/JAS3520.1.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1029/2008RG000266.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, doi:10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • 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, doi:10.1175/2008MWR2459.1.

    • Search Google Scholar
    • Export Citation
  • Ling, J., C. Li, W. Zhou, X. Jia, and C. Zhang, 2013: Effect of boundary layer latent heating on MJO simulations. Adv. Atmos. Sci., 30, 101115, doi:10.1007/s00376-012-2031-x.

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

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in tropics with a 40–50 day period. J. Atmos. Sci., 29, 11091123, doi:10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Matsueda, M., and H. Endo, 2011: Verification of medium-range MJO forecasts with TIGGE. Geophys. Res. Lett., 38, L11801, doi:10.1029/2011GL047480.

    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., 2004: Intraseasonal variability over tropical Africa during northern summer. J. Climate, 17, 24272440, doi:10.1175/1520-0442(2004)017<2427:IVOTAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Renwick, J. A., and M. J. Revell, 1999: Blocking over the South Pacific and Rossby wave propagation. Mon. Wea. Rev., 127, 22332247, doi:10.1175/1520-0493(1999)127<2233:BOTSPA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Straub, K. H., 2013: MJO initiation in the real-time multivariate MJO index. J. Climate, 26, 1130–1151, doi:10.1175/JCLI-D-12-00074.1.

    • Search Google Scholar
    • Export Citation
  • Waliser, D., and Coauthors, 2009: MJO simulation diagnostics. J. Climate, 22, 30063030, doi:10.1175/2008JCLI2731.1.

  • Vialard, J., F. Vitart, M. A. Balmaseda, T. Stockdale, and D. L. T. Anderson, 2005: An ensemble generation method for seasonal forecasting with an ocean–atmosphere coupled model. Mon. Wea. Rev., 133, 441453, doi:10.1175/MWR-2863.1.

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

  • Vitart, F., and T. Jung, 2010: Impact of the Northern Hemisphere extratropics on the skill in predicting the Madden–Julian Oscillation. Geophys. Res. Lett., 37, L23805, doi:10.1029/2010GL045465.

    • 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, doi:10.1002/qj.623.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1175/MWR3415.1.

    • Search Google Scholar
    • Export Citation
  • Wang, W., M.-P. Hung, S. J. Weaver, A. Kumar, and X. Fu, 2013: MJO prediction in the NCEP Climate Forecast System version 2. Climate Dyn., 10.1007/s00382-013-1806-9, in press.

    • 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, doi:10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wolff, J. O., E. Maier-Raimer, and S. Legutke, 1997: The Hamburg ocean primitive equation model. Deutsches Klimarechenzentrum Tech. Rep. 13, Hamburg, Germany, 98 pp.

  • Yoneyama, K., C. Zhang, and C. N. Long, 2013: Tracking pulses of the Madden–Julian Oscillation. Bull. Amer. Meteor. Soc., 94, 1871–1891, doi:10.1175/BAMS-D-12-00157.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2013: Madden–Julian Oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 1849–1870, doi:10.1175/BAMS-D-12-00026.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., and S. P. Anderson, 2003: Sensitivity of intraseasonal perturbations in SST to the structure of the MJO. J. Atmos. Sci., 60, 21962207, doi:10.1175/1520-0469(2003)060<2196:SOIPIS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhao, C., T. Li, and T. Zhou, 2013: Precursor signals and processes associated with MJO initiation over the tropical Indian Ocean. J. Climate, 26, 291307, doi:10.1175/JCLI-D-12-00113.1.

    • Search Google Scholar
    • Export Citation
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