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James B. Elsner, Sarah E. Strazzo, Thomas H. Jagger, Timothy LaRow, and Ming Zhao

University Center for Ocean–Atmospheric Prediction Studies (FSU-COAPS) global spectral model ( Cocke and LaRow 2000 ; LaRow et al. 2008 ). We apply the same algorithm used on the observations to interpolate the 6-hourly model data to hourly values. The GFDL HiRAM data are from a control simulation forced with prescribed SST and sea ice concentrations from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003 ). We use data from three realizations of the HiRAM that

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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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Wei Mei, Shang-Ping Xie, and Ming Zhao

: 10.1061/(ASCE)1527-6988(2008)9:1(29) . Raymond , D. J. , and S. L. Sessions , 2007 : Evolution of convection during tropical cyclogenesis. Geophys. Res. Lett., 34, L06811 , doi: 10.1029/2006GL028607 . Rayner , N. A. , D. E. Parker , E. B. Horton , C. K. Folland , L. V. Alexander , D. P. Rowell , E. C. Kent , and A. Kaplan , 2003 : Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century . J. Geophys

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Malcolm J. Roberts, Pier Luigi Vidale, Matthew S. Mizielinski, Marie-Estelle Demory, Reinhard Schiemann, Jane Strachan, Kevin Hodges, Ray Bell, and Joanne Camp

Surface Temperature and Sea Ice Analysis (OSTIA; Donlon et al. 2012 ) daily SST and sea ice dataset, which has a native resolution of ° and is a synthesis of satellite and in situ observations covering from 1985 to the present day, where the period 1985–2008 is a reanalysis ( Roberts-Jones et al. 2012 ). The present climate (PC) simulations use this surface forcing, together with CMIP5 Atmospheric Model Intercomparison Project phase 2 (AMIP-II) standard forcings for aerosols and greenhouse gases

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Enrico Scoccimarro, Silvio Gualdi, Gabriele Villarini, Gabriel A. Vecchi, Ming Zhao, Kevin Walsh, and Antonio Navarra

Group dataset. More specifically, we use the following four experiments: CLIM: This is a climatological run obtained by repeating the SST climatology over the period 1982–2005 [based on the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST); Rayner et al. 2003 ] for 10 years. It is used to provide a baseline to contrast with the perturbation studies. Ozone and aerosol forcings are climatological as provided by the IPCC. Also, radiative gas concentrations are defined according to

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Yohei Yamada and Masaki Satoh

global nonhydrostatic model with explicit cloud microphysics processes; such a global atmospheric model is different from climate models ( Satoh et al. 2008 ). Miura et al. (2005) and Wyant et al. (2006) also showed cloud responses different from those obtained by relatively coarse-resolution climate models. Upper clouds are related to the ice phase of water. Satoh et al. (2012a) showed that the ice water path (IWP) generally decreases under a global warming condition for a different set of

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

Model Intercomparison Project II (AMIP-II) Reanalysis (NCEP-2) ( Kanamitsu et al. 2002 ). SST and sea ice are based on the monthly 1.0° × 1.0° Hadley Centre Global Sea Ice and Sea Surface Temperature dataset (HadISST) ( Rayner et al. 2003 ). The 21-yr control integration ( Table 1 ) is initialized on 15 January 1980 and run through 31 December 2000, and is named “year”-e1 corresponding to each of 21 years. Additional simulations for the 1987 (1987-e2 and 1987-e3) and 1999 (1999-e2 and 1999-e3

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

in section 5 . 2. Data and methods a. Observational and reanalysis data The observed WNP TC tracks are from the Joint Typhoon Warning Center best-track dataset ( Chu et al. 2002 ), which provides the location and intensity of TCs at 6-h intervals since 1945. SSTs from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003 ) and atmospheric variables [including sea level pressure (SLP), 850- and 200-hPa winds, and 500-hPa vertical pressure velocity] from the

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Hui Wang, Lindsey Long, Arun Kumar, Wanqiu Wang, Jae-Kyung E. Schemm, Ming Zhao, Gabriel A. Vecchi, Timothy E. Larow, Young-Kwon Lim, Siegfried D. Schubert, Daniel A. Shaevitz, Suzana J. Camargo, Naomi Henderson, Daehyun Kim, Jeffrey A. Jonas, and Kevin J. E. Walsh

observations and simulations with five atmospheric GCMs. For observations, the SST data are taken from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset ( Rayner et al. 2003 ) on a 1° × 1° (latitude × longitude) grid. The 28-yr monthly mean SSTs were also prescribed as low boundary forcing for the GCMs. The Atlantic TC track data are from the National Hurricane Center second-generation Atlantic hurricane database (HURDAT2; Landsea and Franklin 2013 ). The precipitation data are from

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Sarah Strazzo, James B. Elsner, Timothy LaRow, Daniel J. Halperin, and Ming Zhao

://www.usclivar.org/working-groups/hurricane ). We use data from two different high-resolution atmospheric (uncoupled) GCMs. As with the observational data, the modeled track data are provided at 6-hourly intervals and have been interpolated to hourly intervals using the same algorithm as used for the observations. We first use cyclone tracks from the GFDL HiRAM, version 2.2 ( Zhao et al. 2009 , 2012 ). The model data come from a control simulation forced with monthly prescribed SSTs and sea ice concentrations for each simulated year from the

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