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The response of land-falling tropical cyclone characteristics to projected climate change in northeast Australia

Abstract

Land-falling tropical cyclones along the Queensland coastline can result in serious and widespread damage. However, the effects of climate change on cyclone characteristics such as intensity, trajectory, rainfall, and especially translation speed and size are not well-understood. This study explores the relative change in the characteristics of three case studies by comparing the simulated tropical cyclones under current climate conditions with simulations of the same systems under future climate conditions. Simulations are performed with the Weather Research and Forecasting Model and environmental conditions for the future climate are obtained from the Community Earth System Model using a pseudo global warming technique. Results demonstrate a consistent response of increasing intensity through reduced central pressure (by up to 11 hPa), increased wind speeds (by 5–10% on average), and increased rainfall (by up to 27% for average hourly rainfall rates). The responses of other characteristics were variable and governed by either the location and trajectory of the current climate cyclone or the change in the steering flow. The cyclone that traveled furthest poleward encountered a larger climate perturbation, resulting in a larger proportional increase in size, rainfall rate, and wind speeds. The projected monthly average change in the 500 mb winds with climate change governed the alteration in the both the trajectory and translation speed for each case. The simulated changes have serious implications for damage to coastal settlements, infrastructure, and ecosystems through increased wind speeds, storm surge, rainfall, and potentially increased size of some systems.

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References

  1. Abbs D (2012) The impact of climate change on the climatology of tropical cyclones in the Australian region. CSIRO Climate Adaptation Flagship Working paper No. 11

  2. Alexander LV, Hope P, Collins D, Trewin B, Lynch A, Nicholls N (2007) Trends in Australia’s climate means and extremes: a global context. Aust Meteorol Mag 56(1):1–18

    Google Scholar 

  3. Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):224–232

    Google Scholar 

  4. Allen JT, Tippett MK, Sobel AH, Lepore C (2016) Understanding the drivers of variability in severe convection: bringing together the scientific and insurance communities. Bull Am Meteorol Soc 97(11):ES221–ES223

    Google Scholar 

  5. Anderson-Berry LJ (2003) Community vulnerability to tropical cyclones: Cairns, 1996–2000. Nat Hazards 30(2):209–232

    Google Scholar 

  6. Anthony KR, Fabricius KE (2000) Shifting roles of heterotrophy and autotrophy in coral energetics under varying turbidity. J Exp Marine Biol Ecol 252(2):221–253

    Google Scholar 

  7. AON Benfield (2011) http://thoughtleadership.aonbenfield.com/Documents/201103_ab_if_tropical_cyclone_yasi_recap.pdf

  8. AON Benfield (2014) April 2014 global catastrophe recap. http://thoughtleadership.aonbenfield.com/Documents/20140507_if_april_global_recap.pdf. Accessed 31 Oct 2016

  9. Australian Government, Bureau of Meteorology (2015) Severe tropical cyclone Marcia. http://www.bom.gov.au/cyclone/history/marcia.shtml. Accessed Jan 31 2017

  10. Authority, Great Barrier Reef Marine Park (2015) Marine Monitoring Program results for 2013–2014: summary report

  11. Authority, Great Barrier Reef Marine Park (2016) Marine Monitoring Program summary report: results for 2014–2015

  12. Beeden R, Maynard J, Puotinen M, Marshall P, Dryden J, Goldberg J et al (2015) Impacts and recovery from severe tropical cyclone Yasi on the great barrier reef. PLoS One 10(4):e0121272. https://doi.org/10.1371/journal.pone.0121272

    Article  Google Scholar 

  13. Bell R, Strachan J, Vidale PL, Hodges K, Roberts M (2013) Response of tropical cyclones to idealized climate change experiments in a global high-resolution coupled general circulation model. J Clim 26(20):7966–7980

    Google Scholar 

  14. Bender MA, Knutson TR, Tuleya RE, Sirutis JJ, Vecchi GA, Garner ST, Held IM (2010) Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science 327(5964):454–458

