ABSTRACT The optimal temperature for photosynthesis (Topt), the temperature at which photosynthesis peaks, is crucial for estimating gross primary production (GPP). Most models, however, apply biome‐specific Topt, overlooking the spatial and temporal variability driven by the genetic adaptation and interannual thermal acclimation of plants, thus introducing systematic error in GPP estimation. We mapped Topt of Tibetan Plateau (TP) grassland averaged over 1982–2018, using satellite‐derived near‐infrared reflectance of vegetation as a proxy of photosynthesis, and projected future Topt under different scenarios of shared socioeconomic pathways (SSPs) using space‐for‐time substitution. We assessed the effects of Topt adaptation on current GPP estimation and acclimation on projected GPP using a light‐use efficiency model (i.e., EC‐LUE model). The spatial heterogeneity of Topt was pronounced across the TP grassland, with higher values in the northeast and lower values in the southwest, averaging 9.32°C ± 3.15°C. Topt tended to increase from 2040 to 2080 across all SSPs, with the slowest increase under SSP1‐2.6 (0.015°C y −1 ) and the fastest increase under SSP5‐8.5 (0.061°C y −1 ). Incorporating Topt adaptation into the EC‐LUE model improved the accuracy of GPP estimation, with R 2 increasing by 7.19% and RMSE decreasing by 11.40%, compared to the model with a fixed Topt. In contrast, ignoring the adaptation of Topt led to systematically lower GPP values across 77.9% of the TP grassland, resulting in an overall reduction of estimated GPP by 7.07% (0.07 g C m −2 d −1 ). Ignoring the acclimation of Topt led to systematically higher projected GPP on the TP, ranging from 2.87% ± 8.96% (SSP1‐2.6) to 3.83% ± 5.97% (SSP3‐7.0) across different scenarios, covering more than 70% of TP regions. These findings highlight the necessity of incorporating both the adaptation and acclimation of Topt into GPP models to enhance the monitoring of the carbon cycle.
Yin et al. (2026) studied this question.