Climate Dynamics

, Volume 31, Issue 7–8, pp 927–940 | Cite as

Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain

  • Philippe Lucas-Picher
  • Daniel Caya
  • Ramón de Elía
  • René Laprise


Previous investigations on regional climate models’ (RCM) internal variability (IV) were limited owing to small ensembles, short simulations and small domains. The present work extends previous studies with a ten-member ensemble of 10-year simulations performed with the Canadian Regional Climate Model over a large domain covering North America. The results show that the IV has no long-term tendency but rather fluctuates in time following the synoptic situation within the domain. The IV of mean-sea-level pressure (MSLP) and screen temperature (ST) show a small annual cycle with larger values in spring, which differs from previous studies. For precipitation (PCP), the IV shows a clear annual cycle with larger values in summer, as previously reported. The 10-year climatology of the IV for MSLP and ST shows a well-defined spatial distribution with larger values in the northeast of the domain, near the outflow boundary. A comparison of the IV of MSLP and ST in summer with the transient-eddy variance reveals that the IV is close to its maximum in a small region near the outflow boundary. Same analysis for PCP in summer shows that the IV reaches its maximum in most parts of the domain, except for a small region on the western side near the inflow boundary. Finally, a comparison of the 10-year climate of each simulation of the ensemble showed that the IV may have a significant impact on the climatology of some variables.


Internal variability Regional climate models Ensemble of simulations North American climate Ten-year simulations 



This work is part of the Ph.D. thesis of Philippe Lucas-Picher in Environmental Sciences at Université du Québec at Montréal. The authors wish to thank Claude Desrochers and Mourad Labassi for maintaining a user-friendly local computing environment at the Ouranos Consortium. A special thank is directed to Sébastien Biner and Samuel Somot, which generously devoted time to the discussion of some sections of the manuscript. This work was carried out as part of the research programs of the Canadian Climate Variability Research Network (CLIVAR), the Canadian Network for Regional Climate Modelling and Diagnostics (CRCMD) and the Ouranos Consortium. It was financially supported by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS), the National Science and Engineering Research Council of Canada (NSERC) and the Ouranos Consortium. Finally, the authors would like to thank Dr. Maryse Picher for her careful revision that has improved the readability of the manuscript.


