Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

SummaryUncertainty 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.

[1]  Clare M. Goodess,et al.  The identification and evaluation of suitable scenario development methods for the estimation of future probabilities of extreme weather events , 2001 .

[2]  T. Wigley,et al.  Precipitation predictors for downscaling: observed and general circulation model relationships , 2000 .

[3]  R. Katz.  Use of conditional stochastic models to generate climate change scenarios , 1996 .

[4]  Christian W. Dawson,et al.  Multi-site simulation of precipitation by conditional resampling , 2003 .

[5]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[6]  W. D. Hogg,et al.  Homogenization of Daily Temperatures over Canada , 2002 .

[7]  René Laprise,et al.  The effects of interactions between surface forcings in the development of a model-simulated polar low in Hudson Bay , 2003 .

[8]  Clare M. Goodess,et al.  Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development , 2003 .

[9]  F. Giorgi,et al.  Guidelines for Use of Climate Scenarios Developed from Regional Climate Model Experiments , 2003 .

[10]  Paulin Coulibaly,et al.  Uncertainty analysis of statistical downscaling methods , 2006 .

[11]  P. Whetton,et al.  Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods , 2004 .

[12]  P. Coulibaly,et al.  Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models , 2005 .

[13]  N. Bond,et al.  Regional Variation of Winter Temperatures in the Arctic , 1997 .

[14]  G. Boer,et al.  Warming asymmetry in climate change simulations , 2001 .

[15]  W. Collins,et al.  The NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation , 2001 .

[16]  G. Meehl,et al.  The seasonal cycle in coupled ocean-atmosphere general circulation models , 2000 .

[17]  John E. Walsh,et al.  Comparison of Arctic Climate Simulations by Uncoupled and Coupled Global Models , 2002 .

[18]  John F. B. Mitchell,et al.  The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments , 2000 .

[19]  P. Coulibaly,et al.  Downscaling Precipitation and Temperature with Temporal Neural Networks , 2005 .

[20]  Christian W. Dawson,et al.  SDSM - a decision support tool for the assessment of regional climate change impacts , 2002, Environ. Model. Softw..

[21]  Paulin Coulibaly,et al.  Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors , 2006 .

[22]  T. Wigley,et al.  Statistical downscaling of general circulation model output: A comparison of methods , 1998 .

[23]  F. Giorgi,et al.  PRUDENCE employs new methods to assess European climate change , 2002 .

[24]  P. Gachon,et al.  CLIMATE VARIABILITY AND CHANGE IN CANADA , 2004 .

[25]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[26]  M. Semenov,et al.  USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS , 1997 .

[27]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[28]  A. Weaver,et al.  The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate , 2000 .