• Bell, G. D., and M. Chelliah, 2006: Leading tropical modes associated with interannual and multidecadal fluctuations in North Atlantic hurricane activity. J. Climate, 19, 590612, https://doi.org/10.1175/JCLI3659.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, G. D., and Coauthors, 2000: Climate assessment for 1999. Bull. Amer. Meteor. Soc., 81 (6), S1S50, https://doi.org/10.1175/1520-0477(2000)81[s1:CAF]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bister, M., and K. A. Emanuel, 1998: Dissipative heating and hurricane intensity. Meteor. Atmos. Phys., 65, 233240, https://doi.org/10.1007/BF01030791.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bister, M., and K. A. Emanuel, 2002: Low frequency variability of tropical cyclone potential intensity. 1. Interannual to interdecadal variability. J. Geophys. Res., 107, 4801, https://doi.org/10.1029/2001JD000776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484, 228232, https://doi.org/10.1038/nature10946.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bove, M. C., J. B. Elsner, C. W. Landsea, X. Niu, and J. J. O’Brien, 1998: Effect of El Niño on U.S. landfalling hurricanes, revisited. Bull. Amer. Meteor. Soc., 79, 24772482, https://doi.org/10.1175/1520-0477(1998)079<2477:EOENOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bruyère, C. L., G. J. Holland, and E. Towler, 2012: Investigating the use of a genesis potential index for tropical cyclones in the North Atlantic basin. J. Climate, 25, 86118626, https://doi.org/10.1175/JCLI-D-11-00619.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., and A. H. Sobel, 2005: Western North Pacific tropical cyclone intensity and ENSO. J. Climate, 18, 29963006, https://doi.org/10.1175/JCLI3457.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 48194834, https://doi.org/10.1175/JCLI4282.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. C. Wheeler, and A. H. Sobel, 2009: Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J. Atmos. Sci., 66, 30613074, https://doi.org/10.1175/2009JAS3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. Ting, and Y. Kushnir, 2013: Influence of local and remote SST on North Atlantic tropical cyclone potential intensity. Climate Dyn., 40, 15151529, https://doi.org/10.1007/s00382-012-1536-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, X., S. F. Chen, G. H. Chen, W. Chen, and R. G. Wu, 2015: On the weakened relationship between spring Arctic Oscillation and following summer tropical cyclone frequency over the western North Pacific: A comparison between 1968–1986 and 1989–2007. Adv. Atmos. Sci., 32, 13191328, https://doi.org/10.1007/s00376-015-4256-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P.-S., 2004: ENSO and tropical cyclone activity. Hurricanes and Typhoons: Past, Present, and Future, R. J. Murnane and K.-B. Liu, Eds., Columbia University Press, 297–332.

  • Chu, P.-S., and X. Zhao, 2004: Bayesian change-point analysis of tropical cyclone activity: The central North Pacific case. J. Climate, 17, 48934901, https://doi.org/10.1175/JCLI-3248.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 20762087, https://doi.org/10.1175/1520-0469(1996)053<2076:TEOVSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunstone, N. J., D. M. Smith, B. B. B. Booth, L. Hermanson, and R. Eade, 2013: Anthropogenic aerosol forcing of Atlantic tropical storms. Nat. Geosci., 6, 534539, https://doi.org/10.1038/ngeo1854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430, https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsner, J. B., 2003: Tracking hurricanes. Bull. Amer. Meteor. Soc., 84, 353356, https://doi.org/10.1175/BAMS-84-3-353.

  • Elsner, J. B., 2006: Evidence in support of the climate change–Atlantic hurricane hypothesis. Geophys. Res. Lett., 33, L16705, https://doi.org/10.1029/2006GL026869.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsner, J. B., X. Niu, and T. H. Jagger, 2004: Detecting shifts in hurricane rates using a Markov chain Monte Carlo approach. J. Climate, 17, 26522666, https://doi.org/10.1175/1520-0442(2004)017<2652:DSIHRU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1988: The maximum intensity of hurricanes. J. Atmos. Sci., 45, 11431155, https://doi.org/10.1175/1520-0469(1988)045<1143:TMIOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2000: A statistical analysis of tropical cyclone intensity. Mon. Wea. Rev., 128, 11391152, https://doi.org/10.1175/1520-0493(2000)128<1139:ASAOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686688, https://doi.org/10.1038/nature03906.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2007: Environmental factors affecting tropical cyclone power dissipation. J. Climate, 20, 54975509, https://doi.org/10.1175/2007JCLI1571.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2011: Global warming effects on U.S. hurricane damage. Wea. Climate Soc., 3, 261268, https://doi.org/10.1175/WCAS-D-11-00007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. USA, 110, 12 21912 224, https://doi.org/10.1073/pnas.1301293110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., and D. S. Nolan, 2004: Tropical cyclone activity and the global climate system. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 10A.2, https://ams.confex.com/ams/pdfpapers/75463.pdf.

  • Emanuel, K. A., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347367, https://doi.org/10.1175/BAMS-89-3-347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • England, M. H., and Coauthors, 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Climate Change, 4, 222227, https://doi.org/10.1038/nclimate2106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evan, A. T., 2012: Atlantic hurricane activity following two major volcanic eruptions. J. Geophys. Res., 117, D06101, https://doi.org/10.1029/2011JD016716.

    • Search Google Scholar
    • Export Citation
  • Fisher, R. A., 1921: On the “probable error” of a coefficient of correlation deduced from a small sample. Metron, 1, 332.

  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447462, https://doi.org/10.1002/qj.49710644905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nuñez, and W. M. Gray, 2001: The recent increase in Atlantic hurricane activity: Causes and implications. Science, 293, 474479, https://doi.org/10.1126/science.1060040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669700, https://doi.org/10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155–218.

  • Gray, W. M., J. D. Sheaffer, and C. W. Landsea, 1997: Climate trends associated with multi-decadal variability of Atlantic hurricane activity. Hurricanes: Climate and Socioeconomic Impacts, H. F. Diaz and R. S. Pulwarty, Eds., Springer, 15–53.

    • Crossref
    • Export Citation
  • Harada, Y., and Coauthors, 2016: The JRA-55 Reanalysis: Representation of atmospheric circulation and climate variability. J. Meteor. Soc. Japan, 94, 269302, https://doi.org/10.2151/jmsj.2016-015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., R. W. Lee, and L. Bengtsson, 2011: A comparison of extratropical cyclones in recent reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25. J. Climate, 24, 48884906, https://doi.org/10.1175/2011JCLI4097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, G. J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 25192541, https://doi.org/10.1175/1520-0469(1997)054<2519:TMPIOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the North Atlantic: Natural variability or climate trend? Philos. Trans. Roy. Soc. London, 365A, 26952716, https://doi.org/10.1098/rsta.2007.2083.

    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., P.-S. Chu, H. Murakami, and X. Zhao, 2014: An abrupt decrease in the late-season typhoon activity over the western North Pacific. J. Climate, 27, 42964312, https://doi.org/10.1175/JCLI-D-13-00417.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2015: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4). Part I: Upgrades and intercomparisons. J. Climate, 28, 911930, https://doi.org/10.1175/JCLI-D-14-00006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2016: Further exploring and quantifying uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). J. Climate, 29, 31193142, https://doi.org/10.1175/JCLI-D-15-0430.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., M. Zhao, and D. E. Waliser, 2012: Modulation of tropical cyclones over the eastern Pacific by the intraseasonal variability simulated in an AGCM. J. Climate, 25, 65246538, https://doi.org/10.1175/JCLI-D-11-00531.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, https://doi.org/10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and Coauthors, 2015: Possible artifacts of data biases in the recent global surface warming hiatus. Science, 348, 14691472, https://doi.org/10.1126/science.aaa5632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendall, M. G., 1975: Rank Correlation Methods. Charles Griffin, 202 pp.

  • Kim, H.-M., and P. J. Webster, 2010: Extended-range seasonal hurricane forecasts for the North Atlantic with a hybrid dynamical-statistical model. Geophys. Res. Lett., 37, L21705, https://doi.org/10.1029/2010GL044792.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2006: Trends in global tropical cyclone activity over the past twenty years (1986–2005). Geophys. Res. Lett., 33, L10805, https://doi.org/10.1029/2006GL025881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282293, https://doi.org/10.1175/2009JCLI2978.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2014: The Madden–Julian oscillation’s impacts on worldwide tropical cyclone activity. J. Climate, 27, 23172330, https://doi.org/10.1175/JCLI-D-13-00483.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., J. J. Sirutis, S. T. Garner, I. M. Held, and R. E. Tuleya, 2007: Simulation of the recent multidecadal increase of Atlantic hurricane activity using an 18-km-grid regional model. Bull. Amer. Meteor. Soc., 88, 15491565, https://doi.org/10.1175/BAMS-88-10-1549.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., J. J. Sirutis, S. T. Garner, G. A. Vecchi, and I. M. Held, 2008: Simulated reduction in Atlantic hurricane frequency under twenty-first-century warming conditions. Nat. Geosci., 1, 359364, https://doi.org/10.1038/ngeo202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163, https://doi.org/10.1038/ngeo779.

  • Kosaka, Y., and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403407, https://doi.org/10.1038/nature12534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., and D. J. Vimont, 2007: A more general framework for understanding Atlantic hurricane variability and trends. Bull. Amer. Meteor. Soc., 88, 17671781, https://doi.org/10.1175/BAMS-88-11-1767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. J. Climate, 23, 30573076, https://doi.org/10.1175/2010JCLI3497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., 1993: A climatology of intense (or major) Atlantic hurricanes. Mon. Wea. Rev., 121, 17031713, https://doi.org/10.1175/1520-0493(1993)121<1703:ACOIMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., B. A. Harper, K. Horau, and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones? Science, 313, 452454, https://doi.org/10.1126/science.1128448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., G. A. Vecchi, L. Bengtsson, and T. R. Knutson, 2010: Impact of duration thresholds on Atlantic tropical cyclone counts. J. Climate, 23, 25082519, https://doi.org/10.1175/2009JCLI3034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LaRow, T. E., L. Stefanova, D.-W. Shin, and S. Cocke, 2010: Seasonal Atlantic tropical cyclone hindcasting/forecasting using two sea surface temperature datasets. Geophys. Res. Lett., 37, L02804, https://doi.org/10.1029/2009GL041459.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Latif, M., N. Keenlyside, and J. Bader, 2007: Tropical sea surface temperature, vertical wind shear, and hurricane development. Geophys. Res. Lett., 34, L01710, https://doi.org/10.1029/2006GL027969.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, W., L. Li, and Y. Deng, 2015: Impact of the interdecadal Pacific oscillation on tropical cyclone activity in the North Atlantic and eastern North Pacific. Sci. Rep., 5, 12358, https://doi.org/10.1038/srep12358.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malkus, J. S., and H. Riehl, 1960: On the dynamics and energy transformations in steady-state hurricanes. Tellus, 12, 120, https://doi.org/10.3402/tellusa.v12i1.9351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and D. L. Hartmann, 2000: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Climate, 13, 14511460, https://doi.org/10.1175/1520-0442(2000)013<1451:MOENPH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and J. Shaman, 2008: Intraseasonal variability of the West African monsoon and Atlantic ITCZ. J. Climate, 21, 28982918, https://doi.org/10.1175/2007JCLI1999.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245259, https://doi.org/10.2307/1907187.

