Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign

Schulte, Ruben Bastiaan; Vilà-Guerau de Arellano, Jordi; van der Graaf, Shelley; Rutledge-Jonker, Susanna; Zhang, Jun; van Zanten, Margreet Catharijne

doi:https://doi.org/10.5194/egusphere-2023-1526

Preprints

https://doi.org/10.5194/egusphere-2023-1526

Preprints

20 Jul 2023

| 20 Jul 2023

Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign

Ruben Bastiaan Schulte, Jordi Vilà-Guerau de Arellano, Shelley van der Graaf, Susanna Rutledge-Jonker, Jun Zhang, and Margreet Catharijne van Zanten

Abstract. We present a comprehensive observational approach, aiming to establish relations between the surface-atmosphere exchange of ammonia (NH₃) and the CO₂ uptake and transpiration by vegetation. In doing so, we study relationships useful for the the improvement and development of NH₃ flux representations and their dependences. The NH₃ concentration and flux are measured using a novel open-path miniDOAS measurement setup, taken during the five week RITA-2021 campaign (25 August until 12 October 2021) at the Ruisdael Observatory at Cabauw, the Netherlands. After filtering for unobstructed flow, sufficient turbulent mixing and CO₂ uptake, we find the diurnal variability of the NH₃ flux to be characterized by daytime emissions (0.05 μg m^-2s^-1 on average) and deposition at sunrise and sunset (-0.05 μg m^-2 s^-1 on average). We first compare the NH₃ flux to the observed gross primary production (GPP), representing CO₂ uptake, and latent heat flux (L_vE), representing to evapotranspiration. Next we study the observations following the main drivers of the dynamic vegetation response, which are photosynthetically active radiation (PAR), temperature (T) and the water vapor pressure deficit (VPD). Our findings show indication of the dominance of stomatal emission of NH₃, with high correlation between the observed emissions and both net L_vE (0.70) and PAR (0.72), as well as close similarities in the diurnal variability of the NH₃ flux and GPP. However, the efforts to establish relationships are hampered due to the amount of diversity of NH₃ sources of the active agricultural region and low data availability after filtering. Our findings show the need to collocate meteorological, carbon and nitrogen studies to advance on our understanding of NH₃ deposition and its representation.

Received: 05 Jul 2023 – Discussion started: 20 Jul 2023

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Journal article(s) based on this preprint

26 Jan 2024

Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign

Ruben B. Schulte, Jordi Vilà-Guerau de Arellano, Susanna Rutledge-Jonker, Shelley van der Graaf, Jun Zhang, and Margreet C. van Zanten

Biogeosciences, 21, 557–574, https://doi.org/10.5194/bg-21-557-2024,https://doi.org/10.5194/bg-21-557-2024, 2024

Short summary

Ruben Bastiaan Schulte, Jordi Vilà-Guerau de Arellano, Shelley van der Graaf, Susanna Rutledge-Jonker, Jun Zhang, and Margreet Catharijne van Zanten

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1526', Anonymous Referee #1, 03 Oct 2023

general comments
This paper shows the relationship between stomatal activity and dynamics with respect to ammonia, exploring the correlations between several environmental variables involved in the process. I found the topic very interesting, and the work is well written: the data quality is very good in my opinion, and the data are explored very carefully, and discussed thoroughly. The authors addressed with the appropriate references the arguments posed by the study.
The weak point of this study is the poor temporal representativity of the dataset: this aspect is exposed in section 4.1, which I completely agree with. I think despite of the impossibility of setting new relationships to model the NH3 dynamics, this dataset provides a very useful verification of the known environmental dynamics, and especially shows very clearly what are the issues with NH3 measurements, setting a good standard for potential new measurements of NH3 fluxes.
Given the good quality of the paper, I agree with the authors that it is a shame the amount of data that needed to be rejected is so large: however, I believe this work is very worth publishing, not only for the scarcity of datasets on atmospheric ammonia fluxes in general (and even less of these standards!), but also for the analysis structure of the data, that provides a good methodology to be used not only by the future measurements that are taking places at the same site. Therefore I recommend its publication almost in its current shape, and I list below some minor points.
specific comments :
The authors operate a filtering procedure that leads to considering 9% of all data valid for the evaluation of ammonia dynamics. While I understand the logic of excluding data for all the reasons listed, I think eliminating 91% of data is a fierce manipulation exercise. The conclusions of such reasoning are, by definition, not representative of the behaviour of the vegetation as such, but of particular conditions. This is addressed in the discussion and conclusions, but I suggest to reinforce the fact that this kind of dataset should not be used to aid annual inventories, perhaps in the abstract (but I’d leave it to the authors’ and editor’s choice where to insert it).
technical corrections :
P4 L30: remove either “ammonia” or NH3 in the sentence.

