Evaluation of Frozen soil characteristics in JSBACH and CLM standalone simulations

Seasonally frozen ground (SFG), a critical component of the cryosphere, covers approximately 50% of the Northern Hemisphere’s land area and significantly influences land surface and overlying atmospheric processes. The coexistence of ice and liquid water in soil alters its hydraulic and thermal properties, thereby influencing the distribution of water and energy fluxes. Accurately modeling parameters such as soil moisture, soil temperature, snow depth, etc., is particularly challenging due to processes like soil freezing and subsequent thawing. Variability in model performance remains a key challenge, highlighting the need for improved representation of SFG in cold regions.

The study compares standalone global simulations from two land surface models (LSMs) - JSBACH (Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg) and CLM (Community Land Model) driven by 3-hourly ERA5 reanalysis data from 1941 to 2022, with a focus on the representation of SFG across the mid- to high latitudes of the Northern Hemisphere. CLM produces a more realistic distribution of frozen ground, whereas JSBACH significantly underestimates the extent of SFG, and ERA5-Land underrepresents permafrost extent. All models tend to underestimate SFG compared to the IPA (International Permafrost Association) map, with JSBACH showing the largest deviation. Global gridded outputs from JSBACH and CLM, along with ERA5-Land data, were intercompared to quantify differences between models. Results indicate that JSBACH performs poorly in cold regions, possibly due to issues in its multilayer snow scheme and the representation of snow thermal properties, which may contribute to underestimated snow depth and lower soil temperatures at 20 cm depth. In contrast, JSBACH performs better than CLM in mid-latitude snow-free regions, particularly over the Tibetan Plateau. Additionally, site-level analyses focusing on the onset and duration of SFG and snow cover revealed notable biases in simulating SFG onset and duration across all models due to snow insulation effects. The modal value of the soil-air temperature difference PDF (Probability Density Function) increases by ~5°C from shallow to thick snow, reflecting the stronger insulating effect of thicker snow. CLM tends to overestimate the insulating effect of snow, while JSBACH underestimates it. The study highlights the challenges of simulating SFG processes in cold regions, emphasizing the need to improve the representation of freeze-thaw dynamics through the thermal and hydraulic properties of soil and snow in land surface models (LSMs) to enhance model performance.