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John G. Dwyer, Suzana J. Camargo, Adam H. Sobel, Michela Biasutti, Kerry A. Emanuel, Gabriel A. Vecchi, Ming Zhao, and Michael K. Tippett

to change due to increased greenhouse gases and other anthropogenic effects and relate these changes to changes in environmental characteristics, using both the downscaling model and HiRAM forced with CMIP3 and CMIP5 data. In the following section we describe the data and explain the methods we use. In section 3 , we describe the twentieth-century seasonal cycles in HiRAM and the downscaling model and compare them with observations. In sections 4 and 5 we describe the projected changes in

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

interferences between interannual tropical Pacific (ENSO) and Atlantic (AMM) climate modes influence tropical cyclone variability in the Atlantic basin. The objective of this study is to address the following questions. What is the impact of concurrent extreme phases of ENSO and AMM on seasonal Atlantic TC activity, and how do various phases of ENSO and AMM together shape the atmospheric environment for Atlantic TCs? In the next section we review observed relationships between ENSO and Atlantic TC activity

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Wei Mei, Shang-Ping Xie, and Ming Zhao

of seasonal predictions and long-term projections of TC activity, which in turn helps the community to be better prepared for TC-imposed threats. Research in this field has received much attention because of the strong rise of TC activity in the North Atlantic (NA) starting in the mid-1990s (e.g., Goldenberg et al. 2001 ; Holland and Webster 2007 ; Klotzbach and Gray 2008 ). There are several measures of TC activity, including genesis; counts; intensity; tracks; and some other derivatives

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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

). The climate models are forced with prescribed climatological sea surface temperatures (SSTs): that is, with the same values every year, varying monthly with the seasonal cycle for a climatological season. As the models are forced with climatological SSTs, it will not be possible to consider climate variability in our analysis. The modulation of North Atlantic tropical cyclones by El Niño–Southern Oscillation (ENSO) in the same set of models was analyzed in Wang et al. (2014) (global climate

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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

. Tellus , 57A , 589 – 604 , doi: 10.1111/j.1600-0870.2005.00117.x . Camargo , S. J. , K. A. Emanuel , and A. H. Sobel , 2007 : Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis . J. Climate , 20 , 4819 – 4834 , doi: 10.1175/JCLI4282.1 . Cocke , S. , and T. E. LaRow , 2000 : Seasonal predictions using a regional spectral model embedded within a coupled ocean–atmospheric model . Mon. Wea. Rev. , 128 , 689 – 708 , doi: 10

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

WNP (e.g., Aiyyer and Thorncroft 2011 ). At this stage the reason why the importance of vertical wind shear differs between these two basins is unclear and needs further investigation. One possible explanation is that relative humidity is higher and has weaker gradients in the WNP than in the NA ( Fig. S5 in the supplemental material), making shear-induced drying/ventilation effects weaker in suppressing TCs in the former basin. c. Seasonal evolution of the ENSO effect on TC track density Above

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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

being integrated over multidecadal time scales ( Shaffrey et al. 2009 ; Strachan et al. 2013 ; Demory et al. 2014 ; Mizielinski et al. 2014 ). Many other groups are also progressing quickly in this direction, often using higher-resolution components of existing weather/seasonal forecasting or climate models (e.g., Zhao et al. 2009 ; Murakami and Sugi 2010 ; Wehner et al. 2010 ; Manganello et al. 2012 ; Rathmann et al. 2013 ; Bacmeister et al. 2013 ), as significant progress in model

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

displacement of hundreds of thousands of people, around 15 fatalities, and a recovery budget of 268.4 million (U.S. dollars; UNESCO 2015 ). The effects of climate change on TCs in the Southern Hemisphere have received relatively less attention in the scientific literature compared to some other regions of the world (most notably, the North Atlantic region) despite the vulnerability of the Southern Hemisphere. That a warming climate will likely result in a small decrease in overall TC frequency but a small

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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

WNP. In La Niña, the anomalous TC activity and large-scale circulations show almost a mirror image of the EP El Niño. There is substantial skill in predicting seasonal TC activity over various basins using dynamical models (e.g., Zhao et al. 2010 ; Chen and Lin 2011 , 2013 ; Vecchi et al. 2014 ). Although the interannual variability in some aspects of TC behavior could be estimated by the type of ENSO, the capability of global climate models (GCMs) in simulating the spatial distribution of the

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

test a number of different tropical cyclone genesis indices in this fashion. The indices differ in the predictors that are used. While our interest here is in the changes due to warming, our procedure also ensures that the indices capture the climatological spatial distribution and seasonal cycle of tropical cyclogenesis in the control simulation from which the index is derived. This feature is an important difference between our method and those involving statistical models, which are designed

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