Progress toward globally complete frontal ablation estimates of marine-terminating glaciers
Knowledge of frontal ablation from marine-terminating glaciers (i.e., mass lost at the calving face) is critical for constraining glacier mass balance, improving projections of mass change, and identifying the processes that govern frontal mass loss. Here, we discuss the challenges involved in compu...
Published in: | Annals of Glaciology |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2023
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Subjects: | |
Online Access: | https://pure.uhi.ac.uk/en/publications/244239c9-72dd-4661-9671-87e8962b5e56 https://doi.org/10.1017/aog.2023.35 https://pureadmin.uhi.ac.uk/ws/files/43507938/progress_toward_globally_complete_frontal_ablation_estimates_of_marine_terminating_glaciers.pdf https://www.cambridge.org/core/product/identifier/S0260305523000356/type/journal_article |
Summary: | Knowledge of frontal ablation from marine-terminating glaciers (i.e., mass lost at the calving face) is critical for constraining glacier mass balance, improving projections of mass change, and identifying the processes that govern frontal mass loss. Here, we discuss the challenges involved in computing frontal ablation and the unique issues pertaining to both glaciers and ice sheets. Frontal ablation estimates require numerous datasets, including glacier terminus area change, thickness, surface velocity, density, and climatic mass balance. Observations and models of these variables have improved over the past decade, but significant gaps and regional discrepancies remain, and better quantification of temporal variability in frontal ablation is needed. Despite major advances in satellite-derived large-scale datasets, large uncertainties remain with respect to ice thickness, depth-averaged velocities, and the bulk density of glacier ice close to calving termini or grounding lines. We suggest ways in which we can move toward globally complete frontal ablation estimates, highlighting areas where we need improved datasets and increased collaboration. |
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