Citation: https://doi.org/10.5194/egusphere-2023-1526-RC1
- AC2: 'Reply on RC1', M.C. van Zanten, 12 Nov 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1526/egusphere-2023-1526-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1526-AC2
RC2:
'Comment on egusphere-2023-1526, Schulte et al.', Anonymous Referee #2, 06 Oct 2023
The manuscript by Schulte et al. presents campaign data of ammonia measurements by miniDOAS instrumentation in a gradient setup together with CO2 and water vapor fluxes and meteorological parameters from the Ruisdael Observatory at Cabauw in the Netherlands. The authors investigate relationships between fluxes and meteorology and try to establish a link between NH3 and CO2 exchange through the stomatal pathway.
The approach is good and the dataset useful. Most of the text is well written and easy to follow. Methods are robust, figures are clear and easy to grasp. Ammonia flux measurements are still highly experimental and it is good to see more campaign data being presented using relatively novel methods with high accuracy concentration readings. The shown filtering scheme for high quality data assurance applied in the main analysis is thoroughly done. However, there are some shortcomings that need to be fixed to make this a study of broader interest for the ammonia flux and modelling community.

Major comments:
The campaign was conducted in a region with agricultural activity. The authors state that the observed emissions are likely originating from fertilization or animal droppings. This means that much of the exchange is due to volatilization directly from the surface (soil or fertilizer). The analysis, however, is solely associated with stomatal exchange of ammonia. Both pathways are known to exhibit distinct diurnal patterns, basically following the course of temperature, radiation, and turbulence, making a differentiation between stomatal and non-stomatal exchange difficult. In this view, I suggest to revisit the aims of the paper. A number of correlations between fluxes and meteorological parameters are shown, which is surely useful and some findings are impressive. But I clearly miss a coherent storyline apart from “establish relationships”. What is the focus of investigation? Much can be fixed by rephrasing, refining conclusions, and interpretation of the validity of the findings.

There seems to be a mixture of the terms “evaporation”, “transpiration”, “evapotranspiration”, and “latent heat flux”. Please check throughout the manuscript. It is really confusing. See also specific comments.

Ecosystem respiration strongly depends on soil temperature. Taking a campaign average to derive gross primary productivity may induce considerable uncertainty. Why didn’t you use one of the well-established partitioning methods of the flux community? See specific comments for further details.

Specific comments:
Page 1, Line 14: “flux representations”, what is meant here? Flux representations in models? Please clarify.

Page 2, Line 49: How was the accuracy of the 30-min average concentration determined? Is it a statistical parameter based on a calibration procedure? Is the number coming from own test? Please provide more information.

Overall, Figure 1 is very informative, but what is meant by “larger structures” (see caption)? Also, in the legend, is the unit kg N per year or kg NH3 per year?

Page 3, Line 8: What has the McDermitt et al. (2011) paper to do with standard Fluxnet methodology? The paper is about a novel open-path methane instrument. Please check.

Table 1 is very nice, but it should be described that the given filters are applied in series and after applying all of them, 9% of the data pass the filter (if I got it correct). Otherwise, it could lead to misunderstanding.

Page 4, Line 11: How do you get to a number of 0.01 ug m-2 s-1? Please describe the procedure. And what exactly do you mean by “accuracy”? Is it the flux detection limit?

Page 4, Line 18: Sentence starting with “While these processes…”: What’s the message? Either elaborate a bit more and add context or delete.

Page 4, Line 33: NEE = GPP + RESP, check sign convention. GPP and RESP are usually given as positive fluxes, then it should either be NEE = GPP – RESP (biological sign convention) indicating that a positive NEE represents a net carbon gain for the ecosystem or NEE = RESP – GPP (atmospheric sign convention) indicating that a positive NEE represents a net carbon gain for the atmosphere.

Page 4, Line 34: Average campaign nighttime flux? Why do you do that? Ecosystem respiration strongly depends on soil temperature. This may induce considerable uncertainty on your GPP estimates. Why didn’t you use the one of the well-established methods in the flux community based on either daytime (Lasslop et al., 2010) or nighttime data (Reichstein et al., 2005) for partitioning measured NEE into GPP and RESP? See also Wutzler et al. (2018) and Pastorello et al. (2020).