  • Mann, H. B., and D. R. Whitney, 1947: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 5060, https://doi.org/10.1214/aoms/1177730491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, Trans. Amer. Geophys. Union, 87, 233241, https://doi.org/10.1029/2006EO240001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsuno, T., 1966: Quasi-geostrophic motions in the equatorial area. J. Meteor. Soc. Japan, 44, 2543, https://doi.org/10.2151/jmsj1965.44.1_25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McBride, J. L., and R. Zehr, 1981: Observational analyses of tropical cyclone formation. Part II: Comparison of non-developing versus developing systems. J. Atmos. Sci., 38, 11321151, https://doi.org/10.1175/1520-0469(1981)038<1132:OAOTCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012: The impact of climate change on global tropical cyclone damage. Nat. Climate Change, 2, 205209, https://doi.org/10.1038/nclimate1357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menkes, C. E., M. Lengaigne, P. Marchesiello, N. C. Jourdain, E. M. Vincent, J. Lefèvre, F. Chauvin, and J.-F. Royer, 2012: Comparison of tropical cyclogenesis indices on seasonal to interannual timescales. Climate Dyn., 38, 301321, https://doi.org/10.1007/s00382-011-1126-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K. C., 2000: The association between intraseasonal oscillations and tropical storms in the Atlantic basin. Mon. Wea. Rev., 128, 40974107, https://doi.org/10.1175/1520-0493(2000)129<4097:TABIOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., T. Li, and P.-C. Hsu, 2014: Contributing factors to the recent high level of accumulated cyclone energy (ACE) and power dissipation index (PDI) in the North Atlantic. J. Climate, 27, 30233034, https://doi.org/10.1175/JCLI-D-13-00394.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., E. D. Rappin, and K. A. Emanuel, 2007: Tropical cyclogenesis sensitivity to environmental parameters in radiative–convective equilibrium. Quart. J. Roy. Meteor. Soc., 133, 20852107, https://doi.org/10.1002/qj.170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peduzzi, P., B. Chatenoux, H. Dao, A. De Bono, C. Herold, J. Kossin, F. Mouton, and O. Nordbeck, 2012: Global trends in tropical cyclone risk. Nat. Climate Change, 2, 289294, https://doi.org/10.1038/nclimate1410.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., Jr., and C. W. Landsea, 1998: Normalized hurricane damages in the United States: 1925–95. Wea. Forecasting, 13, 621631, https://doi.org/10.1175/1520-0434(1998)013<0621:NHDITU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, M. A., and A. R. Harris, 1997: Statistical evidence links exceptional 1995 Atlantic hurricane season to record sea warming. Geophys. Res. Lett., 24, 12551258, https://doi.org/10.1029/97GL01164.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schade, L. R., 2000: Tropical cyclone intensity and sea surface temperature. J. Atmos. Sci., 57, 31223130, https://doi.org/10.1175/1520-0469(2000)057<3122:TCIASS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, W., R. E. Tuleya, and I. A. Ginis, 2000: A sensitivity study of the thermodynamic environment on GFDL model hurricane intensity: Implications for global warming. J. Climate, 13, 109121, https://doi.org/10.1175/1520-0442(2000)013<0109:ASSOTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., S. M. Uppala, and D. P. Dee, 2007: Update on ERA Interim. ECMWF Newsletter, No. 111, ECMWF, Reading, United Kingdom, 5.

  • Smirnov, D., and D. J. Vimont, 2011: Variability of the Atlantic meridional mode during the Atlantic hurricane season. J. Climate, 24, 14091424, https://doi.org/10.1175/2010JCLI3549.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 22832296, https://doi.org/10.1175/2007JCLI2100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A. H., I. M. Held, and C. S. Bretherton, 2002: The ENSO signal in tropical tropospheric temperature. J. Climate, 15, 27022706, https://doi.org/10.1175/1520-0442(2002)015<2702:TESITT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A. H., S. J. Camargo, T. M. Hall, C.-Y. Lee, M. K. Tippett, and A. A. Wing, 2016: Human influence on tropical cyclone intensity. Science, 353, 242246, https://doi.org/10.1126/science.aaf6574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century SST trends in the North Atlantic. J. Climate, 22, 14691481, https://doi.org/10.1175/2008JCLI2561.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ting, M., S. J. Camargo, C. Li, and Y. Kushnir, 2015: Natural and forced North Atlantic hurricane potential intensity change in CMIP5 models. J. Climate, 28, 39263942, https://doi.org/10.1175/JCLI-D-14-00520.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., S. J. Camargo, and A. H. Sobel, 2011: A Poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis. J. Climate, 24, 23352357, https://doi.org/10.1175/2010JCLI3811.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. T. Fasullo, 2013: An apparent hiatus in global warming? Earth’s Future, 1, 1932, https://doi.org/10.1002/2013EF000165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tu, J.-Y., C. Chou, and P.-S. Chu, 2009: The abrupt shift of typhoon activity in the vicinity of Taiwan and its association with western North Pacific–East Asian climate change. J. Climate, 22, 36173628, https://doi.org/10.1175/2009JCLI2411.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and B. J. Soden, 2007: Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature, 450, 10661070, https://doi.org/10.1038/nature06423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I. M. Held, and R. Gudgel, 2011: Statistical–dynamical predictions of seasonal North Atlantic hurricane activity. Mon. Wea. Rev., 139, 10701082, https://doi.org/10.1175/2010MWR3499.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., T. L. Olander, and S. Wanzong, 1998: The impact of multispectral GOES-8 wind information on the Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description, and case analysis. Mon. Wea. Rev., 126, 12021218, https://doi.org/10.1175/1520-0493(1998)126<1202:TIOMGW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 11951210, https://doi.org/10.1175/BAMS-87-9-1195.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vergados, P., A. J. Mannucci, C. O. Ao, J. H. Jiang, and H. Su, 2015: On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data set. Atmos. Meas. Tech., 8, 17891797, https://doi.org/10.5194/amt-8-1789-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., and G. A. Vecchi, 2012a: Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models. Nat. Climate Change, 2, 604607, https://doi.org/10.1038/nclimate1530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., and G. A. Vecchi, 2012b: North Atlantic power dissipation index (PDI) and accumulated cyclone energy (ACE): Statistical modeling and sensitivity to sea surface temperature changes. J. Climate, 25, 625637, https://doi.org/10.1175/JCLI-D-11-00146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., and G. A. Vecchi, 2013: Projected increases in North Atlantic tropical cyclone intensity from CMIP5 models. J. Climate, 26, 32313240, https://doi.org/10.1175/JCLI-D-12-00441.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., G. A. Vecchi, T. R. Knutson, and J. A. Smith, 2011: Is the recorded increase in short-duration North Atlantic tropical storms spurious? J. Geophys. Res., 116, D10114, https://doi.org/10.1029/2010JD015493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, K. J. E., and Coauthors, 2015: Hurricane and climate: The U.S. CLIVAR Working Group on hurricanes. Bull. Amer. Meteor. Soc., 96, 9971017, https://doi.org/10.1175/BAMS-D-13-00242.1; Corrigendum, 96, 1140, https://doi.org/10.1175/BAMS-D-15-00232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., S. Dong, A. T. Evan, G. R. Foltz, and S.-K. Lee, 2012: Multidecadal covariability of North Atlantic sea surface temperature, African dust, Sahel rainfall, and Atlantic hurricanes. J. Climate, 25, 54045415, https://doi.org/10.1175/JCLI-D-11-00413.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., J.-K. E. Schemm, A. Kumar, W. Wang, L. Long, M. Chelliah, G. D. Bell, and P. Peng, 2009: A statistical forecast model for Atlantic seasonal hurricane activity based on the NCEP dynamical seasonal forecast. J. Climate, 22, 44814500, https://doi.org/10.1175/2009JCLI2753.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, R., and L. Wu, 2013: Climate changes of Atlantic tropical cyclone formation derived from twentieth-century reanalysis. J. Climate, 26, 89959005, https://doi.org/10.1175/JCLI-D-13-00056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waters, J. J., J. L. Evans, and C. E. Forest, 2012: Large-scale diagnostics of tropical cyclogenesis potential using environment variability metrics and logistic regression models. J. Climate, 25, 60926107, https://doi.org/10.1175/JCLI-D-11-00359.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., G. J. Holland, J. A. Curry, and H.-R. Chang, 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 18441846, https://doi.org/10.1126/science.1116448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilcoxon, F., 1945: Individual comparisons by ranking methods. Biom. Bull., 1, 8083, https://doi.org/10.2307/3001968.

  • Wu, L., 2007: Impact of Saharan air layer on hurricane peak intensity. Geophys. Res. Lett., 34, L09802, https://doi.org/10.1029/2007GL029564.

  • Wu, L., and H. Zhao, 2012: Dynamically derived tropical cyclone intensity changes over the western North Pacific. J. Climate, 25, 8998, https://doi.org/10.1175/2011JCLI4139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, L., B. Wang, and S. A. Braun, 2008: Implications of tropical cyclone power dissipation index. Int. J. Climatol., 28, 727731, https://doi.org/10.1002/joc.1573.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiang, B., B. Wang, and T. Li, 2013: A new paradigm for the predominance of standing central Pacific warming after the late 1990s. Climate Dyn., 41, 327340, https://doi.org/10.1007/s00382-012-1427-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, L., T. Yan, L. J. Pietrafesa, J. M. Morrison, and T. Karl, 2005: Climatology and interannual variability of North Atlantic hurricane tracks. J. Climate, 18, 53705381, https://doi.org/10.1175/JCLI3560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., H. Xu, W. S. Kessler, and M. Nonaka, 2005: Air–sea interaction over the eastern Pacific warm pool: Gap winds, thermocline dome, and atmospheric convection. J. Climate, 18, 520, https://doi.org/10.1175/JCLI-3249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., Y. Zou, S. T. Kim, and T. Lee, 2012: The changing impact of El Niño on US winter temperatures. Geophys. Res. Lett., 39, L15702, https://doi.org/10.1029/2012GL052483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., H. Paek, E. S. Saltzman, and T. Lee, 2015a: The early 1990s change in ENSO–PSA–SAM relationships and its impact on Southern Hemisphere climate. J. Climate, 28, 93939408, https://doi.org/10.1175/JCLI-D-15-0335.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., P.-K. Kao, H. Paek, H.-H. Hsu, C.-W. Hung, M.-M. Lu, and S.-I. An, 2015b: Linking emergence of the central Pacific El Niño to the Atlantic multidecadal oscillation. J. Climate, 28, 651662, https://doi.org/10.1175/JCLI-D-14-00347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, R., and T. L. Delworth, 2006: Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett., 33, L17712, https://doi.org/10.1029/2006GL026267.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, R., and Coauthors, 2013: Have aerosols caused the observed Atlantic multidecadal variability? J. Atmos. Sci., 70, 11351144, https://doi.org/10.1175/JAS-D-12-0331.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., and G. B. Raga, 2015: On the distinct interannual variability of tropical cyclone activity over the eastern North Pacific. Atmósfera, 28, 161178, https://doi.org/10.20937/ATM.2015.28.03.02.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., and C. Wang, 2016: Interdecadal modulation on the relationship between ENSO and typhoon activity during the late season in the western North Pacific. Climate Dyn., 47, 315328, https://doi.org/10.1007/s00382-015-2837-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., L. Wu, and W. Zhou, 2011: Interannual changes of tropical cyclone intensity in the western North Pacific. J. Meteor. Soc. Japan, 89, 243253, https://doi.org/10.2151/jmsj.2011-305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., P.-S. Chu, P.-C. Hsu, and H. Murakami, 2014: Exploratory analysis of extremely low tropical cyclone activity during the late-season of 2010 and 1998 over the western North Pacific and the South China Sea. J. Adv. Model. Earth Syst., 6, 11411153, https://doi.org/10.1002/2014MS000381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., X. Jiang, and L. Wu, 2015a: Modulation of northwest Pacific tropical cyclone genesis by the intraseasonal variability. J. Meteor. Soc. Japan, 93, 8197, https://doi.org/10.2151/jmsj.2015-006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., R. Yoshida, and G. B. Raga, 2015b: Impact of the Madden–Julian oscillation on western North Pacific tropical cyclogenesis associated with large-scale patterns. J. Appl. Meteor. Climatol., 54, 14131429, https://doi.org/10.1175/JAMC-D-14-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and I. M. Held, 2012: TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late-twenty-first century. J. Climate, 25, 29953009, https://doi.org/10.1175/JCLI-D-11-00313.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 66536678, https://doi.org/10.1175/2009JCLI3049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, X., and P.-S. Chu, 2010: Bayesian changepoint analysis for extreme events (typhoons, heavy rainfall, and heat waves): An RJMCMC approach. J. Climate, 23, 10341046, https://doi.org/10.1175/2009JCLI2597.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, B., and X. Cui, 2014: Interdecadal change of the linkage between the North Atlantic Oscillation and the tropical cyclone frequency over the western North Pacific. China Earth Sci., 57, 21482155, https://doi.org/10.1007/s11430-014-4862-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, Y., J.-Y. Yu, T. Lee, M.-M. Lu, and S. T. Kim, 2014: CMIP5 model simulations of the impacts of the two types of El Niño on the U.S. winter temperature. J. Geophys. Res. Atmos., 119, 30763092, https://doi.org/10.1002/2013JD021064.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    Time series of June–October 600-hPa relative humidity for five reanalysis datasets (NCEP-1, NCEP-2, JRA-55, ERA-Interim, and MERRA) over the NATL MDR (5°–25°N, 75°–15°W).

