Modeling Annual Gross Primary Productivity through Climatic Variables by Integrating Key Vegetation Functional Traits

Vegetation gross primary production (GPP), defined as the rate of carbon compound via photosynthesis, is a fundamental metric for assessing terrestrial carbon uptake. Vegetation functional traits, such as carbon uptake period and maximum photosynthetic capacity (GPPmax), are known to drive the interannual variability in GPP; however, existing frameworks are mainly effective in ecosystems with idealized bell-shaped GPP trajectories. This gap highlights the need for a globally consistent framework that accounts for biome-specific differences. In this study, we utilized flux tower observations to uncover a robust linear relationship between total annual GPP (GPPann) and the product of GPPmax and the annual average growing season index. Building on this relationship, we developed the Prognostic Ecosystem Annual Productivity Model (PEAPM) to simulate GPPann under historical and future climate scenarios. Site-scale evaluations demonstrated the simulative performance of PEAPM, with a Pearson's r of 0.86 and a low root mean square error of 333.8 gC/m²/year. At the global scale, PEAPM effectively reproduces the spatiotemporal patterns of GPPann across diverse biomes, achieving Pearson's r values ranging from 0.94 to 0.98 compared to process-based, light use efficiency, and upscaling models. PEAPM estimates a global total GPPann of 136.3 ± 2.5 PgC/year and detects a positive trend of 0.41 PgC/year from 2001 to 2015. PEAPM projects increasing GPPann trends across most terrestrial regions by 2100 under various shared socioeconomic pathways scenarios. This study underscores the critical role of vegetation functional traits, particularly phenology and photosynthetic capacity, in explaining GPP variability.