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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

Abstract

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.

Keywords

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

Abbreviations

CW

Comprehensive weighting factor

RW

RCM/reanalysis weighting factor

Weighting_DE

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

Weighting_EQ

Weighting factor with the equal relative importance

SEP

Simple equal probability

RCM/AOGCM_DE

CDF derived from the CW with DE optimal relative importance

RCM/AOGCM_EQ

CDF derived from the CW with equal relative importance

RCM/reanalysis_DE

CDF derived from the RW with DE optimal relative importance

RCM/reanalysis_EQ

CDF derived from the RW with equal relative importance

DE (RCM/AOGCM)

CW with DE optimal relative importance

EQ (RCM/AOGCM)

CW with equal relative importance

Notes

Acknowledgments

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; http://loki.qc.ec.gc.ca/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.

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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

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