the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jan 2024
Canopy structure modulates the sensitivity of subalpine forest stands to interannual snowpack and precipitation variability
Abstract. Declining spring snowpack is expected to have widespread effects on montane and subalpine forests in western North America and across the globe. However, the effect of this forcing at the species and hillslope scale are difficult to predict from remote sensing or eddy covariance. Here, we present data from a network of sap velocity sensors and xylem water isotope measurements from three common subalpine tree species (Picea engelmannii, Abies lasiocarpa, Populus tremuloides) across a hillslope transect in a subalpine watershed in the Upper Colorado River Basin. We use these data to compare tree- and stand-level responses to the historically high spring snowpack but low summer rainfall of 2019 against the low spring snowpacks but high summer rains of 2021 and 2022. From the sap velocity data, we found that only 40 % of the trees showed an increase in cumulative transpiration in response to the large snowpack year (2019), illustrating the absence of a common response to a major decline in snowpack. The trees that benefited from the large snow year were all found in dense canopy stands – irrespective of species – while trees in open canopy stands were more active during the years with modest snow and higher summer rains. This pattern reflects how persistent access to soil moisture recharged by snowmelt in topographically-mediated convergence zones shapes stand density. These locally dense canopies also experience high levels of summer rainfall interception that reduce summer precipitation inputs to the soil perpetuating their greater sensitivity to snowmelt inputs. The results illustrate that the progression towards a low snowpack future will manifest at the sub-hillslope scale in dense stands with significant rainfall interception and high water demands reflecting their historical reliance on snowmelt water.
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RC1: 'Comment on egusphere-2023-3063', Anonymous Referee #1, 19 Feb 2024
The document titled.
"Canopy structure modulates the sensitivity of subalpine forest stands to interannual snowpack and precipitation variability."
This article thoroughly studies the effects of varying snowpack and summer rainfall on subalpine forests, focusing on three common tree species. It utilizes sap velocity sensors and xylem water isotope measurements to compare tree and stand-level responses with contrasting weather conditions across different years.
Methodology The variables measured included Sap velocity, xylem water isotopes (δ18O and δ2H), canopy density, and meteorological data (temperature, humidity, snow depth, and precipitation).
The techniques used are based on installing sap velocity sensors across a hillslope transect, cryogenic water extraction for isotopic analysis, airborne LiDAR scanning for canopy structure measurement, and a Bayesian mixing model to assess water source proportions.
The results were then focused on the sap velocity data revealing diurnal and seasonal cycles, with variations between species and across years reflecting different precipitation conditions.
The isotopic data indicated a mix of water sources with a significant reliance on snowmelt, varying by species and canopy cover over the seasons.
The main discussion points, in my opinion, the authors highlight the nuanced response of forest stands to changes in snowpack and summer precipitation, emphasizing the role of canopy structure in modulating water use strategies.
They found that forests with dense canopies are more sensitive to snowmelt, benefiting from high spring snowpack. In contrast, open canopies are more active during years with higher summer rains due to the less canopy interception during summer.
Here are some possible overlooked aspects; the paper might benefit from addressing the long-term climate change implications on these dynamics in more detail, such as the potential for increased frequency of droughts or low snowpack years and its impact on forest ecosystems. Also, some crucial figures in this version seem to be a bit too small, making it difficult to read without zooming a lot.
Comments by line.
Line 35, incomplete citation. (and more citations also inclmplete on the ms.)
The hypothesis in line 90 is not fully clear to me. Does it mean that the nature of water availability will determine the stand sensitivity to changes in annual precipitation distribution? The second hypothesis made me think about the fact that canopy structure is a much more complex and dynamic outcome that depends on site conditions and not just water availability. Is having access to snowmelt indicative of deeper and better soil conditions?
Line 130 related to the deuterium correction might be a good idea to compare notes with https://hess.copernicus.org/articles/26/5835/2022/ that seem to relate this methodological "offset" with the volume of the water samples exposed to the CVD.
In methods, we have the seasonal origin index, which has not been introduced or has been introduced well enough.
