CAREER: What's Past is Prologue: Seamless Assimilation of Past Observations into Simulations of Future Ice Sheets

职业：过去的只是序幕：将过去的观察无缝同化为未来冰盖的模拟

• 批准号: 2235920
• 负责人: Alexander Robel
• 金额: $ 78.09万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235920&HistoricalAwards=false
• 关键词: CAREER; What; Past; Prologue; Seamless

Awareness of sea level rise and its potential to impact coastal communities is a pre-requisite for informed decision-making at all levels of government and private industry. However, most of the uncertainty in future ice sheet contribution to sea level rise under particular emissions pathways comes from incompletely constrained ice sheet models. In practice, differences in calibration data and procedures between models lead to large uncertainties in sea level projections at 2100 and beyond. To make optimal use of the benefits of both modern and geological observations of ice sheet change, the proposed project will adapt state-of-the-art computational methods from applied mathematics and computer science to assimilate many different types of observations into ice sheet models. Ultimately, these methods will be used to produce gapless estimates of past glacier state over multimillennial time scales and improved projections of future ice sheet change. The educational component of this project will extend an existing standards-based curriculum on sea level rise through high school computer science courses. The main focus of the research component of this project is the development of an open-source software package for applying transient ensemble Kalman filtering techniques to a wide range of ice sheet models, enabling the seamless integration of glaciological and geological observations with physical models. Two initial case studies will focus on reconstructing past glacier changes and using these reconstructions to initialize future predictions of change at Sermeq Kujalleq (aka Jakobshavn Isbrae) in Greenland and Thwaites Glacier in West Antarctica. The final part of this project will add these data assimilation capabilities to a cloud-based ice sheet model with a browser-based interface so that it can be used to directly assimilate data from a community repository of ice sheet observations during simulations of past and future glacier behavior. The educational component will develop a new high school curriculum focused on data-based exploration of past and future sea level rise and its driving processes, in alignment with existing Georgia (and national) standards in Computer Science and Statistics courses. This curriculum will be developed in collaboration with an Atlanta-area teacher and an experienced education researcher at Georgia Tech, and piloted at a local high school.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.

对海平面上升及其对沿海社区影响的潜力的认识是各级政府和私营企业做出明智决策的先决条件。然而，在特定排放途径下，未来冰盖对海平面上升的贡献的大部分不确定性来自于不完全约束的冰盖模型。实际上，模型之间校准数据和程序的差异导致 2100 年及以后海平面预测存在很大的不确定性。为了充分利用现代和地质观测对冰盖变化的优势，拟议项目将采用应用数学和计算机科学中最先进的计算方法，将许多不同类型的观测结果吸收到冰盖模型中。最终，这些方法将用于对数千年时间尺度上过去的冰川状态进行无缝估计，并改进对未来冰盖变化的预测。该项目的教育部分将通过高中计算机科学课程扩展现有的基于标准的海平面上升课程。该项目研究部分的主要重点是开发一个开源软件包，将瞬态系综卡尔曼滤波技术应用于各种冰盖模型，从而实现冰川学和地质观测与物理模型的无缝集成。两个初始案例研究将侧重于重建过去的冰川变化，并利用这些重建来初始化格陵兰岛 Sermeq Kujalleq（又名 Jakobshavn Isbrae）和南极洲西部的 Thwaites 冰川未来变化的预测。该项目的最后部分将通过基于浏览器的界面将这些数据同化功能添加到基于云的冰盖模型中，以便在模拟过去和未来期间直接从冰盖观测社区存储库中同化数据冰川行为。教育部分将开发新的高中课程，重点是基于数据的过去和未来海平面上升及其驱动过程的探索，与现有的乔治亚州（和国家）计算机科学和统计课程标准保持一致。该课程将与亚特兰大地区的一名教师和佐治亚理工学院的一名经验丰富的教育研究员合作开发，并在当地一所高中进行试点。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。