Page 4, Line 37: In the context of CO2, I think it is not “deposition”. Please replace with “uptake” or “sequestration”.

Table 2 caption: “Any” number? Consider rephrasing to “at which the observational data passes the filters”.

Table 2 caption: I do not understand the whole sentence starting with “A requirement of non-zero…”. Please rephrase.

Table 2 caption: What is a “footprint anemometer”?

Table 2: The entry “Daytime maximum sonic anemometer footprint (70%)” requires more explanation.

Page 5, Line 7: Please describe what “are actively managed for the agricultural activity” means.

Page 5, Line 16: See my comment on Table 2. This sentence is hard to understand. I would at least suggest to add that 70% represents the value of the isoline confining the area that contributed 70% to the measured flux (if that is what you mean).

Page 5, Line 24: For clarification, I suggest to replace “observations” in the title by “concentrations”.

Page 5, Lines 26-27: I think I understand what you mean with “long tail”, but I’m not sure this is a good expression. Consider rephrasing to something like “The histogram shows a highly skewed distribution with most concentrations being lower than 7 ug m-3 and a strong frequency decline for values >7 ug m-3.”

Page 6, Line 3: “the diurnal variability”, do you mean “their diurnal variability”? Otherwise, where does “variability” refer to?

Page 6, Lines 10-19: This is text book or literature repetition. Is it really needed? Together with the rest of Section 2.4 it appears a bit incoherent and as a sequence of rather loose facts.

Page 6, Line 20: Why sonic temperature? It is not the same as air temperature.

Page 6, Line 36: Sentence starting with “While there were only small variations…”: check grammar, there is something wrong.

Page 6, Line 40: “observations” – do you mean “concentrations”?

Page 6, Lines 47-50: I’m not sure I can follow the reasoning here. LvE is not just transpiration, but also evaporation from soil and water droplets, which has nothing to do with stomatal exchange of water. I think the two sentences are not wrong as they are, but I don’t understand the message.

Figure 2 caption: Add at the end: “…i.e., negative numbers indicate deposition and positive numbers indicate emission.”

Figure 3 caption: “observed NH3” – “observed NH3 concentration”?

Page 8, Line 16: “evaporation”? Aren’t you talking about “transpiration”? Plotted in Figure 4b is LvE measured by eddy covariance, which is evapotranspiration, i.e., evaporation plus transpiration, right? Please clarify.

Page 8, Line 20: “between 10 and 30%” – where do these numbers come from?

Page 9, Line 1: “…the diurnal variability influences the correlation coefficient” – What’s the reasoning here? Please explain. Have you checked for hysteresis? Looking at Figure 4c, it may well be that on single days FNH3 is lagging behind H by probably a few half hours causing a hysteretic relationship between the two. Accounting for the lag (if there is one), may significantly increase the correlation coefficient.

Figure 5: What do correlations between temperature and LvE, GPP, and H tell us about drivers of and correlations between FNH3 and GPP, ET?

Figure 6 caption: Has “ABL” been defined before?

Page 11, Line 3 and Line 4: “evaporation” – again, aren’t we looking at evapotranspiration? Please make it clear here and throughout the manuscript that there is a difference between transpiration and evaporation, and that LvE represents the sum called evapotranspiration.

Page 13, Lines 2-6: Needed? This is well known and should be shortened.

Page 13, Lines 30-43: See my major comment on this topic.

Page 14, Lines 18-25: I appreciate the honesty here and that the limitations of the study are clearly pointed out. However, I disagree with the statement that the ammonia flux could be linked to stomatal exchange.

Page 15, Lines 26-27: “we managed to attribute the observed NH3 emission to stomatal exchange and identify outliers” – again, I’m not convinced.

Conclusion section: Most of the section sounds like a summary with many repetitions that the reader has seen before. Please shorten and concentrate on real conclusions, i.e., what have we learned, what is new?

Technical corrections:
Page 1, Line 14: Remove the second “the”.

Page 1, Line 14: “dependencies”

Page 1, Line 20: Remove “to” before “evapotranspiration”.

Page 2, Line 27: Either “These surface exchanges” or “This surface exchange”.

Page 4, Line 4: “of” between “reduction” and “data” missing.

Page 4, Line 7: “by” between “characterized” and “frontal” missing.

Page 4, Line 15: Singular “pathway”.

Page 4, Line 16: Check phrasing. What depends on RH? The external leaf pathway? If so, consider replacing “depending” with “and depends on”.

