Skip to main content

Advertisement

Log in

Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23 years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Blenkinsop S, Fowler JH (2007) Changes in drought frequency and severity over the British Isles projected by the PRUDENCE regional climate models. J Hydrol 342:50–71

    Article  Google Scholar 

  • 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):19–35

    Google Scholar 

  • Caya D, Laprise R (1999) A semi-implicit semi-Lagrangian regional climate model: the Canadian RCM. Mon Weather Rev 127:341–362

    Article  Google Scholar 

  • Christensen JH, Carter TR, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of regional climate models: the PRUDENCE project. Clim Change 81(Suppl 1):1–6

    Google Scholar 

  • Christensen JH, Kjellstrom E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Clim Res 44:179–194

    Article  Google Scholar 

  • Collins WD, Bitz CM, Blackmon M, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney S, Hack JJ, Henderson TB, Kiehl JT, Large WG, Mckenna DS, Santer BD, Smith RD (2006) The community climate system model: CCSM3. J Clim 19:2122–2143

    Article  Google Scholar 

  • 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 44:121–134

    Article  Google Scholar 

  • De Elía 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 30:113–132

    Article  Google Scholar 

  • Deque 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 Change 81:53–70

    Article  Google Scholar 

  • Dessai S, Lu X, Hulme M (2005) Limited sensitivity analysis of regional climate change probabilities for the 21st century. J Geophys Res 110:D19108

    Article  Google Scholar 

  • Doblas-Reyes FJ, Hagendorn R, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—II. Calibration and combination. Tellus A57:234–252

    Google Scholar 

  • Fiorino M (1997) AMIP II sea surface temperature and sea ice concentration observations. http://www-pcmdi.llnl.gov/projects/amip2/AMIP2EXPDSN/BCS/amip2bcs.html#Introduction

  • Fowler HJ, Ekstrom M (2009) Multi-model ensemble estimates of climate change impacts on UK seasonal precipitation extremes. Int J Climatol 29:385–416

    Article  Google Scholar 

  • Gachon P, Dibike Y (2007) Temperature change signals in northern Canada: convergence of statistical downscaling results using two driving GCMs. Int J Climatol 27:1623–1641

    Article  Google Scholar 

  • Gent PR, McWilliams JC (1990) Isopycnal mxing in ocean circulation models. J Phys Oceanogr 20:150–155

    Article  Google Scholar 

  • 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 15:1141–1158

    Article  Google Scholar 

  • Hall J (2007) Probabilistic climate scenarios may misrepresent uncertainty and lead to bad adaptation decisions. Hydrol Process 21:1127–1129

    Article  Google Scholar 

  • 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. In: Contribution of Working Group I to the 3rd Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge

  • 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 48:725–741

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: the physical science basis. In: Contribution of the Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  • IPCC (2010) Meeting report of the intergovernmental panel on climate change expert meeting on assessing and combining multi model climate projections. In: Stocker T, Dahe Q, Plattner G-K, Tignor M, Midgley P (eds) IPCC Working Group I Technical Support Unit. University of Bern, Bern, p 117

  • Jenkins G, Lowe J (2003) Handling uncertainties in the UKCIP02 scenarios of climate change. Hadley Cent. Tech. Note 44, Exeter

  • Jun M, Knutti R, Nychka DW (2008) Spatial analysis to quantify numerical model bias and dependence: how many climate models are there? J Am Stat Assoc 103:934–947

    Article  Google Scholar 

  • Jungclaus JH, Keenlyside N, Botzet M, Haak H, Luo J-J, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the coupled model ECHAC5/MPI-OM. J Clim 19:3952–3972

    Article  Google Scholar 

  • Kendon EJ, Rowell DP, Jones RG, Buonomo E (2008) Robustness of future changes in local precipitation extremes. J Clim 21:4280–4297

    Article  Google Scholar 

  • Kevin W, Brandon D, Clark M, Gangopadhyay S (2004) Climate index weighting schemes for NWS ESP-based seasonal volume forecasts. J Hydrometeorol 5:1076–1090

    Article  Google Scholar 

  • Kim Y-O, Lee J-K (2010) Addressing heterogeneities in climate change studies for water resources in Korea. Curr Sci 98(8):1077–1083

    Google Scholar 

  • Knutti R, Furrer R, Tebaldi C, Cermak J (2010) Challenges in combining projections from multiple climate models. J Clim 23:2739–2758

    Article  Google Scholar 

  • Lapen DR, Hayhoe HN (2003) Spatial analysis of seasonal and annual temperature and precipitation normals in Southern Ontario, Canada. J Great Lakes Res 29(4):529–544

    Article  Google Scholar 

  • Laprise R, Caya D, Giguere M, Bergeron G, Cote H, Blanchet J-P, Boer GJ, McFarlane NA (1998) Climate and climate change in western Canada as simulated by the Canadian Regional Climate Model. Atmos Ocean 36(2):119–167

    Article  Google Scholar 

  • 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 21:405–421

    Article  Google Scholar 

  • Maidment D (1993) Handbook of hydrology. McGraw-Hill, New York

    Google Scholar 

  • Manning LJ, Hall JW, Fowler JJ, Kilsby CG, Tebaldi C (2009) Using probabilistic climate change information from a multimodel ensemble for water resources assessment. Water Resour Res 45:W11411

    Article  Google Scholar 

  • Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Jović D, Woollen J, Rogers E, Berbery EH, Ek MB, Fan Y, Grumbine R, Higgins W, Li H, Lin Y, Manikin G, Parrish D, Shi W (2006) North American regional reanalysis. Bull Am Meteorol Soc 87(3):343–360

