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Assessing the performance of 33 CMIP6 models in simulating the large-scale environmental fields of tropical cyclones

Abstract

General circulation model (GCM) biases are one of the important sources of biases and uncertainty in dynamic downscaling–based simulations. The ability of regional climate models to simulate tropical cyclones (TCs) is strongly affected by the ability of GCMs to simulate the large-scale environmental field. Thus, in this work, we employ a recently developed multivariable integrated evaluation method to assess the performance of 33 CMIP6 (phase 6 of the Coupled Model Intercomparison Project) models in simulating multiple fields in terms of their climatology. The CMIP6 models are quantitatively evaluated against two reanalysis datasets over five ocean areas. The results show that most of the CMIP6 models overestimate the mid-level humidity in almost all tropical oceans. The multi-model ensemble mean overestimates the vertical shear of the horizontal winds in the Northeast Pacific and North Atlantic. An increase in model horizontal resolution appears to be helpful in improving the model simulations. For example, there are 6–8 models with higher resolution among the top 10 models in terms of overall model performance in simulating the climatology and interannual variability of multiple variables. Similarly, there are 7–8 models with lower resolution among the bottom 10 models. The model skill varies depending on the region and variable being evaluated. Although no model performs best in all regions and for all variables, some models do show relatively good capability in simulating the large-scale environmental field of TCs.

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Availability of data and material (data transparency)

All data generated during this study are included in this published article.

Code availability (software application or custom code)

All code used during this study have been published.

References

  1. Bruyère CL, Done JM et al (2014) Bias corrections of global models for regional climate simulations of high-impact weather. Clim Dyn 43:1847–1856. https://doi.org/10.1007/s00382-013-2011-6

    Article  Google Scholar 

  2. Camargo SJ (2013) Global and regional aspects of tropical cyclone activity in the CMIP5 models. J Clim 26:9880–9902

    Article  Google Scholar 

  3. Camargo SJ, Emanuel KA, Sobel AH (2007a) Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J Clim 20(19):4819–4834

    Article  Google Scholar 

  4. Camargo SJ, Sobel AH, Barnston AG et al (2007b) Tropical cyclone genesis potential index in climate models. Tellus(A) 59(4):428–443

    Article  Google Scholar 

  5. Chan JCL, Shi JE, Liu KS (2001) Improvements in the seasonal forecasting of tropical cyclone activity over the western North Pacific. Weather Forecast 16:491–498

    Article  Google Scholar 

  6. Chen J, Wang Z, Tam C-Y et al (2020) Investigating climate change impacts on western North Pacific tropical cyclones and induced storm surges over the Pearl River Delta region based on pseudo-global warming experiments. Sci Rep 10:1965. https://doi.org/10.1038/s41598-020-58824-8

    Article  Google Scholar 

  7. Elsberry RL (1994) Global view of tropical cyclones.

  8. Emanuel KA (2010) Tropical cyclone activity downscaled from NOAA-CIRES reanalysis. J Adv Model Earth Syst 2:1908–1958. https://doi.org/10.3894/JAMES.2010.2.1

    Article  Google Scholar 

  9. Emanuel K, Center L (2020) Response of global tropical cyclone activity to increasing co2: results from downscaling cmip6 models. J Clim 34(1):1–54. https://doi.org/10.1175/JCLI-D-20-0367.1

    Article  Google Scholar 

  10. Emanuel KA, Sundararajan R, Williams J (2008) Hurricanes and global warming: results from downscaling IPCC AR4 simulations. Bull Am Meteorol Soc 89:347–368. https://doi.org/10.1175/BAMS-89-3-347

    Article  Google Scholar 

  11. Emanuel KA, Nolan DS (2004) Tropical cyclone activity and the global climate system. In: Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Am Meteorol Soc 40–241.

  12. Eyring V, Bony S, Meehl GA et al (2016) Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016

    Article  Google Scholar 

  13. Fan K, Wang HJ (2009) A new approach to forecasting typhoon frequency over the western North Pacific. Weather Forecast 24:974–986

    Article  Google Scholar 

  14. Gleckler PJ, Taylor KE, Doutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104. https://doi.org/10.1029/2007JD008972

    Article  Google Scholar 

  15. Gray WM (1968) Global view of the origin of tropical disturbances and storms. Mon Wea Rev 96:669–700

    Article  Google Scholar 

  16. Gray WM (1984) Atlantic seasonal hurricane frequency. Part II: forecasting its variability. Mon Wea Rev 112:1669–1683

    Article  Google Scholar 

  17. Gray WM (1979) Hurricanes: their formation, structure and likely role in the tropical circulation. In: Shaw DB (ed) Meteorology over the Tropical Oceans, Royal Meteor. Soc., James Glaisher House, Grenville Place, Bracknell, Berkshire, 155–218.

  18. Holland GJ, Done J, Bruyere C, et al (2010) Model investigations of the effects of climate variability and change on future Gulf of Mexico tropical cyclone activity. OTC Metocean.