Page 4, Line 19: “millimeter”?

References:
Lasslop et al. (2010), https://doi.org/10.1111/j.1365-2486.2009.02041.x
Pastorello et al. (2020), https://www.nature.com/articles/s41597-020-0534-3;
Reichstein et al. (2005), https://doi.org/10.1111/j.1365-2486.2005.001002.x
Wutzler et al. (2018), https://doi.org/10.5194/bg-15-5015-2018
Citation: https://doi.org/10.5194/egusphere-2023-1526-RC2
- AC1: 'Reply on RC2', M.C. van Zanten, 12 Nov 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1526/egusphere-2023-1526-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1526-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1526', Anonymous Referee #1, 03 Oct 2023

general comments
This paper shows the relationship between stomatal activity and dynamics with respect to ammonia, exploring the correlations between several environmental variables involved in the process. I found the topic very interesting, and the work is well written: the data quality is very good in my opinion, and the data are explored very carefully, and discussed thoroughly. The authors addressed with the appropriate references the arguments posed by the study.
The weak point of this study is the poor temporal representativity of the dataset: this aspect is exposed in section 4.1, which I completely agree with. I think despite of the impossibility of setting new relationships to model the NH3 dynamics, this dataset provides a very useful verification of the known environmental dynamics, and especially shows very clearly what are the issues with NH3 measurements, setting a good standard for potential new measurements of NH3 fluxes.
Given the good quality of the paper, I agree with the authors that it is a shame the amount of data that needed to be rejected is so large: however, I believe this work is very worth publishing, not only for the scarcity of datasets on atmospheric ammonia fluxes in general (and even less of these standards!), but also for the analysis structure of the data, that provides a good methodology to be used not only by the future measurements that are taking places at the same site. Therefore I recommend its publication almost in its current shape, and I list below some minor points.
specific comments :
The authors operate a filtering procedure that leads to considering 9% of all data valid for the evaluation of ammonia dynamics. While I understand the logic of excluding data for all the reasons listed, I think eliminating 91% of data is a fierce manipulation exercise. The conclusions of such reasoning are, by definition, not representative of the behaviour of the vegetation as such, but of particular conditions. This is addressed in the discussion and conclusions, but I suggest to reinforce the fact that this kind of dataset should not be used to aid annual inventories, perhaps in the abstract (but I’d leave it to the authors’ and editor’s choice where to insert it).
technical corrections :
P4 L30: remove either “ammonia” or NH3 in the sentence.

Citation: https://doi.org/10.5194/egusphere-2023-1526-RC1
- AC2: 'Reply on RC1', M.C. van Zanten, 12 Nov 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1526/egusphere-2023-1526-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1526-AC2
RC2:
'Comment on egusphere-2023-1526, Schulte et al.', Anonymous Referee #2, 06 Oct 2023
The manuscript by Schulte et al. presents campaign data of ammonia measurements by miniDOAS instrumentation in a gradient setup together with CO2 and water vapor fluxes and meteorological parameters from the Ruisdael Observatory at Cabauw in the Netherlands. The authors investigate relationships between fluxes and meteorology and try to establish a link between NH3 and CO2 exchange through the stomatal pathway.
The approach is good and the dataset useful. Most of the text is well written and easy to follow. Methods are robust, figures are clear and easy to grasp. Ammonia flux measurements are still highly experimental and it is good to see more campaign data being presented using relatively novel methods with high accuracy concentration readings. The shown filtering scheme for high quality data assurance applied in the main analysis is thoroughly done. However, there are some shortcomings that need to be fixed to make this a study of broader interest for the ammonia flux and modelling community.

Major comments:
The campaign was conducted in a region with agricultural activity. The authors state that the observed emissions are likely originating from fertilization or animal droppings. This means that much of the exchange is due to volatilization directly from the surface (soil or fertilizer). The analysis, however, is solely associated with stomatal exchange of ammonia. Both pathways are known to exhibit distinct diurnal patterns, basically following the course of temperature, radiation, and turbulence, making a differentiation between stomatal and non-stomatal exchange difficult. In this view, I suggest to revisit the aims of the paper. A number of correlations between fluxes and meteorological parameters are shown, which is surely useful and some findings are impressive. But I clearly miss a coherent storyline apart from “establish relationships”. What is the focus of investigation? Much can be fixed by rephrasing, refining conclusions, and interpretation of the validity of the findings.

