the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Feb 2025
The thermal state of permafrost in under climate change on the Qinghai-Tibet Plateau from 1980 to 2022: A case study of the West Kunlun
Abstract. The thermal regime is a key indicator of permafrost evolution and thaw trajectories in response to climate change, yet it remains inadequately represented in global models. In this study, an efficient and integrated numerical model, the Moving-Grid Permafrost Model (MVPM) was used to simulate the permafrost thermal regime in West Kunlun (WKL), which is approximately 55,669 km² in northwest Qinghai-Tibet Plateau with extreme arid climate conditions. We employed clustering approaches and parallel computing techniques to enhance computational efficiency. The model forcing data, remote-sensing-based land surface temperature (LST) dating back to 1980 with a spatial resolution of 1 km×1 km and a temporal resolution of 1month, was constructed using machine learning techniques that integrate field observations, satellite data and reanalysis products. Our simulations achieved high accuracies of ±0.25 °C for ground temperature and ±0.25 m for active layer thickness, significantly outperforming previous simulations reported to date. The results indicated that the WKL experienced a pronounced warming trend in LST, with an average increase of 0.40 °C per decade from 1980 to 2022. The responses of the permafrost regime to climate warming were closely related to the original thermal conditions shaped by historical climatic evolution. These responses exhibited a distinct altitude-dependent spatial variation and differed according to soil stratigraphic types. Despite the thermal warming trend, the areal extent of permafrost remained relatively stable across the WKL region over the past 43 years, reflecting the slow and lagged response of permafrost to climate warming. These findings are essential for enhancing our understanding of permafrost thaw trajectories, and improving projections of potential future consequences of permafrost degradation with greater accuracy.
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Notice on discussion status
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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RC1: 'Comment on egusphere-2024-3956', Anonymous Referee #1, 22 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3956/egusphere-2024-3956-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2024-3956-RC1 - AC1: 'Reply on RC1', Lin Zhao, 02 May 2025
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RC2: 'Comment on egusphere-2024-3956', Joel Fiddes, 08 Apr 2025
This study investigates permafrost dynamics in the West Kunlun region of the Qinghai-Tibet Plateau (QTP) using a high-resolution permafrost modelling approach. The authors employ the previously developed Moving-Grid Permafrost Model (MVPM) to reconstruct permafrost thermal conditions over the past four decades, integrating remote sensing data from a previously published dataset, machine learning techniques, and field observations. The study provides valuable insights into the effects of climate change on permafrost, demonstrating a significant warming trend in land surface temperature (LST) while indicating a relatively stable permafrost extent. The methodology is reasonably innovative and well-structured, offering an improvement over previous large-scale permafrost models by considering detailed processes at depth. While the study is well-executed and contributes to our understanding of permafrost changes in this region, there are areas that require further clarification and refinement as detailed below.
Major Comments
1. Model validation and uncertainties
The authors report high model accuracy (±0.25 °C for ground temperature and ±0.25 m for active layer thickness). However, the discussion of model uncertainties could be expanded. Key areas for improvement include:
* A sensitivity analysis of key model parameters such as soil thermal properties and initial boundary conditions to assess their impact on the results.
* A deeper discussion on the limitations of the forcing datasets, particularly the machine-learning-based reconstruction of LST prior to 2003 and impact of the cold bias of 0.8degC (l.612: Compared to in situ measurements, we found a slight cold bias in our reconstructed LST series, averaging approximately -0.80°C)* The effect of snow cover on LST reconstruction and subsurface thermal dynamics is not sufficiently addressed. How are snow-insulated periods accounted for? Is there any bias introduced by cloud-covered or snow-covered days in the satellite-derived LST? (Linked to point 2).
2. Use of MODIS LST as forcing data
While MODIS LST provides high spatial resolution and extensive temporal coverage, it poses several challenges when used to force permafrost models:
* MODIS measures skin temperature rather than subsurface ground temperature, which can differ significantly—especially under snow cover or vegetation (vegetation is acknowledged in the study).
* Snow cover introduces thermal insulation, decoupling surface LST from the subsurface thermal regime (albeit often non existent or thin snowcover in this region). However wind driven snow drifts can be significant and give spatial heterogeneity at high model resolutions. The significance of these effects needs to be addressed in this study region.
* Cloud cover causes data gaps, which can lead to temporal inconsistencies or biases if gap-filling methods are not robust—particularly problematic in winter months?
* MODIS LST captures only clear-sky conditions, potentially biasing the dataset toward colder nighttime or warmer daytime extremes, depending on retrieval timing.
The paper should better articulate how these limitations are mitigated in the LST reconstruction, and what implications they have for subsurface heat fluxes and permafrost thermal state. A comparison with measured air-temperature or reanalysis-based forcing would also help.3. Representation of soil stratigraphy
The study highlights variations in permafrost responses based on soil stratigraphy, but further clarity is needed regarding:
* The specific role of soil moisture and ice content in modulating permafrost temperature trends.
* Potential biases in stratigraphic classification and how these might affect regional variability in permafrost degradation.
