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  • DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation x
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Samson M. Hagos, Zhe Feng, Casey D. Burleyson, Chun Zhao, Matus N. Martini, and Larry K. Berg

. Longwave radiative forcing associated with moisture and cloud anomalies is also often cited as the main source of moist static energy for the MJO ( Andersen and Kuang 2012 ; Sobel et al. 2014 ). For example, in the Chikira and Sugiyama (2013) cumulus scheme, radiative heating anomalies moisten the lower and middle troposphere through vertical advection. Finally, a convection–surface flux feedback through nonlinear wind-induced surface heat exchange (WISHE) was proposed by Maloney and Sobel (2004

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Ji-Hyun Oh, Xianan Jiang, Duane E. Waliser, Mitchell W. Moncrieff, Richard H. Johnson, and Paul Ciesielski

findings by Tung and Yanai (2002a , b ) to their theoretical model experiments, Khouider et al. (2012) highlighted two-way interactions between convectively coupled waves (CCWs) and the background MJO winds through the CMT. In addition, Lin et al. (2005) examined a zonal momentum budget associated with the MJO over the equatorial western Pacific using 15 years of daily global reanalysis data. According to their study, the pressure gradient force (PGF) plays a major role in driving MJO zonal winds

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Richard H. Johnson, Paul E. Ciesielski, James H. Ruppert Jr., and Masaki Katsumata

independent estimates of surface fluxes to compute surface precipitation and net tropospheric radiative heating rates for the months of October and November 2011. Two prominent MJO events occurred during this period ( Gottschalck et al. 2013 ; Yoneyama et al. 2013 ; Johnson and Ciesielski 2013 ). The findings are then compared to satellite-based estimates of those quantities. The DYNAMO sounding array analyses have already formed the basis for large-scale forcing fields being used by various authors, so

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Jian Ling, Peter Bauer, Peter Bechtold, Anton Beljaars, Richard Forbes, Frederic Vitart, Marcela Ulate, and Chidong Zhang

increase with increasing speed up beyond 12 m s −1 . Even if a track with maximum averaged precipitation can be found at a speed greater than 12 m s −1 , it cannot be considered as an MJO event. Therefore, no track is identified in the forecast for the December MJO event. d. Numerical experiments Three sets of numerical experiments were conducted: observational data denial, humidity relaxation, and SST forcing ( Table 3 ). The observational denial experiments were designed to explore the impact of

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Adrian J. Matthews, Dariusz B. Baranowski, Karen J. Heywood, Piotr J. Flatau, and Sunke Schmidtko

how this varies under different environmental forcing conditions, particularly those associated with active and inactive phases of the MJO. Simple models of the diurnal warm layer under different environmental conditions are then developed, with the aim of informing (climate) model development. 2. Data processing a. External data sources Sea surface temperature data were extracted from the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation (OI) version 2 dataset

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Tim Li, Chongbo Zhao, Pang-chi Hsu, and Tomoe Nasuno

between day-to-day weather and El Niño–Southern Oscillation, MJO is a major predictability source for extended-range (10–30 days) weather prediction. The least understood aspect of MJO is its initiation process [see Li (2014) for a recent review on this topic]. A number of theories have been advanced in an attempt to understand the initiation mechanism. These theories can be classified according to a tropical or an extratropical origin. The tropical origin hypotheses include a forcing from upstream

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Eric D. Skyllingstad and Simon P. de Szoeke

active phase of the MJO develops. Fig . 1. Skew T –log p temperature profile (solid) for the average DYNAMO conditions from the R/V Revelle along with a histogram of (a) observed temperature and (b) observed dewpoint temperature. The dashed line signifies the dewpoint temperature used in the model initial conditions. In this study we employ a cloud-resolving large-eddy simulation (LES) model to examine how convection responds to external forcing from prescribed domain-scale moisture convergence

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Xiouhua Fu, Wanqiu Wang, June-Yi Lee, Bin Wang, Kazuyoshi Kikuchi, Jingwei Xu, Juan Li, and Scott Weaver

external SST forcing on intra-seasonal time scales may enhance the atmospheric responses toward an eventual satisfactory simulation of intra-seasonal oscillation.” Two MJO events along with coherent upper-ocean variability were first documented by the TOGA COARE field campaign from November 1992 to February 1993 ( Yanai et al. 2000 ; Richards et al. 1995 ; Weller and Anderson 1996 ), although this program was geared to advance the understanding and prediction of El Niño–Southern Oscillation (ENSO

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Aurélie J. Moulin, James N. Moum, and Emily L. Shroyer

1. Introduction Upper ocean heat content changes daily due to radiative forcing from the sun that creates a thin diurnal warm layer (DWL) extending several meters beneath the sea surface. While sea surface temperature (SST) controls the instantaneous air–sea turbulent heat flux, upper ocean heat content provides a diurnally varying heat reservoir that regulates the phase between solar heat flux and the diurnal SST cycle. The thickness and average temperature of this reservoir, which constitutes

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Tomoe Nasuno, Tim Li, and Kazuyoshi Kikuchi

: those stressing tropical internal dynamics [e.g., a circumnavigating signal of the previous episode: Lau and Peng (1987) ; Wang and Li (1994) ; Kikuchi and Takayabu (2003) ; Matthews (2008) ; or local processes in the tropics: Hu and Randall (1994) ; Jiang and Li (2005) ; Straub (2013) ] and those emphasizing extratropical forcing (e.g., Hsu et al. 1990 ; Bladé and Hartmann 1993 ; Matthews and Kiladis 1999 ; Pan and Li 2007 ; Lin et al. 2007 ; Ray and Zhang 2010 ; Wang et al. 2012

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