Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 97–109 | Cite as

Hurricane Matthew (2016) and its impact under global warming scenarios

  • Mansur Ali Jisan
  • Shaowu Bao
  • Leonard J. Pietrafesa
  • Dongliang Shen
  • Paul T. Gayes
  • Jason Hallstrom
Original Article

Abstract

A coupled atmosphere–ocean model was used to study the impact of future ocean warming, both at and below the water surface, on hurricane track and intensity and the associated coastal storm surge and inundation. A strong Saffir–Simpson Category-5 hurricane, Hurricane Matthew made landfall on the South Carolina (SC) coast of the United States (US) in September 2016 and was used as our study case. Future ocean warming was calculated based on the Inter-Governmental Panel on Climate Change (IPCC) RCP 2.6 and RCP 8.5 scenarios. Validated setup of the model was used to simulate the changes in track, intensity, storm surge, and inundation of Hurricane Matthew under future climate ocean warming scenarios. Results showed that the future ocean warming could make the hurricanes stronger in intensity, which, in turn, will greatly increase subsequent coastal storm surge and inundation. For example, under the RCP 8.5 scenario, Matthew’s maximum wind speed would increase by 18 knots (12.97%), its minimum sea-level pressure would deepen by 26 hPa (2.78%), and the coastal area inundated would increase by 70.20% from that of the present day. Moreover, the increases in coastal surge and inundation could likely lead to a downstream blocking of upstream water systems, thereby exacerbating upstream lateral flooding as the rivers go into storage modes; but that potential is beyond the scope of this study.

Keywords

Hurricane Ocean warming Storm surge Climate change Inundation 

Notes

Acknowledgements

The National Science Foundation (NSF) is acknowledged for undergirding this research effort. Coastal Carolina University’s (CCU) Cyber Infrastructure Project is used to perform the simulations in this study, which is funded by NSF Major Research Instrument under contract AGS-1624068. Two NSF awards supporting the investigations of the processes of storm-induced coastal surge and inundation and inland flooding are CNS-1541917 and CNS-1713922. The SC State Guard is acknowledged for encouraging that prognostic studies such as this be conducted, so that they may be better prepared for future environmental hazardous events. CCU is acknowledged for providing the facility computational time support for this study.