    Google Scholar 

  • 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 84:165–185

    Article  Google Scholar 

  • Murphy JM et al (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772

    Article  Google Scholar 

  • 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 8:969–988

    Google Scholar 

  • Nakicenovic N et al (2000) IPCC special report on emission scenarios. Cambridge University Press, Cambridge

  • New M, Hulme M (2000) Representing uncertainty in climate change scenarios: a Monte-Carlo approach. Integr Assess 1:203–214

    Article  Google Scholar 

  • Palmer TN, Alessandri A, Andersen U, Cantelaube P, Davey M, Delecluse P, Deque M, Diez E, Doblas-Reyes FJ, Grahan R, Gualdi S, Gueremy J-F, Hagedorn R, Hoshen M, Keenlyside N, Latif M, Lazar A, Maisonnave E, Marletto V, Morse AP, Orfila B, Rogel P, Terres J-M, Thomson MC (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (Demeter). Bull Am Meteorol Soc 85(6):853–872

    Article  Google Scholar 

  • Pope VD, Gallani ML, Rowntree PR, Stratton A (2000) The impact of new physical parametrizations in the Hadley Centre Climate Model–HadAM3. Clim Dyn 16:123–146

    Article  Google Scholar 

  • Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133:1155–1174

    Article  Google Scholar 

  • Raisanen J, Palmer TN (2001) A probability and decision-model analysis of a multimodel ensemble of climate change simulation. J Clim 14:3212–3226

    Article  Google Scholar 

  • Rajagopalan B, Lall U, Zebiak SE (2002) Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Mon Weather Rev 130:1792–1811

    Article  Google Scholar 

  • Rinke A, Marbaix P, Dethloff K (2004) Internal variability in arctic regional climate simulations: case study for the SHEBA year. Clim Res 27:197–209

    Article  Google Scholar 

  • 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 17:339–359

    Article  Google Scholar 

  • Sanchez E, Romera R, Gaertner MA, Gallardo C, Castro M (2009) A weighting proposal for an ensemble of regional climate models over Europe driven by 1961–2000 ERA40 based on monthly precipitation probability density functions. Atmos Sci Lett 10:241–248

    Google Scholar 

  • Sharma A, O’Neill R (2002) A nonparametric approach for representing interannual dependence in monthly streamflow sequences. Water Resour Res 38(7):5.1–5.10. doi:10.1029/2001WR000953

    Article  Google Scholar 

  • Sharma A, Tarboton DG, Lall U (1997) Streamflow simulation: a nonparametric approach. Water Resour Res 33:291–308

    Article  Google Scholar 

  • Sloughter JM, Raftery AE, Gneiting T, Fraley C (2007) Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon Weather Rev 135:3209–3220

    Article  Google Scholar 

  • Straus DM, Shukla J (2000) Distinguishing between the SST-forced variability and internal variability in mid-latitudes: analysis of observations and GCM simulations. Q J R Meteorol Soc 126:2323–2350

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Phil Trans R Soc A 365:2053–2075

    Article  Google Scholar 

  • Uppala SM (2001) ECMWF reanalysis, 1957– 2001, ERA-40, paper presented at the Workshop on Reanalysis, ECMWF, Reading, UK

  • Uppala S, Kallberg P, Simmons A, Andrae U, da Costa Bechtold V, Fiorino M, Gibson J, Haseler J, Hernandez A, Kelly G, Li X, Onogi K, Saarinen S, Sokka N, Allan R, Andersson E, Arpe K, Balmaseda M, Beljaars A, Van de Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hlm E, Hoskins B, Isaksen L, Janssen P, Jenne R, McNally A, Mahfouf J-F, Morcrette J-J, Rayner N, Saunders R, Simon P, Sterl A, Trenberth K, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012. doi:10.1256/qj.04.176

  • 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

    Google Scholar 

  • Visser H, Folkert RJM, Hoekstra J, deWolff JJ (2000) Identifying key sources of uncertainty in climate change projections. Clim Change 45:421–457

    Article  Google Scholar 

  • Weigel AP, Knutti R, Liniger MA, Appenzeller C (2010) Risks of model weighting in multimodel climate projections. J Clim 23:4175–4191

    Article  Google Scholar 

  • Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour Res 42:W02419

    Article  Google Scholar 

Download references

Acknowledgments

This research was made possible by a financial support from Québec’s Ministère du Développement Économique, de l’Innovation et de l’Exportation (MDEIE) and National Sciences and Engineering Research Council (NSERC) of Canada. The authors would like to acknowledge the Data Access Integration (DAI, see http://quebec.ccsn.ca/DAI/) Team for providing the data and technical support, in particular the help of Milka Radojevic in preparing the data. The DAI data download gateway is made possible through collaboration among the FQRNT-funded Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Section (AIRS) of Environment Canada, and the Drought Research Initiative (DRI). The Ouranos Consortium also provides IT support to the DAI team. The CRCM time series data has been generated and supplied by Ouranos’ Climate Simulations Team. We would like also to acknowledge the National Centers for Environmental Prediction (NCEP) for the access of the North American Regional Reanalysis (NARR) datasets (see http://www.emc.ncep.noaa.gov/mmb/rreanl/). Finally, the authors would like also to express their gratitude to the two anonymous reviewers for their constructive comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyung-Il Eum.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eum, HI., Gachon, P., Laprise, R. et al. Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme. Clim Dyn 38, 1433–1457 (2012). https://doi.org/10.1007/s00382-011-1149-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-011-1149-3

Keywords

Navigation