Assessing the effect of land cover on ISBA snow water equivalent and land surface temperature simulations over Europe

An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover (LC) representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe LC. The most recent version uses the latest LC data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) and land surface temperature (LST) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite-derived SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI LC data reduces the ISBA SWE bias by around 23 %, with this reduction being observed over most of the domain. Further analysis of LC transitions shows that the reduction in SWE bias is driven primarily by a few vegetation changes, particularly the transition from grasslands to forests. Changes in SWE are also found in areas where the dominant LC remains unchanged. This indicates that modifications in sub-grid vegetation fractions can affect snow-vegetation interactions and SWE bias. The updated LC has a very limited impact on the model's performance for land surface temperature, indicating that the impact of LC updates is more noticeable for SWE than for LST. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.