Abstract: Ecosystem temporal stability (TS) determines its ability to maintain structure, function, and services under external disturbances, playing a critical role in the global carbon cycle and climate regulation. However, the capability of numerical models to simulate the TS of ecosystem carbon uptake remains insufficiently assessed. This study evaluates the performance of nine terrestrial ecosystem models in simulating gross primary productivity (GPP) and its TS and employs Random Forest (RF) models with Shapley Additive Explanations (SHAP) to identify key factors contributing to model biases. Site-scale analysis based on flux tower observations indicates that most models underestimate GPP while overestimating its TS, with the most pronounced biases occurring at the interannual scale. These discrepancies primarily stem from errors in simulating vegetation phenology, specifically the carbon uptake period, and physiological traits, particularly peak GPP within a year. At the global scale, regions with higher carbon uptake tend to exhibit greater TS, yet significant discrepancies exist among models. Notably, RF and SHAP analyses indicate that leaf area index is more important than climate and geographical factors in explaining model divergence for simulating GPP and its TS. The study reveals systematic biases in the current models’ representation of TS, highlighting the potential vulnerability of ecosystems. These uncertainties among models may lead to an overestimation of ecosystem resilience, introducing uncertainties in global carbon budget estimates and potentially misguiding scientific assessments and policy decisions regarding future climate change responses. Therefore, improving carbon cycle simulation mechanisms is essential for enhancing model predictive capabilities.

Key words: terrestrial ecosystem models, gross primary productivity, climate change, carbon sinks, ecosystem stability, remote sensing