Climate Dynamics

, Volume 43, Issue 1–2, pp 11–35 | Cite as

Developing a likely climate scenario from multiple regional climate model simulations with an optimal weighting factor

  • Hyung-Il Eum
  • Philippe Gachon
  • René Laprise
Original Article


This study presents a performance-based comprehensive weighting factor that accounts for the skill of different regional climate models (RCMs), including the effect of the driving lateral boundary condition coming from either atmosphere–ocean global climate models (AOGCMs) or reanalyses. A differential evolution algorithm is employed to identify the optimal relative importance of five performance metrics, and corresponding weighting factors, that include the relative absolute mean error (RAME), annual cycle, spatial pattern, extremes and multi-decadal trend. Based on cumulative density functions built by weighting factors of various RCMs/AOGCMs ensemble simulations, current and future climate projections were then generated to identify the level of uncertainty in the climate scenarios. This study selected the areas of southern Ontario and Québec in Canada as a case study. The main conclusions are as follows: (1) Three performance metrics were found essential, having the greater relative importance: the RAME, annual variability and multi-decadal trend. (2) The choice of driving conditions from the AOGCM had impacts on the comprehensive weighting factor, particularly for the winter season. (3) Combining climate projections based on the weighting factors significantly increased the consistency and reduced the spread among models in the future climate changes. These results imply that the weighting factors play a more important role in reducing the effects of outliers on plausible future climate conditions in regions where there is a higher level of variability in RCM/AOGCM simulations. As a result of weighting, substantial increases in the projected warming were found in the southern part of the study area during summer, and the whole region during winter, compared to the simple equal weighting scheme from RCM runs. This study is an initial step toward developing a likelihood procedure for climate scenarios on a regional scale using equal or different probabilities for all models.


Ensemble simulations Regional climate models Probabilistic climate change scenarios Comprehensive weighting factor Differential evolution algorithm Uncertainties analysis 



Comprehensive weighting factor


RCM/reanalysis weighting factor


Weighting factor with the optimal relative importance derived from the differential evolutionary (DE) algorithm


Weighting factor with the equal relative importance


Simple equal probability


CDF derived from the CW with DE optimal relative importance


CDF derived from the CW with equal relative importance


CDF derived from the RW with DE optimal relative importance


CDF derived from the RW with equal relative importance


CW with DE optimal relative importance


CW with equal relative importance



This research was made possible by financial support from Québec’s Ministère du Développement Économique, de l’Innovation et de l’Exportation and the Natural Sciences and Engineering Research Council of Canada. The authors would like to acknowledge the Data Access Integration portal team (DAI; for providing data and technical support, in particular the help of Milka Radojevic and Guillaume Dueymes in preparing the data. The DAI data download gateway is made possible through collaboration among the Fonds de recherche du Québec—Nature et technologies (FQRNT)-funded Global Environmental and Climate Change Centre, the Adaptation and Impacts Research Section of Environment Canada, and the Drought Research Initiative. The Ouranos Consortium also provides IT support to the DAI team. The CRCM time series data were generated and supplied by Ouranos’ Climate Simulations Team. We wish to thank NARCCAP for providing the data used herein. NARCCAP is funded by the U.S. National Science Foundation, the U.S. Department of Energy, the National Oceanic and Atmospheric Administration, and the U.S. Environmental Protection Agency Office of Research and Development. We would also like to acknowledge CRCMD from UQAM’s team for providing the GEMCLIM data, with a special thanks to Dr. Colin Jones, Pr. Laxmi Sushama and Katja Winger.


