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  • DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation x
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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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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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Paul E. Ciesielski, Richard H. Johnson, Wayne H. Schubert, and James H. Ruppert Jr.

(CombRet) product ( Feng et al. 2014 ). Combining the all-sky and clear retrievals gives us an estimate of the cloud radiative forcing (CRF). Satellite-estimated rainfall data were from the TRMM 3B42 V7 product at 0.25°, 3-h resolution ( Huffman et al. 2007 ). Radar rainfall data from the Mirai , Revelle , and Gan-S-Pol at 10-min resolution covering a 320 km × 320 km domain at each site were obtained from the DYNAMO legacy data archive and averaged into 3-hourly bins to facilitate comparison with

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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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Angela K. Rowe, Robert A. Houze Jr, Stacy Brodzik, and Manuel D. Zuluaga

Abstract

The Madden–Julian oscillation (MJO) dominates the intraseasonal variability of cloud populations of the tropical Indian and Pacific Oceans. Suppressed MJO periods consist primarily of shallow and isolated deep convection. During the transition to an active MJO, the shallow and isolated deep clouds grow upscale into the overnight hours. During active MJO periods, mesoscale convective systems occur mostly during 2–4-day bursts of rainfall activity with a statistically significant early morning peak. Yet when these rain events are separated into individual active periods, some periods do not follow the mean pattern, with the November events in particular exhibiting an afternoon peak. The radar-observed microphysical processes producing the precipitation during the major rain events of active MJO periods evolve in connection with synoptic-scale wave passages with varying influences of diurnal forcing. MJO studies that do not account for the intermittency of rainfall during active MJO phases through averaging over multiple events can lead to the misimpression that the primary rain-producing clouds of the MJO are modulated solely by the diurnal cycle.

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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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