Climate Dynamics

, Volume 46, Issue 3–4, pp 1241–1256 | Cite as

How well do CMIP5 climate models reproduce explosive cyclones in the extratropics of the Northern Hemisphere?

  • C. SeilerEmail author
  • F. W. Zwiers


Extratropical explosive cyclones are rapidly intensifying low pressure systems with severe wind speeds and heavy precipitation, affecting livelihoods and infrastructure primarily in coastal and marine environments. This study evaluates how well the most recent generation of climate models reproduces extratropical explosive cyclones in the Northern Hemisphere for the period 1980–2005. An objective-feature tracking algorithm is used to identify and track cyclones from 25 climate models and three reanalysis products. Model biases are compared to biases in the sea surface temperature (SST) gradient, the polar jet stream, the Eady growth rate, and model resolution. Most models accurately reproduce the spatial distribution of explosive cyclones when compared to reanalysis data (R = 0.94), with high frequencies along the Kuroshio Current and the Gulf Stream. Three quarters of the models however significantly underpredict explosive cyclone frequencies, by a third on average and by two thirds in the worst case. This frequency bias is significantly correlated with jet stream speed in the inter-model spread (R \(\ge\) 0.51), which in the Atlantic is correlated with a negative meridional SST gradient (R = −0.56). The importance of the jet stream versus other variables considered in this study also applies to the interannual variability of explosive cyclone frequency. Furthermore, models with fewer explosive cyclones tend to underpredict the corresponding deepening rates (R \(\ge\) 0.88). A follow-up study will assess the impacts of climate change on explosive cyclones, and evaluate how model biases presented in this study affect the projections.


Explosive cyclones CMIP5 climate models Model biases 



The authors gratefully acknowledge the financial support of the Marine Environmental Observation Prediction and Response Network (MEOPAR) for this research. We thank Dr. Kevin Hodges from the University of Reading (UK) for supporting our application of his cyclone tracking algorithm. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 2 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank the respective centers for providing their reanalysis data products ERA-Interim, NCEP-CFSR and NASA-MERRA. We are grateful for the constructive comments from two anonymous reviewers. Please contact Christian Seiler ( for obtaining the output data presented in this research.


