Global decadal variability of plant carbon isotope discrimination and its link to gross primary production

Carbon isotope discrimination (Δ(13)C) in C(3) woody plants is a key variable for the study of photosynthesis. Yet how Δ(13)C 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 Δ(13)C modelling capability in the land‐surface model JULES incorporating both photorespiratory and mesophyll‐conductance fractionations. We test the ability of four leaf‐internal CO(2) 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 Δ(13)C values, and to underestimate interannual variability in Δ(13)C. 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 Δ(13)C 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 Δ(13)C 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 Δ(13)C 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 Δ(13)C 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 Δ(13)C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ(13)C in those forests. Our results demonstrate that the combined analysis of Δ(13)C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.