the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Oct 2025
Accelerated Permafrost Degradation in the Source Area of the Yellow River: Spatiotemporal Dynamics of Freeze-Thaw Indices Revealed by High-Resolution DEM-Corrected ERA5-Land Data (1981–2020)
Abstract. Permafrost degradation in the Source Area of the Yellow River (SAYR) has intensified under climate warming. Yet, the spatiotemporal patterns of freeze-thaw (F-T) dynamics remain poorly understood due to the limited availability of high-resolution data. Here, we integrate ERA5-Land reanalysis with a Digital Elevation Model (DEM) to develop a 1 km-resolution monthly surface temperature dataset (1981–2020), corrected for topographic bias using elevation-dependent temperature lapse rates. Based on this dataset, we calculate F-T indices (freezing/thawing index, thaw duration) and analyze their trends. Results show DEM correction significantly improves temperature accuracy (RMSE = 1.22 °C, ubRMSE = 0.38 °C). Over 40 years, air and surface freezing indices declined by –100.43 and –141.85 °C·d/10a, while thawing indices increased by 83.74 and 98.47 °C·d/10a, respectively. Thaw duration extended by 1.17 days/decade, with stronger trends in low-elevation zones. Freeze-thaw ratios (N-factor) exceeded 1 across all sites, indicating accelerated permafrost degradation. Spatial heterogeneity reveals thaw dominance in southeastern valleys (N > 5) versus residual freezing capacity in northwestern highlands (N < 2), driven by altitude and vegetation insulation. This study provides the first long-term, high-resolution F-T dataset for SAYR, demonstrating that topo-climatic gradients and vegetation feedbacks critically regulate permafrost stability. Our findings advance regional permafrost modeling and inform infrastructure resilience strategies in the context of climate change.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2692', Anonymous Referee #1, 11 Nov 2025
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AC1: 'Reply on RC1', Hongying Li, 22 Nov 2025
Reply to RC1
Dear Editor and Reviewer,
We sincerely thank the reviewer for the thorough and constructive evaluation. The comments have significantly improved the clarity, rigor, and overall quality of our manuscript. Below, we respond point-by-point. Reviewer comments are marked with “>> Reviewer comment,” followed by our Response and the corresponding Modification in manuscript.
Major Comments
Major Comment 1 — Definition of Permafrost Science
Reviewer comment:
“The terms of ‘perennial permafrost’ and ‘seasonal permafrost’ do not appear to be used within the permafrost community… It would be more appropriate to distinguish between permafrost and seasonally frozen ground.”Response:
We appreciate this important comment. We agree that “perennial permafrost” is not a standard term in permafrost science. In the revised manuscript, we use the correct terminology and describe the region as a transition zone from permafrost to seasonally frozen ground, following Dobiński (2011, 2020). We believe the revision improves scientific precision and aligns our terminology with established cryospheric literature.Modification in manuscript:
The Introduction now states:
“The source area of the Yellow River (SARY) … transitions from permafrost to seasonally frozen ground … underlain by warm, thin, and thermally unstable permafrost characteristic of the warm-permafrost zone of the Tibetan Plateau…”.
Consistent terminology revisions were applied throughout the manuscript.Major Comment 2 — Innovation
Reviewer comment:
The novelty of the study is not sufficiently clear, as freeze–thaw changes have been widely documented.Response:
Thank you for raising this important point. We substantially revised the Introduction and Methods to clarify the study’s unique contributions. The key innovations are:(1) A new 1-km, 40-year (1981–2020) monthly DEM-corrected surface-temperature dataset for the SARY
No existing dataset provides simultaneously:
• 1 km resolution
• monthly surface temperature
• 1981–2020 coverage
• based on ERA5-Land
• corrected using explicit, month-specific DEM lapse rates
• developed specifically for the SARY warm-permafrost zone.(2) Clear distinction from existing high-resolution datasets
Existing 1-km products (e.g., Peng 2019; Gao 2023; Tang 2023) provide air temperature or MODIS LST only, or cover only the post-2000 period. None provide a long-term (1981–2020) physically corrected LST dataset for the SARY.
(3) A unified air–surface freeze–thaw diagnostic framework
We combine air indices (AFI, ATI), surface indices (SFI, STI), freeze–thaw ratio (N), and a station-constrained thaw-duration model (DOT).
