the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Modeling Approach to Investigate Drivers, Variability and Uncertainties in O2 Fluxes and the O2 : CO2 Exchange Ratios in a Temperate Forest
Anne Klosterhalfen
Fernando Moyano
Matthias Cuntz
Andrew C. Manning
Alexander Knohl
Abstract. The O2 : CO2 exchange ratio (ER) between terrestrial ecosystems and the atmosphere is a key parameter for partitioning global ocean and land carbon fluxes. The long-term terrestrial ER is considered to be close to 1.10 moles of O2 consumed per mole of CO2 produced. Due to the technical challenge in measuring directly the ER of entire terrestrial ecosystems (EReco), little is known about the variations in ER at the hourly and seasonal scales as well as how different components contribute to EReco. In this modeling study, we explore the variability and drivers of EReco and evaluate the hypothetical uncertainty in determining ecosystem O2 fluxes based on current instrument precision. We adapted the one-dimensional, multi-layer atmosphere-biosphere gas exchange model, CANVEG, to simulate hourly EReco from modeled O2 and CO2 fluxes in a temperate beech forest in Germany.
We found that the annual mean EReco ranged from 1.06 to 1.12 mol mol-1 within the five years’ study period. Hourly EReco showed strong variations over diel and seasonal cycles and within the vertical canopy profile. Determination of ER from O2 and CO2 mole fractions in air above and within the canopy (ERconc) varied between 1.115 and 1.15 mol mol-1. CANVEG simulations also indicated that ecosystem O2 fluxes could be derived using the flux-gradient method in combination with measurements of vertical scalar gradients and CO2, sensible heat or latent heat fluxes obtained with the eddy covariance technique. Owing to measurement uncertainties, however, the uncertainty in estimated O2 fluxes derived with the flux-gradient approach could be as high as 15 μmol m-2 s-1, which represented the 90 % quantile of the uncertainty in hourly data with a high-accuracy instrument. We also demonstrated that O2 fluxes can be used to partition net CO2 exchange fluxes into their component fluxes of photosynthesis and respiration, if EReco is known. The uncertainty of the partitioned gross assimilation ranged from 1.43 to 4.88 μmol m-2 s-1 assuming a measurement uncertainty of 0.1 or 2.5 μmol m-2 s-1 for net ecosystem CO2 exchange and from 0.1 to 15 μmol m-2 s-1 for net ecosystem O2 exchange, respectively. Our analysis suggests that O2 measurements at ecosystem scale have the potential for partitioning net CO2 fluxes into their component fluxes, but further improvement in instrument precision is needed.
Yuan Yan et al.
Status: open (until 13 Apr 2023)
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RC1: 'Comment on bg-2023-30', Anonymous Referee #1, 07 Mar 2023
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The reviewed manuscript presents an interesting study that models the O2 and CO2 fluxes in and above a forest canopy, and aims to determine if the actual measurement of such fluxes will enable the partitioning of the CO2 fluxes into its components. This is a new and interesting modeling exercise, and the manuscript is generally well-written and clear.
Major comments:
I find the way the manuscript is structured somewhat confusing. In the method section, the effect of nitrate assimilation on the ER is ignored, and a fixed value for stem and soil ER is assumed, although the introduction mentioned a range found in field studies. That leaves the reader to wonder why these important variations are ignored. Then the results are detailed, based on the simplified assumption of a fixed ER, and only in the discussion, the variability in the sources ER is discussed in detail and a sensitivity test is performed. If the authors want to keep this structure, they should state clearly in the methods and the results sections, that the effect of variability in ER will be discussed and tested later. For me, it seems it will be even better if some of this discussion will be moved to the introduction and the sensitivity analysis will be included in the methods and results.
This issue has also important implications for the conclusions. If chamber studies at a given site show a constant and large difference between the respiration components, there is a much better chance to use the O2 approach for CO2 fluxes partitioning. Maybe this could be also demonstrated by a test run of the model.
