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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

. From the time series of RMM indices over the analysis period (1998–2017 for rainfall and 1988–2017 for others), the corresponding spatial patterns in other fields are obtained on the basis of regressions of unfiltered daily anomalies of respective fields against daily values of RMM indices. From the spatial regression patterns and RMM indices, daily values of MJO-related components are linearly reconstructed for the fields of interest. Variance percentage associated with MJO variability is then

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Daehyun Kang, Daehyun Kim, Min-Seop Ahn, and Soon-Il An

IO. Fig . 1. The 20–100-day bandpass-filtered anomalies of OLR (W m −2 ; shaded) and column-integrated moisture tendency (kg m −2 ; contour) regressed onto the reference OLR time series from the IO base point (5°S–5°N, 75°–85°E). The column-integrated moisture tendency anomalies are weighted by 1 / [ τ c ¯ ] . c. MJO moisture budget weighted by the convective moisture adjustment timescale To understand MJO propagation under the moisture mode framework, a moisture budget analysis is conducted. We

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Yuntao Wei and Zhaoxia Pu

the statistical regression and composite analysis documented in previous studies: weak and shallow convection followed by strong and deep convection and finally a stratiform-like mode (e.g., Johnson et al. 1999 ; Kiladis et al. 2005 ; Mapes et al. 2006 ; Benedict and Randall 2007 ; Haertel et al. 2008 ; Kiladis et al. 2009 ). During 21–23 July, another synoptic precipitation episode occurs. In the WRF simulation, the peak of this precipitation episode is somewhat stronger and occurs 1 day

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

experiments. c. Analysis method and observations Anomalies are calculated as departures from seasonal climatology, which is defined as annual mean plus the first four harmonics of long-term average. To focus on the intraseasonal variability, most analyses are based on intraseasonal anomalies obtained by applying 20–100-day bandpass filtering to the raw daily mean anomalies. When evaluating the zonal propagation features of the simulated MJO, lead–lag correlations or regressions are calculated for the 10°S

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Xingwen Jiang, Jianchuan Shu, Xin Wang, Xiaomei Huang, and Qing Wu

. 2003 ); monthly mean rainfall from the Global Precipitation Climatology Project (GPCP), version 2.2, monthly rainfall analysis dataset from 1979 to 2015 ( Adler et al. 2003 ); and the rain gauge data (1979–2015) from the latest version [version 3 (V3)] of surface climatological data compiled by the China National Meteorological Information Center. To investigate different roles of convection over various regions, partial correlations (e.g., Behera and Yamagata 2003 ) and partial regression are

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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

2012–14 mooring site). Then, the meridional velocity at M00 is used in a linear regression model to approximate the 2014–16 transports estimated from the three moorings. The correlation coefficients between the regression model and the three mooring transports are 0.80 and 0.84 for the free-slip and nonslip conditions, respectively, above the 99.9% significance level, suggesting the success of the regression model. The root-mean-square differences between the regressed and calculated transports

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Andung Bayu Sekaranom and Hirohiko Masunaga

part of the land ( Qian 2008 ). The environmental characteristics specific to the MC are responsible not only for the high annual precipitation, which is approximately 1500–3000 mm yr −1 over land ( As-Syakur et al. 2013 ), but also for the high frequency of extreme precipitation that can trigger hazards over the MC. An analysis of 11-yr records of global flood frequency in 1998–2008 conducted by Adhikari et al. (2010) shows that Indonesia (which constitutes the largest part of the MC) is listed

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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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