the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Oct 2023
Synoptic-intraseasonal variability control on high chlorophyll-a events in the Puyuhuapi Fjord, Chilean Patagonia
Abstract. Intraseasonal climate variability as, the Madden-Julian Oscillation (MJO), and synoptic-scale systems modify the normal conditions of the atmosphere and ocean, causing anomalies in sea surface temperature (SST) and salinity (S) which could create an environment conducive to algal bloom events in fjord systems, which in some cases can be toxic (HABs). In this work, an analysis of the atmospheric forcings on the synoptic-to-intraseasonal scale (SY-IS), that precede and proceeds to extreme high chlorophyll-a (chl-a) events was made in the Puyuhuapi fjord (44.7º S 72.8º W), during the summer season (December–February, DJF) between the years 2010–2018. Extreme events of high chl-a are defined when chl-a anomalies exceed the 90th percentile, and day 0 was defined as the maximum anomalous value. Six extreme events, corresponding to 83 % of the total, were detected in the year 2016, a year with strong El Niño southern Oscillation (ENSO). From the analysis of the SY-IS patterns of persistent atmospheric anomalies during these 2016 events and their similarities, we detected that 4 events presented the characteristic of the passage of a low-pressure system, starting at least 7 days before the extreme chl-a event, with negative anomalies of sea level pressure and surface temperature, a change in wind direction and an increase in salinity at surface waters. we propose an atmospheric-oceanographic mechanism that induces favourable conditions for high phytoplanktonic activity in summertime: the passage of a low-pressure system, that weakens stratification and induces upwelling of deeper, colder and nutrient-rich waters favouring an increase in phytoplankton activity and the occurrence of extreme events of high chl-a in Puyuhuapi fjord. Furthermore, this work suggests that active phases 6 and 7 of the MJO might reinforce, on the SY-IS time scale, in DJF 2016. In the case of microalgae blooms, in addition to the well-known seasonal and interannual behaviors, it is important to superimpose the high-frequency variability. To improve the predictive ability of algal blooms and their relationship with climate conditions is essential for managing and mitigating their negative impacts on aquatic ecosystems, human health, and the economy.
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Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2272', Anonymous Referee #1, 13 Nov 2023
Review of "Synoptic-intraseasonal variability control on high
chlorophyll-a events in the Puyuhuapi Fjord, Chilean Patagonia" by
Bada-Diaz et al., submitted to Ocean Science.Recommendation: reject.
This paper documents the occurrence of extreme events in chl-a in the
Puyuhuapi Fjord system in Chile and relates them to atmospheric
forcing in an attempt to find an atmospheric causal relationship. The
methodology appears sound. However, the interpretation, that the chl-a
events are caused by the MJO is not supported by the data.MAJOR COMMENTS
1. The number of extreme chl-a events in the study is too low to base
any reliable statistical conclusion on. Also, it is not clear how many
events there actually are. In the abstract, it is stated that 6 (83%)
of the extreme events occur in 2016. This would imply 7
events. However, in table 4, there are only 6 events listed in total
(with 3 of these in (December) 2016). So it is not clear how many
events there are, but either 6 or 7 is far too small a number to base
any reliable conclusions on.2. Of the 6 high chl-a events listed in Table 4, the MJO phases are
also listed. These are, for the 6 events, MJO phases 4,6,7,2,6,5. In
the abstract, it is concluded that MJO phases 6-7 tend to reinforce
chl-a events. This is an incorrect conclusion. 3 out of 6 of the high
chl-a events occur in MJO phases 6-7. With such a small number of
events, and no prior reason to think that MJO phases 6-7 re important,
this can easily happen by pure chance. Also, it is stated that ENSO
is an important modulator of the high chl-a events, as 2016 is an ENSO
year. The paper only analyses DJF data, and it is not clear whether
the 2015/16 or 2016/17 DJF season is being discussed, which is very
confusing. What is the state of ENSO in these two DJF seasons. The
whole discussion here is confusing and not backed up reliably by the
data.Citation: https://doi.org/10.5194/egusphere-2023-2272-RC1 - AC1: 'Reply on RC1', Reynier Bada Diaz, 07 Jan 2024
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RC2: 'Comment on egusphere-2023-2272', Anonymous Referee #2, 10 Dec 2023
Bada-Diaz et al present a study that uses chlorophyll-a data from a fjord in Chile spanning 2010-2018 and seeks to relate high chlorophyll-a events to intraseasonal climate variability. While I do see the potential relevance of the data presented in the study (8 years of in-situ data from a buoy + regular sampling across several locations across the fjord during 2012-2018), the study has several major flaws, some of which make me question the validity of the results and conclusions presented. Since addressing these flaws would require rewriting most of the manuscript, I recommend that the manuscript is rejected.