  • View in gallery

    (a) Time series of the entire season (January–December) tropical cyclone count in the NATL basin during the period of 1970–2014. Horizontal black lines denote the means for the periods 1970–94 and 1995–2011, respectively. (b) Posterior probability for each candidate hypothesis under which there presumably exist a certain number of abrupt shifts in event series. (c) Posterior probability mass function (PMF) for the years of the changepoint under the hypothesis of a single changing point. (d)–(f) As in (a)–(c), but for the peak season (July–October).

  • View in gallery

    The occurrence of TC genesis over NATL basin during (a) P2 (1995–2014) and (b) P1 (1970–2014) and (c) their difference (P2 minus P1). (d)–(f) As in (a)–(c) but for track density over the NATL basin. The plus signs indicate that the difference is significant at a 95% confidence level. The dashed box corresponds to the MDR (5°–25°N, 75°–15°W).

  • View in gallery

    Time series of the normalized July–October TC frequency (black) and the GPI calculated with JRA-55 (blue) and with NCEP-1 (green) datasets during 1970–2014 over the NATL MDR (5°–25°N, 75°–15°W). The GPI follows Emanuel and Nolan (2004). The correlations between GPI using the JRA-55 (NCEP-1) and TC frequency are 0.38 (0.68), 0.78 (0.80), and 0.71 (0.67) during the P1 (1970–94), P2 (1995–2014), and the whole period (1970–2014), respectively.

  • View in gallery

    (a) Spatial distribution of the difference (P2 minus P1) of GPI-Total and (b) the difference (P2 minus P1) for the GPI-Total, GPI-PI, GPI-Shear, GPI-VOR, and GPI-RH during the peak TC season over the NATL MDR (5°–25°N, 75°–15°W) plotted by the dashed line. The GPI developed by Emanuel and Nolan (2004) uses the NCEP-1 dataset.

  • View in gallery

    (a) Difference (P2 minus P1) for the GPI-Total, GPI-PI, GPI-Shear, GPI-VOR, and GPI-RH during July–October over the NATL MDR (5°–25°N, 75°–15°W). (b) Correlation of TC frequency and basinwide-averaged GPI-Total, GPI with varying factors including GPI-RH, GPI-VOR, GPI-PI, and GPI-Shear over the NATL MDR during the two subperiods (P1 and P2) and the whole period (1970–2014). The GPI developed by Emanuel and Nolan (2004) was used, and the computation of this GPI uses the JRA-55 dataset.

  • View in gallery

    (a) Correlation of TC frequency and basinwide-averaged GPI-Total and GPI with varying factors including GPI-RH, GPI-VOR, GPI-PI, and GPI-Shear over the NATL MDR (5°–25°N, 75°–15°W) during the two subperiods (P1 and P2) and the whole period (1970–2014). (b) As in (a), but for the large-scale factors including 600-hPa relative humidity, 850-hPa relative vorticity, vertical wind shear, and local NATL SST. Correlation coefficients marked with two asterisks are significant at a 95% confidence level. The GPI developed by Emanuel and Nolan (2004) was used, and the computation of this GPI uses the NCEP-1 dataset.

  • View in gallery

    Correlation of TC frequency and GPI with varying factors (i.e., dynamic term, with varying shear and 850-hPa vorticity; shear term, only with varying shear; vorticity term, only with varying 850-hPa vorticity; thermal term, with varying 600-hPa relative humidity and PI; humidity term, only with varying 600-hPa relative humidity; and PI term, only with varying potential intensity) over the NATL MDR (5°–25°N, 75°–15°W) using the (a) JRA-55, (b) NCEP-1, (c) NCEP-2, (d) MERRA, and (e) ERA-Interim datasets. Correlation coefficients indicated in blue (red) are significant at a 95% confidence level during 1979–94 (1995–2014). The GPI developed by Emanuel and Nolan (2004) is used.

  • View in gallery

    As in Fig. 8, but using the GPI developed by Tippett et al. (2011).

  • View in gallery

    Correlation of TC frequency and GPI with varying factors (i.e., shear term, with varying shear and PI terms, with varying potential intensity) over the NATL MDR (5°–25°N, 75°–15°W) using the (a) JRA-55, (b) NCEP-1, (c) NCEP-2, (d) MERRA, and (e) ERA-Interim datasets. Correlation coefficients indicated in blue (red) are significant at a 95% confidence level during 1979–94 (1995–2014). The GPI developed by Bruyère et al. (2012) is used.

  • View in gallery

    The composite SSTA (shading) and 850-hPa streamfunction relative to the climatological value (1970–2014) during (a) P2 and (b) P1.

  • View in gallery

    Correlation map between the TC frequency over the NATL basin and global SSTA averaged from July to October during (a) P2 and (b) P1. The plus signs indicate correlation significant at a 95% confidence level.

  • View in gallery

    Correlation of large-scale factors averaged over the NATL MDR (5°–25°N, 75°–15°W) and TC frequency during the two subperiods and the (a) Niño-3.4 index, (b) local WNP SST, and (c) local NATL SST. Correlation coefficients marked with two asterisks are significant at a 95% confidence level.

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Potential Large-Scale Forcing Mechanisms Driving Enhanced North Atlantic Tropical Cyclone Activity since the Mid-1990s

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  • 1 Key Laboratory of Meteorological Disaster, Ministry of Education, and Joint International Research Laboratory of Climate and Environment Change, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, and Pacific Typhoon Research Center, and Earth System Modeling Center, Nanjing University of Information Science and Technology, Nanjing, China
  • 2 Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
  • 3 Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Mexico City, Mexico
  • 4 Department of Geosciences, University of Missouri–Kansas City, Kansas City, Missouri
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Abstract

A significant increase of tropical cyclone (TC) frequency is observed over the North Atlantic (NATL) basin during the recent decades (1995–2014). In this study, the changes in large-scale controls of the NATL TC activity are compared between two periods, one before and one since 1995, when a regime change is observed. The results herein suggest that the significantly enhanced NATL TC frequency is related mainly to the combined effect of changes in the magnitudes of large-scale atmospheric and oceanic factors and their association with TC frequency. Interdecadal changes in the role of vertical wind shear and local sea surface temperatures (SSTs) over the NATL appear to be two important contributors to the recent increase of NATL TC frequency. Low-level vorticity plays a relatively weak role in the recent increase of TC frequency. These changes in the role of large-scale factors largely depend on interdecadal changes of tropical SST anomalies (SSTAs). Enhanced low-level westerlies to the east of the positive SSTAs have been observed over the tropical Atlantic since 1995, with a pattern nearly opposite to that seen before 1995. Moreover, the large-scale contributors to the NATL TC frequency increase since 1995 are likely related to both local and remote SSTAs. Quantification of the impacts of local and remote SSTAs on the increase of TC frequency over the NATL basin and the physical mechanisms require numerical simulations and further observational analyses.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Haikun Zhao, zhk2004y@gmail.com

Abstract

A significant increase of tropical cyclone (TC) frequency is observed over the North Atlantic (NATL) basin during the recent decades (1995–2014). In this study, the changes in large-scale controls of the NATL TC activity are compared between two periods, one before and one since 1995, when a regime change is observed. The results herein suggest that the significantly enhanced NATL TC frequency is related mainly to the combined effect of changes in the magnitudes of large-scale atmospheric and oceanic factors and their association with TC frequency. Interdecadal changes in the role of vertical wind shear and local sea surface temperatures (SSTs) over the NATL appear to be two important contributors to the recent increase of NATL TC frequency. Low-level vorticity plays a relatively weak role in the recent increase of TC frequency. These changes in the role of large-scale factors largely depend on interdecadal changes of tropical SST anomalies (SSTAs). Enhanced low-level westerlies to the east of the positive SSTAs have been observed over the tropical Atlantic since 1995, with a pattern nearly opposite to that seen before 1995. Moreover, the large-scale contributors to the NATL TC frequency increase since 1995 are likely related to both local and remote SSTAs. Quantification of the impacts of local and remote SSTAs on the increase of TC frequency over the NATL basin and the physical mechanisms require numerical simulations and further observational analyses.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Haikun Zhao, zhk2004y@gmail.com

1. Introduction

Tropical cyclones (TCs) are among the most devastating weather events around the globe. The North Atlantic (NATL) basin experiences on average about 12 TCs (of tropical storm and higher intensity) per year (Chu 2004). TCs forming over the NATL basin often affect the United States Atlantic and Gulf coasts, Mexico, Central America, the Caribbean islands, and Bermuda, and can lead to substantial loss of life and property damage (Pielke and Landsea 1998). The damage associated with TC activity has shown a significant increase during recent decades (Mendelsohn et al. 2012; Peduzzi et al. 2012), which has in turn raised concerns about the relationship between climate change and TC activity. Therefore, better understanding of changes of TC activity over the NATL basin and the associated mechanisms has a profound socioeconomic impact and scientific significance (Emanuel 2011; Mendelsohn et al. 2012).

It is well known that TC activity depends on regional thermodynamic and dynamic conditions (Gray 1968; McBride and Zehr 1981; DeMaria 1996; Camargo et al. 2007; Nolan et al. 2007). Higher sea surface temperatures (SSTs), less vertical wind shear, higher midlevel relative humidity, and larger low-level relative vorticity have been widely documented as favorable large-scale controls of TC genesis and intensification. Over the NATL basin, these large-scale conditions can be significantly modulated by various climate modes including intraseasonal oscillations such as the Madden–Julian oscillation (Maloney and Hartmann 2000; Mo 2000; Maloney and Shaman 2008; Camargo et al. 2009; Klotzbach 2010, 2014), the Atlantic meridional mode (AMM) (Kossin and Vimont 2007; Smirnov and Vimont 2011), El Niño–Southern Oscillation (ENSO) (Bove et al. 1998; Elsner 2003; Smith et al. 2008; Camargo et al. 2009), the interdecadal Pacific oscillation (IPO) (Li et al. 2015), the North Atlantic Oscillation (NAO) (L. Xie et al. 2005), and the Atlantic multidecadal oscillation (AMO) (Wang and Wu 2013). Most of these studies have focused on the changes of large-scale controls of NATL TC activity and possible qualitative explanations were given for the corresponding changes. However, quantitative analyses on how these changes in large-scale factors affect TC activity remain relatively less studied.