  19. Huang F, Xu Z, Guo W (2019) Evaluating vector winds in the Asian–Australian monsoon region simulated by 37 CMIP5 models. Clim Dyn 53:491–507. https://doi.org/10.1007/s00382-018-4599-z

    Article  Google Scholar 

  20. Huang F, Xu Z, Guo W (2020) The linkage between CMIP5 climate models’ abilities to simulate precipitation and vector winds. Clim Dyn 54:4953–4970. https://doi.org/10.1007/s00382-020-05259-6

    Article  Google Scholar 

  21. Jiang JH et al (2012) Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA ’A-Train’ satellite observations. J Geophys Res 117(D14):D14105. https://doi.org/10.1029/2011jd017237

    Article  Google Scholar 

  22. Knutson TR et al (2013) Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J Clim 26:6591–6617. https://doi.org/10.1175/JCLI-D-12-00539.1

    Article  Google Scholar 

  23. Li X (1956) The comprehensive theory for typhoon genesis. Acta Meteor Sin 27:97–99

    Google Scholar 

  24. Lin JL (2007) The double-ITCZ problem in IPCC AR4 coupled GCMs: ocean-atmosphere feedback analysis. J Clim 20(18):4497–4525. https://doi.org/10.1175/jcli4272.1

    Article  Google Scholar 

  25. Liu HL, Zhang MH, Lin WY (2012) An investigation of the initial development of the double-ITCZ warm SST biases in the CCSM. J Clim 25(1):140–155. https://doi.org/10.1175/2011jcli4001.1

    Article  Google Scholar 

  26. Liu Z, Schwartz CS, Snyder C et al (2012) Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon Wea Rev 140:4017–4034. https://doi.org/10.1175/MWR-D-12-00083.1

    Article  Google Scholar 

  27. Lu XQ, Yu H, Ying M et al (2021) Western north pacific tropical cyclone database created by the china meteorological administration. Adv Atmos Sci 38(4):690–699. https://doi.org/10.1007/s00376-020-0211-7

    Article  Google Scholar 

  28. Lui YS, Tse K-S, Tam C-Y et al (2020) Performance of western north Pacific tropical cyclone track and intensity evolution in MPAS-A and WRF simulations. Appl Clim Theor. https://doi.org/10.1007/s00704-020-03444-5

    Article  Google Scholar 

  29. Mei W, Xie S-P, Zhao M (2014) Variability of tropical cyclone track density in the North Atlantic: observations and high-resolution simulations. J Clim 27:4797–4814. https://doi.org/10.1175/JCLI-D-13-00587.1

    Article  Google Scholar 

  30. Mei W, Xie S-P, Zhao M et al (2015) Forced and internal variability of tropical cyclone track density in the western North Pacific. J Clim 28:143–167. https://doi.org/10.1175/JCLI-D-14-00164.1

    Article  Google Scholar 

  31. Mei W, Kamae Y, Xie SP et al (2019) Variability and predictability of north atlantic hurricane frequency in a large ensemble of high-resolution atmospheric simulations. J Clim 32(11):3153

    Article  Google Scholar 

  32. Palmen E (1948) On the formation and structure of tropical hurricane. Geophys 4:26–38

    Google Scholar 

  33. Riehl H (1948) On the formation of typhoon. J Meteor 5:247–264

    Article  Google Scholar 

  34. Riehl H (1950) A model of hurricane formation. J App Phys 21:917–925

    Article  Google Scholar 

  35. Roberts MJ, Camp J, Seddon J et al (2020) Projected future changes in tropical cyclones using the CMIP6 HighResMIP multimodel ensemble. Geophys Res Lett 47:e2020GL088662. https://doi.org/10.1029/2020GL088662

    Article  Google Scholar 

  36. Shapiro LJ (1982) Hurricane climate fluctuation. Part II: Relation to large-scale circulation. Mon Weather Rev 111:1014–1023. https://doi.org/10.1175/1520-0493(1982)110%3C1014:HCFPIR%3E2.0.CO;2

    Article  Google Scholar 

  37. Song YJ, Wang L, Lei XY et al (2015) Tropical cyclone genesis potential index over the western North Pacific simulated by CMIP5 models. Adv Atmos Sci 32(11):1539–1550. https://doi.org/10.1007/s00376-015-4162-3

    Article  Google Scholar 

  38. Su H, Jiang JH, Zhai C et al (2013) Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using “A-Train” satellite observations and reanalysis data. J Geophys Res Atmos 118(7):2762–2780. https://doi.org/10.1029/2012JD018575

    Article  Google Scholar 

  39. Sun Y, Ding YH (2002) Anomalous activities of tropical cyclone over the western north pacific and the related large-scale circulation features during 1998 and 1999. Act Meteor Sin 60(5):527–537