  1. Brochu R, Laprise R (2007) Surface water and energy budgets over the Mississippi and Columbia River basins as simulated by two generations of the Canadian regional climate model. Atmos Ocean 45(1):19–35CrossRefGoogle Scholar
  2. Caya D, Laprise R (1999) A semi-implicit semi-Lagrangian regional climate model: the Canadian RCM. Mon Weather Rev 127:341–362CrossRefGoogle Scholar
  3. Chai Y, Du Z, Chen Y (2009) A stepwise optimization algorithm of clustered streaming media servers. J Syst Softw 82:1344–1361CrossRefGoogle Scholar
  4. Christensen JH, Carter TR, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of regional climate models: the PRUDENCE project. Clim Chang 81(Suppl 1):1–6CrossRefGoogle Scholar
  5. Christensen JH, Boberg F, Christensen O, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35:L20709. doi: 10.1029/2008GL035694 CrossRefGoogle Scholar
  6. Christensen JH, Kjellstrom E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Clim Res 44:179–194CrossRefGoogle Scholar
  7. Coppola E, Giorgi F, Rauscher SA, Piani C (2010) Model weighting based on mesoscale structures in precipitation and temperature in an ensemble of regional climate models. Clim Res 44:121–134CrossRefGoogle Scholar
  8. Côté J, Gravel S, Méthot A, Patoine A, Roch M, Staniforth A (1998) The operational CMC–MRB global environmental multiscale (GEM) model. Part I: design considerations and formulation. Mon Weather Rev 126:1373–1395CrossRefGoogle Scholar
  9. de Elìa R, Caya D, Côté H, Frigon A, Biner S, Giguère M, Paquin D, Harvey R, Plummer D (2008) Evaluation of uncertainties in the CRCM-simulated North American climate. Clim Dyn 30:113–132CrossRefGoogle Scholar
  10. Denis B, Laprise R, Caya D (2003) Sensitivity of a regional climate model to the resolution of the lateral boundary conditions. Clim Dyn 20(2):107–126Google Scholar
  11. Déqué M, Piedeliévre JP (1995) High resolution climate simulation over Europe. Clim Dyn 11:321–339CrossRefGoogle Scholar
  12. Déqué M, Rowell DP, Luthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellstrom E, Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Chang 81:53–70CrossRefGoogle Scholar
  13. Ehret U, Zehe E, Wulfmeyer V, Warrach K (2012) Bias correction used for climate projections from climate models—a critique. Geophys Res Abst 14:EGU2012-12952Google Scholar
  14. Eum H-I, Gachon P, Laprise R, Ouarda T (2012) Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme. Clim Dyn 38:1433–1457. doi: 10.1007/s00382-011-1149-3 CrossRefGoogle Scholar
  15. Giorgi F, Coppola E (2010) Does the model regional bias affect the projected regional climate change? An analysis of global model projections. Clim Chang 100:787–795CrossRefGoogle Scholar
  16. Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble averaging (REA) method. J Clim 15:1141–1158CrossRefGoogle Scholar
  17. Hutchinson M, Mckenney DW, Lawrence K, Pedlar JH (2009) Development and testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003. J Appl Meteorol Climatol 48:725–741CrossRefGoogle Scholar
  18. Imtiza R, Joshph B, Karen C, Jason N, James P (2012) Mid-21st century projections in temperature extremes in the southern Colorado Rocky Mountains from regional climate models. Clim Dyn 39:1823–1840CrossRefGoogle Scholar
  19. IPCC (2007) Climate change 2007: The physical science basis. Contribution of the Working Group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge (UK)Google Scholar
  20. IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge (UK) and New York (NY)Google Scholar
  21. Jenkins G, Lowe J (2003) Handling uncertainties in the UKCIP02 scenarios of climate change. Hadley Centre Technical Note 44, ExeterGoogle Scholar
  22. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  23. Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DEO AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643CrossRefGoogle Scholar
  24. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple models. J Clim 23:2739–2758CrossRefGoogle Scholar
  25. Laprise R (2008) Regional climate modelling. J Comput Phys 227:3641–3666CrossRefGoogle Scholar
  26. Laprise R, Caya D, Frigon A, Paquin D (2003) Current and perturbed climate as simulated by the second-generation Canadian regional climate model (CRCM-II) over northwestern North America. Clim Dyn 21:405–421CrossRefGoogle Scholar
