• Arakawa, O., and A. Kitoh, 2004: Comparison of local precipitation-SST relationship between the observation and a reanalysis dataset. Geophys. Res. Lett., 31 .L12206, doi:10.1029/2004GL020283.

    • Search Google Scholar
    • Export Citation
  • Chen, T-C., and J. C. Alpert, 1990: Systematic errors in the annual and intraseasonal variations of the planetary-scale divergent circulation in NMC medium-range forecasts. Mon. Wea. Rev., 118 , 26072623.

    • Search Google Scholar
    • Export Citation
  • Dumenil, L., and E. Todini, 1992: A rainfall-runoff scheme for use in the Hamburg climate model. Advances in Theoretical Hydrology, A Tribute to James Dooge, European Geophysical Society Series on Hydrological Sciences, Vol. 1, Elsevier Press, 129–157.

    • Search Google Scholar
    • Export Citation
  • Flatau, M., P. Flatau, P. Phoebus, and P. Niller, 1997: The feedback between equatorial convection and local radiative and evaporative processes: The implications for intraseasonal oscillations. J. Atmos. Sci., 54 , 23732386.

    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2001: A coupled modeling study of the seasonal cycle of the Pacific cold tongue. Part I: Simulation and sensitivity experiments. J. Climate, 14 , 765779.

    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2004a: Differences of boreal-summer intraseasonal oscillations simulated in an atmosphere–ocean coupled model and an atmosphere-only model. J. Climate, 17 , 12631271.

    • Search Google Scholar
    • Export Citation
  • Fu, X., and B. Wang, 2004b: The boreal-summer intraseasonal oscillations simulated in a hybrid coupled atmosphere–ocean model. Mon. Wea. Rev., 132 , 26282649.

    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Wang, T. Li, and J. P. McCreary, 2003: Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J. Atmos. Sci., 60 , 17331753.

    • Search Google Scholar
    • Export Citation
  • Fu, X., B. Wang, and L. Tao, 2006: Satellite data reveal the 3-D moisture structure of Tropical Intraseasonal Oscillation and its coupling with underlying ocean. Geophys. Res. Lett., 33 .L03705, doi:10.1029/2005GL025074.

    • Search Google Scholar
    • Export Citation
  • Gadgil, S., and P. R. S. Rao, 2000: Famine strategies for a variable climate—A challenge. Curr. Sci., 78 , 12031215.

  • Gaspar, P., 1988: Modeling the seasonal cycle of the upper ocean. J. Phys. Oceanogr., 18 , 161180.

  • Goswami, B. N., and R. S. Ajayamohan, 2001: Intraseasonal oscillations and interannual variability of the Indian summer monsoon. J. Climate, 14 , 11801198.

    • Search Google Scholar
    • Export Citation
  • Goswami, B. N., and P. K. Xavier, 2003: Potential predictability and extended range prediction of Indian summer monsoon breaks. Geophys. Res. Lett., 30 .1966, doi:10.1029/2003GL017810.

    • Search Google Scholar
    • Export Citation
  • Goswami, B. N., R. S. Ajayamohan, P. K. Xavier, and D. Sengupta, 2003: Clustering of low pressure systems during the Indian summer monsoon by intraseasonal oscillations. Geophys. Res. Lett., 30 .1431, doi:10.1029/2002GL016734.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., 2000: Impact of air–sea coupling on the Madden–Julian oscillation in a general circulation model. J. Atmos. Sci., 57 , 39393952.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., B. Liebmann, M. Newman, J. D. Glick, and J. E. Schemm, 2000: Medium-range forecast errors associated with active episodes of the Madden–Julian oscillation. Mon. Wea. Rev., 128 , 6986.

    • Search Google Scholar
    • Export Citation
  • Hollingsworth, A., K. Arpe, M. Tiedtke, M. Capaldo, and H. Savijarvi, 1980: The performance of a medium-range forecast model in winter—Impact of physical parameterizations. Mon. Wea. Rev., 108 , 17361773.

    • Search Google Scholar
    • Export Citation
  • IFRC, 2000: World Disaster Report: Focus on Recovery. IFRC, 392 pp.