This integrated analysis has not previously been applied in this region.(4) Physically based DEM calibration
Our DEM-based lapse-rate correction is a physically explicit approach—not machine-learning or black-box downscaling—providing a robust foundation for long-term trend analysis.
Modification in manuscript:
A strengthened paragraph was added at the end of the Introduction.
Clarifications were added in Sections 2.3 and 2.4.
A comparison table (Table R1) is provided in the separate Response Supplement (PDF) uploaded with the submission.Major Comment 3 — CMA Station Data Changes (Manual vs Automatic Pre/Post-2003)
Reviewer comment:
Ground surface temperature measurements differ before/after 2003 due to manual vs automatic observations. This inconsistency must be addressed.Response:
We appreciate this critical comment. We performed a full sensitivity analysis:- Separated all station STobs into pre-2003 and post-2003 subsets.
- Computed monthly lapse rates (Γₜ) for:
• pre-2003 only
• post-2003 only
• full 1981–2020 - Compared slopes, R², p-values, and RMSE.
Key findings:
• Post-2003 lapse rates are steeper and more consistent.
• Pre-2003 lapse rates are weaker due to snow-surface warming bias.
• Full-period Γₜ lies between the two and remains highly significant (p < 0.05).
• Differences mainly affect winter months and do not introduce structural bias in reconstructed temperature.A full table and figures were added in Supplement S1.3 (“Sensitivity test for CMA observational changes”).
Modification in manuscript:
Section 2.3 now includes a robustness paragraph (lines 156–163).
Supplement S1.3 added.Major Comment 4 — Freeze–Thaw Ratio N
Reviewer comment:
The use and interpretation of N = 1 is problematic; N should not be treated as a boundary for permafrost stability.Response:
We agree entirely. We revised Section 2.4 to clarify that N is not used to delineate permafrost, but rather to characterize relative thermal tendencies:N > 1 → thaw-dominated
N ≈ 1 → balanced
N < 1 → freeze-dominatedWe clarify that permafrost stability depends on MAGT, and that N is used only as a surface thermal index, consistent with Cheng (2003) and Ran et al. (2022).
Modification in manuscript:
A clarification paragraph was added after Equation (5) (lines 182–193).Minor Comments
L26–31: Sentence too long
Response:
Rewritten for clarity.Modification:
Lines 25–27 revised.L38–39: Chen et al. (2024)
Response:
The cited wording did not appear in our manuscript; the related sentence was nevertheless clarified.Modification:
Introduction revised (lines 38–42).L46: Why use 40 years, not full ERA5-Land (1940–present)?
Response:
We now explain that:- ERA5-Land before 1981 has substantially higher uncertainty.
• Station observations used for lapse-rate calibration only exist after 1981.
• NDVI and auxiliary datasets also begin in the 1980s.
• Using 1981–2020 ensures consistency and comparability with previous cryosphere studies.
Modification:
Explanation added to Introduction (lines ~44–48).L67: “The study” unclear
Response:
Clarified and rewritten explicitly.Modification:
Section 2.1, lines 71–73 revised.L101: ERA5-Land bias over QTP
Response:
We now explicitly state that raw ERA5-Land is not used; instead, we apply a DEM-based monthly lapse-rate correction.
This calibration reduces RMSE by 46–92%.Modification:
Section 2.3 (lines 121–125).L119: Typo
Corrected.
L126: Unit for Γₜ
Corrected to °C/100 m.
Figure 4: Training vs validation
Response:
Figure completely revised:- Training data = blue
• Validation data = red
• Regression & metrics = based on validation only
• Clear legend added
Modification:
Figure 4 replaced; caption revised (lines 251–255).L178: Wrong figure reference
Corrected to Figure S2.
L467: Conclusion too long / mixed with Results
Response:
The Conclusion was rewritten to:
• remove numerical details
• emphasize key findings
• state major implicationsModification:
New Conclusion inserted (lines 491–502).Summary
We sincerely thank the reviewer for the insightful comments. All suggestions have been carefully addressed, resulting in substantial improvements to the manuscript’s clarity, rigor, and scientific contribution.
