Statistical downscaling of general circulation model output: A comparison of methods

By Robert L. Wilby, T. Wigley, D. Conway, Peter G. Jones, B. Hewitson, J. Main, D. Wilks. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Does climate policy need probabilities (2003, methodology): “…uncertainty in a number of settings (e.g., water and agriculture), between statistical downscaling and raw GCM output (e.g., Wilby et al., 1999), between statistical methods (e.g., Wilby et al., 1998), between RCMs and statistical downscaling (e.g., Mearns et al., 1999; Wilby et al., 2000).”
N–PLS regression as empirical downscaling tool in climate change studies (2005, unknown): “The most common linear approaches are canonical correlation analysis (CCA) (von Storch et al., 1993; Busuioc et al., 1999, Landman and Tennant, 2000; Benestad, 2001b; Busuioc et al., 2001), principal components regression (PCR) (Schubert, 1998; Benestad et al., 2002; Bergant et al., 2002) and multip”
Statistics of extremes in hydrology (2002, background): “, of ‘‘smooth’’ variables such as atmospheric pressure) much better than they do regional or local weather or climate variables (especially, ‘‘erratic’’ variables like precipitation) [60,98].”
Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. (2009, background): “The disparity can either be ignored (Trnka et al., 2004) or it can be dealt with by downscaling climate model output (Wilby and Wigley, 1997; Kidson and Thompson, 1998; Wilby et al., 1998).”
Automated regression-based statistical downscaling tool (2008, background): “Also, as suggested by Crane and Hewitson (1998) and Wilby et al. (1998) including humidity not only improves the transfer function predictions for the present climate but also reduces the stationarity issue noted above, as the dynamics and the humidity fields become separate predictor variables in…”
Uncertainty analysis of statistical downscaling methods (2006, unknown): “While the distribution of data is nonGaussian, analysis is further extended beyond comparison of means and variances (Wilby et al., 1998).”
A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM (2008, background): “See Wilby and Wigley (1997), Wilby et al. (1998), Zorita and von Storch (1999), von Storch et al. (2000) for general reviews.”
PERCEPTIONS AND ADAPTATIONS OF BEEKEEPERS AND HONEY HUNTERS TO CLIMATE CHANGE: THE CASE OF THE COMMUNES OF NATITINGOU AND TANGUIETA IN NORTHWEST OF BENIN (2012, unknown): “Model outputs variance is mainly due to difference in methods used in the different studies and the fact that rainfall structures change greatly over space and time (Wilby et al., 1998; Wood et al., 2004; Louw, 2007; Ruane and Roads, 2007; Rockel and Geyer, 2008), and the influence of El Niño Southe”
Statistical Downscaling of Daily Temperature in Central Europe (2002, background): “More recent studies avoid that questionable assumption and include among predictors other variables such as free atmospheric temperature, or equivalently thickness (Kaas and Frich 1995; Cavazos 1997), and moisture variables (Crane and Hewitson 1998; Wilby et al. 1998).”
Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada (2012, methodology): “…studies, the relationships between AOGCM predictors and local predictands have been modelled by linear transfer functions (e.g., Enke and Spekat 1997; Wilby et al. 1998a; Huth et al. 2001; Palutikof et al. 2002; Wilby et al. 2002; Huth 2004; Guo et al. 2011; Huang et al. 2011; Hashmi et al. 2011).”
Scaling of water flow through porous media and soils (2007, background): “, 1999), the upscaling of evaporation fluxes to input into weather models or, conversely, the downscaling of their output to estimate soil evaporation (Hantel & Acs, 1998), and the generation of input for hydrologic simulations from global circulation models (Wilby et al., 1998).”
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment (2016, methodology): “Several studies have shown the convenience of combining circulation and thermodynamic predictors in order to include signal-carrying predictors linked to changes in the radiation budget, avoiding to model future climate from changes in circulation alone (Huth, 2004; Wilby et al., 1998).”