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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 (2014, unpublished manuscript).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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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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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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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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Malcolm J. Roberts, Pier Luigi Vidale, Matthew S. Mizielinski, Marie-Estelle Demory, Reinhard Schiemann, Jane Strachan, Kevin Hodges, Ray Bell, and Joanne Camp

general circulation models (CGCMs) implemented at horizontal resolutions that allow multicentennial integrations under a variety of forcing scenarios, often with full Earth system biogeochemistry components. To address such issues, a long-standing collaboration exists between the Met Office and the University of Reading to develop “weather resolving” climate models, which are able to capture typical weather features such as fronts and atmospheric rivers (as found in a weather forecast) while also

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

University ( Gray and Klotzbach 2005 ) called for one of the most active hurricane seasons on record, both underpredicted the number of tropical storms and hurricanes. In addition, the midseason forecast by NOAA predicted a seasonal accumulated cyclone energy (ACE) ( Bell et al. 2000 ), which is defined as the sum of the squares of the 6-hourly maximum sustained wind speed throughout the life of a tropical cyclone, of 158–236 (×10 4 kt 2 ) (180%–270% of the median), noting that the primary uncertainty

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

Hemisphere ( Ramsay et al. 2012 ), as well as to typhoon post-landfall tracks over China ( Zhang et al. 2013 ). Other recent applications of this cluster analysis include a comparison of tracks of TCs in a reanalysis dataset to observations ( Bell et al. 2018 ), analysis of the skill of tropical cyclone forecasts ( Don et al. 2016 , Kowaleski and Evans 2016 ), and in the development of statistical–dynamical seasonal forecasts ( Zhang et al. 2016 ). The application we focus on here is to examine the

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Wei Mei, Shang-Ping Xie, and Ming Zhao

the operational forecast of the path for a specific TC at a lead time of a few days, which has improved steadily in recent decades (e.g., Cangialosi and Franklin 2013 ). These findings also have important implications in the context of climate change: even if the multimodel ensemble can well project changes in total seasonal TC counts under global warming, it remains difficult to assess changes in local TC occurrence, particularly near the coast, where landfall TCs incur the greatest societal and

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

Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-40) and National Centers for Environmental Protection (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis, as well as retrievals of column water vapor from satellite passive microwave observations. The regression methodology is objective and provides a framework for the selection of the climate variables to be used in the index. This method led us to select four environmental variables for the index similar but not identical

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