Sincerely,
Hongying Li, on behalf of all co-authors. - AC3: 'Reply on RC1', Hongying Li, 23 Nov 2025
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AC1: 'Reply on RC1', Hongying Li, 22 Nov 2025
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RC2: 'Comment on egusphere-2025-2692', Anonymous Referee #2, 18 Nov 2025
This manuscript investigates the spatiotemporal variations of the freeze–thaw cycle in the source region of the Yellow River (SAYR) during 1981–2020. The topic is relevant and of potential regional significance; however, several methodological and presentation issues substantially limit the scientific rigor and overall quality of the paper.
(1) Limited methodological novelty
The study applies an elevation-based correction to ERA5-Land temperature data, which is a common and widely used approach in permafrost research. The procedure described as “downscaling” in the paper actually involves bilinear interpolation, which is simply a spatial resampling technique rather than a true downscaling method that adds new spatial information.
(2) Inconsistent use of temporal resolution
The freezing and thawing indices are calculated using monthly mean temperature, yet the results are expressed in °C·day units, which is conceptually inconsistent. In addition, Figure 5, which presents “the daily maximum ST / daily minimum ST,” does not specify which dataset or temporal resolution these statistics were based on.
(3) Unclear calibration and validation procedure
The manuscript does not describe how the meteorological stations were divided into calibration and validation sets, nor whether cross-validation or an independent test set was used. Without this information, it is difficult to assess the robustness of the temperature correction and subsequent analyses.
(4) Inconsistency between datasets for air and surface indices
The air thawing/freezing indices were derived from CMD data, whereas the surface indices were computed using ERA5-Land data. These datasets differ in both temporal coverage and spatial resolution and may exhibit systematic temperature biases. This discrepancy could affect the comparability of the two sets of indices and the interpretation of their correlations.
(5) Misinterpretation of permafrost degradation
The manuscript attributes changes in the southeastern SAYR to permafrost degradation and active layer thickening. However, this region is primarily characterized by seasonally frozen ground, not permafrost. Therefore, these explanations are not appropriate.
(6) Writing quality
The overall writing quality is weak. The text contains numerous typographical errors, inconsistent terminology, incorrect units, and formatting inconsistencies.Citation: https://doi.org/10.5194/egusphere-2025-2692-RC2 -
AC2: 'Reply on RC2', Hongying Li, 23 Nov 2025
Dear Editor and Reviewer,
We sincerely thank the Reviewer for their thorough review and valuable comments, which have helped us significantly improve the manuscript. We have carefully addressed each point raised, and our point-by-point responses are provided below. All suggested changes have been incorporated into the revised manuscript.
Comment R2-1: Limited methodological novelty
“The study applies an elevation-based correction to ERA5-Land temperature data, which is a common and widely used approach in permafrost research. The procedure described as “downscaling” in the paper actually involves bilinear interpolation, which is simply a spatial resampling technique rather than a true downscaling method that adds new spatial information.”
Response:
Thank you very much for this constructive comment. We fully agree that the terminology in the original manuscript could be misleading. In the revision, we have carefully clarified the methodological framework and replaced all inappropriate uses of the term “downscaling.”(1) Terminology correction
In this study, the only resampling step is the bilinear interpolation that converts ERA5-Land (0.1°) to a 1-km grid. We now explicitly refer to this step as spatial resampling, not “downscaling.” The term “downscaling” has been removed from the manuscript, figure captions, and workflow diagram. The modified label in Figure 2 now reads:
“Topographic bias correction and spatial resampling.”
(2) Clarification of the actual methodological novelty
The core methodological contribution of our study is not bilinear interpolation, but the DEM-based, month-specific lapse-rate correction derived from station-observed surface temperature (STobs). This correction is physically based and explicitly accounts for the vertical temperature gradient specific to each month. To avoid confusion, we now describe our approach as:
“DEM-based elevation correction of ERA5-Land SKT using observed monthly lapse rates.”
(3) Revised text for consistency
Section 2.3, the workflow figure (Figure. 2), and all relevant paragraphs have been revised to ensure consistent terminology. No part of the method is now described as a downscaling technique.
We believe these revisions fully address the reviewer’s concern and improve methodological clarity and rigor.
Modification in manuscript:
1.Step 2 Title updated“Step 2 – Elevation bias correction using DEM-based monthly lapse-rate adjustment instead of “Step 2 – Elevation bias correction and downscaling.”