Figure 2: I recommend using one more color on the gradient of A, b, and C so the upper sap velocities >40 are easily distinguished between trees. Also, I would add the months to the figures. Not everyone is familiar with relating DOY and the months…
Figure 4 could be larger for easy reading, and the dots of stem water might be better to plot with smaller dots and maybe somewhat transparent. (Just a suggestion). Also, what is the difference between SOI and the relative or reliance snowmelt use? Maybe it would be nice to make this clearer, as I am getting confused with the rest of the figures
Line 250: I am not sure if I am following why there is reliance on seasonal rain if the snowmelt represents 80% of the annual.
Line 365, maybe there is a bit of an offset between this and the message from the introduction. Maybe it would be good for the readers to be able to connect more clearly with this reminder on the discussion, and the canopy structure is supposed to be a key element of the manuscript, which was a bit in the shadows, in my opinion, throughout the manuscript also edit the citations years.
Line 385 might be something to consider: reference the result sections to some figures or particular sections where this is supported instead of sending the readers to the supplementary info. It seems like this should be an important part of the manuscript.
On the supplemental material,
I would recommend adding a list of the figures in the first page.
FigS2 seems to be cut on the text above the figure also could be nice to make reference to the literature and the methods on how this was addressed.
FigS3 here is something related to the SOI and relative contribution that was creating some confusion explained above, where it was not clear to me why the SOI is used in the main ms.
FigS6. I'm not sure if the y-axis labels are correct in both figures.
Citation: https://doi.org/10.5194/egusphere-2023-3063-RC1 -
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3063/egusphere-2023-3063-AC1-supplement.pdf
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AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
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RC2: 'Comment on egusphere-2023-3063', Anonymous Referee #2, 06 Mar 2024
Review of Berkelhammer et al for HESS
This article leverages diverse field measurements concentrated in a single site to explore complex interactions between species responsiveness to moisture available, canopy structural affects on water fluxes, and interannual variability in water availability as controlled by snow. The study uncovers some interesting trends (those captured in Figure 3 and Figure 6, for example), and presents other findings that could be valuable to many readers and the field at large. However, I found four critical issues with manuscript, the first of which seems unfixable.
1) The overall narrative and conclusions go beyond what should be inferred from the data. While the authors admit that the investigation was not established to pursue the narrative as it is presented, the truer statement is that the investigation falls short of what is needed to support the arguments regarding the role of canopy structure (which is reinforced by the scarcity of statistical tests).
Far more study sites with varying ecosystem structure would be needed to support the argument that topography shapes density, or that density relates to sensitivity, let alone to assert “the progression towards a low snowpack future will manifest at the sub-hillslope scale in dense stands”. The story-telling narrative style involves of web of speculations, making it challenging to connect evidence with statements made throughout. A reasonable level of evidence is crucially unmet in many areas. One key result is presented in line 281-283, “we see that the response of trees to the large spring snowpack of 2019 can best be predicted based on whether a given tree was located in one of the dense stands (i.e. where sapwood to ground area was ∼38 cm2 m2) or open stands (i.e. sapwood to ground area ∼12 m2 m2) (Figure 6)”; let’s ignore the typo (it’s not “m2 m2” but “cm^2 m^-2” ). Figure 6b shows 5 data points, and there is no aspen stand with low density and there is no mixed stand with high density, and thus there is no variation in density for a given composition of stand. This alone invalidates the primary argument of the paper (especially because we can see that species does matter in Figure 6a, Figure 4, and Figure 2). This critically small sample size does not allow for assessing stand effects.
2) There are major communications issues throughout, including use of imprecise language, omitted details, and insufficient copy editing.
Throughout, there is much suggestive and imprecise language, making it challenging to interpret and certainly not reproducible. Let us start with a few examples of uninterpretable statements:
-“we consolidate the numerous processes acting across sites by collapsing the measurement clusters…” (page 4 line 84)
-“it is also common to see areas where winter precipitation and groundwater may be of equal or greater importance to the species’ water demands illustrating an overriding impact of hillslope context relative to species-specific traits” (line 64-65)
-“The sensitivity of annual tree activity to snow inputs did not clearly map onto species or elevational position on the hillslope.” (line 280)
I do not know what these statements mean. The imprecise language is even evident in the (arguably) most important sentences of the paper, with these two hypotheses: “Within this context, we first test two fundamental hypotheses: (1) the seasonal origin of water used by trees influences a stand’s sensitivity to changes in precipitation seasonality and (2) that canopy structure is a reflection of the source of water accessible to the trees in that stand.” I cannot understand how hypotheses of “influences” and “is a reflection” could be tested, establishing murkiness early on.
