What is the global glacier ice volume outside the ice sheets?

Hock, Regine; Maussion, Fabien; Marzeion, Ben; Nowicki, S.

doi:10.1017/jog.2023.1

Cited by 14 publications

(12 citation statements)

References 63 publications

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“…Should the models' outputs align closely with the GPR data from the ablation zone, the variance in ice volume could also be attributed to differences in the ice thickness distribution within the accumulation zone. Additionally, the accuracy of the ice thickness inversion models is highly dependent on the assumptions made regarding model parameters, particularly those pertaining to ice flow dynamics [35]. Although the model parameters have been optimized to some extent, there is still room for optimizing certain parameters, such as basal shear stress.…”

Section: Comparison With Previous Researchmentioning

confidence: 99%

“…Moreover, a synergistic approach, blending a 3D high-order numerical model with a 1D streamline model, might offer a more refined methodology for capturing the ice thickness and volume attributes of individual glaciers [18]. The 3D model, by accounting for additional variables like ice movement and temperature, can provide a detailed physical representation of the target glacier, when integrated with remote sensing information (such as adjusted basal sliding velocities of glaciers and derived ice temperatures), which may reduce inaccuracies associated with the basic ice approximation principle [12,20,35].…”

Section: Comparison With Previous Researchmentioning

confidence: 99%

“…However, this database only covers approximately 2% of the world's glaciers [34], which is insufficient. Meanwhile, the distribution of ice thickness observation data in different regions is uneven, and there are very few available ice thickness observations in many regions [35]. This leads to the calibration of the model for the entire mountain range based on only a few glaciers and then applying these calibration factors to all other glaciers, introducing uncertainty [30,31,36].…”

Section: Introductionmentioning

confidence: 99%

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Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

Yang,

Li,

Wang

et al. 2024

Remote Sensing

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As a heavily glaciated region, the Eastern Pamir plays a crucial role in regional water supply. However, considerable ambiguity surrounds the distribution of glacier ice thickness and the details of ice volume. Accurate data at the local scale are largely insufficient. In this study, ground-penetrating radar (GPR) was applied to assess the ice thickness at Muztagh Glacier No.16 (MG16) in Muztagh Ata, Eastern Pamir, for the first time, detailing findings from four distinct profiles, bridging the gap in regional measurements. We utilized a total of five different methods based on basic shear stress, surface velocity, and mass conservation, aimed at accurately delineating the ice volume and distribution for MG16. Verification was conducted using measured data, and an aggregated model outcome provided a unified view of ice distribution. The different models showed good agreement with the measurements, but there were differences in the unmeasured areas. The composite findings indicated the maximum ice thickness of MG16 stands at 115.87 ± 4.55 m, with an ice volume calculated at 0.27 ± 0.04 km3. This result is relatively low compared to the findings of other studies, which lies in the fact that the GPR measurements somewhat constrain the model. However, the model parameters remain the primary source of uncertainty. The results from this study can be used to enhance water resource assessments for future glacier change models.

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Section: Comparison With Previous Researchmentioning

confidence: 99%

Section: Comparison With Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

Yang,

Li,

Wang

et al. 2024

Remote Sensing

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“…PSK glaciers have exhibited some of the highest retreat rates in Antarctica throughout the satellite observing record, with their grounding lines receding over 30 km in recent decades (Scheuchl et al, 2016;Goldberg and Holland, 2022). Their catchment can potentially contribute up to 6 cm to the global mean sea level (Morlighem et al, 2020), double the global mean sea level contribution of the inventory of Earth's mountain glaciers (when excluding the Antarctic and Greenland periphery, Hock et al, 2023). A complete collapse of the ice shelves in this area would likely lead to accelerated mass loss from adjacent ice streams, including Thwaites Glacier (Goldberg and Holland, 2022).…”

Section: Study Area Model Domain and Data Sourcesmentioning

confidence: 99%

A framework for time-dependent Ice Sheet Uncertainty Quantification, applied to three West Antarctic ice streams

Recinos¹,

Goldberg²,

Maddison³

et al. 2023

Preprint

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Abstract. Ice sheet models are the main tool to generate forecasts of ice sheet mass loss; a significant contributor to sea-level rise, thus knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice-sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely-sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea-level rise contribution or volume above flotation (VAF), due to the choice of different regularisation strengths (prior strengths), sliding laws and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L-curve analysis, as is typically done in ice-sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularisation suggested by the L-curves is too strong and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our 2-member “sample” of observed velocity fields. Additionally, our experiments suggest that large amounts of data points may be redundant, with implications for the error propagation of VAF.

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“…If we upscale our results to all 100486 lakes in S, SW and NW (< 71° N) Greenland based on the strong relationship between excess energy and surface area while assuming similar radiation conditions and lake ice break-up variabilities, we estimate an additional energy input of 1.8 * 10 6 TJ which corresponds to melting 5.8 Gt ice at the melting point or warming 432.3 Gt water by 1 K. This number of lakes corresponds to 64.5 % of all lakes or 62.1 % of the overall lake area in the inventory (Table A1, Table A2). To put this into perspective, the upscaled mass estimate of ice melt corresponds to approximately 30 to 60 % of the volume of Greenland peripheral glaciers published in recent studies (Hock et al, 2023).…”

Section: Excess Radiation and Energy From Earlier Lake Ice Break-upmentioning

confidence: 99%

Lake Ice Break-Up in Greenland: Timing and Spatio-Temporal Variability

Posch

Abermann

Silva

2023

Preprint

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Abstract. Synthetic aperture radar (SAR) data from the Sentinel-1 (S1) mission with its high temporal and spatial resolution allows for an automated detection of lake ice break-up timings from surface backscatter differences across South (S), Southwest (SW) and Northwest (NW) Greenland (< 71° N latitude) during the period 2017 to 2021. Median break-up dates of the 563 studied lakes range between 8 June and 10 July, being earliest in 2019 and latest in 2018. There is a strong correlation between break-up date and elevation, while no relationship with latitude and lake area could be observed. Lake-specific median break-up timings for 2017–2021 increase (i.e., are later) by 3 days per 100 m elevation gain. When assuming an earlier break- up timing of 8 days which corresponds to the observed median variability of ± 8 days, the introduced excess energy due to a changing surface albedo from ice to water translates to melting 0.5 m thick ice at the melting point or heating up a water depth down to 35 m by 1 K across the entire surface area of each respective lake. Upscaling the results to 100486 lakes across the regions S, SW and NW which correspond to 64.5 % of all lakes or 62.1 % of the overall lake area in Greenland yields an estimate of 1.8 * 106 TJ additional energy input. This translates to melting 5.8 Gt ice at the melting point or warming 432.3 Gt water by 1 K.

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What is the global glacier ice volume outside the ice sheets?

Cited by 14 publications

References 63 publications

Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

A framework for time-dependent Ice Sheet Uncertainty Quantification, applied to three West Antarctic ice streams

Lake Ice Break-Up in Greenland: Timing and Spatio-Temporal Variability

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