Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model

  • Julio T. Bacmeister
  • Kevin A. Reed
  • Cecile Hannay
  • Peter Lawrence
  • Susan Bates
  • John E. Truesdale
  • Nan Rosenbloom
  • Michael Levy
Article

Abstract

This study examines how characteristics of tropical cyclones (TCs) that are explicitly resolved in a global atmospheric model with horizontal resolution of approximately 28 km are projected to change in a warmer climate using bias-corrected sea-surface temperatures (SSTs). The impact of mitigating from RCP8.5 to RCP4.5 is explicitly considered and is compared with uncertainties arising from SST projections. We find a reduction in overall global TC activity as climate warms. This reduction is somewhat less pronounced under RCP4.5 than under RCP8.5. By contrast, the frequency of very intense TCs is projected to increase dramatically in a warmer climate, with most of the increase concentrated in the NW Pacific basin. Extremes of storm related precipitation are also projected to become more common. Reduction in the frequency of extreme precipitation events is possible through mitigation from RCP8.5 to RCP4.5. In general more detailed basin-scale projections of future TC activity are subject to large uncertainties due to uncertainties in future SSTs. In most cases these uncertainties are larger than the effects of mitigating from RCP8.5 to RCP4.5.

Keywords

Tropical cyclones Climate change High-resolution 

Supplementary material

10584_2016_1750_MOESM1_ESM.pdf (7.3 mb)
ESM 1(PDF 7455 kb)

