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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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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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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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Jun-Ichi Yano and Joseph J. Tribbia

model channel domain typically placed at 30°–45° latitude. Once the lateral forcing is turned off, the planetary-scale MJO circulations disappear ( Ray et al. 2009 ; Vitart and Jung 2010 ; Ray and Li 2013 ). Gustafson and Weare (2004) , Ray and Zhang (2010) , Ray and Li (2013) , and Zhao et al. (2013) suggest that the eastward-propagating Rossby wave train arriving from higher latitudes induces the MJO. However, the above argument suggests that any eastward-moving structure with a

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Brian Mapes, Arunchandra S. Chandra, Zhiming Kuang, Siwon Song, and Paquita Zuidema

verifications of high-resolution simulations driven by NWP analyses (e.g., Takemi 2015 ; Hagos et al. 2014 ) or by sounding array–derived forcing sets. Given the sheer abundance of global operational data entering cutting-edge NWP, and the technical hurdles of research with such complex systems, special field data may not find their highest and best use there. Still, analysis needs some framework. Here we use only basic statistics (regression coefficients). The first step in any statistical analysis is a

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Emily M. Riley Dellaripa, Eric Maloney, and Susan C. van den Heever

scale. No other large-scale forcing was utilized. The lateral boundary-nudging scheme follows Davies (1976) , in which the nudging time scale increases parabolically from the user-defined time scale at the boundary to infinity at the interior grid point of the relaxation region (i.e., the seventh innermost grid cell from the boundary in our setup). Nudging at the lateral boundaries to observations ensures that both the control and sensitivity simulations accurately capture the overall evolution of

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Hungjui Yu, Richard H. Johnson, Paul E. Ciesielski, and Hung-Chi Kuo

conceptual mesoscale process was proposed involving the nonlinear interaction between clouds, radiation, and surface processes, the so-called “diurnal dancing” of convective systems, to explain the near 2-day periodicity. In their scenario, despite diurnal radiative forcing, boundary layer (BL) recovery extends to a second day, likely due to the expanded stratiform clouds of MCSs, which impacts the timing of the next round of convection. The BL recovery for a future convective event over a given region

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Adam Sobel, Shuguang Wang, and Daehyun Kim

energy in the denominator follows Sobel and Maloney (2012 , 2013) ; Raymond et al. (2009) , for example, normalize by the moisture convergence. In practice, the difference is minor. By either definition, quantifies the relationship between the precipitation and the net column forcing of MSE (surface fluxes plus radiation) in steady state, with smaller NGMS giving greater steady-state precipitation for a given net positive forcing. If we assume steady state and neglect horizontal advection of dry

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Matthew A. Janiga and Chidong Zhang

to drive the evolution of clouds in a CRM with periodic boundary conditions. The simulation was performed using 3-hourly data from the DYNAMO northern sounding array, since this array captured more convective variability on the MJO time scale ( Johnson and Ciesielski 2013 ). Version 2a of the sounding data, which is based only on observations, was used. The goal of the simulation is not to determine the total value of Q 1 and Q 2 , since these are constrained by the forcing, but the

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