• Bengtsson, L., , M. Botzet, , and M. Esch, 1995: Hurricane-type vortices in a general circulation model. Tellus, 47A, 175196.

  • Bengtsson, L., , K. I. Hodges, , M. Esch, , N. Keenlyside, , L. Kornblueh, , J.-J. Luo, , and T. Yamagata, 2007a: How may tropical cyclones change in a warmer climate? Tellus, 59A, 539561.

    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., , K. I. Hodges, , and M. Esch, 2007b: Tropical cyclones in a T159 resolution global climate model: Comparison with observations and re-analyses. Tellus, 59A, 396416.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., , A. G. Barnston, , P. J. Klotzbach, , and C. W. Landsea, 2007: Seasonal tropical cyclone forecasts. WMO Bull., 56, 297309.

    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., , M. K. Tippett, , A. H. Sobel, , G. A. Vecchi, , M. Zhao, , and I. M. Held, 2012: Analysis of tropical cyclone genesis indices for climate change using the HIRAM model. Preprints, Conf. on Hurricanes and Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Meteor. Soc., 4B.1. [Available online at https://ams.confex.com/ams/30Hurricane/webprogram/Paper205593.html.]

  • Catto, J. L., , L. C. Shaffrey, , and K. I. Hodges, 2011: Northern Hemisphere extratropical cyclones in a warming climate in the HiGEM high-resolution climate model. J. Climate, 24, 53365352.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim Reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 483485.

  • Emanuel, K. A., 2008: The hurricane–climate connection. Bull. Amer. Meteor. Soc., 89, ES10ES20.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., , K. Oouchi, , M. Satoh, , H. Tomita, , and Y. Yamada, 2010: Comparison of explicitly simulated and downscaled tropical cyclone activity in a high-resolution global climate model. J. Adv. Model. Earth Syst., 2 (4), doi:10.3894/JAMES.2010.2.9.

    • Search Google Scholar
    • Export Citation
  • Evans, J. L., , and R. E. Hart, 2003: Objective indicators of the life cycle evolution of extratropical transition for Atlantic tropical cyclones. Mon. Wea. Rev., 131, 909925.

    • Search Google Scholar
    • Export Citation
  • Garner, S. T., , I. M. Held, , T. Knutson, , and J. Sirutis, 2009: The roles of wind shear and thermal stratification in past and projected changes of Atlantic tropical cyclone activity. J. Climate, 22, 47234734.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., , and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 14831506.

    • Search Google Scholar
    • Export Citation
  • Gualdi, S., , E. Scoccimarro, , and A. Navarra, 2008: Changes in tropical cyclone activity due to global warming: Results from a high-resolution coupled general circulation model. J. Climate, 21, 52045228.

    • Search Google Scholar
    • Export Citation
  • Guo, L., , E. J. Highwood, , L. Shaffrey, , and A. Turner, 2013: The effect of regional changes in anthropogenic aerosols on rainfall of the East Asian summer monsoon. Atmos. Chem. Phys., 13, 15211534.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699.

  • Held, I. M., , and M. Zhao, 2011: The response of tropical cyclone statistics to an increase in CO2 with fixed sea surface temperatures. J. Climate, 24, 53535364.

    • Search Google Scholar
    • Export Citation
  • Hopsch, S. B., , C. D. Thorncroft, , and K. R. Tyle, 2010: Analysis of African easterly wave structures and their role in influencing tropical cyclogenesis. Mon. Wea. Rev., 138, 13991419.

    • Search Google Scholar
    • Export Citation
  • Johns, T. C., and Coauthors, 2006: The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. J. Climate, 19, 13271353.

    • Search Google Scholar
    • Export Citation
  • Kang, S., , and J. Lu, 2012: Expansion of the Hadley cell under global warming: Winter versus summer. J. Climate, 25, 83878393.

  • Kim, J.-H., , S. J. Brown, , and R. E. McDonald, 2010: Future changes in tropical cyclone genesis in fully dynamic ocean- and mixed layer ocean-coupled climate models: A low-resolution model study. Climate Dyn., 37, 737758.

    • 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). Bull. Amer. Meteor. Soc., 91, 363376.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163.

  • Landsea, C. W., 2007: Counting Atlantic tropical cyclones back to 1900. Eos, Trans. Amer. Geophys. Union, 88, 197208.

  • Landsea, C. W., , B. A. Harper, , K. Hoarau, , and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones? Science, 313, 452454.

    • Search Google Scholar
    • Export Citation
  • Lee, S.-K., , D. B. Enfield, , and C. Wang, 2011: Future impact of differential interbasin ocean warming on Atlantic hurricanes. J. Climate, 24, 12641275.

    • Search Google Scholar
    • Export Citation
  • Li, T., , M. Kwon, , M. Zhao, , J.-S. Kug, , J.-J. Luo, , and W. Yu, 2010: Global warming shifts Pacific tropical cyclone location. Geophys. Res. Lett., 37, L21804, doi:10.1029/2010GL045124.

    • Search Google Scholar
    • Export Citation
  • Lu, J., , G. A. Vecchi, , and T. Reichler, 2007: Expansion of the Hadley cell under global warming. Geophys. Res. Lett.,34, L06805, doi:10.1029/2006GL028443.

  • 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.

    • Search Google Scholar
    • Export Citation
  • McDonald, R. E., , D. G. Bleaken, , D. R. Creswell, , V. D. Pope, , and C. A. Senior, 2005: Tropical storms: Representation and diagnosis in climate models and the impacts of climate change. J. Climate, 18, 12621275.

    • Search Google Scholar
    • Export Citation
  • Murakami, H., , and M. Sugi, 2010: Effect of model resolution on tropical cyclone climate projections. SOLA, 6, 7376.

  • Murakami, H., , R. Mizuta, , and E. Shindo, 2012a: Future changes in tropical cyclone activity projected by multi-physics and multi-SST ensemble experiments using the 60-km-mesh MRI-AGCM. Climate Dyn., 39, 25692584.

    • Search Google Scholar
    • Export Citation
  • Murakami, H., and Coauthors, 2012b: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Climate, 25, 32373260.

    • 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 analyses. J. Meteor. Soc. Japan, 84, 259276.

    • Search Google Scholar
    • Export Citation
  • Ringer, M. A., , G. M. Martin, , C. Z. Greeves, , T. J. Hinton, , P. M. James, , V. D. Pope, , A. A. Scaife, , and R. A. Stratton, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of variability and regional climate. J. Climate, 19, 13021326.

    • Search Google Scholar
    • Export Citation
  • Roberts, M. J., and Coauthors, 2009: Impact of resolution on the tropical Pacific circulation in a matrix of coupled models. J. Climate, 22, 25412556.

    • Search Google Scholar
    • Export Citation
  • Schneider, T., , P. A. O'Gorman, , and X. J. Levine, 2010: Water vapor and the dynamics of climate changes. Rev. Geophys., 48, RG3001, doi:10.1029/2009RG000302.

    • Search Google Scholar
    • Export Citation
  • Scoccimarro, E., and Coauthors, 2011: Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. J. Climate, 24, 43684384.

    • Search Google Scholar
    • Export Citation
  • Shaffrey, L. C., and Coauthors, 2009: U.K. HiGEM: The new U.K. High-Resolution Global Environment Model—Model description and basic evaluation. J. Climate, 22, 18611896.

    • Search Google Scholar
    • Export Citation
  • Smith, D. M., , R. Eade, , N. J. Dunstone, , D. Fereday, , J. M. Murphy, , H. Pohlmann, , and A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. Nat. Geosci., 3, 846849.

    • Search Google Scholar
    • Export Citation
  • Strachan, J., , P. L. Vidale, , K. Hodges, , M. Roberts, , and M.-E. Demory, 2013: Investigating global tropical cyclone activity with a hierarchy of AGCMs: The role of model resolution. J. Climate, 26, 133152.

