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Chao Wang and Liguang Wu

positions during July–October over 1979–2005. The climatologic mean TUTT is elongated from northeast to southwest across the North Pacific Ocean, extending to the tropical North Pacific near 160°E. The prominent westerly wind shear to the southeast of the TUTT trough line generally tends to suppress the TC formation east of 160°E. As a result, Most TCs formed to west of the TUTT. As shown in Fig. 2 , the north and south boundaries of the TC formation area are consistent with the subtropical westerly

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Michael Wehner, Prabhat, Kevin A. Reed, Dáithí Stone, William D. Collins, and Julio Bacmeister

2011 ). The base configuration, called CLIMO in this paper, calls for a multiyear integration of AGCMs with the surface boundary conditions (SST and sea ice extent) set to early 1990s average values and atmospheric carbon dioxide (CO 2 ) concentrations set to 330 ppm. The second configuration, called SSTplus2 here, simply adds 2°C uniformly to the SSTs. The third configuration, called 2xCO2, uses the 1990 surface climatology but doubles the CO 2 concentration to 660 ppm. The fourth configuration

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Young-Kwon Lim, Siegfried D. Schubert, Oreste Reale, Myong-In Lee, Andrea M. Molod, and Max J. Suarez

1. Introduction This article is inspired by a recent research on tropical cyclone (TC) simulation coordinated by the U.S. Climate Variability and Predictability (CLIVAR) Hurricane Working Group ( ) Among various science issues raised in the research, it was found that many current general circulation models (GCMs) seriously underestimate TC activity over the North Atlantic when run at ~0.5° latitude/longitude or coarser horizontal grid spacing as

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Gabriele Villarini, David A. Lavers, Enrico Scoccimarro, Ming Zhao, Michael F. Wehner, Gabriel A. Vecchi, Thomas R. Knutson, and Kevin A. Reed

changes, there are minor retunings of the atmospheric parameters in the cloud and surface boundary layer parameterizations necessary to achieve the top-of-atmosphere (TOA) radiative balance ( Zhao et al. 2012 ). This model is also the version of HiRAM used for the GFDL participation in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) high-resolution time-slice simulations. The CAM5, developed by the U.S. Department of Energy and the National Science Foundation

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Wei Mei, Shang-Ping Xie, Ming Zhao, and Yuqing Wang

. These simulations are constrained by observed atmospheric conditions on the lateral boundaries. Because of this and the short period of simulation (1982–2001), we do not use these simulations to explore the SST-forced response in TC activity. Instead, we only use deviations of the four-member simulations from the ensemble mean to understand the effects of downscaling on the internal variability of simulated TC track density. Detailed descriptions of iRAM and the procedures for identifying TCs in

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Hiroyuki Murakami, Pang-Chi Hsu, Osamu Arakawa, and Tim Li

and Wang 2010 ; Murakami et al. 2011 ). Using a “time slice” method ( Bengtsson et al. 1996 ), the AGCMs were forced by prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) as the lower boundary conditions. The present-day simulations were styled after the Atmospheric Model Intercomparison Project (AMIP) models, in which the lower boundary conditions are prescribed by observed monthly mean SSTs and SICs during 1979–2003, obtained from the first Hadley Centre Global Sea Ice

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Christina M. Patricola, R. Saravanan, and Ping Chang

2008 ) through changes in the local boundary layer and tropospheric vertical wind shear in the Atlantic main development region (MDR) ( Landsea et al. 1999 ; Goldenberg et al. 2001 ; Vitart and Anderson 2001 ). Two prominent modes of climate variability expressed through Atlantic SST include 1) the Atlantic multidecadal oscillation, which describes multidecadal North Atlantic SST variations and 2) the Atlantic meridional mode ( Chiang and Vimont 2004 ), or the “dipole mode” ( Chang et al. 1997

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Suzana J. Camargo, Michael K. Tippett, Adam H. Sobel, Gabriel A. Vecchi, and Ming Zhao

based on our current physical understanding of the factors that control genesis; but that understanding is imperfect, and the relationships between the predictors and the index are found using data from the present climate. Thus, it is possible that they will fail to capture the influence of future climate changes on TCs. We cannot perform empirical tests of the indices’ ability to capture these changes using observations, since there are no observations of future tropical cyclone activity. As an

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John G. Dwyer, Suzana J. Camargo, Adam H. Sobel, Michela Biasutti, Kerry A. Emanuel, Gabriel A. Vecchi, Ming Zhao, and Michael K. Tippett

then simulated with an idealized axisymmetric dynamical tropical cyclone model, following a track determined using the GCM wind field ( Emanuel et al. 2008 ); while the other method, a high-resolution global atmospheric model (HiRAM; Held and Zhao 2011 ), can explicitly, albeit crudely, resolve TCs when given the SST as a boundary condition. Despite the advances of GCMs and new modeling approaches and techniques, uncertainty remains in projections of TC frequency with global warming. High

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Michael Horn, Kevin Walsh, Ming Zhao, Suzana J. Camargo, Enrico Scoccimarro, Hiroyuki Murakami, Hui Wang, Andrew Ballinger, Arun Kumar, Daniel A. Shaevitz, Jeffrey A. Jonas, and Kazuyoshi Oouchi

schemes not already described by differences in the detected genesis densities are apparent. Overall, the representation of the geographic pattern and tracks of TC activity in the current climate is reasonably similar in each model across schemes. Between models, the most notable difference is in the Atlantic basin for JAS, where NCEP performs well but all other models show very little genesis. We should also note that, unlike the IBTrACS observations, the model tracks do not show extratropical

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