Carbon isotope discrimination (Δ¹³C) in C₃ woody plants is a key variable for the study of photosynthesis. Yet how Δ¹³C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ¹³C modelling capability in the land-surface model JULES incorporating both photorespiratory and mesophyll-conductance fractionations. We test the ability of four leaf-internal CO₂ concentration models embedded in JULES to reproduce leaf and tree-ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ¹³C values, and to underestimate interannual variability in Δ¹³C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post-photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR-derived Δ¹³C values. Nonetheless, the “least-cost” (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy-level carbon and water fluxes. Overall, modelled Δ¹³C trends vary strongly between regions during the recent (1979–2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ¹³C trend by 0.0015 ± 0.005‰ and –0.0006 ± 0.001‰ ppm⁻¹, respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ¹³C and GPP tend to be negatively correlated in wet-humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ¹³C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ¹³C in those forests. Our results demonstrate that the combined analysis of Δ¹³C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.

中文翻译：

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植物碳同位素鉴别的全球年代际变化及其与初级生产总值的联系

_{C 3}木本植物中的碳同位素鉴别（Δ ¹³ C）是光合作用研究的关键变量。然而，Δ ¹³ C 如何在十年尺度和跨地区变化，以及它与初级生产总值 (GPP) 的关系如何，仍不完全清楚。在这里，我们通过在结合光呼吸和叶肉电导分馏的陆面模型 JULES 中实施新的 Δ ^{13 C 建模能力来解决这些问题。}我们测试了嵌入在 JULES 中的四种叶片内部 CO ₂浓度模型重现叶片和树木年轮 (TR) 碳同位素数据的能力。我们表明，所有测试模型都倾向于高估平均 Δ ^{13C 值，并低估了 Δ 13} C的年际变化。这可能是因为他们忽略了土壤水分胁迫对气孔行为的影响。^{不同物种、地点和年份的光合后同位素分馏的变化也可以部分解释预测的和 TR 衍生的 Δ 13} C 值之间的差异。尽管如此，“最低成本”（Prentice）模型显示同位素测量的偏差最小，并导致对冠层碳和水通量的预测得到改进。总体而言，在最近（1979-2016 年）的历史时期，模拟的 Δ ^{13 C 趋势在不同地区之间差异很大，但在全球平均时几乎保持不变。}光呼吸和叶肉效应调节模拟的全局 Δ ¹³C 趋势分别为 0.0015 ± 0.005‰ 和 –0.0006 ± 0.001‰ ppm ^-1。这些预测与先前基于大气碳同位素测量的发现形成对比。预测的 Δ ¹³ C 和 GPP 在湿湿和寒冷地区以及热带非洲森林中往往呈负相关，但在其他地方呈正相关。^{Δ 13} C 和 GPP之间的负相关部分是由于温度对 GPP 和蒸汽压不足对 Δ ¹³ C 的强烈影响。我们的研究结果表明，Δ ¹³ C 和 GPP 的联合分析有助于了解不同气候区域光合作用变化的驱动因素。