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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. [ABSTRACT FROM AUTHOR]

Elevated atmospheric CO 2 (eCO 2 ) 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 eCO 2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO 2 effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO 2 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 CO 2 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 eCO 2 effects. The eCO 2 effect on gross primary productivity (GPP) emerges at relatively low CO 2 increase (∆[CO 2 ] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO 2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO 2 increase (∆[CO 2 ] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO 2 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 CO 2 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., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)