I will now list several overall concerns and doubts I identified, hoping they can be constructive towards a future, better version of the manuscript.
- The Introduction should be more straightforward and introduce the reader to your objectives, following an easily understandable order. The section starts describing the seasonal drivers of phytoplankton for the Chilean Patagonia and then jumps to the Maden-Julian Oscillation (MJO) without first mentioning why it is doing all this and what is the relevance of studying phytoplankton and, more precisely, what is the relevance of studying it in a fjord in Patagonia. It is important that the overall public that reads the article understands how globally relevant it is. Moreover, if the main goal of the study is to understand how intraseasonal variability drives phytoplankton biomass, why not focus more on phytoplankton and then gradually move to how intraseasonal climate variability may affect it? This is more a matter of restructuring the Introduction to make it more appealing for the reader.
- The objectives are not clear. “The research aims to suggest a mechanism by which SY-IS variability modulated water column conditions, triggering extreme chl-a events”. Shouldn’t it be the other way? For instance, the goal should be to first understand what is driving these events, then find out the main frequency of these environmental factors and finally what are the overall process? Yet, in the beginning of the paragraph (line 70), the authors mention that the study will also focus on seasonal variability. Finally, I do not understand lines 75-77, it is not clear if this still part of the objectives or not or what is the clear relationship between it and the extreme chl-a events. I suggest rethinking the objectives – keep it simple, avoid repeating objectives and using vague terms that the reader is not familiar with at this point text.
- The Methods section has several major problems. First, the section lacks several details and is confusing, which makes it difficult to follow at times and, more importantly, difficult to reproduce. I would suggest adding a summary table with all variables used, with their sources and resolution. I have also indicated several instances where the methods could be improved below, after my general comments.
- Chl-a is the main variable of this study. Yet, all results are based on in-vivo fluorometry chl-a measured on a buoy within a highly productive and potentially turbid fjord. Without validating these measurements against lab-measured chl-a (HPLC, preferentially), it is difficult to be sure that these results are reliable as currently shown. Particularly, when chlorophyll-a concentrations above 100 mg/m3 are common from what I can see in Figure 4. In-vivo fluorometry are often less accurate and frequently overestimate chl-a (over 2-fold) due to the fluorescence of coloured dissolved organic matter. Other matters such as turbidity, biofouling, and nonphotochemical quenching can also interfere with the measurements of chl-a. Note that many in-vivo fluorometers are not prepared to handle very high concentrations. Therefore, it is very important to ensure that your measurements are accurate, particularly when the entire goal of the work is evaluating the drivers of extreme events of chl-a. Personally, I have doubts that the values presented are accurate, which might affect the validity of the results.
- I also did not understand the choices made in the Methodology. For instance, if you have a rich dataset with summer and winter samples scattered along the fjord, why average them for the entire seasons? First, by doing this the authors are “throwing away” the intraseasonal information (the one which is the focus of the work) in order to have a single value for three months. Second, this makes it difficult for the reader to understand when these samples were collected and if they were equally collected along each season. I think the authors may be wasting the potential of their data as it is not clear why they only use intraseasonal chl-a data from the buoy and for looking at the extreme events.
- I would really advise against using a product with such low resolution such as ERA5 for studying such a small, coastal, and complex region as a fjord. Not only it may not represent the full picture, but I am sure most pixels would also include land.