References

  1. Balaji V, Numrich RW (2005) A uniform memory model for distributed data objects on parallel architectures. In: Use of high-performance computing in meteorology. World Scientific, pp 272–294Google Scholar
  2. Bao S, Li X, Shen D, Yang Z, Pietrafesa LJ, Zheng W (2017) Ocean upwelling along the Yellow Sea coast of China revealed by satellite observations and numerical simulation. IEEE Trans Geosci Remote Sens 55(1):526–536.  https://doi.org/10.1109/TGRS.2016.2610761 CrossRefGoogle Scholar
  3. Bender MA, Ginis I (2000) Real-case simulations of hurricane-ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon Weather Rev 128(4):917–946.  https://doi.org/10.1175/1520-0493(2000)128%3C0917:RCSOHO%3E2.0.CO;2 CrossRefGoogle Scholar
  4. Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon Weather Rev 129(4):569–585.  https://doi.org/10.1175/1520-0493(2001)129%3C0569:CAALSH%3E2.0.CO;2 CrossRefGoogle Scholar
  5. Cione JJ, Uhlhorn EW (2003) Sea surface temperature variability in hurricanes: Implications with respect to intensity change. Mon Weather Rev.  https://doi.org/10.1175//2562.1 Google Scholar
  6. Das Y, Mohanty UC, Jain I (2016) Development of tropical cyclone wind field for simulation of storm surge/sea surface height using numerical ocean model. Model Earth Syst Environ 2(1):13.  https://doi.org/10.1007/s40808-015-0067-5 CrossRefGoogle Scholar
  7. Divins DL, Metzger D (2008) NGDC coastal relief model. National Geophysical Data Center, National Oceanic and Atmospheric Administration, US Department of Commerce. https://www.ngdc.noaa.gov/mgg/
  8. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46(20): 3077–3107.  https://doi.org/10.1175/1520-0469(1989)046%3C3077:NSOCOD%3E2.0.CO;2 CrossRefGoogle Scholar
  9. Dyer AJ, Hicks BB (1970) Flux-gradient relationships in the constant flux layer. Q J R Meteorol Soc 96(410):715–721.  https://doi.org/10.1002/qj.49709641012 CrossRefGoogle Scholar
  10. Emanuel KA (1987) The dependence of hurricane intensity on climate. Nature 326(6112): 483–485.  https://doi.org/10.1038/326483a0 CrossRefGoogle Scholar
  11. Emanuel KA (2003) Tropical cyclones. Annu Rev Earth Planet Sci 31.  https://doi.org/10.1146/annurev.earth.31.100901.141259
  12. Emanuel KA (2013) Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc Natl Acad Sci 110(30):12219–12224.  https://doi.org/10.1073/pnas.1301293110 CrossRefGoogle Scholar
  13. Emanuel KA, Živković-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 56(11): 1766–1782.  https://doi.org/10.1175/1520-0469(1999)056%3C1766:DAEOAC%3E2.0.CO;2 CrossRefGoogle Scholar
  14. Emanuel K, Solomon S, Folini D, Davis S, Cagnazzo C (2013) Influence of tropical tropopause layer cooling on Atlantic hurricane activity. J Clim 26(7):2288–2301.  https://doi.org/10.1175/JCLI-D-12-00242.1 CrossRefGoogle Scholar
  15. Gualdi S, Scoccimarro E, Navarra A (2008) Changes in tropical cyclone activity due to global warming: results from a high-resolution coupled general circulation model. J Clim 21(20):5204–5228.  https://doi.org/10.1175/2008JCLI1921.1 CrossRefGoogle Scholar
  16. Hill C, DeLuca C, Balaji V, Suarez M, Silva AD (2004) The architecture of the earth system modeling framework. Comput Sci Eng 6(1):18–28.  https://doi.org/10.1109/MCISE.2004.1255817 CrossRefGoogle Scholar
  17. Hong JSG, Lancaster MJ (2004) Microstrip filters for RF/microwave applications, vol 167. Wiley, New YorkGoogle Scholar
  18. Jisan MA (2017) An ensemble study of the sea level rise impact on storm surge and inundation in the coastal Bangladesh. Dissertation, Coastal Carolina UniversityGoogle Scholar
  19. Jisan MA, Bao S, Pietrafesa LJ (2016) Investigating tropical cyclones and its related storm surge & inundation in coastal Bangladesh using a coupled atmosphere-ocean model [A43I-0382] presented at 2016 Fall Meeting, American Geophysical Union, San Francisco, CA, 12–16 DecGoogle Scholar
  20. Jisan MA, Bao S, Pietrafesa LJ (2017) Ensemble Projection of the Sea Level Rise Impact on Storm Surge and Inundation in the Coastal Bangladesh. Nat Hazards Earth Syst Sci Discuss.  https://doi.org/10.5194/nhess-2017-216 (in review, 2017) Google Scholar
  21. Kimura F, Kitoh A (2007) Downscaling by pseudo global warming method. In: Report of research projection on impact of climate changes on agricultural production system in arid areas, Kyoto, pp 43–46Google Scholar
  22. Knutson TR, Tuleya RE, Shen W, Ginis I (2001) Impact of CO2-induced warming on hurricane intensities as simulated in a hurricane model with ocean coupling. J Clim 14(11):2458–2468.  https://doi.org/10.1175/1520-0442(2001)014%3C2458:IOCIWO%3E2.0.CO;2 CrossRefGoogle Scholar
  23. Knutson TR, Tuleya RE (2004) Impact of CO2-induced warming on simulated hurricane intensity and precipitation: sensitivity to the choice of climate model and convective parameterization. J Clim 17(18):3477–3495.  https://doi.org/10.1175/1520-0442(2004)017%3C3477:IOCWOS%3E2.0.CO;2 CrossRefGoogle Scholar
  24. Knutson TR, McBride JL, Chan J et al (2010) Tropical cyclones and climate change. Nat Geosci 3(3):157–163.  https://doi.org/10.1038/ngeo779 CrossRefGoogle Scholar
