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Title of Journal: Theor Appl Climatol

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Abbravation: Theoretical and Applied Climatology

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Springer-Verlag

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DOI

10.1002/gea.3340050403

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1434-4483

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Uncertainty analysis of statistically downscaled t

Authors: Y B Dibike P Gachon A StHilaire T B M J Ouarda Van TV Nguyen
Publish Date: 2007/04/12
Volume: 91, Issue: 1-4, Pages: 149-170
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Abstract

Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada In this region most global climate models GCMs have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover The performance of the multiple regressionbased Statistical Downscaling model in reproducing the observed daily minimum/maximum temperature and precipitation for a reference period 1961–1990 is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs the Canadian CGCM2 and the British HadCM3 The Wilcoxon Signed Rank test and bootstrap confidenceinterval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables The results show that the NCEPdriven downscaling results mostly reproduced the mean and variability of the observed climate very well Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3 In general all downscaling results reveal that the regressionbased Statistical Downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios developmentAuthors’ addresses Dr Yonas B Dibike NSERC Research Fellow OURANOS Consortium 550 Sherbrooke Street West 19th Floor Montreal QC H3A 1B9 Canada Philippe Gachon Adaptation and Impact Research Division AIRD Atmospheric Science and Technology Directorate Environment Canada at Ouranos Montreal QC Canada André StHilaire and Taha B M J Ouarda Institut National de la Recherche Scientifique Centre Eau Terre Environnement INRSETE University of Québec 490 Rue de La Couronne Québec QC G1K 9A9 Canada Van TV Nguyen Department of Civil Engineering and Applied Mechanics McGill University 817 Sherbrooke Street West Montreal QC H3A 2K6 Canada

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