There seems to be a mixture of the terms “evaporation”, “transpiration”, “evapotranspiration”, and “latent heat flux”. Please check throughout the manuscript. It is really confusing. See also specific comments.

Ecosystem respiration strongly depends on soil temperature. Taking a campaign average to derive gross primary productivity may induce considerable uncertainty. Why didn’t you use one of the well-established partitioning methods of the flux community? See specific comments for further details.

Specific comments:
Page 1, Line 14: “flux representations”, what is meant here? Flux representations in models? Please clarify.

Page 2, Line 49: How was the accuracy of the 30-min average concentration determined? Is it a statistical parameter based on a calibration procedure? Is the number coming from own test? Please provide more information.

Overall, Figure 1 is very informative, but what is meant by “larger structures” (see caption)? Also, in the legend, is the unit kg N per year or kg NH3 per year?

Page 3, Line 8: What has the McDermitt et al. (2011) paper to do with standard Fluxnet methodology? The paper is about a novel open-path methane instrument. Please check.

Table 1 is very nice, but it should be described that the given filters are applied in series and after applying all of them, 9% of the data pass the filter (if I got it correct). Otherwise, it could lead to misunderstanding.

Page 4, Line 11: How do you get to a number of 0.01 ug m-2 s-1? Please describe the procedure. And what exactly do you mean by “accuracy”? Is it the flux detection limit?

Page 4, Line 18: Sentence starting with “While these processes…”: What’s the message? Either elaborate a bit more and add context or delete.

Page 4, Line 33: NEE = GPP + RESP, check sign convention. GPP and RESP are usually given as positive fluxes, then it should either be NEE = GPP – RESP (biological sign convention) indicating that a positive NEE represents a net carbon gain for the ecosystem or NEE = RESP – GPP (atmospheric sign convention) indicating that a positive NEE represents a net carbon gain for the atmosphere.

Page 4, Line 34: Average campaign nighttime flux? Why do you do that? Ecosystem respiration strongly depends on soil temperature. This may induce considerable uncertainty on your GPP estimates. Why didn’t you use the one of the well-established methods in the flux community based on either daytime (Lasslop et al., 2010) or nighttime data (Reichstein et al., 2005) for partitioning measured NEE into GPP and RESP? See also Wutzler et al. (2018) and Pastorello et al. (2020).

Page 4, Line 37: In the context of CO2, I think it is not “deposition”. Please replace with “uptake” or “sequestration”.

Table 2 caption: “Any” number? Consider rephrasing to “at which the observational data passes the filters”.

Table 2 caption: I do not understand the whole sentence starting with “A requirement of non-zero…”. Please rephrase.

Table 2 caption: What is a “footprint anemometer”?

Table 2: The entry “Daytime maximum sonic anemometer footprint (70%)” requires more explanation.

Page 5, Line 7: Please describe what “are actively managed for the agricultural activity” means.

Page 5, Line 16: See my comment on Table 2. This sentence is hard to understand. I would at least suggest to add that 70% represents the value of the isoline confining the area that contributed 70% to the measured flux (if that is what you mean).

Page 5, Line 24: For clarification, I suggest to replace “observations” in the title by “concentrations”.

Page 5, Lines 26-27: I think I understand what you mean with “long tail”, but I’m not sure this is a good expression. Consider rephrasing to something like “The histogram shows a highly skewed distribution with most concentrations being lower than 7 ug m-3 and a strong frequency decline for values >7 ug m-3.”

Page 6, Line 3: “the diurnal variability”, do you mean “their diurnal variability”? Otherwise, where does “variability” refer to?

Page 6, Lines 10-19: This is text book or literature repetition. Is it really needed? Together with the rest of Section 2.4 it appears a bit incoherent and as a sequence of rather loose facts.

Page 6, Line 20: Why sonic temperature? It is not the same as air temperature.

Page 6, Line 36: Sentence starting with “While there were only small variations…”: check grammar, there is something wrong.

Page 6, Line 40: “observations” – do you mean “concentrations”?

Page 6, Lines 47-50: I’m not sure I can follow the reasoning here. LvE is not just transpiration, but also evaporation from soil and water droplets, which has nothing to do with stomatal exchange of water. I think the two sentences are not wrong as they are, but I don’t understand the message.

Figure 2 caption: Add at the end: “…i.e., negative numbers indicate deposition and positive numbers indicate emission.”

Figure 3 caption: “observed NH3” – “observed NH3 concentration”?

