the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Extreme Glacier Melt in the Central Tibetan Plateau during the Summer of 2022: Detection and Mechanisms
Abstract. Extreme glacier melt events accelerate mass loss, increase glacier instability, and temporarily mitigate downstream drought. However, the glacier energy-mass balance in the Geladandong region—the headwater of both the Yangtze River (China’s longest river) and Siling Co (Tibet’s largest lake)—and its connection to the unprecedented 2022 summer melt remain insufficiently quantified. To address this gap, we integrated in situ mass balance observations (October 2019–July 2022) and glacio- meteorological data from a 5,700 m monitoring site (October 2020–July 2022) with a surface energy-mass balance model, and applied a novel dual-threshold framework (based on mean and standard deviation) to identify extreme mass loss/melt events in short-term glacier mass balance records. Our novel methodology identified 2021–2022 as a period of extreme mass loss and melt intensity, with melt during the 2022 summer heatwave reaching unprecedented extremes. Over 52 days, the heatwave generated 1,135 mm w.e. of melt—accounting for 65.2 % of the total 2021–2022 melt, equivalent to 1.8 and 2.3 times the melt recorded in 2019–2020 and 2020–2021, respectively. This extreme melt was driven by energy balance anomalies over glacier surface, including reduced albedo, increased incoming longwave radiation, and enhanced sensible heat fluxes, with these processes strongly linked to persistent high temperatures and diminished precipitation. Such anomalies in the energy balance were driven by large-scale atmospheric circulation anomalies—specifically, the concurrent intensification and westward expansion of the Western Pacific Subtropical High (WPSH) and the eastward extension of the South Asian High (SAH). This study establishes a novel framework for identifying extreme mass loss events and substantially advances understanding of glacier mass balance responses to extreme weather and climate events.
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Status: open (until 03 Dec 2025)
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CC1: 'Comment on egusphere-2025-2579', Baijun Shang, 09 Oct 2025
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AC1: 'Reply on CC1', Zhu. Meilin, 14 Oct 2025
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Thank you for your comment.
Citation: https://doi.org/10.5194/egusphere-2025-2579-AC1
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AC1: 'Reply on CC1', Zhu. Meilin, 14 Oct 2025
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RC1: 'Comment on egusphere-2025-2579', Anonymous Referee #1, 27 Oct 2025
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This manuscript by Zhu et al. investigates a critical topic. However, in my opinion, there are fundamental issues with model validation, methodological justification, and presentation. If the author can address these issues, I would recommend publication with major revisions.
Weaknesses in Model Validation, Methodology, and Presentation
1. Inadequate Temporal Validation of Core Model Outputs (Figs. 2, 3, 4 & 5):
Figs. 2 & 3 (Scatter Plots): Presenting albedo, radiation, and mass balance as scatter plots obscures the model's performance in simulating the temporal evolution of these variables. Time-series plots are essential to reveal whether the model correctly captures the timing and duration of key events, such as the rapid albedo drop during heatwaves. A good correlation (R-value) can mask significant temporal lags or failures to replicate specific events.
Fig. 3 (Mass Balance): The scatter plots validate only the annual net result. To truly validate the melt process, the authors must present observed vs. modeled cumulative mass balance over time for each year. This would indicate whether the model accurately simulates the rate and timing of ablation. Furthermore, disaggregating into observed vs. modeled ablation and accumulation separately is critical, as a correct net balance can result from compensating errors in melt and snowfall.
Unclear Representativeness: The manuscript does not clearly state how the point measurements from a limited number of stakes (Fig 1d) are representative of the glacier-wide means presented in Figs. 4 and 5.
Redundancy (Figs. 4 & 5): Fig. 4 shows only modeled monthly fluxes. If sub-annual observed data exist, this figure should be updated to include the observed cumulative mass balance overlaid on the modeled one, making it the primary validation figure. This would render the interannual bar chart in Fig. 5 redundant, but it would also provide valuable insights into the model's temporal performance.
