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

Carbon isotope discrimination (Δ¹³ C) in C₃ woody plants is a key variable for the study of photosynthesis. Yet how Δ¹³ 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 Δ¹³ C 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 Δ¹³ C values, and to underestimate interannual variability in Δ¹³ 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 Δ¹³ 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 Δ¹³ 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 Δ¹³ 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 Δ¹³ 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 Δ¹³ C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ¹³ C in those forests. Our results demonstrate that the combined analysis of Δ¹³ C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.