RAPID: New Acceleration in Ordonnansbreen in the Negribreen Glacier System

这项研究的动机是在2019年5月使用卫星数据观测到北极斯瓦尔巴群岛内格里布林冰川系统（NGS）中的Ordonannsbreen冰川出现了新的加速现象。冰川加速是评估海平面上升不确定性的一个主要来源，对冰川涌动的观测很少，因此了解甚少。在这个快速响应项目中，本研究将收集航空地球物理数据，并结合卫星图像和来自邻近内格里布林冰川的单独研究的数据，将解决冰川激增以及冰川加速与气候变暖之间相互作用的问题。研究人员还将推进机器学习的使用，以分析复杂的卫星数据。为了扩大范围，调查人员将通过一个网站与公众分享关于冰川涌流的观察结果，并在当地K-12学校展示结果。此外，该项目将支持对研究生和本科生的培训，使他们参与实地工作，并采用新的数学分析方法。研究人员最近通过卫星数据发现了Ordonannsbreen冰川的新加速，这项研究将利用之前对邻近Negribreen冰川激增的研究来了解更大的冰川系统的变化。令人感兴趣的问题是，这一事件是否仅仅是北极冰川的又一次激增，还是整个冰川系统更大规模解体的一部分。为了实现研究目标，研究者将在2019年夏季的一个野外季节收集机载激光高度计、图像、全球定位系统（GPS）和惯性测量单元（IMU）数据；2)推导出裂隙地形地表高程和高程变化的三维表示，估算质量损失；3)解决加速是否可归因于冰川系统和/或变暖环境中的物理过程的问题。以裂缝为中心的数学方法，将物理过程和机器学习相结合，应用于观察、分析和建模。裂缝类型和其他表面特征的自动分类将导致加速度类型的分类，并有助于约束热机械冰川模型的参数，这将在未来的工作中得到发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The motivation for this study is detection of a new acceleration in Ordonannsbreen, a glacier in the Negribreen Glacier System (NGS) in Arctic Svalbard, observied in May 2019 using satellite data. Glacial acceleration is a major source of uncertainty in assessments of sea level rise, and observations of glacial surging are rare and therefore poorly understood. In this RAPID response project, airborne geophysical data will be collected in this study, and in combination with satellite images and data leveraged from a separate study of neighboring Negribreen glacier, will address questions about glacier surge and the interaction between glacial acceleration and climate warming. The investigator will also advance use of machine learning to analyze complex satellite data. For outreach, the investigator will share observations with the public via a website about glacial surge and present results at local K-12 schools. In addition, this project will support training for graduate and undergraduate students, engaging them in field work and new methods for mathematical analysis.The investigator has recently detected a new acceleration in Ordonannsbreen glacier via satellite data, and this study will leverage prior work on a surge in neighboring Negribreen glacier to understand changes in the larger glacier system. The motivating question is whether this event is simply another surge in an Arctic glacier or part of the larger disintegration of an entire glacier system. To address the research objective, the investigator will 1) collect airborne laser altimeter, image, Global Positioning System (GPS), and Inertial Measurement Unit (IMU) data in one field season in summer 2019; 2) derive a 3-dimensional representation of surface elevation in crevassed terrain and elevation change to estimate mass loss; and 3) address the question of whether the acceleration can be attributed to physical processes in the glacier system and/or in a warming environment. A crevasse-centered mathematical approach that integrates physical processes and machine learning will be applied in observation, analysis, and modeling. Automated classification of crevasse types and other surface signatures will lead to a classification of acceleration types and aid in constraining parameters in thermo-mechanical glacier models, to be developed in future work.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.