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Anne S. Daloz, S. J. Camargo, J. P. Kossin, K. Emanuel, M. Horn, J. A. Jonas, D. Kim, T. LaRow, Y.-K. Lim, C. M. Patricola, M. Roberts, E. Scoccimarro, D. Shaevitz, P. L. Vidale, H. Wang, M. Wehner, and M. Zhao

easterly wave activity. These issues will also be examined for some models in this study (depending on availability). Here, both explicit and downscaled North Atlantic tropical cyclone tracks are examined. The explicit tropical cyclone tracks originated from nine climate models (global and regional) with a spatial resolution varying from 0.25° to 1°. Tracks were obtained using detection and tracking algorithms that find and track storms in the output of these climate models. Typically, each modeling

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

uncertainties specified in annual storm numbers is determined by the 5%–95% confidence range based on interannual variability. The observed numbers per year from the (IBTrACS) observed track database ( Knapp et al. 2010 ) during this period were 85 tropical storms, 48 tropical cyclones, and 28 intense tropical cyclones over all ocean basins. The simulated storm counts were produced using the tracking algorithm from the Geophysical Fluid Dynamics Laboratory (GFDL) with the threshold values for vorticity

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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

explicit feature-tracking algorithms ( Bengtsson et al. 2007a ; Zhao et al. 2009 ). Alternatively, measures based on the larger-scale climatology of factors known to influence TC formation (e.g., wind shear, thermodynamic instability, and humidity) can be computed, such as the genesis potential index (GPI; Emanuel 1988 ; Camargo et al. 2007 ; Emanuel 2010 ) or measures combining aspects of both such as Tory et al. (2013a) . Walsh et al. (2013) made a comparison between GPI-based and explicit

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

centers of cyclones, cyclone intensities (maximum 1-min surface wind speeds), and sea level pressures at 6-hourly intervals from 1851 to 2009. We only used TCs with tropical storm intensities or stronger (i.e., TCs possessing 1-min sustained surface winds of 35 kt or greater; 1 kt ≈ 0.514 m s −1 ) during the period 1979–2003. c. Detection algorithm for tropical cyclones Model-generated TCs were detected directly from 6-hourly output using the following model-dependent globally uniform criteria

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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

al. 2008 ; Molod et al. 2012 ), and the NCEP Global Forecast System (GFS) model ( Saha et al. 2014 ). More detailed descriptions of the models can be found in K. J. E. Walsh et al. (2014, unpublished manuscript). Table 1 lists the number of ensemble runs and model data resolutions, which are also close to model resolutions, as well as the references for TC tracking algorithms for the five models. The ensemble members vary from two to five with a total of 16 realizations. Horizontal resolutions

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

( Tiedtke 1989 ), modified following Nordeng (1994) . Moist processes are treated using a mass-conserving algorithm for the transport ( Lin and Rood 1996 ) of the different water species and potential chemical tracers. The transport is resolved on the Gaussian grid. A more detailed description of the ECHAM5 atmospheric model performance can be found in Roeckner et al. (2006) . Rather than running the same TC tracking algorithm on both the GFDL and CMCC models, we used the tracks provided by each

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

of TC landfall. Fig . 15. SNR for (a) annual, (b) early-season, (c) peak-season, and (d) late-season TC track density calculated based on an ensemble of three members of HiRAM simulations. (e) As in (c), but for the peak-season TC track density based on an ensemble of four members of iRAM simulations. White contours show values of 1. Wu et al. (2012) recently found that TC detection algorithm can contribute to the large internal variability in TC activity since TCs detected in models need to

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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

concept ( Tiedtke 1989 ) modified following Nordeng (1994) . Moist processes are treated using a mass-conserving algorithm for the transport of the different water species and potential chemical tracers ( Lin and Rood 1996 ). The transport is resolved on the Gaussian grid. A more detailed description of the ECHAM5 atmospheric model performance can be found in Roeckner et al. (2006) . The model used for this study is a newer version of the GFDL HiRAM utilized in Zhao et al. (2009) , Zhao and Held

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