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A Surge in a Complex Glacier System: Results from Observations, Data Analysis and Numerical Experiments of the Bering-Bagley Glacier System

复杂冰川系统的激增：白令-巴格利冰川系统的观测、数据分析和数值实验的结果

基本信息

批准号：
1504533
负责人：
Ute Herzfeld
金额：
$ 20万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2020-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1504533&HistoricalAwards=false
关键词：
Surge Complex Glacier System Results

项目摘要

This research project will bring together results from several years of observations of the surge of the Bering-Bagley Glacier System in Alaska BBGS surge and experiments with a finite-element model in order to enhance knowledge of the dynamic within processes in a complex glacier system during a surge.. The Bering-Bagley Glacier System in Alaska is the largest surge-type glacier on Earth and the largest glacier system outside of the Greenland and Antarctic ice sheets. Project results will increase knowledge about the processes that occur during glacial accelerations in Alaska and in the outlet glaciers of the Greenland Ice Sheet, thereby helping to reduce uncertainty in predictions of sea-level rise. The project will provide education and training opportunities for a graduate student and for three undergraduate students from interdisciplinary and diverse backgrounds. The project also will result in the development of education units for direct use in Alaska and Colorado high schools and a web-based curricular unit for schools elsewhere that focuses on the Bering Glacier surges and the surge phenomenon in general.Understanding fast-flowing glaciers and accelerations in ice flow is critical to understanding changes in the cryosphere as well as changes in sea level. Glacial acceleration is the main source of uncertainty in assessment of sea-level rise in the current realm of climatic warming. Surging is the least-studies of the four types of glacial accelerations. Because they suddenly accelerate and advance in a quasi-cyclic and unpredictable manner, surging glaciers are a counterpoint to the seemingly omnipresent retreat in the cryosphere. The current surge of the Bering-Bagley Glacier System provides a rare opportunity to observe and analyze a surge of a large and complex glacier system. This project will include observation, data analysis, data parameterization, and comparison of results from data analysis and model-based numerical experiments. The investigators will analyze data collected during several years of airborne campaigns conducted by the research team together with airborne and satellite laser altimeter and image data from NASA, European Space Agency and commercial satellites and radar data of subglacial topography. The investigators continue their efforts to bring together Earth observations, remote sensing, and numerical modeling. They will apply an automated classification system they previously developed to identify physical parameters from images of crevasse fields. The rapid changes in ice flow during the characteristically different phases of the surge, which often lasts several years although with regionally different slow-downs and accelerations, are manifest in complex crevasse patterns. Derived physical parameters relate directly to deformation and other variables of ice dynamics that are hard to retrieve otherwise but necessary for physical modeling. The image-based classification method is expected to be generally applicable in other areas of environmental and spatial sciences, remote sensing-data analysis, geography, geology, and geophysics.

该研究项目将汇集来自阿拉斯加BBGS浪涌Bering-Bagley冰川系统浪涌的几年观测结果和有限元模型实验，以增强对浪涌期间复杂冰川系统中过程的动态知识。阿拉斯加的白令-巴格利冰川系统是地球上最大的浪涌型冰川，也是格陵兰和南极冰盖以外最大的冰川系统。项目成果将增加对阿拉斯加冰川加速和格陵兰冰盖出口冰川过程的了解，从而有助于减少海平面上升预测的不确定性。该项目将为来自跨学科和不同背景的一名研究生和三名本科生提供教育和培训机会。该项目还将导致开发教育单元供阿拉斯加和科罗拉多高中直接使用，并为其他地方的学校开发一个基于网络的课程单元，重点是白令冰川涌动和一般的涌动现象，了解快速流动的冰川和冰流加速对于了解冰冻圈的变化以及海平面的变化至关重要。在目前气候变暖的情况下，冰川加速是评估海平面上升不确定性的主要来源。涌浪是四种冰川加速度中研究最少的一种。因为它们以准周期和不可预测的方式突然加速和前进，所以涌动的冰川与冰冻圈中似乎无处不在的退缩形成了对比。 Bering-Bagley冰川系统目前的激增为观察和分析大型复杂冰川系统的激增提供了难得的机会。该项目将包括观测、数据分析、数据参数化以及数据分析和基于模式的数值试验结果的比较。研究人员将分析研究小组在几年的空中活动中收集的数据，以及来自美国宇航局，欧洲航天局和商业卫星的机载和卫星激光高度计和图像数据以及冰下地形的雷达数据。研究人员继续努力将地球观测、遥感和数值模拟结合在一起。他们将应用他们以前开发的自动分类系统，从裂缝场的图像中识别物理参数。冰流的快速变化，在不同阶段的激增，这往往持续几年，虽然与区域不同的减速和加速，表现在复杂的裂缝模式。推导出的物理参数直接关系到冰的变形和其他变量的动态，很难检索，否则，但必要的物理建模。基于图像的分类方法预计将普遍适用于环境和空间科学，遥感数据分析，地理，地质和地球物理学的其他领域。