the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Dec 2024
Dynamic identification of snow phenology in the Northern Hemisphere
Abstract. Snow phenology characterizes the cyclical changes in snow and has become an important indicator of climate change in recent decades. Changes in snow phenology can significantly impact climate and hydrological conditions. Previous studies commonly employed fixed threshold methods to extract snow phenology. However, these methods do not account for the variability in snow distribution across the Northern Hemisphere, leading to potential biases of snow phenology. In this study, we observe that snow phenology extracted from different snow data and methods shows significant differences, but consistently underestimates snow duration at low and middle latitudes. Our analysis further indicates that the changes in snow depth exhibits a significant shift around 10 % of peak value across the Northern Hemisphere, marking the transition between the snow and non-snow seasons. We further apply the 10 % snow depth threshold and investigate the differences between original and newly extracted snow phenology. At low and middle latitudes, the snow cover duration (SCD) extends, the snow cover onset day (SCOD) advances, and the snow cover end day (SCED) delays, especially on the Tibetan Plateau, where the SCD differences can reach 28 days. The change at higher latitudes is reversed. The dynamic snow phenology accounts for the spatial heterogeneity of Northern Hemisphere snow cover, and excludes the influence of inter-annual variability of snow cover on snow phenology extraction, providing a novel perspective for identifying and understanding snow cover variations in the Northern Hemisphere.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Comment on egusphere-2024-3431', Xiongxin Xiao, 06 Jan 2025
I am interested in your great work on snow phenology analysis. I have several questions that need your clarification.
- According to numerous previous works and my analysis (https://doi.org/10.1016/j.isprsjprs.2024.07.018), the uncertainty in snow phenology is mainly derived from the accuracy of daily snow cover products. You always mentioned there are potential bias when using the fixed threshold to determine snow phenology. Through your whole text, you didn’t specifically illustrate this type of uncertainties. May I ask you to give some examples to clarify this uncertainty what you mentioned?
---In the Abstract, “Previous studies commonly employed … leading to potential biases of snow phenology.” This description is too general.
--- Lines 65-66, “The fixed threshold for snow phenology fails to account for the variations in snow cover across the NH”. Please give us more explanation on this. Do you have any related references to support your statement? Additionally, the following sentence “In fact, snow cover increase …. Especially on the TP” cannot support your statement on the uncertainties due to using fixed threshold.
--- Lines 68-69: “Snow conditions are variable, … underestimation or overestimation of snow phenology.” Please add more explanations on this underestimation and overestimation. Is there any published works to support this statement? - Line 14-15: “At low and middle latitudes, the snow cover duration (SCD) extends, the snow cover onset day (SCOD) advances, and the snow cover end day (SCED) delays, …” This conclusion is quite different from what we got up to now. Does your analysis conclusion tell us we have more snow in this region in a warming world?
- MOD10C2 products provide a maximum snow cover extent during this 8-days period. That means you would potentially overestimate the snow cover phenology metrics (snow cover days, snow onset date and snow end date) when you used this data. “For the SCF dataset, … after the last identified snow cover.” (Line 112-113) Please cite references to support this processing’s reasonability.
- 1: which part of data do you use to plot this figure? 1989-2018 or 2000-2018? Single data and which data? Additionally, this curve is like for vegetation growth instead of snow cover evolution. Please check it again. The fact is that Summer is no snow (DOY=180 days). Finally, how can I understand the term “percentage of snow depth”? For your reference, here is a in-situ observation of snow evolution within a snow cover year (10/1 – 9/31) https://www.climatehubs.usda.gov/hubs/northwest/topic/30-year-normals. Regarding only snow depth variable used in your method, I am confused how you use snow cover products (IMS and SCF) to calculate snow phenology metrics.
- Through your manuscript, you used a 5% or 10% to determine snow phenology metrics. Is this another type of “fixed threshold” method. Additionally, you didn’t include some comparisons, validations, and evaluations. How do you convince our readers of the responsibility (not accuracy) of your snow phenology results? Because your method quite largely different the method we usually used.
