Evaluating Predictor Strategies for Regression-Based Downscaling with a Focus on Glacierized Mountain Environments

Abstract

This study explores the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity, and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in close proximity to mountain glaciers: 1) the Vernagtbach station in the European Alps, 2) the Artesonraju measuring site in the tropical South American Andes, and 3) the Mount Brewster measuring site in the Southern Alps of New Zealand. The large-scale dataset being evaluated is the ERA-Interim dataset. In the downscaling procedure, particular emphasis is put on developing efficient yet not overfit models from the limited information in the temporally short (typically a few years) observational records of the high mountain sites. For direct (univariate) predictors, optimum scale analysis turns out to be a powerful means to improve the forecast skill without the need to increase the downscaling model complexity. Yet the traditional (multivariate) predictor sets show generally higher skill than the direct predictors for all variables, sites, and days of the year. Only in the case of large sampling uncertainty (identified here to particularly affect observed precipitation) is the use of univariate predictor options justified. Overall, the authors find a range in forecast skill among the different predictor options applied in the literature up to 0.5 (where 0 indicates no skill, and 1 represents perfect skill). This highlights that a sophisticated predictor selection (as presented in this study) is essential in the development of realistic, local-scale scenarios by means of downscaling.

Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach

“…With regard to the selection of RCM grid points for the downscaling to the point scale, we followed the approach by Hofer et al (2017) (who however used linear regressions rather than QM) to find the optimum scale (OS) for each station and target variable: for each station and variable, spatial averages of the closest 1 × 1, 2 × 2, . .…”

“…. Histograms of the optimum scales for bias correction found using the approach by Hofer et al (2017) for the variables minimum temperature (T min ), maximum temperature (T max ), precipitation (P ), relative humidity (RH), global radiation (G), and wind speed (WS). Histograms are calculated using the derived optimum scales for all available stations for a given variable and all 14 GCM-RCM combinations (Table 2).…”

Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach

“…Histograms of the optimum scales for bias correction found using the approach by Hofer et al (2017) for the variables minimum temperature (Tmin), maximum temperature (Tmax), precipitation (P), relative humidity (RH), global radiation (G), and wind speed (WS).…”

An imbalancing act: the delayed dynamic response of the Kaskawulsh Glacier to sustained mass loss

“…Precipitation downscaling is achieved using a regression-based method that incorporates daily total surface precipitation at NARR grid points and geographic predictors of precipitation on the 200 m grid (Easting, Northing, elevation) (Guan and others, 2005, 2009), but does not include other reanalysis-derived climatic variables (cf. Hofer and others, 2017). A rain-to-snow threshold of 1 ° C is used to calculate accumulation.…”