Projected Response of Tropical Cyclone Intensity and Intensification in a Global Climate Model

Kieran Bhatia Princeton University, and NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Gabriel Vecchi Geosciences Department, and Princeton Environmental Institute, Princeton University, Princeton, New Jersey

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Hiroyuki Murakami Princeton University, and NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Seth Underwood NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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James Kossin NOAA/National Centers for Environmental Information, Center for Weather and Climate, University of Wisconsin–Madison, Madison, Wisconsin

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Abstract

As one of the first global coupled climate models to simulate and predict category 4 and 5 (Saffir–Simpson scale) tropical cyclones (TCs) and their interannual variations, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model at the Geophysical Fluid Dynamics Laboratory (GFDL) represents a novel source of insight on how the entire TC intensification distribution could be transformed because of climate change. In this study, three 70-yr HiFLOR experiments are performed to identify the effects of climate change on TC intensity and intensification. For each of the experiments, sea surface temperature (SST) is nudged to different climatological targets and atmospheric radiative forcing is specified, allowing us to explore the sensitivity of TCs to these conditions. First, a control experiment, which uses prescribed climatological ocean and radiative forcing based on observations during the years 1986–2005, is compared to two observational records and evaluated for its ability to capture the mean TC behavior during these years. The simulated intensification distributions as well as the percentage of TCs that become major hurricanes show similarities with observations. The control experiment is then compared to two twenty-first-century experiments, in which the climatological SSTs from the control experiment are perturbed by multimodel projected SST anomalies and atmospheric radiative forcing from either 2016–35 or 2081–2100 (RCP4.5 scenario). The frequency, intensity, and intensification distribution of TCs all shift to higher values as the twenty-first century progresses. HiFLOR’s unique response to climate change and fidelity in simulating the present climate lays the groundwork for future studies involving models of this type.

© 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: Kieran T. Bhatia, kbhatia@princeton.edu

Abstract

As one of the first global coupled climate models to simulate and predict category 4 and 5 (Saffir–Simpson scale) tropical cyclones (TCs) and their interannual variations, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model at the Geophysical Fluid Dynamics Laboratory (GFDL) represents a novel source of insight on how the entire TC intensification distribution could be transformed because of climate change. In this study, three 70-yr HiFLOR experiments are performed to identify the effects of climate change on TC intensity and intensification. For each of the experiments, sea surface temperature (SST) is nudged to different climatological targets and atmospheric radiative forcing is specified, allowing us to explore the sensitivity of TCs to these conditions. First, a control experiment, which uses prescribed climatological ocean and radiative forcing based on observations during the years 1986–2005, is compared to two observational records and evaluated for its ability to capture the mean TC behavior during these years. The simulated intensification distributions as well as the percentage of TCs that become major hurricanes show similarities with observations. The control experiment is then compared to two twenty-first-century experiments, in which the climatological SSTs from the control experiment are perturbed by multimodel projected SST anomalies and atmospheric radiative forcing from either 2016–35 or 2081–2100 (RCP4.5 scenario). The frequency, intensity, and intensification distribution of TCs all shift to higher values as the twenty-first century progresses. HiFLOR’s unique response to climate change and fidelity in simulating the present climate lays the groundwork for future studies involving models of this type.

© 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: Kieran T. Bhatia, kbhatia@princeton.edu
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  • Bacmeister, J. T., M. F. Wehner, R. B. Neale, A. Gettelman, C. Hannay, P. H. Lauritzen, J. M. Caron, and J. E. Truesdale, 2014: Exploratory high-resolution climate simulations using the Community Atmosphere Model (CAM). J. Climate, 27, 30733099, https://doi.org/10.1175/JCLI-D-13-00387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., K. A. Reed, C. Hannay, P. Lawrence, S. Bates, J. E. Truesdale, N. Rosenbloom, and M. Levy, 2018: Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model. Climatic Change, 146, 547560, https://doi.org/10.1007/s10584-016-1750-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bender, M. A., and I. Ginis, 2000: Real-case simulations of hurricane–ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon. Wea. Rev., 128, 917946, https://doi.org/10.1175/1520-0493(2000)128<0917:RCSOHO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., and A. A. Wing, 2016: Tropical cyclones in climate models. Wiley Interdiscip. Rev.: Climate Change, 7, 211237, https://doi.org/10.1002/wcc.373.