    Google Scholar 

  15. Bengtsson L, Hodges KI, Esch M, Keenlyside N, Kornblueh L, Luo JJ, Yamagata T (2007) How may tropical cyclones change in a warmer climate? Tellus A 59(4):539–561

    Google Scholar 

  16. Brodie J, Schroeder T, Rohde K, Faithful J, Masters B, Dekker A, Maughan M (2010) Dispersal of suspended sediments and nutrients in the Great Barrier Reef lagoon during river-discharge events: conclusions from satellite remote sensing and concurrent flood-plume sampling. Marine Freshw Res 61(6):651–664

    Google Scholar 

  17. Brodie JE, Kroon FJ, Schaffelke B, Wolanski EC, Lewis SE, Devlin MJ et al (2012) Terrestrial pollutant runoff to the Great Barrier Reef: an update of issues, priorities and management responses. Mar Pollut Bull 65(4):81–100

    Google Scholar 

  18. Bruyère CL, Done JM, Holland GJ, Fredrick S (2014) Bias corrections of global models for regional climate simulations of high-impact weather. Clim Dyn 43(7–8):1847–1856

    Google Scholar 

  19. Carroll F, Authority QR (2015) Building it back better to reduce risks after multiple disaster events. In: Queensland Reconstruction Authority, Queensland, presented to the Floodplain Management Association National Conference

  20. Ceccarelli DM, Emslie MJ, Richards ZT (2016) Post-disturbance stability of fish assemblages measured at coarse taxonomic resolution masks change at finer scales. PLoS One 11(6):e0156232. https://doi.org/10.1371/journal.pone.0156232

    Article  Google Scholar 

  21. Chand SS, McBride LJ, Tory KJ, Wheeler MC, Walsh KJ (2013) Impact of different ENSO regimes on southwest Pacific tropical cyclones. J Clim 26(2):600–608

    Google Scholar 

  22. 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

    Google Scholar 

  23. Chen M, Huang XY (2006) Digital filter initialization for MM5. Mon Weather Rev 134(4):1222–1236

    Google Scholar 

  24. Collins M, An SI, Cai W, Ganachaud A, Guilyardi E, Jin FF, et al (2010) The impact of global warming on the tropical Pacific Ocean and El Niño. Nat Geosci 3(6):391–397

    Google Scholar 

  25. Czajkowski J, Done J (2014) As the wind blows? Understanding hurricane damages at the local level through a case study analysis. Weather Clim Soc 6(2):202–217

    Google Scholar 

  26. Dai A, Rasmussen RM, Ikeda K, Liu C (2017) A new approach to construct representative future forcing data for dynamic downscaling. Clim Dyn. https://doi.org/10.1007/s00382-017-3708-8

    Article  Google Scholar 

  27. Davidson NE, Xiao Y, Ma Y, Weber HC, Sun X, Rikus LJ, Fraser J et al (2014) ACCESS-TC: Vortex specification, 4DVAR initialization, verification, and structure diagnostics. Mon Weather Rev 142(3):1265–1289

    Google Scholar 

  28. Davies PL, Eyre BD (2005) Estuarine modification of nutrient and sediment exports to the Great Barrier Reef Marine Park from the Daintree and Annan River catchments. Mar Pollut Bull 51(1):174–185

    Google Scholar 

  29. De’ath G, Fabricius KE, Sweatman H, Puotinen M (2012) The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc Natl Acad Sci 109(44):17995–17999

    Google Scholar 

  30. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Bechtold P, et al. (2011) The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J Roy Meteorol Soc 137(656):553–597

    Google Scholar 

  31. Devlin MJ, Brodie J (2005) Terrestrial discharge into the Great Barrier Reef Lagoon: nutrient behavior in coastal waters. Mar Pollut Bull 51(1):9–22

    Google Scholar 

  32. Devlin M, Schaffelke B (2009) Spatial extent of riverine flood plumes and exposure of marine ecosystems in the Tully coastal region, Great Barrier Reef. Mar Freshw Res 60(11):1109–1122