  • Inness, P. M., and J. M. Slingo, 2003: Simulation of the Madden–Julian oscillation in a coupled general circulation model. Part I: Comparison with observations and an atmosphere-only GCM. J. Climate, 16 , 345364.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., T. Li, and B. Wang, 2004: Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillations. J. Climate, 17 , 10221039.

    • Search Google Scholar
    • Export Citation
  • Jones, C., D. E. Waliser, J-K. E. Schemm, and W. K. M. Lau, 2000: Prediction skill of the Madden and Julian Oscillation in dynamical extended range forecasts. Climate Dyn., 16 , 273289.

    • Search Google Scholar
    • Export Citation
  • Jones, C., D. E. Waliser, K. M. Lau, and W. Stern, 2004: Global occurrences of extreme precipitation events and the Madden–Julian oscillation: Observations and predictability. J. Climate, 17 , 45754589.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.

  • Kemball-Cook, S., B. Wang, and X. Fu, 2002: Simulation of the ISO in the ECHAM4 model: The impact of coupling with an ocean model. J. Atmos. Sci., 59 , 14331453.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., M. Subramaniam, D. K. Oosterhof, and G. Daughenbaugh, 1990: Predictability of low-frequency modes. Meteor. Atmos. Phys., 44 , 6383.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., M. Subramaniam, G. Daughenbaugh, D. Oosterhof, and J. H. Xue, 1992: One-month forecasts of wet and dry spells of the monsoon. Mon. Wea. Rev., 120 , 11911223.

    • Search Google Scholar
    • Export Citation
  • Lau, K. H., and N. C. Lau, 1990: Observed structure and propagation characteristics of tropical summertime synoptic scale disturbances. Mon. Wea. Rev., 118 , 18881913.

    • 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 outgoing longwave radiation. Mon. Wea. Rev., 114 , 13541367.

    • Search Google Scholar
    • Export Citation
  • Lau, K. M., and F. C. Chang, 1992: Tropical intraseasonal oscillation and its prediction by the NMC operational model. J. Climate, 5 , 13651378.

    • Search Google Scholar
    • Export Citation
  • Lau, K. M., and C-H. Sui, 1997: Mechanisms of short-term sea surface temperature regulation: Observations during TOGA COARE. J. Climate, 10 , 465472.

    • Search Google Scholar
    • Export Citation
  • Liess, S., D. E. Waliser, and S. D. Schubert, 2005: Predictability studies of the intraseasonal oscillation in ECHAM5 GCM. J. Atmos. Sci., 62 , 33203336.

    • Search Google Scholar
    • Export Citation
  • Lo, F., and H. H. Hendon, 2000: Empirical extended-range prediction of the Madden–Julian oscillation. Mon. Wea. Rev., 128 , 25282543.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34 , 505513.

  • Matthews, A. J., 2004: Atmospheric response to observed intraseasonal tropical sea surface temperature anomalies. Geophys. Res. Lett., 31 .L14107, doi:10.1029/2004GL020474.

    • Search Google Scholar
    • Export Citation
  • Miyakoda, K., G. D. Hembree, R. F. Strickler, and I. Shulman, 1972: Cumulative results of extended forecast experiment. I. Model performance for winter cases. Mon. Wea. Rev., 100 , 836855.

    • Search Google Scholar
    • Export Citation
  • McCreary, J. P., and Z. J. Yu, 1992: Equatorial dynamics in a 2.5-layer model. Progress in Oceanography, Vol. 29, Pergamon. 61132.

  • Mo, K. C., 2001: Adaptive filtering and prediction of intraseasonal oscillations. Mon. Wea. Rev., 129 , 802817.

  • Nordeng, T. E., 1994: Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Research Dept. Tech. Memo. 206, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom, 41 pp.

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

    • Search Google Scholar
    • Export Citation
  • Roeckner, E., and Coauthors, 1996: The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate. Tech. Rep., Max-Plank-Institute for Meteorology, Rep. 218, 90 pp.

  • Sengupta, D., and M. Ravichandran, 2001: Oscillations of Bay of Bengal sea surface temperature during the 1998 summer monsoon. Geophys. Res. Lett., 28 , 20332036.

    • Search Google Scholar
    • Export Citation
  • Seo, K-H., J-K. Schemm, and C. Jones, 2005: Forecast skill of the Tropical Intraseasonal Oscillation in the NCEP GFS dynamical extended range forecasts. Climate Dyn., 25 , 265284.

