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Hamish A. Ramsay, Savin S. Chand, and Suzana J. Camargo

simulations, six models are evaluated: HiRAM (hereafter HIRAM), CMCC-ECHAM5 (hereafter CMCC), GISS, CAM5.1 (hereafter CAM5), FSU COAPS (hereafter FSU), and GFS ( Table 1 ), with the first four of these models also used for evaluating the downscaled tracks. Following the approach of Held and Zhao (2011) , these simulations contained four climate scenarios: a controlled twentieth-century climate and the three idealized warming climate scenarios. The control climate (20C) was forced with climatological

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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

for Global Environmental Risk (UPSCALE) project ran the Met Office Unified Model (MetUM), using a forced atmosphere–land configuration named Global Atmosphere 3.0 (GA3.0; Walters et al. 2011 ), on the Cray XE6 supercomputer Hermit at the High Performance Computing Center Stuttgart (HLRS) in Stuttgart, Germany. Using a hierarchy of models with midlatitude resolutions of N96 (130 km), N216 (60 km), and N512 (25 km) with consistent physics and dynamics settings, our goal was to investigate the

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

provide insight into the model strengths and weaknesses. For example, Camargo et al. (2005) examined genesis location, TC counts, intensity, and storm lifetimes in a statistical analysis of TC-like vortices in three low-resolution GCMs. They found that basin-scale global TC statistics match the observed statistics reasonably well, even for these lower-resolution models. With the suite of higher-resolution models, individual modeling groups have presented TC performance statistics for their specific

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

relative roles of increased ocean temperature and greenhouse gases on future tropical storm behavior as well as some guidance to the interpretation of multimodel datasets, including the current generation of the Coupled Model Intercomparison Project, CMIP5. 2. The Community Atmospheric Model and simulated tropical cyclone performance The Community Atmospheric Model developed by the U.S. Department of Energy and the National Science Foundation is one of several global atmospheric models currently being

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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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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

warming climate, the HWG initiated a series of simulations with high-resolution atmospheric GCMs (K. J. E. Walsh et al. 2014, unpublished manuscript). One set of simulations is the interannual experiment, which is Atmospheric Model Intercomparison Project (AMIP)-type simulations with multiple GCMs forced with the same observed time-varying SST from 1982 to 2009. This set of simulations provides necessary data to characterize TC response to ENSO in climate models. This study aims to evaluate the

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

quantitative analyses on future changes. 3. Results a. Present-day performance A Taylor diagram ( Taylor 2001 ) ( Fig. 3 ) was used to evaluate model performance in terms of the global distribution of FOCs in the present-day simulation. Table 1 lists the root-mean-square error (RMSE) and the Taylor skill score II ( S 2 ; Taylor 2001 ) for each model; S 2 is defined as where R is the spatial correlation coefficient between simulated and observed FOC fields, R 0 is the maximum correlation attainable

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Chao Wang and Liguang Wu

intensity (17.2 m s −1 ). The TC formation location is defined as the latitude and longitude when a TC for the first time reaches tropical storm intensity. The monthly wind data are from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis dataset (ERA-I; Dee et al. 2011 ). The monthly SST from the National Oceanic and Atmospheric Administration (NOAA) (ERSST.v3b; Smith et al. 2008 ) is used in this study. To evaluate the performance of CMIP5 models against the reanalysis

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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

modeled rainfall data. These observational records will be used to evaluate the capability of three climate models in reproducing the rainfall characteristics associated with TCs. In this study we will use outputs from the Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM), the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) model, and the Community Atmosphere Model, version 5.1 (CAM5). The models are forced by climatological SSTs [1981–2005 average from

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

-resolution general circulation models (GCMs) and simulations performed following common protocols, within the U.S. Climate Variability and Predictability (CLIVAR) Hurricane Working Group ( ). In our analyses, we will first assess whether these GCMs are able to reproduce TC rainfall contribution to the total rainfall at the global scale, with particular emphasis on the coastal regions. After the model evaluation, we will examine the changes in TCP for three

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