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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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James H. Ruppert Jr., Xingchao Chen, and Fuqing Zhang

in order to test the study objectives, which are as follows: To assess the dynamic origins of diurnal gravity waves in the MC region and the specific role of orography for these waves. To quantify the nonlinear response to the synchronized diurnal forcing of Borneo and Sumatra. 2. Methodology The regional cloud-permitting numerical model framework of this study is based on that of Wang et al. (2015) , who invoked the Weather Research and Forecasting (WRF) Model, version 3.4.1 ( Skamarock et al

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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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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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Jian Ling, Yuqing Zhao, and Guiwan Chen

forecast systems than its global reanalysis. Wang et al. (2019) found that prediction skill for MJO convection is lowest when it is over the MC during boreal winter in most WMO/subseasonal to seasonal (S2S) models. Several possible reasons were proposed to explain the MC barrier effect on MJO propagation. They include the reduced surface flux due to the islands of the MC ( Maloney and Sobel 2004 ; Sobel et al. 2008 ), distorted low-level circulation by topography ( Hsu and Lee 2005 ; Inness and

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Chidong Zhang and Jian Ling

numerical models ( Inness and Slingo 2003 ; Kim et al. 2009 ; Seo et al. 2009 ), creating an MJO “prediction barrier” ( Weaver et al. 2011 ; Fu et al. 2013 ). For example, the fraction of MJO events that fail to propagate through the MC is 30% in a global reanalysis product but 50% in the ECMWF forecast system ( Vitart and Molteni 2010 ). The MJO prediction barrier would inevitably undermine the model capability of forecasting global influences of the MJO ( Hendon et al. 2000 ) and hinder the overall

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