Collaborative Research: Machine-enabled modeling of terminus ablation for Greenland's outlet glaciers

对未来海平面的预测依赖于气候如何迫使冰盖变化的改进模型。这在冰-海边界尤其具有挑战性，因为那里同时发生多个过程。目前，没有单一的方程能充分描述冰-海边界的变化，这给冰盖模式与气候模式的耦合带来了问题。该项目将提高对影响冰-海边界的变量如何随时间和空间变化的理解，使用机器学习来搜索可用数据中的关系。这项技术将允许研究小组对冰川终端行为进行分类，并确定格陵兰冰盖周围特定冰川迫使变化的相关参数。机器学习练习的结果将用于在冰盖模型中开发一个表示冰-海洋相互作用的方程，该模型将用于确定海洋对未来冰盖的影响。只有当控制冰盖演变的所有关键过程都得到适当考虑时，未来冰盖变化模型才能对海平面上升作出可靠的预测。然而，目前人们对许多物理过程知之甚少。其中一个过程是冰川出口末端的消融（冰山崩解和海底融化），这已被证明是特定冰川质量变化的主要控制因素。该项目的目标是通过使用冰川学观测的机器学习分析来改进格陵兰岛海平面的模型预测，从而为出口冰川的基于物理的建模提供信息，重点是冰-海边界。机器学习工具将用于确定在一个明显的历史变化时期（卫星时代），是什么控制了格陵兰岛所有冰川在一系列时间尺度上的末端位置变化。对模型性能的分析将使研究小组能够确定冰川个体和冰川群的终点位置的主要控制因素，并在获得新的冰川学和环境数据时测试模型的性能。终端位置的机器学习模型将用于使用基于物理的数值冰流模型来改进出口冰川质量变化的预测。随着项目过程中获得的数据越来越多，研究小组将研究模型预测在不同时间尺度上的可靠性。该项目将对格陵兰冰盖的动态损失进行精确预测，这对需要在全球范围内做出关键基础设施和资源决策的决策者非常重要。这一目标是美国国家科学基金会极地项目办公室、美国国家科学基金会新北极导航以及其他国家（如美国国家航空航天局、美国国家海洋和大气管理局）和国际优先事项研究的中心焦点。该项目整合了不同学科、性别和职业阶段的研究人员。通过这个项目产生的数据产品和方法将公开提供，并将对更广泛的科学界有用。该项目由地球科学理事会和先进网络基础设施办公室共同资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。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。

Predicting future sea level relies on improved modeling of how the climate forces the ice sheet to change. This is particularly challenging at the ice-ocean boundary where there are multiple processes occurring simultaneously. At present, no single equation adequately describes the changing ice-ocean boundary, which poses problems for coupling ice sheet models to climate models. This project will improve understanding of how the variables that influence the ice-ocean boundary may change over time and space using machine learning to search for relationships amidst available data. This technique will allow the research team to categorize glacier terminus behavior and identify the relevant parameters forcing change for a particular glacier around the Greenland Ice Sheet. Results of the machine learning exercise will be used to develop an equation to represent ice-ocean interactions in an ice sheet model which will be used to determine future changes to the ice sheet forced by the ocean into the future. Models of future ice sheet change yield reliable forecasts of sea level rise only when all the critical processes controlling ice sheet evolution are appropriately accounted for. However, many physical processes are currently poorly understood. One such process is ablation (iceberg calving and submarine melt) at the terminus of outlet glaciers, which has been shown to be the dominant control on mass change at particular glaciers. The goal of this project is to improve model forecasts of sea-level from Greenland by using machine learning analyses of glaciological observations to inform physics-based modeling of outlet glaciers, with a focus on the ice-ocean boundary. Machine learning tools will be used to determine what controls changes in terminus position over a range of time scales for all glaciers in Greenland over a period of pronounced historical change (the satellite era). Analysis of the model performance will enable the research team to determine the dominant controls on terminus position for individual and groups of glaciers and to test how well the model performs as new glaciological and environmental data become available. The machine learning model of terminus positions will be used to improve projections of outlet glacier mass change using a physically-based numerical ice flow model. The team will examine how robust model prediction is on various time-scales as more and more data become available over the course of this project. The project will result in refined projections of dynamic loss from the Greenland Ice Sheet, which is important for policy makers needing to make critical infrastructure and resource decisions globally. This goal is a central focus for research within NSF's Office of Polar Programs, NSF's Navigating the New Arctic, and other national (e.g., NASA, NOAA) and international priorities. The project integrates researchers across disciplines, genders, and career stages. Data products and methods produced through this project will be make publicly available and will be useful to the broader scientific community. This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.