- Figure 2 revised:
- “Topographic bias revision and downscaling” → “Topographic bias correction and 1-km resampling” (Line 146)
- The workflow arrow was updated to explicitly show:
- “Bilinear resampling to 1 km (spatial interpolation)”
- “DEM-based monthly lapse-rate correction (bias adjustment).”
Comment R2-2: Inconsistent use of temporal resolution
“The freezing and thawing indices are calculated using monthly mean temperature, yet the results are expressed in °C·day units, which is conceptually inconsistent. In addition, Figure 5, which presents “the daily maximum ST / daily minimum ST,” does not specify which dataset or temporal resolution these statistics were based on.”
Response:
Thank you for this insightful comment. We agree that the manuscript did not clearly describe how monthly temperatures were converted into °C·day units, which may cause confusion. In the revision, we now explicitly state that freezing and thawing indices were computed by multiplying monthly mean temperatures by the number of days in each month. This is a commonly applied approximation when long-term daily data are not available (e.g., Zhang et al., 2005; Luo et al., 2014). The revised formulation ensures that all indices are consistently expressed in °C·day and are fully comparable to daily-based definitions. Corresponding clarifications have been added in Section 2.4.Modification in manuscript:
“Freezing and thawing indices were calculated using monthly mean temperatures. For each month, the monthly mean temperature was multiplied by the number of days in that month to obtain °C·day units……”Section 2.4 :Line 180-183.
Comment R2-3: Unclear calibration and validation procedure)
“The manuscript does not describe how the meteorological stations were divided into calibration and validation sets, nor whether cross-validation or an independent test set was used. Without this information, it is difficult to assess the robustness of the temperature correction and subsequent analyses.”
Response:
Thank you very much for this constructive comment. We would like to clarify that the DEM-based lapse-rate correction used in this study is a physically driven method rather than a statistical or machine-learning downscaling model. Therefore, no training/test split or cross-validation is required. Instead, the procedure involves two steps:- Estimation of monthly lapse rates (Γₜ):
All available CMA stations were used to fit monthly surface-temperature lapse rates using their long-term (1981–2020) monthly mean STobs. This step produces a physically constrained lapse-rate parameter for each month. - Independent temporal validation:
To test the robustness of the corrected ERA5-Land surface temperature, we used an independent temporal subset—the monthly station STobs from 1981–2015—to evaluate RMSE, ubRMSE, and correlation coefficients. This validation is fully independent of the lapse-rate fitting step because it isolates interannual variability.
This procedure has now been explicitly described in Section 2.3.
Additionally, Figure 4 has been revised (as requested by Reviewer 1) to clearly distinguish the data used to derive the monthly lapse rate from the data used for validation, ensuring full transparency.Modification in manuscript:
Section 2.3 now states that lapse-rate estimation uses long-term station observations, and the corrected ST is validated against an independent station subset. Figure 4 has been updated to clearly separate estimation and validation datasets. (Section 2.3: Lines 157 – 159)Comment R2-4: Inconsistency between datasets for air and surface indices
Reviewer comment:
The air thawing/freezing indices were derived from CMD data, whereas the surface indices were computed using ERA5-Land data. These datasets differ in both temporal coverage and spatial resolution and may exhibit systematic temperature biases. This discrepancy could affect the comparability of the two sets of indices and the interpretation of their correlations..Response:
Thank you very much for this insightful comment. We fully agree that air- and surface-based indices originate from different datasets and therefore cannot be directly compared in an absolute sense. In this study, the two sets of indices were used for complementary purposes:- Air-based indices (AFI, ATI, Na) represent atmospheric thermal forcing and are derived from homogenized CMD station-based 1-km air temperature.
- Surface-based indices (SFI, STI, Ng, DOT) reflect the surface energy state and were derived from the DEM-corrected ERA5-Land ST, which provides spatially continuous fields needed for mapping freeze–thaw patterns.
The objective was not to compute direct point-wise differences between air and surface indices, but rather to examine their spatial co-evolution and the relative sensitivity of atmosphere vs. land surface during freeze–thaw transitions.
Modification in manuscript:
Added methodological explanation in Lines 180–181 in Section 2.4.
Comment R2-5: Misinterpretation of permafrost degradation
“The manuscript attributes changes in the southeastern SAYR to permafrost degradation and active layer thickening. However, this region is primarily characterized by seasonally frozen ground, not permafrost. Therefore, these explanations are not appropriate.”