  1. Allen JT, Pezza AB, Black MT (2010) Explosive cyclogenesis: a global climatology comparing multiple reanalyses. J Clim 23(24):6468–6484CrossRefGoogle Scholar
  2. Anstey JA et al (2013) Multi-model analysis of Northern Hemisphere winter blocking: model biases and the role of resolution. J Geophys Res Atmos 118(10):3956–3971CrossRefGoogle Scholar
  3. Arora V, Scinocca J, Boer G, Christian J, Denman K, Flato G, Kharin V, Lee W, Merryfield W (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38(5)Google Scholar
  4. Bengtsson L, Hodges KI, Roeckner E (2006) Storm tracks and climate change. J Clim 19(15):3518–3543CrossRefGoogle Scholar
  5. Bentsen M et al (2013) The norwegian earth system model, NorESM1-M-Part 1: description and basic evaluation of the physical climate. Geosci Model Dev 6:687–720CrossRefGoogle Scholar
  6. Black MT, Pezza AB (2013) A universal, broad-environment energy conversion signature of explosive cyclones. Geophys Res Lett 40(2):452–457CrossRefGoogle Scholar
  7. Chang EK (2014) Impacts of background field removal on CMIP5 projected changes in Pacific winter cyclone activity. J Geophys Res Atmos 119(8):4626–4639CrossRefGoogle Scholar
  8. Chang EK, Lee S, Swanson KL (2002) Storm track dynamics. J Clim 15(16):2163–2183CrossRefGoogle Scholar
  9. Chang EK, Guo Y, Xia X (2012) CMIP5 multimodel ensemble projection of storm track change under global warming, J Geophys Res Atmos (1984–2012), 117(D23)Google Scholar
  10. Charlton-Perez AJ et al (2013) On the lack of stratospheric dynamical variability in low-top versions of the CMIP5 models. J Geophys Res Atmos 118(6):2494–2505CrossRefGoogle Scholar
  11. Dee D et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  12. Donner LJ et al (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24(13):3484–3519CrossRefGoogle Scholar
  13. Dufresne J-L et al (2013) Climate change projections using the IPSL-CM5 earth system model: from CMIP3 to CMIP5. Clim Dyn 40(9–10):2123–2165CrossRefGoogle Scholar
  14. Dunne JP et al (2012) GFDL’s ESM2 global coupled climate-carbon earth system models, part I: physical formulation and baseline simulation characteristics. J Clim 25(19):6646–6665CrossRefGoogle Scholar
  15. Fink AH, Pohle S, Pinto JG, Knippertz P (2012) Diagnosing the influence of diabatic processes on the explosive deepening of extratropical cyclones. Geophys Res Lett 39(7):1–8CrossRefGoogle Scholar
  16. Finnis J, Holland MM, Serreze MC, Cassano JJ (2007) Response of Northern Hemisphere extratropical cyclone activity and associated precipitation to climate change, as represented by the community climate system model. J Geophys Res Biogeosci (2005–2012) 112(G4):1–14Google Scholar
  17. Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W, Cox P, et al. (2013) Evaluation of climate models. In: Climate change 2013: The Physical science basis. Working Group I contribution to the fifth assessment report of the intergovernmental panel on climate change, Tech. rep., Groupe d’experts intergouvernemental sur l’evolution du climat/Intergovernmental Panel on Climate Change-IPCC, C/O World Meteorological Organization, 7bis Avenue de la Paix, CP 2300 CH-1211 Geneva 2 (Switzerland)Google Scholar
  18. Gent PR et al (2011) The community climate system model version 4. J Clim 24(19):4973–4991CrossRefGoogle Scholar
  19. Gilhousen DB (1994) The value of NDBC observations during march 1993s “Storm of the century”. Weather Forecast 9(2):255–264CrossRefGoogle Scholar
  20. Giorgetta MA et al (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. Journal of Advances in Modeling Earth Systems 5(3):572–597CrossRefGoogle Scholar
  21. Harrell FE (2014) Hmisc: harrell miscellaneous, r package version 3.14-4Google Scholar
  22. Hodges K (1999) Adaptive constraints for feature tracking. Mon Weather Rev 127(6):1362–1373CrossRefGoogle Scholar
  23. Hoskins BJ, Valdes PJ (1990) On the existence of storm-tracks. J Atmos Sci 47(15):1854–1864CrossRefGoogle Scholar
  24. Kocin PJ, Schumacher PN, Morales RF Jr, Uccellini LW (1995) Overview of the 12–14 March 1993 superstorm. Bull Am Meteorol Soc 76(2):165–182CrossRefGoogle Scholar
  25. Lambert SJ (1996), Intense extratropical Northern Hemisphere winter cyclone events: 1899–1991, J Geophys Res Atmos (1984–2012), 101(D16), 21319–21325Google Scholar
  26. Lambert SJ, Fyfe JC (2006) Changes in winter cyclone frequencies and strengths simulated in enhanced greenhouse warming experiments: results from the models participating in the IPCC diagnostic exercise. Clim Dyn 26(7–8):713–728CrossRefGoogle Scholar
  27. Li L et al (2013) The flexible global ocean-atmosphere-land system model, grid-point version 2: FGOALS-g2. Adv Atmos Sci 30:543–560CrossRefGoogle Scholar
