Deriving optimal air temperature from light use efficiency to improve ecosystem productivity estimates

Accurately representing photosynthetic optimum temperature (Topt) is essential for predicting terrestrial carbon uptake, yet conventional ecosystem-scale estimates based on peak gross primary productivity (GPP) are confounded by concurrent variations in radiation, moisture and phenology. Here, we develop a physiologically grounded approach that defines Topt as the air temperature where light use efficiency (LUE) reaches its maximum, thereby isolating the intrinsic thermal response of photosynthesis. We derived efficiency-based Topt (Topt-LUE) from 131 flux observation sites. By replacing biome-based Topt used in Vegetation Photosynthesis Model (VPM), we significantly improved GPP estimation (R2 = 0.71, RMSE = 1.93 gC m−2 d−1) compared to the biome-based approach (R2 = 0.62, RMSE = 2.84 gC m−2 d−1). We then used a Random Forest framework to generate global and time-varying Topt-LUE fields (2001–2020). The results revealed that biome-based Topt systematically overestimated Topt-LUE across ∼94% of global vegetated areas, with a mean bias of ∼10°C. The global Topt-LUE exhibit clear latitudinal gradients and biome-specific contrasts, providing evidence for widespread thermal acclimation of ecosystem photosynthesis. This acclimation is reflected in a mean increase in Topt-LUE of 0.021 ± 0.102 °C per year, underscoring a measurable response to long-term climate changes. When integrated into VPM, the dynamic Topt-LUE fields reshape the spatial pattern of simulated carbon uptake, mitigating overestimation in tropical areas (∼5 gC m−2 d−1) and enhancing underestimation in frigid areas and temperate regions including China, India and Europe. This study established a mechanistically grounded framework for quantifying ecosystem thermal acclimation, advancing the representation of temperature responses in terrestrial carbon cycle models.