* The extent to which subsurface heterogeneity is accounted for in the model.4. Permafrost stability and warming trends
The study concludes that despite significant warming trends in LST, permafrost extent remains stable. While plausible given the thermal inertia of deep permafrost, the paper could benefit from:
* A clearer discussion on why this stability is observed and how it compares with degradation rates reported in other high-altitude or Arctic regions.
* Consideration of potential threshold effects (e.g., rapid degradation once a critical warming threshold is exceeded).
* More discussion on subsurface processes such as latent heat effects and talik formation, which may delay degradation despite rising surface temperatures.
* The observed increase in permafrost area despite a warming trend of 0.4 °C per decade is surprising and warrants closer examination and justification.5. Implications for future projections
While the paper effectively documents historical changes, it lacks a forward-looking component. While I realise this is beyond the scope of the paper perhaps some additional points could be added to the discussion to enhance its relevance, such as:
* Discuss how the observed trends might evolve under different climate scenarios.
* Assess the potential for abrupt future permafrost degradation / non-linearity of the system and its implications for infrastructure and carbon release.
* Offer suggestions for integrating MVPM outputs into Earth System Models to improve global climate projections.6. Spatial resolution
The use of 1 km spatial resolution may not adequately capture topographic effects (e.g., slope, aspect), which can critically influence local permafrost dynamics. The paper should further justify the adequacy of this resolution, particularly in complex mountainous terrain and variable snow cover (controlled by wind redistribution, slope, aspect).
7. Figure 5 and Figure 10
These figures display a gridded pattern at approximately 25 km resolution, which appears inconsistent with the stated 1 km model resolution. The source of this pattern should be clarified and discussed. Is it an artefact of the clustering used? This deserves explanation, especially given the emphasis on fine-scale modelling.Minor Comments
* Line 283: Typo: "account for only 28.02% of the total model grid cells, remarkable reducing computation time"
* Line 300: Typo: Section title should read "3.3 Field investigation and borehole monitoring datasets."
* Figure 7: Missing units on the legend; overall presentation could be improved.
* Line 538: Add space in "with74.20%" to read "with 74.20%".
* Figure 11: Legend and labelling could be enhanced for readability.
* Line 600: Discussion: "Most previous evaluations indicated that soil temperature products derived from atmospheric circulation models or ESMs, which typically have coarse resolutions (~300 km)..." Consider replacing "ESMs" with "GCMs" for clarity. Or revise to a more moderate number typical of historical forcing datasets such as ERA5 (25km) or ERA5-Land (9km) as you do not use GCMS in this study.
* Line 613: Clarify what is meant by "Compared to in situ measurements, we found a slight cold bias in our reconstructed LST series, averaging approximately -0.80°C" - is this computed over the entire period? Please specify the period.
* Line 858: Typo: "experiencing recover or degradation" should be revised to "experiencing recovery or degradation."
* l.185 How and what insitu measurements integrated? Please add additional clarification here. (This dataset was created by integrating in situ observations with satellite-based LST from the Moderate Resolution Imaging Spectroradiometer (MODIS). )Citation: https://doi.org/10.5194/egusphere-2024-3956-RC2 - AC2: 'Reply on RC2', Lin Zhao, 02 May 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3956', Anonymous Referee #1, 22 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3956/egusphere-2024-3956-RC1-supplement.pdf
- AC1: 'Reply on RC1', Lin Zhao, 02 May 2025
-
RC2: 'Comment on egusphere-2024-3956', Joel Fiddes, 08 Apr 2025
This study investigates permafrost dynamics in the West Kunlun region of the Qinghai-Tibet Plateau (QTP) using a high-resolution permafrost modelling approach. The authors employ the previously developed Moving-Grid Permafrost Model (MVPM) to reconstruct permafrost thermal conditions over the past four decades, integrating remote sensing data from a previously published dataset, machine learning techniques, and field observations. The study provides valuable insights into the effects of climate change on permafrost, demonstrating a significant warming trend in land surface temperature (LST) while indicating a relatively stable permafrost extent. The methodology is reasonably innovative and well-structured, offering an improvement over previous large-scale permafrost models by considering detailed processes at depth. While the study is well-executed and contributes to our understanding of permafrost changes in this region, there are areas that require further clarification and refinement as detailed below.
Major Comments
1. Model validation and uncertainties
The authors report high model accuracy (±0.25 °C for ground temperature and ±0.25 m for active layer thickness). However, the discussion of model uncertainties could be expanded. Key areas for improvement include:
* A sensitivity analysis of key model parameters such as soil thermal properties and initial boundary conditions to assess their impact on the results.
* A deeper discussion on the limitations of the forcing datasets, particularly the machine-learning-based reconstruction of LST prior to 2003 and impact of the cold bias of 0.8degC (l.612: Compared to in situ measurements, we found a slight cold bias in our reconstructed LST series, averaging approximately -0.80°C)* The effect of snow cover on LST reconstruction and subsurface thermal dynamics is not sufficiently addressed. How are snow-insulated periods accounted for? Is there any bias introduced by cloud-covered or snow-covered days in the satellite-derived LST? (Linked to point 2).
