Advertisement

Climate Dynamics

, Volume 53, Issue 1–2, pp 173–192 | Cite as

The impact of climate model sea surface temperature biases on tropical cyclone simulations

  • Wei-Ching HsuEmail author
  • Christina M. Patricola
  • Ping Chang
Article

Abstract

Sea surface temperature (SST) patterns both local to and remote from tropical cyclone (TC) development regions are important drivers of the variability of TC activity. Therefore, reliable simulations and predictions of TC activity depend on a realistic representation of tropical SST. Nevertheless, severe SST biases are common to the current generation of global climate models, especially in the tropical Pacific and Atlantic. These biases are strongly positive in the southeastern tropical basins, and negative, but weaker, in the northwestern tropical basins. To investigate the impact of the tropical SST biases on simulated TC activity, an atmospheric-only tropical channel model was used to conduct several sets of ensemble simulations. The simulations suggest an underrepresentation in Atlantic TC activity caused by the Atlantic cold bias alone, and an overrepresentation in Eastern North Pacific (ENP) TC activity due to the Atlantic cold bias and Pacific warm bias jointly. While the local impact of SST biases on TC activity is generally induced by the local anomalous SST and the associated changes in atmospheric conditions, the remote impact of the Atlantic bias on the ENP TCs is strongly driven by the change in topographically forced regional circulation. Moreover, an eastward shift in Western North Pacific TCs was generated by the Pacific SST biases, even though basin-wide TC activity indicators change insignificantly. The results indicate the importance of considering SST bias effects on simulated TC activity in climate model studies and highlight key regions where reducing SST biases could potentially improve TC representation in climate models.

Keywords

SST bias Tropical cyclones Tropical channel model Climate model bias 

Notes

Acknowledgements

The authors wish to thank the editor and two anonymous reviewers for comments that greatly improved the quality of this paper. This research is supported by U.S. National Science Foundation Grants OCE-1334707, AGS-1347808 and AGS-1462127, and National Oceanic and Atmospheric Administration Grant NA13OAR4310136. PC acknowledges the Natural Science Foundation of China (41490644 and 41490640). C.M.P. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division, Regional & Global Climate Modeling Program, under Award Number DE-AC02-05CH11231. High-performance computing resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE). Simulations were performed at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin and the Texas A&M Supercomputing Facility.

