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Paul E. Ciesielski and Richard H. Johnson

1. Introduction Islands can have a wide range of impacts on their surrounding atmosphere, depending on their size and topographic features. When stratified flow is blocked by larger islands with high topography such as Hawaii, Sri Lanka, and Sumatra, significant flow blocking and lee wake generation is likely ( Smith and Grubisic 1993 ; Fine et al. 2016 ; Ciesielski et al. 2014b ). In fact, using 2.5 years of model reanalyses, Fine et al. (2016) found that 25% of the tropical cyclones that

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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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Samson M. Hagos, Zhe Feng, Casey D. Burleyson, Chun Zhao, Matus N. Martini, and Larry K. Berg

statistical relationship between the CMIP5 models’ representation of this effect and their performance in simulating the amplitude of the MJO. Del Genio et al. (2015) examined sensitivity of MJO simulation to entrainment, rain evaporation, downdrafts, and cold pools. They found that for the various configurations, a version that produces strong column heating for weak precipitation also produces improved initiation and progression of MJO. However, improvements in MJO simulations are often accompanied by

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

; Renwick and Revell 1999 ). A models’ capability of forecasting the global teleconnection pattern of the MJO depends on its skill of forecasting MJO convection centers in terms of their strengths, propagation speeds, and timing. The objectives of this study are to document the ECMWF model’s forecast skill during DYNAMO as a benchmark for the comparative evaluation of forecast and hindcast skill of other operational and research models for the same MJO events; advocate the need for both global and local

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Jianhao Zhang, Paquita Zuidema, David D. Turner, and Maria P. Cadeddu

Raju et al. (2013) ; the latter is a case study of the water vapor field surrounding a tropical water spout from a scanning microwave radiometer. We extend this research area into the equatorial Indian Ocean and adopt two approaches for evaluating surface-based microwave radiometry humidity profiling. One approach examines the retrieval accuracy itself, whereas the second approach assesses radiometry’s ability to capture equatorial humidity variability at a range of time scales. Examined time

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Eric D. Skyllingstad and Simon P. de Szoeke

( Rotunno et al. 1988 ; Weckwerth and Parsons 2006 ; Houston and Wilhelmson 2012 ). In the deep tropics, cold pools can cause a more than doubling of the local latent heat flux ( Jabouille et al. 1996 ). Understanding how cold pools interact and potentially enhance tropical convection is important for accurate representation of convection in large-scale models where these processes are not well resolved. On average, heating by deep convection through condensation is the primary mechanism that balances

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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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Shuguang Wang, Adam H. Sobel, Fuqing Zhang, Y. Qiang Sun, Ying Yue, and Lei Zhou

model layer. Note that we have neglected the diffusion process, which smooths out the moisture field; therefore, it is not a source/sink in a global domain but it may still be a net source/sink at local area. As shown below, the rhs and lhs of Eq. (1) agree quite well, indicating that this omission in Eq. (1) is justified. The two nonlinear advection terms at the resolved scales are evaluated using variables averaged over 7 adjacent grid cells. Skamarock (2004) demonstrated that 6–7 times the

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