Using the genesis potential index (GPI) developed by Emanuel and Nolan (2004), Camargo et al. (2007, 2009) investigated the relative importance of environmental factors in controlling TC genesis frequency over the global oceans at the interannual and intraseasonal time scales. They showed that over the NATL basin, midlevel relative humidity and vertical wind shear were important to reductions in genesis during El Niño years. On the intraseasonal time scale, midlevel relative humidity remains a primary contributor, followed by low-level absolute vorticity with weak contributions from vertical wind shear and potential intensity (PI), which is different from what is found at the interannual time scale. It may be plausible that the role of large-scale controls of NATL TC activity is modulated by different time scales. Associated with the well-documented significant increase of NATL TC frequency since the mid-1990s (Goldenberg et al. 2001; Elsner et al. 2004; Emanuel 2005, 2013; Webster et al. 2005; Bell and Chelliah 2006; Klotzbach 2006; Kossin and Vimont 2007; Knutson et al. 2007, 2008, 2010; Holland and Webster 2007; Emanuel et al. 2008; LaRow et al. 2010; Wang et al. 2009; Zhao et al. 2009; Vecchi et al. 2011; Villarini and Vecchi 2012a,b, 2013; Murakami et al. 2014), a question naturally arises: is there a difference in the role of environmental factors affecting NATL TC genesis before and after the mid-1990s?

The hiatus in the global warming trend in recent decades has garnered extensive attention (Kosaka and Xie 2013; Trenberth and Fasullo 2013; Xiang et al. 2013; England et al. 2014; Karl et al. 2015). Associated with the hiatus, extreme weather events in many regions have shown significant changes. Studies have suggested that the relationships at interannual time scales have experienced an interdecadal change. Zhao and Wang (2016) found that an interdecadal change of the interannual relationship between the Pacific decadal oscillation (PDO) and ENSO in 1998 had a strong impact on the abrupt shift in late season (i.e., October–December) TC activity over the western North Pacific (WNP). Similarly, the interannual relationships of WNP TC frequency with the NAO (Zhou and Cui 2014) and the Arctic Oscillation (AO) (Cao et al. 2015) have experienced a significant interdecadal shift. However, the changes of the interannual relationship between TC frequency over the NATL basin and large-scale factors have been relatively less studied. Interestingly, the significant change in the NATL TC frequency after the mid-1990s coincides with the global warming hiatus (Gray et al. 1997). Another question naturally arises: Has the climate regime shift changed the interannual relationship between large-scale factors and NATL TC frequency?

Motivated by the aforementioned discussion, this study investigates the individual roles of environmental factors contributing to the significant increase in NATL TC frequency since the mid-1990s, and examines whether the interannual relationship between the environmental factors and NATL TC frequency is modulated by the climate regime shift and, if so, then explores the underlying physical mechanisms. The rest of this paper is arranged as follows. Section 2 presents the datasets and methods. The general characteristics of NATL TC activity during the two subperiods considered (before and since 1995) are shown in section 3. Section 4 investigates the changes of the role of the environmental factors in contributing to the significant increase of NATL TC frequency since 1995. The interdecadal changes of the interannual relationship between the large-scale factors and NATL TC frequency are shown in section 5. The associated plausible physical causes are investigated in section 6, followed by a summary in section 7.

2. Data and methods

a. TC data

The TC data are obtained from the U.S. best-track hurricane database (HURDAT2) (Landsea and Franklin 2013) through the National Hurricane Center. It includes the position and maximum sustained wind speed of each TC every 6 h (available online from http://www.nhc.noaa.gov/data/#hurdat). The HURDAT2 TC intensity estimates in the Atlantic since the late 1960s are a blend of satellite estimates, aircraft reconnaissance, and other in situ observations (Dvorak 1975; Emanuel 2005; Velden et al. 2006). Although continuous aircraft reconnaissance has occurred over the NATL basin, substantial changes have occurred in the manner in which the Dvorak technique has been applied (Velden et al. 1998, 2006), introducing uncertainty in the TC intensity data (Landsea 1993; Emanuel 2005). In addition, Landsea (1993) and Emanuel (2005) pointed out the need to adjust TC intensity before 1970 in order to understand the trends in TC over the NATL basin. Because of these limitations, we deliberately limit this study to the period 1970–2014. The occurrences of TC genesis and track are counted for each 2.5° × 2.5° grid box over the NATL basin. The TC genesis location is defined as the first position at which the maximum sustained winds reach 34 kt (1 kt ≈ 0.51 m s−1). The annual frequency of TC occurrence is used for representing the TC track pattern.

As suggested in previous studies (Goldenberg et al. 2001; Emanuel 2005; Wu 2007; Holland and Webster 2007; Kossin et al. 2010; Wu and Zhao 2012; Zhao and Raga 2015), we use several TC intensity indices to compare TC activity before and after the shift identified in this study. The selected indices are accumulated cyclone energy (ACE) (Bell et al. 2000), the power dissipation index (PDI) (Emanuel 2005), peak intensity, average intensity, and number of category 3–5 TCs (based on the Saffir–Simpson hurricane categories with winds ≥96 kt; http://www.nhc.noaa.gov/aboutsshs.shtml). Here, the average intensity is defined by averaging the maximum wind speed over the lifetime for each TC and then for all of the TCs. The peak intensity is obtained by averaging the peak intensity over the lifetime for each TC and then for all of the TCs.

b. Atmospheric fields and SST data

The monthly mean SST is obtained from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST, version 4 (ERSST.v4), data at a horizontal resolution of 2° × 2° (Huang et al. 2015, 2016).

The atmospheric fields used in this study—winds, relative humidity, air temperature, specific humidity, and surface pressure—are obtained from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) monthly reanalysis (NCEP-1) dataset on a 2.5° × 2.5° grid (Kalnay et al. 1996). To confirm the robustness of results presented in this manuscript, four other reanalysis datasets are used: the Japanese 55-year Reanalysis Project (JRA-55) available since 1958 (Harada et al. 2016), the NCEP–DOE AMIP-II reanalysis (NCEP-2) available since 1979 (Kanamitsu et al. 2002), the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) available since 1979 (Rienecker et al. 2011), and the ECMWF interim reanalysis (ERA-Interim) available since 1979 (Simmons et al. 2007). The atmospheric fields will be analyzed within the main development region (MDR) of the NATL basin, delimited by 5°–25°N, 75°–15°W. In particular, the quality of the midlevel relative humidity field from NCEP-1 has been called into question (Hodges et al. 2011; Vergados et al. 2015), so using a variety of reanalysis datasets provides confidence on the results on the role of interdecadal change of midlevel relative humidity. For example, Fig. 1 shows the time series of the relative humidity at 600 hPa averaged over the MDR for the TC season for each of the reanalysis datasets mentioned, highlighting the differences in the overall trends and the interannual variability. Furthermore, the correlation coefficients between midlevel relative humidity averaged over the MDR and the TC frequency again highlight the difference in correlation between the different reanalysis datasets. The trends and correlations calculated using JRA-55, ERA-Interim, and MERRA are consistent and depart from results using NCEP-1 and NCEP-2 (not shown).

Fig. 1.
Fig. 1.

Time series of June–October 600-hPa relative humidity for five reanalysis datasets (NCEP-1, NCEP-2, JRA-55, ERA-Interim, and MERRA) over the NATL MDR (5°–25°N, 75°–15°W).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

c. Diagnostic tool to assess the role of environmental factors

The GPI is used as a diagnostic tool to assess the relative importance of the large-scale controls of NATL TC activity in contributing to TC genesis. The GPI was originally developed by Emanuel and Nolan (2004) with the following expression:
e1
where ξ is the 850-hPa absolute vorticity (s−1), H is the 600-hPa relative humidity (%), Vpot is the PI (m s−1), providing an upper bound of TC intensity (Bister and Emanuel 2002), and Vshear is the vertical wind shear, computed as the magnitude of the vector difference between 850 and 200 hPa (m s−1).

Following Camargo et al. (2007), in order to further investigate the separate role of each of the four factors included in the GPI in determining the difference of TC genesis potential before and after the changepoint year determined in this study, the GPI is calculated by varying one variable at a time and keeping the other three variables at their climatological values (1970–2014). The terms GPI-RH, GPI-VOR, GPI-Shear, and GPI-PI indicate the distinct contribution of the midlevel relative humidity, low-level absolute vorticity, vertical wind shear, and PI, respectively. The GPI with all four varying variables is referred to as GPI-Total. Although the net anomaly cannot be described as the sum of the contributions from the four factors included in the GPI due to its nonlinearity, the index can provide an adequate quantification of the role of each of the different factors. This methodology has been widely adopted in a number of previous studies (Camargo et al. 2009; Jiang et al. 2012; Zhao et al. 2014, 2015a,b).

To confirm the robustness of the relative importance of large-scale factors based on the analyses of GPI from Emanuel and Nolan (2004), two additional genesis indices from Tippett et al. (2011) and Bruyère et al. (2012) are used. Tippett et al. (2011) constructed a GPI for TC genesis using a Poisson regression between the observed climatology of TC genesis and large-scale climate variables, which comprised four variables including clipped low-level absolute vorticity, midlevel relative humidity, relative SST, and vertical wind shear. The relative SST is the difference between the local SST and the mean tropical SST from 20°S to 20°N. The GPI from Bruyère et al. (2012) comprises only potential intensity and vertical wind shear. Detailed information on these two GPIs can be found in Tippett et al. (2011), Bruyère et al. (2012), and Menkes et al. (2012).

d. Detection of the changepoint and statistical significance

The Bayesian changepoint analysis approach proposed by Chu and Zhao (2004) is applied to the time series of TC counts to detect any changepoints in NATL TC frequency during the period of 1970–2014. In this approach, the annual TC frequency is considered as a discrete Poisson process. The only parameter is the Poisson intensity, which is codified by a conjugate gamma distribution. Rather than a deterministic estimation of the changepoint, the Bayesian inference provides the probability estimate of the shifts. Such changepoint analysis has been extensively adopted in several previous studies (Tu et al. 2009; Zhao and Chu 2010; Hsu et al. 2014; Zhao et al. 2014; Zhao and Wang 2016). Details of this methodology can be found in Chu and Zhao (2004) and Zhao and Chu (2010).

Once a changepoint is identified, the resulting two subperiods (i.e., before and after the changepoint) are then considered for further analyses. The Student’s t test is typically used to test the statistical significance with the assumption that two random variables fit a Gaussian distribution. However, the assumption that the samples fit a Gaussian distribution may not be valid because of the relatively small sample size in our 45-yr analysis. Thus, instead of the Student’s t test, the nonparametric Mann–Kendall test (Mann 1945; Kendall 1975) and the Wilcoxon–Mann–Whitney test (Wilcoxon 1945; Mann and Whitney 1947) are used to evaluate the significance of the correlations and differences between the two subperiods.