    • Search Google Scholar
    • Export Citation
  • Sugi, M., , A. Noda, , and N. Sato, 2002: Influence of global warming on tropical cyclone climatology: An experiment with the JMA global model. J. Meteor. Soc. Japan, 80, 249272.

    • Search Google Scholar
    • Export Citation
  • Sugi, M., , H. Murakami, , and J. Yoshimura, 2009: A reduction in global tropical cyclone frequency due to global warming. SOLA, 5, 164167.

    • Search Google Scholar
    • Export Citation
  • Sugi, M., , H. Murakami, , and J. Yoshimura, 2012: On the mechanism of tropical cyclone frequency changes due to global warming. J. Meteor. Soc. Japan, 90A, 397408.

    • Search Google Scholar
    • Export Citation
  • Tang, B. H., , and J. D. Neelin, 2004: ENSO influence on Atlantic hurricanes via tropospheric warming. Geophys. Res. Lett., 31, L24204, doi:10.1029/2004GL021072.

    • Search Google Scholar
    • Export Citation
  • Tsutsui, J., 2002: Implications of anthropogenic climate change for tropical cyclone activity: A case study with the NCAR CCM2. J. Meteor. Soc. Japan, 80, 4565.

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

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., , and B. J. Soden, 2007b: Increased tropical Atlantic wind shear in model projections of global warming. Geophys. Res. Lett., 34, L08702, doi:10.1029/2006GL028905.

    • Search Google Scholar
    • Export Citation
  • Walsh, K. J. E., , M. Fiorino, , C. W. Landsea, , and K. L. McInnes, 2007: Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J. Climate, 20, 23072314.

    • Search Google Scholar
    • Export Citation
  • Yoshimura, J., , and M. Sugi, 2005: Tropical cyclone climatology in a high-resolution AGCM—Impacts of SST warming and CO2 increase. SOLA, 1, 133136.

    • 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, doi:10.1029/2006GL026267.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Tropical cyclone track density (storm transits per month per 106 km2, equivalent to a 5° radius) during May–November in the Northern Hemisphere and October–May in the Southern Hemisphere for (a) IBTrACS, (b) ERA-Interim, (c) HiGAM AMIP II simulation (Strachan et al. 2013), and (d) HiGAM forced with HiGEM SST. The numbers shown in each subdomain are the climatology annual count of tropical cyclones. Note: IBTrACS removes extratropical position.

  • View in gallery

    As in Fig. 1 but for (a) for HiGEM present-day simulation, (b) the North Atlantic, (c) 2CO2 minus present-day simulation, (d) the North Atlantic 2CO2 minus present-day simulation, (e) 4CO2 minus present-day simulation, and (f) North Atlantic 4CO2 minus present-day simulation. Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

  • View in gallery

    Percentage change of annual tropical cyclone counts. The error bars denote the maximum and minimum 5 × 30-yr present-day simulations. The present-day climatology is shown at the bottom of the x axis label.

  • View in gallery

    Normalized distributions of storm maximum intensities in terms of 850-hPa wind speed from HiGEM for the (a) North Atlantic, (b) northwest Pacific, (c) northeast Pacific, (d) north Indian Ocean, (e) South Pacific, and (f) south Indian Ocean. The error bars denote the maximum and minimum 5 × 30-yr present-day simulations. Note the difference in the scaling of the y axis for (c) and (d). Bin widths are 5 m s−1.

  • View in gallery

    As in Fig. 4, but from HiGAM–HiGEM time slice experiments.

  • View in gallery

    Sea surface temperature (°C), July–October for (a) 2CO2 minus present-day simulation, (b) 2CO2 tropical (30°S–30°N) mean sea surface temperature change, (c) 4CO2 minus present-day simulation, and (d) 4CO2 tropical mean sea surface temperature change. In (b) and (c) the number at the top right shows the tropical mean anomaly (TM). The present-day simulation climatology is shown in black contours in (a). Significance was found everywhere in (a) and (c), so stippling is not shown.

  • View in gallery

    Height–longitude cross section of the Walker circulation, 0°–10°N, in July–October for (a) present-day simulation, (b) 2CO2 minus present-day, and (c) 4CO2 minus present-day. The colors show mean ascent (−ω) and the vectors are mean ascent and a change in the velocity potential with respect to latitude. Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

  • View in gallery

    Height–latitude cross section of the Hadley circulation, 0°–360°E, in July–October for (a) present-day simulation, (b) 2CO2 minus present-day, and (c) 4CO2 minus present-day. The zonal average includes regions of updrafts only using the 500-hPa level. The colors show mean ascent (−ω) and the vectors are mean ascent and a change in the velocity potential with respect to longitude. Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

  • View in gallery

    Vertical wind shear (m s−1) in July–October for (a) 2CO2 minus present-day simulation and (b) 4CO2 minus present-day simulation. The present-day simulation climatology is shown in black contours in (a). Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

  • View in gallery

    Percentage change of ocean-only large-scale monthly-mean parameters for (a)–(d) July–October and (e),(f) December–March. The area defined is shown above the plot. The error bars show the maximum and minimum variability of the 5 × 30-yr present-day simulations. The climatologies in the control simulation are shown at the bottom: SST (°C), tropical relative SST (30°S–30°N, °C), relative humidity at 700 hPa (RH, %), precipitation (ppt, mm day−1), mean ascent at 500 hPa (−ω500, Pa s−1), vertical wind shear (vws, m s−1), tropical cyclone track density (TCden, as in Fig. 1), and tropical cyclone frequency (TCfreq, as in Fig. 3). The percentage change of −ω500 in the North Atlantic (NATL) is shown as minus because the region has subsiding air in the present-day simulation. Note the difference in scaling of the y axis.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 132 132 7
PDF Downloads 85 85 5

Response of Tropical Cyclones to Idealized Climate Change Experiments in a Global High-Resolution Coupled General Circulation Model

View More View Less
  • 1 Department of Meteorology, University of Reading, Reading, United Kingdom
  • | 2 National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, United Kingdom
  • | 3 National Centre for Earth Observation, University of Reading, Reading, United Kingdom
  • | 4 Met Office Hadley Centre, Exeter, United Kingdom
© Get Permissions
Full access

Abstract

The authors present an assessment of how tropical cyclone activity might change owing to the influence of increased atmospheric carbon dioxide concentrations, using the U.K. High-Resolution Global Environment Model (HiGEM) with N144 resolution (~90 km in the atmosphere and ~40 km in the ocean). Tropical cyclones are identified using a feature-tracking algorithm applied to model output. Tropical cyclones from idealized 30-yr 2×CO2 (2CO2) and 4×CO2 (4CO2) simulations are compared to those identified in a 150-yr present-day simulation that is separated into a five-member ensemble of 30-yr integrations. Tropical cyclones are shown to decrease in frequency globally by 9% in the 2CO2 and 26% in the 4CO2. Tropical cyclones only become more intense in the 4CO2; however, uncoupled time slice experiments reveal an increase in intensity in the 2CO2. An investigation into the large-scale environmental conditions, known to influence tropical cyclone activity in the main development regions, is used to determine the response of tropical cyclone activity to increased atmospheric CO2. A weaker Walker circulation and a reduction in zonally averaged regions of updrafts lead to a shift in the location of tropical cyclones in the Northern Hemisphere. A decrease in mean ascent at 500 hPa contributes to the reduction of tropical cyclones in the 2CO2 in most basins. The larger reduction of tropical cyclones in the 4CO2 arises from further reduction of the mean ascent at 500 hPa and a large enhancement of vertical wind shear, especially in the Southern Hemisphere, North Atlantic, and northeast Pacific.

