Novel subglacial ocean models to accurately predict Ice-shelf Ablation rates at high resolution and low computational cost
This project aims to enhance predictions of Antarctic ice shelf dynamics and ablation rates by developing innovative models and data-driven simulations to reduce computational costs and improve understanding of ocean interactions.
Projectdetails
Introduction
Ice shelves fringing the Antarctic coastline limit sea-level rise by slowing down the flow of grounded ice into the sea. They are thinning because of warming and intensifying ocean currents. Building upon my expertise in multi-scale flow dynamics and subglacial oceanography, I will resolve the two main bottlenecks that impair our ability to assess and project the state of Antarctic ice shelves. These are:
- A poor understanding of the relationship between ice-shelf ablation rates and ocean properties.
- The high computational cost of ocean simulations.
Development of New Models
I will first build new models of the metre-thick ice-shelf—ocean boundary layer (ISOBL), using innovative laboratory experiments and simulations that will unravel its dynamics. The lack of accurate ISOBL models is responsible for leading-order errors in predicting ice loss rates and freshwater production.
The new models will capture how turbulence controls heat fluxes over an unprecedented range of sub ice-shelf conditions, accurately predicting ice ablation rates from the grounding line to the shelf front.
Paradigm Shift in Ocean Modelling
I will then promote a paradigm shift in the modelling of oceans beneath ice shelves, which are hundreds of kilometres wide. At present, ensemble simulations of ice-shelf ocean cavities on 100-year time scales are about 1000 times computationally too costly to resolve mesoscales, which influence the mean circulation and ice ablation.
To circumvent this issue, I will train novel data-driven reduced-order models (ROMs), which will emulate the ocean dynamics at high resolution and unprecedentedly low computational cost. The ROMs will learn the key fingerprints of ocean cavities and their time evolution from short-term high-resolution simulations.
They will then be extended to longer times and different forcing conditions, enabling eddy-resolving IPCC-level large ensemble simulations of subglacial oceans, which will help reduce uncertainty related to the timing of abrupt regime changes of the Antarctic ice sheet.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.497.861 |
Totale projectbegroting | € 1.497.861 |
Tijdlijn
Startdatum | 1-7-2024 |
Einddatum | 30-6-2029 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITE LYON 1 CLAUDE BERNARDpenvoerder
- ECOLE NORMALE SUPERIEURE DE LYON
Land(en)
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A physics-based study of ice stream dynamics
PHAST aims to develop a comprehensive theory and simulation tools for ice stream dynamics to enhance understanding of ice sheet behavior and its impact on future sea level rise.
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The IceDaM project aims to use deep learning and high-order ice flow models to quantify ice shelf damage in Antarctica, enhancing predictions of sea-level rise through real-time fracture monitoring.
Probing and predicting the dynamical response of the Greenland-Ice-Sheet to surface melt water
This project aims to reassess the impact of surface meltwater on Greenland Ice Sheet dynamics by linking glacier morphology to ice loss, using innovative monitoring and modeling techniques.
Arctic Summer Sea Ice in 3D
SI/3D aims to enhance Arctic sea ice forecasting by integrating satellite altimetry data and deep learning to produce uninterrupted summer sea ice thickness records, improving climate models and stakeholder insights.
ICE³: Modelling the global multi-century evolution of glacier ICE in 3D
ICE³ aims to enhance global glacier modeling by reducing uncertainties and simulating past evolution to improve future projections of sea-level rise and water availability under various emission scenarios.