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Glacier Calving-observations and Modelling

Glacier Calving-observations and Modelling
冰川崩解-观测和建模
批准号:
2743624
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
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英文摘要
Glacier calving is the process of ice loss through the breaking of ice from the edge of a glacier. Ice-flow models describe calving in a number of different ways, and there is still no consensus on the best approach. Recent work suggests that calving can give rise to an unstable run-away process and some of the higher-end predictions of near-future global sea level rely on models implementing such an unstable frontal retreat. In this project, both observational and numerical methods are used to better understand the calving process and how best to implement calving numerically. The aim is to arrive at a description that results in good agreement with observations, while at the same time being numerically robust and flexible enough to fit into an existing modelling framework used in large-scale ice-sheet modelling today. Of particular interest is the possibility of some types of calving-laws giving rise to an unstable retreat. This idea will be investigated and tested using numerical ice-flow models and simplified analytical models. Observational data will be primarily based on remote sensing data from Greenland, Iceland and Antarctica with additional new data sets to be collected from lake-terminating glaciers in Iceland. The near-future evolution of Pine-Island glacier, Antarctica, will be modelled using different types of calving laws, and the importance of calving formulations assess through formal uncertainty-quantification methodology. The work will be tightly integrated with existing international research projects and other groups currently working on similar problems. The student will benefit and contribute to work conduced at Northumbria University as a part of the NSFPLR-NERC funded project: Processes, drivers, predictions: Modeling the response of Thwaites Glacier over the next century using ice/ocean coupled models.
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