Evaluation of snow depth from multiple observation-based, reanalysis, and regional climate model datasets over a low-altitude Central European region

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006-2010 and 1985-2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2-3 cm for some stations and display lower correlations (0.7-0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February-March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land-atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

Automated summary

This study evaluates different data sources for snow depth (SD) estimation, including satellite-based and in situ measurements, reanalyses, and regional climate simulations. The results show that the ERA5 reanalysis and the CGLS product perform well in representing SD, while ERA5-Land and CARPATCLIM overestimate SD and WRF simulations have significant biases. The excessive snow cover in the WRF simulations can negatively impact land-atmosphere interactions and lead to biases in temperature estimation. The study highlights the importance of considering SD errors in model evaluation and adaptation.