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Identifying a key physical factor sensitive to the performance of Madden–Julian oscillation simulation in climate models

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Abstract

A key physical factor in regulating the performance of Madden–Julian oscillation (MJO) simulation is examined by using 26 climate model simulations from the World Meteorological Organization’s Working Group for Numerical Experimentation/Global Energy and Water Cycle Experiment Atmospheric System Study (WGNE and MJO-Task Force/GASS) global model comparison project. For this, intraseasonal moisture budget equation is analyzed and a simple, efficient physical quantity is developed. The result shows that MJO skill is most sensitive to vertically integrated intraseasonal zonal wind convergence (ZC). In particular, a specific threshold value of the strength of the ZC can be used as distinguishing between good and poor models. An additional finding is that good models exhibit the correct simultaneous convection and large-scale circulation phase relationship. In poor models, however, the peak circulation response appears 3 days after peak rainfall, suggesting unfavorable coupling between convection and circulation. For an improving simulation of the MJO in climate models, we propose that this delay of circulation in response to convection needs to be corrected in the cumulus parameterization scheme.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A11051818 & No. NRF-2015R1A2A2A01006663). We would like to acknowledge the support from the Korea Institute of Science and Technology Information (KISTI). We are very grateful to the two anonymous reviewers for their helpful and constructive comments, which contributed to improving this paper.

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  1. Department of Atmospheric Sciences, Pusan National University, Busan, 609-735, South Korea

    Go-Un Kim & Kyong-Hwan Seo

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Kim, GU., Seo, KH. Identifying a key physical factor sensitive to the performance of Madden–Julian oscillation simulation in climate models. Clim Dyn 50, 391–401 (2018). https://doi.org/10.1007/s00382-017-3616-y

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