Impact of air–sea coupling on the simulated global tropical cyclone activity in the high-resolution Community Earth System Model (CESM)

Abstract

Atmosphere–ocean coupling is critical for tropical cyclones (TC) formation and development. TCs derive their energy from the upper ocean, and the associated TC-ocean interactions can in turn modulate storm evolution. This study assesses the impact of ocean coupling on directly-simulated global TC activity in the high-resolution “TC-permitting” Community Earth System Model (CESM). Model-simulated global TC activity is evaluated in a 30-year fully-coupled CESM simulation (CPL), in which the 0.25° atmosphere component is coupled to the nominal 1° dynamic ocean (with ~ 0.27° horizontal grid spacing in the tropics). An atmosphere-only simulation (ATM) is branched from CPL, with sea surface temperature (SST) specified from CPL, which we use to isolate the effect of ocean coupling on TC activity. We find that the two-way ocean coupling can affect global TC frequency, geographical distribution, storm intensity, and interannual variability. ATM on average simulates 27% more major TC events than CPL globally, and the TC power dissipation is higher than CPL poleward of 12° latitude in both hemispheres. The lack of negative SST feedbacks in ATM allows TCs to have a longer intensification period and reach the maximum intensity at a higher latitude. In CPL, TC interannual variability is heavily influenced by El Nino/La Nina events. This relationship can be captured in ATM under strong events but is less predictable during weak and neutral years. Results help to better understand the connections and feedbacks linking air–sea coupling, tropical variability, and the directly simulated TC activity within the high-resolution Earth System Models.

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    http://eaps4.mit.edu/faculty/Emanuel/products.

References

  1. Bacmeister JT, Wehner MF, Neale RB et al (2013) Exploratory high-resolution climate simulations using the community atmosphere model (CAM). J Clim 27:3073–3099. https://doi.org/10.1175/JCLI-D-13-00387.1

    Article  Google Scholar 

  2. Bacmeister JT, Reed KA, Hannay C et al (2016) Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model. Clim Change. https://doi.org/10.1007/s10584-016-1750-x

    Article  Google Scholar 

  3. Banzon V, Smith TM, Chin TM et al (2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst Sci Data 8:165–176. https://doi.org/10.5194/essd-8-165-2016

    Article  Google Scholar 

  4. 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 128:917–946

    Article  Google Scholar 

  5. Bessafi M, Wheeler MC (2006) Modulation of South Indian Ocean Tropical Cyclones by the Madden–Julian Oscillation and Convectively Coupled Equatorial Waves. Mon Weather Rev 134:638–656. https://doi.org/10.1175/MWR3087.1

    Article  Google Scholar 

  6. Bueti MR, Ginis I, Rothstein LM, Griffies SM (2014) Tropical cyclone-induced thermocline warming and its regional and global impacts. J Clim. https://doi.org/10.1175/JCLI-D-14-00152.1

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. Chan JCL (2000) Tropical cyclone activity over the Western North Pacific associated with El Niño and La Niña Events. J Clim 13:2960–2972. https://doi.org/10.1175/1520-0442(2000)013%3C2960:TCAOTW%3E2.0.CO;2

    Article  Google Scholar 

  10. Cheng L, Zhu J, Sriver RL (2015) Global representation of tropical cyclone-induced short-term ocean thermal changes using Argo data. Ocean Sci 11:719–741. https://doi.org/10.5194/os-11-719-2015

    Article  Google Scholar 

  11. Dickinson M, Molinari J (2002) Mixed Rossby–gravity waves and western pacific tropical cyclogenesis. Part I: Synoptic evolution. J Atmos Sci 59:2183–2196. https://doi.org/10.1175/1520-0469(2002)059%3C2183:MRGWAW%3E2.0.CO;2

    Article  Google Scholar 

  12. Emanuel K (2001) Contribution of tropical cyclones to meridional heat transport by the oceans. J Geophys Res Atmos 106:14771–14781. https://doi.org/10.1029/2000JD900641

    Article  Google Scholar 

  13. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688. https://doi.org/10.1038/nature03906

    Article  Google Scholar 

  14. Gent PR, Mcwilliams JC (1990) Isopycnal mixing in ocean circulation models. J Phys Oceanogr 20:150–155. https://doi.org/10.1175/1520-0485(1990)020%3C0150:IMIOCM%3E2.0.CO;2

    Article  Google Scholar 

  15. Gray WM (1984) 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

    Article  Google Scholar 

  16. Hart RE (2011) An inverse relationship between aggregate northern hemisphere tropical cyclone activity and subsequent winter climate. Geophys Res Lett 38:L01705. https://doi.org/10.1029/2010GL045612

    Article  Google Scholar 

  17. Huang A, Li H, Sriver RL et al (2017) Regional variations in the ocean response to tropical cyclones: ocean mixing versus low cloud suppression. Geophys Res Lett 44:2016GL072023. https://doi.org/10.1002/2016GL072023

