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

合作研究：格陵兰岛出口冰川终点消融的机器模型

• 批准号: 2146702
• 负责人: Ginny Catania
• 金额: $ 50.45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146702&HistoricalAwards=false
• 关键词: Collaborative; Research; Machine; enabled; modeling

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.

预测未来海平面依赖于改进气候如何迫使冰盖变化的模型。这在冰海边界尤其具有挑战性，因为那里有多个过程同时发生。目前，没有一个单一的方程能够充分描述变化的冰-海边界，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之间的关系。这项技术将使研究小组能够对冰川终点行为进行分类，并确定格陵兰冰盖周围特定冰川的相关参数。机器学习练习的结果将用于开发一个方程，以代表冰盖模型中的冰-海洋相互作用，该模型将用于确定未来海洋对冰盖的影响。 未来冰盖变化的模型只有在控制冰盖演变的所有关键过程都得到适当解释的情况下，才能对海平面上升做出可靠的预测。然而，许多物理过程目前还知之甚少。其中一个过程是出口冰川末端的消融（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观测的机器学习分析来改进格陵兰海平面的模型预测，以告知出口冰川的物理建模，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），是什么控制着格陵兰岛所有冰川在一系列时间尺度上的终点位置变化。对模型性能的分析将使研究小组能够确定对个别和各组冰川终点位置的主要控制，并在新的冰川学和环境数据可用时测试模型的性能。终点位置的机器学习模型将用于改进出口冰川质量变化的预测，使用基于物理的数值冰流模型。该团队将研究模型预测在不同时间尺度上的稳健性，因为在该项目的过程中，越来越多的数据变得可用。该项目将导致格陵兰冰盖动态损失的精确预测，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NSF极地项目办公室、NSF新北极导航和其他国家（例如，NASA，NOAA）和国际优先事项。该项目整合了跨学科，性别和职业阶段的研究人员。通过该项目产生的数据产品和方法将向公众提供，并将对更广泛的科学界有用。该项目由地球科学理事会和高级网络基础设施办公室共同资助，旨在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NSF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。