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Wei Mei, Shang-Ping Xie, and Ming Zhao

calculated as the duration of TC tracks within each 8° × 8° grid within the study area of 108°W–0° and 0°–50°N during the NA hurricane season (i.e., 1 June–30 November). (We use this large grid to reduce the noise level; using a smaller grid, such as 5° × 5° or 6° × 6°, gives similar results.) The leading modes of variability in TC track density are extracted using an empirical orthogonal function (EOF) analysis. Linear correlation and regression analyses are applied to identify the SST pattern(s) and

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

. (1982) suggests that this mode is significant. Fig . 7. (a) Regression of low-pass-filtered SST anomalies (°C) on the PC of mode L2 from HiRAM simulations shown in Fig. 6c . Stippling indicates linear correlation coefficient exceeding 0.5. (b) As in (a), but for SLP (contours; hPa) and 850-hPa vorticity (shading; s −1 ). b. High-frequency variability of annual TC track density Applying EOF analysis to the high-frequency component of observed WNP TC track density depicts only one physically

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

, and easily reproducible procedure. Such a procedure allows the index to be rederived easily when new datasets become available for either the environmental fields or tropical cyclones or if new hypotheses about which environmental fields should be used as predictors are developed. The statistical method used is Poisson regression. The TCGI in Tippett et al. (2011) was constructed using the observed climatology of tropical cyclogenesis and large-scale variables from the 40-yr European Centre for

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James B. Elsner, Sarah E. Strazzo, Thomas H. Jagger, Timothy LaRow, and Ming Zhao

made using quantile regression ( Elsner et al. 2008 ); however, because the variation of sea surface temperature (SST) over time is rather small, it is difficult to obtain a precise value. Studies using paired values of intensity and SST ( Evans 1993 ; DeMaria and Kaplan 1994 ; Emanuel 2000 , 2007 ) might also be limited because most pairs come from hurricanes in environments that are less than dynamically optimal. Because a strong hurricane is more likely, on average, to be in a dynamically

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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

storms are more often “recurving.” In this study, we first want to verify that the characteristics of the observed tropical cyclone tracks [as discussed in Kossin et al. (2010) and shown in Fig. 1 ] are simulated by the climate models. Fig . 1. Observed North Atlantic tropical cyclone tracks, genesis locations, and landfall locations during the period 1950–2013 for HURDAT, as separated by the cluster analysis. This figure is similar to what was shown in Kossin et al. (2010) for the period 1950

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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

temperature (SST) anomalies in the tropical eastern Pacific, whereas CP El Niño or El Niño Modoki ( Ashok et al. 2007 ) is a nonconventional El Niño with the warmest SST anomalies in the tropical central Pacific. The zonal shift of the warm SST anomalies indicates a change in tropical heating and consequent changes in atmospheric response. A composite analysis of TC track density anomaly in Kim et al. (2009 , their Fig. 2) displays coherent weakening in TC activity over the Caribbean Sea, Gulf of Mexico

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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

depressions from our analysis). Because TCs in D5 have been characterized at a higher threshold (40 kt), we perform a correction to account for this small discrepancy in threshold wind speed. Observations show the global average number of TCs per year is 85.1 with a 35-kt threshold and 79.1 with a 40-kt threshold, giving a ratio of 1.08. We multiply the twentieth- and twenty-first-century D5 data in each basin by this ratio to roughly account for the stricter threshold. Note that this procedure has little

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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

Surface Temperature and Sea Ice Analysis (OSTIA; Donlon et al. 2012 ) daily SST and sea ice dataset, which has a native resolution of ° and is a synthesis of satellite and in situ observations covering from 1985 to the present day, where the period 1985–2008 is a reanalysis ( Roberts-Jones et al. 2012 ). The present climate (PC) simulations use this surface forcing, together with CMIP5 Atmospheric Model Intercomparison Project phase 2 (AMIP-II) standard forcings for aerosols and greenhouse gases

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Yohei Yamada and Masaki Satoh

-LWP become more abundant for the same intensity of TC under the warmer condition, as inferred from Figs. 1 – 4 . Fig . 5. Relationships between the minimum SLP and amount of the averaged (a) the IWP and (b) the LWP for one TC. Solid lines denote the regression lines. Black and red lines indicate the CTL and GW experiments, respectively. b. The contributions of TC on the environmental IWP and LWP The above analysis shows that both TC-IWP and TC-LWP generally increase under the GW condition. However, as

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