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Tropical Intraseasonal Variability in the MRI-20km60L AGCM

Cited 21 time in Web of Science Cited 21 time in Scopus
Authors

Liu, Ping; Kajikawa, Yoshiyuki; Wang, Bin; Kitoh, Akio; Yasunari, Tetsuzo; Li, Tim; Annamalai, H.; Fu, Xiouhua; Kikuchi, Kazuyoshi; Mizuta, Ryo; Rajendran, Kavirajan; Waliser, Duane E.; Kim, Daehyun

Issue Date
2009-04
Publisher
AMER METEOROLOGICAL SOC
Citation
JOURNAL OF CLIMATE, Vol.22 No.8, pp.2006-2022
Abstract
This study documents the detailed characteristics of the tropical intraseasonal variability (TISV) in the MRI-20km60L AGCM that uses a variant of the Arakawa-Schubert cumulus parameterization. Mean states, power spectra, propagation features, leading EOF modes, horizontal and vertical structures, and seasonality associated with the TISV are analyzed. Results show that the model reproduces the mean states in winds realistically and in convection comparable to that of the observations. However, the simulated TISV is less realistic. It shows low amplitudes in convection and low-level winds in the 30-60-day band. Filtered anomalies have standing structures. Power spectra and lag correlation of the signals do not propagate dominantly either in the eastward direction during boreal winter or in the northward direction during boreal summer. A combined EOF (CEOF) analysis shows that winds and convection have a loose coupling that cannot sustain the simulated TISV as realistically as that observed. In the composited mature phase of the simulated MJO, the low-level convergence does not lead convection clearly so that the moisture anomalies do not tilt westward in the vertical, indicating that the low-level convergence does not favor the eastward propagation. The less realistic TISV suggests that the representation of cumulus convection needs to be improved in this model.
ISSN
0894-8755
URI
https://hdl.handle.net/10371/201029
DOI
https://doi.org/10.1175/2008JCLI2406.1
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Climate Change, Earth & Environmental Data, Severe Weather, 기후과학, 위험기상, 지구환경 데이터과학

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