References

  1. Bell GD, Halpert MS, Schnell RC et al (2000) Climate assessment for 1999. Bull Am Meteorol Soc 81:s1–s50.  https://doi.org/10.1175/1520-0477(2000)81%5BS1:CAF%5D2.0.CO;2 CrossRefGoogle Scholar
  2. Bender MA, Knutson TR, Tuleya RE et al (2010) Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science 327:454–458.  https://doi.org/10.1126/science.1180568 CrossRefGoogle Scholar
  3. Biasutti M, Sobel AH, Kushnir Y (2006) AGCM precipitation biases in the tropical Atlantic. J Clim 19:935–958.  https://doi.org/10.1175/JCLI3673.1 CrossRefGoogle Scholar
  4. Blake ES, Rappaport EN, Jarrell JD et al (2007) The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006 (and other frequently requested hurricane facts). NOAA/National Weather Service, National Centers for Environmental Prediction, National Hurricane Center MiamiGoogle Scholar
  5. Blake ES, Kimberlain TB, Berg RJ et al (2013) Tropical cyclone report hurricane Sandy. National Hurricane Center 12: 1–10Google Scholar
  6. Camargo SJ (2013) Global and regional aspects of tropical cyclone activity in the CMIP5 models. J Clim 26:9880–9902.  https://doi.org/10.1175/JCLI-D-12-00549.1 CrossRefGoogle Scholar
  7. Camargo SJ, Hsiang SM (2016) From the influence of climate to their socioeconomic impacts. In: Tropical cyclones, pp 303–342Google Scholar
  8. Camargo SJ, Sobel AH (2005) Western North Pacific tropical cyclone intensity and ENSO. J Clim 18:2996–3006.  https://doi.org/10.1175/JCLI3457.1 CrossRefGoogle Scholar
  9. Camargo SJ, Emanuel KA, Sobel AH (2007) Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J Clim 20:4819–4834.  https://doi.org/10.1175/JCLI4282.1 CrossRefGoogle Scholar
  10. Chan JCL (1985) Tropical cyclone activity in the Northwest Pacific in relation to the El Niño/Southern Oscillation phenomenon. Mon Weather Rev 113:599–606.  https://doi.org/10.1175/1520-0493(1985)113%3C0599:TCAITN%3E2.0.CO;2 CrossRefGoogle Scholar
  11. Chan JCL (2005) Interannual and interdecadal variations of tropical cyclone activity over the western North Pacific. Meteorol Atmos Phys 89:143–152.  https://doi.org/10.1007/s00703-005-0126-y CrossRefGoogle Scholar
  12. Colas F, McWilliams JC, Capet X, Kurian J (2012) Heat balance and eddies in the Peru-Chile current system. Clim Dyn 39:509–529.  https://doi.org/10.1007/s00382-011-1170-6 CrossRefGoogle Scholar
  13. Ding H, Keenlyside NS, Latif M (2012) Impact of the equatorial Atlantic on the El Niño Southern Oscillation. Clim Dyn 38:1965–1972.  https://doi.org/10.1007/s00382-011-1097-y CrossRefGoogle Scholar
  14. Emanuel KA (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688.  https://doi.org/10.1038/nature03906 CrossRefGoogle Scholar
  15. Emanuel KA (2007) Environmental factors affecting tropical cyclone power dissipation. J Clim 20:5497–5509.  https://doi.org/10.1175/2007JCLI1571.1 CrossRefGoogle Scholar
  16. Emanuel KA (2017) Assessing the present and future probability of Hurricane Harvey’s rainfall. Proc Natl Acad Sci 114:12681–12684.  https://doi.org/10.1073/pnas.1716222114 CrossRefGoogle Scholar
  17. Emanuel KA, Nolan DS (2004) Tropical cyclone activity and the global climate system. In: 26th conference on hurricanes and tropical meteorology. American Meteorological Society, Miami, FLGoogle Scholar
  18. Graham NE, Barnett TP (1987) Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science 238:657–659.  https://doi.org/10.1126/science.238.4827.657 CrossRefGoogle Scholar
  19. Gray WM (1984a) Atlantic seasonal hurricane frequency. Part II: forecasting its variability. Mon Weather Rev 112:1669–1683.  https://doi.org/10.1175/1520-0493(1984)112%3C1669:ASHFPI%3E2.0.CO;2 CrossRefGoogle Scholar
  20. Gray WM (1984b) Atlantic seasonal hurricane frequency. Part I: El Niño and 30 mb Quasi-Biennial Oscillation influences. Mon Weather Rev 112:1649–1668.  https://doi.org/10.1175/1520-0493(1984)112%3C1649:ASHFPI%3E2.0.CO;2 CrossRefGoogle Scholar
  21. Ham Y-G, Kug J-S, Park J-Y (2013a) Two distinct roles of Atlantic SSTs in ENSO variability: North tropical Atlantic SST and Atlantic Niño. Geophys Res Lett 40:4012–4017.  https://doi.org/10.1002/grl.50729 CrossRefGoogle Scholar