2. Use of MODIS LST as forcing data
While MODIS LST provides high spatial resolution and extensive temporal coverage, it poses several challenges when used to force permafrost models:
* MODIS measures skin temperature rather than subsurface ground temperature, which can differ significantly—especially under snow cover or vegetation (vegetation is acknowledged in the study).
* Snow cover introduces thermal insulation, decoupling surface LST from the subsurface thermal regime (albeit often non existent or thin snowcover in this region). However wind driven snow drifts can be significant and give spatial heterogeneity at high model resolutions. The significance of these effects needs to be addressed in this study region.
* Cloud cover causes data gaps, which can lead to temporal inconsistencies or biases if gap-filling methods are not robust—particularly problematic in winter months?
* MODIS LST captures only clear-sky conditions, potentially biasing the dataset toward colder nighttime or warmer daytime extremes, depending on retrieval timing.
The paper should better articulate how these limitations are mitigated in the LST reconstruction, and what implications they have for subsurface heat fluxes and permafrost thermal state. A comparison with measured air-temperature or reanalysis-based forcing would also help.3. Representation of soil stratigraphy
The study highlights variations in permafrost responses based on soil stratigraphy, but further clarity is needed regarding:
* The specific role of soil moisture and ice content in modulating permafrost temperature trends.
* Potential biases in stratigraphic classification and how these might affect regional variability in permafrost degradation.
* The extent to which subsurface heterogeneity is accounted for in the model.4. Permafrost stability and warming trends
The study concludes that despite significant warming trends in LST, permafrost extent remains stable. While plausible given the thermal inertia of deep permafrost, the paper could benefit from:
* A clearer discussion on why this stability is observed and how it compares with degradation rates reported in other high-altitude or Arctic regions.
* Consideration of potential threshold effects (e.g., rapid degradation once a critical warming threshold is exceeded).
* More discussion on subsurface processes such as latent heat effects and talik formation, which may delay degradation despite rising surface temperatures.
* The observed increase in permafrost area despite a warming trend of 0.4 °C per decade is surprising and warrants closer examination and justification.5. Implications for future projections
While the paper effectively documents historical changes, it lacks a forward-looking component. While I realise this is beyond the scope of the paper perhaps some additional points could be added to the discussion to enhance its relevance, such as:
* Discuss how the observed trends might evolve under different climate scenarios.
* Assess the potential for abrupt future permafrost degradation / non-linearity of the system and its implications for infrastructure and carbon release.
* Offer suggestions for integrating MVPM outputs into Earth System Models to improve global climate projections.6. Spatial resolution
The use of 1 km spatial resolution may not adequately capture topographic effects (e.g., slope, aspect), which can critically influence local permafrost dynamics. The paper should further justify the adequacy of this resolution, particularly in complex mountainous terrain and variable snow cover (controlled by wind redistribution, slope, aspect).
7. Figure 5 and Figure 10
These figures display a gridded pattern at approximately 25 km resolution, which appears inconsistent with the stated 1 km model resolution. The source of this pattern should be clarified and discussed. Is it an artefact of the clustering used? This deserves explanation, especially given the emphasis on fine-scale modelling.Minor Comments
* Line 283: Typo: "account for only 28.02% of the total model grid cells, remarkable reducing computation time"
* Line 300: Typo: Section title should read "3.3 Field investigation and borehole monitoring datasets."
* Figure 7: Missing units on the legend; overall presentation could be improved.
* Line 538: Add space in "with74.20%" to read "with 74.20%".
* Figure 11: Legend and labelling could be enhanced for readability.
* Line 600: Discussion: "Most previous evaluations indicated that soil temperature products derived from atmospheric circulation models or ESMs, which typically have coarse resolutions (~300 km)..." Consider replacing "ESMs" with "GCMs" for clarity. Or revise to a more moderate number typical of historical forcing datasets such as ERA5 (25km) or ERA5-Land (9km) as you do not use GCMS in this study.
* Line 613: Clarify what is meant by "Compared to in situ measurements, we found a slight cold bias in our reconstructed LST series, averaging approximately -0.80°C" - is this computed over the entire period? Please specify the period.
* Line 858: Typo: "experiencing recover or degradation" should be revised to "experiencing recovery or degradation."
* l.185 How and what insitu measurements integrated? Please add additional clarification here. (This dataset was created by integrating in situ observations with satellite-based LST from the Moderate Resolution Imaging Spectroradiometer (MODIS). )Citation: https://doi.org/10.5194/egusphere-2024-3956-RC2 - AC2: 'Reply on RC2', Lin Zhao, 02 May 2025
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Jianting Zhao
Ze Sun
Guojie Hu
Minxuan Xiao
Guangyue Liu
Qiangqiang Pang
Erji Du
Zhibin Li
Xiaodong Wu
Yao Xiao
Lingxiao Wang
Wenxin Zhang
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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