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

seesaw. Seasonal prediction skill of ENSO has been significantly improved over the past 2–3 decades through the development of coupled ocean–atmosphere general circulation models (OAGCMs) (e.g., Cane et al. 1986 ; Barnston et al. 1999 ; Jin et al. 2008 ; Luo et al. 2008 , 2015 ; Graham et al. 2011 ; Cottrill et al. 2013 ; MacLachlan et al. 2015 ). While the forecast skill of ENSO varies with target seasons, ENSO phases, and ENSO strength (e.g., Jin et al. 2008 ), ENSO can be generally

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

-Pacific summer climate . Meteor. Atmos. Phys. , 113 , 171 – 180 , doi: 10.1007/s00703-011-0146-8 . 10.1007/s00703-011-0146-8 Jiang , X. , S. Yang , J. Li , Y. Li , H. Hu , and Y. Lian , 2013 : Variability of the Indian Ocean SST and its possible impact on summer western North Pacific anticyclone in the NCEP Climate Forecast System . Climate Dyn. , 41 , 2199 – 2212 , doi: 10.1007/s00382-013-1934-2 . 10.1007/s00382-013-1934-2 Jiang , X. , Y. Li , S. Yang , J. Shu , and G. He , 2015

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Joshua Chun Kwang Lee, Anurag Dipankar, and Xiang-Yu Huang

data assimilation configurations to gain insights on their weaknesses. Faccani et al. (2009) and Sun et al. (2020) assessed the impact of assimilating additional radiosonde data on forecasts over western Africa and Antarctica, respectively. However, despite the availability of data from recent intensive observation projects over the western Maritime Continent, such case studies in this region are still absent. Here, we perform a case study over western the Maritime Continent, building on

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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

1. Introduction There has been growing interest in forecasts at subseasonal time scales (i.e., 3–4 weeks; National Research Council 2010 ; National Academies of Sciences, Engineering, and Medicine 2016 ), which fills the gap between medium-range weather forecast and seasonal prediction. The Madden–Julian oscillation (MJO; Madden and Julian 1971 ), the primary mode of tropical intraseasonal climate variability in the boreal winter and spring, is considered to be a major source of global

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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

Climate Forecast System version 2 (CFSv2) 6-hourly products ( Saha 2011 ). The CLIVAR MJO Working Group diagnostics package ( Waliser et al. 2009 ) is used to isolate and analyze the intraseasonal (20–100 day) variability. MJO phase composites are computed based on the Real-Time Multivariate MJO index ( Wheeler and Hendon 2004 ). We use the ECHAM5.4 ( Roeckner 2003 ) AGCM coupled with the Snow-Ice-Thermocline (SIT) one-column ocean model ( Tu and Tsuang 2005 ; Tsuang et al. 2009 ) to simulate the

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

and December 2013 was chosen to compute OLR climatology. The OLR for December 2016 is obtained from NOAA Climate Data Record (CDR) of OLR version 1.2 ( Lee and NOAA CDR Program 2011 ), which is estimated from High-Resolution Infrared Radiation Sounder (HIRS) radiance observations with a 2-day lag. It is given daily with 1° × 1° horizontal resolution. The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, hereinafter ERA-Int; Dee et al. 2011 ) is utilized for zonal

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

region running regional models for weather prediction using input conditions from the big centers like the European Centre for Medium-Range Weather Forecasts (ECMWF,) the Met Office (United Kingdom), and the National Oceanic and Atmospheric Administration (United States). A novelty of the current study is that it utilizes results from a convection-permitting state-of-the-art NWP model to highlight the biases in the input conditions from the high-resolution (9 km) deterministic forecast from ECMWF

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Giuseppe Torri, David K. Adams, Huiqun Wang, and Zhiming Kuang

; Higgins and Shi 2001 ; Bond and Vecchi 2003 ; Jones et al. 2004 ; Becker et al. 2011 ; Schreck et al. 2013 ; Thompson and Roundy 2013 ; Matsueda and Takaya 2015 ; Klotzbach et al. 2016 ; Zhou et al. 2016 ; Zheng et al. 2018 ; Tippett 2018 ; Barrett 2019 ), it is important to forecast the MJO accurately. Upon reaching the Maritime Continent, some MJO events weaken and do not propagate farther (e.g., Rui and Wang 1990 ; Salby and Hendon 1994 ; Zhang and Hendon 1997 ; Hsu and Lee 2005

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Jieshun Zhu, Wanqiu Wang, and Arun Kumar

, based on the NCEP Climate Forecast System, version 1 (CFSv1), Seo and Wang (2010) performed a series of experiments to explore the impacts of various factors on the simulation of the MJO. They found that the simulation strongly depended on the convection parameterization, and the use of the relaxed Arakawa–Schubert (RAS) cumulus parameterization of Moorthi and Suarez (1999) produced a significantly better representation of the MJO with more realistic periodicity, spectral power, and eastward

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Ming Feng, Yongliang Duan, Susan Wijffels, Je-Yuan Hsu, Chao Li, Huiwu Wang, Yang Yang, Hong Shen, Jianjun Liu, Chunlin Ning, and Weidong Yu

-Pacific region. The MJO offers promising perspectives to forecast tropical rainfall with a lead time of a couple of weeks; however, current state-of-art weather forecasting models have systematic biases and cannot reproduce the MJO well (e.g., Kim et al. 2014 ), possibly because they do not represent air–sea interaction processes associated with the MJO ( DeMott et al. 2015 ). During boreal summer, intraseasonal oscillations display prominent northward–northeastward propagation and variability, extending

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