著者

SHIBUYA Ryosuke NAKANO Masuo KODAMA Chihiro NASUNO Tomoe KIKUCHI Kazuyoshi SATOH Masaki MIURA Hiroaki MIYAKAWA Tomoki

出版者

Meteorological Society of Japan

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2021-046, (Released:2021-04-08)

被引用文献数

5

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In this study, we assessed the prediction skill of the Boreal Summer Intra-Seasonal Oscillation (BSISO) mode of one-month simulations using a global non-hydrostatic atmospheric model (NICAM) with explicit cloud microphysics and with a grid spacing of 14 km. The simulations were run as a series of hindcast experiments every day of August during 2000-2014; a total of 465 simulations were run with a 13950-day integration. On using forecast skill scores for statistical measurements, it was found that the model showed an overall BSISO prediction skill of approximately 24 days. The prediction skill tended to be slightly higher (∼ 2 days) when BSISO events began in the initial phases 7 to 1, which corresponded to the re-initiation phase of the BSISO, where a major convective center over the Philippine Sea decayed and a new convective envelope began aggregating over the western Indian Ocean. The phase speed and the evolution of the amplitude of the BSISO were well simulated by the model with a clear northwestward-southeastward tilted outgoing longwave radiation (OLR) structure over the Maritime continent and the western Pacific. However, the propagation speed was slower during phases 6-7, and the amplitude of the BSISO largely decayed during phases 8-1, which was likely to have been associated with the stagnant behavior of the convective cells over the Philippines. This stagnation of the propagation over the Philippines may be largely attributed to the small background southerlies bias in the model over the Philippines based on regression coefficient analysis using the moist static energy. The bias in the large-scale circulation was likely to have been associated with the bias in the moisture field and the associated background monsoonal circulation. We concluded that the model physics controlling the background fields are important factors for improving the BSISO prediction skill.