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Climate Dynamics

, Volume 38, Issue 7–8, pp 1433–1457 | Cite as

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

  • Hyung-Il Eum
  • Philippe Gachon
  • René Laprise
  • Taha Ouarda
Article

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.

Keywords

Ensemble simulations Regional climate model Weighting scheme Criteria of evaluation 

Notes

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.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Hyung-Il Eum
    • 1
  • Philippe Gachon
    • 1
    • 2
  • René Laprise
    • 1
  • Taha Ouarda
    • 3
  1. 1.ESCER (Étude et Simulation du Climat à l’Échelle Régionale)University of Québec at MontrealMontrealCanada
  2. 2.Adaptation and Impacts Research Section, Climate Research DivisionEnvironment CanadaMontrealCanada
  3. 3.INRS-ETE (Institut National de la Recherche Scientifique, Centre Eau-Terre-Environnement)University of QuébecQuebecCanada

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