The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO₂ concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO₂ fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.

光合作用对环境变化的敏感性对于理解碳循环对全球气候变化的响应以及开发解释其时空变异性的建模方法至关重要。我们收集了大量已发表的总初级生产力 (GPP) 对不同强迫变量的敏感性函数，以评估 GPP 对环境因素的响应。这些包括 GPP 对温度的响应；蒸气压不足，其中一些包括对大气 CO ₂的响应浓度；土壤水分可用性（W）；光照强度；和多云。这些函数结合在一个全因子光利用效率 (LUE) 模型结构中，从而形成了 5600 个不同的 LUE 模型的集合。每个模型都针对来自 196 个 FLUXNET 站点的每日 GPP 和蒸散通量进行了优化，并基于引导方法在站点之间进行了排名。GPP 对每个环境因素的敏感性，包括 CO ₂受精，被证明是重要的，并且之前发布的模型结构都没有表现得像所选的最佳模型一样。从每日和每周到每月的尺度，最佳模型的跨站点 Nash-Sutcliffe 模型效率中值分别为 0.73、0.79 和 0.82，但在年度尺度 (0.23) 上较差，强调当前模型在描述年际变化方面的共同局限性GPP。尽管最佳全球模型与每个站点的本地最佳模型不匹配，但该选择在生态系统类型中是稳健的。在所有生物群落中都观察到了光饱和度和云度对 GPP 的贡献（从 23% 到 43%）。温度和 W 在 GPP 和 LUE 中占主导地位，但 GPP 对温度和 W 的响应分别在寒冷和干旱生态系统中滞后。