    • Search Google Scholar
    • Export Citation
  • Chen, J.-H., and S.-J. Lin, 2011: The remarkable predictability of inter-annual variability of Atlantic hurricanes during the past decade. Geophys. Res. Lett., 38, L11804, https://doi.org/10.1029/2011GL047629.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J.-H., and S.-J. Lin, 2013: Seasonal predictions of tropical cyclones using a 25-km-resolution general circulation model. J. Climate, 26, 380398, https://doi.org/10.1175/JCLI-D-12-00061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, J.-H., C. R. Sampson, A. S. Levine, and E. Fukada, 2002: The Joint Typhoon Warning Center tropical cyclone best-tracks, 1945–2000. Naval Research Laboratory Rep. NRL/MR/7540-02-16, 22 pp.

  • Colbert, A. J., B. J. Soden, G. A. Vecchi, and B. P. Kirtman, 2013: The impacts of anthropogenic climate change on North Atlantic tropical cyclone tracks. J. Climate, 26, 40884095, https://doi.org/10.1175/JCLI-D-12-00342.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C. A., 2018: Resolving tropical cyclone intensity in models. Geophys. Res. Lett., 45, 20822087, https://doi.org/10.1002/2017GL076966.

  • Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19, 643674, https://doi.org/10.1175/JCLI3629.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2012: Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J. Climate, 25, 27552781, https://doi.org/10.1175/JCLI-D-11-00316.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2006: Climate and tropical cyclone activity: A new model downscaling approach. J. Climate, 19, 47974802, https://doi.org/10.1175/JCLI3908.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2013: Increased global tropical cyclone activity from global warming: Results of downscaling CMIP5 climate models. Proc. Natl. Acad. Sci. USA, 110, 12 21912 224, https://doi.org/10.1073/pnas.1301293110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2017: Will global warming make hurricane forecasting more difficult? Bull. Amer. Meteor. Soc., 98, 495501, https://doi.org/10.1175/BAMS-D-16-0134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., S. Ravela, E. Vivant, and C. Risi, 2006: A statistical deterministic approach to hurricane risk assessment. Bull. Amer. Meteor. Soc., 87, 299314, https://doi.org/10.1175/BAMS-87-3-299.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K., K. 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
  • Gnanadesikan, A., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part II: The baseline ocean simulation. J. Climate, 19, 675697, https://doi.org/10.1175/JCLI3630.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harper, B. A., J. D. Kepert, and J. D. Ginger, 2010: Guidelines for converting between various wind averaging periods in tropical cyclone conditions. World Meteorological Organization Tech. Rep. WMO/TD-1555, 54 pp.

  • Harris, L. M., S.-J. Lin, and C. Tu, 2016: High-resolution climate simulations using GFDL HiRAM with a stretched global grid. J. Climate, 29, 42934314, https://doi.org/10.1175/JCLI-D-15-0389.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holland, G., and C. L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dyn., 42, 617627, https://doi.org/10.1007/s00382-013-1713-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, A., H. Li, R. L. Sriver, A. V. Fedorov, and C. M. Brierley, 2017: Regional variations in the ocean response to tropical cyclones: Ocean mixing versus low cloud suppression. Geophys. Res. Lett., 44, 19471955, https://doi.org/10.1002/2016GL072023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, L., and Coauthors, 2015: Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. J. Climate, 28, 20442062, https://doi.org/10.1175/JCLI-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., 2008a: Calibration of long-term geostationary infrared observations using HIRS. J. Atmos. Oceanic Technol., 25, 183195, https://doi.org/10.1175/2007JTECHA910.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., 2008b: Scientific data stewardship of International Satellite Cloud Climatology Project B1 global geostationary observations. J. Appl. Remote Sens., 2, 023548, https://doi.org/10.1117/1.3043461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., and J. P. Kossin, 2007: New global tropical cyclone data set from ISCCP B1 geostationary satellite observations. J. Appl. Remote Sens., 1, 013505, https://doi.org/10.1117/1.2712816.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91, 363376, https://doi.org/10.1175/2009BAMS2755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and R. E. Tuleya, 2004: Impact of CO2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization. J. Climate, 17, 34773495, https://doi.org/10.1175/1520-0442(2004)017<3477:IOCWOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., R. E. Tuleya, and Y. Kurihara, 1998: Simulated increase of hurricane intensities in a CO2-warmed climate. Science, 279, 10181020, https://doi.org/10.1126/science.279.5353.1018.

    • 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 condition. 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.

