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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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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

, which represents an intrinsic property of the climate system and quantifies the upper limit of MJO prediction skill. Relative to the quantification of MJO prediction skill, however, there are relatively fewer attempts to characterize the predictability of the MJO. The “perfect model” approach, first introduced by Waliser et al. (2003) in the MJO research, is a commonly used way to characterize the MJO predictability, which assesses a model’s ability to predict its own MJO variability with

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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

surface temperature, which are of importance in the context of decision making. Empirical forecast tools have been developed that exploit this link and utilize MJO information for predictions ( Zhou et al. 2012 ; Riddle et al. 2013 ; Johnson et al. 2014 ). In the last decade, advances have been made in the prediction of MJO using dynamical models (e.g., Vitart 2017 ). These are due to improvements in the observations and data assimilation systems, improvements in the physical parameterization

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Marvin Xiang Ce Seow, Yushi Morioka, and Tomoki Tozuka

oceans, and those of land rain gauges and soundings ( Adler et al. 2003 ). Anomalies are calculated by removing the monthly climatologies, and the long-term linear trend is removed via a least squares fit in all observational and reanalysis data and model results. 3. Model and experiment design We use the version 2 of the Scale Interaction Experiment Frontier (SINTEX-F2) coupled model ( Masson et al. 2012 ). The atmospheric component is ECHAM 5.3, which has a horizontal resolution of T106 with 31

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Ewan Short, Claire L. Vincent, and Todd P. Lane

0400 and 0700 LST. Propagation behavior was explained in terms of the land–sea breeze. Hassim et al. (2016) and Vincent and Lane (2016a) examined the diurnal cycle of precipitation around New Guinea using the Weather Research and Forecasting (WRF) Model and satellite precipitation radar data. They found that precipitation associated with convective clouds propagated offshore at two distinct speeds. Within 100–200 km of the coast, precipitation propagated at with density currents associated

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

significant topography, mountains also form an integral part of the land and sea interaction. The rainfall peaks over coastal regions tend to show a maximum over the mountains in the early afternoon, followed by another maximum at the mountain foot early in the evening that then migrates offshore in the night. It has been reported and discussed extensively in the literature using both models and observations. See for example, Houze et al. (1981) , Yang and Slingo (2001) , Neale and Slingo (2003

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D. Argüeso, R. Romero, and V. Homar

using either the same model ( Hassim et al. 2016 ; Vincent and Lane 2017 ) or different ones ( Love et al. 2011 ; Birch et al. 2016 ; Im and Elthair 2018 ). Increasing resolution has a positive effect on DP experiments by reducing the wet bias both over land and water, but the other two experiments (SH and EX) seem to worsen at higher resolution over land and show only some improvement over the ocean. For example, EX runs deviate from the observations average over land between 44% (32 km) and 75

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Jian Ling, Yuqing Zhao, and Guiwan Chen

resolution of 0.25° × 0.25° is used to identify individual MJO events in the observation. Other variables covering the same period are three-dimensional wind fields, air temperature, specific humidity (0.75° × 0.75°) provided by European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-I) ( Dee et al. 2011 ) and NOAA Optimum Interpolation (OI) High Resolution Sea Surface Temperature, version 2 (OISSTv2; Reynolds et al. 2007 ), with a horizontal resolution of 0.25° × 0.25° provided by

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Andung Bayu Sekaranom and Hirohiko Masunaga

study is that the relation between the cloud microphysics of ice particles aloft and surface rainfall depends systematically on the environmental conditions. This may introduce a potential bias in rainfall estimates between the PR and TMI products, particularly over land. Although a number of studies have used TRMM data to measure precipitation over the study area, particularly related to the variation of diurnal cycles ( Mori et al. 2004 ; Ichikawa and Yasunari 2006 ), very few studies have

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Hironari Kanamori, Tomo’omi Kumagai, Hatsuki Fujinami, Tetsuya Hiyama, and Tetsuzo Yasunari

precipitation variability on the MJO time scale ( Chen et al. 1995 ). In addition, Waliser et al. (2009) used satellite data to show that increasing (decreasing) precipitation is maintained by the preceding moisture flux convergence (divergence) on MJO time scales and that the contribution of evaporation to precipitation is relatively small. Tropical rain forests represent a major source of global and regional hydrologic fluxes ( Aragão 2012 ), and evapotranspiration over land might also play a major role

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