Snow bias in EURO-CORDEX regional climate models and its dependence on topography mismatch and cold bias in the European Alps
SnowHydro
Poster presented at SnowHydro 2020 in Bolzano:
Snow bias in EURO-CORDEX regional climate models and its dependence on topography mismatch and cold bias in the European Alps
Abstract
Snow is a key environmental parameter in mountains, and in this changing climate reductions in snow are expected. Traditionally, future estimates of snow are based on dedicated snow/hydrological models forced by climate projections, which, however, are computationally intensive and which decouple hydrology from climate forcing. Recently, regional climate models (RCM) have been used as an alternative, although snow is only an auxiliary parameter in RCMs and not as accurately represented as compared to dedicated snow models. Nonetheless, RCMs encompass the climate-hydrology feedbacks, cover large areas, and have become available in moderate horizontal resolutions (e.g. the EURO-CORDEX models are at approx. 12km).
Here, we show that snow in RCMs is well represented given its moderate resolution and that most deficiencies are because of the mismatch in model topography and the cold bias in winter. For this, we compared the ERA-Interim driven EURO-CORDEX RCMs with a) snow cover from MODIS and temperature from E-OBS for the whole alpine area and the common period 2002-2008, and b) with station data for the province of Bolzano in Northern Italy for the period 1980-2008. The remaining differences between RCMs and biases to observations could then be attributed to the different snow modules in RCMs, since they were forced with the same climate (ERA-Interim).
Consequently, the simple snow representation in RCMs might be enough for adequate large-scale assessments. As such, they provide a complementary view to the traditional approach of running dedicated snow models, which can provide a much more detailed view but only on a smaller area, typically catchments.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 795310.