  22. Ham Y-G, Kug J-S, Park J-Y, Jin F-F (2013b) Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events. Nat Geosci 6:112–116.  https://doi.org/10.1038/ngeo1686 CrossRefGoogle Scholar
  23. Holland GJ, Webster PJ (2007) Heightened tropical cyclone activity in the North Atlantic: natural variability or climate trend? Philos Trans R Soc A Math Phys Eng Sci 365:2695–2716.  https://doi.org/10.1098/rsta.2007.2083 CrossRefGoogle Scholar
  24. Jien JY, Gough WA, Butler K (2015) The influence of El Niño–Southern Oscillation on tropical cyclone activity in the eastern North Pacific basin. J Clim 28:2459–2474.  https://doi.org/10.1175/JCLI-D-14-00248.1 CrossRefGoogle Scholar
  25. Jin F-F, Boucharel J, Lin I-I (2014) Eastern Pacific tropical cyclones intensified by El Niño delivery of subsurface ocean heat. Nature 516:82–85.  https://doi.org/10.1038/nature13958 CrossRefGoogle Scholar
  26. Kanamitsu M, Ebisuzaki W, Woollen J et al (2002) NCEP–DOE AMIP-II Reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1644.  https://doi.org/10.1175/BAMS-83-11-1631 CrossRefGoogle Scholar
  27. Klotzbach PJ, Bowen SG, Pielke R, Bell M (2018) Continental United States hurricane landfall frequency and associated damage: observations and future risks. Bull Am Meteorol Soc BAMS-D-17-0184.1.  https://doi.org/10.1175/BAMS-D-17-0184.1 Google Scholar
  28. Knutson T, Landsea C, Emanuel K (2010)  Tropical cyclones and climate change: a review. In: Chan JCL, Kepert J (eds) Global perspectives on tropical cyclones. World Scientific, Singapore, pp 243–286CrossRefGoogle Scholar
  29. Kucharski F, Parvin A, Rodriguez-Fonseca B et al (2016) The teleconnection of the tropical Atlantic to Indo-Pacific sea surface temperatures on inter-annual to centennial time scales: A review of recent findings. Atmosphere (Basel) 7:29.  https://doi.org/10.3390/atmos7020029 CrossRefGoogle Scholar
  30. LaRow TE, Stefanova L, Shin D-W, Cocke S (2010) Seasonal Atlantic tropical cyclone hindcasting/forecasting using two sea surface temperature datasets. Geophys Res Lett 37:L02804.  https://doi.org/10.1029/2009GL041459 CrossRefGoogle Scholar
  31. Li G, Xie S-P (2012) Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys Res Lett 39:L22703.  https://doi.org/10.1029/2012GL053777 Google Scholar
  32. Lin I-I, Black P, Price JF et al (2013) An ocean coupling potential intensity index for tropical cyclones. Geophys Res Lett 40:1878–1882.  https://doi.org/10.1002/grl.50091 CrossRefGoogle Scholar
  33. Liu H, Wang C, Lee S-K, Enfield D (2012) Atlantic warm-pool variability in the IPCC AR4 CGCM simulations. J Clim 25:5612–5628.  https://doi.org/10.1175/JCLI-D-11-00376.1 CrossRefGoogle Scholar
  34. Masunaga H, Nakajima TY, Nakajima T et al (2002) Physical properties of maritime low clouds as retrieved by combined use of tropical rainfall measuring mission (TRMM) microwave imager and visible/infrared scanner 2. Climatology of warm clouds and rain. J Geophys Res 107:4367.  https://doi.org/10.1029/2001JD001269 CrossRefGoogle Scholar
  35. Murakami H, Vecchi GA, Villarini G et al (2016) Seasonal forecasts of major hurricanes and landfalling tropical cyclones using a high-resolution GFDL coupled climate model. J Clim 29:7977–7989.  https://doi.org/10.1175/JCLI-D-16-0233.1 CrossRefGoogle Scholar
  36. Nobre P, Shukla J (1996) Variations of sea surface temperature, wind stress, and rainfall over the tropical Atlantic and South America. J Clim 9:2464–2479.  https://doi.org/10.1175/1520-0442(1996)009<2464:VOSSTW>2.0.CO;2 CrossRefGoogle Scholar
  37. Painemal D, Minnis P (2012) On the dependence of albedo on cloud microphysics over marine stratocumulus clouds regimes determined from Clouds and the Earth’s Radiant Energy System (CERES) data. J Geophys Res Atmos 117:D06203.  https://doi.org/10.1029/2011JD017120 Google Scholar
  38. Patricola CM, Li M, Xu Z et al (2012) An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model. Clim Dyn 39:2443–2463.  https://doi.org/10.1007/s00382-012-1320-5 CrossRefGoogle Scholar