It is not just a writing-style issue because there are also major omissions and indicators of insufficient proofreading. Grammar problems occur throughout (e.g., a lack of appropriate punctuation). Methodological details are too vague (although the Methods section is otherwise nicely structured). The sap flow study design is insufficiently communicated as it is unclear which trees and which trees in which sites were actually instrumented (Figure 1 only partially helps because there are places where the yellow circle could be over a deciduous or needleleaf tree, and there are issues such as only two yellow circles being visible in a site that supposedly had 4 to 6 trees instrumented). It repeatedly says that ‘live stems’ were sampled for isotope data, which seems unfathomable for mature trees (were they live twigs? cores from live stems?). Lines 156-157 require more details because I do not know what they mean or how they apply to the Bayesian approach used. Figure 3 axes labels are unclear and/or inaccurate. Figure 7 has a polynomial fit but what order is that polynomial and why is it justified? What are the error bars in Figure 7B? Figure 2 has no x-axis labels on some panels. It is dubious for such problems to occur despite this paper having so many (native English speaking) authors approve its submission.
3) There are citation problems. Many of the citation choices are odd; for example, often papers are cited for ideas they mention as opposed what they actually demonstrate. E.g., Brooks et al., 2015 is overused and is used in odd places. Another odd one was Graup et al., 2022 (page 4 line 87), cited (I think) because Graup et al make a related assumptions; but, they do not show evidence to justify those assumptions. There are many of these throughout. Another common occurrence was confusing use of past citations when referring to the present study: e.g. “is a critical absence in terms of our capacity to close the transpiration budget (Cooper et al., 2020)” (213, page 8). How can a paper from 4 years ago be cited regarding this study’s inability to close the balance? It needs to be clear why it is being cited. A similar issue arises again sentences later: “we estimate that aggregate hillslope water use was more similar between the conifers and aspens than implied from Figure 2 (Pataki et al., 2000). I do not know what the authors mean by this. There are many of these atypical citation usages in the discussion. The figures need to be more precisely cited in the text too; often figures would be referenced but it was unclear why.
Despite the main conclusions relating to stand structure, very little literature on stand structure and its relationships with physiology, anatomy, and functional ecology is cited. The authors could greatly benefit from considering, for example, works by Hank Margolis and Jim Long on relationships between stand structure, sapwood area, and sapwood permeability that are problematically not considered in the interpretation of data shown in Figure 6. Statements such as “Much of the way we have come to understand the sensitivity of forests in the region to precipitation variability is through a handful of longer eddy covariance records (e.g. (Knowles et al., 2015)) and analysis of satellite greenness indices (e.g. (Trujillo et al., 2012))” are incorrect, as there have been decades of research on tree hydraulics using tree level or leaf level techniques to study drought responses in forests (also note that Trujillo et al did not focus on forests in this region!).
4) I am unclear how the authors justify the conclusions about interception and its relationship with stand structure.
It seems that inferences on interception are made from soil moisture data, and few details are provided on these soil moisture measurements. How many soil water measurements were used per plot? How do the soil moisture plots differ from the other study plots? How does density vary across the soil moisture plots? Is there enough spatial coverage by soil moisture sensors to rule out the possibility that the moisture differences are not random, or are not influenced by points where water drips from the canopy?
More importantly, the choice of data presentation is highly concerning. Data were measured at 5 15 and 50 cm depths, and the values in Figure 5 show interpolations from that 5cm-50cm range. Given that the axis bounds are 5 (the upper y-axis limit) to 50 (the lower), essentially all of the range shown is influenced by the values at the 15-cm depth. It appears that the pattern being interpreted as moisture differences are mostly only a product of measurements at that one depth (15 cm). However, the y-axis seemingly uses a strange non-linear scale to emphasize the region of interpolated data most influenced by that 15 cm depth (15 +/- 10 makes up ~75% of the height of the figure!). So, the blue patches in summer (Figure 5 c), critical to core arguments of this paper, may only be a product of measurements at 1 depth (15 cm), and effect is visually stretched to skew what readers might perceive. The axis scale and the interpolation approach together create a misleading picture.
