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Marvin Xiang Ce Seow, Yushi Morioka, and Tomoki Tozuka

Temperature (OISST), version 2, dataset. It is based on the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) infrared satellite data from 1982 to 2005 and operational AVHRR data from 2006 onward ( Reynolds et al. 2002 ). The atmospheric data of 850-hPa zonal and meridional winds and surface heat fluxes are from the ERA-Interim reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF). They have a T255 spectral resolution and 60 vertical levels from the surface to 0

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Lei Song and Renguang Wu

1. Introduction East Asia is subject to frequent cold events during boreal winter. The long-lasting cold event in January and early February 2008 caused large economic and life losses in eastern China ( Zhou et al. 2009 ; Wen et al. 2009 ). In December 2009, several cold events occurred in the United States, Europe, and East Asia, bringing grave damage to these regions ( Wang and Chen 2010 ). A strong cold event struck East Asia in January 2016, causing snowfall and frigid weather in many

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Claire L. Vincent and Todd P. Lane

of 10%–30% over continental Europe relative to TRMM, and in particular less precipitation over the European Alps than TRMM. Vincent and Lane (2016a) compared TRMM and CMORPH estimates to rain gauges in New Guinea during a 1-month period and found a systematic overprediction in most locations for CMORPH, with many sites exceeding 10 mm day −1 and smaller positive and negative biases of less than about 5 mm day −1 for TRMM. That these results are inconsistent with those of Skok et al. (2016

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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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Kevin E. Trenberth and Yongxin Zhang

Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.0 radiation on 1° × 1° grids, from Langley Atmospheric Science Data Center ( http://ceres.larc.nasa.gov/order_data.php ) ( Loeb et al. 2009 ). Observations from CERES begin in March 2000 and have been extended back in time ( Allan et al. 2014 ) using model results and other constraints, so that we use results from January 2000 on. The atmospheric computations here all utilize only the European Centre for Medium

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D. Argüeso, R. Romero, and V. Homar

available for convection is provided. These two aspects are interesting directions to continue identifying the dominant factors that may help simulate realistic tropical convective precipitation. Acknowledgments This work was supported by the REHIPRE project. REHIPRE is funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Actions Individual Fellowship Grant Agreement 743547. This study was also supported by the COASTEPS project (CGL2017-82868-R

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

influence weather and climate over remote places including East Asia ( Chang and Lau 1982 ; Lau and Chang 1987 ), North America ( Yanai and Tomita 1998 ; Yang et al. 2002 ; Chan and Li 2004 ), and Europe ( Neale and Slingo 2003 ). Convection over the SCS–MC region exhibits significant multiscale variability, which remains a great challenge to the global atmospheric models, owing to the difficulties in representing convective processes in the tropical environment with contrasting land–ocean difference

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Chu-Chun Chen, Min-Hui Lo, Eun-Soon Im, Jin-Yi Yu, Yu-Chiao Liang, Wei-Ting Chen, Iping Tang, Chia-Wei Lan, Ren-Jie Wu, and Rong-You Chien

different resolution. The initial and lateral boundary conditions are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) with a resolution of 1.5° × 1.5° at 6-h intervals. The sea surface temperatures (SSTs) are prescribed by ERA-Interim with a resolution of 1.5° × 1.5° at 6-h intervals. Both simulations (deforestation and control experiments) span 23 years from January 1979 to December 2001. The first 3 years were used as the spinup period and

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

humidity ( q ) from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) ( Dee et al. 2011 ). All data above are daily data with horizontal resolution of 2.5° latitude × 2.5° longitude for 1984–2005. In this study, we focus on the boreal winter season (November–April). b. Methodology For the data analysis, we first removed the climatological annual cycle. Then a Lanczos bandpass filter ( Duchon 1979 ) was used to derive the HFW component ( A ′; <20 days), MJO

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Jieshun Zhu, Arun Kumar, and Wanqiu Wang

significantly better than its previous version (CFSv1) with skillful predictions of 10–15 days ( Seo et al. 2009 ). Similar skills were also reported in the dynamical MJO predictions at other operational centers such as the Predictive Ocean Atmosphere Model for Australia (POAMA; Rashid et al. 2011 ), the European Centre for Medium-Range Weather Forecasts (ECMWF; Vitart et al. 2010 ; Vitart 2014 ), and Beijing Climate Center, China ( Liu et al. 2017 ). Vitart et al. (2017) and Lim et al. (2018

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