Page 8, Line 16: “evaporation”? Aren’t you talking about “transpiration”? Plotted in Figure 4b is LvE measured by eddy covariance, which is evapotranspiration, i.e., evaporation plus transpiration, right? Please clarify.

Page 8, Line 20: “between 10 and 30%” – where do these numbers come from?

Page 9, Line 1: “…the diurnal variability influences the correlation coefficient” – What’s the reasoning here? Please explain. Have you checked for hysteresis? Looking at Figure 4c, it may well be that on single days FNH3 is lagging behind H by probably a few half hours causing a hysteretic relationship between the two. Accounting for the lag (if there is one), may significantly increase the correlation coefficient.

Figure 5: What do correlations between temperature and LvE, GPP, and H tell us about drivers of and correlations between FNH3 and GPP, ET?

Figure 6 caption: Has “ABL” been defined before?

Page 11, Line 3 and Line 4: “evaporation” – again, aren’t we looking at evapotranspiration? Please make it clear here and throughout the manuscript that there is a difference between transpiration and evaporation, and that LvE represents the sum called evapotranspiration.

Page 13, Lines 2-6: Needed? This is well known and should be shortened.

Page 13, Lines 30-43: See my major comment on this topic.

Page 14, Lines 18-25: I appreciate the honesty here and that the limitations of the study are clearly pointed out. However, I disagree with the statement that the ammonia flux could be linked to stomatal exchange.

Page 15, Lines 26-27: “we managed to attribute the observed NH3 emission to stomatal exchange and identify outliers” – again, I’m not convinced.

Conclusion section: Most of the section sounds like a summary with many repetitions that the reader has seen before. Please shorten and concentrate on real conclusions, i.e., what have we learned, what is new?

Technical corrections:
Page 1, Line 14: Remove the second “the”.

Page 1, Line 14: “dependencies”

Page 1, Line 20: Remove “to” before “evapotranspiration”.

Page 2, Line 27: Either “These surface exchanges” or “This surface exchange”.

Page 4, Line 4: “of” between “reduction” and “data” missing.

Page 4, Line 7: “by” between “characterized” and “frontal” missing.

Page 4, Line 15: Singular “pathway”.

Page 4, Line 16: Check phrasing. What depends on RH? The external leaf pathway? If so, consider replacing “depending” with “and depends on”.

Page 4, Line 19: “millimeter”?

References:
Lasslop et al. (2010), https://doi.org/10.1111/j.1365-2486.2009.02041.x
Pastorello et al. (2020), https://www.nature.com/articles/s41597-020-0534-3;
Reichstein et al. (2005), https://doi.org/10.1111/j.1365-2486.2005.001002.x
Wutzler et al. (2018), https://doi.org/10.5194/bg-15-5015-2018
Citation: https://doi.org/10.5194/egusphere-2023-1526-RC2
- AC1: 'Reply on RC2', M.C. van Zanten, 12 Nov 2023
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1526/egusphere-2023-1526-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2023-1526-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Publish subject to minor revisions (review by editor) (14 Nov 2023) by Ivonne Trebs

AR by M.C. van Zanten on behalf of the Authors (27 Nov 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (29 Nov 2023) by Ivonne Trebs

AR by M.C. van Zanten on behalf of the Authors (02 Dec 2023) Author's response Manuscript

Journal article(s) based on this preprint

26 Jan 2024

Observational relationships between ammonia, carbon dioxide and water vapor under a wide range of meteorological and turbulent conditions: RITA-2021 campaign

Ruben B. Schulte, Jordi Vilà-Guerau de Arellano, Susanna Rutledge-Jonker, Shelley van der Graaf, Jun Zhang, and Margreet C. van Zanten

Biogeosciences, 21, 557–574, https://doi.org/10.5194/bg-21-557-2024,https://doi.org/10.5194/bg-21-557-2024, 2024

Short summary

Ruben Bastiaan Schulte, Jordi Vilà-Guerau de Arellano, Shelley van der Graaf, Susanna Rutledge-Jonker, Jun Zhang, and Margreet Catharijne van Zanten

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Short summary

We analysed measurements, aiming to find relations between the surface atmosphere exchange of NH₃ and the CO₂ uptake and transpiration by vegetation. We found high correlation between the daytime NH₃ emissions and latent heat flux and the photosynthetically active radiation. There exist very few simultaneous measurements of NH₃, CO₂ fluxes and meteorological variables at subdiurnal timescales. This study paves the way for finding more robust relations between NH₃ exchange flux and CO₂ uptake.


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