Corresponding author address: Ray Bell, Department of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading, RG6 6BB, United Kingdom. E-mail: r.j.bell@pgr.reading.ac.uk

Abstract

The authors present an assessment of how tropical cyclone activity might change owing to the influence of increased atmospheric carbon dioxide concentrations, using the U.K. High-Resolution Global Environment Model (HiGEM) with N144 resolution (~90 km in the atmosphere and ~40 km in the ocean). Tropical cyclones are identified using a feature-tracking algorithm applied to model output. Tropical cyclones from idealized 30-yr 2×CO2 (2CO2) and 4×CO2 (4CO2) simulations are compared to those identified in a 150-yr present-day simulation that is separated into a five-member ensemble of 30-yr integrations. Tropical cyclones are shown to decrease in frequency globally by 9% in the 2CO2 and 26% in the 4CO2. Tropical cyclones only become more intense in the 4CO2; however, uncoupled time slice experiments reveal an increase in intensity in the 2CO2. An investigation into the large-scale environmental conditions, known to influence tropical cyclone activity in the main development regions, is used to determine the response of tropical cyclone activity to increased atmospheric CO2. A weaker Walker circulation and a reduction in zonally averaged regions of updrafts lead to a shift in the location of tropical cyclones in the Northern Hemisphere. A decrease in mean ascent at 500 hPa contributes to the reduction of tropical cyclones in the 2CO2 in most basins. The larger reduction of tropical cyclones in the 4CO2 arises from further reduction of the mean ascent at 500 hPa and a large enhancement of vertical wind shear, especially in the Southern Hemisphere, North Atlantic, and northeast Pacific.

Corresponding author address: Ray Bell, Department of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading, RG6 6BB, United Kingdom. E-mail: r.j.bell@pgr.reading.ac.uk

1. Introduction

The study of tropical cyclones and climate change has received much attention in recent years owing to the large socioeconomic impacts associated with these extreme weather systems and the possible changes in associated risk. Due to recent advances in available computing resources, general circulation models (GCMs) can now be run with a high enough resolution to simulate different aspects of tropical cyclone activity (e.g., Zhao et al. 2009; Smith et al. 2010; Murakami et al. 2012b; Manganello et al. 2012; Strachan et al. 2013). The response of tropical cyclones to changes in the large-scale environment associated with climate change can also be investigated using high-resolution GCMs, which provide a platform to examine the mechanisms.

There have been a number of previous studies investigating tropical cyclones and climate change using atmospheric general circulation models (AGCMs) (e.g., McDonald et al. 2005; Bengtsson et al. 2007a; Held and Zhao 2011; Murakami et al. 2012a,b). A consistent result from these studies is that there is a general decrease in the global number of tropical cyclones [see supplementary material Table S1 in Knutson et al. (2010) for a summary]. The models also predict an increase in tropical cyclone intensity [see Table S3 in Knutson et al. (2010)] in line with the theory presented by Emanuel (1987). However, the ability of models to simulate a change in tropical cyclone intensity is largely resolution dependent (Bengtsson et al. 2007a; Murakami and Sugi 2010). The simulated decrease of global tropical cyclone frequency has previously been related to changes in dynamical parameters, such as an increase in vertical wind shear over the main development regions (Tsutsui 2002; Garner et al. 2009) and also a decrease in convective mass flux (Sugi et al. 2002; Yoshimura and Sugi 2005; Oouchi et al. 2006; Bengtsson et al. 2007a; Held and Zhao 2011; Sugi et al. 2012). In the study of Murakami et al. (2012a), an AGCM was integrated at 60 km (TL319) to investigate the influence of different physical parameterizations and sea surface temperature (SST) on future tropical cyclone changes. They showed that variations in the projected SST pattern affected the dynamical parameters of vertical motion at 500 hPa (ω500) and relative vorticity at 850 hPa, which caused larger deviations in global tropical cyclone activity than changes in the model physics. Other studies point to changes in thermodynamics, such as the midtroposphere saturation deficit, being more important (Emanuel et al. 2008).

The AGCM studies typically use the “time slice” method (Bengtsson et al. 1995) to allow the use of higher resolution in the atmosphere than would otherwise be possible. The time slice approach utilizes SST and sea ice distributions taken from relatively low-resolution coupled atmosphere–ocean GCM (AOGCM) experiments as boundary conditions. Time slice experiments typically have short integration lengths of ~20–30 years. This makes it difficult to address whether the changes in tropical cyclones seen in these simulations are likely to be outside the range of natural variability. Additionally, AGCMs do not allow tropical cyclones to feedback onto the SST and ocean heat content (Scoccimarro et al. 2011), which may influence future tropical cyclone activity (Emanuel 2008). Investigating tropical cyclones and climate change using AOGCMs has received less attention due to the larger computational costs involved. The study of Tsutsui (2002) used the National Centre for Atmospheric Research (NCAR) Community Climate Model, version 2 (CCM2), which had a resolution of T42, and Bengtsson et al. (2007a) used the Max Planck Institute (MPI) ECHAM5 at T63. Gualdi et al. (2008) assessed 30 years at present-day, 2×CO2 (2CO2), and 4×CO2 (4CO2) using the Scale Interaction Experiment-Frontier Research Center for Global Change system (SINTEX-G), which had a 2° × 2° resolution ocean model and a T106 resolution atmospheric model. More recently, Scoccimarro et al. (2011) used a higher resolution T159 atmospheric model; however, they only compared two 20-yr periods of a transient experiment. Other studies have used output from low-resolution AOGCMs to allow a focus on inferred changes of tropical cyclone activity, for example, changing genesis parameters (Kim et al. 2010). It should be noted that the AOGCM simulations give similar results of tropical cyclone frequency changes compared to the AGCM experiments, but simulate a smaller increase in intensity, related to negative feedback associated with wind-stress-induced cold water upwelling (Scoccimarro et al. 2011).

Previous work within the High-Resolution Global Environment Model (HiGEM) framework has focused on the ability of the HiGEM atmospheric component, the High-Resolution Global Atmospheric Model (HiGAM), to simulate present-day tropical cyclone climatology (Strachan et al. 2013). In that study, HiGAM was forced with observed SST and sea ice concentration boundary conditions from the second phase of the Atmospheric Model Intercomparison Project (AMIP II), integrated for the period 1979 until 2002. Strachan et al. (2013) found that HiGAM simulates slightly fewer tropical cyclones in the North Atlantic and northeast Pacific compared to observations, shown in Fig. 1. HiGAM also simulates a more even split in tropical cyclone numbers between hemispheres than observed, with almost twice as many tropical cyclones identified in the South Pacific.

Fig. 1.
Fig. 1.

Tropical cyclone track density (storm transits per month per 106 km2, equivalent to a 5° radius) during May–November in the Northern Hemisphere and October–May in the Southern Hemisphere for (a) IBTrACS, (b) ERA-Interim, (c) HiGAM AMIP II simulation (Strachan et al. 2013), and (d) HiGAM forced with HiGEM SST. The numbers shown in each subdomain are the climatology annual count of tropical cyclones. Note: IBTrACS removes extratropical position.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

In this study, we make use of a long integration of 150 years at present-day CO2 concentration using a high-resolution AOGCM, with the aim of obtaining a good representation of natural variability on multidecadal time scales. This is compared to idealized climate change experiments stabilized at 2×CO2 and 4×CO2 to investigate the response of tropical cyclone activity with climate change. The focus here is an understanding of the mechanisms, not a prediction, so we have chosen to investigate the impact of only increasing CO2 without the further complexity of aerosol or time-varying radiative forcing.

The paper is structured as follows. In section 2 the model and tracking algorithm are described. Section 3 will show the ability of the model to represent present-day tropical cyclone climatology. The results of changing tropical cyclone location, frequency, and intensity are shown in section 4 along with discussions of the changing large-scale environmental conditions in section 5. The results are summarized in section 6 along with concluding remarks.

