DOI: 10.5061/dryad.3xsj3txk5
title: Data associated with: Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water

description: 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] &gt;&gt; 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.
description: We develop the concept of the
 emergence of the eCO<sub>2</sub> effect (EoC), which
 allows us to determine to which extent the eCO<sub>2</sub> 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 eCO<sub>2</sub> effects: (i) a reference
 simulation with transient CO<sub>2</sub> concentrations and
 observation-based meteorological forcing; (ii) a simulation where the
 CO<sub>2</sub> 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 CO<sub>2</sub> 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 CO<sub>2</sub> concentration in our
 findings. Analyzing the differences in carbon and water fluxes between
 simulations with transient and constant CO<sub>2</sub> concentration, we
 develop a statistical method based on the signal-noise ratio to detect the
 emergence of significant eCO<sub>2</sub> effects on these fluxes
 given their natural variability. The signal refers to the
 eCO<sub>2</sub>
 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.
description: Model Code is available under the DOI: 10.17871/quincy-model-2019 (branch name: quincy-land/release04)<br>Data and code to support this analysis are uploaded.