Automated regression-based statistical downscaling tool

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 e a decision support tool for the assessment of regional climate change impacts, Environmental Modelling and Software 17, 147e159] 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...

• Type: Journal
• Year: 2008
• Where: ENVSOFT
• Authors: Masoud Hessami, Philippe Gachon, Taha B. M. J. Ouarda, André St-Hilaire
• Keywords: Climate Change, Climate Model, ENVSOFT 2008, Global Climate Model