2. Contradictory Evidence for Heatwave Identification (Fig. 7):
The central premise of the study is the impact of "extreme heatwaves." However, Fig. 7b shows that air temperature during the identified heatwave periods (grey shading) does not appear anomalously high compared to other times in the same melt season. Some peaks outside the grey areas appear just as high or higher. This directly undermines the classification of these periods as thermally extreme events at the glacier surface and calls into question the applicability of the standardized heatwave index (S_HI) derived from a distant valley station (Tuotuhe). The authors should provide a rigorous justification for why these periods are classified as heatwaves despite the apparent lack of a strong temperature signal at the AWS.
3. Unsupported Extrapolation from Point to Glacier-Wide Data:
The study relies on a single Automatic Weather Station (AWS) at 5,700 m a.s.l. (Fig. 1). Yet, the energy and mass balance results are presented as "glacier-wide" (e.g., Figs. 4, 8).
The method for spatially interpolating meteorological data across the entire glacier's elevation range (5,400–6,104 m a.s.l.) from this single point needs to be sufficiently justified. Using a constant lapse rate and assuming wind speed and relative humidity are uniform across the glacier is a major simplification that likely introduces significant errors, especially during extreme and stable atmospheric conditions. The manuscript should explicitly discuss the uncertainties introduced by this extrapolation and provide evidence (e.g., from other studies or model sensitivity tests) that the chosen method is valid for deriving glacier-wide energy balance.
4. Unjustified Application of the Framework to Other Glaciers (Fig. 6):
The application of the dual-threshold method to other glaciers is presented without a clear rationale. If intended as validation, it is circular, as it applies the method to other short records without an independent benchmark. If intended to show prevalence, it dilutes the focus from the detailed process-based analysis of Sangqu Glacier. The purpose needs to be clearly stated and justified.
Major Revisions Required:
1) Fundamental Validation: Replace scatter plots in Figs. 2 and 3 with time-series comparisons. Crucially, include time series of observed vs. modeled cumulative mass balance to validate the ablation process.
2) Justify Heatwave Classification: Reconcile the apparent discrepancy in Fig. 7. Provide a compelling explanation for why the grey-shaded periods are classified as extreme heatwaves based on the in-situ AWS data, or re-evaluate the analysis periods.
3) Address Spatial Representation: Justify the extrapolation from a single AWS to a glacier-wide scale. Discuss the associated uncertainties and limitations explicitly. A sensitivity analysis of the model to the spatial interpolation scheme would significantly strengthen the study.
4) Streamline Narrative and Clarify Rationale:
Update Fig. 4 to include observed mass balance time series.
Remove or modify redundant figures (e.g., Fig. 5).
Provide a clear and convincing rationale for the analysis presented in Fig. 6.
Conclusion
In my opinion, the manuscript requires major revisions. The current validation is insufficient to prove the model accurately simulates ice melt dynamics. The identification of the key driver, the heatwaves, is not convincingly supported by the presented in-situ temperature data, and the extrapolation of point measurements to the entire glacier is a major, unquantified source of uncertainty. Until these fundamental issues are addressed, the analysis of energy balance mechanisms remains speculative and is built on unverified premises.
Citation: https://doi.org/10.5194/egusphere-2025-2579-RC1
Model code and software
The Dual-Threshold Framework Fei Zhu https://github.com/zhuzhufeifei/detect_extreme_events
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This study is a standout contribution to glaciology and climate science. It skillfully addresses a critical knowledge gap in the hydrologically vital Geladandong region by integrating in-situ observations, high-altitude data, and energy-mass balance modeling. Its novel dual-threshold framework offers a replicable tool for identifying extreme glacier melt events, while its clear quantification of the 2022 summer extreme melt (1,135 mm w.e. over 52 days, 65.2% of annual melt) and linkages to large-scale atmospheric circulation (WPSH, SAH) deepen understanding of glacier-climate interactions. It advances both methodology and practical insights for climate projections and water resource management.