the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Jul 2025
Quantifying Temperature-sliding Inconsistency in Thermomechanical Coupling: A Comparative Analysis of Geothermal Heat Flux Datasets at Totten Glacier
Abstract. Rapid sliding of ice sheets requires warm basal temperatures and lubricating basal meltwater, whereas slow velocities typically correlate with a frozen bed. However, ice sheet models often infer basal sliding by inverting surface velocity observations with the vertical structure of temperature and hence rheology held constant. If the inversion is allowed to freely vary sliding over the model domain, then inconsistencies between the basal thermal state and ice motion can arise lowering simulation realism. In this study, we propose a new method that quantifies inconsistencies when inferring warm and cold-bedded regions of ice sheets. This method can be used to evaluate the quality of ice sheet simulation results without requiring any englacial or subglacial measurements. We apply the method to evaluate simulation results for Totten Glacier using an isotropic 3D full-Stokes ice sheet model with eight geothermal heat flux (GHF) datasets and compare our evaluation results with inferences on basal thermal state from radar specularity. The rankings of GHF datasets based on inconsistency are closely aligned with those using the independent specularity content data. Examples of the method utility are 1. an inconsistency characterizing overcooling with all GHFs near the western boundary of Totten Glacier between 70° S–72° S, where there is a bedrock canyon and fast surface ice velocities, which suggests that GHF is low in all published datasets; 2. an overheating inconsistency in the eastern Totten Glacier with all GHFs that leads to overestimation of ice temperature due, in this case, to an unrealistically warm surface temperature. Our approach opens a new avenue for assessing the self-consistency and reliability of ice sheet model results and GHF datasets, which may be widely applicable.
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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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Supplement
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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.
- Preprint
(1908 KB) - Metadata XML
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Supplement
(1097 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-3296', Anonymous Referee #1, 01 Aug 2025
- AC1: 'Reply on RC1', Junshun Wang, 19 Oct 2025
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RC2: 'Comment on egusphere-2025-3296', Anonymous Referee #2, 19 Sep 2025
In this study, Wang et al. compare modelled velocities derived from a full-Stokes thermomechanical model (from a previous study, Huang et al., 2024) with surface velocity observations from MEaSUREs. I do not think this study is a novel contribution to the field, and found the manuscript confusing, so I do not recommend it for publication.
Major comments
In a preceding study, Huang et al. (2024) conduct full-Stokes thermomechanical simulation forced by 8 different geothermal heat flux models. The strength of this study is the comparison of the model results with an independent constraint – the radar specularity content of the bed. To make this comparison, Huang et al. perform an inversion for the basal parameters, based on modelled vs. surface velocities. So in this study, Wang et al. are calculating metrics based on the residuals of the modelled vs surface velocities, and are thus evaluating the performance of the Huang et al. inversions. While it seems reasonable that better performing inversions reflect more accurate GHF map, a range of other factors can be at play. For instance, the authors note the possible influence of anisotropic viscosity. Poorly-performing inversions could also be the result of uncertainties in the form of the basal sliding law, or in the basal topography model. Wang et al. present a ranking of GHF maps that is similar but not identical to that of Huang et al. It is unclear why Wang et al. think that this ranking is more accurate, especially given the circularity of the velocity residual argument mentioned above. It is also unclear that the Wang et al study presents results which are novel, relative to the Huang et al. study.
Wang et al. justify their study by saying that they are introducing a new method – this is misleading. They are instead introducing new terminology for “metrics” based on velocity residuals, which is a common practice in the field of glaciological inversion. One metric masks the thawed bed, another metric masks the frozen bed, but both metrics are used in the final evaluation – so it is unclear why the masking was necessary to start with. I don’t understand why you need “bidirectional constraints” when you could take the root-mean-square error. Even if they were presenting a new method, they do not provide any tests to show that this method improves upon existing ones. Finally, the terminology of these metrics is confusing, and I found did not provide me with a better physical understanding of why some models performed better than others (although this last point can be rectified with clearer language).
Minor comments
In Figure 3 & 4, I think it would be clearer to change the term “warm bed” to “thawed bed”. And in Figure 5, “cold bed” to “frozen bed”.
The authors should note where they obtain their velocity observations within the main text, and maybe plot them.
I think the authors should add a section describing the model methodology, so that the reader can understand this paper without having to read Huang et al.
Citation: https://doi.org/10.5194/egusphere-2025-3296-RC2 - AC2: 'Reply on RC2', Junshun Wang, 19 Oct 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-3296', Anonymous Referee #1, 01 Aug 2025
- AC1: 'Reply on RC1', Junshun Wang, 19 Oct 2025
-
RC2: 'Comment on egusphere-2025-3296', Anonymous Referee #2, 19 Sep 2025
In this study, Wang et al. compare modelled velocities derived from a full-Stokes thermomechanical model (from a previous study, Huang et al., 2024) with surface velocity observations from MEaSUREs. I do not think this study is a novel contribution to the field, and found the manuscript confusing, so I do not recommend it for publication.
Major comments
In a preceding study, Huang et al. (2024) conduct full-Stokes thermomechanical simulation forced by 8 different geothermal heat flux models. The strength of this study is the comparison of the model results with an independent constraint – the radar specularity content of the bed. To make this comparison, Huang et al. perform an inversion for the basal parameters, based on modelled vs. surface velocities. So in this study, Wang et al. are calculating metrics based on the residuals of the modelled vs surface velocities, and are thus evaluating the performance of the Huang et al. inversions. While it seems reasonable that better performing inversions reflect more accurate GHF map, a range of other factors can be at play. For instance, the authors note the possible influence of anisotropic viscosity. Poorly-performing inversions could also be the result of uncertainties in the form of the basal sliding law, or in the basal topography model. Wang et al. present a ranking of GHF maps that is similar but not identical to that of Huang et al. It is unclear why Wang et al. think that this ranking is more accurate, especially given the circularity of the velocity residual argument mentioned above. It is also unclear that the Wang et al study presents results which are novel, relative to the Huang et al. study.
Wang et al. justify their study by saying that they are introducing a new method – this is misleading. They are instead introducing new terminology for “metrics” based on velocity residuals, which is a common practice in the field of glaciological inversion. One metric masks the thawed bed, another metric masks the frozen bed, but both metrics are used in the final evaluation – so it is unclear why the masking was necessary to start with. I don’t understand why you need “bidirectional constraints” when you could take the root-mean-square error. Even if they were presenting a new method, they do not provide any tests to show that this method improves upon existing ones. Finally, the terminology of these metrics is confusing, and I found did not provide me with a better physical understanding of why some models performed better than others (although this last point can be rectified with clearer language).
Minor comments
In Figure 3 & 4, I think it would be clearer to change the term “warm bed” to “thawed bed”. And in Figure 5, “cold bed” to “frozen bed”.
The authors should note where they obtain their velocity observations within the main text, and maybe plot them.
I think the authors should add a section describing the model methodology, so that the reader can understand this paper without having to read Huang et al.
Citation: https://doi.org/10.5194/egusphere-2025-3296-RC2 - AC2: 'Reply on RC2', Junshun Wang, 19 Oct 2025
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Junshun Wang
Liyun Zhao
Michael Wolovick
John C. Moore
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1908 KB) - Metadata XML
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Supplement
(1097 KB) - BibTeX
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- Final revised paper
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