Climate Dynamics

, Volume 50, Issue 1–2, pp 677–692 | Cite as

How does dynamical downscaling affect model biases and future projections of explosive extratropical cyclones along North America’s Atlantic coast?

  • C. SeilerEmail author
  • F. W. Zwiers
  • K. I. Hodges
  • J. F. Scinocca


Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America’s Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America’s Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (−0.1 day\(^{-1}\)). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to −22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (−15%, −18%) and Eady growth rate (−0.2 day\(^{-1}\), −0.2 day\(^{-1}\)), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.


Explosive extratropical cyclones Dynamical downscaling Model biases Climate change projections 



The authors gratefully acknowledge the financial support of the Marine Environmental Observation Prediction and Response Network (MEOPAR) for this research. We thank Dr. Yanjun Jiao from the Canadian Centre for Climate Modelling and Analysis (CCCma) for providing us with data from CanRCM4. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank CCCma and ECMWF 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 are grateful for the constructive comments from two anonymous reviewers.


  1. Anderson D, Hodges KI, Hoskins BJ (2003) Sensitivity of feature-based analysis methods of storm tracks to the form of background field removal. Mon Weather Rev 131(3):565–573CrossRefGoogle Scholar
  2. Arora VK, Scinocca JF, Boer GJ, Christian JR, Denman KL, Flato GM, Kharin VV, Lee WG, Merryfield WJ (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38(5):1–6CrossRefGoogle Scholar
  3. Bengtsson L, Hodges KI, Keenlyside N (2009) Will extratropical storms intensify in a warmer climate? J Clim 22(9):2276–2301CrossRefGoogle Scholar
  4. Bengtsson L, Hodges KI, Roeckner E (2006) Storm tracks and climate change. J Clim 19(15):3518–3543CrossRefGoogle Scholar
  5. Chang EKM, Guo Y, Xia X (2012) CMIP5 multimodel ensemble projection of storm track change under global warming. J Geophys Res Atmos 117(D23):1–19CrossRefGoogle Scholar
  6. Christensen J, Kumar KK, Aldrian E, An S-I, Cavalcanti I, de Castro M, Dong W, Goswami P, Hall A, Kanyanga J, Kitoh A, Kossin J, Lau NC, Renwick J, Stephenson D, Xie SP, Zhou T (2013) Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Technical report, 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
  7. Colle BA, Booth JF, Chang EK (2015) A review of historical and future changes of extratropical cyclones and associated impacts along the us east coast. Curr Clim Change Rep 1(3):125–143CrossRefGoogle Scholar
  8. Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski Jr. WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Technical report, 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
  9. Côté H, Grise KM, Son S-W, de Elía R, Frigon A (2015) Challenges of tracking extratropical cyclones in regional climate models. Clim Dyn 44(11–12):3101–3109CrossRefGoogle Scholar
  10. Davis CA, Emanuel KA (1991) Potential vorticity diagnostics of cyclogenesis. Mon Weather Rev 119(8):1929–1953CrossRefGoogle Scholar
  11. Dee D, Uppala S, Simmons A, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M, Balsamo G, Bauer P et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  12. Denis B, Côté J, Laprise R (2002) Spectral decomposition of two-dimensional atmospheric fields on limited-area domains using the discrete cosine transform (DCT). Mon Weather Rev 130(7):1812–1829CrossRefGoogle Scholar
  13. 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
  14. Froude LS, Bengtsson L, Hodges KI (2007) The predictability of extratropical storm tracks and the sensitivity of their prediction to the observing system. Mon Weather Rev 135(2):315–333CrossRefGoogle Scholar
  15. Giorgi F, Jones C, Asrar GR et al (2009) Addressing climate information needs at the regional level: the CORDEX framework. World Meteorological Organization (WMO). Bulletin 58(3):175Google Scholar