    Google Scholar 

  33. Diamond HJ, Lorrey AM, Renwick JA (2013) A southwest Pacific tropical cyclone climatology and linkages to the El Niño–Southern Oscillation. J Clim 26(1):3–25

    Google Scholar 

  34. Done TJ (1992) Effects of tropical cyclone waves on ecological and geomorphological structures on the Great Barrier Reef. Cont Shelf Res 12(7):859–872

    Google Scholar 

  35. Done JM, Holland GJ, Bruyère CL, Leung LR, Suzuki-Parker A (2013) Modeling high-impact weather and climate: Lessons from a tropical cyclone perspective. Clim Change. https://doi.org/10.1007/s10584-013-0954-6

    Article  Google Scholar 

  36. Done JM, Ge M, Holland GJ, Bruyère CL (2014) Future changes in Gulf of Mexico hurricane wave climatology. In: Offshore technology conference. Offshore Technology Conference

  37. Done JM, Holland GJ, Bruyère CL, Leung LR, Suzuki-Parker A (2015) Modeling high-impact weather and climate: lessons from a tropical cyclone perspective. Clim Change 129(3–4):381–395

    Google Scholar 

  38. Dowdy AJ (2014) Long-term changes in Australian tropical cyclone numbers. Atmos Sci Lett 15(4):292–298

    Google Scholar 

  39. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V et al (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J Geophys Res Atmos (1984–2012) 108:D22

    Google Scholar 

  40. Elsner JB, Kara AB (1999) Hurricanes of the North Atlantic: Climate and Society. Oxford University Press, Oxford, UK

  41. Emanuel KA (1987) The dependence of hurricane intensity on climate. Nature 326(6112):483–485

    Google Scholar 

  42. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436(7051):686–688

    Google Scholar 

  43. Emanuel K (2006) Climate and tropical cyclone activity: a new model downscaling approach. J Clim 19(19):4797–4802

    Google Scholar 

  44. Emanuel K, Sundararajan R, Williams J (2008) Hurricanes and global warming: results from downscaling IPCC AR4 simulations. Bull Am Meteorol Soc 89(3):347

    Google Scholar 

  45. Fabricius KE, Wolanski E (2000) Rapid smothering of coral reef organisms by muddy marine snow. Estuar Coast Shelf Sci 50(1), 115–120

    Google Scholar 

  46. Fabricius KE, De’Ath G, Puotinen ML, Done T, Cooper TF, Burgess SC (2008) Disturbance gradients on inshore and offshore coral reefs caused by a severe tropical cyclone. Limnol Oceanogr 53(2):690–704

    Google Scholar 

  47. Ferrier BS (1994) A double-moment multiple-phase four-class bulk ice scheme. Part I: description. J Atmos Sci 51(2):249–280

    Google Scholar 

  48. Ferrier BS, Jin Y, Lin Y, Black T, Rogers E, DiMego G (2002) Implementation of a new grid-scale cloud and rainfall scheme in the NCEP Eta Model. In: Preprints, 15th Conf. on Numerical Weather Prediction, San Antonio, TX, American Meteorological Society, pp 280–283

  49. Fraser RH, Currie DJ (1996) The species richness-energy hypothesis in a system where historical factors are thought to prevail: coral reefs. The Am Nat 148(1):138–159

    Google Scholar 

  50. Furnas M, Mitchell A, Skuza M, Brodie J (2005) In the other 90%: phytoplankton responses to enhanced nutrient availability in the Great Barrier Reef Lagoon. Mar Pollut Bull 51(1):253–265

    Google Scholar 

  51. Gall RJ, Franklin J, Marks F, Rappaport EN, Toepfer F (2013) The hurricane forecast improvement project. Bull Am Meteorol Soc 94:329–334

    Google Scholar 

  52. Garner ST, Held IM, Knutson T, Sirutis J (2009) The roles of wind shear and thermal stratification in past and projected changes of Atlantic tropical cyclone activity. J Clim 22(17):4723–4734

    Google Scholar 

  53. Gentry MS, Lackmann GM (2010) Sensitivity of simulated tropical cyclone structure and intensity to horizontal resolution. Mon Weather Rev 138(3):688–704

