Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water

Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants and thereby can affect 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 signal in gross primary production (GPP) emerges at relatively low CO2 increase (Δ[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 (Δ[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 of 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.Model Code is available under the DOI: 10.17871/quincy-model-2019 (branch name: quincy-land/release04)Data and code to support this analysis are uploaded.Funding provided by: German Research FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: 391059971Funding provided by: H2020 European Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/100010663Award Number: 647204Funding provided by: H2020 European Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/100010663Award Number: 855187Funding provided by: International Max Planck Research School for Global Biogeochemical Cycles *Crossref Funder Registry ID: Award Number:We develop the concept of the emergence of the eCO2 effect (EoC), which allows us to determine to which extent the eCO2 effect can exceed the natural variability and confounding factors. Here, we perform three simulations with the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system; Thum et al., 2019) to isolate the eCO2 effects: (i) a reference simulation with transient CO2 concentrations and observation-based meteorological forcing; (ii) a simulation where the CO2 is kept constant at the level of 1901 while the meteorological forcing is identical to the reference simulation; and (iii) a simulation with the same set up of (i) but CO2 is kept constant after the year 1988 at the level of 1988. The simulation (iii) is used to test our method in the recent time period when the FLUXNET observations start to be recorded (Baldocchi et al., 2001). Additionally, simulation (iii) is designed to study the role of the baseline CO2 concentration in our findings. Analyzing the differences in carbon and water fluxes between simulations with transient and constant CO2 concentration, we develop a statistical method based on the signal-noise ratio to detect the emergence of significant eCO2 effects on these fluxes given their natural variability. The signal refers to the eCO2 effects, and the noise indicates the inter-annual variability which is related mostly to climate variations. In other words, we seek to identify the point in time when the signal is distinguishable from the noise