  16. Haarsma R, Roberts M, Vidale P, Senior C, Bellucci A, Corti S, Fučkar N, Guemas V, von Hardenberg J, Hazeleger W et al (2016) High resolution model intercomparison project (HighResMIP). Geosci Model Dev 9:4185–4208CrossRefGoogle Scholar
  17. Hall NM, Hoskins BJ, Valdes PJ, Senior CA (1994) Storm tracks in a high-resolution GCM with doubled carbon dioxide. Quart J R Meteorol Soc 120(519):1209–1230CrossRefGoogle Scholar
  18. Hodges K (1994) A general method for tracking analysis and its application to meteorological data. Mon Weather Rev 122(11):2573–2586CrossRefGoogle Scholar
  19. Hodges K (1999) Adaptive constraints for feature tracking. Mon Weather Rev 127(6):1362–1373CrossRefGoogle Scholar
  20. Hodges K et al (1995) Feature tracking on the unit-sphere. Mon Weather Rev 123(12):3458–3465CrossRefGoogle Scholar
  21. Hoskins BJ, Valdes PJ (1990) On the existence of storm-tracks. J Atmos Sci 47(15):1854–1864CrossRefGoogle Scholar
  22. 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
  23. 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
  24. Long Z, Perrie W, Gyakum J, Laprise R, Caya D (2009) Scenario changes in the climatology of winter midlatitude cyclone activity over eastern North America and the Northwest Atlantic. J Geophys Res Atmos 114(D112):1–13Google Scholar
  25. Marciano CG, Lackmann GM, Robinson WA (2015) Changes in US East Coast cyclone dynamics with climate change. J Clim 28(2):468–484CrossRefGoogle Scholar
  26. Martin JE (2013) Mid-latitude atmospheric dynamics: a first course. John Wiley & SonsGoogle Scholar
  27. McDonald RE (2011) Understanding the impact of climate change on Northern Hemisphere extra-tropical cyclones. Clim Dyn 37(7–8):1399–1425CrossRefGoogle Scholar
  28. Core Team R (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  29. Roebber PJ (1984) Statistical analysis and updated climatology of explosive cyclones. Mon Weather Rev 112(8):1577–1589CrossRefGoogle Scholar
  30. Sanders F, Gyakum JR (1980) Synoptic-dynamic climatology of the “bomb”. Mon Weather Rev 108(10):1589–1606CrossRefGoogle Scholar
  31. Scinocca J, Kharin V, Jiao Y, Qian M, Lazare M, Solheim L, Flato G, Biner S, Desgagne M, Dugas B (2016) Coordinated global and regional climate modeling*. J Clim 29(1):17–35CrossRefGoogle Scholar
  32. Seiler C, Zwiers F (2016a) How well do CMIP5 climate models reproduce explosive cyclones in the extratropics of the Northern Hemisphere? Clim Dyn 46(3–4):1241–1256CrossRefGoogle Scholar
  33. Seiler C, Zwiers F (2016b) How will climate change affect explosive cyclones in the extratropics of the Northern Hemisphere? Clim Dyn 46(11):3633–3644CrossRefGoogle Scholar
  34. Stull, R. B. (2000). Meteorology for scientists and engineers: a technical companion book with Ahrens’ Meteorology Today. Brooks/ColeGoogle Scholar
  35. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498CrossRefGoogle Scholar
  36. 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
  37. von Salzen K, Scinocca JF, McFarlane NA, Li J, Cole JN, Plummer D, Verseghy D, Reader MC, Ma X, Lazare M et al (2013) The Canadian fourth generation atmospheric global climate model (CanAM4). Part I: representation of physical processes. Atmos Ocean 51(1):104–125CrossRefGoogle Scholar
  38. Willison J, Robinson WA, Lackmann GM (2013) The importance of resolving mesoscale latent heating in the North Atlantic storm track. J Atmos Sci 70(7):2234–2250CrossRefGoogle Scholar
  39. Willison J, Robinson WA, Lackmann GM (2015) North Atlantic storm-track sensitivity to warming increases with model resolution. J Clim 28(11):4513–4524CrossRefGoogle Scholar
  40. 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
  41. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada
  2. 2.University of ReadingReadingUnited Kingdom
  3. 3.Canadian Centre for Climate Modelling and AnalysisVictoriaCanada

Personalised recommendations