A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments

Abstract. This study presents a statistical downscaling (SD) method for high-altitude, glaciated mountain ranges. The SD method uses an a priori selection strategy of the predictor (i.e., predictor selection without data analysis). In the SD model validation, emphasis is put on appropriately considering the pitfalls of short observational data records that are typical of high mountains. An application example is shown, with daily mean air temperature from several sites (all in the Cordillera Blanca, Peru) as target variables, and reanalysis data as predictors. Results reveal strong seasonal variations of the predictors' performance, with the maximum skill evident for the wet (and transitional) season months January to May (and September), and the lowest skill for the dry season months June and July. The minimum number of observations (here, daily means) required per calendar month to obtain statistically significant skill ranges from 40 to 140. With increasing data availability, the SD model skill tends to increase. Applied to a choice of different atmospheric reanalysis predictor variables, the presented skill assessment identifies only air temperature and geopotential height as significant predictors for local-scale air temperature. Accounting for natural periodicity in the data is vital in the SD procedure to avoid spuriously high performances of certain predictors, as demonstrated here for near-surface air temperature. The presented SD procedure can be applied to high-resolution, Gaussian target variables in various climatic and geo-environmental settings, without the requirement of subjective optimization.