    Google Scholar 

  54. Ginger JD, Henderson DJ, Leitch CJ, Boughton GN (2007) Tropical Cyclone Larry: estimation of wind field and assessment of building damage. Aust J Struct Eng 7(3):209–224

    Google Scholar 

  55. Hara M, Yoshikane T, Kawase H, Kimura F (2008) Estimation of the impact of global warming on snow depth in Japan by the pseudo-global-warming method. Hydrol Res Lett 2:61–64

    Google Scholar 

  56. Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19(21):5686–5699

    Google Scholar 

  57. Hill KA, Lackmann GM (2009) Influence of environmental humidity on tropical cyclone size. Mon Weather Rev 137(10):3294–3315

    Google Scholar 

  58. Hill KA, Lackmann GM (2011) The impact of future climate change on TC intensity and structure: a downscaling approach. J Clim 24(17):4644–4661

    Google Scholar 

  59. Holland GJ (1983) Tropical cyclone motion: Environmental interaction plus a beta effect. J Atmos Sci 40(2):328–342

    Google Scholar 

  60. Holland G, Bruyère CL (2014) Recent intense hurricane response to global climate change. Clim Dyn 42:617–617. https://doi.org/10.1007/s00382-013-1713-0

    Article  Google Scholar 

  61. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341

    Google Scholar 

  62. Hughes TP, Day JC, Brodie J (2015) Securing the future of the Great Barrier Reef. Nat Clim Change 5(6):508–511

    Google Scholar 

  63. Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J Geophys Res 113:D13103. https://doi.org/10.1029/2008JD009944

    Article  Google Scholar 

  64. Irish JL, Resio DT (2010) A hydrodynamics-based surge scale for hurricanes. Ocean Eng 37(1):69–81

    Google Scholar 

  65. Kain JS (1993) Convective parameterization for mesoscale models: the Kain Fritsch scheme. Represent Cumulus Convect Numer Models Meteorol Monogr 46:165–170

    Google Scholar 

  66. Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol Climatol 43:170–181

    Google Scholar 

  67. Kain JS, Fritsch JM (1990) A one-dimensional entraining detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802

    Google Scholar 

  68. Kawase, Yoshikane T, Hara M, Kimura F, Yasunari T, Ailikun B, Ueda H, Inoue T (2009) Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method J Geophys Res 114:D24110. https://doi.org/10.1029/2009JD011803

  69. Knaff JA, Longmore SP, Molenar DA (2014) An objective satellite-based tropical cyclone size climatology. J Clim 27(1):455–476

    Google Scholar 

  70. Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) The international best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data. Bull Am Meteorol Soc 91(3):363–376

    Google Scholar 

  71. 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

    Google Scholar 

  72. Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C et al (2010) Tropical cyclones and climate change. Nat Geosci 3(3):157–163

    Google Scholar 

  73. Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40(6):1194–1199

    Google Scholar 

  74. Kuleshov Y, Fawcett R, Qi L, Trewin B, Jones D, McBride J, Ramsay H (2010) Trends in tropical cyclones in the South Indian Ocean and the South Pacific Ocean. J Geophys Res 115:D01101. https://doi.org/10.1029/2009JD012372

  75. Kurihara Y, Bender MA, Ross RJ (1993) An initialization scheme of hurricane models by vortex specification. Mon Weather Rev 121:2030–2045

    Google Scholar 

  76. Lackmann GM (2015) Hurricane Sandy before 1900 and after 2100. Bull Am Meteorol Soc 96(4):547–560

    Google Scholar 

  77. Landsea CW, Vecchi GA, Bengtsson L, Knutson TR (2010) Impact of duration thresholds on Atlantic tropical cyclone counts. J Clim 23(10):2508–2519

    Google Scholar 

  78. Laurance WF, Curran TJ (2008) Impacts of wind disturbance on fragmented tropical forests: a review and synthesis. Aust Ecol 33(4):399–408

    Google Scholar 

  79. Lavender SL, Walsh KJE (2011) Dynamically downscaled simulations of Australian region tropical cyclones in current and future climates. Geophys Res Lett 38:L10705. https://doi.org/10.1029/2011GL047499