3. Significant increase of NATL TC activity since the mid-1990s

Figure 2a shows the annual (i.e., January–December) TC frequency over the NATL basin during the period of 1970–2014. While an increasing linear trend can be fitted to this time series, note that there is a change in the series evident since 1995. The average TC frequency for 1970–94 is about 9 TCs per year, significantly smaller than the roughly15 TCs per year for 1995–2014 (Fig. 2a and Table 1). Bayesian changepoint analysis applied to the time series of the NATL annual TC frequency over the whole period 1970–2014 clearly identifies the year 1995 (Figs. 2b,c) as a changepoint year, corresponding to a regime shift. The terms “changepoint” and “regime shift” will be used interchangeably in the remainder of the text to refer to the changes that are observed in 1995.

Fig. 2.
Fig. 2.

(a) Time series of the entire season (January–December) tropical cyclone count in the NATL basin during the period of 1970–2014. Horizontal black lines denote the means for the periods 1970–94 and 1995–2011, respectively. (b) Posterior probability for each candidate hypothesis under which there presumably exist a certain number of abrupt shifts in event series. (c) Posterior probability mass function (PMF) for the years of the changepoint under the hypothesis of a single changing point. (d)–(f) As in (a)–(c), but for the peak season (July–October).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

Table 1.

Monthly mean of NATL TC frequency over the TC season from June to November during P1 (1970–94) and P2 (1995–2014), and the differences between the two periods (P2 − P1) and the p value. An asterisk (no asterisk) indicates that the changes are statistically significant (nonsignificant) at the 95% confidence level based on the nonparametric Wilcoxon–Mann–Whitney rank sum test.

Table 1.

To examine whether the shift of NATL TC frequency occurs in a specific month of the hurricane season from June to November, the TC frequency during different months is compared before and after the changepoint, that is, during the first subperiod (1970–94) and the second subperiod (1995–2014). It is worth noting that most TCs form between July and October, and those that form outside this period do not typically form in the MDR of the NATL basin, where a significant difference of TC genesis frequency before and since 1995 can be clearly observed (Fig. 2c). Therefore, we would not expect these TCs to be as closely correlated with the regime shift from the inactive to the active period. Indeed, it is readily observed that the increase in the annual TC frequency since 1995 is mainly due to a significant increase in TC genesis number over the four months that correspond with the peak TC season from July to October (Table 1). During this peak TC season, the total of the difference in TC frequency between the two subperiods is 4.9, accounting for 88% of the total difference of the annual TC frequency with 5.6. Similarly, the reduced TC frequency over the peak TC season during 1970–94 is responsible for the overall reduced frequency (Fig. 2d). Further changepoint analyses also indicate a clear shift of peak season TC frequency in 1995 (Figs. 2e,f). The rest of the analyses presented in the following sections mainly focus on the peak season from July to October, unless otherwise specified.

Recently, Murakami et al. (2014) developed an empirical–statistical method for assessing the relative importance of the factors that contributed toward the high values of ACE and PDI over the NATL basin during recent decades. They showed that the significant increase of TC frequency is the most important contributor to the increased ACE and PDI over the NATL basin. Coincident with the significant increase in TC frequency since 1995, the two indices, ACE and PDI, over the peak TC season indeed experience a significant increase in the second subperiod from the first subperiod (Table 2). Moreover, increases in the average and peak intensity and a significant increase in the number of category 3–5 TCs are found in the second subperiod. Previous studies (Camargo and Sobel 2005; Wu et al. 2008; Zhao et al. 2011; Wu and Zhao 2012) suggested that the duration effect is an important factor in controlling basinwide TC intensity and the frequency of intense TCs, Consistent with those studies, a significant increase in TC duration in 1995–2014 compared with 1970–94 is found, which is possibly in part associated with the significant southward shift and the moderate eastward shift of TC genesis locations during 1995–2014, shown in Fig. 3. Over the NATL MDR and especially over its southeastern sector, there are significantly more TCs during the second subperiod. Accompanying the shift in the location of TC genesis (Fig. 3c), more TCs during the second subperiod have longer tracks that tend to move westward and northwestward (Fig. 3f) and thus have a greater probability of becoming more intense. Additionally, more TCs that form in the southeastern part of the NATL basin typically encounter warmer SSTs and lower vertical wind shear for a longer period, which could also help TCs reach stronger intensities.

Table 2.

Characteristics of NATL TC activity during the two subperiods and their differences and the p value. An asterisk (no asterisk) indicates that the changes are statically significant (nonsignificant) at the 95% confidence level based on the nonparametric Wilcoxon–Mann–Whitney rank sum test.

Table 2.
Fig. 3.
Fig. 3.

The occurrence of TC genesis over NATL basin during (a) P2 (1995–2014) and (b) P1 (1970–2014) and (c) their difference (P2 minus P1). (d)–(f) As in (a)–(c) but for track density over the NATL basin. The plus signs indicate that the difference is significant at a 95% confidence level. The dashed box corresponds to the MDR (5°–25°N, 75°–15°W).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

4. Factors contributing to the enhanced TC frequency after the mid-1990s

In this section, the changes in the roles of various large-scale factors associated with the interdecadal change are examined based on the diagnosis of the GPI, following the methodology presented in Camargo et al. (2007).

After the mid-1990s, significantly larger low-level relative vorticity, higher local NATL SST, and smaller vertical wind shear are found (Table 3). As expected, all these large-scale factors positively contribute to the increase in TCs during the second subperiod. Somewhat unexpectedly, 600-hPa relative humidity from the NCEP-1 dataset during the second subperiod is significantly smaller than during the first subperiod, which tends to be unfavorable for TC genesis, and seems contrary to the observed recent increase of the NATL TC frequency. In fact, we will show in section 5 that inconsistent results on interdecadal changes of the relationship between midlevel relative humidity from various reanalysis datasets and TC frequency can be obtained.

Table 3.

The magnitude of factors affecting TCs averaged over the NATL MDR (5°–25°N, 75°–15°W) during the two subperiods (P1 and P2) and their differences and the p value. An asterisk indicates that the changes are statically significant at the 95% confidence level based on the nonparametric Wilcoxon–Mann–Whitney rank sum test.

Table 3.

To further quantify the relative importance of the large-scale factors in contributing to the higher NATL TC frequency during the second subperiod, we use the GPI from Emanuel and Nolan (2004) as a diagnostic tool and the large-scale factors included in GPI from the NCEP-1 dataset. As shown in Fig. 4, the average GPI-Total over the NATL MDR exhibits a highly significant correlation with TC frequency, with a correlation coefficient of 0.71 for the whole period (1970–2014). In addition, the significant increase in TC frequency and TC genesis distribution over the NATL MDR since 1995 is well represented by the significant difference of the average GPI-Total between the two subperiods (Fig. 4) and by the spatial distribution of GPI-Total (Fig. 5a). The consistency of temporal and spatial NATL TC genesis enhances our confidence in using the GPI to further quantify the individual role of the large-scale controls of the TC activity.

Fig. 4.
Fig. 4.

Time series of the normalized July–October TC frequency (black) and the GPI calculated with JRA-55 (blue) and with NCEP-1 (green) datasets during 1970–2014 over the NATL MDR (5°–25°N, 75°–15°W). The GPI follows Emanuel and Nolan (2004). The correlations between GPI using the JRA-55 (NCEP-1) and TC frequency are 0.38 (0.68), 0.78 (0.80), and 0.71 (0.67) during the P1 (1970–94), P2 (1995–2014), and the whole period (1970–2014), respectively.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

Fig. 5.
Fig. 5.

(a) Spatial distribution of the difference (P2 minus P1) of GPI-Total and (b) the difference (P2 minus P1) for the GPI-Total, GPI-PI, GPI-Shear, GPI-VOR, and GPI-RH during the peak TC season over the NATL MDR (5°–25°N, 75°–15°W) plotted by the dashed line. The GPI developed by Emanuel and Nolan (2004) uses the NCEP-1 dataset.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

The role of each large-scale factor is assessed by calculating different GPIs, GPI-RH, GPI-VOR, GPI-PI, and GPI-Shear, following Zhao et al. (2015a). This approach was also described in section 2c. Figure 5b compares the difference of the magnitude of the average GPI with varying factors and GPI-Total over the NATL basin. Results show that GPI-RH and GPI-PI are the two largest terms contributing to the difference of GPI-Total between the two subperiods. The PI is a theoretical upper bound of TC intensity (Emanuel 1988; Holland 1997), which can be predicted from a given SST and atmospheric thermodynamic profile. The local SST determines the energy input available for TC development and maintenance (Malkus and Riehl 1960; Schade 2000; Saunders and Harris 1997) and is a key factor controlling the PI of TCs. These results indicate that the midlevel relative humidity and local NATL SST are the two most important factors contributing to the increased TC frequency during the second subperiod. Low-level vorticity and vertical wind shear also play positive contributions to the significant increase of TC frequency since 1995. Similar results are obtained based on the spatial correlations between the different variables of GPI-RH, GPI-PI, GPI-Shear, GPI-VOR, and GPI-Total during the two subperiods. These correlations are 0.70, 0.51, 0.33, and 0.38, respectively, confirming the relatively great significance of the midlevel relative humidity and local SST in contributing to the recent increase in TC frequency over the NATL basin, with moderate contributions from the low-level vorticity and vertical wind shear. These results on the relative importance of large-scale factors are confirmed by recalculating the indices with the JRA-55 dataset (Figs. 4 and 6a). Unfortunately, it seems contradictory that both the significantly decreased midlevel relative humidity from the NCEP-1 and the moderate increase of midlevel relative humidity from the JRA-55 during 1995–2014 are in response to enhanced GPI. Therefore, note that caution should be taken in interpreting the great importance of midlevel relative humidity.

Fig. 6.
Fig. 6.

(a) Difference (P2 minus P1) for the GPI-Total, GPI-PI, GPI-Shear, GPI-VOR, and GPI-RH during July–October over the NATL MDR (5°–25°N, 75°–15°W). (b) Correlation of TC frequency and basinwide-averaged GPI-Total, GPI with varying factors including GPI-RH, GPI-VOR, GPI-PI, and GPI-Shear over the NATL MDR during the two subperiods (P1 and P2) and the whole period (1970–2014). The GPI developed by Emanuel and Nolan (2004) was used, and the computation of this GPI uses the JRA-55 dataset.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

5. Intensified association between large-scale factors and TC frequency since the mid-1990s

An interesting result can be inferred from Fig. 4 related to the interdecadal change of the interannual relationship between TC frequency and the average GPI-Total over the NATL basin. During 1970–94, TC frequency moderately correlates with GPI with a correlation coefficient of 0.38, which is not significant at a 95% confidence level. Note that this nonsignificant correlation during the first period is dominated by the pre-1980s section of the time series, since a good correlation is found in 1980–94. In contrast, a significant correlation of 0.78 is observed during 1995–2014. It is valid to hypothesize that the relationship between TC frequency over the NATL basin and factors included in the calculation of the GPI should experience interdecadal modulations associated with regime shifts. Therefore, the correlations of the average GPI with varying factors and TC frequency over the NATL basin between the two subperiods are compared to infer the relative importance of each factor in contributing to the highly significant interannual correlation during the second subperiod.

