12 Sep 2023
 | 12 Sep 2023
Status: this preprint is currently under review for the journal TC.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Benjamin Poschlod and Anne Sophie Daloz

Abstract. Snow dynamics play a critical role in the climate system as they affect the water cycle, ecosystems and society. Within climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany over the time period 1987 – 2018. Two simulations stem from high-resolution regional climate models, the Weather Research & Forecasting Model (WRF) at 1.5 km resolution and the COSMO-CLM (CCLM) at 3 km resolution. Additionally, the hydrometeorological snow model AMUNDSEN is run at the point scale of the climate stations based on the atmospheric output of CCLM. The ERA5-Land dataset (9 km) complements the comparison as state-of-the-art reanalysis land surface product. All four simulations are driven by the same atmospheric boundary conditions of ERA5. The WRF simulation features a cold bias of -1.2 °C and slightly overestimates snow depth (+0.4 cm) with a root-mean-square error (RMSE) of 4.3 cm. Snow cover duration slightly exceeds the observations (+6.8 d; RMSE = 20.5 d). The CCLM reproduces the winter climate very well, but shows a strong negative bias at snow depth (-2.5 cm; RMSE = 5.6 cm) and snow cover duration (-20.0 d; RMSE = 27.1 d). AMUNDSEN improves the reproduction of snow cover duration (+6.5 d; RMSE = 16.6 cm) and snow depth (+2.2 cm; RMSE = 6.2 cm). ERA5-Land shows a strong positive bias in mean winter snow depth (+3.6 cm; RMSE = 14.5 cm) and snow cover duration (+33.9 d; RMSE = 44.0 d). All models fail to skilfully predict white Christmas. For extreme events of snow dynamics such as annual maximum snow depths, maximum daily snow accumulation and melting, the ERA5L and CCLM show large biases in amplitude and deviations in seasonality. WRF and AMUNDSEN can improve the representation of extremes but still with considerable limitations.

The high spatial resolution of convection-permitting climate models shows potential in reproducing the winter climate in southern Germany. However, the uncertainties within the snow modelling prevent a further straightforward use for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, also in the context of climate change research.

Benjamin Poschlod and Anne Sophie Daloz

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-137', Anonymous Referee #1, 28 Sep 2023
    • AC1: 'Reply on RC1', Benjamin Poschlod, 17 Nov 2023
  • RC2: 'Comment on tc-2023-137', Anonymous Referee #2, 03 Oct 2023
    • AC2: 'Reply on RC2', Benjamin Poschlod, 17 Nov 2023
Benjamin Poschlod and Anne Sophie Daloz
Benjamin Poschlod and Anne Sophie Daloz


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Short summary
Information about snow depth, snow accumulation, and snow melt are important within climate research but also for many different sectors, such as tourism, mobility, civil engineering, and ecology. Climate models often feature a spatial resolution, which is too coarse to investigate snow depth. Here, we analyse high-resolution simulations and identify added value compared to a state-of-the-art product. Still, daily extremes are represented with limitations and need to be carefully evaluated.