  39. Patricola CM, Saravanan R, Chang P (2014) The impact of the El Niño–Southern Oscillation and Atlantic Meridional Mode on seasonal Atlantic tropical cyclone activity. J Clim 27:5311–5328.  https://doi.org/10.1175/JCLI-D-13-00687.1 CrossRefGoogle Scholar
  40. Patricola CM, Chang P, Saravanan R (2016) Degree of simulated suppression of Atlantic tropical cyclones modulated by flavour of El Niño. Nat Geosci 9:155–160.  https://doi.org/10.1038/ngeo2624 CrossRefGoogle Scholar
  41. Patricola CM, Saravanan R, Chang P (2017) A teleconnection between Atlantic sea surface temperature and eastern and central North Pacific tropical cyclones. Geophys Res Lett 44:1167–1174.  https://doi.org/10.1002/2016GL071965 CrossRefGoogle Scholar
  42. Polo I, Martin-Rey M, Rodriguez-Fonseca B et al (2015) Processes in the Pacific La Niña onset triggered by the Atlantic Niño. Clim Dyn 44:115–131.  https://doi.org/10.1007/s00382-014-2354-7 CrossRefGoogle Scholar
  43. Rayner NA, Parker DE, Horton EB et al (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407.  https://doi.org/10.1029/2002JD002670 CrossRefGoogle Scholar
  44. Reed A, Mann M, Emanuel K, Titley D (2015) An analysis of long-term relationships among count statistics and metrics of synthetic tropical cyclones downscaled from CMIP5 models. J Geophys Res Atmos 120:7506–7519.  https://doi.org/10.1002/2015JD023357 CrossRefGoogle Scholar
  45. Reynolds RW, Smith TM, Liu C et al (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496.  https://doi.org/10.1175/2007JCLI1824.1 CrossRefGoogle Scholar
  46. Richter I (2015) Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. Wiley Interdiscip Rev Clim Chang 6:345–358.  https://doi.org/10.1002/wcc.338 CrossRefGoogle Scholar
  47. Richter I, Xie S-P (2008) On the origin of equatorial Atlantic biases in coupled general circulation models. Clim Dyn 31:587–598.  https://doi.org/10.1007/s00382-008-0364-z CrossRefGoogle Scholar
  48. Rodríguez-Fonseca B, Polo I, García-Serrano J et al (2009) Are Atlantic Niños enhancing Pacific ENSO events in recent decades? Geophys Res Lett 36:L20705.  https://doi.org/10.1029/2009GL040048 CrossRefGoogle Scholar
  49. Shuckburgh E, Mitchell D, Stott P (2017) Hurricanes Harvey, Irma and Maria: how natural were these ‘natural disasters’? Weather 72:353–354.  https://doi.org/10.1002/wea.3190 CrossRefGoogle Scholar
  50. Skamarock WC, Klemp JB, Dudhia J et al (2008) A description of the advanced research WRF version 3NCAR/TN–475+STR.  https://doi.org/10.5065/D68S4MVH.
  51. Small RJ, Curchitser E, Hedstrom K et al (2015) The Benguela upwelling system: quantifying the sensitivity to resolution and coastal wind representation in a global climate model. J Clim 28:9409–9432.  https://doi.org/10.1175/JCLI-D-15-0192.1 CrossRefGoogle Scholar
  52. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498.  https://doi.org/10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  53. Toniazzo T, Mechoso CR, Shaffrey LC, Slingo JM (2010) Upper-ocean heat budget and ocean eddy transport in the south-east Pacific in a high-resolution coupled model. Clim Dyn 35:1309–1329.  https://doi.org/10.1007/s00382-009-0703-8 CrossRefGoogle Scholar
  54. Tory KJ, Chand S, McBride J, Ye H, Dare R (2013) Projected changes in Late-twenty-first-century tropical cyclone frequency in 13 coupled climate models from phase 5 of the coupled model intercomparison project. J Clim 26:9946–9959.  https://doi.org/10.1175/JCLI-D-13-00010.1 CrossRefGoogle Scholar
  55. Vecchi GA, Delworth T, Gudgel R et al (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27:7994–8016.  https://doi.org/10.1175/JCLI-D-14-00158.1 CrossRefGoogle Scholar
  56. Vimont DJ, Kossin JP (2007) The Atlantic meridional mode and hurricane activity. Geophys Res Lett 34:L07709.  https://doi.org/10.1029/2007GL029683 CrossRefGoogle Scholar
  57. Walsh K (1997) Objective detection of tropical cyclones in high-resolution analyses. Mon Weather Rev 125:1767–1779.  https://doi.org/10.1175/1520-0493(1997)125%3C1767:ODOTCI%3E2.0.CO;2 CrossRefGoogle Scholar