Minor comments:
Line 35: I guess there are much older references for this, or this can be just assumed as common knowledge.
Line 49: How important is this 0.05 Pg uncertainty compared to other uncertainties, like the effect of ocean warming on O2 solubility?
Line 707: As in the major comments above – is it worth showing some sensitivity test for this?
Citation: https://doi.org/10.5194/bg-2023-30-RC1 -
AC1: 'Reply on RC1', Yuan Yan, 22 Mar 2023
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Dear referee,
thank you for your fast and helpful review. We will consider revising the structure as suggested to more clearly show why and how we used, for the most part, fixed exchange ratios (ER) as model parameters and their spatial and temporal variability as model output on ecosystem scale. We are aware of the role of N assimilation on ER but decided deliberately to leave this out of the current manuscript to keep a clear focus. We are currently working on a study investigating the N assimilation effect on ER variability. To illustrate the role of possible ER variability, we did a sensitivity analysis by testing a change of ±10% in ERA, ERstem, and ERsoil (model parameters) on the variation of O2 flux. We will move the sensitivity analysis to the methods and results sections to make it more obvious. Besides the impact on O2 fluxes, we will also test the effect of ER variability on flux partitioning in the sensitivity analysis. We still would expect that the main uncertainty on flux partitioning is caused by the uncertainty of measured O2 fluxes.
Minor comments:
line 35: Yes, we agree that there are older references. Nevertheless, we prefer these two references cited as they nicely summarize the exchange processes of O2 and CO2, both at the land and the ocean interface. But we will also have a look into older references and consider adding these too.
line 49: Following the reference Keeling and Manning (2014), ocean warming of 1 Watt per square meter of ocean area would lead to a correction of the global and ocean sinks by about 0.1 Pg C per year due to the combined N2 and O2 solubility effect (section 5.15.4.6 in Keeling and Manning, 2014, citing Manning, 2001). So, the 0.05 Pg C per year uncertainty due to the uncertainty in ER is smaller than the effect of O2 solubility under 1 Watt per square meter warming, nevertheless still relevant. A better understanding of the ER of land-atmosphere exchange could help to reduce this uncertainty.
line 707: We will add the magnitude of O2 flux variation and impact on simulated EReco and flux partitioning due to the change of ERA, ERstem, and ERsoil by ±10%, as mentioned above.
Citation: https://doi.org/10.5194/bg-2023-30-AC1
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AC1: 'Reply on RC1', Yuan Yan, 22 Mar 2023
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CC1: 'Comment on bg-2023-30', Andrew Kowalski, 13 Mar 2023
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If the paper by Yuan Yan et al. demonstrates valid simulations of ecosystem O2 fluxes, and furthermore provides understanding of the relationship between environmental drivers and O2 fluxes and O2:CO2 exchange ratios, it somehow manages these achievements despite completely misrepresenting the physics of O2 transport in the boundary layer.
The authors note that fluxes of O2 and CO2 between the terrestrial biosphere and atmosphere are inversely linked in terms of stoichiometry. But what is true about biogeochemistry is not necessarily true regarding turbulent transport. Rather O2, like CO2, generally diffuses downward, towards the forest canopy. This can be seen by examining the paper's data in units that are revelatory if unorthodox. From Figure 2, it is safe to say that H2O emissions of 98.2 mmol m-2 min-1 characterize a modest rate of evaporation (about 74 W m-2 of LE), while oxygen emissions of 1.8 mmol m-2 min-1 represent robust photosynthesis (30 μmol m-2 s-1). From these values, we can see clearly that a typical 1-m2 section of surface emits 100 mmol of gas during one minute, of which just 1.8% is O2, far lower than the 21% O2 that is typical in the atmosphere. Thus, surface emissions have a net effect of O2 dilution, and provoke its downward diffusion, even for the modest evaporation and vigorous photosynthesis that have been specified to illustrate this.
