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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

simulated the intraseasonal wind variability resulting in significant biases in latent heat flux and in SST variability. Unrealistic SST variations, in turn, degraded the MJO simulation by affecting SST-modulated heat fluxes and the boundary layer moisture convergence or surface moist static energy (e.g., Flatau et al. 1997 ; Maloney and Sobel 2004 ). Given the critical role of convective parameterization in MJO simulations, it is possible that the large uncertainties in current estimates for MJO

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Chu-Chun Chen, Min-Hui Lo, Eun-Soon Im, Jin-Yi Yu, Yu-Chiao Liang, Wei-Ting Chen, Iping Tang, Chia-Wei Lan, Ren-Jie Wu, and Rong-You Chien

4, we use the radiative transfer scheme of the modified NCAR Community Climate Model version 3 (CCM3), the nonlocal planetary boundary layer scheme of Holtslag ( Holtslag et al. 1990 ), the ocean flux scheme of Zeng ( Zeng et al. 1998 ), and the Subgrid Explicit Moisture (SUBEX) scheme for the resolved scale precipitation, which are default schemes of RegCM4 ( Giorgi et al. 2012 ) or applied schemes for RegCM4 simulations of the Southeast Asia domain ( Chung et al. 2018 ). We also performed

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Ching-Shu Hung and Chung-Hsiung Sui

have been proposed to explain the growth and propagation of the MJO. They can be roughly separated into two categories: dynamics and thermodynamics. The former explains the MJO by forced tropical wave dynamics, whereas the latter emphasizes the role of physical processes (e.g., moistening, surface turbulent fluxes, and radiative fluxes). Yet, it should be noted that most of the theories in the second set also require coupling to the dynamics, such as the coupled Kelvin–Rossby wave (e.g., Emanuel

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Kevin E. Trenberth and Yongxin Zhang

an El Niño event, and then movement of heat laterally and major adjustments in the vertical distribution of heat and the thermocline during the course of the event as the trade winds relax and the Bjerknes feedback processes kick in. The atmosphere plays a vital role as a bridge among the oceans and to the extratropics through changes in the atmospheric circulation and associated surface fluxes, resulting in a significant diabatic component, as heat is ultimately radiated to space and lost

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

of air–sea interaction. Observational diagnoses have shown coherent variations in surface heat fluxes, SST, and convection associated with the MJO (e.g., Krishnamurti et al. 1988 ; Shinoda et al. 1998 ; Woolnough et al. 2000 ; Kumar et al. 2013 ). Many numerical studies also noted improved MJO simulations when an atmosphere-only GCM (AGCM) is coupled to an ocean model (e.g., Flatau et al. 1997 ; Waliser et al. 1999 ; Kemball-Cook et al. 2002 ; Inness et al. 2003 ; Zhang et al. 2006

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Chidong Zhang and Jian Ling

model capability of subseasonal prediction. Several possible reasons for the MC barrier effect on MJO propagation have been suggested. If surface fluxes, especially latent heat flux, are important to the MJO ( Maloney and Sobel 2004 ; Sobel et al. 2008 ), then the MJO would be weakened or diminished by the reduction in surface fluxes in the MC region because of its many islands. If moisture convergence of the low-level circulation is essential to the MJO ( Wang 1988 ; 2005 ), then its distortion

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Lei Song and Renguang Wu

activity fluxes ( Takaya and Nakamura 2001 ). All the variables are filtered by the Butterworth bandpass filter to extract the 30–60-day intraseasonal variations. Composite analysis is performed for intraseasonal cold events over eastern China with a time lag with respect to the MJO convection anomalies over the tropical Indian Ocean and the Maritime Continent during boreal winters (December to February) from 1979/80 to 2015/16. The significance of the composite analysis is estimated by the Student

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Giuseppe Torri, David K. Adams, Huiqun Wang, and Zhiming Kuang

significant seasonal shifts in its location ( Wheeler and Kiladis 1999 ). For both simulations, the initial and boundary conditions, as well as SSTs, were provided by ERA-Interim ( Dee et al. 2011 ). Cloud microphysics were parameterized using the WRF Double-Moment (WDM) scheme ( Lim and Hong 2010 ), while radiative transfer was parameterized using the GCM version of the Rapid Radiative Transfer Model (RRTM) longwave radiation scheme ( Iacono et al. 2008 ) and the updated Goddard shortwave scheme ( Chou

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Benjamin A. Toms, Susan C. van den Heever, Emily M. Riley Dellaripa, Stephen M. Saleeby, and Eric D. Maloney

troposphere, which has been found to be an important factor in the overall evolution of the MJO and other convectively coupled equatorial waves ( Peters and Bretherton 2006 ; Benedict and Randall 2007 ; Adames and Kim 2016 ). It would therefore be interesting to expand upon the existing literature citing the importance of convective features in the upscale redistribution of water vapor using methods such as spectral flux, as detailed in Arbic et al. (2012) and Hayashi (1980) , to identify the scales

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Wan-Ling Tseng, Huang-Hsiung Hsu, Noel Keenlyside, Chiung-Wen June Chang, Ben-Jei Tsuang, Chia-Ying Tu, and Li-Chiang Jiang

using an atmospheric general circulation model (AGCM) in an aquaplanet setting, which is forced by the prescribed MJO-like moving SST. However, this AGCM poorly simulated the MJO and could not resolve the complex orography and land–sea contrast in the MC because of a very coarse model resolution. Takasuka et al. (2015) conduct high-resolution model simulations with and without flat land in the MC and suggest that the land–ocean zonal contrast of latent heat flux is the major reason for the slower

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