    • Search Google Scholar
    • Export Citation
  • Shinoda, T., H. H. Hendon, and J. Glick, 1998: Intraseasonal variability of surface fluxes and sea surface temperature in the tropical western Pacific and Indian Oceans. J. Climate, 11 , 16851702.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., P. J. Webster, R. H. Johnson, R. Engelen, and T. L’Ecuyer, 2004: Observational evidence for the mutual regulation of the tropical hydrological cycle and tropical sea surface temperatures. J. Climate, 17 , 22132224.

    • Search Google Scholar
    • Export Citation
  • Tam, C. Y., and T. Li, 2006: The origin and dispersion characteristics of the observed summertime synoptic-scale waves over the western Pacific. Mon. Wea. Rev., 134 , 16301646.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., D. Williamson, and F. Zwiers, 2000: The sea surface temperature and sea-ice concentration boundary condition for AMIP II simulations. PCMDI Rep. 60, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, 25 pp.

  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117 , 17791800.

    • Search Google Scholar
    • Export Citation
  • Tracton, M. S., and E. Kalnay, 1993: Ensemble forecasting at NMC: Practical aspects. Wea. Forecasting, 8 , 379398.

  • Van den Dool, H. M., and S. Saha, 1990: Frequency dependence in forecast skill. Mon. Wea. Rev., 118 , 128137.

  • Waliser, D. E., K. M. Lau, and J. H. Kim, 1999a: A statistical extended-range tropical forecast model based on the slow evolution of the Madden–Julian oscillation. J. Climate, 12 , 19181939.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., K. M. Lau, and J. H. Kim, 1999b: The influence of coupled sea surface temperatures on the Madden–Julian oscillation: A model perturbation experiment. J. Atmos. Sci., 56 , 333358.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and Coauthors, 2003a: AGCM simulations of intraseasonal variability associated with the Asian summer monsoon. Climate Dyn., 21 , 423446.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., W. Stern, S. Schubert, and K. M. Lau, 2003b: Dynamic predictability of intraseasonal variability associated with the Asian summer monsoon. Quart. J. Roy. Meteor. Soc., 129 , 28972925.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., K. M. Lau, W. Stern, and C. Jones, 2003c: Potential predictability of the Madden–Julian oscillation. Bull. Amer. Meteor. Soc., 84 , 3350.

    • Search Google Scholar
    • Export Citation
  • Wang, B., 1988: Dynamics of tropical low-frequency waves: An analysis of the moist Kelvin waves. J. Atmos. Sci., 45 , 20512065.

  • Wang, B., and H. Rui, 1990: Synoptic climatology of transient tropical intraseasonal convection anomalies: 1975–1985. Meteor. Atmos. Phys., 44 , 4361.

    • Search Google Scholar
    • Export Citation
  • Wang, B., and X. Xie, 1998: Coupled modes of the warm pool climate system. Part I: The role of air–sea interaction in maintaining Madden–Julian oscillation. J. Climate, 11 , 21162135.

    • Search Google Scholar
    • Export Citation
  • Wang, B., T. Li, and P. Chang, 1995: An intermediate model of the tropical Pacific Ocean. J. Phys. Oceanogr., 25 , 15991616.

  • Webster, P. J., and C. Hoyos, 2004: Forecasting monsoon rainfall and river discharge variability on 15–30-day time scales. Bull. Amer. Meteor. Soc., 85 , 17451765.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., V. O. Magana, T. N. Palmer, J. Shukla, R. A. Tomas, M. Yanai, and T. Yasunari, 1998: Monsoons: Processes, predictability, and the prospects for prediction. J. Geophys. Res., 103 , (C7) (TOGA special issue). 1445114510.

    • Search Google Scholar
    • Export Citation
  • Yasunari, T., 1980: A quasi-stationary appearance of 30 to 40 day period in the cloudiness fluctuations during the summer monsoon over India. J. Meteor. Soc. Japan, 58 , 225229.

