Snow representation in seasonal forecasts and climate simulations: sensitivities of seasonal snow simulation and impact on frozen soils

Snow cover is a critical component of the Earth's climate system, covering up to 44 % of the Northern Hemisphere's land during winter and influencing energy exchange, water storage, and atmospheric circulation patterns. However, operational seasonal forecasting systems and climate models still encounter challenges in accurately representing snow processes. This leads to biases in temperature predictions and frozen soil simulations. This cumulative thesis investigates snow representation across different modeling frameworks to identify sources of error and quantify sensitivities. The first study evaluated five operational seasonal forecasting systems over Siberia, distinguishing between initialization and parameterization errors. The results showed that initial snow biases persisted throughout the forecast period, influencing snow melt timing and duration. Although multi-layer snow schemes offer theoretical advantages, they did not outperform single-layer schemes among the five systems due to initialization challenges. Both initialization and parameterization contributed to snow biases, with their relative importance varying by system. The second study used the TERRA Standalone land surface model to quantify the sensitivity of snow simulations to initialization, parameterization, and atmospheric forcing. A +10 % precipitation perturbation during early winter simulations increased total snow mass by 28 % (more than three times the interannual variability), while a +10 % albedo perturbation during spring simulations increased the area of snow cover by 24 %. Snow margin zones were particularly sensitive to albedo and precipitation perturbations, with positive perturbations having larger effects than negative ones due to the snow/no-snow threshold. The third study compared the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP) models to evaluate frozen soil simulations in Siberia. LS3MIP showed a higher winter soil temperature bias (-3.6 °C) than CMIP6 (-2.7 °C), indicating that coupled models partially compensate for errors in the land surface component. All models underestimated the effects of snow insulation, with temperature differences of up to 10 °C less than observed at shallow snow depths (below 0.2 m). Models with updated snow thermal conductivity formulations demonstrated improved insulation representation. Together, these studies highlight the importance of accurately representing snow in climate and seasonal forecasting systems. The results demonstrate that accurate snow initialization is essential for reliable seasonal snow forecasts. Precipitation forcing and albedo parameterization dominate uncertainties in snow mass and snow cover area, and improving parameterization, especially for snow thermal conductivity and snow cover fraction is critical for realistically simulating frozen soil conditions in climate models.