    Article  Google Scholar 

  18. Hurrell JW, Ghan S, Kay JE et al (2013) The community earth system model: a framework for collaborative research. Bull Am Meteorol Soc 94:1339–1360. https://doi.org/10.1175/BAMS-D-12-00121.1

    Article  Google Scholar 

  19. Jaimes B, Shay LK (2009) Mixed layer cooling in mesoscale oceanic eddies during Hurricanes Katrina and Rita. Mon Weather Rev 137(12):4188–4207

    Article  Google Scholar 

  20. Jien JY, Gough WA, Butler K (2014) 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

    Article  Google Scholar 

  21. Jullien S, Marchesiello P, Menkes CE et al (2014) Ocean feedback to tropical cyclones: climatology and processes. Clim Dyn 1–24. https://doi.org/10.1007/s00382-014-2096-6

  22. Kim H-S, Vecchi GA, Knutson TR et al (2014) Tropical cyclone simulation and response to CO2 doubling in the GFDL CM2.5 high-resolution coupled climate model. J Clim 27:8034–8054. https://doi.org/10.1175/JCLI-D-13-00475.1

    Article  Google Scholar 

  23. Klingaman NP, Woolnough SJ, Weller H, Slingo JM (2010) The Impact of finer-resolution air–sea coupling on the intraseasonal oscillation of the Indian Monsoon. J Clim 24:2451–2468. https://doi.org/10.1175/2010JCLI3868.1

    Article  Google Scholar 

  24. Kossin JP, Emanuel KA, Vecchi GA (2014) The poleward migration of the location of tropical cyclone maximum intensity. Nature 509:349–352. https://doi.org/10.1038/nature13278

    Article  Google Scholar 

  25. Lawrence DM, Oleson KW, Flanner MG et al (2012) The CCSM4 land simulation, 1850–2005: assessment of surface climate and new capabilities. J Clim 25:2240–2260. https://doi.org/10.1175/JCLI-D-11-00103.1

    Article  Google Scholar 

  26. Lengaigne M, Neetu S, Samson G et al (2018) Influence of air–sea coupling on Indian Ocean tropical cyclones. Clim Dyn. https://doi.org/10.1007/s00382-018-4152-0

    Article  Google Scholar 

  27. Li H, Sriver RL (2016) Effects of ocean grid resolution on tropical cyclone-induced upper ocean responses using a global ocean general circulation model: resolution affects ocean response to TC. J Geophys Res Oceans 121:8305–8319. https://doi.org/10.1002/2016JC011951

    Article  Google Scholar 

  28. Li H, Sriver RL (2018a) Impact of tropical cyclones on the global ocean: results from multi-decadal global ocean simulations isolating tropical cyclone forcing. J Clim. https://doi.org/10.1175/JCLI-D-18-0221.1

    Article  Google Scholar 

  29. Li H, Sriver RL (2018b) Tropical cyclone activity in the high-resolution community earth system model and the impact of ocean coupling. J Adv Model Earth Syst 10:165–186. https://doi.org/10.1002/2017MS001199

    Article  Google Scholar 

  30. Li H, Sriver RL, Goes M (2016) Modeled sensitivity of the Northwestern Pacific upper-ocean response to tropical cyclones in a fully coupled climate model with varying ocean grid resolution: ocean response to TCs IN coupled model. J Geophys Res Oceans 121:586–601. https://doi.org/10.1002/2015JC011226

    Article  Google Scholar 

  31. Lin I-I, Wu C-C, Pun I-F, Ko D-S (2008) Upper-ocean thermal structure and the Western North Pacific Category 5 Typhoons. Part I: Ocean features and the category 5 Typhoons’ intensification. Mon Weather Rev 136:3288–3306. https://doi.org/10.1175/2008MWR2277.1

    Article  Google Scholar 

  32. Lin I-I, Pun I-F, Lien C-C (2014) “Category-6” supertyphoon Haiyan in global warming hiatus: contribution from subsurface ocean warming. Geophys Res Lett 41:8547–8553. https://doi.org/10.1002/2014GL061281

    Article  Google Scholar 

  33. Liu B, Liu H, Xie L et al (2010) A coupled atmosphere–wave–ocean modeling system: simulation of the intensity of an idealized tropical cyclone. Mon Weather Rev 139:132–152. https://doi.org/10.1175/2010MWR3396.1

    Article  Google Scholar 

  34. Lloyd ID, Marchok T, Vecchi GA (2011) Diagnostics comparing sea surface temperature feedbacks from operational hurricane forecasts to observations. J Adv Model Earth Syst. https://doi.org/10.1029/2011MS000075

    Article  Google Scholar 

  35. Ma Z, Fei J, Liu L, Huang X, Li Y (2017) An investigation of the influences of mesoscale ocean eddies on tropical cyclone intensities. Mon Weather Rev 145(4):1181–1201