  28. Liberato ML (2014) The 19 January 2013 windstorm over the North Atlantic: large-scale dynamics and impacts on Iberia. Weather Clim Extremes 5:16–28CrossRefGoogle Scholar
  29. Lim E-P, Simmonds I (2002) Explosive cyclone development in the Southern Hemisphere and a comparison with Northern Hemisphere events. Mon Weather Rev 130(9):2188–2209CrossRefGoogle Scholar
  30. Martin G et al (2011) The HadGEM2 family of met office unified model climate configurations. Geosci Model Dev Discuss 4(2):765–841CrossRefGoogle Scholar
  31. McDonald RE (2011) Understanding the impact of climate change on Northern Hemisphere extra-tropical cyclones. Clim dyn 37(7–8):1399–1425CrossRefGoogle Scholar
  32. Neu U, Caballero R, Hanley J (2012) IMILAST-a community effort to intercompare extratropical cyclone detection and tracking algorithms: assessing method-related uncertainties, Bulletin of The American Meteorological Society-(BAMS), pp. 529–547Google Scholar
  33. Pinto JG, Zacharias S, Fink AH, Leckebusch GC, Ulbrich U (2009) Factors contributing to the development of extreme North Atlantic cyclones and their relationship with the NAO. Clim Dyn 32(5):711–737CrossRefGoogle Scholar
  34. R Core Team (2013) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  35. Rienecker MM et al (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24(14):3624–3648CrossRefGoogle Scholar
  36. Roebber PJ (1984) Statistical analysis and updated climatology of explosive cyclones. Mon Weather Rev 112(8):1577–1589CrossRefGoogle Scholar
  37. Roeckner E, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kornblueh L, Manzini E, Schlese U, Schulzweida U (2006) Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J Clim 19(16):3771–3791CrossRefGoogle Scholar
  38. Rudeva I, Gulev SK (2007) Climatology of cyclone size characteristics and their changes during the cyclone life cycle. Mon Weather Rev 135(7):2568–2587CrossRefGoogle Scholar
  39. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91(8):1015–1057CrossRefGoogle Scholar
  40. Sakamoto T et al (2012) MIROC4h–a new high-resolution atmosphere-ocean coupled general circulation model. J Meteorol Soc Jpn 90(3):325–359CrossRefGoogle Scholar
  41. Sanders F, Gyakum JR (1980) Synoptic-dynamic climatology of the “bomb”. Mon Weather Rev 108(10):1589–1606CrossRefGoogle Scholar
  42. Stull RB (2000) Meteorology for scientists and engineers: a technical companion book with Ahrens’ Meteorology Today, Brooks/ColeGoogle Scholar
  43. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498CrossRefGoogle Scholar
  44. Ulbrich U, Pinto J, Kupfer H, Leckebusch G, Spangehl T, Reyers M (2008) Changing Northern Hemisphere storm tracks in an ensemble of IPCC climate change simulations. J Clim 21(8):1669–1679CrossRefGoogle Scholar
  45. Voldoire A et al (2013) The CNRM-CM5. 1 global climate model: description and basic evaluation. Clim Dyn 40(9–10):2091–2121CrossRefGoogle Scholar
  46. Volodin E, Dianskii N, Gusev A (2010) Simulating present-day climate with the INMCM4. 0 coupled model of the atmospheric and oceanic general circulations, Izvestiya, Atmospheric and Oceanic. Physics 46(4):414–431Google Scholar
  47. Watanabe S et al (2011) MIROC-ESM: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev Discuss 4:1063–1128CrossRefGoogle Scholar
  48. Xin X, Zhang L, Zhang J, Wu T, Fang Y (2013) Climate change projections over East Asia with \(\text{ BCC }\_\text{ CSM1. }\) 1 climate model under RCP scenarios. J Meteorol Soc Jpn 91(4):413–429CrossRefGoogle Scholar
  49. Yukimoto S, Kenkyūjo K (2011) Meteorological Research institute earth system model version 1 (MRI-ESM1): model description. Meteorol Res Inst Tech Rep 64:83Google Scholar
  50. Yukimoto S et al (2012) A new global climate model of the meteorological research institute: MRI-CGCM3 - model description and basic performance. J Meteorol Soc Jpn 90:23–64CrossRefGoogle Scholar
  51. Zappa G, Shaffrey LC, Hodges KI (2013a) The ability of CMIP5 models to simulate north atlantic extratropical cyclones*. J Clim 26(15):5379–5396CrossRefGoogle Scholar
  52. Zappa G, Shaffrey LC, Hodges KI, Sansom PG, Stephenson DB (2013b) A multimodel assessment of future projections of north atlantic and european extratropical cyclones in the cmip5 climate models*. J Clim 26(16):5846–5862CrossRefGoogle Scholar
  53. Zhu X, Sun J, Liu Z, Liu Q, Martin JE (2007) A synoptic analysis of the interannual variability of winter cyclone activity in the Aleutian low region. J Clim 20(8):1523–1538CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada

Personalised recommendations