- Based on your analysis, I wonder if your approach means that snow phenology metrics will vary depending on the study area used. For example, the snow depth in the Northern-Xinjiang is generally higher, while it is lower in the TP. ---Case1: Only TP data is used to analyze snow phenology metrics for TP. ---Case2: The data both in the TP and Northern-Xinjiang are used to analyze snow phenology metrics for TP. Are the snow phenology metrics in the TP region different between Case 1 and Case 2?
Minor comments:
Change “on” to “in” in line 67.
Change “24 hours” to “daily” in line 95
Change “the hydrological year” to “a hydrological year” in line 111
How do I understand the “snow index” in Line 132? Snow depth? Snow cover days? NDSI?
Line 101-102: “This data has been validated against … less than 5 cm account for approximately 65% of all the data” Please add the reference for this statement to support this “5cm - 65%” number pair.
Line 141: Why do you use “30-day” moving window? Any specific reasons?
Section 2.1: how did you handle the different spatial resolution of these snow products?
Line 246-247: “Given that the zonal variations … compared to a fixed threshold.” It is not clear.
Are the days in Fig 2 and Table 2 DOYs (Day of Year)? If it is, 66 of SCOD in table 2 means the snow starts in March. Please check the whole paper’s figures again.
Citation: https://doi.org/10.5194/egusphere-2024-3431-CC1 -
AC3: 'Reply on CC1', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC3-supplement.pdf
- According to numerous previous works and my analysis (https://doi.org/10.1016/j.isprsjprs.2024.07.018), the uncertainty in snow phenology is mainly derived from the accuracy of daily snow cover products. You always mentioned there are potential bias when using the fixed threshold to determine snow phenology. Through your whole text, you didn’t specifically illustrate this type of uncertainties. May I ask you to give some examples to clarify this uncertainty what you mentioned?
-
RC1: 'Comment on egusphere-2024-3431', Anonymous Referee #1, 17 Jan 2025
In this paper, the authors examine the snow phenology over the Northern Hemisphere (NH), based on satellite observations of snow cover by MODIS and IMS, and of snow depths by microwave instruments. The study focuses on snow onset, snow end date and snow cover days, as well as snow peak day.
They propose a dynamic threshold selection in constructing the snow phenology indicators, based on the local seasonal snow evolution, rather than a fixed threshold. The method has significant advantages, e.g., over the Tibetan Plateau (TP) where the snowpack can be shallow (Fig7) and the dynamic method allows for a considerably longer snow phenology (Fig9). These differences are strongly influenced by topography, and it is in the mountainous areas that the dynamic method offers the greatest benefits. In the mid and high latitudes, the difference between the two methods is small (Fig7) though, 4-5 days at most. Over the NH overall, the differences appear small (compare Fig8 c and d), albeit it influences the indicator trends.
The study is detailed and comprehensive, and the article is fairly clearly written. It should prove a valuable study to understand the phenology and snow variability, over the TP in particular. I recommend the paper for publication provided the main comments are addressed.
Main comments:
- I question the choice of treating on the same footing snow cover and snow depth (SD) in the first part of the paper. Indeed, some phenology indicators (like SCED, end of snow season) exhibit large differences exceeding one month over the TP, depending if one is considering snow cover or depth. In the 2nd part of the paper, only SD is considered. One possible way to clarify this paper is to re-structure it to address snow cover phenology (comparing the 2 instruments) in a first part, and then focus SD in a next part.
- I am concerned about the methodology at locations where the snow curve is not monotonous across the season and has several maxima and minima linked to episodic snowfall and melt. Smoothing or climatological averaging should alleviate this potential problem. This seems be the case for the snow cover data over the TP (Fig4). This restricts the applicability of the method, and the authors expressed this concern (L445-446), even for SD at some locations over the TP where the snow layer is shallow. Please discuss this issue in the Methodology section.
Minor comments:
- L 36: Concerning the decreasing length of the snow season in Notarcola (2022): wasn’t this paper focused only on the mountainous regions?
- L98: It is a bit unclear what the authors mean by “replacing the dataset”?
