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

relatively short record of reliable TC intensity data (e.g., Kossin et al. 2013 ), and questions about the quality of the historical best track datasets. Meaningful and robust future projections are highly dependent on the ability of GCMs to accurately simulate the observed characteristics of TC tracks (i.e., their frequency, genesis locations, movement, and intensity). Previous studies have explored observed TC track types using cluster analysis in different geographical regions, including the western

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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

technique and describes the models and the tracking algorithms used. In section 3 , the characteristics of the explicitly simulated North Atlantic tropical cyclone clusters are analyzed. In section 4 , the same analysis is performed for the downscaled tropical cyclones. Section 5 explores, using clusters, future changes in frequency and intensity of North Atlantic tropical cyclones. Finally, a summary and a discussion of our results are presented in section 6 . 2. Clustering method and data a

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

) would be a useful strategy. However, a number of questions remain regarding the multimodel ensemble approach. Is it appropriate to include models with marked biases in their control simulations as ensemble members? To what extent do biases in present-day simulations influence projected future changes in FOCs? Is it reasonable to assume that model biases do not substantially influence estimates of future changes, based on the assumption that subtracting simulated present-day means from projected

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

in the two experiments with 1990 climatological surface boundary conditions at the 0.23° × 0.31° model resolution. This increase occurs when the storms are at intensities of category 1 and above. The data in Fig. 5 labeled “Cat 0” shows that the average duration in days of all identified storms in the increased SST experiments with maximum winds between 17 and 33 m s −1 decreases slightly. However, the average duration of simulated storms at tropical cyclone strength or greater is lengthened

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