References

  1. Bacmeister JT et al. (2014) Exploratory high-resolution climate simulations using the community atmosphere model (CAM). J Clim 27(9):3073–3099CrossRefGoogle Scholar
  2. Bell GD, Chelliah M (2006) Leading tropical modes associated with interannual and multidecadal fluctuations in North Atlantic hurricane activity. J Clim 19(4):590–612CrossRefGoogle Scholar
  3. Camargo SJ, Sobel AH (2005) Western North Pacific tropical cyclone intensity and ENSO. J Clim 18(15):2996–3006CrossRefGoogle Scholar
  4. Dennis J et al. (2011) CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model. International Journal of High Performance Computing Applications 26:74–89CrossRefGoogle Scholar
  5. Done JM, PaiMazumder D, Towler E et al (2015) Estimating Impacts of North Atlantic Tropical Cyclones Using an Index of Damage Potential. Clim Change. doi:10.1007/s10584-015-1513-0
  6. Efron B, Tibshirani RJ (1998) An Introduction to the bootstrap. Monographs on Statistics and Applied Probablity 57. Chapman and Hall/CRC Press, Boca Raton FL. 435 pp.Google Scholar
  7. Emanuel KA, D Nolan, 2004: Tropical cyclone activity and the global climate system. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 10 A.2. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75463.htm.]
  8. Gettelman, A., D. Bresch, C. C. Chen, et al. (2016) Projections of future tropical cyclone damage with a high resolution global climate model. Climatic Change. (in review)Google Scholar
  9. Gray WM (1984) Atlantic seasonal hurricane frequency. Part I: El Nino and 30 mb quasi-biennial oscillation influences. Mon Weather Rev 112(9):1649–1668CrossRefGoogle Scholar
  10. Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19(21):5686–5699CrossRefGoogle Scholar
  11. Hurrell JW, Hack JJ, Shea D, Caron JM, Rosinski J (2008) A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model. J Clim 21:5145–5153. doi:10.1175/2008JCLI2292.1. CrossRefGoogle Scholar
  12. Jiang H, Zipser E (2010) Contribution of tropical cyclones to the global precipitation from eight seasons of TRMM data: Regional, seasonal, and interannual variations. J Clim 23:1526–1543CrossRefGoogle Scholar
  13. Kay J et al. (2015) The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability. Bull. Amer. Meteorol. Soc. doi:10.1175/BAMS-D-13-00255.1 Google Scholar
  14. Knapp KM et al. (2010) The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone data. BAMS 91:363–376CrossRefGoogle Scholar
  15. Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava AK, Sugi M (2010) Tropical cyclones and climate change. Nat Geosci 3(3):157–163CrossRefGoogle Scholar
  16. Knutson TR et al. (2015) Global Projections of Intense Tropical Cyclone Activity for the Late Twenty-First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. J Clim 28(18). doi:10.1175/JCLI-D-15-0129.1
  17. Manganello JV et al. (2012) Tropical cyclone climatology in a 10-km global atmospheric GCM: toward weather-resolving climate modeling. J Clim 25(11):3867–3893CrossRefGoogle Scholar
  18. Murakami H, Mizuta R, Shindo E (2012a) Future changes in tropical cyclone activity projected by multi-physics and multi-SST ensemble experiments using the 60-km-mesh MRI-AGCM. Clim Dyn 39(9–10):2569–2584CrossRefGoogle Scholar
  19. Murakami H et al. (2012b) Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J Clim 25:3237–3260CrossRefGoogle Scholar
  20. Murakami H, Hsu P-C, Arakawa O, Li T (2014) Influence of model biases on projected future changes in tropical cyclone frequency of occurrence. J Clim 27:2159–2181CrossRefGoogle Scholar
  21. Neale, R. J. et al. (2012) Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCARTN-4861STR, 274 ppGoogle Scholar
  22. O’Gorman PA, Schneider T (2009) The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc Natl Acad Sci 106(35):14773–14777CrossRefGoogle Scholar
  23. Pielke Jr RA, Landsea CN (1999) La nina, el nino and atlantic hurricane damages in the united states. Bull Am Meteorol Soc 80(10):2027–2033CrossRefGoogle Scholar
  24. Reed KA, Bacmeister JT, Rosenbloom N et al. (2015) Impact of the dynamical core on the direct simulation of tropical cyclones in a high-resolution global model. Geophys Res Lett 42(9):3603–3608Google Scholar
  25. Small RJ et al. (2014) A new synoptic scale resolving global climate simulation using the Community Earth System Model. Journal of Advances in Modeling Earth Systems 6(4):1065–1094CrossRefGoogle Scholar
  26. VanVuuren DP et al. (2011) Representative concentration pathways: an overview. Clim Chang 109:1–2 5–31CrossRefGoogle Scholar
  27. Villarini G et al. (2014) Sensitivity of tropical cyclone rainfall to idealized global-scale forcings. J Clim 20(12):2307–2314Google Scholar
  28. Walsh KJ et al. (2015) Hurricanes and climate: the US CLIVAR working group on hurricanes. Bull Am Meteorol Soc 96:997–1017. doi:10.1175/BAMS-D-13-00242.1 CrossRefGoogle Scholar
  29. Wang C, Zhang L, Lee SK, Wu L, Mechoso CR (2014) A global perspective on CMIP5 climate model biases. Nat Clim Chang 4(3):201–205CrossRefGoogle Scholar
  30. Wehner M et al. (2015) Resolution dependence of future tropical cyclone projections of CAM5. 1 in the US CLIVAR Hurricane Working Group idealized configurations. J Clim 28(10):3905–3925CrossRefGoogle Scholar
  31. Zhao M, Held IM (2012) TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late-twenty-first century. J Clim 25(8):2995–3009CrossRefGoogle Scholar
  32. Zhao M, Held IM, Lin SJ, Vecchi GA (2009) Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J Clim 22(24):6653–6678CrossRefGoogle Scholar
  33. Zhao M, Held IM, Lin S-J (2012) Some counter-intuitive dependencies of tropical cyclone frequency on parameters in a GCM. J Atmos Sci 69(7). doi:10.1175/JAS-D-11-0238.

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Julio T. Bacmeister
    • 1
  • Kevin A. Reed
    • 2
  • Cecile Hannay
    • 1
  • Peter Lawrence
    • 1
  • Susan Bates
    • 1
  • John E. Truesdale
    • 1
  • Nan Rosenbloom
    • 1
  • Michael Levy
    • 1
  1. 1.Climate and Global Dynamics Division, National Center for Atmospheric ResearchBoulderUSA
  2. 2.School of Marine and Atmospheric SciencesStony Brook UniversityStony BrookUSA

Personalised recommendations