Automated regression-based statistical downscaling tool

By Masoud Hessami, Philippe Gachon, Taha B. M. J. Ouarda, A. St‐Hilaire. Environmental Modelling & Software, 2008

310 citations recorded by the source corpus.

Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach inspired by the SDSM method (statistical downscaling model) developed by Wilby, R.L., Dawson, C.W., Barrow, E.M. [2002. SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling and Software 17, 147-159] is presented and assessed to reconstruct the observed climate in eastern Canada based extremes as well as mean state. In the ASD model, automatic predictor selection methods are based on backward stepwise regression and partial correlation coefficients. The ASD model also gives the possibility to use ridge regression to alleviate the effect of the non-orthogonality of predictor vectors. Outputs from the first generation Canadian Coupled Global Climate Model (CGCM1) and the third version of the coupled global Hadley Centre Climate Model (HadCM3) are used to test this approach over the current period (i.e. 1961-1990), and compare results with observed temperature and precipitation from 10 meteorological stations of Environment Canada located in eastern Canada. All ASD and SDSM models, as these two models are evaluated and inter-compared, are calibrated using NCEP (National Center for Environmental Prediction) reanalysis data before the use of GCMs atmospheric fields as input variables. The results underline certain limitations to downscale the precipitation regime and its strength to downscale the temperature regime. When modeling precipitation, the most commonly combination of predictor variables were relative and specific humidity at 500hPa, surface airflow strength, 850hPa zonal velocity and 500hPa geopotential height. For modeling temperature, mean sea level pressure, surface vorticity and 850hPa geopotential height were the most dominant variables. To evaluate the performance of the statistical downscaling approach, several climatic and statistical indices were developed. Results indicate that the agreement of simulations with observations depends on the GCMs atmospheric variables used as ''predictors'' in the regression-based approach, and the performance of the statistical downscaling model varies for different stations and seasons. The comparison of SDSM and ASD models indicated that neither could perform well for all seasons and months. However, using different statistical downscaling models and multi-sources GCMs data can provide a better range of uncertainty for climatic and statistical indices.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d8ebdd876f6e7647bf97e3e712963664c7aed1b6

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Citations in the corpus

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada (2012, methodology): “D. I. Jeong (&) A. St-Hilaire T. B. M. J. Ouarda INRS-ETE, University of Québec, 490 de la Couronne, Montréal, QC G1K 9A9, Canada e-mail: dae_il_jeong@ete.inrs.ca P. Gachon Adaptation and Impacts Research Section (AIRS), Climate Research Division, Environment Canada, Montréal, QC, Canada The adva”
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution (2017, methodology): “For instance, ASD approaches perform reasonably well at downscaling average precipitation [16] but performs poorly at the extremes [4].”
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment (2016, methodology): “Although MLR has been applied to downscale daily precipitation in previous studies (see, e.g., Chen, Brissette, & Leconte, 2014; Hessami et al., 2008; San-Martín et al., 2017), these techniques are not suitable to model daily precipitation, even after transforming precipitation—using, for example,…”
CORRESPONDENCE Comment on ''Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue'' (2014, unknown): “(Hessami et al. 2008), is of limited quality when”
Statistical downscaling of precipitation using quantile regression (2013, background): “, 2002) and Automated Statistical Downscaling (ADS; Hessami et al., 2008).”