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Yan Zhu, Tim Li, Ming Zhao, and Tomoe Nasuno

summary is given in section 5 . 2. Data, methodology, and model description a. Data Primary observational datasets used in the present analysis include 1) interpolated outgoing longwave radiation (OLR) from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites ( Liebmann and Smith 1996 ) and 2) atmospheric three-dimensional fields including zonal and meridional wind ( u and υ ), temperature ( T ), pressure vertical velocity ( ω ), geopotential height ( ϕ ), and specific

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Shijian Hu, Ying Zhang, Ming Feng, Yan Du, Janet Sprintall, Fan Wang, Dunxin Hu, Qiang Xie, and Fei Chai

freedom of 12 is indicated in blue. The significant lagged correlation between the southeastern Indian Ocean salinity anomaly and the PDO index makes it possible to assess its predictability. Here we propose a statistical prediction model based on a regression analysis: (5) S SEIO ′ ⁡ ( τ ) = γ 0 + γ 1 PDO ⁡ ( τ − 10 ) , where S SEIO ′ ⁡ ( τ ) is the mean salinity anomaly in the southeastern Indian Ocean at month τ , and γ 0 and γ 1 are coefficient estimates for a multilinear regression of the

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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

2008 ). The early models used in these MJO predictability studies, however, were generally poor in simulating the MJO (e.g., Zhang et al. 2006 ). During the recent years when extensive hindcast datasets (e.g., the S2S hindcast dataset) became available, the MJO predictability was reevaluated (e.g., Rashid et al. 2011 ; Kim et al. 2014 ; Neena et al. 2014 ; Liu et al. 2017 ). For example, Neena et al. (2014) conducted a comprehensive analysis about the MJO predictability based on hindcasts by

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Wei-Ting Chen, Shih-Pei Hsu, Yuan-Huai Tsai, and Chung-Hsiung Sui

, complex coastlines, and steep topography ( Birch et al. 2015 ). This region is surrounded by islands and continents with complex topography, which cultivates prominent diurnal variability of convection. Periodically and zonally propagating modes of tropical convection at different temporal and spatial scales can be found active over the SCS–MC. These are regarded as convectively coupled tropical waves based on the theoretical study of Matsuno (1966) and the analysis of Wheeler and Kiladis (1999

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Ching-Shu Hung and Chung-Hsiung Sui

patterns of 25–90-day filtered OLR during boreal winter (DJF). Percentages in parentheses show the contribution of each EOF mode to total variance. OLR values are multiplied by one standard deviation of the corresponding PCs to obtain a typical value and unit (W m −2 ) for ISOs. c. Successive and primary events Since some conventional MJO analysis techniques (e.g., lag regression) tend to produce a repeating MJO cycle, it is difficult to separate the individual attribution from current and previous

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Satoru Yokoi, Shuichi Mori, Masaki Katsumata, Biao Geng, Kazuaki Yasunaga, Fadli Syamsudin, Nurhayati, and Kunio Yoneyama

). The cooling dominated only during period I, whereas warming took place in the following days (13 December onward) when the diurnal cycle of precipitation was obscured. Using this time series in period I as a reference, we then perform regression analysis of daily time series of the radar-estimated precipitation at a particular location and hour of day ( Fig. 12b ). The top of this figure plots the regression coefficients between the late-afternoon cooling and precipitation at 0700 LT of the same

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Chen Li, Jing-Jia Luo, Shuanglin Li, Harry Hendon, Oscar Alves, and Craig MacLachlan

the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ) for the period 1982 to August 2002, and to the global analyses from BoM’s numerical weather prediction system thereafter (e.g., Hudson et al. 2011 ). Initial conditions for the ocean are obtained from the POAMA Ensemble Ocean Data Assimilation System (PEODAS), which assimilates available ocean observations using an ensemble Kalman filter (e.g., Yin et al. 2011 ). 2) The SINTEX-F model

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Jian Ling, Yuqing Zhao, and Guiwan Chen

; Zhang and Ling 2017 ) was applied to identify individual MJO events based on intraseasonally filtered precipitation anomalies in both GCMs and observations. We introduce the data and method in section 2 , present the results in section 3 , and discuss their implications in section 4 . 2. Data and method Daily precipitation from Tropical Rainfall Measuring Mission (TRMM) 3B42, version 7, Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ) from 1998 to 2015 with a horizontal

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