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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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Beata Latos, Thierry Lefort, Maria K. Flatau, Piotr J. Flatau, Donaldi S. Permana, Dariusz B. Baranowski, Jaka A. I. Paski, Erwin Makmur, Eko Sulystyo, Philippe Peyrillé, Zhe Feng, Adrian J. Matthews, and Jerome M. Schmidt

1. Introduction Indonesia, with its tropical and monsoonal climate, is exposed to extreme rain accumulation ( Ramage 1968 ; Chang et al. 2005 ; Moron et al. 2015 ) and thus weather-driven hazards, such as landslides ( Liao et al. 2010 ) and floods ( Aryastana et al. 2015 ; Sekaranom and Masunaga 2017 ; Sugiartha et al. 2017 ; Paski et al. 2020 ). Population growth, deforestation and significant changes in land use have resulted in shrinking retention areas, which along with climate change

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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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Lei Zhou, Ruomei Ruan, and Raghu Murtugudde

the Maritime Continent near the equator, while some are detoured southward and cross the Maritime Continent around 10°S. There are also cases where MJOs are blocked by the Maritime Continent and cannot reach the Pacific Ocean ( DeMott et al. 2015 ; Feng et al. 2015 ; Kerns and Chen 2016 ; Kim et al. 2016 ; Zhang and Ling 2017 ). The complex land–sea distributions and orography with multiscale air–sea interactions over the Maritime Continent have resulted in different mechanisms being proposed

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Yuntao Wei and Zhaoxia Pu

the WRF single-moment 6-class scheme. Subgrid-scale vertical turbulent eddy mixing is parameterized using the Yonsei University planetary boundary layer scheme. Radiative processes are calculated using the Rapid Radiative Transfer Model longwave radiation scheme and the Dudhia shortwave scheme. The cumulus scheme is turned off under the 3-km resolution. The unified Noah land surface model is used to simulate surface processes. The revised Fifth-generation Pennsylvania State University

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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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