<p>Snow cover is a critical element of the Earth’s climate system, influencing energy exchange and water storage. This study examines the sensitivity of seasonal snow simulations to initialization, parameterization, and atmospheric forcing to assess the influence on snow representation in seasonal simulations. Using the TERRA Standalone (TSA) land surface model with ERA5 reanalysis forcing, we investigate the effects of perturbations in initial snow water equivalent (SWE), albedo (parameterization), and precipitation (forcing) affect snow dynamics across Eurasia over ten winters. A +10% perturbation in the snow albedo during 3-month spring simulations leads to a median increase in the snow cover area of 24%, which closely matches the interannual variability. Similarly, a +10% perturbation in precipitation during early winter simulations leads to a median of 28% increase in total snow mass, more than three times the interannual variability. Sensitivity analysis shows that seasonal snow simulations are most sensitive to precipitation perturbations, especially during the melt season, while initialization effects persist throughout the winter. The results show that snow cover during the melt season is strongly influenced by albedo and precipitation perturbations, with cumulative positive perturbations having a larger impact than negative perturbations. Mid- and late-winter initializations effectively reduce snow bias from precipitation and albedo perturbations. In addition, improved albedo parameterization can improve snow simulation accuracy during the melt season. Spatial analysis identifies snow margin zones as particularly sensitive to albedo and precipitation perturbations. These findings underscore the importance of accurate precipitation forcing and snow parameterization to ensure reliable seasonal snow simulations for climate predictions and projections. </p> Graphical Abstract <p> The graphical abstract provides a visual summary of the study setup and results generation, providing the reader with an efficient way to grasp the essence of the research. The first section highlights the input data, where ERA5 reanalysis forcing and initialization are used for the TERRA Standalone (TSA) land surface model to simulate snow cover dynamics over Eurasia for 10 winters and with perturbations. The second section illustrates the simulation timeline, showing how the experiments are conducted with different starting months ranging from October to May. The final section presents the sensitivity analysis of the perturbing factors: initialization (SWE), parameterization (albedo), and atmospheric forcing (precipitation). The figures show the effects of the perturbations, such as how changes in precipitation and albedo affect snow mass and snow cover area over winter and spring. The maps also highlight snow margin zones as areas of increased sensitivity to these factors. This graphical summary highlights the study’s contribution to understanding snow simulation sensitivity and the importance of accurate initialization, parameterization, and forcing in seasonal simulations.</p>