  25. Knutson TR, Sirutis JJ, Vecchi GA et al (2013) Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J Clim 26(17): 6591–6617.  https://doi.org/10.1175/JCLI-D-12-00539.1 CrossRefGoogle Scholar
  26. Lin II, Pun IF, Wu CC (2009) Upper-ocean thermal structure and the western North Pacific category 5 typhoons. Part II: Dependence on translation speed. Mon Weather Rev 137(11):3744–3757.  https://doi.org/10.1175/2008MWR2277.1 CrossRefGoogle Scholar
  27. Lin II, Pun IF, Lien CC (2014) “Category-6” supertyphoon Haiyan in global warming hiatus: Contribution from subsurface ocean warming. Geophys Res Lett 41(23):8547–8553.  https://doi.org/10.1002/2014GL061281 CrossRefGoogle Scholar
  28. Lloyd ID, Vecchi GA (2011) Observational evidence for oceanic controls on hurricane intensity. J Clim 24(4):1138–1153.  https://doi.org/10.1175/2010JCLI3763.1 CrossRefGoogle Scholar
  29. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 102(D14):16663–16682.  https://doi.org/10.1029/97JD00237 CrossRefGoogle Scholar
  30. Noh Y, Cheon WG, Hong SY, Raasch S (2003) Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound Layer Meteorol 107(2):401–427.  https://doi.org/10.1023/A:1022146015946 CrossRefGoogle Scholar
  31. Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteorol 9(6): 857–861.  https://doi.org/10.1175/1520-0450(1970)009%3C0857:TMROWS%3E2.0.CO;2 CrossRefGoogle Scholar
  32. Price JF (1981) Upper ocean response to a hurricane. J Phys Oceanogr 11(2): 153–175.  https://doi.org/10.1175/1520-0485(1981)011%3C0153:UORTAH%3E2.0.CO;2 CrossRefGoogle Scholar
  33. Rahaman KM, Ahmed FRS, Islam MN (2016) Modeling on climate induced drought of north-western region, Bangladesh. Model Earth Syst Environ 2(1):45.  https://doi.org/10.1007/s40808-016-0089-7 CrossRefGoogle Scholar
  34. Sakib M, Nihal F, Haque A, Rahman M, Ali M (2015) Sundarban as a buffer against storm surge flooding. World J Eng Technol 3:59–64.  https://doi.org/10.4236/wjet.2015.33C009 CrossRefGoogle Scholar
  35. Sato T, Kimura F, Kitoh A (2007) Projection of global warming onto regional precipitation over Mongolia using a regional climate model. J Hydrol 333(1):144–154.  https://doi.org/10.1016/j.jhydrol.2006.07.023 CrossRefGoogle Scholar
  36. Shay LK, Goni GJ, Black PG (2000) Effects of a warm oceanic feature on Hurricane Opal. Mon Weather Rev 128(5): 1366–1383.  https://doi.org/10.1175/1520-0493(2000)128%3C1366:EOAWOF%3E2.0.CO;2 CrossRefGoogle Scholar
  37. Shen W, Tuleya RE, Ginis I (2000) A sensitivity study of the thermodynamic environment on GFDL model hurricane intensity: implications for global warming. J Clim 13(1):109–121.  https://doi.org/10.1175/1520-0442(2000)013%3C0109:ASSOTT%3E2.0.CO;2 CrossRefGoogle Scholar
  38. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version 2. NCAR Tech Notes- NCAR/TN-468 + STRGoogle Scholar
  39. Stewart SR (2017) National Hurricane Center Tropical Cyclone Report: Hurricane Matthew (AL142016). http://www.nhc.noaa.gov/data/tcr/AL142016_Matthew.pdf. Accessed 7 Apr 2017
  40. Stocker T (ed) (2014) Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  41. Temam R (1984) Navier-stokes equations, vol 2. North-Holland, Amsterdam, pp xii+-526Google Scholar
  42. Vecchi GA, Fueglistaler S, Held IM, Knutson TR, Zhao M (2013) Impacts of atmospheric temperature trends on tropical cyclone activity. J Clim 26(11):3877–3891.  https://doi.org/10.1175/JCLI-D-12-00503.1
  43. Wang S, Camargo SJ, Sobel AH, Polvani LM (2014) Impact of the tropopause temperature on the intensity of tropical cyclones—an idealized study using a mesoscale model. J Atmos Sci 71(11):4333–4348.  https://doi.org/10.1175/JAS-D-14-0029.1 CrossRefGoogle Scholar
  44. Wear DN, Greis JG (2012) The Southern Forest Future Project: summary report. General Technical Report SRS-GTR-168. USDA-Forest Service, Southern Research Station, AshevilleGoogle Scholar
  45. Webb EK (1970) Profile relationships: the log-linear range, and extension to strong stability. Q J R Meteorol Soc 96(407): 67–90.  https://doi.org/10.1002/qj.49709640708 CrossRefGoogle Scholar
  46. Williams JJ, Esteves LS, Rochford LA (2015) Modelling storm responses on a high-energy coastline with XBeach. Model Earth Syst Environ 1(1–2):3.  https://doi.org/10.1007/s40808-015-0003-8 CrossRefGoogle Scholar
  47. Yospin GI, Wood SW, Holz A, Bowman DM, Keane RE, Whitlock C (2015) Modeling vegetation mosaics in sub-alpine Tasmania under various fire regimes. Model Earth Syst Environ 1(3):16.  https://doi.org/10.1007/s40808-015-0019-0 CrossRefGoogle Scholar
  48. Zhao M, Held IM (2010) An analysis of the effect of global warming on the intensity of Atlantic hurricanes using a GCM with statistical refinement. J Clim 23(23):6382–6393.  https://doi.org/10.1175/2010JCLI3837.1 CrossRefGoogle Scholar
  49. 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):6653–667.  https://doi.org/10.1175/2009JCLI3049.1 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Coastal and Marine Systems ScienceCoastal Carolina UniversityConwayUSA
  2. 2.Shanghai Ocean UniversityShanghaiChina
  3. 3.Florida Atlantic UniversityBoca RatonUSA

Personalised recommendations