This change in correlation between TC frequency and GPI-Total, shown in Fig. 7a, is mainly due to the significant interdecadal changes of the relationship between TC frequency and the average GPI-RH and GPI-PI before and after the changepoint on 1995. During 1970–94, there is a very weak correlation (0.05) between GPI-RH and TC frequency, while a significant correlation (0.53) is found for 1995–2014. Following Fisher (1921), we assess the statistical difference of the two correlation coefficients between the two periods and find that it is significant at a 95% confidence level, indicating an increased relationship with the recent increase in TC activity. In contrast, the low-level vorticity plays a much smaller role, based on the very weak correlation between TC frequency and the average GPI-VOR over the NATL basin. Consistent with analyses of the GPI proposed by Tippett et al. (2011), low-level vorticity has virtually no role in a genesis index. One of the possible reasons is that the low-level environment vorticity reaches a sufficiently large value during the two subperiods that the vorticity no longer is a limiting factor and the other factors become more critical, so that further increases in vorticity do not increase the probability of TC genesis. As suggested by previous studies (Gray 1968; McBride and Zehr 1981; Kim and Webster 2010), vertical wind shear and local NATL SST appear to be the two important factors affecting TC frequency, which is reflected by the highly significant correlation for both subperiods and the whole period. The significant interannual relationships between TC frequency and GPI-Shear and GPI-PI remain stable between the two subperiods, contrasting the change in correlation seen for the 600-hPa relative humidity discussed above.

Fig. 7.
Fig. 7.

(a) Correlation of TC frequency and basinwide-averaged GPI-Total and GPI with varying factors including GPI-RH, GPI-VOR, GPI-PI, and GPI-Shear over the NATL MDR (5°–25°N, 75°–15°W) during the two subperiods (P1 and P2) and the whole period (1970–2014). (b) As in (a), but for the large-scale factors including 600-hPa relative humidity, 850-hPa relative vorticity, vertical wind shear, and local NATL SST. Correlation coefficients marked with two asterisks are significant at a 95% confidence level. The GPI developed by Emanuel and Nolan (2004) was used, and the computation of this GPI uses the NCEP-1 dataset.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

The direct relationships between TC frequency and large-scale factors averaged over the NATL MDR during the two subperiods are further examined. During 1970–2014, TC frequency has a weak correlation with 600-hPa relative humidity with a correlation coefficient of −0.17. However, the interannual relationship between the 600-hPa relative humidity and TC frequency, shown in Fig. 7b, exhibits a significant interdecadal change. A weak correlation between them, with a correlation coefficient of 0.11, is found during the first subperiod, whereas they are significantly correlated with a correlation coefficient of 0.46 during the second subperiod. Following Fisher (1921), the difference of these two correlation coefficients is significant at a 95% level.

The correlation of the local NATL SST and TC frequency is larger during the second subperiod than during the first subperiod (Fig. 7b). A significant correlation between them is found during the two subperiods (at 95% confidence level), but the correlation is slightly larger (0.55) during the second subperiod than during the first subperiod (0.47). Moreover, significant correlations between vertical wind shear and TC frequency are found at a 95% confidence level for the period of 1970–2014, with a correlation coefficient of −0.73, and a correlation coefficient of −0.61 for the first subperiod and of −0.76 for the second subperiod (Fig. 7b). During the two subperiods, the low-level relative vorticity is weakly correlated with the TC frequency over the NATL basin with a correlation coefficient of 0.30 for the first subperiod and of 0.34 for the second subperiod, although a significant correlation between them is found for the whole period, with a correlation coefficient of 0.56 (Fig. 7b).

The results reported above are based on the GPI calculated from Emanuel and Nolan (2004) using the NCEP-1 dataset. These results on the relationship between factors from the NCEP-1 and NATL TC frequency are further confirmed using the JRA-55 dataset. Results show that a closer correlation is found between TC frequency and entire GPI with the JRA-55 during the first period, 1970–94, which is mainly due to a better match between them before 1980 (Fig. 4). Our calculation reveals that this difference in results stems mainly from the difference in 600-hPa relative humidity from these two reanalysis datasets (not shown). Nevertheless, a consistent result of an increasing relationship between midlevel relative humidity in these two reanalysis datasets and TC frequency can be clearly detected from the first subperiod to the second subperiod. We note that the correlations between TC frequency and GPI with varying factors from these two reanalysis datasets also show almost identical interdecadal change, with some slight differences found (Figs. 6b and 7b). Associated with a paradoxical fact shown in section 4 that both the decreased midlevel moisture from NCEP-1 and the increase from JRA-55 contribute to enhanced GPI, it is possible that a closer relationship between the midlevel moisture and TC frequency plays a major contribution to the enhanced GPI since 1995. Additionally, we speculate that the average midlevel moisture during both periods is sufficient for TC genesis as with the role of low-level vorticity suggested by Tippett et al. (2011), and, thus, their differences between the two periods are not important for interdecadal changes of TC genesis frequency. These speculations need more detailed analysis.

Various GPIs have been proposed for understanding the relationship between climate and TC formation (Gray 1979; Emanuel and Nolan 2004; Tippett et al. 2011; Bruyère et al. 2012; Waters et al. 2012). Menkes et al. (2012) performed a comprehensive comparison of the characteristics of various GPIs and pointed out that some results based on these different GPIs are inconsistent or even contradictory and vary depending on the regions and the time scales considered. Together with the homogeneity problems over time, especially crossing 1979 when there were major satellite-associated changes, the widely used three GPIs from Emanuel and Nolan (2004), Tippett et al. (2011), and Bruyère et al. (2012) are calculated from the five reanalysis datasets (JRA-55, NCEP-1, NCEP-2, MERRA, and ERA-Interim) to confirm the results. By assessing the relative importance of large-scale factors included in these GPIs, a fairly consistent result is found with the clear importance of dynamic and thermodynamic terms for all three GPIs and all five reanalysis datasets (Figs. 810). Detailed analyses indicate that the importance of the dynamic term is mainly from the contribution of vertical wind shear and the thermodynamic term is largely from the contribution of PI (Figs. 810).

Fig. 8.
Fig. 8.

Correlation of TC frequency and GPI with varying factors (i.e., dynamic term, with varying shear and 850-hPa vorticity; shear term, only with varying shear; vorticity term, only with varying 850-hPa vorticity; thermal term, with varying 600-hPa relative humidity and PI; humidity term, only with varying 600-hPa relative humidity; and PI term, only with varying potential intensity) over the NATL MDR (5°–25°N, 75°–15°W) using the (a) JRA-55, (b) NCEP-1, (c) NCEP-2, (d) MERRA, and (e) ERA-Interim datasets. Correlation coefficients indicated in blue (red) are significant at a 95% confidence level during 1979–94 (1995–2014). The GPI developed by Emanuel and Nolan (2004) is used.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

Fig. 9.
Fig. 9.

As in Fig. 8, but using the GPI developed by Tippett et al. (2011).

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

Fig. 10.
Fig. 10.

Correlation of TC frequency and GPI with varying factors (i.e., shear term, with varying shear and PI terms, with varying potential intensity) over the NATL MDR (5°–25°N, 75°–15°W) using the (a) JRA-55, (b) NCEP-1, (c) NCEP-2, (d) MERRA, and (e) ERA-Interim datasets. Correlation coefficients indicated in blue (red) are significant at a 95% confidence level during 1979–94 (1995–2014). The GPI developed by Bruyère et al. (2012) is used.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

Also shown in Figs. 8 and 9, inconsistent interdecadal changes in relationships between the midlevel moisture and GPI-RH using three GPIs and five reanalysis datasets can be found. Additionally, changes of the relationships between NATL TC frequency and midlevel moisture from various reanalysis datasets are also calculated and yield inconsistent results. An increasing relationship between 600-hPa relative humidity and July–October TC frequency over the NATL basin from 1970–94 to 1995–2014 is found using both the NCEP-1 and JRA-55 datasets (not shown). Although such an increasing relationship between them from 1979–94 to 1995–2014 is also confirmed using the ERA-Interim dataset, a decreasing relationship is observed between TC frequency and the 600-hPa relative humidity from the MERRA dataset. Therefore, the relative importance of the midlevel moisture for the recent increase of TC activity since 1995 remains uncertain and needs further investigation from both observations and numerical simulations.

In summary, a fairly consistent description of the relative importance of the factors associated with the regime shift in contributing to the recent increase in TC activity over the NATL basin emerges based on 1) the interdecadal changes of the interannual relationship between TC frequency and large-scale factors or GPI and 2) the interdecadal difference of large-scale factors or GPI. The local SST and vertical wind shear appear to be the two most important contributors to the recent significant increase in TC activity over the NATL basin. Significantly higher local SST and smaller vertical wind shear together increased associations between them and TC frequency since 1995, exerting an increasing impact on TC frequency during the second subperiod. The low-level vorticity appears to play a role in the recent increase of NATL TC activity. Although there is no increased relationship between low-level vorticity and TC frequency during the second period, significantly larger low-level vorticity over the NATL during the second period is found compared to that during the first period. Because of inconsistent results on the role of the midlevel relative humidity, its relative importance for the significant recent increase of TC frequency should be taken with caution and needs further investigation.

6. Plausible physical causes of changes in the role of large-scale factors

Based on the analysis of GPI presented above, the recent increase in TC frequency over the NATL basin largely depends on the interdecadal changes closely associated with the regime shift. The composite 850-hPa streamfunction anomaly and SSTA during the two subperiods are displayed in Fig. 11. In response to the Pacific heating associated with the second subperiod, a typical Gill–Matsuno-type response of the atmosphere can be observed over the tropical Atlantic and Pacific basins (Gill 1980; Matsuno 1966). At 850 hPa, intensified westerlies to the east of the heating can be observed over the tropical Atlantic during the second subperiod. Moreover, note that the circulation and SSTA distribution during the second subperiod are almost opposite to those during the first subperiod. It is possible that the large-scale controls of the NATL TC activity could be significantly modulated by the associated SSTA pattern in response to the regime shift. This is proposed here as a hypothesis to be tested in the future. Details of the link between the changes of SSTA patterns associated with the regime shift and the changes of large-scale factors that can cause the significant increase of TC frequency over the NATL basin since 1995 remain unclear and need further study.

Fig. 11.
Fig. 11.

The composite SSTA (shading) and 850-hPa streamfunction relative to the climatological value (1970–2014) during (a) P2 and (b) P1.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

A large region of warm SSTA over the tropical and subtropical regions can be observed during the second subperiod (Fig. 11) compared to the climatological value (1970–2014), whereas an almost opposite SSTA pattern is seen during the first subperiod. As suggested by previous studies (S. P. Xie et al. 2005; Vecchi and Soden 2007; Latif et al. 2007; Wang et al. 2012; Knutson et al. 2010), the large-scale controls of the NATL TC activity can be substantially impacted by changes of SSTA in different oceanic basins. Preliminary statistical analyses are conducted here, and the results suggest that the interdecadal changes of the SSTA over the different tropical oceans associated with the regime shift may modulate the large-scale factors affecting the NATL TC genesis frequency.

A correlation map between NATL TC frequency and global SSTA is displayed in Fig. 12. Significant interdecadal changes of the interannual correlation are seen over the three tropical regions: the WNP, the equatorial central-eastern Pacific, and the NATL. The SSTAs over the equatorial central-eastern Pacific and NATL basins both have an intensified teleconnection with TC frequency over the NATL basin after 1995 (Figs. 12 and 13a,c), while a weakened teleconnection of WNP SSTA with NATL TC frequency is found (Figs. 12 and 13b). Such an intensified impact of the equatorial central-eastern Pacific SSTA pattern on the NATL TC frequency during recent decades manifests in the increasing significant correlation between the equatorial central-eastern Pacific SSTA (represented by the Niño-3.4 index) and 600-hPa relative humidity and vertical wind shear during the second subperiod (Fig. 13a). The interannual relationship between the WNP SSTA experiences a significant correlation with the vertical wind shear and local NATL SSTA during the first subperiod, which is not found during the second subperiod (Fig. 13b). These changes contribute to the intensified (weakened) impact of the WNP SSTA on NATL basin TC frequency during the first (second) subperiod. An increasing significant relationship between local NATL SSTA and TC frequency can be found during recent decades in contrast to the first subperiod, which is mainly due to the closer interannual relationship between vertical wind shear and low-level vorticity and TC frequency over the NATL basin (Fig. 13c). In summary, the large-scale contributors to the recent significant increase in TC frequency over the NATL basin are closely associated with the impact of local and remote SSTA associated with the regime shift. The relative contributions of the local and remote SSTA to the recent increase in TC frequency over the NATL basin are not resolved in this study and need numerical simulations and more observations to explore the detailed mechanisms. Additionally, several studies (e.g., Yu et al. 2015a; Zou et al. 2014; Yu et al. 2012) have indicated that a recent change of El Niño type from the eastern Pacific to the central Pacific in response to the climate shift occurred in the early 1990s, coinciding with the significantly enhanced NATL TC frequency during the recent decade. Although some studies have suggested that the recent climate shift is likely associated with the phase changes of the AMO (Goldenberg et al. 2001; Yu et al. 2015b), the mechanism responsible for the significant change observed in 1995 remains unclear and needs further investigation.

