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Marvin Xiang Ce Seow, Yushi Morioka, and Tomoki Tozuka

Abstract

Influences from the tropical Pacific and Indian Oceans, and atmospheric internal variability on the South China Sea (SCS) atmospheric circulation and cold tongue (CT) variability in boreal winter and the relative roles of remote forcings at interannual time scales are studied using observational data, reanalysis products, and coupled model experiments. In the observation, strong CT years are accompanied by local cyclonic wind anomalies, which are an equatorial Rossby wave response to enhanced convection over the warmer-than-normal western equatorial Pacific associated with La Niña. Also, the cyclonic wind anomalies are an atmospheric Kelvin wave response to diabatic cooling anomalies linked to both the decaying late fall negative Indian Ocean Dipole (IOD) and winter atmospheric internal variability. Partially coupled experiments reveal that both the tropical Pacific air-sea coupling and atmospheric internal variability positively contribute to the coupled variability of the SCS CT, while the air-sea coupling over the tropical Indian Ocean weakens such variabilities. The northwest Pacific anticyclonic wind anomalies that usually precede El Niño–Southern Oscillation-independent negative IOD generated under the tropical Indian Ocean air-sea coupling undermine such variabilities.

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Ya Yang, Xiang Li, Jing Wang, and Dongliang Yuan

climatological wind stress was averaged over the entire time period of 2007–18. d. The LICOM ocean model The LASG/IAP Climate Ocean Model (LICOM) version 1.0 developed by the Institute of Atmosphere Physics of the Chinese Academy of Sciences was used to evaluate the influence of equatorial waves on the NESC. The model has a horizontal resolution of 0.5° latitude × 0.5° longitude and 30 vertical levels of varying thickness from 12.5 to 5319.1 m. A 900-yr spinup was forced with the climatological forcing of

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Arun Kumar, Jieshun Zhu, and Wanqiu Wang

.5460.2002 McPhaden , M. J. , X. Zhang , H. H. Hendon , and M. C. Wheeler , 2006 : Large scale dynamics and MJO forcing of ENSO variability . Geophys. Res. Lett. , 33 , L16702 , https://doi.org/10.1029/2006GL026786 . 10.1029/2006GL026786 Moore , R. W. , O. Martius , and T. Spengler , 2010 : The modulation of the subtropical and extratropical atmosphere in the Pacific basin in response to the Madden–Julian oscillation . Mon. Wea. Rev. , 138 , 2761 – 2779 , https://doi.org/10

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James H. Ruppert Jr. and Fuqing Zhang

; namely, their role in forcing and coupling with long-lived gravity waves. Among the most dominant drivers of weather variability in the MC is the Madden–Julian oscillation (MJO; Madden and Julian 1972 ). The MJO is a convectively coupled tropical wave that propagates slowly eastward (~5 m s −1 ) through the Indo-Pacific warm pool region, modulating deep overturning motion and moist convection on intraseasonal time scales ( Zhang 2005 ). Yet since the diurnal cycle is the primary rainfall mechanism

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Jian Ling, Yuqing Zhao, and Guiwan Chen

-propagation speed, and seasonal cycle. Hung et al. (2013) indicated only one GCM was able to simulate the observed eastward propagation of the MJO in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Jiang et al. (2015) showed that only a quarter of the GCMs that participated in the MJO Task Force (MJOTF) ( Moncrieff et al. 2012 ; Waliser et al. 2012 ) and the GEWEX Atmospheric System Study (GASS) ( Petch et al. 2011 ; we refer to this by the abbreviation MJOTF/GASS in this study) could produce

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Claire L. Vincent and Todd P. Lane

maintain consistency with observed intraseasonal variability, spectral nudging toward ERA-Interim was performed for the 12-km domain only for wavelengths longer than 1000 km above the boundary layer, with an inverse nudging time scale of 0.0003 s −1 for all nudged variables. Liu et al. (2012) showed that spectral nudging achieved a better balance between maintaining consistency with large-scale forcing while allowing smaller-scale variance to develop than grid nudging, and Vincent and Hahmann (2015

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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

using fast ocean profiling platforms to resolve the very near-surface ocean temperature structures, as well as the key atmospheric variables such as humidity, air temperature, vector winds, and radiation parameters; such measurements in the region are unprecedented, to assess the upper-ocean response to the MJO forcing in the Indonesian–Australian Basin as compared to other regions in the Indian Ocean. The data will be used to understand the coupled model performance in capturing the scale, strength

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Chen Li, Jing-Jia Luo, and Shuanglin Li

layer” parameterization scheme is based on that of Lock et al. (2000) with the modifications described in Lock (2001) and Brown et al. (2008) . It is a first-order turbulence closure mixing adiabatically conserved heat and moisture variables, momentum, and tracers. For more details of the model physics, readers are referred to Walters et al. (2017) . We examine UM-GA6’s performance in the Atmospheric Model Intercomparison Project (AMIP) runs from 1982 to 2008 with observed SST forcing. The

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

conspire to promote nocturnal low-level convergence, moistening, and destabilization, and hence provide explanations for the triggering and maintenance of these MCSs, though not their propagation. Yet in other regions, nocturnal propagating convective systems exist both without low-level jets and without continuous orographic forcing, as exemplified by the many examples of offshore-propagating nocturnal systems: in the Tiwi Islands ( Carbone et al. 2000 ); the Panama Bight region ( Mapes et al. 2003a

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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

represent the wind forcing in the Maluku Channel. Surface drifter trajectories are obtained from the Global Lagrangian Drifter Data of AOML/NOAA from 1979 to 2011 ( http://www.aoml.noaa.gov/envids/gld/dirkrig/parttrk_spatial_temporal.php ). The 2-Minute Gridded Global Relief Data (ETOPO2v2) of the U.S. National Geophysical Data Center are used to calculate the width of the Maluku Channel section ( https://ngdc.noaa.gov/mgg/global/etopo2.html ). The drifters released in the western Pacific Ocean during

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