    Google Scholar 

  40. Takahashi H, Su H, Jiang JH (2016) Error analysis of upper tropospheric water vapor in CMIP5 models using “A-Train” satellite observations and reanalysis data. Clim dyn 46(9–10):2787–2803. https://doi.org/10.1007/s00382-015-2732-9

    Article  Google Scholar 

  41. Tao L, Zhang YF, Wang XB (2020) Improvement of genesis potential index for western North Pacific tropical cyclones. Trans Atmos Sci 43(4):603–616. https://doi.org/10.13878/j.cnki.dqkxxb.20171228001

    Article  Google Scholar 

  42. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192

    Article  Google Scholar 

  43. Tian B, Fetzer EJ, Kahn BH et al (2013) Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology. J Geophys Res Atmos 118:114–134. https://doi.org/10.1029/2012JD018607

    Article  Google Scholar 

  44. Villarini G, Vecchi GA (2012) Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models. Nat Clim Change 2:604–607. https://doi.org/10.1038/nclimate1530

    Article  Google Scholar 

  45. Villarini G, Vecchi GA (2013) Projected increases in North Atlantic tropical cyclone intensity from CMIP5 models. J. Clim 26:3231–3240. https://doi.org/10.1175/JCLI-D-12-00441.1

    Article  Google Scholar 

  46. Wu W, Jin-hua Yu (2011) Scenarios of 21st century tropical cyclone activity over western north pacific as projected by GFDL_RegCM. J Trop Meteorol 27(6):843–852

    Google Scholar 

  47. Xu ZF, Yang Z-L (2012) An improved dynamical downscaling methods with GCM bias corrections and its validation with 30 years of climate simulations. J Clim 25:6271–6286. https://doi.org/10.1175/JCLI-D-12-00005.1

    Article  Google Scholar 

  48. Xu ZF, Yang Z-L (2015) A new dynamical downscaling approach with GCM bias corrections and spectral nudging. J Geophys Res Atmos. https://doi.org/10.1002/2014JD022958

    Article  Google Scholar 

  49. Xu ZF, Hou Z, Han Y et al (2016) A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geosci Model Dev 9:4365–4380. https://doi.org/10.5194/gmd-9-4365-2016

    Article  Google Scholar 

  50. Xu ZF, Han Y, Fu C (2017) Multivariable integrated evaluation of model performance with the vector field evaluation diagram. Geosci Model Dev 10:3805–3820. https://doi.org/10.5194/gmd-10-3805-2017

    Article  Google Scholar 

  51. Xu ZF, Han Y, Yang Z-L (2019) Dynamical downscaling of regional climate: a review of methods and limitations. Sci China Earth Sci 62:365–375. https://doi.org/10.1007/s11430-018-9261-5

    Article  Google Scholar 

  52. Ying M, Zhang W, Yu H et al (2014) An overview of the China Meteorological Administration tropical cyclone database. J Atmos Oceanic Technol 31:287–301. https://doi.org/10.1175/JTECH-D-12-00119.1

    Article  Google Scholar 

  53. Zhang Y, Wang H, Sun J et al (2010) Changes in the tropical cyclone genesis potential index over the western North Pacifc in the SRES A2 scenario. Adv Atmos Sci. https://doi.org/10.1007/x00376-010-9096-1

    Article  Google Scholar 

  54. Zhang M-Z, Xu Z, Han Y et al (2021) An improved multivariable integrated evaluation method and tool (MVIETool) v1.0 for multimodel intercomparison. Geosci Model Dev 14:3079–3094. https://doi.org/10.5194/gmd-14-3079-2021

    Article  Google Scholar 

  55. Zhao J, Wu L, Zhao H (2012) Improvement of tropical cyclone genesis potential index in the western North Pacific Basin. J Met Sci 32(6):591–599

    Google Scholar 

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Acknowledgements

We thank the climate modeling groups involved in CMIP6 for producing and making available their model outputs. The ERA5 data were provided by the European Centre for Medium-Range Weather Forecasts. The JRA-55 data were provided by the Japan Meteorological Agency. This study was supported jointly by the National Key Research and Development Program of China (2017YFA0603803) and the National Science Foundation of China (42075152, 42075170, 41675105, 41775075). The study was also supported by the Jiangsu Collaborative Innovation Centre for Climate Change.

Funding

This study was supported jointly by the National Key Research and Development Program of China (2017YFA0603803) and the National Science Foundation of China (42075152, 42075170, 41675105, 41775075). The study was also supported by the Jiangsu Collaborative Innovation Centre for Climate Change.

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YH and ZX designed the study. YH carried out the analysis and drafted the manuscript. M-ZZ and YH prepared the figures. All authors discussed the results and commented on the manuscript.

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Correspondence to Zhongfeng Xu.

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Han, Y., Zhang, MZ., Xu, Z. et al. Assessing the performance of 33 CMIP6 models in simulating the large-scale environmental fields of tropical cyclones. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-05986-4

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Keywords

  • Tropical cyclone
  • Multivariable integrated evaluation
  • CMIP6
  • Large-scale environmental field