  • Knutson, T. R., and Coauthors, 2013: Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Climate, 26, 65916617, https://doi.org/10.1175/JCLI-D-12-00539.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., J. J. Sirutis, M. Zhao, R. E. Tuleya, M. Bender, G. A. Vecchi, G. Villarini, and D. Chavas, 2015: Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios. J. Climate, 28, 72037224, https://doi.org/10.1175/JCLI-D-15-0129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kodama, C., and Coauthors, 2015: A 20-year climatology of a NICAM AMIP-type simulation. J. Meteor. Soc. Japan, 93, 393424, https://doi.org/10.2151/jmsj.2015-024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Korty, R. L., K. A. Emanuel, M. Huber, and R. A. Zamora, 2017: Tropical cyclones downscaled from simulations with very high carbon dioxide levels. J. Climate, 30, 649667, https://doi.org/10.1175/JCLI-D-16-0256.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., 2017: Hurricane intensification along United States coast suppressed during active hurricane periods. Nature, 541, 390393, https://doi.org/10.1038/nature20783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., K. R. Knapp, D. J. Vimont, R. J. Murnane, and B. A. Harper, 2007: A globally consistent reanalysis of hurricane variability and trends. Geophys. Res. Lett., 34, L04815, https://doi.org/10.1029/2006GL028836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., T. L. Olander, and K. R. Knapp, 2013: Trend analysis with a new global record of tropical cyclone intensity. J. Climate, 26, 99609976, https://doi.org/10.1175/JCLI-D-13-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kowch, R., and K. Emanuel, 2015: Are special processes at work in the rapid intensification of tropical cyclones? Mon. Wea. Rev., 143, 878882, https://doi.org/10.1175/MWR-D-14-00360.1.

    • 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
  • Lee, C.-Y., M. K. Tippett, A. H. Sobel, and S. J. Camargo, 2016: Rapid intensification and the bimodal distribution of tropical cyclone intensity. Nat. Commun., 7, 10625, https://doi.org/10.1038/ncomms10625.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, I. I., G. J. Goni, J. A. Knaff, C. Forbes, and M. M. Ali, 2013: Ocean heat content for tropical cyclone intensity forecasting and its impact on storm surge. Nat. Hazards, 66, 14811500, https://doi.org/10.1007/s11069-012-0214-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, M., G. A. Vecchi, J. A. Smith, and H. Murakami, 2017: The present-day simulation and twenty-first-century projection of the climatology of extratropical transition in the North Atlantic. J. Climate, 30, 27392756, https://doi.org/10.1175/JCLI-D-16-0352.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lloyd, I. D., and G. A. Vecchi, 2011: Observational evidence for oceanic controls on hurricane intensity. J. Climate, 24, 11381153, https://doi.org/10.1175/2010JCLI3763.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manganello, J. V., and Coauthors, 2012: Tropical cyclone climatology in a 10-km global atmospheric GCM: Toward weather-resolving climate modeling. J. Climate, 25, 38673893, https://doi.org/10.1175/JCLI-D-11-00346.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manganello, J. V., and Coauthors, 2014: Future changes in the western North Pacific tropical cyclone activity projected by a multidecadal simulation with a 16-km global atmospheric GCM. J. Climate, 27, 76227646, https://doi.org/10.1175/JCLI-D-13-00678.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McClean, J. L., and Coauthors, 2011: A prototype two-decade fully-coupled fine-resolution CCSM simulation. Ocean Modell., 39, 1030, https://doi.org/10.1016/j.ocemod.2011.02.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., and B. Wang, 2010: Future change of North Atlantic tropical cyclone tracks: Projection by a 20-km-mesh global atmospheric model. J. Climate, 23, 26992721, https://doi.org/10.1175/2010JCLI3338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., and Coauthors, 2012: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Climate, 25, 32373260, https://doi.org/10.1175/JCLI-D-11-00415.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., B. Wang, T. Li, and A. Kitoh, 2013: Projected increase in tropical cyclones near Hawaii. Nat. Climate Change, 3, 749754, https://doi.org/10.1038/nclimate1890.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., and Coauthors, 2015: Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model. J. Climate, 28, 90589079, https://doi.org/10.1175/JCLI-D-15-0216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., and Coauthors, 2016: Seasonal forecasts of major hurricanes and landfalling tropical cyclones using a high-resolution GFDL coupled climate model. J. Climate, 29, 79777989, https://doi.org/10.1175/JCLI-D-16-0233.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olander, T. L., and C. S. Velden, 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298, https://doi.org/10.1175/WAF975.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S. Kusunoki, and A. Noda, 2006: Tropical cyclone climatology in a global-warming climate as simulated in a 20 km-mesh global atmospheric model: Frequency and wind intensity analysis. J. Meteor. Soc. Japan, 84, 259276, https://doi.org/10.2151/jmsj.84.259.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., Jr., J. Gratz, C. W. Landsea, D. Collins, M. Saunders, and R. Musulin, 2008: Normalized hurricane damages in the United States: 1900–2005. Nat. Hazards Rev., 9, 2942, https://doi.org/10.1061/(ASCE)1527-6988(2008)9:1(29).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rathmann, N. M., S. Yang, and E. Kaas, 2014: Tropical cyclones in enhanced resolution CMIP5 experiments. Climate Dyn., 42, 665681, https://doi.org/10.1007/s00382-013-1818-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schade, L. R., and K. A. Emanuel, 1999: The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere–ocean model. J. Atmos. Sci., 56, 642651, https://doi.org/10.1175/1520-0469(1999)056<0642:TOSEOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scoccimarro, E., P. G. Fogli, K. A. Reed, S. Gualdi, S. Masina, and A. Navarra, 2017: Tropical cyclone interaction with the ocean: The Role of high-frequency (subdaily) coupled processes. J. Climate, 30, 145162, https://doi.org/10.1175/JCLI-D-16-0292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaevitz, D. A., and Coauthors, 2014: Characteristics of tropical cyclones in high-resolution models in the present climate. J. Adv. Model. Earth Syst., 6, 11541172, https://doi.org/10.1002/2014MS000372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Small, R. J., and Coauthors, 2014: A new synoptic scale resolving global climate simulation using the Community Earth System Model. J. Adv. Model. Earth Syst., 6, 10651094, https://doi.org/10.1002/2014MS000363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suissa, S., and J. J. Shuster, 1985: Exact unconditional sample sizes for the 2 × 2 binomial trial. J. Roy. Stat. Soc., 148A, 317327, https://doi.org/10.2307/2981892.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takagi, H., and M. Esteban, 2016: Statistics of tropical cyclone landfalls in the Philippines: Unusual characteristics of 2013 Typhoon Haiyan. Nat. Hazards, 80, 211222, https://doi.org/10.1007/s11069-015-1965-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., and D. S. Nolan, 2012: Observational undersampling in tropical cyclones and implications for estimated intensity. Mon. Wea. Rev., 140, 825840, https://doi.org/10.1175/MWR-D-11-00073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., P. G. Black, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 30703085, https://doi.org/10.1175/MWR3454.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van der Wiel, K., S. B. Kapnick, and G. A. Vecchi, 2017: Shifting patterns of mild weather in response to projected radiative forcing. Climatic Change, 140, 649658, https://doi.org/10.1007/s10584-016-1885-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 531, https://doi.org/10.1007/s10584-011-0148-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and Coauthors, 2014: On the seasonal forecasting of regional tropical cyclone activity. J. Climate, 27, 79948016, https://doi.org/10.1175/JCLI-D-14-00158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, K. J. E., and Coauthors, 2015: Hurricanes 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, K. J. E., and Coauthors, 2016: Tropical cyclones and climate change. Wiley Interdiscip. Rev.: Climate Change, 7, 6589, https://doi.org/10.1002/wcc.371.

