Research Abstract


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

2014年5月6日 Nature Communications 5 : 3769 doi: 10.1038/ncomms4769


宮川 知己1, 佐藤 正樹1,2, 三浦 裕亮1,3, 富田 浩文1,4, 八代 尚4, 野田 暁1, 山田 洋平1, 小玉 知央1, 木本 昌秀2 & 米山 邦夫1

  1. 海洋研究開発機構
  2. 東京大学 大気海洋研究所
  3. 東京大学 理学系研究科
  4. 理化学研究所 計算科学研究機構
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.