  58. Wang B, Chan JCL (2002) How strong ENSO events affect tropical storm activity over the western North Pacific. J Clim 15:1643–1658.  https://doi.org/10.1175/1520-0442(2002)015%3C1643:HSEEAT%3E2.0.CO;2 CrossRefGoogle Scholar
  59. Wang C, Lee S-K (2009) Co-variability of tropical cyclones in the North Atlantic and the eastern North Pacific. Geophys Res Lett 36:L24702.  https://doi.org/10.1029/2009GL041469 CrossRefGoogle Scholar
  60. Wang B, Li T (1993) A simple tropical atmosphere model of relevance to short-term climate variations. J Atmos Sci 50:260–284.  https://doi.org/10.1175/1520-0469(1993)050%3C0260:ASTAMO%3E2.0.CO;2 CrossRefGoogle Scholar
  61. Wang C, Li C, Mu M, Duan W (2013) Seasonal modulations of different impacts of two types of ENSO events on tropical cyclone activity in the western North Pacific. Clim Dyn 40:2887–2902.  https://doi.org/10.1007/s00382-012-1434-9 CrossRefGoogle Scholar
  62. Wang C, Zhang L, Lee S-K et al (2014) A global perspective on CMIP5 climate model biases. Nat Clim Chang 4:201–205.  https://doi.org/10.1038/nclimate2118 CrossRefGoogle Scholar
  63. Webster PJ, Holland GJ, Curry JA, Chang H-R (2005) Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309:1844–1846.  https://doi.org/10.1126/science.1116448 CrossRefGoogle Scholar
  64. Whitney LD, Hobgood JS (1997) The relationship between sea surface temperatures and maximum intensities of tropical cyclones in the eastern North Pacific ocean. J Clim 10:2921–2930.  https://doi.org/10.1175/1520-0442(1997)010%3C2921:TRBSST%3E2.0.CO;2 CrossRefGoogle Scholar
  65. Whyte FS, Taylor MA, Stephenson TS, Campbell JD (2008) Features of the Caribbean low level jet. Int J Climatol 28:119–128.  https://doi.org/10.1002/joc.1510 CrossRefGoogle Scholar
  66. Xu Z, Chang P, Richter I et al (2014a) Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble. Clim Dyn 43:3123–3145.  https://doi.org/10.1007/s00382-014-2247-9 CrossRefGoogle Scholar
  67. Xu Z, Li M, Patricola CM, Chang P (2014b) Oceanic origin of southeast tropical Atlantic biases. Clim Dyn 43:2915–2930.  https://doi.org/10.1007/s00382-013-1901-y CrossRefGoogle Scholar
  68. Yu J-Y, Kao P, Paek H et al (2015) Linking emergence of the Central Pacific El Niño to the Atlantic Multidecadal Oscillation. J Clim 28:651–662.  https://doi.org/10.1175/JCLI-D-14-00347.1 CrossRefGoogle Scholar
  69. Zhang L, Wang C, Song Z, Lee S-K (2014) Remote effect of the model cold bias in the tropical North Atlantic on the warm bias in the tropical southeastern Pacific. J Adv Model Earth Syst 6:1016–1026.  https://doi.org/10.1002/2014MS000338 CrossRefGoogle Scholar
  70. Zhang W, Vecchi GA, Villarini G et al (2017) Modulation of western North Pacific tropical cyclone activity by the Atlantic Meridional Mode. Clim Dyn 48:631–647.  https://doi.org/10.1007/s00382-016-3099-2 CrossRefGoogle Scholar
  71. Zhao M, Held IM, Vecchi GA (2010) Retrospective forecasts of the hurricane season using a global atmospheric model assuming persistence of SST anomalies. Mon Weather Rev 138:3858–3868.  https://doi.org/10.1175/2010MWR3366.1 CrossRefGoogle Scholar
  72. Zuidema P, Chang P, Medeiros B et al (2016) Challenges and prospects for reducing coupled climate model SST biases in the eastern tropical Atlantic and Pacific Oceans: The U.S. CLIVAR eastern tropical oceans synthesis working group. Bull Am Meteorol Soc 97:2305–2328.  https://doi.org/10.1175/BAMS-D-15-00274.1 CrossRefGoogle Scholar
  73. Landsea CW, Franklin JL, Beven JL (2015) The revised Atlantic hurricane database (HURDAT2). United States National Oceanic and Atmospheric Administration’s National Weather ServiceGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of OceanographyTexas A&M UniversityCollege StationUSA
  2. 2.Climate and Ecosystem Sciences DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Department of Atmospheric ScienceTexas A&M UniversityCollege StationUSA
  4. 4.Physical Oceanography Laboratory/Qingdao Collaborative Innovation Center of Marine Science and TechnologyOcean University of ChinaQingdaoChina

Personalised recommendations