    • Search Google Scholar
    • Export Citation
  • Wehner, M., Prabhat, K. A. Reed, D. Stone, W. D. Collins, and J. Bacmeister, 2015: Resolution dependence of future tropical cyclone projections of CAM5.1 in the U.S. CLIVAR Hurricane Working Group idealized configurations. J. Climate, 28, 39053925, https://doi.org/10.1175/JCLI-D-14-00311.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 676 pp.

  • Wittenberg, A. T., A. Rosati, N. Lau, and J. J. Ploshay, 2006: GFDL’s CM2 global coupled climate models. Part III: Tropical Pacific climate and ENSO. J. Climate, 19, 698722, https://doi.org/10.1175/JCLI3631.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamada, Y., K. Oouchi, M. Satoh, H. Tomita, and W. Yanase, 2010: Projection of changes in tropical cyclone activity and cloud height due to greenhouse warming: Global cloud-system-resolving approach. Geophys. Res. Lett., 37, L07709, https://doi.org/10.1029/2010GL042518.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamada, Y., M. Satoh, M. Sugi, C. Kodama, A. T. Noda, M. Nakano, and T. Nasuno, 2017: Response of tropical cyclone activity and structure to global warming in a high-resolution global nonhydrostatic model. J. Climate, 30, 97039724, https://doi.org/10.1175/JCLI-D-17-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., K. B. Karnauskas, J. P. Donnelly, and K. Emanuel, 2017: Response of the North Pacific tropical cyclone climatology to global warming: Application of dynamical downscaling to CMIP5 models. J. Climate, 30, 12331243, https://doi.org/10.1175/JCLI-D-16-0496.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, W., and Coauthors, 2016: Improved simulation of tropical cyclone responses to ENSO in the western North Pacific in the high-resolution GFDL HiFLOR coupled climate model. J. Climate, 29, 13911415, https://doi.org/10.1175/JCLI-D-15-0475.1.

    • 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
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