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Madden–Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer

Author

Listed:
  • Tomoki Miyakawa

    (Japan Agency for Marine-Earth Science and Technology)

  • Masaki Satoh

    (Japan Agency for Marine-Earth Science and Technology
    Atmosphere and Ocean Research Institute, The University of Tokyo)

  • Hiroaki Miura

    (Japan Agency for Marine-Earth Science and Technology
    The University of Tokyo)

  • Hirofumi Tomita

    (Japan Agency for Marine-Earth Science and Technology
    Advanced Institute for Computational Science, RIKEN)

  • Hisashi Yashiro

    (Advanced Institute for Computational Science, RIKEN)

  • Akira T. Noda

    (Japan Agency for Marine-Earth Science and Technology)

  • Yohei Yamada

    (Japan Agency for Marine-Earth Science and Technology)

  • Chihiro Kodama

    (Japan Agency for Marine-Earth Science and Technology)

  • Masahide Kimoto

    (Atmosphere and Ocean Research Institute, The University of Tokyo)

  • Kunio Yoneyama

    (Japan Agency for Marine-Earth Science and Technology)

Abstract

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden–Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003–2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts.

Suggested Citation

  • Tomoki Miyakawa & Masaki Satoh & Hiroaki Miura & Hirofumi Tomita & Hisashi Yashiro & Akira T. Noda & Yohei Yamada & Chihiro Kodama & Masahide Kimoto & Kunio Yoneyama, 2014. "Madden–Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer," Nature Communications, Nature, vol. 5(1), pages 1-6, September.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4769
    DOI: 10.1038/ncomms4769
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