In conclusion: I think the findings could be presented in an entirely rewritten manuscript that might be acceptable for publication. There are interesting data and findings shown but their presentation would likely have to be as an entirely new manuscript because of the major issues cited above.
Citation: https://doi.org/10.5194/egusphere-2023-3063-RC2 -
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3063/egusphere-2023-3063-AC1-supplement.pdf
-
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
Status: closed
-
RC1: 'Comment on egusphere-2023-3063', Anonymous Referee #1, 19 Feb 2024
The document titled.
"Canopy structure modulates the sensitivity of subalpine forest stands to interannual snowpack and precipitation variability."
This article thoroughly studies the effects of varying snowpack and summer rainfall on subalpine forests, focusing on three common tree species. It utilizes sap velocity sensors and xylem water isotope measurements to compare tree and stand-level responses with contrasting weather conditions across different years.
Methodology The variables measured included Sap velocity, xylem water isotopes (δ18O and δ2H), canopy density, and meteorological data (temperature, humidity, snow depth, and precipitation).
The techniques used are based on installing sap velocity sensors across a hillslope transect, cryogenic water extraction for isotopic analysis, airborne LiDAR scanning for canopy structure measurement, and a Bayesian mixing model to assess water source proportions.
The results were then focused on the sap velocity data revealing diurnal and seasonal cycles, with variations between species and across years reflecting different precipitation conditions.
The isotopic data indicated a mix of water sources with a significant reliance on snowmelt, varying by species and canopy cover over the seasons.
The main discussion points, in my opinion, the authors highlight the nuanced response of forest stands to changes in snowpack and summer precipitation, emphasizing the role of canopy structure in modulating water use strategies.
They found that forests with dense canopies are more sensitive to snowmelt, benefiting from high spring snowpack. In contrast, open canopies are more active during years with higher summer rains due to the less canopy interception during summer.
Here are some possible overlooked aspects; the paper might benefit from addressing the long-term climate change implications on these dynamics in more detail, such as the potential for increased frequency of droughts or low snowpack years and its impact on forest ecosystems. Also, some crucial figures in this version seem to be a bit too small, making it difficult to read without zooming a lot.
Comments by line.
Line 35, incomplete citation. (and more citations also inclmplete on the ms.)
The hypothesis in line 90 is not fully clear to me. Does it mean that the nature of water availability will determine the stand sensitivity to changes in annual precipitation distribution? The second hypothesis made me think about the fact that canopy structure is a much more complex and dynamic outcome that depends on site conditions and not just water availability. Is having access to snowmelt indicative of deeper and better soil conditions?
Line 130 related to the deuterium correction might be a good idea to compare notes with https://hess.copernicus.org/articles/26/5835/2022/ that seem to relate this methodological "offset" with the volume of the water samples exposed to the CVD.
In methods, we have the seasonal origin index, which has not been introduced or has been introduced well enough.
Figure 2: I recommend using one more color on the gradient of A, b, and C so the upper sap velocities >40 are easily distinguished between trees. Also, I would add the months to the figures. Not everyone is familiar with relating DOY and the months…
Figure 4 could be larger for easy reading, and the dots of stem water might be better to plot with smaller dots and maybe somewhat transparent. (Just a suggestion). Also, what is the difference between SOI and the relative or reliance snowmelt use? Maybe it would be nice to make this clearer, as I am getting confused with the rest of the figures
Line 250: I am not sure if I am following why there is reliance on seasonal rain if the snowmelt represents 80% of the annual.
Line 365, maybe there is a bit of an offset between this and the message from the introduction. Maybe it would be good for the readers to be able to connect more clearly with this reminder on the discussion, and the canopy structure is supposed to be a key element of the manuscript, which was a bit in the shadows, in my opinion, throughout the manuscript also edit the citations years.
