Advertisement

Climate Dynamics

, Volume 50, Issue 5–6, pp 1581–1596 | Cite as

Simulating the characteristics of tropical cyclones over the South West Indian Ocean using a Stretched-Grid Global Climate Model

  • Molulaqhooa L. Maoyi
  • Babatunde J. AbiodunEmail author
  • Joseph M. Prusa
  • Jennifer J. Veitch
Article

Abstract

Tropical cyclones (TCs) are one of the most devastating natural phenomena. This study examines the capability of a global climate model with grid stretching (CAM-EULAG, hereafter CEU) in simulating the characteristics of TCs over the South West Indian Ocean (SWIO). In the study, CEU is applied with a variable increment global grid that has a fine horizontal grid resolution (0.5° × 0.5°) over the SWIO and coarser resolution (1° × 1°—2° × 2.25°) over the rest of the globe. The simulation is performed for the 11 years (1999–2010) and validated against the Joint Typhoon Warning Center (JTWC) best track data, global precipitation climatology project (GPCP) satellite data, and ERA-Interim (ERAINT) reanalysis. CEU gives a realistic simulation of the SWIO climate and shows some skill in simulating the spatial distribution of TC genesis locations and tracks over the basin. However, there are some discrepancies between the observed and simulated climatic features over the Mozambique channel (MC). Over MC, CEU simulates a substantial cyclonic feature that produces a higher number of TC than observed. The dynamical structure and intensities of the CEU TCs compare well with observation, though the model struggles to produce TCs with a deep pressure centre as low as the observed. The reanalysis has the same problem. The model captures the monthly variation of TC occurrence well but struggles to reproduce the interannual variation. The results of this study have application in improving and adopting CEU for seasonal forecasting over the SWIO.

Notes

Acknowledgements

The project was supported with grants and bursaries from National Research Foundation (NRF, South Africa), Water Research Commission (WRC, South Africa), and the Future Resilience for African Cities and Lands (FRACTAL) project. Computing facility was provided by Centre for High Performance Computing (CHPC, South Africa). We thank the two anonymous reviewers, whose comments have helped in improving the quality of the manuscript.