    Article  Google Scholar 

  36. Ma Z, Fei J, Liu L, Huang X, Cheng X (2018) Modulating effects of mesoscale oceanic eddies on sea surface temperature response to tropical cyclones over the Western North Pacific. J Geophys Res Atmos 123(1):367–379

    Article  Google Scholar 

  37. Meehl GA, Arblaster JM, Hu A, Teng H (2013) Climate change projections in CESM1(CAM5) compared to CCSM4. J Clim 26:6287–6308. https://doi.org/10.1175/JCLI-D-12-00572.1

    Article  Google Scholar 

  38. Mei W, Pasquero C, Primeau F (2012) The effect of translation speed upon the intensity of tropical cyclones over the tropical ocean: TC translation speed affects intensity. Geophys Res Lett. https://doi.org/10.1029/2011GL050765

    Article  Google Scholar 

  39. Moon I-J, Ginis I, Hara T, Thomas B (2007) A physics-based parameterization of air–sea momentum flux at high wind speeds and its impact on hurricane intensity predictions. Mon Weather Rev 135:2869–2878. https://doi.org/10.1175/MWR3432.1

    Article  Google Scholar 

  40. Neale RB, Gettelman A, Park S et al (2012) Description of the NCAR community atmosphere model (CAM 5.0), Tech. Note NCAR/TN-486 + STR, Natl. Cent. for Atmos. In: 6of7 ZHAO ET AL.: AEROSOL FIE SIMULATED BY CAMS L08806. pp 2009–038451

  41. Newman M, Sardeshmukh PD, Penland C (2009) How important is air–sea coupling in ENSO and MJO evolution? J Clim 22:2958–2977. https://doi.org/10.1175/2008JCLI2659.1

    Article  Google Scholar 

  42. Ogata T, Mizuta R, Adachi Y et al (2015) Effect of air–sea coupling on the frequency distribution of intense tropical cyclones over the northwestern Pacific. Geophys Res Lett 42:10,415 –415 10,421. https://doi.org/10.1002/2015GL066774

    Article  Google Scholar 

  43. Ogata T, Mizuta R, Adachi Y et al (2016) Atmosphere–ocean coupling effect on intense tropical cyclone distribution and its future change with 60 km-AOGCM. Sci Rep 6:29800. https://doi.org/10.1038/srep29800

    Article  Google Scholar 

  44. Powell MD, Vickery PJ, Reinhold TA (2003) Reduced drag coefficient for high wind speeds in tropical cyclones. Nature 422:279–283. https://doi.org/10.1038/nature01481

    Article  Google Scholar 

  45. Reed KA, Bacmeister JT, Rosenbloom NA et al (2015) Impact of the dynamical core on the direct simulation of tropical cyclones in a high-resolution global model. Geophys Res Lett 42:3603–3608. https://doi.org/10.1002/2015GL063974

    Article  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. Sandery PA, Brassington GB, Craig A, Pugh T (2010) Impacts of ocean–atmosphere coupling on tropical cyclone intensity change and ocean prediction in the Australian region. Mon Weather Rev 138:2074–2091. https://doi.org/10.1175/2010MWR3101.1

    Article  Google Scholar 

  48. Schade LR, Emanuel KA (1999) The ocean’s effect on the intensity of tropical cyclones: results from a simple coupled atmosphere–ocean model. J Atmos Sci 56:642–651

    Article  Google Scholar 

  49. Schreck CJ, Molinari J (2011) Tropical cyclogenesis associated with Kelvin waves and the Madden–Julian oscillation. Mon Weather Rev 139:2723–2734. https://doi.org/10.1175/MWR-D-10-05060.1

    Article  Google Scholar 

  50. Schreck CJ, Molinari J, Mohr KI (2010) Attributing tropical cyclogenesis to equatorial waves in the western North Pacific. J Atmos Sci 68:195–209. https://doi.org/10.1175/2010JAS3396.1

    Article  Google Scholar 

  51. Schreck CJ, Molinari J, Aiyyer A (2011) A global view of equatorial waves and tropical cyclogenesis. Mon Weather Rev 140:774–788. https://doi.org/10.1175/MWR-D-11-00110.1

    Article  Google Scholar 

  52. Scoccimarro E, Fogli PG, Reed KA et al (2017) Tropical cyclone interaction with the ocean: the role of high-frequency (subdaily) coupled processes. J Clim 30:145–162. https://doi.org/10.1175/JCLI-D-16-0292.1

    Article  Google Scholar 

  53. Small RJ, Bacmeister J, Bailey D et al (2014) A new synoptic scale resolving global climate simulation using the Community Earth System Model. J Adv Model Earth Syst 6:1065–1094. https://doi.org/10.1002/2014MS000363

