Elevated atmospheric CO₂ (eCO₂ ) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO₂ in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO₂ effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO₂ effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO₂ concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO₂ effects. The eCO₂ effect on gross primary productivity (GPP) emerges at relatively low CO₂ increase (∆[CO₂ ] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO₂ effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO₂ increase (∆[CO₂ ] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO₂ is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO₂ effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.

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