Line 385 might be something to consider: reference the result sections to some figures or particular sections where this is supported instead of sending the readers to the supplementary info. It seems like this should be an important part of the manuscript.
On the supplemental material,
I would recommend adding a list of the figures in the first page.
FigS2 seems to be cut on the text above the figure also could be nice to make reference to the literature and the methods on how this was addressed.
FigS3 here is something related to the SOI and relative contribution that was creating some confusion explained above, where it was not clear to me why the SOI is used in the main ms.
FigS6. I'm not sure if the y-axis labels are correct in both figures.
Citation: https://doi.org/10.5194/egusphere-2023-3063-RC1 -
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3063/egusphere-2023-3063-AC1-supplement.pdf
-
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
-
RC2: 'Comment on egusphere-2023-3063', Anonymous Referee #2, 06 Mar 2024
Review of Berkelhammer et al for HESS
This article leverages diverse field measurements concentrated in a single site to explore complex interactions between species responsiveness to moisture available, canopy structural affects on water fluxes, and interannual variability in water availability as controlled by snow. The study uncovers some interesting trends (those captured in Figure 3 and Figure 6, for example), and presents other findings that could be valuable to many readers and the field at large. However, I found four critical issues with manuscript, the first of which seems unfixable.
1) The overall narrative and conclusions go beyond what should be inferred from the data. While the authors admit that the investigation was not established to pursue the narrative as it is presented, the truer statement is that the investigation falls short of what is needed to support the arguments regarding the role of canopy structure (which is reinforced by the scarcity of statistical tests).
Far more study sites with varying ecosystem structure would be needed to support the argument that topography shapes density, or that density relates to sensitivity, let alone to assert “the progression towards a low snowpack future will manifest at the sub-hillslope scale in dense stands”. The story-telling narrative style involves of web of speculations, making it challenging to connect evidence with statements made throughout. A reasonable level of evidence is crucially unmet in many areas. One key result is presented in line 281-283, “we see that the response of trees to the large spring snowpack of 2019 can best be predicted based on whether a given tree was located in one of the dense stands (i.e. where sapwood to ground area was ∼38 cm2 m2) or open stands (i.e. sapwood to ground area ∼12 m2 m2) (Figure 6)”; let’s ignore the typo (it’s not “m2 m2” but “cm^2 m^-2” ). Figure 6b shows 5 data points, and there is no aspen stand with low density and there is no mixed stand with high density, and thus there is no variation in density for a given composition of stand. This alone invalidates the primary argument of the paper (especially because we can see that species does matter in Figure 6a, Figure 4, and Figure 2). This critically small sample size does not allow for assessing stand effects.
2) There are major communications issues throughout, including use of imprecise language, omitted details, and insufficient copy editing.
Throughout, there is much suggestive and imprecise language, making it challenging to interpret and certainly not reproducible. Let us start with a few examples of uninterpretable statements:
-“we consolidate the numerous processes acting across sites by collapsing the measurement clusters…” (page 4 line 84)
-“it is also common to see areas where winter precipitation and groundwater may be of equal or greater importance to the species’ water demands illustrating an overriding impact of hillslope context relative to species-specific traits” (line 64-65)
-“The sensitivity of annual tree activity to snow inputs did not clearly map onto species or elevational position on the hillslope.” (line 280)
I do not know what these statements mean. The imprecise language is even evident in the (arguably) most important sentences of the paper, with these two hypotheses: “Within this context, we first test two fundamental hypotheses: (1) the seasonal origin of water used by trees influences a stand’s sensitivity to changes in precipitation seasonality and (2) that canopy structure is a reflection of the source of water accessible to the trees in that stand.” I cannot understand how hypotheses of “influences” and “is a reflection” could be tested, establishing murkiness early on.
