Reply on RC3

Abstract

The exchange ratio (ER) between atmospheric O2 and CO2 is a useful tracer on global and local scales to better understand the carbon budget. The variability of ER (in mol O2 per mol CO2) between terrestrial ecosystems is not well-known, and there is no consensus on how to derive the ER signal to represent an ecosystem, as there are different approaches available, either based on concentration (ERatmos) or flux measurements (ERforest). In this study we measured atmospheric O2 and CO2 concentrations at two heights above the boreal forest in Hyytiälä, Finland. Such measurements of O2 are unique and enable us to potentially identify which forest carbon loss and production mechanisms dominate over various hours of the day. We found that the ERatmos signal at 23 m is not representative for the forest exchange alone but is also influenced by other factors, including for example entrainment of air masses with different thermodynamic and atmospheric composition characteristics in the atmospheric boundary layer. To derive ERforest we infer O2 fluxes using multiple theoretical and observation-based micro-meteorological formulations to determine the most suitable approach. Our resulting ERforest shows a distinct difference in behaviour between daytime (0.92 ± 0.17 mol/mol) and nighttime (1.03 ± 0.05 mol/mol). These insights demonstrate the diurnal variability of different ER signals above a boreal forest and we also confirmed that the signals of ERatmos and ERforest can not be used interchangeably. Therefore, we recommend measurements on multiple vertical levels to derive O2 and CO2 fluxes for the ERforest signal, instead of a single level time series of the concentrations for the ERatmos signal. We show that ERforest can be further split into specific signals for respiration (1.03 ± 0.05 mol/mol) and photosynthesis (0.96 ± 0.12 mol/mol). This estimation allows us to separate the Net Ecosystem Exchange (NEE) into Gross Primary Production (GPP) and Total Ecosystem Respiration (TER), giving comparable results to the more commonly used eddy covariance approach. Our study shows the potential of using atmospheric O2 as an alternative method to gain new insights on the different CO2 signals that contribute to the forest carbon budget.