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Ice-sheet thickness reconstruction with uncertainty quantification

Project description

Using probability theories to reconstruct ice sheet thickness distribution

Ice sheets have a significant impact on rising sea levels. The estimated distribution of ice sheet thickness, however, is based on limited measurements to create reconstructions, resulting in a considerable level of uncertainty. This uncertainty also makes it difficult to adequately predict rates of sea level rise. Supported by the Marie Skłodowska-Curie Actions programme, the IceSTRUQ project aims to develop ice thickness reconstructions based on a Bayesian inversion approach that combines data and mathematical models for ice sheet dynamics, such as Markov chain Monte Carlo (MCMC) and stochastic Newton MCMC methods. It will apply the resulting method to reconstruct the ice thickness distribution of the Antarctic Peninsula Ice Sheet, whose complex geometry has challenged existing ice thickness reconstruction methods.

Objective

The Antarctic and Greenland ice sheets are fundamental to the Earth's climate system and the main contributors to sea-level rise. The overall ice-thickness distribution of an ice sheet is computed from sparse ice-thickness measurements using reconstruction methods. This data sparsity results in a marked uncertainty of the ice-thickness reconstructions, which is never taken into account in a mathematically rigorous manner. This implies large deviations between different ice-thickness reconstructions, delivering inconsistent estimates of sea-level rise rates.
To remedy this, we propose formulating the ice-thickness reconstruction as a Bayesian inverse problem that combines data and mathematical models for ice-sheet dynamics. The solution of this inverse problem is a probability distribution for the ice-thickness, resulting in a rigorous quantification of uncertainty. However, conventional numerical methods either break down or become impractically slow when solving this problem. To circumvent this, we propose the design of a Monte-Carlo Markov-Chain (MCMC) method that combines ideas from multi-fidelity and the stochastic Newton MCMC methods. These are two sophisticated mathematical techniques that have achieved extraordinary reductions in computational time, allowing for previously unfeasible computations.
We will apply our method to reconstruct the ice-thickness of the Antarctic Peninsula Ice sheet (APIS). This important Antarctic region has a complex geometry that has defied existing ice-thickness reconstruction methods. Our Bayesian inversion approach has the potential of reconciling dissimilar estimates of ice-discharge by quantifying confidence intervals via uncertainty.
This highly interdisciplinary project will develop the applicant's mathematical and glaciological knowledge in an internationally-recognized research team. Research will be complemented with enriching activities such as Antarctic fieldwork (as training) and supervision of PhD students.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSIDAD POLITECNICA DE MADRID
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 194 074,56
Address
CALLE RAMIRO DE MAEZTU 7 EDIFICIO RECTORADO
28040 MADRID
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
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Total cost

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