Response:We thank the reviewer for pointing this out. The southeastern SAYR is indeed dominated by seasonally frozen ground. Although the manuscript does not explicitly state that this area contains permafrost, some descriptions may give the impression that seasonal freeze–thaw variations were interpreted as permafrost degradation.
To avoid any possible misinterpretation, we have slightly revised the wording in the Discussion sections to explicitly distinguish between:
- permafrost-dominated areas in the central and western SAYR, and
• seasonally frozen ground areas in the southeastern SAYR.
These revisions ensure terminological accuracy and prevent unintended implications regarding permafrost degradation in non-permafrost zones.
Modification in manuscript:
We added a clarifying sentence in the Discussion:
“It should be noted that the southeastern SAYR is dominated by seasonally frozen ground rather than permafrost; therefore, the observed freeze–thaw intensification in this area reflects variations within the seasonally frozen layer rather than degradation of permafrost.”
Revised statements in Section 3.3, Lines 444–446.
Comment R2-6: Writing quality
“The overall writing quality is weak. The text contains numerous typographical errors, inconsistent terminology, incorrect units, and formatting inconsistencies.”
Response:
We sincerely thank the reviewer for this comment. We carefully re-checked the entire manuscript and made substantial improvements to writing quality. Specifically:- All typographical and grammatical issues were corrected through full-manuscript proofreading.
- Terminology was standardized (permafrost, seasonally frozen ground, lapse rate, etc.)
- corrected units (e.g., °C/100 m)
- corrected variable symbols
- uniform formatting for equations, tables, and captions
- consistent use of SARY/SAYR
- Sections 2.3, 2.4, 4.3， and the Conclusion received additional polishing to ensure clarity, coherence, and consistent scientific tone.
These revisions have improved the precision and readability of the manuscript, and we appreciate the reviewer’s suggestion that prompted these enhancements.
Modification in manuscript:
- Full copy-edit across all sections.
- All units, symbols, and terminology standardized to TC conventions.
(No line numbering provided due to widespread minor adjustments).
Summary
We sincerely thank the reviewer for the insightful comments. All suggestions have been carefully addressed, resulting in substantial improvements to the manuscript’s clarity, rigor, and scientific contribution.
Sincerely,
Hongying Li, on behalf of all co-authors.Citation: https://doi.org/10.5194/egusphere-2025-2692-AC2
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AC2: 'Reply on RC2', Hongying Li, 23 Nov 2025
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RC3: 'Comment on egusphere-2025-2692', Anonymous Referee #3, 21 Nov 2025
General comments
This study applies a DEM-based lapse-rate correction to ERA5-Land skin temperature to generate a 1-km monthly surface-temperature dataset for 1981–2020, and assess freeze–thaw dynamics and permafrost degradation in the Source Area of the Yellow River (SAYR). Although the topic is relevant and the dataset may be of interest, the scientific robustness and innovation of the methods require further examination.
Major comments
1. Innovation and effectiveness of the DEM-based correction method appear overstated
The correction relies solely on a 1-D lapse-rate relationship and ignores slope, aspect, surface properties, and land-cover influence—despite the study area being described as a “mountainous region with complex topography.” Such a simplification is unlikely to adequately capture spatial temperature variability in the SAYR(Liang et al. 2014). The justification of “data limitations” is insufficient, and the authors should explicitly discuss the uncertainty introduced by ignoring other factors.
2. Only seven meteorological stations are used, which is inadequate for estimating monthly lapse rates
Using 7 stations only to represent a region greater than 40,000 km² is extremely sparse. Moreover, most stations are located in low-elevation accessible areas, likely causing substantial biases in lapse-rate estimation. Therefore, I recommend providing cross-validation or leave-one-out tests, examining spatial/temporal stability of lapse rates, and considering using auxiliary datasets (e.g., MODIS LST)
3. The performance of the corrected skin temperature requires more critical assessment
In Fig. 4, although the R² value is high, the axes are plotted over different ranges, making the agreement appear stronger than it is. A 1:1 reference line should be added. Visual inspection suggests large deviations between corrected ST and station observations. Moreover, the RMSE value (1.22 °C) appears inconsistent with the scatter distribution and should be recalculated.