- In section 2.4, why are the authors using these “intraseasonal anomalies”? This must be clear in the text. Also, does not this equation remove the same information twice? For instance, first it removes the average value for that day (let us say January 1st) then removes the seasonal anomaly (which already includes the value from January 1st). I may be misunderstanding the equation, but I do not believe this would be the best way to remove the seasonal and interannual variability to your datapoints, which is what it seems the authors are trying to do here. Moreover, since the authors are calculating anomalies from buoy chl-a, I would be really careful with the outliers prior to calculating the anomalies. Finally, I am not sure I agree with the approach of defining day 0 as the maximum day of the event and analysing the conditions prior and after it. There are several environmental factors that can lead to the abrupt ending of a bloom and there is often a lag between the environmental change and their effect on phytoplankton biomass. Plus, a bloom in December can be completely different from a bloom in February, both in terms of community composition, nutrient availability, and grazing influence (all factors not included in this work). Overall, this analysis might be too simplistic for the authors’ goal.
- In the results, why are we looking at the extreme events in the summer if (according to Figure 4) the highest biomass are observed in other periods of the year (autumn and winter)?
- What is the rationale behind the “extreme low chl-a” events? Why are these events even relevant for the objectives of this study?
Overall, the manuscript is characterised by a lack of focus, which must be corrected. As mentioned above, I do think the study has potential but it is clear that the authors need to review it carefully.
Minor comments:
The manuscript would benefit if its English were reviewed, I found several typos and grammatical errors throughout the text.
Lines 55-58 – How does MJO influence intraseasonal temperature and precipitation then?
Line 62 – What are these phases of MJO? You should explain what each phase means or, at least, make a general distinction between low-numbered phases and high-numbered phases. Assume the overall reader is not familiar with these terms.
Line 63 – Species names should be in italic.
Line 67 – What is negative cloudiness? Rephrase.
Lines 77-80 – These sentences can be removed; an article does not require an index unless it is a lengthy review.
Figure 2 – It is not clear at this stage of the study why the reader is looking at images from summer 2016 and 2017 and it will not become clear until section 3.2.1.
Line 113 – Remove this sentence, this is discussing the results prior to presenting them.
Line 116 – Why are you calculating stratification? To what goal? Will you use it to understand how it drives the extreme events? Make it clear to the reader.
Line 122 – What do you define as extreme? In line 161 you mention 9.1 mg/m3, yet you do not mention if this is threshold was used for winter or summer or both.
Line 124 – Add units for each variable introduced in methods. This is recurrent along the Methods.
Line 126 – Which sensors did you use to measure temperature, salinity, and chl-a?
Table 1 – This is not an adequate caption for a table in scientific research article. Also, the number of days with values in summer and winter is not truly relevant if the temporal distribution of the samples is not shown (all samples could be from January or June). Think about plotting, for each year, the distribution of your samples within each season.
Line 138 – What is SLP? The reader will not likely know.
Lines 141-142 – These are just tide height charts, right?
Lines 163-165 – Just a minimum of two days for an event seems rather short.
Figure 3 – Are these climatological means? If so, this information should be included in the caption. Again, it is really important to state how your data is distributed within each season. For instance, the mean for a given station can change a lot if 50% of the samples were collected in December.
Lines 206 – I may be wrong but an anomaly of 30 psu would mean that is 30 psu above the average salinity expected for that time of year. If a maximum salinity of 31.8 psu was observed on 27th May, how could the mean salinity for that day be ~1 or 2 psu? This anomalies analysis should be reviewed.
Figure 4 – These chl-a values are very high, particularly when the average values for summer and winter were around 1-3 mg/m3 according to Figure 1. How do these values from the buoy compared to the values measured from field campaigns? Also, you should mention these data gaps in chl-a (as opposed to temperature and salinity) in the Methods. Finally, you should try to align the vertical lines (ticks) with the years (each major vertical tick in the X axis should coincide with the 1st of January). Do not forget to include units in the caption.
Line 221 – It is difficult to justify an extreme event corresponding to 9.1 mg/m3 if we can easily find many events with chl-a above 100 mg/m3 in Figure 4.
Figure 5 – This analysis can be very difficult to interpret. For instance, the trends seen in temperature and salinity can just be a matter of temperature and salinity increasing over the summer, particularly if a lot of events occur in December.
Table 3 – Should include the dates of start and end of each event.
Line 255 – First mention of the results in Fig. 2c?
Figure 7 – What is tm2? Again, do not assume the reader knows your variables as well as you may do.
Lines 387-394 – Repeated information from the Introduction + Methods. Remove
Lines 405-408 - Repeated information from the Results.