    • Search Google Scholar
    • Export Citation
  • Zheng, Y., D. E. Waliser, W. F. Stern, and C. Jones, 2004: The role of coupled sea surface temperatures in the simulation of the tropical intraseasonal oscillation. J. Climate, 17 , 41094134.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 231 79 9
PDF Downloads 161 59 10

Impact of Atmosphere–Ocean Coupling on the Predictability of Monsoon Intraseasonal Oscillations

View More View Less
  • 1 IPRC, SOEST, University of Hawaii at Manoa, Honolulu, Hawaii
  • | 2 IPRC, and Department of Meteorology, SOEST, University of Hawaii at Manoa, Honolulu, Hawaii
  • | 3 JPL, California Institute of Technology, Pasadena, California
  • | 4 IPRC, SOEST, University of Hawaii at Manoa, Honolulu, Hawaii
Restricted access

Abstract

The impact of air–sea coupling on the predictability of monsoon intraseasonal oscillations (MISO) has been investigated with an atmosphere–ocean coupled model and its atmospheric component. From a 15-yr coupled control run, 20 MISO events are selected. A series of twin perturbation experiments have been conducted for all the selected events using both the coupled model and the atmosphere-only model. Two complementary measures are used to quantify the MISO predictability: (i) the ratio of signal-to-forecast error and (ii) the spatial anomaly correlation coefficient (ACC).

In the coupled model, the MISO predictability is generally higher over the Indian sector than that over the western Pacific with a maximum of 35 days in the eastern equatorial Indian Ocean. Air–sea coupling significantly improves the predictability in almost the entire Asian–western Pacific region. The mean predictability of the MISO-related rainfall over its active area (10°S–30°N, 60°–160°E) reaches about 24 days in the coupled model and is about 17 days in the atmosphere-only model. This result suggests that including an interactive ocean allows the MISO predictability of an atmosphere-only model to be extended by about a week. The extended predictability is primarily due to the coupled model capturing the two-way interactions between the MISO and underlying sea surface. The MISO forces a coherent intraseasonal SST response in underlying ocean that in return exerts an external control on the future evolutions of the MISO.

The break phase of the MISO is more predictable than the active phase in both the atmosphere-only model and the coupled model as revealed in the observations. Air–sea coupling appears to extend the MISO predictability uniformly regardless of the active or break phases.

* School of Ocean and Earth Science and Technology Contribution Number 7004 and International Pacific Research Center Contribution Number 422

Corresponding author address: Dr. Xiouhua (Joshua) Fu, IPRC, SOEST, University of Hawaii at Manoa, 1680 East West Road, 401 POST Bldg., Honolulu, HI 96822. Email: xfu@hawaii.edu

Abstract

The impact of air–sea coupling on the predictability of monsoon intraseasonal oscillations (MISO) has been investigated with an atmosphere–ocean coupled model and its atmospheric component. From a 15-yr coupled control run, 20 MISO events are selected. A series of twin perturbation experiments have been conducted for all the selected events using both the coupled model and the atmosphere-only model. Two complementary measures are used to quantify the MISO predictability: (i) the ratio of signal-to-forecast error and (ii) the spatial anomaly correlation coefficient (ACC).

In the coupled model, the MISO predictability is generally higher over the Indian sector than that over the western Pacific with a maximum of 35 days in the eastern equatorial Indian Ocean. Air–sea coupling significantly improves the predictability in almost the entire Asian–western Pacific region. The mean predictability of the MISO-related rainfall over its active area (10°S–30°N, 60°–160°E) reaches about 24 days in the coupled model and is about 17 days in the atmosphere-only model. This result suggests that including an interactive ocean allows the MISO predictability of an atmosphere-only model to be extended by about a week. The extended predictability is primarily due to the coupled model capturing the two-way interactions between the MISO and underlying sea surface. The MISO forces a coherent intraseasonal SST response in underlying ocean that in return exerts an external control on the future evolutions of the MISO.

The break phase of the MISO is more predictable than the active phase in both the atmosphere-only model and the coupled model as revealed in the observations. Air–sea coupling appears to extend the MISO predictability uniformly regardless of the active or break phases.

* School of Ocean and Earth Science and Technology Contribution Number 7004 and International Pacific Research Center Contribution Number 422

Corresponding author address: Dr. Xiouhua (Joshua) Fu, IPRC, SOEST, University of Hawaii at Manoa, 1680 East West Road, 401 POST Bldg., Honolulu, HI 96822. Email: xfu@hawaii.edu

Save