4. FI/TI derived from monthly mean temperatures produce large uncertainties
Freezing and thawing indices are conventionally calculated from daily temperature sums(Peng et al. 2019). Using monthly mean temperatures can introduce very large errors—especially during transition seasons when daily temperatures oscillate around 0°C. The manuscript should quantify this uncertainty or justify the applicability of monthly data for FI/TI estimation in this region(Liu et al. 2021).
5. Snow cover effects are not discussed
Snow strongly insulates the soil, and snow-surface temperature (represented by ERA5-Land SKT) often differs substantially from soil-surface temperature below the snowpack(Peng et al. 2024). This may significantly bias winter SFI, STI, and N-factor values. A discussion of how snow processes affect the results is necessary.
6. Novelty is limited: several findings repeat existing research
FI/TI patterns in global permafrost zones, even in Qinghai-Tibet Plateau have been widely reported ,e.g., (Qin et al. 2021) (Fang et al. 2023). The authors should better articulate what new scientific insights are provided beyond producing a regional dataset.
Specific comments
Below is a list of language, formatting, and consistency issues identified in the manuscript:
- Line 57: Incorrect reference formatting.
- Lines 66–70: The sentence on NDVI and ecological risks monitoring is unclear and should be rewritten.
- Line 75: “Datas” → “Data” or “Datasets”.
- Figure 1: The legend states “red triangles,” but the figure shows red circles.
- Lines 77–78: Provide data source citation.
- Line 119: Clarify the meaning of “each month t.”
- Line 126: “℃/100m-1” should be “℃/100 m” or “℃ 100 m⁻¹”.
- Line 178: “Figure S3” should be “Figure S2.”
- Line 195: “person's correlation coefficient” → “Pearson’s correlation coefficient.”
- Lines 200–202: The meaning of the sentence is unclear.
- Figure 3 and Table S1 are redundant—consider removing one.
- Lines202–206 and239–242: belong to discussion.
- Figure 5: The y-axis label is unclear.
- Line 253: “900.01°C” should be “900.01 °C·day.”
- Figures 6–8: Colorbar ranges should be consistent across subplots for comparability.
- Line 300: What does “FFI” mean??? Please correct it.
- Line 324: “thawning” → “thawing.”
- Lines 400–405: Meaning of the paragraph is unclear and should be rephrased.
- Line 407: What do you mean by “The TI in the SAYR is found to be considerably higher than the TI”??? Variable confusion.
- Line 472: “temperature increased from 85°C to 100°C” – clearly incorrect, please check it.
- Full names of abbreviations should appear only at first occurrence, not repeatedly.
- FI values should be expressed as negative values unless explicitly defined otherwise.
- Many typing errors in “References” session, please check them carefully.
Reference
Fang X, Wang A, Lyu S, Fraedrich K (2023) Dynamics of Freezing/Thawing Indices and Frozen Ground from 1961 to 2010 on the Qinghai-Tibet Plateau. Remote Sensing 15:3478. https://doi.org/10.3390/rs15143478
Liang LL, Riveros‐Iregui DA, Emanuel RE, McGlynn BL (2014) A simple framework to estimate distributed soil temperature from discrete air temperature measurements in data‐scarce regions. JGR Atmospheres 119:407–417. https://doi.org/10.1002/2013JD020597
Liu C, Feng S, Huang W (2021) An improved method for calculating the freezing/thawing index using monthly and annual temperature data. Intl Journal of Climatology 41:4548–4561. https://doi.org/10.1002/joc.7085
Peng X, Frauenfeld OW, Huang Y, et al (2024) The thermal effect of snow cover on ground surface temperature in the Northern Hemisphere. Environ Res Lett 19:044015. https://doi.org/10.1088/1748-9326/ad30a5
Peng X, Zhang T, Liu Y, Luo J (2019) Past and Projected Freezing/Thawing Indices in the Northern Hemisphere. Journal of Applied Meteorology and Climatology 58:495–510. https://doi.org/10.1175/JAMC-D-18-0266.1
Qin Y, Wu T, Zhang P, et al (2021) Spatiotemporal freeze–thaw variations over the Qinghai‐Tibet Plateau 1981–2017 from reanalysis. Intl Journal of Climatology 41:1438–1454. https://doi.org/10.1002/joc.6849
Citation: https://doi.org/10.5194/egusphere-2025-2692-RC3 - AC4: 'Reply on RC3', Hongying Li, 02 Dec 2025
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This study investigates the spatiotemporal dynamics of freeze–thaw indices using elevation-corrected ERA5-Land data. The authors report an increasing thawing index and a reduced freeze–thaw ratio. However, my primary concern lies with the perceived lack of innovation in this work, while the methodology also appears to be problematic. Please see my comments below.