- L106: What is SCE and where is it defined ? Or is it a Typo and should it be SCA as it refers to IMS ?
- IMS also measures snow cover fraction: hence the labels (e.g. in Table 2, figures 3-4) should be more consistent: using IMS, SCF, SD mixes instruments names and the variables.
- L145: The first derivative also goes to zero at the maximum of the snow curve.
- When adapting the formula from the vegetation index, is it true that SnowMin is actually zero throughout this study?
- A couple of points are not clear in the Methodology: on one hand, “The above process is carried out for each grid in the NH (hence locally), yet above, it seems that the ratio is defined in “latitudinal zones” (implying a zonal average). The authors also mention multi-annual averages of the threshold, which implies that the threshold is defined for each year and then averaged, as opposed to using a climatological evolution to define a threshold. Please clarify.
- Caption of Fig 4 over which years is this intra-annual (climatological?) variation established?
- L242-265: there are lot of repeats with the Methodology section 2.3
- The early onset of snow annual cycle for the TP is interesting; is it governed by the high-altitude areas? It might be worthed to split this Fig 6 into mountainous and non-mountainous areas, like done in the later part of the paper.
Typos & English.
- L125 (and many other places) ratio not radio
- Caption of Fig 1 : “has not started … has ended”
- L217: snow conditions at given grid points
- Caption of Fig 4 : replace …”snow elements” by the annual snow maxima over the respective areas.
Citation: https://doi.org/10.5194/egusphere-2024-3431-RC1 -
AC1: 'Reply on RC1', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-3431', Anonymous Referee #2, 29 Jan 2025
General:
The manuscript presents a valuable contribution to the field of snow phenology by proposing a dynamic threshold method for snow phenology extraction in the Northern Hemisphere. The study effectively highlights the limitations of the traditional fixed threshold approach and demonstrates the advantages of the dynamic method in capturing the spatial heterogeneity and temporal variability of snow cover.
Specific:
- The sentence “Our analysis further indicates that the changes in snow depth exhibits a significant shift around 10% of peak value across the Northern Hemisphere, marking the transition between the snow and non-snow seasons.” need to clarity. I feel it’s difficult to understand.
- The introduction could be more focused on the specific research gap addressed by the study. Could you rewrite it?
- If possible, could you add a sentence or two to explicitly state the research objectives and the main contributions of the study.
- It would be helpful to provide a brief explanation of the rationale behind choosing the 30-day moving window for smoothing the snow depth data.
- The conclusions clearly summarize the main findings and contributions of the study.
Citation: https://doi.org/10.5194/egusphere-2024-3431-RC2 -
AC2: 'Reply on RC2', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC2-supplement.pdf
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CC2: 'Comment on egusphere-2024-3431', David Robinson, 12 Feb 2025
The authors introduce a method of defining start dates, end dates, and durations of snow cover over Northern Hemisphere lands that differs from fixed definitions for these variables. This reviewer is left with considerable uncertainties as to how the author's dynamic method is any more useful than the fixed method. One major concern has to do with the use of microwave derived snow depth data to "drive" this approach. Many uncertainties remain to this day regarding the accuracy of this spectral region to accurately estimate depth. This can especially be true at shallow depths, with wet snow, and with deep snow that may have ice lenses, depth hoar, etc. within the pack. Unless the authors can explain how these issues do not impact their dynamic phenology method, I would place greater faith in the IMS and SCF estimates. This brings into question just how specific one can get with timing using an 8-day window for the SCF. Not that the IMS is a full-hemisphere daily evaluation of snow cover given persistent cloud cover that may mask surface conditions for multiple days. These issues are mainly more challenging in the fall for all sources, in part due to greater cloudiness then than in the spring melt season. Also, due to shallower depths, potential wet conditions, and ephemeral snow cover that can be found everywhere during seasonal snow onset (not that it doesn't exist throughout the season at lower elevation, low-mid latitude regions. Also, the challenges of using microwave data to map snow cover over the Tibetan Plateau has long been recognized, in part, as the authors suggest, due to its shallow depth (over non-mountainous areas) and ephemeral nature.