  27. Leduc M, Laprise R (2009) Regional climate model sensitivity domain size. Clim Dyn 32(6):833–854CrossRefGoogle Scholar
  28. Leduc M, Laprise R, Moretti-Poisson M, Morin J-P (2011) Sensitivity to domain size of mid-latitude summer simulations with a regional climate model. Clim Dyn 37(1–2):343–356. doi: 10.1007/s00382-011-1008-2 CrossRefGoogle Scholar
  29. Mandal KK, Chakraborty N (2008) Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems. Electr Power Syst Res 78:1972–1979CrossRefGoogle Scholar
  30. Maraun D (2012) Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett. doi: 10.1029/2012GL051210 Google Scholar
  31. Mearns LO, Gutowski WJ, Jones R, Leung L-Y, McGinnis S, Nunes AMB, Qian Y (2009) A regional climate change assessment program for North America. EOS 90(36):311–312CrossRefGoogle Scholar
  32. Mizuta R, Oouchi K, Yoshimura H, Noda A, Katayama K, Yukimoto S, Hosaka M, Kusunoki S, Kawai H, Nakagawa M (2006) 20-km-mesh global climate simulations using JMA-GSM model—mean climate states. J Meteorol Soc Jpn 84:165–185CrossRefGoogle Scholar
  33. Muerth MJ, Gauvin St-Denis B, Ricard S, Velazquez JA, Schmid J, Minville M, Caya D, Chaumont D, Ludwig R, Turcotte R (2012) On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff. Hydrol Earth Syst Sci Discuss 9:10205–10243. doi: 10.5194/hessd-9-10205-2012 CrossRefGoogle Scholar
  34. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  35. Music B, Caya D (2007) Evaluation of the hydrological cycle over the Mississippi River Basin as simulated by the Canadian regional climate model (CRCM). J Hydrometeorol 8(5):969–988CrossRefGoogle Scholar
  36. Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T et al (2000) IPCC special report on emissions scenarios. Cambridge University Press, Cambridge (UK), p 599Google Scholar
  37. Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theoret Appl Climatol 99:187–192. doi: 10.1007/s00704-009-0134-9 CrossRefGoogle Scholar
  38. Plummer DA, Caya D, Frigon A, Côté H, Giguère M, Paquin D, Biner S, Harvey R, de Elía R (2006) Climate and climate change over North America as simulated by the Canadian regional climate model. J Clim 19(13):3112–3132CrossRefGoogle Scholar
  39. Price VK, Storn MR (1997) Differential evolution—a simple evolution strategy for fast optimization. Dr. Dobb’s J 22:18–24Google Scholar
  40. Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133:1155–1174CrossRefGoogle Scholar
  41. Rummukainen M, Raissanen J, Bringfelt B, Ullerstig A, Omstedt A, Willen U, Hansson U, Jones C (2001) A regional climate model for northern Europe: model description and results from the downscaling of two GCM control simulations. Clim Dyn 17:339–359CrossRefGoogle Scholar
  42. Sanchez-Gomez E, Somot S, Deque M (2009) Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961–2000. Clim Dyn 33:723–736CrossRefGoogle Scholar
  43. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A 365:2053–2075CrossRefGoogle Scholar
  44. Teutschbein C, Seibert J (2012) Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol Earth Syst Sci Discuss 9:12765–12795CrossRefGoogle Scholar
  45. Uppala SM (2001) ECMWF reanalysis, 1957–2001, ERA-40. Workshop on reanalysis. ECMWF, Reading (UK)Google Scholar
  46. Uppala S, Kallberg P, Simmons A, Andrae U, da Costa Bechtold V, Fiorino M, Gibson J, Haseler J, Hernandez A, Kelly G et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  47. van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Exeter (UK)Google Scholar
  48. Wehner MF (2013) Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Clim Dyn 40(1–2):59–80CrossRefGoogle Scholar
  49. Weigel AP, Knutti R, Liniger MA, Appenzeller C (2010) Risks of model weighting in multimodel climate projections. J Clim 23:4175–4191CrossRefGoogle Scholar
  50. Yeh KS, Côté J, Gravel S, Méthot A, Patoine A, Roch M, Staniforth A (2002) The operational CMC–MRB global environmental multiscale (GEM) model. Part III: non-hydrostatic formulation. Mon Weather Rev 130:339–356CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Water-Climate Impact Research Centre (W-CIRC)Environment CanadaVictoriaCanada
  2. 2.Étude et Simulation du Climat à l’Échelle Régionale (ESCER)University of Québec at MontrealMontrealCanada
  3. 3.Canadian Centre for Climate Modelling and Analysis Section, Climate Research DivisionEnvironment CanadaMontrealCanada

Personalised recommendations