Fig. 12.
Fig. 12.

Correlation map between the TC frequency over the NATL basin and global SSTA averaged from July to October during (a) P2 and (b) P1. The plus signs indicate correlation significant at a 95% confidence level.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

Fig. 13.
Fig. 13.

Correlation of large-scale factors averaged over the NATL MDR (5°–25°N, 75°–15°W) and TC frequency during the two subperiods and the (a) Niño-3.4 index, (b) local WNP SST, and (c) local NATL SST. Correlation coefficients marked with two asterisks are significant at a 95% confidence level.

Citation: Journal of Climate 31, 4; 10.1175/JCLI-D-17-0016.1

7. Summary and discussion

This study presents an exploratory analysis of the observed significant increase in TC frequency over the NATL basin since 1995. Changes in large-scale controls of the NATL TC activity are compared between two subperiods: before (1970–94) and after (1995–2014) the changepoint year. Results suggest that the high level of TC activity over the NATL basin since 1995 is mainly due to a combined effect of the changes of magnitude of large-scale controls of the NATL TC activity and their association with the NATL TC frequency on the interannual time scale coinciding with the regime shift.

The magnitude of average low-level vorticity, vertical wind shear, and local SST over the NATL basin are found to be favorable for TC genesis during the second subperiod (1995–2014) compared to the first subperiod (1970–94). A significant decrease of the midlevel relative humidity during the second subperiod is observed, which is typically unfavorable for TC genesis, and likely associated with the uncertainty in the NCEP-1 dataset. Inconsistent interdecadal changes of relationship between the TC frequency and the midlevel relative humidity over the NATL from different reanalysis datasets are found. The vertical wind shear and local SST remain very important in controlling TC genesis frequency, in agreement with previous studies (Gray 1968; McBride and Zehr 1981; Kim and Webster 2010), and their significance remains stable through the regime shift. Moreover, the low-level vorticity is identified to play a role in the recent significant increase of TC frequency, given the significant increase of low-level relative vorticity and weak correlation between TC frequency and both GPI-VOR and low-level vorticity. In summary, the significantly higher local SST and smaller vertical wind shear over the NATL basin appear to be the two most important contributors to the increased NATL TC frequency during the recent decades based on their intensified associations with TC frequency since 1995. Further investigation shows that all of these interdecadal changes in the role of large-scale factors are possibly related to the associated global SSTA pattern in response to the regime shift. Details of the physical mechanism of the impact of the remote and local SSTA patterns need more observations and numerical simulations to elucidate.

The results presented in this manuscript show the clear importance of the PI included in the GPI developed by Emanuel and Nolan (2004) in contributing to the interdecadal changes of TC frequency over the NATL basin. PI is a measure that provides an upper bound on cyclone intensity (Bister and Emanuel 1998, 2002; Holland 1997; Emanuel 2000) and can also reflect on the likelihood of TC genesis and development (Emanuel and Nolan 2004; Camargo et al. 2007). Its computation is based on SST and local vertical thermodynamic structure of the atmosphere (Emanuel 2007; Bister and Emanuel 1998, 2002). Therefore, a local warming SST would act to increase PI. Indeed, warmer SST over the MDR of the NATL basin during the second subperiod (1995–2014) compared to the first subperiod (1970–94) leads to higher PI and an enhanced effect of PI on the TC genesis frequency during the second subperiod. However, some studies suggest that remote SST changes can influence PI through their influence on upper-atmospheric temperatures (Vecchi and Soden 2007; Camargo et al. 2013; Shen et al. 2000; Elsner 2006), which are determined by changes in the tropical-mean SST (Sobel et al. 2002). The clear importance of relative SST based on the GPI proposed by Tippett et al. (2011) was confirmed (Fig. 9), which is partly manifested by the spatial statistical linkages to the central Pacific and western Pacific (Fig. 12). Overall, the local PI in the tropics is influenced by both local and remote SST changes between the two subperiods; nevertheless, further study is needed on the role of the respective SST changes over different basins on the PI over the NATL basin.

Studies suggest that climate models largely represent the observed variability of TC frequency in the NATL region with prescribed SSTs from observations (Knutson et al. 2010; Zhao and Held 2012; Zhang and Delworth 2006). However, the specific observed SST pattern is the result of a combined effect of natural variability and external forcing, with their respective relative roles remaining highly uncertain (Dunstone et al. 2013; Ting et al. 2009; Booth et al. 2012; Mann and Emanuel 2006; Evan 2012; Camargo et al. 2013; Walsh et al. 2015). As suggested by recent studies (Booth et al. 2012; Dunstone et al. 2013; Mann and Emanuel 2006; Ting et al. 2015; Sobel et al. 2016), the external forcing may be partly responsible for the increased TC formation since the 1970s, especially the possible influence of atmospheric aerosols in the decreased TC activity over the North Atlantic basin during the pre-1995 period. The importance of anthropogenic aerosols on the NATL TC frequency was emphasized by Dunstone et al. (2013), indicating that anthropogenic aerosols over the NATL are important in the inactive TC seasons over the twentieth century and that the sharp decline of anthropogenic aerosol levels at the end of the twentieth century resulted in increased TC activity. These studies imply that anthropogenic aerosols may play a role in modulating an interdecadal change of TC frequency over the NATL basin, especially for the pre-1995 low levels of TC activity. However, Villarini and Vecchi (2013) argued that a combination of increased greenhouse gases, decreased aerosols, and the Atlantic multidecadal variability led to the observed increase in PDI of the North Atlantic tropical cyclone over the past 30 years. Zhang et al. (2013) also questioned the conclusions of Booth et al. (2012) and Dunstone et al. (2013) about the role of aerosols in the NATL basin. Therefore, the impact of aerosols in the changes of TC activity over the NATL basin warrants further detailed investigation.

The GPIs have been used widely to evaluate TC genesis potential in many previous studies (Bruyère et al. 2012; Emanuel and Nolan 2004; Camargo et al. 2007, 2009; Jiang et al. 2012; Tippett et al. 2011; Zhao et al. 2015a,b). However, the results of this study indicate that the change of the interannual relationship between the GPI and NATL TC frequency may be significantly affected by the climate regime shift. It suggests that substantial changes of the role of large-scale factors included in the definitions of the GPI and/or other factors not included in the GPI contribute to TC genesis. Therefore, to provide better understanding of the impact of climate change on the large-scale controls of TC activity and to assess the representation of climate modulation on TC genesis in climate simulations, a better or at least different genesis potential index may be needed. In particular, this new index should be more applicable to climate changes and to provide further insights into the reasons for the significant increase of TCs during 1995–2014. Additionally, we should caution that the uncertainty in the TC data (Landsea et al. 2006, 2010; Landsea and Franklin 2013; Villarini et al. 2011) could lead to some different results, which needs further investigation with more homogenous TC records. For example, Landsea et al. (2010) argued that the previously documented increase in Atlantic total TC frequency is due primarily to an increase in short-lived (less than 2 days) TCs. Consistent with Landsea et al. (2010), an increase in short-lived TCs can be found during the higher level of TC activity during the recent decades over the NATL basin. Further examination also suggested a consistent significant increase in long-lived (greater than 2 days) TC frequency since 1995. It also raises our confidence in the significant increase of TC frequency during the recent decades, although some results of this manuscript may be sensitive to TC data quality. Additionally, this study shows a relatively robust statistical interdecadal change between these factors and TC frequency, which increases our understanding of the relationship between climate change and TC activity over the NATL basin. Note that these relationships are statistical and as such the large-scale forcing mechanisms on the recent increase of TC frequency over the NATL basin need further investigation to confirm them using more observations and numerical simulations.

As is well known, a significant linear upward trend is clearly observed in peak season TC frequency during 1979–2014. Consistently, an upward trend of the MDR SST and a downward trend of the MDR vertical wind shear can also be found (not shown). As a final remark, one may be concerned about how the linear upward trend of the large-scale factors and TC frequency might contribute to the changes in their associations before 1995 and since 1995. The standard deviations of the peak season TC frequency, as shown in Fig. 2d, and its detrended interannual component are 4.2 and 3.1, respectively. The variability of the detrended interannual component contributes about 81% to the total variance, suggesting that the interannual change dominates the NATL TC variability. Following Wang et al. (2009), we further compute changes of correlations between the large-scale factors and TC frequency based on the original and detrended time series (not shown). A stronger relationship is found between detrended large-scale factors and TC frequency since 1995, indicating that such changes of relationship between them is possibly closely associated with the multidecadal variations of the mean state. Moreover, the linear trend also contributes to the interdecadal changes in their associations, implying the possibility of an amplified response to the climate trend. The underlying physical mechanisms on how natural variability and climate trends affect the recent increase of TC frequency over the NATL basin will be discussed in a follow-up study.

Acknowledgments

The authors are grateful to Dr. Phil Klotzbach from the Department of Atmospheric Science, Colorado State University, and the additional anonymous reviewers for a number of helpful comments that have improved this manuscript. This research was jointly supported by the National Natural Science Foundation of China (Grants 41675072, 41475091, and 41675051), the Qing Lan Project of Jiangsu Province (R2017Q01), the Natural Science Foundation for Higher Education Institutions in Jiangsu Province (12KJA170002), the National Basic Research Program of China (Grant 2015CB452803), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). TC data are obtained from the U.S. hurricane best-track database (HURDAT) through the National Hurricane Center. Atmospheric fields were obtained from the reanalysis datasets JRA-55, ERA-Interim, NCEP-1 and NCEP-2, and MERRA, which are provided from JMA, ECMWF, NCEP, and NASA. The monthly mean SST is from the National Oceanic and Atmospheric Administration (NOAA).

