Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations

Offline land surface models (LSMs) require atmospheric forcing data sets for simulating water, energy, and biogeochemical fluxes. However, available forcing data sets remain highly uncertain and can introduce additional differences in LSM simulations. This study explored the impact of various forcing data sets, ranging from widely used to newly developed, on hydrological simulations using the Common Land Model 2024 (CoLM2024). We conducted 12 global experiments using different forcing data sets to force CoLM2024. We evaluated the model's performance against plot-scale observations and globally gridded reference data. We examined the uncertainties in forcings and their impact on output variables such as latent heat, sensible heat, net radiation, and total runoff. Globally, precipitation has the highest degree of uncertainty at 4.4%. The forcing uncertainties propagate to the model simulations and cause significant differences in simulated variables. Runoff uncertainty is about 15.7% globally, with a greater impact in low latitudes. Our evaluation shows that the newly developed data sets, such as CRUJRA and ERA5LAND, generally outperform the others. However, the optimal forcing data set varies depending on the variable of interest and the targeted region. Partial Least Squares Regression analysis reveals that different simulated variables are associated with dominant forcing variables, highlighting the importance of selecting forcing data sets for specific applications and regions. This study confirms the importance of improving the quality and consistency of meteorological data. This would help reduce simulation biases and guide the improvement of the model structure and parameterization of CoLM2024.