I will go no further with this evaluation as I see earlier reviewers have posted excellent comments regarding manuscript specifics, to which I agree. At this point, I will conclude that while the manuscript addresses an interesting topic and employs the key snow cover extent data products at regional to continental scales (IMS and MODIS), I am not convinced that the SD data and the subsequent conclusions built upon it are fully supported.
Citation: https://doi.org/10.5194/egusphere-2024-3431-CC2 -
AC4: 'Reply on CC2', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC4-supplement.pdf
-
AC4: 'Reply on CC2', Xin Miao, 12 Mar 2025
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2024-3431', Xiongxin Xiao, 06 Jan 2025
I am interested in your great work on snow phenology analysis. I have several questions that need your clarification.
- According to numerous previous works and my analysis (https://doi.org/10.1016/j.isprsjprs.2024.07.018), the uncertainty in snow phenology is mainly derived from the accuracy of daily snow cover products. You always mentioned there are potential bias when using the fixed threshold to determine snow phenology. Through your whole text, you didn’t specifically illustrate this type of uncertainties. May I ask you to give some examples to clarify this uncertainty what you mentioned?
---In the Abstract, “Previous studies commonly employed … leading to potential biases of snow phenology.” This description is too general.
--- Lines 65-66, “The fixed threshold for snow phenology fails to account for the variations in snow cover across the NH”. Please give us more explanation on this. Do you have any related references to support your statement? Additionally, the following sentence “In fact, snow cover increase …. Especially on the TP” cannot support your statement on the uncertainties due to using fixed threshold.
--- Lines 68-69: “Snow conditions are variable, … underestimation or overestimation of snow phenology.” Please add more explanations on this underestimation and overestimation. Is there any published works to support this statement? - Line 14-15: “At low and middle latitudes, the snow cover duration (SCD) extends, the snow cover onset day (SCOD) advances, and the snow cover end day (SCED) delays, …” This conclusion is quite different from what we got up to now. Does your analysis conclusion tell us we have more snow in this region in a warming world?
- MOD10C2 products provide a maximum snow cover extent during this 8-days period. That means you would potentially overestimate the snow cover phenology metrics (snow cover days, snow onset date and snow end date) when you used this data. “For the SCF dataset, … after the last identified snow cover.” (Line 112-113) Please cite references to support this processing’s reasonability.
- 1: which part of data do you use to plot this figure? 1989-2018 or 2000-2018? Single data and which data? Additionally, this curve is like for vegetation growth instead of snow cover evolution. Please check it again. The fact is that Summer is no snow (DOY=180 days). Finally, how can I understand the term “percentage of snow depth”? For your reference, here is a in-situ observation of snow evolution within a snow cover year (10/1 – 9/31) https://www.climatehubs.usda.gov/hubs/northwest/topic/30-year-normals. Regarding only snow depth variable used in your method, I am confused how you use snow cover products (IMS and SCF) to calculate snow phenology metrics.
- Through your manuscript, you used a 5% or 10% to determine snow phenology metrics. Is this another type of “fixed threshold” method. Additionally, you didn’t include some comparisons, validations, and evaluations. How do you convince our readers of the responsibility (not accuracy) of your snow phenology results? Because your method quite largely different the method we usually used.
- Based on your analysis, I wonder if your approach means that snow phenology metrics will vary depending on the study area used. For example, the snow depth in the Northern-Xinjiang is generally higher, while it is lower in the TP. ---Case1: Only TP data is used to analyze snow phenology metrics for TP. ---Case2: The data both in the TP and Northern-Xinjiang are used to analyze snow phenology metrics for TP. Are the snow phenology metrics in the TP region different between Case 1 and Case 2?
Minor comments:
Change “on” to “in” in line 67.
Change “24 hours” to “daily” in line 95
Change “the hydrological year” to “a hydrological year” in line 111
How do I understand the “snow index” in Line 132? Snow depth? Snow cover days? NDSI?
Line 101-102: “This data has been validated against … less than 5 cm account for approximately 65% of all the data” Please add the reference for this statement to support this “5cm - 65%” number pair.
