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Application Papers

The Effects of Climate Change on Extreme Precipitation Events in the Upper Thames River Basin: A Comparison of Downscaling Approaches

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Pages 253-274 | Published online: 23 Jan 2013
 

Abstract

Future changes in climatic conditions from increasing greenhouse gas concentrations will have a major impact on the hydrologic cycle. It is important to understand and predict future changes in temperature and precipitation in order to effectively manage water resources. Atmosphere-Ocean coupled Global Climate Models (AOGCMs) are used widely to predict the effects of greenhouse-gas forcing on global climate conditions. However, their spatial and temporal resolutions are quite large so their outputs must be modified to represent local climate conditions. This process is called downscaling, and there are a variety of tools available to achieve this goal. This study compares three downscaling approaches, namely the Statistical DownScaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG), and the K-NN Weather Generator with Principal Component Analysis (WG-PCA). Each weather generator is used to simulate the historical climate for the Upper Thames River Basin in Ontario, Canada for use in a comparison of downscaling tools. Future climate conditions are simulated by LARS-WG and WG-PCA from six different AOGCMs, each with two to three emissions scenarios, for a total of 15 different models. In simulation of historical climate variability, the models generally perform better in terms of mean daily precipitation and total monthly precipitation. LARS-WG simulates precipitation events well but cannot reproduce means and variances in the daily temperature series. SDSM adequately simulates both temperatures and precipitation events. WG-PCA reproduces daily temperatures very well but overestimates the occurrence of some extreme precipitation events. Results are variable for the downscaling of AOGCMs; however, the downscaling tools generally predict a rise in winter, spring and fall precipitation totals, as well as an overall increase in mean annual precipitation in future decades.

Les changements futurs lis aux conditions climatiques qui dcouleront des concentrations croissantes de gaz effet de serre auront une incidence majeure sur le cycle hydrologique. Il est important de comprendre et de prdire les changements futurs lis aux tempratures et aux prcipitations afin de pouvoir grer efficacement les ressources en eau. Les modles de circulation gnrale coupls atmosphre-ocan (AOGCM) sont largement utiliss pour prdire les effets des gaz effet de serre sur les conditions climatiques mondiales. Cependant, leurs rsolutions spatiales et temporelles sont trs grandes. Leurs rsultats doivent donc tre modifis afin de reprsenter les conditions climatiques locales. Ce processus a pour nom rduction dchelle et il existe divers outils permettant d'atteindre cet objectif. La prsente tude compare trois mthodes de rduction dchelle, c'est--dire le modle de rduction dchelle statistique (SDSM), le gnrateur stochastique de climat LARS-WG et le gnrateur de climat KNN avec analyse en composantes principales (GC-ACP). Chaque gnrateur de climat sert simuler le climat historique pour le bassin hydrographique du cours suprieur de la rivire Thames en Ontario, au Canada, des fins de comparaison des outils de rduction dchelle. Les conditions climatiques futures sont simules l'aide du gnrateur LARS-WG et du GC-ACP partir de six AOGCM diffrents, chacun s'accompagnant de deux trois scnarios dmissions, pour un total de 15 modles diffrents.Pour la simulation de la variabilit climatique historique, les modles offrent en gnral un meilleur rendement en ce qui concerne les prcipitations quotidiennes moyennes et les prcipitations mensuelles totales. Le LARS-WG simule bien les vnements de prcipitations mais ne peut reproduire les moyennes et les carts dans les sries de tempratures quotidiennes. Le modle SDSM simule convenablement la fois les tempratures et les prcipitations. Le GC-ACP reproduit trs bien les tempratures quotidiennes mais surestime la survenance de certains vnements de prcipitations extrmes. Les rsultats sont variables pour la rduction dchelle des modles AOGCM; cependant, les outils de rduction dchelle prdisent en gnral une hausse des prcipitations totales d'hiver, de printemps et d'automne, ainsi qu'une hausse globale des prcipitations annuelles moyennes dans les dcennies venir.

Acknowledgements

The authors are thankful to the North American Regional Reanalysis (NARR) and Environment Canada for providing the climate data used in this study, as well as the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) and Ontario Graduate Scholarship (OGS) for funding this research.

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