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Predicting Ocean-Induced Ice-Shelf Melt Rates Using Deep Learning

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AbstractMelt rates at the base of Antarctic ice shelves are needed to drive projections of the Antarctic ice sheet mass loss. Current basal melt parameterizations struggle to link open

Ocean-induced melt volume directly paces ice loss from Pine Island ...

Widespread supraglacial lakes (SGLs) across the Antarctic surface can accelerate land ice flow and be linked to ice shelf disintegration, further affecting sea-level rise projections. Download Citation | Roughness of Ice Shelves Is Correlated With Basal Melt Rates | Ice shelf collapse could trigger widespread retreat of marine‐based portions of the Melt rates at the base of Antarctic ice shelves are needed to drive projections of the Antarctic ice sheet mass loss. Current basal melt parameterizations struggle to link open

Deep learning for predicting rate-induced tipping

Abstract. Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused Download scientific diagram | Ice shelf buttressing and grounding line flux. Confined ice shelves restrain the flow of upstream ice streams and glaciers.

Authorea (preprint). 2023. LINK. Rosier, S. H. R., Bull, C. Y. S., Woo, W. L., and Gudmundsson, G. H.: Predicting ocean-induced ice-shelf melt rates using deep learning, The Cryosphere, 17, Polar-ice-sheet melting and associated sea-level rise is an alarming con-sequence of human-induced climate change. In the Antarctic, rapidly accelerating rates of ice-sheet melting,

Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased

Also in the eld of Arctic science, we have witnessed an increasing trend in the adoption of AI, especially deep learning, to support the analysis of Arctic big data and facilitate new The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital

Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea-level change. Reduction in ice-shelf buttressing caused by increased The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017-2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital

Committed future ice-shelf melt

This study aims to demonstrate the potential of improving surface melt simulations by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface Deep Learning for predicting rate-induced tipping Y u Huang 1,2*, Sebastian Bathiany1,2, Peter Ashwin3, Niklas Boers 1,2,3 1 Earth System Modelling, School of

Basal melt data were derived from ice shelf basal melt rate calculations using CryoSat-2 satellite altimetry data by Liu et al. (2024), at a 5-km resolution. The freezing Ice shelves regulate the ice‐ocean boundary by buttressing the flux of grounded ice into the ocean and are vulnerable to basal melt, which can lead to ice‐shelf thinning and

In this work, however, we use a recently derived parametrization of the melt rates based on a buoyant meltwater plume travelling upward beneath an ice shelf.

Here, we propose using deep learn-ing to emulate ocean model behaviour for the prediction of sub-ice-shelf melt rates. Since the computational cost of a machine learning algorithm is

TC - The complex basal morphology and ice dynamics of the Nansen Ice ...

Towards a fully unstructured ocean model for ice shelf cavity environments: model development and verification using the Firedrake finite element framework. Ocean Modeling, vol 182, 102178. Ocean-induced melting below ice shelves is one of the dominant drivers for mass loss from the Antarctic Ice Sheet at present. An appropriate representation of sub-shelf melt

Ice-shelf freshwater triggers for the Filchner–Ronne Ice Shelf melt tipping point in a global ocean–sea-ice model Basal melt of the southern Filchner Ice Shelf, Antarctica Predicting Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic ice sheet.

Simulated melt rates for the Totten and Dalton ice shelves

We introduce a new approach that brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model

Figure 1. Workflow diagram for the proposed deep learning framework, split into training and prediction. Synthetic ice shelf geometries (Section 2.3.1) and synthetic temperature and salinity Fingerprint Dive into the research topics of ‚Predicting ocean-induced ice-shelf melt rates using deep learning‘. Together they form a unique fingerprint. Sort by Weight Alphabetically The simulated coastal ocean response to changing winds is strongly impacted by the circulation induced by ice-shelf melt Strengthening and poleward shifting

Freshwater is lost from Antarctica mostly by solid ice discharge over the grounding line followed by basal melting under floating ice shelves and by the calving of icebergs from the To address this, we make a rst attempt to develop a deep learning framework to predict transition probabilities of dynamical sys- tems ahead of rate-induced transitions. Our method issues early Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

Current basal melt parameterizations struggle to link open ocean properties to ice‐shelf basal melt rates for the range of current sub‐shelf cavity geometries around Antarctica.

To address this, we make the first attempt to develop a deep learning framework predicting the transition probabilities of dynamical systems ahead of rate-induced transitions. Abstract. Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused In this study, we utilized high-resolution remote sensing data and deep learning method to detect and analyze the spatio-temporal variations of surface melt, ice shelf calving,

Deep learning to predict Antarctic ice shelf melt rates Sebastian H. R. Rosier and Christopher Y. S. Bull Aims and Introduction The current main driver of change in Antarctica is ocean driven Here, we present estimates of basal melt rates of the FIS using ground‐based interferometric radar. We find a low average basal melt rate on the order of 1 m/yr, with the highest rates

This study links basal melt of the Totten and Dalton ice shelves to warm water intrusions across the continental shelf break and atmosphere–ocean heat exchange. Totten ice shelf melting is