Line 141: Why do you use “30-day” moving window? Any specific reasons?
Section 2.1: how did you handle the different spatial resolution of these snow products?
Line 246-247: “Given that the zonal variations … compared to a fixed threshold.” It is not clear.
Are the days in Fig 2 and Table 2 DOYs (Day of Year)? If it is, 66 of SCOD in table 2 means the snow starts in March. Please check the whole paper’s figures again.
Citation: https://doi.org/10.5194/egusphere-2024-3431-CC1 -
AC3: 'Reply on CC1', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC3-supplement.pdf
- According to numerous previous works and my analysis (https://doi.org/10.1016/j.isprsjprs.2024.07.018), the uncertainty in snow phenology is mainly derived from the accuracy of daily snow cover products. You always mentioned there are potential bias when using the fixed threshold to determine snow phenology. Through your whole text, you didn’t specifically illustrate this type of uncertainties. May I ask you to give some examples to clarify this uncertainty what you mentioned?
-
RC1: 'Comment on egusphere-2024-3431', Anonymous Referee #1, 17 Jan 2025
In this paper, the authors examine the snow phenology over the Northern Hemisphere (NH), based on satellite observations of snow cover by MODIS and IMS, and of snow depths by microwave instruments. The study focuses on snow onset, snow end date and snow cover days, as well as snow peak day.
They propose a dynamic threshold selection in constructing the snow phenology indicators, based on the local seasonal snow evolution, rather than a fixed threshold. The method has significant advantages, e.g., over the Tibetan Plateau (TP) where the snowpack can be shallow (Fig7) and the dynamic method allows for a considerably longer snow phenology (Fig9). These differences are strongly influenced by topography, and it is in the mountainous areas that the dynamic method offers the greatest benefits. In the mid and high latitudes, the difference between the two methods is small (Fig7) though, 4-5 days at most. Over the NH overall, the differences appear small (compare Fig8 c and d), albeit it influences the indicator trends.
The study is detailed and comprehensive, and the article is fairly clearly written. It should prove a valuable study to understand the phenology and snow variability, over the TP in particular. I recommend the paper for publication provided the main comments are addressed.
Main comments:
- I question the choice of treating on the same footing snow cover and snow depth (SD) in the first part of the paper. Indeed, some phenology indicators (like SCED, end of snow season) exhibit large differences exceeding one month over the TP, depending if one is considering snow cover or depth. In the 2nd part of the paper, only SD is considered. One possible way to clarify this paper is to re-structure it to address snow cover phenology (comparing the 2 instruments) in a first part, and then focus SD in a next part.
- I am concerned about the methodology at locations where the snow curve is not monotonous across the season and has several maxima and minima linked to episodic snowfall and melt. Smoothing or climatological averaging should alleviate this potential problem. This seems be the case for the snow cover data over the TP (Fig4). This restricts the applicability of the method, and the authors expressed this concern (L445-446), even for SD at some locations over the TP where the snow layer is shallow. Please discuss this issue in the Methodology section.
Minor comments:
- L 36: Concerning the decreasing length of the snow season in Notarcola (2022): wasn’t this paper focused only on the mountainous regions?
- L98: It is a bit unclear what the authors mean by “replacing the dataset”?
- L106: What is SCE and where is it defined ? Or is it a Typo and should it be SCA as it refers to IMS ?
- IMS also measures snow cover fraction: hence the labels (e.g. in Table 2, figures 3-4) should be more consistent: using IMS, SCF, SD mixes instruments names and the variables.
- L145: The first derivative also goes to zero at the maximum of the snow curve.
- When adapting the formula from the vegetation index, is it true that SnowMin is actually zero throughout this study?
- A couple of points are not clear in the Methodology: on one hand, “The above process is carried out for each grid in the NH (hence locally), yet above, it seems that the ratio is defined in “latitudinal zones” (implying a zonal average). The authors also mention multi-annual averages of the threshold, which implies that the threshold is defined for each year and then averaged, as opposed to using a climatological evolution to define a threshold. Please clarify.