Citation: https://doi.org/10.5194/egusphere-2023-2272-RC2 - AC2: 'Reply on RC2', Reynier Bada Diaz, 07 Jan 2024
-
EC1: 'Comment on egusphere-2023-2272', Karen J. Heywood, 10 Dec 2023
Both reviewers have raised serious concerns about your paper. Although your work is interesting, it will take considerable further work to strengthen it sufficiently for publication. I am sorry to say that I will be rejecting the paper in its present form. I encourage you to consider the comments and suggestions of both reviewers carefully in deciding how to take the work further. My view is that it needs a re-think about the approach and analyses discussed in the paper. I wish you well in these endeavours.
Citation: https://doi.org/10.5194/egusphere-2023-2272-EC1 -
AC3: 'Reply on EC1', Reynier Bada Diaz, 08 Jan 2024
Thank you very much for the feedback to our work. We appreciate the effort made and have taken the reviewers' comments into account and will consider them for a revised version of our work. A point-by-point response was prepared for each reviewer. Our best regards.
Citation: https://doi.org/10.5194/egusphere-2023-2272-AC3
-
AC3: 'Reply on EC1', Reynier Bada Diaz, 08 Jan 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2272', Anonymous Referee #1, 13 Nov 2023
Review of "Synoptic-intraseasonal variability control on high
chlorophyll-a events in the Puyuhuapi Fjord, Chilean Patagonia" by
Bada-Diaz et al., submitted to Ocean Science.Recommendation: reject.
This paper documents the occurrence of extreme events in chl-a in the
Puyuhuapi Fjord system in Chile and relates them to atmospheric
forcing in an attempt to find an atmospheric causal relationship. The
methodology appears sound. However, the interpretation, that the chl-a
events are caused by the MJO is not supported by the data.MAJOR COMMENTS
1. The number of extreme chl-a events in the study is too low to base
any reliable statistical conclusion on. Also, it is not clear how many
events there actually are. In the abstract, it is stated that 6 (83%)
of the extreme events occur in 2016. This would imply 7
events. However, in table 4, there are only 6 events listed in total
(with 3 of these in (December) 2016). So it is not clear how many
events there are, but either 6 or 7 is far too small a number to base
any reliable conclusions on.2. Of the 6 high chl-a events listed in Table 4, the MJO phases are
also listed. These are, for the 6 events, MJO phases 4,6,7,2,6,5. In
the abstract, it is concluded that MJO phases 6-7 tend to reinforce
chl-a events. This is an incorrect conclusion. 3 out of 6 of the high
chl-a events occur in MJO phases 6-7. With such a small number of
events, and no prior reason to think that MJO phases 6-7 re important,
this can easily happen by pure chance. Also, it is stated that ENSO
is an important modulator of the high chl-a events, as 2016 is an ENSO
year. The paper only analyses DJF data, and it is not clear whether
the 2015/16 or 2016/17 DJF season is being discussed, which is very
confusing. What is the state of ENSO in these two DJF seasons. The
whole discussion here is confusing and not backed up reliably by the
data.Citation: https://doi.org/10.5194/egusphere-2023-2272-RC1 - AC1: 'Reply on RC1', Reynier Bada Diaz, 07 Jan 2024
-
RC2: 'Comment on egusphere-2023-2272', Anonymous Referee #2, 10 Dec 2023
Bada-Diaz et al present a study that uses chlorophyll-a data from a fjord in Chile spanning 2010-2018 and seeks to relate high chlorophyll-a events to intraseasonal climate variability. While I do see the potential relevance of the data presented in the study (8 years of in-situ data from a buoy + regular sampling across several locations across the fjord during 2012-2018), the study has several major flaws, some of which make me question the validity of the results and conclusions presented. Since addressing these flaws would require rewriting most of the manuscript, I recommend that the manuscript is rejected.
I will now list several overall concerns and doubts I identified, hoping they can be constructive towards a future, better version of the manuscript.
- The Introduction should be more straightforward and introduce the reader to your objectives, following an easily understandable order. The section starts describing the seasonal drivers of phytoplankton for the Chilean Patagonia and then jumps to the Maden-Julian Oscillation (MJO) without first mentioning why it is doing all this and what is the relevance of studying phytoplankton and, more precisely, what is the relevance of studying it in a fjord in Patagonia. It is important that the overall public that reads the article understands how globally relevant it is. Moreover, if the main goal of the study is to understand how intraseasonal variability drives phytoplankton biomass, why not focus more on phytoplankton and then gradually move to how intraseasonal climate variability may affect it? This is more a matter of restructuring the Introduction to make it more appealing for the reader.