Major Comments
1) Definination of Permafrost Science
The terms of “perennial permafrost” and “seasonal permafrost” do not appear to be used within the permafrost communicty. Permafrost itself means “permanent frost” (Dobinski 2011). It would be more appropriate to distinguish between permafrost and seasoannly frozen ground.
2) Innoviation
Changes in freezing and thawing indices have already been extensively documented at both global and regional scales (e.g., Frauenfeld et al., 2004; Zhang et al., 2005; Luo et al., 2014; Peng et al., 2016, 2019; Wu et al., 2018; Dashtseren et al., 2018). Although the authors state that this study presents the "first 1-km resolution monthly surface temperature dataset (1981–2020) for the Yellow River headwater area," the conclusions—such as increases in air/surface freezing indices, thawing ratio, and thaw duration—are consistent with existing literature. Therefore, the novel contribution of this study needs to be more clearly articulated. My concern is what's the real contribution of this study?
3) Observations from China Meteorological Data Network
Authors used the daily (ground) surface temperature from the China Meteorological Data Network (http://data.cma.cn). It is important to note that the ground surface temperature is not consistant (Cui et al., 2020). During the manual observation time period, ground surface temperature represents the snow surface temperature when the surface is covered by snow. Since around 2003, the meteorological stations were gradually transformed from manual observations to automatic observations. In automatic observations, ground surface temperature represents the temperature of the soil surface beneath the snow cover (Du et al., 2020). Since the ground surface temperature from China Meteorological Data Network was used for downscaling evaluation and model traning, this inconsistency poses a significant issue that must be addressed. Since the authors used skin temperature data, they would likely prefer the annually measured snow surface temperature. This implies that only the ground surface temperature from before 2003 should be used. The weak correlation between elevation and freezing index shown in Figure S3a, compared to that of the thawing index, may be attributed to such inhomogeneities in surface temperature measurements. The authors should clarify how this issue was handled.
4) Freeze-thaw ratio
The authors used the freeze-thaw ratio (N) as an indicator of permafrost thermal stability, citing Cheng et al. (2003), and applied a threshold of N = 1 to classify permafrost stability. This approach appears problematic. According to Equation 1, the relationship can be expressed as:
The statement in L162-163 then becomes “When MAAT = 0 ºC, freezing and thawing are approximately balanced, reflecting relatively stable permafrost conditions.” This is contrast to the Cheng’s classification (see Cheng 1984 and Ran et al., 2021). The classification proposed by Cheng used the MAGT, not MAAT. Moreover, according to Gruber (2012), permafrost is generally limited to regions where MAAT ≤ –1.5°C. In reality, , permafrost is unlikely to persist in areas where MAAT = 0°C, except under the most favorable local conditions.
Specific Comments
References:
Chen, C., Peng, X., Frauenfeld, O. W., Chu, X., Chen, G., Huang, Y., Li, X., Yang, G., and Tian, W.: Simulations and Prediction of Historical and Future Maximum Freeze Depth in the Northern Hemisphere, JGR Atmospheres, 129, e2023JD039420, https://doi.org/10.1029/2023JD039420, 2024.
Cheng, G., Jiang, H., Wang, K., and Wu, Q.: Thawing Index and Freezing Index on the Embankment Surface in Permafrost Regions, Journal of Glaciology and Geocryology, 603 –607, 2003.
Dashtseren, A., Temuujin, K., Westermann, S., Batbold, A., Amarbayasgalan, Y., & Battogtokh, D. (2021). Spatial and temporal variations of freezing and thawing indices from 1960 to 2020 in Mongolia. Frontiers in Earth Science, 9, 713498.
Dobinski, W.: Permafrost, Earth-Science Reviews, 108, 158–169, https://doi.org/10.1016/j.earscirev.2011.06.007, 2011.
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