2. Data and methodologies

a. The model

This study uses HiGEM1.1, a high-resolution coupled climate model based on the Met Office Hadley Centre Global Environmental Model, version 1 (HadGEM1), (Johns et al. 2006; Ringer et al. 2006) run on the Japanese Earth Simulator as part of the United Kingdom–Japan Climate Collaboration (UJCC). The horizontal resolution of the atmospheric component is 0.83° latitude × 1.25° longitude (N144, 90 km at 50°N). The atmosphere has 38 vertical levels extending to over 39 km in height. The ocean component also has a high resolution, compared to other coupled model simulations, at 0.3° × 0.3° (40 km at 50°N) with 40 vertical levels. HiGEM uses the mass flux cumulus convective parameterization scheme of Gregory and Rowntree (1990). More details about HiGEM and validation of the large-scale fields can be found in Roberts et al. (2009) and Shaffrey et al. (2009). This model has also been used to investigate extratropical cyclones and climate change by Catto et al. (2011).

The HiGEM control simulation was completed using present-day radiative forcing for 150 years. This period has been split into five 30-yr periods, effectively providing a five-member AOGCM ensemble to aid our understanding of natural variability on multidecadal time scales. From the control simulation, a transient climate change integration was performed with CO2 levels increasing by 2% yr−1. The CO2 levels were then stabilized at 2×CO2 levels and integrated for a further 30 years. The transient integration was continued and the CO2 levels stabilized at 4×CO2. Again this was integrated for another 30 years. The two integrations with the stabilized CO2 levels will be referred to as the 2CO2 and 4CO2. Tropical cyclone activity is investigated in both experiments and assessed to find out whether any changes are outside the range of natural variability given by the 5 × 30-yr control simulation.

The SSTs from these simulations are also prescribed onto HiGAM as time slice experiments, each for 30 yr. The time slice experiments reveal differences in coupled and uncoupled simulations, which are investigated for projections of tropical cyclone intensity.

b. Observational and reanalysis data

Global observed tropical cyclone data from the International Best Track Archive for Climate Stewardship (IBTrACS) (Knapp et al. 2010) for the 1979–2002 period are used to validate the model in terms of its ability to simulate present-day tropical cyclone climatology. IBTrACS provides the best observations of tropical cyclones from multiple regional observational centers, which are merged into one product. However, there are known limitations of IBTrACS, such as global inhomogeneity (Landsea 2007) and changes in observational techniques (Landsea et al. 2006). The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) is based on the predictions of an operational forecast model constrained by assimilation of data (Dee et al. 2011). The resolution of ERA-Interim is ~80 km (T255). The tracking algorithm (described below) is applied to ERA-Interim using the same procedure as for HiGEM, providing a more consistent comparison between tracked model data and observational data (Strachan et al. 2013).

c. Tropical cyclone tracking methodology

In our study we use an objective feature-tracking methodology to identify and track tropical warm-core vortices with cyclonic rotation and tropical origin, which we hereafter refer to as tropical cyclones. The method is fully described in Bengtsson et al. (2007b) and Strachan et al. (2013). For initial identification and tracking, 850-hPa relative vorticity is computed at a spectral resolution of T42. The initial identification is made for vorticity maxima with intensities greater than 0.5 × 10−5 s−1 in the Northern Hemisphere (minima less than −0.5 × 10−5 s−1 in the Southern Hemisphere). Systems with a lifetime of over two days are retained for further analysis. The tropical cyclone features are identified using intensity thresholds and evidence of a warm core using the following criteria.

  1. T63 relative vorticity at 850 hPa is larger than 6 × 10−5 s−1.
  2. A positive T63 vorticity center must exist at 850, 500, and 200 hPa.
  3. There must be a minimum reduction in vorticity at T63 from 850 to 200 hPa of 6 × 10−5 s−1 to provide evidence of a warm core.
  4. There must be a reduction in T63 vorticity with height between pressure levels of 850 and 500 hPa, as well as between 500 and 200 hPa.
  5. Criteria (i) to (iv) must be attained for a minimum of 4 × 6-hourly time steps (one day).
The same criteria above also apply for tracking systems in the Southern Hemisphere but with the values multiplied by −1. For a complete discussion of these criteria the reader is directed to Strachan et al. (2013).

Track density statistics are used to investigate the spatial distribution of tropical cyclones via the tracking algorithm. Tropical cyclones from the analysis period are composited and monthly-mean storm transits per unit area (equivalent to a 5° spherical cap, or approximately 106 km2) are calculated. In the tracking of HiGEM and ERA-Interim identification is based on the T42 vorticity. The tracking algorithm follows the life cycle from “genesis,” when the initial T42 vorticity maxima threshold is achieved, through to “lysis,” when the T42 vorticity maxima threshold is no longer achieved (Strachan et al. 2013). “Cutting” the tracks similar to IBTrACS would remove valuable information, for example easterly wave precursors (Hopsch et al. 2010) and the extratropical transitions (Evans and Hart 2003), often associated with tropical cyclones. However, it should be noted that IBTrACS does not remove all of the associated extratropical storm tracks (Evans and Hart 2003).

3. Tropical cyclones in the present climate

It is important to evaluate the ability of HiGEM to simulate the present-day tropical cyclone climatology to provide a confidence level for the climate change simulations. A comparison of track densities from IBTrACS and reanalysis with HiGAM results, which are based on observed SSTs, reduces the complexity of understanding systematic errors in HiGEM due to inaccurate SST. Figure 2a shows the track density for the HiGEM present-day simulation, as well as the mean number of tropical cyclones simulated in each basin per season. HiGEM simulates slightly fewer tropical cyclones in the North Atlantic compared to HiGAM and observations. A lack of recurving tropical cyclones in the northwest Pacific can also be seen in comparison to those simulated in HiGAM and identified in ERA-Interim (Fig. 1). Cold SST biases in HiGEM (which can be seen in Fig. 3a of Shaffrey et al. 2009) may contribute to both of these systematic errors. HiGEM simulates the same number of tropical cyclones in the South Pacific as HiGAM, although it simulates their genesis more eastward. In addition, HiGEM simulates more tropical cyclones in the south Indian Ocean basin than HiGAM. A positive precipitation bias (which can be seen in Fig. 6 of Shaffrey et al. 2009) is partly explained by higher tropical cyclone counts in both basins compared to observations. Despite these differences, the general spatial distribution of tropical cyclones in HiGEM is in good agreement with the observed tropical cyclone distribution, especially in the Northern Hemisphere. The 150-yr present-day simulation shows multidecadal variability of tropical cyclones on a 30-yr time scale (not shown), similar to what is seen in basins with long time series of observations. HiGEM also captures the spatial variability of tropical cyclones as dependent on the phase of the El Niño–Southern Oscillation (ENSO), which will be presented in a future publication. HiGEM simulates slightly less intense tropical cyclones than those simulated by HiGAM (not shown), as HiGEM allows negative feedback mechanisms between the atmosphere and ocean to be simulated, for example those associated with wind-stress-induced cold water upwelling (Scoccimarro et al. 2011). It can be seen that atmosphere–ocean coupling has little impact on the number of tropical cyclones simulated in each basin, as the coupled HiGEM results look very similar to the HiGAM–HiGEM time slice experiment (Figs. 2a and 1d).

Fig. 2.
Fig. 2.