  1. Alexandru A, de Elía R, Laprise R (2007) Internal variability in regional climate downscaling at the seasonal time scale. Mon Weather Rev 135:3221–3238CrossRefGoogle Scholar
  2. Biner S, Caya D, Laprise R, Spacek L (2000) Nesting of RCMs by imposing large scales. In: Ritchie H (ed) Research activities in atmospheric and oceanic modelling. WMO/TD, No. 987, Report No. 30, pp 7.3–7.4Google Scholar
  3. Caya D, Biner S (2004) Internal variability of RCM simulations over an annual cycle. Clim Dyn 22:33–46CrossRefGoogle Scholar
  4. Caya D, Laprise R (1999) A semi-implicit semi-lagrangian regional climate model: the Canadian RCM. Mon Weather Rev 127:341–362CrossRefGoogle Scholar
  5. Christensen OB, Gaertner MA, Prego JA, Polcher J (2001) IV of regional climate models. Clim Dyn 17:875–887CrossRefGoogle Scholar
  6. Davies HC (1976) A lateral boundary formulation for multilevel prediction models. Q J R Meteorol Soc 102:405–418Google Scholar
  7. de Elía R, Caya D, Frigon A, Côté H, Giguère M, Paquin D, Biner S, Harvey R, Plummer D (2008) Evaluation of uncertainties in the CRCM-simulated North American climate. Clim Dym 30:113–132. doi: 10.1007/s00382-007-0288-z CrossRefGoogle Scholar
  8. Gates WL et al (1999) An overview of the results of the Atmospheric Model Intercomparison Project. Bull Am Meteorol Soc 80:29–55CrossRefGoogle Scholar
  9. Giorgi F, Bi X (2000) A study of IV of regional climate model. J Geophys Res 105:29503–29521CrossRefGoogle Scholar
  10. IPCC, Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (2001) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, p 881Google Scholar
  11. IPCC, Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (2007) Climate change 2007: the physical science basis. In: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, New York, NY, USA, p 996Google Scholar
  12. Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Q J R Meteorol Soc 121:1413–1449Google Scholar
  13. Kalnay E et al (1996) The NCEP/NCAR 40-year Reanalysis Project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  14. Lucas-Picher P, Caya D, Biner S (2004) RCM’s IV as function of domain size. In: Côté J (ed) Research activities in atmospheric and oceanic modelling. WMO/TD, vol 1220, no. 34, pp 7.27–7.28Google Scholar
  15. McFarlane NA, Boer GJ, Blanchet JP, Lazare M (1992) The Canadian climate centre second-generation general circulation model and its equilibrium climate. J Clim 5:1013–1044CrossRefGoogle Scholar
  16. Mearns L (2004) North American Regional Climate Change Assessment Program (NARCCAP): a Multiple AOGCM and RCM climate scenario project over North America, AGU Fall Meeting, San Francisco, USAGoogle Scholar
  17. Miguez-Macho G, Stenchikov GL, Robock A (2004) Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J Geophys Res 109:D13104. doi: 10.1029/2003JD004495 CrossRefGoogle Scholar
  18. Plummer D, Caya D, Côté H, Frigon A, Biner S, Giguère M, Paquin D, Harvey R, de Élía R (2006) Climate and climate change over North America as simulated by the Canadian regional climate model. J Clim 19:3112–3132Google Scholar
  19. Riette S, Caya D (2002) Sensitivity of short simulations to the various parameters in the new CRCM spectral nudging. In: Ritchie H (ed) Research activities in atmospheric and oceanic modelling. WMO/TD, No. 1105, Report No. 32, pp 7.39–7.40Google Scholar
  20. Rinke A, Dethloff K (2000) On the sensitivity of a regional Arctic climate model to initial and boundary conditions. Clim Res 14:101–113CrossRefGoogle Scholar
  21. Rinke A, Marbaix P, Dethloff K (2004) IV in Arctic regional climate simulations: case study for the Sheba year. Clim Res 27:197–209CrossRefGoogle Scholar
  22. von Storch H, Zwiers FW (2001) Statistical analysis in climate research. p 484Google Scholar
  23. von Storch H, Langenberg H, Feser F (2000) A spectral nudging technique for dynamical downscaling purposes. Mon Weather Rev 128:3664–3673CrossRefGoogle Scholar
  24. Takle ES, Roads J, Rockel B, Gutowski WJ, Arritt RW, Meinke I, Jones CG, Zadra A (2007) Transferability intercomparison: an opportunity for new insight on the global water cycle and energy budget. Bull Am Meteorol Soc 88:375–384CrossRefGoogle Scholar
  25. Vannitsem S, Chomé F (2005) One-way nested regional climate simulations and domain size. J Clim 18:229–233CrossRefGoogle Scholar
  26. Weisse R, Heyen H, von Storch H (2000) Sensitivity of a regional atmospheric model to a sea state dependent roughness and the need of ensemble calculations. Mon Weather Rev 128:3631–3642CrossRefGoogle Scholar
  27. Wu W, Lynch A, Rivers A (2005) Estimating the uncertainty in a regional climate model related to initial and lateral boundary conditions. J Clim 18:917–933CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Philippe Lucas-Picher
    • 1
    • 2
  • Daniel Caya
    • 2
    • 3
  • Ramón de Elía
    • 2
    • 3
  • René Laprise
    • 2
  1. 1.Danish Meteorological InstituteCopenhagen EDenmark
  2. 2.Canadian Network for Regional Climate Modelling and Diagnostics, UQÀMMontrealCanada
  3. 3.Ouranos ConsortiumMontrealCanada

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