    Google Scholar 

  80. Leslie LM, Holland GJ (1995) On the bogussing of tropical cyclones in numerical models: a comparison of vortex profiles. Meteorol Atmos Phys 56:101–110

    Google Scholar 

  81. Leslie LM, Karoly DJ, Leplastrier M, Buckley BW (2007) Variability of tropical cyclones over the southwest Pacific Ocean using a high-resolution climate model. Meteorol Atmos Phys 97(1–4):171–180

    Google Scholar 

  82. Lynch P, Huang XY (1992) Initialization of the HIRLAM model using a digital filter. Mon Weather Rev 120(6):1019–1034

    Google Scholar 

  83. Lynch P, Huang XY (1994) Diabatic initialization using recursive filters. Tellus A 46(5):583–597

    Google Scholar 

  84. Lynn BH, Healy R, Druyan LM (2009) Investigation of Hurricane Katrina characteristics for future, warmer climates. Clim Res 39(1):75–86

    Google Scholar 

  85. Marks FD, Shay LK (1998) Landfalling tropical cyclones: Forecast problems and associated research opportunities. Bull Am Meteorol Soc 79(2):305–323

    Google Scholar 

  86. Massel SR, Done TJ (1993) Effects of cyclone waves on massive coral assemblages on the Great Barrier Reef: meteorology, hydrodynamics and demography. Coral Reefs 12(3–4):153–166

    Google Scholar 

  87. McInnes KL, Hubbert GD, Abbs DJ, Oliver SE (2002) A numerical modelling study of coastal flooding. Meteorol Atmos Phys 80(1–4):217–233

    Google Scholar 

  88. McInnes KL, Walsh KJE, Hubbert GD, Beer T (2003) Impact of sea-level rise and storm surges on a coastal community. Nat Hazards 30(2):187–207

    Google Scholar 

  89. Merrill RT (1984) A comparison of large and small tropical cyclones. Mon Weather Rev 112(7):1408–1418

    Google Scholar 

  90. Mitchell C, Brodie J, White I (2005) Sediments, nutrients and pesticide residues in event flow conditions in streams of the Mackay Whitsunday Region, Australia. Mar Pollut Bull 51(1):23–36

    Google Scholar 

  91. Monaghan AJ, Steinhoff DF, Bruyere CL, Yates D (2014) NCAR CESM global bias-corrected CMIP5 output to support WRF/MPAS research. In: Research data archive at the national center for atmospheric research, computational and information systems laboratory. https://doi.org/10.5065/D6DJ5CN4. Accessed Feb 2016

  92. Monin AS, Obukhov A (1954) Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib Geophys Inst Acad Sci USSR 151:163–187

    Google Scholar 

  93. Nguyen KC, Walsh KJE (2001) Interannual, decadal, and transient greenhouse simulation of tropical cyclone—like vortices in a regional climate model of the South Pacific. J Clim 14(13):3043–3054

    Google Scholar 

  94. Nuissier O, Rogers RF, Roux F (2005) A numerical simulation of Hurricane Bret on 22–23 August 1999 initialized with airborne Doppler radar and dropsonde data. Q J R Meteorol Soc 131:155–194

    Google Scholar 

  95. Oouchi K, Yoshimura J, Yoshimura H, Mizuta R, Kusunoki S, Akira 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 Meteorol Soc Jpn Ser II 84(2):259–276

    Google Scholar 

  96. Pall P, Allen MR, Stone DA (2007) Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim Dyn 28(4):351–363

    Google Scholar 

  97. Parker CL, Lynch AH, Spera SA, Spangler KR (2017a) The Relationship between tropical cyclone activity, nutrient loading, and algal blooms over the Great Barrier Reef. Biogeosci Discuss https://doi.org/10.5194/bg-2017-23 (in review)

    Article  Google Scholar 

  98. Parker CL, Lynch AH, Mooney PA (2017b) Factors affecting the trajectory and intensification of tropical cyclone yasi (2011). Atmos Res. https://doi.org/10.1016/j.atmosres.2017.04.002

