Evaluating Dynamic Global Vegetation Models in China: Challenges in capturing trends in Leaf Area and Gross Primary Productivity, but effective seasonal variation representation

Abstract. Terrestrial ecosystems are crucial in mitigating global climate change, and Dynamic Global Vegetation Models (DGVMs) have become essential tools for simulating these ecosystems. However, uncertainties remain in DGVM simulations for China, highlighting the need to systematic evaluations of their dynamics across various time scales to enhance model performance. As such, we utilize reprocessed monthly MODIS Leaf Area Index (LAI) and Contiguous Solar-induced Fluorescence (CSIF) data as observational references to assess the long-term trends and seasonal variations of LAI and Gross Primary Production (GPP) simulated by 14 models (CABLE-POP, CLASSIC, CLM5.0, DLEM, IBIS, ISAM, ISBA-CTRIP, JULES, LPJ-GUESS, LPX, OCN, ORCHIDEEv3, SDGVM, and VISIT) in China from 2003 to 2019. Additionally, we evaluate the trends and seasonal variations of simulated LAI and GPP in response to environmental and climatic factors. Our findings indicate that: (1) While the overall trend of simulated LAI is captured, the spatial performance of simulated LAI and GPP is poor, with underestimation in forested areas, overestimation in grasslands, and misestimation in croplands; (2) The models misestimate the simulated LAI and GPP responses to changes in environmental factors, and their inaccuracy in capturing anthropogenic impacts on vegetation dynamics. We indicate that the main reason for the model's misestimation is that the model's understanding of the CO₂ fertilization effect is inadequate, and thus fails to simulate the vegetation response to CO₂ concentration. (3) Despite these issues, the models can effectively capture the seasonality of LAI and GPP in China, largely due to their robust representation of seasonal responses to climate factors.