Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study

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.