    Article  Google Scholar 

  99. Peng M, Xie L, Pietrafesa LJ (2004) A numerical study of storm surge and inundation in the Croatan–Albemarle–Pamlico Estuary system. Estuar Coast Shelf Sci 59(1), 121–137

    Google Scholar 

  100. Powell MD, Reinhold TA (2007) Tropical cyclone destructive potential by integrated kinetic energy. Bull Am Meteorol Soc 88(4):513

    Google Scholar 

  101. Preen AR, Long WL, Coles RG (1995) Flood and cyclone related loss, and partial recovery, of more than 1000 km 2 of seagrass in Hervey Bay, Queensland, Australia. Aquat Bot 52(1):3–17

    Google Scholar 

  102. Puotinen ML (2007) Modelling the risk of cyclone wave damage to coral reefs using GIS: a case study of the Great Barrier Reef, 1969–2003. Int J Geogr Inf Sci 21(1):97–120

    Google Scholar 

  103. Puotinen ML, Done TJ, Skelly WC (1997) An atlas of tropical cyclones in the Great Barrier Reef region: 1969–1997. CRC Reef Research, Technical Report pp 1–192

  104. Queensland Government (2012) Budget strategy and outloook 2011–2012, 70 pp. http://www.budget.qld.gov.au/current-budget/budget-papers/index.php. Accessed 5 Jan 2015

  105. Puri K, Xiao Y, Sun X, Lee J, Engel C, Steinle P, Sun Z et al (2010) Preliminary results from Numerical Weather Prediction implementation of ACCESS. CAWCR Res Let 5:15–22

    Google Scholar 

  106. Puri K, Dietachmayer G, Steinle P, Dix M, Rikus L, Logan L, Bermous I et al (2013) Implementation of the initial ACCESS numerical weather prediction system. Aust Meteorol Oceanogr J 63:265–284

    Google Scholar 

  107. Rasmussen R, Liu C, Ikeda K, Gochis D, Yates D, Chen F et al (2011) High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate. J Clim 24(12):3015–3048

    Google Scholar 

  108. Rego JL, Li C (2009) On the importance of the forward speed of hurricanes in storm surge forecasting: A numerical study. Geophys Res Lett 36:L07609. https://doi.org/10.1029/2008GL036953

    Google Scholar 

  109. Rennó NO, Ingersoll AP (1996) Natural convection as a heat engine: a theory for CAPE. J Atmos Sci 53(4):572–585

    Google Scholar 

  110. Reuters (2015) Cyclone Yasi to cost insurers AUS $3.5 bln forecaster. http://www.reuters.com/article/2011/02/03/insured-losses-yasi-idUSLDE7121NR20110203. Accessed Apr 2016

  111. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, … Rafaj P (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109(1–2):33–57

    Google Scholar 

  112. Riemann-Campe K, Fraedrich K, Lunkeit F (2009) Global climatology of convective available potential energy (CAPE) and convective inhibition (CIN) in ERA-40 reanalysis. Atmos Res 93(1):534–545

    Google Scholar 

  113. Rogers R, Aberson S, Black M, Black P, Cione J, Dodge P, Uhlhorn E et al (2006) The intensity forecasting experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull Am Meteorol Soc 87(11):1523–1537

    Google Scholar 

  114. Rogers RF et al (2013) NOAA’s hurricane intensity forecasting experiment: a progress report. Bull Am Meteorol Soc 94:859–882

    Google Scholar 

  115. Schär C, Frei C, Lüthi D, Davies HC (1996) Surrogate climate-change scenarios for regional climate models. Geophys Res Lett 23(6):669–672

    Google Scholar 

  116. Shapiro LJ (1983) The asymmetric boundary layer flow under a translating hurricane. J Atmos Sci 40(8):1984–1998

    Google Scholar 

  117. Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J Geophys Res Atmos 118(4):1716–1733

    Google Scholar 

  118. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM (2005) Coauthors, 2008: a description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475 + STR, p 113