As in Fig. 1 but for (a) for HiGEM present-day simulation, (b) the North Atlantic, (c) 2CO2 minus present-day simulation, (d) the North Atlantic 2CO2 minus present-day simulation, (e) 4CO2 minus present-day simulation, and (f) North Atlantic 4CO2 minus present-day simulation. Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

4. Tropical cyclones and climate change simulations

It is important to characterize changes in tropical cyclone activity and to determine those that are outside the range of natural variability given by the 5 × 30-yr present-day simulations. The results in this section focus on the difference between the 2CO2 and 4CO2 for tropical cyclone metrics of location, frequency, and intensity.

a. Tropical cyclone location changes

The track density difference between the 2CO2, 4CO2, and control (CTRL) experiment are shown in Fig. 2. There is a reduction of tropical cyclones in the Southern Hemisphere and in the northwest Pacific in both the 2CO2 and 4CO2, which was also found by Sugi et al. (2002), Oouchi et al. (2006), and Zhao et al. (2009). There is an increase in tropical cyclones in the central North Pacific region, which agrees well with previous studies (e.g., McDonald et al. 2005; Li et al. 2010; Zhao and Held 2012; Murakami et al. 2012a). In the northeast Pacific, tropical cyclones shift toward the southwest of the basin in the 2CO2. However, in the 4CO2 the tropical cyclones are greatly reduced throughout the entire basin. The change in location in the North Atlantic is shown in more detail in Figs. 2d,f: tropical cyclones migrate poleward in the 2CO2, whereas there is a reduction throughout the basin in the 4CO2. In general, the tropical cyclones show a slight poleward shift, which was also found by Zhao and Held (2012), but only in the Northern Hemisphere, similar to the study by Emanuel et al. (2010). The stippled regions show changes that are outside the range of natural variability as represented by 5 × 30-yr present-day simulations. Both the northwest Pacific and some regions in the Southern Hemisphere show a robust reduction of tropical cyclones.

b. Tropical cyclone frequency changes

The global, hemispheric, and regional changes in the mean annual number of tropical cyclones are shown in Fig. 3, which shows tropical cyclones decreasing in frequency globally by 9% in the 2CO2 and 26% in the 4CO2 and also show error bars that are a measure of current climate variability. The tropical cyclone frequency changes are similar to previous studies, which give a range from 6% to 34% (Knutson et al. 2010). There is a larger reduction in the number of tropical cyclones in the Southern Hemisphere: 12% in the 2CO2 (30% in the 4CO2) compared to 6% in the Northern Hemisphere in the 2CO2 (22% in the 4CO2). The North Atlantic basin shows the greatest percentage reduction in tropical cyclones; however, in the control simulation this is the basin with the smallest number of storms. The mean frequency in the northeast Pacific in the 2CO2 is similar to the control simulation (18 yr−1), although the tropical cyclones shift their location (Fig. 2). However, in the 4CO2 the tropical cyclones decrease by 40% (11 yr−1). The reduction is greater than the error bars associated with the 2CO2 and are now outside the range of 5 × 30-yr natural variability.

Fig. 3.
Fig. 3.

Percentage change of annual tropical cyclone counts. The error bars denote the maximum and minimum 5 × 30-yr present-day simulations. The present-day climatology is shown at the bottom of the x axis label.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

c. Tropical cyclone intensity changes

To assess the impact of climate change on the intensity of simulated tropical cyclones, the distributions of maximum intensities in terms of 850-hPa wind speed for the global basins are shown in Fig. 4. Lower-level wind speed data are not used as they are significantly damped in these simulations compared to those observed. However, the main concern regarding the use of model output wind speeds to assess the distribution of maximum intensities against observations is that the winds are not directly comparable in terms of vertical level, temporal sampling, and resolution (Walsh et al. 2007; Strachan et al. 2013).

Fig. 4.
Fig. 4.

Normalized distributions of storm maximum intensities in terms of 850-hPa wind speed from HiGEM for the (a) North Atlantic, (b) northwest Pacific, (c) northeast Pacific, (d) north Indian Ocean, (e) South Pacific, and (f) south Indian Ocean. The error bars denote the maximum and minimum 5 × 30-yr present-day simulations. Note the difference in the scaling of the y axis for (c) and (d). Bin widths are 5 m s−1.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

There is a shift to more intense tropical cyclones along with a reduction of weak storms in the Southern Hemisphere. However, this is only significant in the 4CO2; the 2CO2 changes are within the 5 × 30-yr range of natural variability (indicated by the error bars). The Northern Hemisphere basins also show a similar pattern. The North Atlantic shows the greatest shift to more intense tropical cyclones in the 4CO2, similar to the results by Oouchi et al. (2006) who used a 20-km AGCM. However, Oouchi et al. found a decrease in maximum intensity in other basins, although they used short 10-yr simulations. Tropical cyclones become relatively more intense in the northeast Pacific in the 4CO2, although there is a reduction in weak storms in both 2CO2 and 4CO2. The increase in intensity in all basins, which are barely outside the range of natural variability in the 4CO2, is lower than in most previous studies (Knutson et al. 2010). The majority of studies in Knutson et al. used AGCMs with higher resolution than HiGEM and focus on the late twenty-first-century period, with carbon dioxide levels around 2×CO2. The small increase in simulated tropical cyclone intensity is, in part, an issue of model resolution but is also linked to atmosphere–ocean coupling. Bengtsson et al. (2007a) noted that higher resolution models are needed to simulate more realistic intensities and therefore required to assess the change in intensities with climate change. Gualdi et al. (2008) found no increase in tropical cyclone intensity as measured by mean sea level pressure for their 4×CO2 simulation, albeit using a model with a coarser atmospheric component.

The role of atmosphere–ocean coupling on future tropical cyclone intensity is investigated further using HiGAM–HiGEM time slice experiments, shown in Fig. 5. These results indicate that an increase in tropical cyclone intensity can be seen more so in the 2CO2 experiments compared to those in the coupled simulations, although there are some differences on a regional scale. The northwest Pacific shows a robust response of tropical cyclones becoming more intense in the 2CO2 in the uncoupled simulations. Negative feedback associated with cold water upwelling does not occur in the uncoupled simulations. As a result, tropical cyclones become more intense with increasing CO2 in the uncoupled simulations. There is also a clear shift of more intense tropical cyclones in the Southern Hemisphere in the 2CO2. The response in the northeast Pacific is very similar in the coupled and uncoupled simulations.

Fig. 5.
Fig. 5.

As in Fig. 4, but from HiGAM–HiGEM time slice experiments.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

5. Changes in large-scale environmental conditions

The number of tropical cyclones that form each year and their maximum intensities are largely dependent on the large-scale environmental conditions. An understanding of these relationships has allowed for statistical seasonal forecasts to be developed (Camargo et al. 2007). The discussion in this section focuses on the differences between the 2CO2 and 4CO2 to the 5 × 30-yr present-day simulations for aspects of the large-scale environment that are known to modulate tropical cyclone activity. These include SST, vertical wind shear, tropical circulation, and the relationship between changing thermodynamic and dynamic parameters.

a. Sea surface temperature change

The change in SST during July–October (JASO) shown in Figs. 6a,c indicates a warming everywhere in the tropics, in line with results from phase 3 of the Coupled Model Intercomparison Project (CMIP3) (Zhao et al. 2009; Zhao and Held 2012). One striking feature, which occurs in both 2CO2 and 4CO2, is a tongue of relatively cooler water in the tropical North Atlantic, as shown in Figs. 6b,d. Research by Vecchi and Soden (2007a), Sugi et al. (2009), and Murakami et al. (2012a) suggests that for tropical cyclones the spatial structure of future SST is more important than the absolute SST changes. This reduced SST warming in the North Atlantic, which is less than the tropical average SST warming, has a strong impact on the number of tropical cyclones that can form in the region (Lee et al. 2011). In addition, SSTs are shown to warm more in the Northern Hemisphere than in the Southern Hemisphere during their peak tropical cyclone seasons (not shown), as was also found by Vecchi and Soden (2007b).

Fig. 6.
Fig. 6.