REFERENCES

  • Bell, G. D., and M. Chelliah, 2006: Leading tropical modes associated with interannual and multidecadal fluctuations in North Atlantic hurricane activity. J. Climate, 19, 590612, https://doi.org/10.1175/JCLI3659.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, G. D., and Coauthors, 2000: Climate assessment for 1999. Bull. Amer. Meteor. Soc., 81 (6), S1S50, https://doi.org/10.1175/1520-0477(2000)81[s1:CAF]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bister, M., and K. A. Emanuel, 1998: Dissipative heating and hurricane intensity. Meteor. Atmos. Phys., 65, 233240, https://doi.org/10.1007/BF01030791.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bister, M., and K. A. Emanuel, 2002: Low frequency variability of tropical cyclone potential intensity. 1. Interannual to interdecadal variability. J. Geophys. Res., 107, 4801, https://doi.org/10.1029/2001JD000776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484, 228232, https://doi.org/10.1038/nature10946.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bove, M. C., J. B. Elsner, C. W. Landsea, X. Niu, and J. J. O’Brien, 1998: Effect of El Niño on U.S. landfalling hurricanes, revisited. Bull. Amer. Meteor. Soc., 79, 24772482, https://doi.org/10.1175/1520-0477(1998)079<2477:EOENOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bruyère, C. L., G. J. Holland, and E. Towler, 2012: Investigating the use of a genesis potential index for tropical cyclones in the North Atlantic basin. J. Climate, 25, 86118626, https://doi.org/10.1175/JCLI-D-11-00619.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., and A. H. Sobel, 2005: Western North Pacific tropical cyclone intensity and ENSO. J. Climate, 18, 29963006, https://doi.org/10.1175/JCLI3457.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 48194834, https://doi.org/10.1175/JCLI4282.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. C. Wheeler, and A. H. Sobel, 2009: Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J. Atmos. Sci., 66, 30613074, https://doi.org/10.1175/2009JAS3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. Ting, and Y. Kushnir, 2013: Influence of local and remote SST on North Atlantic tropical cyclone potential intensity. Climate Dyn., 40, 15151529, https://doi.org/10.1007/s00382-012-1536-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, X., S. F. Chen, G. H. Chen, W. Chen, and R. G. Wu, 2015: On the weakened relationship between spring Arctic Oscillation and following summer tropical cyclone frequency over the western North Pacific: A comparison between 1968–1986 and 1989–2007. Adv. Atmos. Sci., 32, 13191328, https://doi.org/10.1007/s00376-015-4256-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P.-S., 2004: ENSO and tropical cyclone activity. Hurricanes and Typhoons: Past, Present, and Future, R. J. Murnane and K.-B. Liu, Eds., Columbia University Press, 297–332.

  • Chu, P.-S., and X. Zhao, 2004: Bayesian change-point analysis of tropical cyclone activity: The central North Pacific case. J. Climate, 17, 48934901, https://doi.org/10.1175/JCLI-3248.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 20762087, https://doi.org/10.1175/1520-0469(1996)053<2076:TEOVSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunstone, N. J., D. M. Smith, B. B. B. Booth, L. Hermanson, and R. Eade, 2013: Anthropogenic aerosol forcing of Atlantic tropical storms. Nat. Geosci., 6, 534539, https://doi.org/10.1038/ngeo1854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430, https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsner, J. B., 2003: Tracking hurricanes. Bull. Amer. Meteor. Soc., 84, 353356, https://doi.org/10.1175/BAMS-84-3-353.

  • Elsner, J. B., 2006: Evidence in support of the climate change–Atlantic hurricane hypothesis. Geophys. Res. Lett., 33, L16705, https://doi.org/10.1029/2006GL026869.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsner, J. B., X. Niu, and T. H. Jagger, 2004: Detecting shifts in hurricane rates using a Markov chain Monte Carlo approach. J. Climate, 17, 26522666, https://doi.org/10.1175/1520-0442(2004)017<2652:DSIHRU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1988: The maximum intensity of hurricanes. J. Atmos. Sci., 45, 11431155, https://doi.org/10.1175/1520-0469(1988)045<1143:TMIOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2000: A statistical analysis of tropical cyclone intensity. Mon. Wea. Rev., 128, 11391152, https://doi.org/10.1175/1520-0493(2000)128<1139:ASAOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686688, https://doi.org/10.1038/nature03906.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2007: Environmental factors affecting tropical cyclone power dissipation. J. Climate, 20, 54975509, https://doi.org/10.1175/2007JCLI1571.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2011: Global warming effects on U.S. hurricane damage. Wea. Climate Soc., 3, 261268, https://doi.org/10.1175/WCAS-D-11-00007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. USA, 110, 12 21912 224, https://doi.org/10.1073/pnas.1301293110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., and D. S. Nolan, 2004: Tropical cyclone activity and the global climate system. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 10A.2, https://ams.confex.com/ams/pdfpapers/75463.pdf.

  • Emanuel, K. A., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347367, https://doi.org/10.1175/BAMS-89-3-347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • England, M. H., and Coauthors, 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Climate Change, 4, 222227, https://doi.org/10.1038/nclimate2106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evan, A. T., 2012: Atlantic hurricane activity following two major volcanic eruptions. J. Geophys. Res., 117, D06101, https://doi.org/10.1029/2011JD016716.

    • Search Google Scholar
    • Export Citation
  • Fisher, R. A., 1921: On the “probable error” of a coefficient of correlation deduced from a small sample. Metron, 1, 332.

  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447462, https://doi.org/10.1002/qj.49710644905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nuñez, and W. M. Gray, 2001: The recent increase in Atlantic hurricane activity: Causes and implications. Science, 293, 474479, https://doi.org/10.1126/science.1060040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669700, https://doi.org/10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155–218.

  • Gray, W. M., J. D. Sheaffer, and C. W. Landsea, 1997: Climate trends associated with multi-decadal variability of Atlantic hurricane activity. Hurricanes: Climate and Socioeconomic Impacts, H. F. Diaz and R. S. Pulwarty, Eds., Springer, 15–53.

    • Crossref
    • Export Citation
  • Harada, Y., and Coauthors, 2016: The JRA-55 Reanalysis: Representation of atmospheric circulation and climate variability. J. Meteor. Soc. Japan, 94, 269302, https://doi.org/10.2151/jmsj.2016-015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., R. W. Lee, and L. Bengtsson, 2011: A comparison of extratropical cyclones in recent reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25. J. Climate, 24, 48884906, https://doi.org/10.1175/2011JCLI4097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, G. J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 25192541, https://doi.org/10.1175/1520-0469(1997)054<2519:TMPIOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the North Atlantic: Natural variability or climate trend? Philos. Trans. Roy. Soc. London, 365A, 26952716, https://doi.org/10.1098/rsta.2007.2083.

    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., P.-S. Chu, H. Murakami, and X. Zhao, 2014: An abrupt decrease in the late-season typhoon activity over the western North Pacific. J. Climate, 27, 42964312, https://doi.org/10.1175/JCLI-D-13-00417.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2015: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4). Part I: Upgrades and intercomparisons. J. Climate, 28, 911930, https://doi.org/10.1175/JCLI-D-14-00006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2016: Further exploring and quantifying uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). J. Climate, 29, 31193142, https://doi.org/10.1175/JCLI-D-15-0430.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., M. Zhao, and D. E. Waliser, 2012: Modulation of tropical cyclones over the eastern Pacific by the intraseasonal variability simulated in an AGCM. J. Climate, 25, 65246538, https://doi.org/10.1175/JCLI-D-11-00531.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, https://doi.org/10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and Coauthors, 2015: Possible artifacts of data biases in the recent global surface warming hiatus. Science, 348, 14691472, https://doi.org/10.1126/science.aaa5632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendall, M. G., 1975: Rank Correlation Methods. Charles Griffin, 202 pp.

  • Kim, H.-M., and P. J. Webster, 2010: Extended-range seasonal hurricane forecasts for the North Atlantic with a hybrid dynamical-statistical model. Geophys. Res. Lett., 37, L21705, https://doi.org/10.1029/2010GL044792.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2006: Trends in global tropical cyclone activity over the past twenty years (1986–2005). Geophys. Res. Lett., 33, L10805, https://doi.org/10.1029/2006GL025881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2010: On the Madden–Julian oscillation–Atlantic hurricane relationship. J. Climate, 23, 282293, https://doi.org/10.1175/2009JCLI2978.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., 2014: The Madden–Julian oscillation’s impacts on worldwide tropical cyclone activity. J. Climate, 27, 23172330, https://doi.org/10.1175/JCLI-D-13-00483.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., J. J. Sirutis, S. T. Garner, I. M. Held, and R. E. Tuleya, 2007: Simulation of the recent multidecadal increase of Atlantic hurricane activity using an 18-km-grid regional model. Bull. Amer. Meteor. Soc., 88, 15491565, https://doi.org/10.1175/BAMS-88-10-1549.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., J. J. Sirutis, S. T. Garner, G. A. Vecchi, and I. M. Held, 2008: Simulated reduction in Atlantic hurricane frequency under twenty-first-century warming conditions. Nat. Geosci., 1, 359364, https://doi.org/10.1038/ngeo202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163, https://doi.org/10.1038/ngeo779.

  • Kosaka, Y., and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403407, https://doi.org/10.1038/nature12534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., and D. J. Vimont, 2007: A more general framework for understanding Atlantic hurricane variability and trends. Bull. Amer. Meteor. Soc., 88, 17671781, https://doi.org/10.1175/BAMS-88-11-1767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. J. Climate, 23, 30573076, https://doi.org/10.1175/2010JCLI3497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., 1993: A climatology of intense (or major) Atlantic hurricanes. Mon. Wea. Rev., 121, 17031713, https://doi.org/10.1175/1520-0493(1993)121<1703:ACOIMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., B. A. Harper, K. Horau, and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones? Science, 313, 452454, https://doi.org/10.1126/science.1128448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., G. A. Vecchi, L. Bengtsson, and T. R. Knutson, 2010: Impact of duration thresholds on Atlantic tropical cyclone counts. J. Climate, 23, 25082519, https://doi.org/10.1175/2009JCLI3034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LaRow, T. E., L. Stefanova, D.-W. Shin, and S. Cocke, 2010: Seasonal Atlantic tropical cyclone hindcasting/forecasting using two sea surface temperature datasets. Geophys. Res. Lett., 37, L02804, https://doi.org/10.1029/2009GL041459.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Latif, M., N. Keenlyside, and J. Bader, 2007: Tropical sea surface temperature, vertical wind shear, and hurricane development. Geophys. Res. Lett., 34, L01710, https://doi.org/10.1029/2006GL027969.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, W., L. Li, and Y. Deng, 2015: Impact of the interdecadal Pacific oscillation on tropical cyclone activity in the North Atlantic and eastern North Pacific. Sci. Rep., 5, 12358, https://doi.org/10.1038/srep12358.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malkus, J. S., and H. Riehl, 1960: On the dynamics and energy transformations in steady-state hurricanes. Tellus, 12, 120, https://doi.org/10.3402/tellusa.v12i1.9351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and D. L. Hartmann, 2000: Modulation of eastern North Pacific hurricanes by the Madden–Julian oscillation. J. Climate, 13, 14511460, https://doi.org/10.1175/1520-0442(2000)013<1451:MOENPH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and J. Shaman, 2008: Intraseasonal variability of the West African monsoon and Atlantic ITCZ. J. Climate, 21, 28982918, https://doi.org/10.1175/2007JCLI1999.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245259, https://doi.org/10.2307/1907187.

  • Mann, H. B., and D. R. Whitney, 1947: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 5060, https://doi.org/10.1214/aoms/1177730491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, Trans. Amer. Geophys. Union, 87, 233241, https://doi.org/10.1029/2006EO240001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsuno, T., 1966: Quasi-geostrophic motions in the equatorial area. J. Meteor. Soc. Japan, 44, 2543, https://doi.org/10.2151/jmsj1965.44.1_25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McBride, J. L., and R. Zehr, 1981: Observational analyses of tropical cyclone formation. Part II: Comparison of non-developing versus developing systems. J. Atmos. Sci., 38, 11321151, https://doi.org/10.1175/1520-0469(1981)038<1132:OAOTCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012: The impact of climate change on global tropical cyclone damage. Nat. Climate Change, 2, 205209, https://doi.org/10.1038/nclimate1357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menkes, C. E., M. Lengaigne, P. Marchesiello, N. C. Jourdain, E. M. Vincent, J. Lefèvre