  119. Stewart MG (2003) Cyclone damage and temporal changes to building vulnerability and economic risks for residential construction. J Wind Eng Ind Aerodyn 91(5):671–691

    Google Scholar 

  120. Stieglitz T (2005) Submarine groundwater discharge into the near-shore zone of the Great Barrier Reef, Australia. Mar Pollut Bull 51(1):51–59

    Google Scholar 

  121. Sugi M, Yoshimura J (2012) Decreasing trend of tropical cyclone frequency in 228-year highresolution AGCM simulations. Geophys Res Lett 39:L19805. https://doi.org/10.1029/2012GL053360

    Google Scholar 

  122. Sugi M, Murakami H, Yoshimura J (2009) A reduction in global tropical cyclone frequency due to global warming. Sola 5:164–167

    Google Scholar 

  123. Tewari M, Chen F, Wang W, Dudhia J, LeMone MA, Mitchell K et al (2004) Implementation and verification of the unified NOAH land surface model in the WRF model. In: 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, pp 11–15

  124. Trenberth K (2005) Uncertainty in hurricanes and global warming. Science 308(5729):1753–1754

    Google Scholar 

  125. Trenberth KE, Fasullo J, Smith L (2005) Trends and variability in column-integrated atmospheric water vapor. Clim Dyn 24(7–8):741–758

    Google Scholar 

  126. Turton SM (2008) Landscape-scale impacts of Cyclone Larry on the forests of northeast Australia, including comparisons with previous cyclones impacting the region between 1858 and 2006. Austral Ecol 33(4):409–416

    Google Scholar 

  127. Ueno M (1989) Operational bogussing and numerical predication in JMA. JMA/NPD Tech Rep 28:48

    Google Scholar 

  128. Vecchi GA, Soden BJ (2007) Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature 450(7172):1066–1070

    Google Scholar 

  129. Velden CS, Leslie LM (1991) The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Weather Forecast 6(2):244–253

    Google Scholar 

  130. Walsh K (2004) Tropical cyclones and climate change: unresolved issues. Climate Res 27(1):77–83

    Google Scholar 

  131. Walsh K (2015) Fine resolution simulations of the effect of climate change on tropical cyclones in the South Pacific. Clim Dyn 45(9–10):2619–2631

    Google Scholar 

  132. Walsh KJ, Katzfey JJ (2000) The impact of climate change on the poleward movement of tropical cyclone–like vortices in a regional climate model. J Clim 13(6):1116–1132

    Google Scholar 

  133. Walsh KJ, Ryan BF (2000) Tropical cyclone intensity increase near Australia as a result of climate change. J Clim 13(16):3029–3036

    Google Scholar 

  134. Walsh KJE, Nguyen KC, McGregor JL (2004) Fine-resolution regional climate model simulations of the impact of climate change on tropical cyclones near Australia. Clim Dyn 22(1):47–56

    Google Scholar 

  135. Walsh KJ, McInnes KL, McBride JL (2012) Climate change impacts on tropical cyclones and extreme sea levels in the South Pacific—a regional assessment. Global Planet Change 80:149–164

    Google Scholar 

  136. Walsh KJ, McBride JL, Klotzbach PJ, Balachandran S, Camargo SJ, Holland G et al (2016) Tropical cyclones and climate change. Wiley Interdiscip Rev Clim Change 7(1):65–89

    Google Scholar 

  137. Wang G, Wang D, Trenberth KE, Erfanian A, Yu M, Bosilovich MG, Parr DT (2017) The peak structure and future changes of the relationships between extreme precipitation and temperature. Nat Clim Change 7:268–274

    Google Scholar 

  138. Weisberg RH, Zheng L (2006) Hurricane storm surge simulations for Tampa Bay. Estuar Coast 29(6):899–913

    Google Scholar 

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

    Google Scholar 

  140. Yoshikane T, Kimura F, Kawase H, Nozawa T (2012) Verification of the performance of the pseudo-global-warming method for future climate changes during June in East Asia. SOLA 8(0):133–136

