Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada |
| |
| Authors: | Y B Dibike P Gachon A St-Hilaire T B M J Ouarda Van T-V Nguyen |
| |
| Institution: | (1) OURANOS Consortium on Regional Climatology and Adaptation to Climate Change, Montreal (QC), Canada;(2) Adaptation and Impact Research Division (AIRD), Atmospheric Science and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada;(3) Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, Québec (QC), Canada;(4) Department of Civil Engineering and Applied Mechanics, McGill University, Montreal (QC), Canada |
| |
| Abstract: | Summary 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 regression-based 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 confidence-interval estimation techniques are used to perform uncertainty analysis
on the downscaled meteorological variables. The results show that the NCEP-driven 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 regression-based 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 development.
Authors’ 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 St-Hilaire and Taha B. M. J. Ouarda,
Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue
de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill
University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
