Predicting ocean-induced ice-shelf melt rates using deep learning
Abstract m yr - 1 compared to the ocean model. MELTNET calculates melt rates several orders of magnitude faster than the ocean model and outperforms more traditional parameterisations for > 96 % of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as changes in thermal forcing and ice-shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, which could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Predicting ocean-induced ice-shelf melt rates using deep learning ; volume:17 ; number:2 ; year:2023 ; pages:499-518 ; extent:20
The Cryosphere ; 17, Heft 2 (2023), 499-518 (gesamt 20)
- Urheber
- DOI
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10.5194/tc-17-499-2023
- URN
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urn:nbn:de:101:1-2023033007243588423650
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:54 MESZ
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Beteiligte
- Rosier, Sebastian
- Bull, Christopher Y. S.
- Woo, Wai L.
- Gudmundsson, G. Hilmar
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