    Google Scholar 

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Acknowledgements

The authors would like to thank James Done, and Greg Holland at the National Center for Atmospheric Research (NCAR) for very helpful discussion of the results; Noel Davidson at the Australian Bureau of Meteorology for the high-resolution ACCESS initialization data and advice; and Daniel P. Moriarty III at the NASA Goddard Spaceflight Center for assistance in editing the manuscript. The authors would also like to thank the anonymous reviewer for their helpful insights, suggestions, and contributions that were instrumental for improving this study and paper. Computational work was supported by NCAR and the Yellowstone supercomputing facilities. This work was funded by Brown University and in part by NCAR summer visitor program. NCAR is sponsored by the National Science Foundation.

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Correspondence to Chelsea L. Parker.

Appendix A

Appendix A

Effect of future changes in relative humidity on tropical cyclone Ita

The pseudo-global warming (PGW) technique used in this study calculated the difference between the future and current climate of a GCM (ΔCC) and added this to the ERA-Interim reanalysis to create forcing data for the future climate. This ΔCC was applied to all variables in the forcing data, including the relative humidity (RH). There is some disagreement in the literature regarding whether the RH should be allowed to change in future simulations. Many PGW studies (e.g. Lynn et al. 2009; Rasmussen et al. 2011; Dai et al. 2017) have applied a delta to the RH, although there are examples where RH is not changed (e.g. Kawase et al. 2009). One argument is that an inconsistency is created when adding a broadly uniform, GCM-derived specific humidity delta onto a specific synoptic pattern. For example, if a relatively homogeneous specific humidity delta were applied to a synoptic cold front, there would be a larger RH increase on the cold side of the front (perhaps even resulting in supersaturation), whereas the RH could decrease in the warm air. A second argument is that RH is generally expected to remain constant in the future (Allen and Ingram 2002). However, this is contradicted by a recent study (Wang et al. 2017) that shows relative humidity will decrease over land as Earth warms which suggests that RH should be allowed to change in the PGW technique. This debate motivates the short study described in this Appendix.

The future climate simulations for Ita described in the main text were repeated without changing relative humidity i.e. ΔCCRH = 0, and the results are shown in Figs. 8, 9 and 10. Figure 8 shows that the tropical cyclone in the future climate simulation with no ΔCCRH moves more southward than the future climate simulation with a ΔCCRH. However, the general pattern is the same regardless of the ΔCCRH. The difference between the two future climate simulations is small compared to the difference between either of the future climate simulations and the current climate simulation.

In the first 72 h of the simulations, there is very little difference between the simulated intensity for Ita in both future climates (as indicated by the minimum sea level pressures shown Fig. 9). After the first 72 h, the differences are not directly caused by the changes in relative humidity, but due to the slight change in the trajectories. Previous studies (e.g. Hill and Lackmann 2009) found that environmental relative humidity exerts an influence on TC size. However, in this sensitivity study, the TC size (as indicated by the average radius of the 34-knot winds in Fig. 9) does not change substantially in the first 72 h. There is a discernable difference after this period, but again this can be attributed to the different trajectories for the two future climate simulations and is not directly due to the changes in relative humidity.

Figure 10 shows the ΔCCRH at 900 hPa in April that was used for the PGW technique for TC Ita. At 900 hPa, ΔCCRH is small (~ − 2 to 4%) and shows little variation spatially. Therefore, it unsurprising that ΔCCRH does not substantially effect the simulated characteristics of Ita. The results suggest that a constant RH assumption may be valid in maritime PGW studies. However, we conclude that the inclusion or exclusion of the ΔCCRH does not affect the overall results described in the main text of this study and has been included for consistency across variables.

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Parker, C.L., Bruyère, C.L., Mooney, P.A. et al. The response of land-falling tropical cyclone characteristics to projected climate change in northeast Australia. Clim Dyn 51, 3467–3485 (2018). https://doi.org/10.1007/s00382-018-4091-9

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Keywords

  • Australia
  • Tropical cyclones
  • Climate change
  • Weather research and forecasting model
  • Pseudo global warming technique