Abstract
The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but they often suffer from inaccuracies, e.g., due to insufficient spatial resolution. sDoG (statistical Downscaling for Glacierized mountain environments) is a reanalysis data postprocessing tool designed to extend short-term weather station data from high mountain sites to the baseline climate. In this study, sDoG is applied to ERA-Interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. First, sDoG is trained and cross-validated using observations from 2002 to 2012 (cross-validation period). Then, the sDoG retrospective forecast and its cross-validation-based uncertainty estimates are evaluated for the period 1979–2001 (hereafter referred to as the true evaluation period). We demonstrate the ability of sDoG to model air temperature in the true evaluation period for different temporal scales: day-to-day variations, year-to-year and season-to-season variations, and the 23-year mean seasonal cycle. sDoG adds significant value over a selection of reference data sets available for the site at different spatial resolutions, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product. However, we identify limitations of sDoG in modeling summer air temperature variations particularly evident in the first part of the true evaluation period. This is most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. When comparing the performance of the considered reference data sets, we cannot demonstrate added value of the higher resolution data sets over the data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA-Interim (80 km resolution) both clearly outperform the higher resolution data sets ERA5-Land (9 km resolution), UERRA HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences among ERA5 and ERA-Interim, by contrast, are comparably small. Our study highlights the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas both for data users and model developers.
Highlights
Availability and quality of in situ meteorological observations dramatically decrease with elevation and topographic complexity (e.g., [1,2,3])
(hereafter referred to as the true evaluation period). sDoG is compared to the measurements for different time aspects, and the sDoG performance is benchmarked with the performance of various state-of-art reference data sets at very distinct spatial resolutions, that are available for the site and extend over the true evaluation period
This study evaluates sDoG and its cross-validation-based uncertainty estimates for daily air temperature at the Vernagtbach climate monitoring site (2640 MSL in the European Alps). sDoG is trained and cross-validated using data from 2002 to 2012, while the evaluation considers data from 1979 to 2001
Summary
Availability and quality of in situ meteorological observations dramatically decrease with elevation and topographic complexity (e.g., [1,2,3]). Long-term regional climate model simulations have the largest potential to add value over their coarse-scale drivers [19], and at the same time, they are the most challenging for the data-scarce areas with complex topography. SDoG relies on statistically adjusting reanalysis data to local-scale conditions with a strategy to circumvent the pitfalls of fitting temporally short and highly autocorrelated records It is one-dimensional in the physical and variable space, and can be applied to various atmospheric quantities at a daily time scale (e.g., air temperature, precipitation, wind speed, relative humidity). SDoG is compared to the measurements for different time aspects, and the sDoG performance is benchmarked with the performance of various state-of-art reference data sets at very distinct spatial resolutions, that are available for the site and extend over the true evaluation period.
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