Sea surface temperature (°C), July–October for (a) 2CO2 minus present-day simulation, (b) 2CO2 tropical (30°S–30°N) mean sea surface temperature change, (c) 4CO2 minus present-day simulation, and (d) 4CO2 tropical mean sea surface temperature change. In (b) and (c) the number at the top right shows the tropical mean anomaly (TM). The present-day simulation climatology is shown in black contours in (a). Significance was found everywhere in (a) and (c), so stippling is not shown.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

b. Circulation change

Catto et al. (2011) showed that HiGEM simulates greater warming in the upper tropical atmosphere compared to the lower tropopause, similar to other studies (Held and Soden 2006; Bengtsson et al. 2007a). The upward mass flux decreases because the top-of-atmosphere radiation cannot compensate for the increase in lower-tropospheric specific humidity and associated latent heating with a fixed mass flux (Held and Soden 2006). This has previously been discussed as the cause of the simulated reduction in global tropical cyclone frequency (e.g., Sugi et al. 2002; Yoshimura and Sugi 2005; Oouchi et al. 2006; Bengtsson et al. 2007a; Held and Zhao 2011; Sugi et al. 2012). Held and Zhao (2011) argue that a reduction of mean ascent at 500 hPa makes the mid-to-upper-level environmental air drier, as there is less detrainment of moist air from convective systems. This reduces the relative humidity at 700 hPa, and the drier air is able to suppress tropical cyclogenesis more effectively via entrainment and downdrafts. When assessing changes in the mean zonal circulation, Fig. 7 shows that there is a weakening of the rising branch of the Walker circulation in the northwest Pacific, particularly in the 4CO2. The weaker mean ascent reduces the convective mass flux, which makes the environment less favorable to tropical cyclone development. In contrast, the descending branch in the central Pacific shows anomalous ascent, which favors the enhanced development of tropical cyclones in this region (see also Li et al. 2010; Murakami et al. 2012a).

Fig. 7.
Fig. 7.

Height–longitude cross section of the Walker circulation, 0°–10°N, in July–October for (a) present-day simulation, (b) 2CO2 minus present-day, and (c) 4CO2 minus present-day. The colors show mean ascent (−ω) and the vectors are mean ascent and a change in the velocity potential with respect to latitude. Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

While previous studies have focused on possible changes in the Walker circulation, a changing Hadley circulation has received less attention. However, this is perhaps even more relevant as tropical cyclones tend to form north of 10°N. To also address a change in the vertical mass flux in the meridional direction, an investigation of a change in the Hadley cell circulation is shown in Fig. 8. We use regions of updraft only in the zonal mean to reflect regions that are conducive for tropical cyclone development. HiGEM simulates a weakening of the intertropical convergence zone (ITCZ) with an increase of updrafts either side of 9°N, related to a weakening of the Hadley circulation similar to the results found by Lu et al. (2007) and Kang and Lu (2012). The weakening suppresses tropical cyclone activity in low latitudes around 10°N, a feature that is more pronounced in the 4CO2. Although the magnitude of the change is much less than the weakening of the Walker circulation, the weakening will also aid in the overall reduction of tropical cyclones. The anomalous updrafts north of 16°N may be responsible for the poleward migration of tropical cyclones. A widening of the Hadley cell is related to a rise in the tropopause under global warming and a poleward shift of midlatitude eddies (Lu et al. 2007; Schneider et al. 2010). In contrast, in the Southern Hemisphere the Hadley cell during the peak tropical cyclone season shows a weakening, with no shift in location (not shown). This explains why the Southern Hemisphere, unlike the Northern Hemisphere, does not show a poleward shift of tropical cyclones.

Fig. 8.
Fig. 8.

Height–latitude cross section of the Hadley circulation, 0°–360°E, in July–October for (a) present-day simulation, (b) 2CO2 minus present-day, and (c) 4CO2 minus present-day. The zonal average includes regions of updrafts only using the 500-hPa level. The colors show mean ascent (−ω) and the vectors are mean ascent and a change in the velocity potential with respect to longitude. Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

c. Vertical wind shear change

Vertical wind shear is defined as the magnitude of the vector difference between winds at 850 and 200 hPa. The weakening of the Walker circulation has previously been related to the increase in vertical wind shear over the main development region (MDR) of the North Atlantic (Vecchi and Soden 2007b). This response is similar to that which occurs during an El Niño event. The increase in vertical wind shear over the Caribbean is also likely to be associated with the tongue of relatively cooler water in the tropical North Atlantic compared to the warmer SST to the north (Zhang and Delworth 2006). This SST pattern creates a larger meridional temperature gradient, which in turn leads to stronger vertical wind shear over the region via thermal wind balance. Vertical wind shear change in the northeast Pacific in the 2CO2 is strongly related to the change in tropical cyclone location (shown in Fig. 9 and Fig. 2), as the tropical cyclones move to the southwest of the basin. In the 4CO2, however, the spatial extent of increased vertical wind shear expands across the entire basin and is likely to be responsible for the reduced tropical cyclone frequency. There is a reduction in vertical wind shear in the central North Pacific, which would favor tropical cyclone development, in both 2CO2 and 4CO2.

Fig. 9.
Fig. 9.

Vertical wind shear (m s−1) in July–October for (a) 2CO2 minus present-day simulation and (b) 4CO2 minus present-day simulation. The present-day simulation climatology is shown in black contours in (a). Stippling shows where changes are outside the range of 5 × 30-yr present-day simulations.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

d. Thermodynamic versus dynamic influences

Figure 10 shows the percentage change of other large-scale environmental conditions that are important for simulated changes in tropical cyclone activity over the MDRs. This includes, using the terminology of Held and Zhao (2011), thermodynamic variables of SST, tropical relative SST, relative humidity at 700 hPa, and precipitation: in addition, dynamic parameters including mean ascent at 500 hPa (−ω500, a proxy for deep convection) and vertical wind shear.

Fig. 10.
Fig. 10.

Percentage change of ocean-only large-scale monthly-mean parameters for (a)–(d) July–October and (e),(f) December–March. The area defined is shown above the plot. The error bars show the maximum and minimum variability of the 5 × 30-yr present-day simulations. The climatologies in the control simulation are shown at the bottom: SST (°C), tropical relative SST (30°S–30°N, °C), relative humidity at 700 hPa (RH, %), precipitation (ppt, mm day−1), mean ascent at 500 hPa (−ω500, Pa s−1), vertical wind shear (vws, m s−1), tropical cyclone track density (TCden, as in Fig. 1), and tropical cyclone frequency (TCfreq, as in Fig. 3). The percentage change of −ω500 in the North Atlantic (NATL) is shown as minus because the region has subsiding air in the present-day simulation. Note the difference in scaling of the y axis.

Citation: Journal of Climate 26, 20; 10.1175/JCLI-D-12-00749.1

The North Atlantic shows a linear reduction in tropical cyclone frequency between the 2CO2 and 4CO2. The mechanism that reduces tropical cyclone frequency is similar to what occurs during an El Niño event. Warming in the tropical Pacific, compared to the North Atlantic, shown by the tropical relative SST, increases the upper-tropospheric temperature across the tropics and decreases the lapse rate over the North Atlantic, increasing vertical stability. This makes the environment less favorable for genesis (Tang and Neelin 2004). The reduction in −ω500 explains the reduction in tropical cyclone frequency, discussed further in Bengtsson et al. (2007a) and Held and Zhao (2011). HiGEM also simulates an increase in vertical shear in the Caribbean, which expands in size and magnitude in the 4CO2. Garner et al. (2009) find vertical wind shear to be more important than large-scale subsidence, in contrast to the results reported here, although they use a regional model as opposed to the global approach used in this study.

The northwest Pacific shows a robust reduction in tropical cyclones for both 2CO2 and 4CO2, even though vertical wind shear decreases. The small decrease in −ω500 of 3% (12%) in the 2CO2 (4CO2) is of the same sign as the tropical cyclone changes, unlike the other variables. This is shown further in the changing Walker circulation (Fig. 7).