- The objectives are not clear. “The research aims to suggest a mechanism by which SY-IS variability modulated water column conditions, triggering extreme chl-a events”. Shouldn’t it be the other way? For instance, the goal should be to first understand what is driving these events, then find out the main frequency of these environmental factors and finally what are the overall process? Yet, in the beginning of the paragraph (line 70), the authors mention that the study will also focus on seasonal variability. Finally, I do not understand lines 75-77, it is not clear if this still part of the objectives or not or what is the clear relationship between it and the extreme chl-a events. I suggest rethinking the objectives – keep it simple, avoid repeating objectives and using vague terms that the reader is not familiar with at this point text.
- The Methods section has several major problems. First, the section lacks several details and is confusing, which makes it difficult to follow at times and, more importantly, difficult to reproduce. I would suggest adding a summary table with all variables used, with their sources and resolution. I have also indicated several instances where the methods could be improved below, after my general comments.
- Chl-a is the main variable of this study. Yet, all results are based on in-vivo fluorometry chl-a measured on a buoy within a highly productive and potentially turbid fjord. Without validating these measurements against lab-measured chl-a (HPLC, preferentially), it is difficult to be sure that these results are reliable as currently shown. Particularly, when chlorophyll-a concentrations above 100 mg/m3 are common from what I can see in Figure 4. In-vivo fluorometry are often less accurate and frequently overestimate chl-a (over 2-fold) due to the fluorescence of coloured dissolved organic matter. Other matters such as turbidity, biofouling, and nonphotochemical quenching can also interfere with the measurements of chl-a. Note that many in-vivo fluorometers are not prepared to handle very high concentrations. Therefore, it is very important to ensure that your measurements are accurate, particularly when the entire goal of the work is evaluating the drivers of extreme events of chl-a. Personally, I have doubts that the values presented are accurate, which might affect the validity of the results.
- I also did not understand the choices made in the Methodology. For instance, if you have a rich dataset with summer and winter samples scattered along the fjord, why average them for the entire seasons? First, by doing this the authors are “throwing away” the intraseasonal information (the one which is the focus of the work) in order to have a single value for three months. Second, this makes it difficult for the reader to understand when these samples were collected and if they were equally collected along each season. I think the authors may be wasting the potential of their data as it is not clear why they only use intraseasonal chl-a data from the buoy and for looking at the extreme events.
- I would really advise against using a product with such low resolution such as ERA5 for studying such a small, coastal, and complex region as a fjord. Not only it may not represent the full picture, but I am sure most pixels would also include land.
- In section 2.4, why are the authors using these “intraseasonal anomalies”? This must be clear in the text. Also, does not this equation remove the same information twice? For instance, first it removes the average value for that day (let us say January 1st) then removes the seasonal anomaly (which already includes the value from January 1st). I may be misunderstanding the equation, but I do not believe this would be the best way to remove the seasonal and interannual variability to your datapoints, which is what it seems the authors are trying to do here. Moreover, since the authors are calculating anomalies from buoy chl-a, I would be really careful with the outliers prior to calculating the anomalies. Finally, I am not sure I agree with the approach of defining day 0 as the maximum day of the event and analysing the conditions prior and after it. There are several environmental factors that can lead to the abrupt ending of a bloom and there is often a lag between the environmental change and their effect on phytoplankton biomass. Plus, a bloom in December can be completely different from a bloom in February, both in terms of community composition, nutrient availability, and grazing influence (all factors not included in this work). Overall, this analysis might be too simplistic for the authors’ goal.
- In the results, why are we looking at the extreme events in the summer if (according to Figure 4) the highest biomass are observed in other periods of the year (autumn and winter)?
- What is the rationale behind the “extreme low chl-a” events? Why are these events even relevant for the objectives of this study?
Overall, the manuscript is characterised by a lack of focus, which must be corrected. As mentioned above, I do think the study has potential but it is clear that the authors need to review it carefully.
Minor comments:
The manuscript would benefit if its English were reviewed, I found several typos and grammatical errors throughout the text.
Lines 55-58 – How does MJO influence intraseasonal temperature and precipitation then?
