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

North Pacific ( Camargo et al. 2007b ), eastern North Pacific ( Camargo et al. 2008 ), North Atlantic ( Kossin et al. 2010 ), South Pacific ( Chand and Walsh 2009 ), and Southern Hemisphere ( Ramsay et al. 2012 ). We apply the same clustering technique as used in these previous studies to explore differences between observed and model-simulated TC tracks in the Southern Hemisphere (refer to section 2e for details). The focus of the current study is to assess the ability of climate models to

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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

to the interannual frequency in the basin. Strazzo et al. (2013) used a spatial lattice technique to analyze two of the models in this study and identified regional biases in the North Atlantic tropical cyclone activity. These studies highlighted the importance of accurately simulating the tropical cyclone tracks in addition to the frequency and intensity of tropical cyclones. To evaluate the ability of modern climate models to represent the North Atlantic tropical cyclone tracks, the

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

ultimate goal is to generate ensemble means using models that reduce the inheritance of biases to increase the reliability of information on projections of future changes in FOCs. On the other hand, the multimodel ensemble approach has been widely discussed in the literature for comprehensive forecast and projection frameworks such as short-range weather forecasting ( Raftery et al. 2005 ; Casanova and Ahrens 2009 ), seasonal forecasting ( Tippet et al. 2005 ; Casanova and Ahrens 2009 ), decadal

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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 (2014, unpublished manuscript).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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Young-Kwon Lim, Siegfried D. Schubert, Oreste Reale, Myong-In Lee, Andrea M. Molod, and Max J. Suarez

and Suarez (1992) ] on GCM hurricane forecasts. Both studies agreed on that explicit representation of cloud processes produces a larger number of TC events, with stronger intensity and longer life cycles ( Reed and Jablonowski 2011 ; Stan 2012 ). However, the details of the atmospheric processes responsible for altering TC activity were not the focus of the above studies. Some of the atmospheric responses to changes in deep convective activity are discussed in Zhao et al. (2012) , which focuses

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Rongqing Han, Hui Wang, Zeng-Zhen Hu, Arun Kumar, Weijing Li, Lindsey N. Long, Jae-Kyung E. Schemm, Peitao Peng, Wanqiu Wang, Dong Si, Xiaolong Jia, Ming Zhao, Gabriel A. Vecchi, Timothy E. LaRow, Young-Kwon Lim, Siegfried D. Schubert, Suzana J. Camargo, Naomi Henderson, Jeffrey A. Jonas, and Kevin J. E. Walsh

TC activity associated with the different phases of ENSO is still considered one of the key issues in dynamical seasonal prediction of TCs. In terms of the routine seasonal forecasts of TC frequency in the WNP, both the model simulations and real-time predictions using high-resolution GCMs in recent years are promising ( Zhao et al. 2009 , 2010 ; Chen and Lin 2011 , 2013 ; Shaevitz et al. 2014 ; Vecchi et al. 2014 ; Wang et al. 2014 ; Mei et al. 2015 ; Murakami et al. 2015 ; Walsh et al

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

other comparable models ( Knutson et al. 2010 ). Our procedure is as follows: Use the model’s own TCs and large-scale environmental fields, taken from a control simulation, to derive a tropical cyclone genesis index. Compute the resulting index from model environmental fields taken from a simulation of a warmer climate. Compare the future changes in the indices to future changes in the model’s own tropical cyclone frequency. We use the technique developed by Tippett et al. (2011) to generate and

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Wei Mei, Shang-Ping Xie, and Ming Zhao

-pass filter based on the fast Fourier transform (FFT) technique. We tested the results using a 10-yr low-pass filter and obtained very similar results. To have a slightly larger degree of freedom for the low-frequency component (considering the 30-yr period of the simulations), we chose to use the 7.5-yr low-pass filter. EOF analysis is applied to both observed and modeled TC track density after the low-pass filtering. In both observations and model simulations, the low-frequency TC track density is

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

more frequently during La Niña events ( Camargo et al. 2007b ). Using a different clustering technique, H.-S. Kim et al. (2011) show that the WNP TC tracks over the TC active season can also be categorized into seven clusters with four of them being linked to either ENSO or the quasi-biennial oscillation. Because of the large variability in its three contributors (i.e., count, genesis location, and track), we expect to see strong variations in TC track density over the WNP. Compared to 1951

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