References

  1. Abiodun BJ, Prusa JM, Gutowski WJ Jr (2008) Implementation of a non-hydrostatic, adaptive-grid dynamics core in CAM3. Part I: comparison of dynamics cores in aqua-planet simulations. Clim Dyn 31(7–8):795–810CrossRefGoogle Scholar
  2. Abiodun BJ, Gutowski WJ, Abatan AA, Prusa JM (2011) CAM-EULAG: a non-hydrostatic atmospheric climate model with grid stretching. Acta Geophys 59(6):1158–1167CrossRefGoogle Scholar
  3. Araujo JA, Abiodun BJ, Crespo O (2014) Impacts of drought on grape yields in Western Cape, South Africa. Theoret Appl Climatol, pp 1–14Google Scholar
  4. Badarinath KVS, Mahalakshmim DV, Ratna SB (2012) Influence of land use land cover on cyclone track prediction—a study during Ailia cyclone. Open Atmos Sci J [Online] 6:6 February 2013. Available from: http://benthamopen.com/toascj/articles/V006/33TOASCJ.pdf
  5. Bell GD, Halpert MS, Schnell RC, Higgins RW, Lawrimore J, Kousky VE, Tinker R, Thiaw W, Chelliah M, Artusa A (2000) Climate assessment for 1999. Bull Am Meteorol Soc 81(6):s1–s50CrossRefGoogle Scholar
  6. Bell J, Hodges K, Vidale PL, Strachan J, Roberts M (2014) Simulation of the global ENSO–tropical cyclone teleconnection by a high-resolution coupled general circulation model. J Clim [Online] 27(17):14 (November 2014-6404-6422). Available from: http://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-13-00559.1
  7. Bengtsson L, Botzet M, Esch M (1995) Hurricane-type vortices in a general circulation model. Tellus [Online] 47A(2):175–196. Available from: http://envsci.rutgers.edu/~toine379/extremeprecip/papers/bengtsson_et_al_1995.pdf. 4 Nov 2014
  8. Bengtsson L, Botzet M, Esch M (1996) Will greenhouse gas-induced warming over the next 50 years lead to higher frequency and greater intensity of hurricanes?. Tellus [Online], 4A(1):3 November 2014-57-73. Available from: http://pubman.mpdl.mpg.de/pubman/item/escidoc:1852493:2/component/escidoc:1852573/11632-38420-1-SM.pdf
  9. Berrisford P, Kallberg P, Kobayashi S, Dee D, Uppala S, Simmons A, Poli P, Sato H (2011) The ERA-Interim archive version 2.0. Eur Centre Medium-Range Weather Forecasts ERA Tech Rep 1:23Google Scholar
  10. Caian M, Geleyn JF (1997) Some limits to the variable-mesh solution and comparison with the nested lam solution. Q J R Meteorol Soc 123(539):743–766CrossRefGoogle Scholar
  11. Camargo SJ, Sobel AH (2005) Western North Pacific tropical cyclone intensity and ENSO. J Clim 18(15):2996–3006CrossRefGoogle Scholar
  12. Chatzidimitriou K, Sutton A (2005) Alternative data mining techniques for predicting tropical cyclone intensification. In: American Association for Artificial Intelligence, vol 37. Citeseer, New Jersey, pp 99–128Google Scholar
  13. Collins WD, Rasch PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Kiehl JT, Briegleb B, Bitz C, Lin S (2004) Description of the NCAR community atmosphere model (CAM 3.0)”Google Scholar
  14. Driver P (2014) Rainfall variability over Southern Africa, PhD Thesis Submitted to the University of Cape TownGoogle Scholar
  15. Dvorak VF (1984) Tropical cyclone intensity analysis using satellite data, US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information ServiceGoogle Scholar
  16. Girishkumar MS, Ravichandran M (2012) The influences of ENSO on tropical cyclone activity in the Bay of Bengal during October–December. J Geophys Res 117:C02033. doi: 10.1029/2011JC007417 CrossRefGoogle Scholar
  17. Goerss JS (2000) Tropical cyclone track forecasts using an ensemble of dynamical models. Mon Weather Rev 128(4):1187–1193CrossRefGoogle Scholar
  18. Gray WM (1968) “Global view of the origin of tropical disturbances and storms. Mon Weather Rev 96(10):669–700CrossRefGoogle Scholar
  19. Gray WM (1979) Hurricanes: their formation, structure, and likely role in the tropical circulation. In: Shaw DB (ed) Meteorology over the Tropical Oceans. Royal meteorological Society, pp 155–218Google Scholar
  20. Hamill TM, Whitaker JS, Fiorino M, Benjamin SG (2011) Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon Weather Rev 139(2):668–688CrossRefGoogle Scholar
  21. Harris LM, Lin SJ, Tu C (2016) High-resolution climate simulations using GFDL HiRAM with a stretched global grid. J Clim 29(11):4293–4314CrossRefGoogle Scholar
  22. Hashimoto A, Done JM, Fowler LD, Bruyère CL (2016) Tropical cyclone activity in nested regional and global grid-refined simulations. Clim Dyn 47(1–2):497–508CrossRefGoogle Scholar
  23. Henderson-Sellers A, Zhang H, Berz G, Emanuel K, Gray W, Landsea C, Holland G, Lighthill J, Shieh S, Webster P (1998) Tropical cyclones and global climate change: a post-IPCC assessment. Bull Am Meteorol Soc 79(1):19–38CrossRefGoogle Scholar
  24. Holland GJ (1993) Ready reckoner. Glob Guide Trop Cyclone Forecast, pp 9–1Google Scholar
  25. IRIN, Tropical Cyclone Haruna hits southwestern Madagascar. 2015, [Homepage of IRINNews], [Online]. Available: http://www.irinnews.org/report/97542/tropical-cyclone-haruna-hits-southwestern-madagascar [2015, April]
  26. Kleppek S, Muccione V, Raible CC, Bresch DN, Koellner-Heck P, Stocker TF (2008) Tropical cyclones in ERA-40: a detection and tracking method. Geophys Res Lett 35:L10705. doi: 10.1029/2008GL033880 CrossRefGoogle Scholar
  27. Klinman M, Reason C (2008) On the peculiar storm track of TC Favio during the 2006–2007 Southwest Indian Ocean tropical cyclone season and relationships to ENSO. Meteorol Atmos Phys 100(1–4):233–242CrossRefGoogle Scholar
  28. Kurowski MJ, Grabowski WW, Smolarkiewicz PS (2014) Anelastic and compressible simulation of moist deep convection. J Atmos Sci V71:3767–3787CrossRefGoogle Scholar