There is a significant nonlinear change in tropical cyclone frequency and track density between the 2CO2 and 4CO2 in the northeast Pacific. Most large-scale parameters remain relatively unchanged in the 2CO2 and the tropical cyclones show a slight reduction in frequency, although not outside the range of 5 × 30-yr control variability (18 yr−1). The magnitude of vertical wind shear increases by 46% over the MDR in the 4CO2, and tropical cyclones are greatly reduced to 11 yr−1. The change in −ω500 is small in both experiments; therefore, it is likely that the tropical cyclones decline in number as a result of an increase in vertical wind shear in this basin.

In the north Indian Ocean tropical cyclones initially increase in frequency in the 2CO2 but do not change in the 4CO2. However, track density increases in the main development region in both experiments, as tropical cyclones tend to form more in the Bay of Bengal. There is an increase in −ω500, also relative humidity at 700 hPa, relating to the tropical cyclone changes.

Both the South Pacific and south Indian Ocean basins have a similar tropical cyclone response. The reduction of tropical cyclones in the South Pacific in the 2CO2 is related to a small decrease in −ω500 and a slight increase in vertical wind shear. In the south Indian Ocean basin in the 2CO2 there is a small decrease in mean ascent at 500 hPa. A further reduction of tropical cyclones in the 4CO2 in both basins is related to less −ω500 and a large increase in vertical wind shear.

The dynamical parameters of −ω500 and vertical wind shear show large percentage changes in the climate change experiments compared to the present-day simulations. The changes are of the same sign as the tropical cyclone changes in most basins. On the other hand, local SST and precipitation changes can be of opposite sign to the tropical cyclone changes. It is known that a small change in the tropical relative SST pattern drives larger changes in dynamical parameters, which ultimately influences tropical cyclone activity (Zhao and Held 2012; Murakami et al. 2012a). We investigated tropical-wide thermodynamical profile changes of temperature, specific humidity, and equivalent potential temperature. However, the results are similar to previous studies (not shown). Current research is being undertaken by Camargo et al. (2012) to produce “climate change genesis potential indices” that will allow for an improved understanding on the role of different environmental parameters on future tropical cyclone activity.

6. Summary and conclusions

Tropical cyclones simulated in a high-resolution AOGCM with a 150-yr present-day control simulation have been compared against idealized climate change simulations of stabilized 2CO2 and 4CO2. Changes in tropical cyclone location, frequency, and intensity are all considered.

Tropical cyclones decrease in frequency globally by 9% and 26% in the 2CO2 and 4CO2, respectively. The reduction arises in the 2CO2 because of a decrease in mean ascent at 500 hPa, similar to what is found in Bengtsson et al. (2007a), Held and Zhao (2011), and Sugi et al. (2012). The further reduction of tropical cyclone frequency in the 4CO2 is thought to be connected to a large increase in vertical wind shear, as well as further reduction of mean ascent at 500 hPa, especially in the Southern Hemisphere. The North Atlantic is the basin that shows the largest decrease in tropical cyclone frequency, although it shows the greatest shift to more intense tropical cyclones. The increase in tropical cyclone intensity only becomes noticeable in the 4CO2 compared to the 5 × 30-yr present-day simulations. The shift to more intense tropical cyclones in the 4CO2 is robust across all basins and is consistent with a shift to fewer weaker tropical cyclones. The increase in intensity was found to be less than found in previous studies (Knutson et al. 2010), owing to the atmosphere–ocean coupling in HiGEM. This was further highlighted by uncoupled simulations that showed tropical cyclones becoming more intense in the 2CO2.

A weaker Walker circulation drives changes in tropical cyclone location: an increase in the central Pacific and eastward shift in the northwest Pacific. Tropical cyclones respond to an increase in vertical wind shear in the northeast Pacific, with a shift to the southwest, which was also found by Vecchi and Soden (2007b). A poleward shift of updrafts associated with the Northern Hemisphere Hadley cell, during peak tropical cyclone season, helps to explain the poleward migration of tropical cyclones.

This study does not address other changing environmental parameters, such as the midtroposphere saturation deficit (Emanuel et al. 2008), due to the limited number of required parameters that were stored as pressure level diagnostics, although other studies show that this changes homogeneously and cannot explain regional changes in future tropical cyclone activity (Sugi et al. 2012). The experiments investigated here assess the tropical cyclone response to an increase in CO2 and do not assess the response to interactive aerosol forcing. Recent work by Booth et al. (2012) highlights the importance of aerosols as a driver for tropical North Atlantic climate variability and, therefore, tropical cyclone variability. Future research using interactive aerosol forcing in HiGEM, such as in Guo et al. (2013), could be exploited to investigate future tropical cyclone activity. The extent to which the response of tropical cyclones to climate change is like an El Niño event will be the focus of future work.

The use of a high-resolution AOGCM with a long present-day integration reveals multidecadal variability of tropical cyclones. Previous limitations of computer resources have not allowed for climate change experiments to be compared to these types of integrations. The increase in atmospheric CO2 simulations reveals robust changes of tropical cyclone activity. In light of this study, high-resolution AOGCMs for integration lengths of multicenturies are needed to assess when tropical cyclone changes are outside the range of natural variability.

Acknowledgments

We thank Chris Holloway for helpful comments. This research was supported by NERC Ph.D. Grant NE/I528569/1 and a collaboration between the University of Reading and Willis. The model described was developed from HadGEM1 by the U.K. High-Resolution Modelling (HiGEM) Project and the United Kingdom–Japan Climate Collaboration. UJCC was jointly funded by NERC and by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). HiGEM is supported by NERC High-Resolution Climate Modelling Grant R8/H12/123. Model integrations were performed using the Japanese Earth Simulator supercomputer, supported by JAMSTEC. We thank three anonymous reviewers and Kevin Walsh for providing thorough reviews with very helpful comments and suggestions for improving the manuscript.

REFERENCES

  • Bengtsson, L., , M. Botzet, , and M. Esch, 1995: Hurricane-type vortices in a general circulation model. Tellus, 47A, 175196.

  • Bengtsson, L., , K. I. Hodges, , M. Esch, , N. Keenlyside, , L. Kornblueh, , J.-J. Luo, , and T. Yamagata, 2007a: How may tropical cyclones change in a warmer climate? Tellus, 59A, 539561.

    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., , K. I. Hodges, , and M. Esch, 2007b: Tropical cyclones in a T159 resolution global climate model: Comparison with observations and re-analyses. Tellus, 59A, 396416.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., , A. G. Barnston, , P. J. Klotzbach, , and C. W. Landsea, 2007: Seasonal tropical cyclone forecasts. WMO Bull., 56, 297309.

    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., , M. K. Tippett, , A. H. Sobel, , G. A. Vecchi, , M. Zhao, , and I. M. Held, 2012: Analysis of tropical cyclone genesis indices for climate change using the HIRAM model. Preprints, Conf. on Hurricanes and Tropical Meteorology, Ponte Vedra Beach, FL, Amer. Meteor. Soc., 4B.1. [Available online at https://ams.confex.com/ams/30Hurricane/webprogram/Paper205593.html.]

  • Catto, J. L., , L. C. Shaffrey, , and K. I. Hodges, 2011: Northern Hemisphere extratropical cyclones in a warming climate in the HiGEM high-resolution climate model. J. Climate, 24, 53365352.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim Reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 483485.

  • Emanuel, K. A., 2008: The hurricane–climate connection. Bull. Amer. Meteor. Soc., 89, ES10ES20.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., , K. Oouchi, , M. Satoh, , H. Tomita, , and Y. Yamada, 2010: Comparison of explicitly simulated and downscaled tropical cyclone activity in a high-resolution global climate model. J. Adv. Model. Earth Syst., 2 (4), doi:10.3894/JAMES.2010.2.9.