The values in the previous paragraph are expressed in the molar percentages perferred by the authors, despite the fact that it is the mass fraction that is diffusion's determinant (Kowalski et al. 2021). Subtle differences between mass fraction and molar fraction (due to molecular mass) do not affect the derived direction of diffusion when the O2 concentration of the atmosphere is an order of magnitude greater than that of gas emitted by the surface.
To be sure, despite downward O2 diffusion, net transport of O2 is upward because it is overwhelmingly non-diffusive. Evaporation plays two roles in determining the transport of any gas near the surface, those of dilution and displacement, the latter described by a Stefan flow measured in μm s-1 (Kowalski 2017). In the case of O2, whose surface exchange is miniscule considering its very high concentration, this tiny upward mean velocity can produce a huge O2 flux density, much of which is offset by downward O2 diffusion.
These issues of distinguishing between physical transport mechanisms are very relevant at different points within the paper, identified below. They are sometimes characterized in terms of discrimination against water vapour, which is what we do when defining fractions with reference to dry air (artificially removing water vapor from the denominator). Such discrimination is not appropriate when describing random motions that bring about mixing.Specific Comments by line number
23: "ecosystem O2 fluxes could be derived using the flux-gradient method in combination with measurements of vertical scalar gradients and CO2". The flux-gradient method is valid for transport by turbulence, which (unlike Yuan Yan et al. and most atmospheric chemists) does not discriminate against water vapor. Clearly, the authors have used molar fractions with reference to dry air, and this is not appropriate in the context of flux-gradient theory.
86: The authors note that the flux-gradient method "assumes that heat and mass are transported in a similar manner". This is certainly not the case for O2.
124: "CANVEG includes within-canopy transport of CO2, water vapor and energy (Baldocchi, 1997; Baldocchi and Wilson, 2001), so that if it were
adapted to O2 processes, one could evaluate the accuracy of different flux measurement techniques such as eddy covariance or flux-gradient approaches." The validity of adapting CANVEG for turbulent transport of O2 is highly dubious if it mischaracterizes the direction of the turbulent flux.
170: "Atmospheric O2 mole fraction (O2atm) as input for the model was deduced from a fixed O2:CO2 mole ratio of -1.15 mol mol-1 ... (Table 1)." This sentence demonstrates the methodological error behind the derivation of turbulent transport. The general comment above illustrates that the fraction of air that is O2, which determines the direction of turbulent transport, is overwhelmingly determined by H2O exchanges. Eliminating the effects of H2O exchanges, and working with the mole fraction with reference to dry air, completely invalidate this means of model parameterization with regard to turbulent transport.
246: "a multi-layer gas flux diffusion determined by a Lagrangian dispersion matrix" does not discriminate against water vapor, and therefore must use the O2 fraction with reference to moist air. That fraction should furthermore be defined in terms of mass, and not moles of moist air.
254: "The CANVEG simulations of ecosystem O2 fluxes and O2 mole fraction gradients provided the opportunity to test the applicability of the flux-gradient approach to estimate FO2." The arguments above demonstrate that this is incorrect. If CANVEG simulates ecosystem O2 fluxes and O2 mole fraction gradients, it is not thanks to properly applied flux-gradient theory.258-259: Equation (5) is not valid while mole fractions (ppm) are used, and furthermore expressed with reference to dry air.
266: O2 is not transported in a similar way.
472: In Figure 5, gradients are presented in different units: g m-3 for water vapor (i.e., it is an absolute humidity), versus ppm for CO2. There are good arguments for presenting the gases with identical units, at least in the context of flux-gradient theory.
642: "This guarantees that ...the eddy diffusivity of O2 is the same as of the other corresponding scalars". I believe this is not so. See general comment above.
710: "According to our simulations, it is feasible to derive ecosystem O2 fluxes with the flux-gradient approach". This seems to be overly optimistic, particularly as part of the Conclusions section, given the above criticism of the methods the authors have applied.Citation: https://doi.org/10.5194/bg-2023-30-CC1
Yuan Yan et al.
Yuan Yan et al.
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