  29. Kurowski MJ, Grabowski WW, Smolarkiewicz PS (2015) Anelastic and compressible simulation of moist dynamics at planetary scales. J Atmos Sci, V72:3975–3995CrossRefGoogle Scholar
  30. LaRow TE, Lim YK, Shin DW, Chassignet EP, Cocke S (2008) Atlantic basin seasonal hurricane simulations. J Clim 21(13):3191–3206CrossRefGoogle Scholar
  31. Manabe S, Holloway JL Jr, Stone HM (1970) Tropical circulation in a time-integration of a global model of the atmosphere. J Atmos Sci 27(4):580–613CrossRefGoogle Scholar
  32. Mavume FA, Rydberg L, Rouault M, Lutjeharms REJ (2009) Climatology and landfall of tropical cyclones in the South West Indian Ocean. West Indian Ocean J Mar Sci 8(1):15–36Google Scholar
  33. Mbedzi MP (2010) Simulation of tropical cyclone-like vortices over the southwestern Indian Ocean, University of PretoriaGoogle Scholar
  34. Misra J (1991) Phase synchronization. Inf Process Lett 38(2):101–105CrossRefGoogle Scholar
  35. Morioka Y, Tozuka T, Masson S, Terray P, Luo J, Yamagata T (2012) Subtropical dipole modes simulated in a coupled general circulation model. J Clim 25(12):4029–4047CrossRefGoogle Scholar
  36. National Weather Service, Tropical Cyclone Structure. 2010, [Homepage of National Weather Service], [Online]. Available: http://www.srh.noaa.gov/jetstream/tropics/tc_structure.htm?&session-id=e2221f2646ce61f563e3a28f11ec4d98. 2015, July
  37. Ogier D (2013) Characteristics of inertial gravity waves over Southern Africa as simulated with CAM-EULAG, A MSc thesis submitted to the University of Cape TownGoogle Scholar
  38. Prusa JM, Gutowski WJ (2010) Multi-scale waves in sound-proof global simulations with EULAG. Acta Geophys V59:1135–1157Google Scholar
  39. Rodrigues RR, Campos EJ, Haarsma R (2015) The impact of ENSO on the South Atlantic subtropical dipole mode. J Clim 28(7):2691–2705CrossRefGoogle Scholar
  40. Saha KK, Wasimi SA (2013) Interrelationship between Indian Ocean Dipole (IOD) and Australian Tropical Cyclones. Int J Environ Sci Dev 4(6):647CrossRefGoogle Scholar
  41. Saji N, Goswami BN, Vinayachandran P, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401(6751):360–363Google Scholar
  42. Schenkel BA, Hart RE (2012) An examination of tropical cyclone position, intensity, and intensity life cycle within atmospheric reanalysis datasets. J Clim 25(10):3453–3475. doi: 10.1175/2011JCLI4208.1 CrossRefGoogle Scholar
  43. Schreck CJ III, Knapp KR, Kossin JP (2014) The impact of best track discrepancies on global tropical cyclone climatologies using IBTrACS. Mon Weather Rev 142(10):3881–3899CrossRefGoogle Scholar
  44. Strachan J, Vidale PL, Hodges K, Roberts M, Demory M (2013) Investigating global tropical cyclone activity with a hierarchy of AGCMs: The role of model resolution. J Clim [Online] 26(1):5 November-133-152. Available from: http://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-12-00012.1
  45. Tozuka T, Abiodun BJ, Engelbrecht FA (2014) Impacts of convection schemes on simulating tropical-temperate troughs over southern Africa. Clim Dyn 42(1–2):433–451CrossRefGoogle Scholar
  46. Vitart F, Anderson J, Stern W (1997) Simulation of interannual variability of tropical storm frequency in an ensemble of GCM integrations. J Clim 10(4):745–760CrossRefGoogle Scholar
  47. Vitart F, Anderson D, Stockdale T (2003) Seasonal forecasting of tropical cyclone landfall over Mozambique. J Clim 16(23):3932–3945CrossRefGoogle Scholar
  48. Walsh K, Fiorino M, Landsea C, McInnes K (2007) Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J Clim 20(10):2307–2314CrossRefGoogle Scholar
  49. Walsh KE, Camargo SJ, Vecchi GA, Daloz A, Elsner J, Emanuel K, Horn M, Lim Y, Roberts M, Patricola C, Scoccimarro E, Sobel AH, Strazzo S, Villarini G, Wehner M, Zhao M, Kossin JP, LaRow T, Oouchi K, Schubert S, Wang H, Bacmeister J, Chang P, Chauvin F, Jablonowski C, Kumar A, Murakami H, Ose T, Reed KA, Saravanan R, Yamada Y, Zarzycki CM, Vidale P, Jonas JA, Henderson N (2015) Hurricanes and climate: the U.S. CLIVAR working group on hurricanes. Bull Am Meteorol Soc 96:997–1017. doi: 10.1175/BAMS-D-13-00242.1 CrossRefGoogle Scholar
  50. Williamson DL (2008) Convergence of aqua-planet simulations with increasing resolution in the Community Atmospheric Model, Version3. Tellus 60A:848–862CrossRefGoogle Scholar
  51. Zarzycki CM, Jablonowski C (2014) A multidecadal simulation of Atlantic tropical cyclones using a variable resolution global atmospheric general circulation model. J Adv Model Earth Syst 6(3):805–828CrossRefGoogle Scholar
  52. Zarzycki CM, Jablonowski C, Taylor MA (2014) Using variable-resolution meshes to model tropical cyclones in the Community Atmosphere Model. Mon Weather Rev 142(3):1221–1239CrossRefGoogle Scholar
  53. 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 [Online] 24(8):15 (November 2014-2995-3009). Available from: http://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-11-00313.1

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Molulaqhooa L. Maoyi
    • 1
  • Babatunde J. Abiodun
    • 1
    Email author
  • Joseph M. Prusa
    • 2
  • Jennifer J. Veitch
    • 3
    • 4
  1. 1.Climate System and Analysis Group, Department of Environmental and Geographical ScienceUniversity of Cape TownCape TownSouth Africa
  2. 2.Teraflux CorporationBoca RatonUSA
  3. 3.Department of OceanographyUniversity of Cape TownCape TownSouth Africa
  4. 4.South African Environmental Observation Network, Egagasini NodeCape TownSouth Africa

Personalised recommendations