Impact of Snow Data Assimilation on Land-Surface Energy Fluxes at Sites Across Europe Using eCLM-PDAF

Snow plays a key role in land-surface processes by modulating the surface energy balance, soil thermal insulation and water availability. However, the influence of snow on water and energy fluxes in land surface models remains insufficiently understood. To improve simulations of the coupled water–energy cycle, we developed a snow data assimilation (snow-DA) within the Encore Community Land Model coupled to the Parallel Data Assimilation Framework (eCLM-PDAF; https://github.com/HPSCTerrSys/eCLM), enabling assimilation of both snow depth and snow water equivalent (SWE). In the Snow-DA experiment, we assimilate daily snow depth with a one-dimensional Ensemble Kalman Filter (EnKF), updating the liquid and ice SWE components across all snow layers; snow depth is then adjusted through its correlation with SWE. We evaluated the performance of the snow-DA framework by comparing snowpack variables as well as heat fluxes such as latent heat flux (LE), sensible heat flux (SH), ground heat flux (GH), and soil temperature, between data assimilation (DA) and open-loop (OL) simulations at eleven selected Integrated Carbon Observation System (ICOS) sites across Europe. The sites span different observation periods within 2017–2024. Each OL and DA experiment used 100 ensemble members, generated through perturbation of meteorological variables and key snow related parameters.  A multiplicative inflation factor of 0.95 and observation error of 0.2m are applied across all sites. Results across ICOS sites showed that DA substantially improved snow variable estimates compared to OL simulations. On average, the root mean square error (RMSE) of SD decreased by 27.3%, and the correlation coefficient (R) increased by 0.06. DA also improved the timing of snow cover duration, yielding a more realistic seasonal snow cover evolution when compared with satellite-based observations. Although overall changes in land-surface heat fluxes were modest, the improved snowpack reduced RMSE during the melt season for LE by 9.5%, evaporative fraction (EF) by 1.6%, and soil temperature by 20.8%. Although the energy balance was evaluated, and LE and EF improved, snow DA degraded the performance of SH and GH at most sites, indicating possible coupling bias between modeled variables for energy partitioning, or representativeness errors between tower-based and modeled fluxes. Overall, this study enhances the representation of snow processes in a land surface model and encourages further research into the modeling of associated water and energy balance mechanisms. Future work will investigate regional responses of water flux components, including runoff, evapotranspiration, and soil water content, to further examine snow data assimilation impacts on water availability.