    Article  Google Scholar 

  54. Smith R, Jones P, Briegleb B et al (2010) The parallel ocean program (POP) reference manual ocean component of the community climate system model (CCSM) and community earth system model (CESM). Rep LAUR-01853 141:

  55. Sriver RL, Huber M (2007) Observational evidence for an ocean heat pump induced by tropical cyclones. Nature 447:577–580. https://doi.org/10.1038/nature05785

    Article  Google Scholar 

  56. Sriver RL, Huber M (2010) Modeled sensitivity of upper thermocline properties to tropical cyclone winds and possible feedbacks on the Hadley circulation: CCSM sensitivity to TC winds. Geophys Res Lett. https://doi.org/10.1029/2010GL042836

    Article  Google Scholar 

  57. Tang BH, Neelin JD (2004) ENSO Influence on Atlantic hurricanes via tropospheric warming. Geophys Res Lett. https://doi.org/10.1029/2004GL021072

    Article  Google Scholar 

  58. Vincent EM, Lengaigne M, Vialard J et al (2012) Assessing the oceanic control on the amplitude of sea surface cooling induced by tropical cyclones. J Geophys Res. https://doi.org/10.1029/2011JC007705

    Article  Google Scholar 

  59. Walker ND, Leben RR, Pilley CT et al (2014) Slow translation speed causes rapid collapse of northeast Pacific Hurricane Kenneth over cold core eddy. Geophys Res Lett 41:7595–7601. https://doi.org/10.1002/2014GL061584

    Article  Google Scholar 

  60. Wang X, Wang C, Han G et al (2014) Effects of tropical cyclones on large-scale circulation and ocean heat transport in the South China Sea. Clim Dyn 1–16. https://doi.org/10.1007/s00382-014-2109-5

  61. Wehner M, Prabhat, Reed KA et al (2015) Resolution dependence of future tropical cyclone projections of CAM5.1 in the U.S. CLIVAR hurricane working group idealized configurations. J Clim 28:3905–3925. https://doi.org/10.1175/JCLI-D-14-00311.1

    Article  Google Scholar 

  62. Wheeler M, Kiladis GN (1999) Convectively coupled equatorial waves: analysis of clouds and temperature in the wavenumber–frequency domain. J Atmosp Sci. https://doi.org/10.1175/1520-0469(1999)056%3C0374:CCEWAO%3E2.0.CO;2

    Article  Google Scholar 

  63. Wu R, Kirtman BP (2005) Roles of Indian and Pacific Ocean air–sea coupling in tropical atmospheric variability. Clim Dyn 25:155–170

    Article  Google Scholar 

  64. Wu C-C, Lee C-Y, Lin I-I (2007) The effect of the ocean eddy on tropical cyclone intensity. J Atmos Sci 64(10):3562–3578. https://doi.org/10.1175/jas4051.1

    Article  Google Scholar 

  65. Wu C-C, Tu W-T, Pun I-F et al (2016) Tropical cyclone-ocean interaction in Typhoon Megi (2010)—a synergy study based on ITOP observations and atmosphere–ocean coupled model simulations. J Geophys Res Atmospheres 121:153–167. https://doi.org/10.1002/2015JD024198

    Article  Google Scholar 

  66. Zarzycki CM (2016) Tropical cyclone intensity errors associated with lack of two-way ocean coupling in high-resolution global simulations. J Clim 29:8589–8610. https://doi.org/10.1175/JCLI-D-16-0273.1

    Article  Google Scholar 

  67. Zhu J, Shukla J (2013) The role of air–sea coupling in seasonal prediction of Asia–Pacific summer monsoon rainfall. J Clim 26:5689–5697. https://doi.org/10.1175/JCLI-D-13-00190.1

    Article  Google Scholar 

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Acknowledgements

This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (Awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana–Champaign and its National Center for Supercomputing Applications (NCSA). This work is funded in part by the NCSA Faculty Fellowship Program and the NSF Petascale Computing Resource Allocations (PRAC) program (Award OAC-1713685). High-resolution daily and 6-h data output are archived at the University of Illinois and are available from the authors upon request. We acknowledge Kerry Emanuel for providing best track data (http://eaps4.mit.edu/faculty/Emanuel/products). NOAA High Resolution OISST data and NCEP reanalysis data are provided by the NOAA/OAR/ESRL PSD, Boulder, CO, from their Web site at http://www.esrl.noaa.gov/psd/.

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Li, H., Sriver, R.L. Impact of air–sea coupling on the simulated global tropical cyclone activity in the high-resolution Community Earth System Model (CESM). Clim Dyn 53, 3731–3750 (2019). https://doi.org/10.1007/s00382-019-04739-8

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Keywords

  • Tropical cyclones
  • Earth system modeling
  • Atmosphere–ocean interactions
  • Ocean coupling
  • Climate variability