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Theory and Models of Ice Sheet Surface Melting Instabilities in the Past and Future

Theory and Models of Ice Sheet Surface Melting Instabilities in the Past and Future
过去和未来冰盖表面融化不稳定性的理论和模型
批准号:
1735715
负责人:
Victor Tsai
金额:
$22.48万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2020-08-31

项目摘要

项目成果

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中文摘要
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英文摘要
Sudden accelerations in melting at the junction of merged ice sheets are thought to have caused some of the most rapid sea level rise events in history. Existing theories of ice sheet surface melting do not consider such regions and are not able to reproduce these sudden accelerations in ice sheet surface melting. To predict the likelihood of such rapid sea level rise events in the future, it is important to understand why such events happened in the past. This project will develop a new mathematical theory for ice sheet surface melting instabilities within ice saddle regions. It will compare the predictions of this theory to complex ice sheet models and perform more realistic simulations to determine whether the Greenland Ice Sheet may undergo such a sudden acceleration in melting under future warming. This project includes a number of broader impacts. Sea level rise threatens life and infrastructure along coastlines throughout the world. Accurate projections of potential periods of rapid future sea level rise will inform strategic decision-making about mitigation strategies and infrastructure design. The project will also contribute to STEM workforce development by supporting the training of a post-doctoral associate and a graduate student. A web-based application employing the simple model developed during this project will be produced as an effective outreach tool for explaining ice sheet dynamics to the public.The primary goal of this project is to develop a theory that explains the occurrence of deglacial meltwater pulses from land-based ice sheets under a warming climate. A new variant on the classical single-dome ice sheet model that relaxes previous assumptions and considers an ice-saddle geometry will be produced. This new ice-saddle model will be numerically tested to determine the conditions under which it produces a significant deglacial meltwater pulse. Asymptotic mathematical techniques, such as multiple time scales analysis, will be used to derive functional expressions for the magnitude and speed of meltwater pulses predicted by these models. The utility of the ice-saddle theory in explaining reality will be evaluated by comparing it to more complex ice sheet models and proxy records of sea level rise during meltwater pulses. Finally, multi-millennial simulations of the Greenland Ice Sheet under different scenarios of future climate change will be developed.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
A simple physics-based improvement to the positive degree day model
对正度日模型的基于物理的简单改进
DOI: 10.1017/jog.2018.55
发表时间: 2018
期刊: Journal of Glaciology
影响因子: 3.4
作者: [TSAI, VICTOR C., RUAN, XIAOZHOU]
通讯作者: RUAN, XIAOZHOU
A Simple Model for Deglacial Meltwater Pulses
冰消融水脉冲的简单模型
DOI: 10.1029/2018gl080884
发表时间: 2018
期刊: Geophysical Research Letters
影响因子: 5.2
作者: [Robel, Alexander A., Tsai, Victor C.]
通讯作者: Tsai, Victor C.
A physics-based neural network approach for geophysical inversions
  • 批准号:
    2309920
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $43.54万
  • 财政年份:
    2023
  • 负责人:
    Victor Tsai
  • 依托单位:
Collaborative Proposal: Testing Collision Versus Frictional Stress-Drop Models of High-Frequency Earthquake Ground Motions
  • 批准号:
    2146640
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $32.47万
  • 财政年份:
    2022
  • 负责人:
    Victor Tsai
  • 依托单位:
Collaborative Research: Improving the Interpretability of Tomographic Images Using Geologically Motivated Parametrizations
  • 批准号:
    2011079
  • 项目类别:
    Standard Grant
  • 资助金额:
    $25.59万
  • 财政年份:
    2020
  • 负责人:
    Victor Tsai
  • 依托单位:
CAREER: Environmental Seismology and Geomechanics
  • 批准号:
    1939227
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $28.4万
  • 财政年份:
    2019
  • 负责人:
    Victor Tsai
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
新型手性NAD(P)H Models合成及生化模拟