    • Search Google Scholar
    • Export Citation
  • Evans, J. L., , and R. E. Hart, 2003: Objective indicators of the life cycle evolution of extratropical transition for Atlantic tropical cyclones. Mon. Wea. Rev., 131, 909925.

    • Search Google Scholar
    • Export Citation
  • Garner, S. T., , I. M. Held, , T. Knutson, , and J. Sirutis, 2009: The roles of wind shear and thermal stratification in past and projected changes of Atlantic tropical cyclone activity. J. Climate, 22, 47234734.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., , and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 14831506.

    • Search Google Scholar
    • Export Citation
  • Gualdi, S., , E. Scoccimarro, , and A. Navarra, 2008: Changes in tropical cyclone activity due to global warming: Results from a high-resolution coupled general circulation model. J. Climate, 21, 52045228.

    • Search Google Scholar
    • Export Citation
  • Guo, L., , E. J. Highwood, , L. Shaffrey, , and A. Turner, 2013: The effect of regional changes in anthropogenic aerosols on rainfall of the East Asian summer monsoon. Atmos. Chem. Phys., 13, 15211534.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699.

  • Held, I. M., , and M. Zhao, 2011: The response of tropical cyclone statistics to an increase in CO2 with fixed sea surface temperatures. J. Climate, 24, 53535364.

    • Search Google Scholar
    • Export Citation
  • Hopsch, S. B., , C. D. Thorncroft, , and K. R. Tyle, 2010: Analysis of African easterly wave structures and their role in influencing tropical cyclogenesis. Mon. Wea. Rev., 138, 13991419.

    • Search Google Scholar
    • Export Citation
  • Johns, T. C., and Coauthors, 2006: The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. J. Climate, 19, 13271353.

    • Search Google Scholar
    • Export Citation
  • Kang, S., , and J. Lu, 2012: Expansion of the Hadley cell under global warming: Winter versus summer. J. Climate, 25, 83878393.

  • Kim, J.-H., , S. J. Brown, , and R. E. McDonald, 2010: Future changes in tropical cyclone genesis in fully dynamic ocean- and mixed layer ocean-coupled climate models: A low-resolution model study. Climate Dyn., 37, 737758.

    • 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). Bull. Amer. Meteor. Soc., 91, 363376.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163.

  • Landsea, C. W., 2007: Counting Atlantic tropical cyclones back to 1900. Eos, Trans. Amer. Geophys. Union, 88, 197208.

  • Landsea, C. W., , B. A. Harper, , K. Hoarau, , and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones? Science, 313, 452454.

    • Search Google Scholar
    • Export Citation
  • Lee, S.-K., , D. B. Enfield, , and C. Wang, 2011: Future impact of differential interbasin ocean warming on Atlantic hurricanes. J. Climate, 24, 12641275.

    • Search Google Scholar
    • Export Citation
  • Li, T., , M. Kwon, , M. Zhao, , J.-S. Kug, , J.-J. Luo, , and W. Yu, 2010: Global warming shifts Pacific tropical cyclone location. Geophys. Res. Lett., 37, L21804, doi:10.1029/2010GL045124.

    • Search Google Scholar
    • Export Citation
  • Lu, J., , G. A. Vecchi, , and T. Reichler, 2007: Expansion of the Hadley cell under global warming. Geophys. Res. Lett.,34, L06805, doi:10.1029/2006GL028443.

  • 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.

    • Search Google Scholar
    • Export Citation
  • McDonald, R. E., , D. G. Bleaken, , D. R. Creswell, , V. D. Pope, , and C. A. Senior, 2005: Tropical storms: Representation and diagnosis in climate models and the impacts of climate change. J. Climate, 18, 12621275.

    • Search Google Scholar
    • Export Citation
  • Murakami, H., , and M. Sugi, 2010: Effect of model resolution on tropical cyclone climate projections. SOLA, 6, 7376.

  • Murakami, H., , R. Mizuta, , and E. Shindo, 2012a: Future changes in tropical cyclone activity projected by multi-physics and multi-SST ensemble experiments using the 60-km-mesh MRI-AGCM. Climate Dyn., 39, 25692584.

    • Search Google Scholar
    • Export Citation
  • Murakami, H., and Coauthors, 2012b: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Climate, 25, 32373260.

    • 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 analyses. J. Meteor. Soc. Japan, 84, 259276.

    • Search Google Scholar
    • Export Citation
  • Ringer, M. A., , G. M. Martin, , C. Z. Greeves, , T. J. Hinton, , P. M. James, , V. D. Pope, , A. A. Scaife, , and R. A. Stratton, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of variability and regional climate. J. Climate, 19, 13021326.

    • Search Google Scholar
    • Export Citation
  • Roberts, M. J., and Coauthors, 2009: Impact of resolution on the tropical Pacific circulation in a matrix of coupled models. J. Climate, 22, 25412556.

    • Search Google Scholar
    • Export Citation
  • Schneider, T., , P. A. O'Gorman, , and X. J. Levine, 2010: Water vapor and the dynamics of climate changes. Rev. Geophys., 48, RG3001, doi:10.1029/2009RG000302.

    • Search Google Scholar
    • Export Citation
  • Scoccimarro, E., and Coauthors, 2011: Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. J. Climate, 24, 43684384.

    • Search Google Scholar
    • Export Citation
  • Shaffrey, L. C., and Coauthors, 2009: U.K. HiGEM: The new U.K. High-Resolution Global Environment Model—Model description and basic evaluation. J. Climate, 22, 18611896.

    • Search Google Scholar
    • Export Citation
  • Smith, D. M., , R. Eade, , N. J. Dunstone, , D. Fereday, , J. M. Murphy, , H. Pohlmann, , and A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. Nat. Geosci., 3, 846849.

    • Search Google Scholar
    • Export Citation
  • Strachan, J., , P. L. Vidale, , K. Hodges, , M. Roberts, , and M.-E. Demory, 2013: Investigating global tropical cyclone activity with a hierarchy of AGCMs: The role of model resolution. J. Climate, 26, 133152.

    • Search Google Scholar
    • Export Citation
  • Sugi, M., , A. Noda, , and N. Sato, 2002: Influence of global warming on tropical cyclone climatology: An experiment with the JMA global model. J. Meteor. Soc. Japan, 80, 249272.

    • Search Google Scholar
    • Export Citation
  • Sugi, M., , H. Murakami, , and J. Yoshimura, 2009: A reduction in global tropical cyclone frequency due to global warming. SOLA, 5, 164167.

    • Search Google Scholar
    • Export Citation
  • Sugi, M., , H. Murakami, , and J. Yoshimura, 2012: On the mechanism of tropical cyclone frequency changes due to global warming. J. Meteor. Soc. Japan, 90A, 397408.

    • Search Google Scholar
    • Export Citation
  • Tang, B. H., , and J. D. Neelin, 2004: ENSO influence on Atlantic hurricanes via tropospheric warming. Geophys. Res. Lett., 31, L24204, doi:10.1029/2004GL021072.

    • Search Google Scholar
    • Export Citation
  • Tsutsui, J., 2002: Implications of anthropogenic climate change for tropical cyclone activity: A case study with the NCAR CCM2. J. Meteor. Soc. Japan, 80, 4565.

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

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., , and B. J. Soden, 2007b: Increased tropical Atlantic wind shear in model projections of global warming. Geophys. Res. Lett., 34, L08702, doi:10.1029/2006GL028905.

    • Search Google Scholar
    • Export Citation
  • Walsh, K. J. E., , M. Fiorino, , C. W. Landsea, , and K. L. McInnes, 2007: Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J. Climate, 20, 23072314.

    • Search Google Scholar
    • Export Citation
  • Yoshimura, J., , and M. Sugi, 2005: Tropical cyclone climatology in a high-resolution AGCM—Impacts of SST warming and CO2 increase. SOLA, 1, 133136.

    • 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, doi:10.1029/2006GL026267.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
Save