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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

for model improvement ( National Research Council 2010 ), and therefore errors in their estimates need to be quantified. In addition to specifics of forecast configuration (e.g., the specification of initial conditions) to estimate MJO predictability, the MJO simulations have been found to be highly sensitive to convective parameterization (e.g., Wang and Schlesinger 1999 ; Zhang and Mu 2005 ; Bechtold et al. 2008 ; Lin et al. 2008 ; Zhu et al. 2017b ), representation of air–sea coupling (e

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Marvin Xiang Ce Seow, Yushi Morioka, and Tomoki Tozuka

. 2000 ; Wang and Zhang 2002 ) in the tropical Pacific and the Indian Ocean basin mode (IOBM; Watanabe and Jin 2002 ; Annamalai et al. 2005 ) and Indian Ocean dipole (IOD; Yang et al. 2010 ; Yuan et al. 2012 ; M. Chen et al. 2016 ) in the tropical Indian Ocean. Those adjacent oceans affect the SCS atmospheric conditions through the Matsuno–Gill response ( Matsuno 1966 ; Gill 1980 ). For example, different ENSO types influence or are influenced by the Indian Ocean, which in turn control the

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James H. Ruppert Jr., Xingchao Chen, and Fuqing Zhang

sensitivities of the diurnally phase-locked gravity waves in the MC through semi-idealized cloud-permitting numerical model experiments. Realistic geography is imposed in these experiments, while the lateral boundary conditions are semi-idealized by repeating a diurnal composite calculated from a regime of interest, following the technique of several past studies ( Trier et al. 2010 ; Sun and Zhang 2012 ; Chen et al. 2016 ). We conduct a series of sensitivity tests modifying the islands or their existence

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Anurag Dipankar, Stuart Webster, Xiang-Yu Huang, and Van Quang Doan

rainfall to the model’s inability to adequately represent entrainment at convection-permitting resolution. The grid resolution is known to be critical in controlling the model biases, regardless of whether it is an underestimation ( Neale and Slingo 2003 ; Qian 2008 ) or an overestimation ( Hassim et al. 2016 ). However, biases in the initial and boundary conditions are important as well, more so for the regional simulations because of their dependency on the lateral boundary conditions (LBC). For

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Dongliang Yuan, Xiang Li, Zheng Wang, Yao Li, Jing Wang, Ya Yang, Xiaoyue Hu, Shuwen Tan, Hui Zhou, Adhitya Kusuma Wardana, Dewi Surinati, Adi Purwandana, Mochamad Furqon Azis Ismail, Praditya Avianto, Dirham Dirhamsyah, Zainal Arifin, and Jin-Song von Storch

using the free-slip and nonslip boundary conditions are 1.53 and 1.23 Sv (1 Sv ≡ 10 6 m 3 s −1 ), respectively, significantly smaller than the standard deviations of the transports of 2.39 and 2.20 Sv, respectively. The linear regression is then extended into the period of December 2012–November 2014 using the only M00 data available in the central Maluku Channel ( Fig. 5a ). Based on the regression model, the mean transports through the Maluku Channel between 60 and 315 m during the 4 years are

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Satoru Yokoi, Shuichi Mori, Fadli Syamsudin, Urip Haryoko, and Biao Geng

environmental winds or the gravity currents excited over another landmass ( Houze et al. 1981 ; Mori et al. 2004 ; Wu et al. 2009 ; Fujita et al. 2010 ; Wapler and Lane 2012 ). Other studies such as Ichikawa and Yasunari (2006 , 2008 ) and Yanase et al. (2017) focused on advective effect of environmental winds. To help verify these proposed mechanisms, high-frequency observations of dynamic and thermodynamic conditions of the offshore atmosphere can provide useful information. However, routine upper

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Casey R. Densmore, Elizabeth R. Sanabia, and Bradford S. Barrett

, MJO amplitudes are stronger when 50-hPa zonal winds are easterly, and MJO amplitudes are weaker when 50-hPa winds are westerly. Several studies (e.g., Yoo and Son 2016 ; Son et al. 2017 ; Nishimoto and Yoden 2017 ) relate wind conditions associated with changes in MJO amplitudes to 100-hPa static stability differences. Increased MJO amplitudes are found to be associated with lower 100-hPa static stabilities associated with QBO winds. Conversely, decreased MJO amplitudes are found to be

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

details of the boreal wintertime MJO, and it accurately captures the distribution of convective cell sizes compared to satellite observations, which further suggests it is a viable tool for simulating the convective identity of the MJO ( Riley Dellaripa et al. 2018 ). Details of the simulation parameters are listed in Table 1 , including the grid spacing, microphysical parameterizations, and lateral boundary conditions. Table 1. Simulation parameters. At the beginning of the simulation, the interior

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Claire L. Vincent and Todd P. Lane

is that the diurnal precipitation cycle tends to be suppressed by the onset of background conditions of cloudiness and convection, possibly with the added contribution of large-scale background winds affecting the strength of local land and sea breezes. For example, Peatman et al. (2014) used observational evidence from the TRMM satellite to show that the diurnal precipitation cycle over the land reached a maximum ahead of the main envelope of the Madden–Julian oscillation (MJO). This maximum

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Ching-Shu Hung and Chung-Hsiung Sui

wave (~19 m s −1 ). The intraseasonal period of the MJO is determined by the time needed for the moist Kelvin wave to circumnavigate the globe. However, the speed of the moist Kelvin wave is still significantly faster than the observed MJO propagation speed (~5 m s −1 ), and the mode is most unstable in small wavelength, which is opposite to the observed planetary zonal scale. To solve these problems, Wang and Li (1994) introduced a boundary layer into the traditional wave-CISK model framework

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