The slow temperature acclimation of photosynthesis has been confirmed through early field experiments and studies. However, this effect is difficult to characterize and quantify with some simple and easily accessible indicators. As a result, the impact of slow temperature acclimation of photosynthesis on gross primary production (GPP) estimation has often been overlooked or not integrated into most GPP models. In this study, we used a theorical variable-state of acclimation (S), to characterize the slow temperature acclimation. This variable represents the temperature to which the photosynthetic machinery adapts and is defined as a function of air temperature () and time constant () required for vegetation to respond to temperature, to discuss its impact on GPP simulation. We used FLUXNET2015 dataset to calculate S and established a GPP model using S and shortwave radiation (SW) based on random forest algorithm (S model). As a comparison, we directly used and SW to build the other GPP model ( model). Moreover, the divergent temperature acclimation capacities of plants are crucial to predict and make preparations for likely temperature stress in the future. Therefore, the spatial distribution of values was also mapped using satellite sun induced chlorophyll fluorescence (SIF) and datasets. The results indicated that: (1) taking into account the slow temperature acclimation of photosynthesis led to a more precise estimation of GPP which mainly reflected in reduction of excessive fluctuations in GPP predictions; (2) considering the slow temperature acclimation of photosynthesis can reduce the sensitivity of vegetation to temperature; (3) the improvement of S model in GPP estimations was different in different vegetation growth stages which was more significant in the springtime recovery stage; (4) values had significant spatial distribution which was strongly affected by the determinants of vegetation growth and seasonal variations in temperature.

中文翻译：

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光合作用缓慢温度驯化对总初级生产力估算的影响


光合作用的缓慢温度适应已通过早期的田间实验和研究得到证实。然而，这种效应很难用一些简单且易于获取的指标来表征和量化。因此，光合作用缓慢的温度适应对总初级生产 (GPP) 估算的影响常常被忽视或未纳入大多数 GPP 模型中。在本研究中，我们使用理论可变驯化状态（S）来表征缓慢的温度驯化。该变量代表光合机制适应的温度，定义为植被响应温度所需的气温（）和时间常数（）的函数，以讨论其对GPP模拟的影响。我们使用FLUXNET2015数据集计算S，并基于随机森林算法（S模型）建立使用S和短波辐射（SW）的GPP模型。作为比较，我们直接使用和SW来构建另一个GPP模型（模型）。此外，植物不同的温度适应能力对于预测和为未来可能的温度胁迫做好准备至关重要。因此，还使用卫星太阳诱导叶绿素荧光（SIF）和数据集绘制了值的空间分布。 结果表明：（1）考虑光合作用缓慢的温度驯化使得GPP的估算更加精确，主要体现在GPP预测中过度波动的减少； (2)考虑到光合作用缓慢的温度驯化会降低植被对温度的敏感性； (3)不同植被生长阶段S模型对GPP估算的改进效果不同，在春季恢复阶段更为显着； (4)数值具有显着的空间分布，受植被生长和温度季节变化的决定因素的强烈影响。