- Caption of Fig 4 over which years is this intra-annual (climatological?) variation established?
- L242-265: there are lot of repeats with the Methodology section 2.3
- The early onset of snow annual cycle for the TP is interesting; is it governed by the high-altitude areas? It might be worthed to split this Fig 6 into mountainous and non-mountainous areas, like done in the later part of the paper.
Typos & English.
- L125 (and many other places) ratio not radio
- Caption of Fig 1 : “has not started … has ended”
- L217: snow conditions at given grid points
- Caption of Fig 4 : replace …”snow elements” by the annual snow maxima over the respective areas.
Citation: https://doi.org/10.5194/egusphere-2024-3431-RC1 -
AC1: 'Reply on RC1', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-3431', Anonymous Referee #2, 29 Jan 2025
General:
The manuscript presents a valuable contribution to the field of snow phenology by proposing a dynamic threshold method for snow phenology extraction in the Northern Hemisphere. The study effectively highlights the limitations of the traditional fixed threshold approach and demonstrates the advantages of the dynamic method in capturing the spatial heterogeneity and temporal variability of snow cover.
Specific:
- The sentence “Our analysis further indicates that the changes in snow depth exhibits a significant shift around 10% of peak value across the Northern Hemisphere, marking the transition between the snow and non-snow seasons.” need to clarity. I feel it’s difficult to understand.
- The introduction could be more focused on the specific research gap addressed by the study. Could you rewrite it?
- If possible, could you add a sentence or two to explicitly state the research objectives and the main contributions of the study.
- It would be helpful to provide a brief explanation of the rationale behind choosing the 30-day moving window for smoothing the snow depth data.
- The conclusions clearly summarize the main findings and contributions of the study.
Citation: https://doi.org/10.5194/egusphere-2024-3431-RC2 -
AC2: 'Reply on RC2', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC2-supplement.pdf
-
CC2: 'Comment on egusphere-2024-3431', David Robinson, 12 Feb 2025
The authors introduce a method of defining start dates, end dates, and durations of snow cover over Northern Hemisphere lands that differs from fixed definitions for these variables. This reviewer is left with considerable uncertainties as to how the author's dynamic method is any more useful than the fixed method. One major concern has to do with the use of microwave derived snow depth data to "drive" this approach. Many uncertainties remain to this day regarding the accuracy of this spectral region to accurately estimate depth. This can especially be true at shallow depths, with wet snow, and with deep snow that may have ice lenses, depth hoar, etc. within the pack. Unless the authors can explain how these issues do not impact their dynamic phenology method, I would place greater faith in the IMS and SCF estimates. This brings into question just how specific one can get with timing using an 8-day window for the SCF. Not that the IMS is a full-hemisphere daily evaluation of snow cover given persistent cloud cover that may mask surface conditions for multiple days. These issues are mainly more challenging in the fall for all sources, in part due to greater cloudiness then than in the spring melt season. Also, due to shallower depths, potential wet conditions, and ephemeral snow cover that can be found everywhere during seasonal snow onset (not that it doesn't exist throughout the season at lower elevation, low-mid latitude regions. Also, the challenges of using microwave data to map snow cover over the Tibetan Plateau has long been recognized, in part, as the authors suggest, due to its shallow depth (over non-mountainous areas) and ephemeral nature.
I will go no further with this evaluation as I see earlier reviewers have posted excellent comments regarding manuscript specifics, to which I agree. At this point, I will conclude that while the manuscript addresses an interesting topic and employs the key snow cover extent data products at regional to continental scales (IMS and MODIS), I am not convinced that the SD data and the subsequent conclusions built upon it are fully supported.
Citation: https://doi.org/10.5194/egusphere-2024-3431-CC2 -
AC4: 'Reply on CC2', Xin Miao, 12 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3431/egusphere-2024-3431-AC4-supplement.pdf
-
AC4: 'Reply on CC2', Xin Miao, 12 Mar 2025
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Le Wang
Xinyun Hu
Yizhuo Li
Bo Qiu
Weidong Guo
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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