Line 62 – What are these phases of MJO? You should explain what each phase means or, at least, make a general distinction between low-numbered phases and high-numbered phases. Assume the overall reader is not familiar with these terms.
Line 63 – Species names should be in italic.
Line 67 – What is negative cloudiness? Rephrase.
Lines 77-80 – These sentences can be removed; an article does not require an index unless it is a lengthy review.
Figure 2 – It is not clear at this stage of the study why the reader is looking at images from summer 2016 and 2017 and it will not become clear until section 3.2.1.
Line 113 – Remove this sentence, this is discussing the results prior to presenting them.
Line 116 – Why are you calculating stratification? To what goal? Will you use it to understand how it drives the extreme events? Make it clear to the reader.
Line 122 – What do you define as extreme? In line 161 you mention 9.1 mg/m3, yet you do not mention if this is threshold was used for winter or summer or both.
Line 124 – Add units for each variable introduced in methods. This is recurrent along the Methods.
Line 126 – Which sensors did you use to measure temperature, salinity, and chl-a?
Table 1 – This is not an adequate caption for a table in scientific research article. Also, the number of days with values in summer and winter is not truly relevant if the temporal distribution of the samples is not shown (all samples could be from January or June). Think about plotting, for each year, the distribution of your samples within each season.
Line 138 – What is SLP? The reader will not likely know.
Lines 141-142 – These are just tide height charts, right?
Lines 163-165 – Just a minimum of two days for an event seems rather short.
Figure 3 – Are these climatological means? If so, this information should be included in the caption. Again, it is really important to state how your data is distributed within each season. For instance, the mean for a given station can change a lot if 50% of the samples were collected in December.
Lines 206 – I may be wrong but an anomaly of 30 psu would mean that is 30 psu above the average salinity expected for that time of year. If a maximum salinity of 31.8 psu was observed on 27th May, how could the mean salinity for that day be ~1 or 2 psu? This anomalies analysis should be reviewed.
Figure 4 – These chl-a values are very high, particularly when the average values for summer and winter were around 1-3 mg/m3 according to Figure 1. How do these values from the buoy compared to the values measured from field campaigns? Also, you should mention these data gaps in chl-a (as opposed to temperature and salinity) in the Methods. Finally, you should try to align the vertical lines (ticks) with the years (each major vertical tick in the X axis should coincide with the 1st of January). Do not forget to include units in the caption.
Line 221 – It is difficult to justify an extreme event corresponding to 9.1 mg/m3 if we can easily find many events with chl-a above 100 mg/m3 in Figure 4.
Figure 5 – This analysis can be very difficult to interpret. For instance, the trends seen in temperature and salinity can just be a matter of temperature and salinity increasing over the summer, particularly if a lot of events occur in December.
Table 3 – Should include the dates of start and end of each event.
Line 255 – First mention of the results in Fig. 2c?
Figure 7 – What is tm2? Again, do not assume the reader knows your variables as well as you may do.
Lines 387-394 – Repeated information from the Introduction + Methods. Remove
Lines 405-408 - Repeated information from the Results.
Citation: https://doi.org/10.5194/egusphere-2023-2272-RC2 - AC2: 'Reply on RC2', Reynier Bada Diaz, 07 Jan 2024
-
EC1: 'Comment on egusphere-2023-2272', Karen J. Heywood, 10 Dec 2023
Both reviewers have raised serious concerns about your paper. Although your work is interesting, it will take considerable further work to strengthen it sufficiently for publication. I am sorry to say that I will be rejecting the paper in its present form. I encourage you to consider the comments and suggestions of both reviewers carefully in deciding how to take the work further. My view is that it needs a re-think about the approach and analyses discussed in the paper. I wish you well in these endeavours.
Citation: https://doi.org/10.5194/egusphere-2023-2272-EC1 -
AC3: 'Reply on EC1', Reynier Bada Diaz, 08 Jan 2024
Thank you very much for the feedback to our work. We appreciate the effort made and have taken the reviewers' comments into account and will consider them for a revised version of our work. A point-by-point response was prepared for each reviewer. Our best regards.
Citation: https://doi.org/10.5194/egusphere-2023-2272-AC3
-
AC3: 'Reply on EC1', Reynier Bada Diaz, 08 Jan 2024
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