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  • Model performance/evaluation x
  • DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation x
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Sue Chen, Maria Flatau, Tommy G. Jensen, Toshiaki Shinoda, Jerome Schmidt, Paul May, James Cummings, Ming Liu, Paul E. Ciesielski, Christopher W. Fairall, Ren-Chieh Lien, Dariusz B. Baranowski, Nan-Hsun Chi, Simon de Szoeke, and James Edson

the time scale for the observed moisture resurgence in the post-MJO dry air mass prior to the subsequent MJO onset? What relative roles do diurnal ocean temperature anomalies and surface fluxes play in regulating or initiating the deep vapor resurgence? Do transient Kelvin, Rossby, mixed Rossby–gravity, and inertio-gravity waves impact the vapor resurgence? We begin with a description of the data and modeling methods used in section 2 . Analyses of observed in situ rawindsondes, surface flux, and

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

homogenized LHFLXs to determine the role of wind-induced LHFLX feedbacks in organizing convection. This work extends the evaluation of surface flux feedbacks to the destabilization and maintenance of MJO convection from the MJO-envelope time and space scale (e.g., Sobel et al. 2010 ; Araligidad and Maloney 2008 ; RDM2015 ) to the smaller convective scale. In this study, the convective scale refers to precipitating cloud clusters (defined below) ranging in size from a single model grid point (i.e., 2

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

in that study. The results here are an additional approach to evaluate this model against local observations in terms of temporal correlations. A third approach (hindcast skill) is relegated to the appendix , since its findings are weak but it slightly affects the regressions here. Figure 8 shows actual and virtual field data regressions, our cleanest comparison involving experimental CCPM-derived sensitivity matrices. Regressions of D on q 550 from AMIE-DYNAMO (red, from Fig. 6 ) are

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Yue Ying and Fuqing Zhang

increase the predictability limit 10 times. The intrinsic predictability limit for KE is about 10 days at l = 500 km and decreases to <1 day at small scales. The same predictability limit is true for other variables such as temperature and humidity but not for precipitation, which has more limited intrinsic predictability. For regional models, the specification of LBC is nontrivial for the accuracy of simulation. To evaluate the relative importance of IC and LBC, an extra set of ensemble simulations

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Sharon L. Sessions, Stipo Sentić, and David J. Raymond

coupling between the dynamics and thermodynamics that characterize the balanced dynamics framework. This framework complements existing theories of convective evolution observed during DYNAMO, and the corresponding diagnostics may be applied to evaluate models and improve convective parameterizations. Whether or not tropical convection is a response to balanced dynamics or is primarily a consequence of stochastic processes is largely a matter of scale. Ooyama (1982) argued that balanced flow is

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Ji-Eun Kim, Chidong Zhang, George N. Kiladis, and Peter Bechtold

, D. Wu , and G. Thompson , 2014a : Evaluation of convection-permitting model simulations of cloud populations associated with the Madden-Julian oscillation using data collected during the AMIE/DYNAMO field campaign . J. Geophys. Res. Atmos. , 119 , 12 052 – 12 068 , . 10.1002/2014JD022143 Hagos , S. , Z. Feng , K. Landu , and C. N. Long , 2014b : Advection, moistening, and shallow-to-deep convection transitions during the initiation and

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H. Bellenger, K. Yoneyama, M. Katsumata, T. Nishizawa, K. Yasunaga, and R. Shirooka

features to be studied with this campaign. The importance of this preconditioning for deep convection associated with the MJO has been stressed by many observational (e.g., Johnson et al. 1999 ; Kikuchi and Takayabu 2004 ; Holloway and Neelin 2009 ) and modeling studies (e.g., Zhang and Song 2009 ; Cai et al. 2013 ). A possible consequence of our lack of understanding of the origin of this preconditioning is the limitation of the forecast skill of the timing of the MJO triggering. Indeed, forecast

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David M. Zermeño-Díaz, Chidong Zhang, Pavlos Kollias, and Heike Kalesse

Island in the western Pacific ( Long et al. 2013 ) provides unique long-term (1996–2014) observations from a suite of instruments, including vertically pointing cloud radars, radiosondes, rain gauges, and others. Data from Manus have been used to evaluate satellite observations ( Hollars et al. 2004 ) and model simulations ( Chen and Del Genio 2009 ) to estimate cloud radiative heating rates ( McFarlane et al. 2007 ; Mather and McFarlane 2009 ; Wang et al. 2010 ), and to document the cloud

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Weixin Xu and Steven A. Rutledge

Roundy 2013 ), ENSO ( Zhang 2005 ; Lau 2012 ), and extratropical climate modes ( Lin et al. 2009 ; L’Heureux and Higgins 2008 ). Despite decades of study, the MJO is not well understood and therefore MJO prediction skill is limited, especially concerning initiation over the Indian Ocean ( Bechtold et al. 2008 ; Kim et al. 2009 ; Vitart and Molteni 2010 ). Meanwhile, the MJO has been poorly simulated by several generations of general circulation models (GCMs) ( Lin et al. 2006 ; Hung et al. 2013

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