It is not just a writing-style issue because there are also major omissions and indicators of insufficient proofreading. Grammar problems occur throughout (e.g., a lack of appropriate punctuation). Methodological details are too vague (although the Methods section is otherwise nicely structured). The sap flow study design is insufficiently communicated as it is unclear which trees and which trees in which sites were actually instrumented (Figure 1 only partially helps because there are places where the yellow circle could be over a deciduous or needleleaf tree, and there are issues such as only two yellow circles being visible in a site that supposedly had 4 to 6 trees instrumented). It repeatedly says that ‘live stems’ were sampled for isotope data, which seems unfathomable for mature trees (were they live twigs? cores from live stems?). Lines 156-157 require more details because I do not know what they mean or how they apply to the Bayesian approach used. Figure 3 axes labels are unclear and/or inaccurate. Figure 7 has a polynomial fit but what order is that polynomial and why is it justified? What are the error bars in Figure 7B? Figure 2 has no x-axis labels on some panels. It is dubious for such problems to occur despite this paper having so many (native English speaking) authors approve its submission.
3) There are citation problems. Many of the citation choices are odd; for example, often papers are cited for ideas they mention as opposed what they actually demonstrate. E.g., Brooks et al., 2015 is overused and is used in odd places. Another odd one was Graup et al., 2022 (page 4 line 87), cited (I think) because Graup et al make a related assumptions; but, they do not show evidence to justify those assumptions. There are many of these throughout. Another common occurrence was confusing use of past citations when referring to the present study: e.g. “is a critical absence in terms of our capacity to close the transpiration budget (Cooper et al., 2020)” (213, page 8). How can a paper from 4 years ago be cited regarding this study’s inability to close the balance? It needs to be clear why it is being cited. A similar issue arises again sentences later: “we estimate that aggregate hillslope water use was more similar between the conifers and aspens than implied from Figure 2 (Pataki et al., 2000). I do not know what the authors mean by this. There are many of these atypical citation usages in the discussion. The figures need to be more precisely cited in the text too; often figures would be referenced but it was unclear why.
Despite the main conclusions relating to stand structure, very little literature on stand structure and its relationships with physiology, anatomy, and functional ecology is cited. The authors could greatly benefit from considering, for example, works by Hank Margolis and Jim Long on relationships between stand structure, sapwood area, and sapwood permeability that are problematically not considered in the interpretation of data shown in Figure 6. Statements such as “Much of the way we have come to understand the sensitivity of forests in the region to precipitation variability is through a handful of longer eddy covariance records (e.g. (Knowles et al., 2015)) and analysis of satellite greenness indices (e.g. (Trujillo et al., 2012))” are incorrect, as there have been decades of research on tree hydraulics using tree level or leaf level techniques to study drought responses in forests (also note that Trujillo et al did not focus on forests in this region!).
4) I am unclear how the authors justify the conclusions about interception and its relationship with stand structure.
It seems that inferences on interception are made from soil moisture data, and few details are provided on these soil moisture measurements. How many soil water measurements were used per plot? How do the soil moisture plots differ from the other study plots? How does density vary across the soil moisture plots? Is there enough spatial coverage by soil moisture sensors to rule out the possibility that the moisture differences are not random, or are not influenced by points where water drips from the canopy?
More importantly, the choice of data presentation is highly concerning. Data were measured at 5 15 and 50 cm depths, and the values in Figure 5 show interpolations from that 5cm-50cm range. Given that the axis bounds are 5 (the upper y-axis limit) to 50 (the lower), essentially all of the range shown is influenced by the values at the 15-cm depth. It appears that the pattern being interpreted as moisture differences are mostly only a product of measurements at that one depth (15 cm). However, the y-axis seemingly uses a strange non-linear scale to emphasize the region of interpolated data most influenced by that 15 cm depth (15 +/- 10 makes up ~75% of the height of the figure!). So, the blue patches in summer (Figure 5 c), critical to core arguments of this paper, may only be a product of measurements at 1 depth (15 cm), and effect is visually stretched to skew what readers might perceive. The axis scale and the interpolation approach together create a misleading picture.
In conclusion: I think the findings could be presented in an entirely rewritten manuscript that might be acceptable for publication. There are interesting data and findings shown but their presentation would likely have to be as an entirely new manuscript because of the major issues cited above.
Citation: https://doi.org/10.5194/egusphere-2023-3063-RC2 -
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3063/egusphere-2023-3063-AC1-supplement.pdf
-
AC1: 'Reply on RC1 and RC2', Max Berkelhammer, 16 Apr 2024
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