EAGER: Exploring a community driven data-model framework for testing the stability of the Greenland Ice Sheet

EAGER：探索社区驱动的数据模型框架来测试格陵兰冰盖的稳定性

• 批准号: 1837544
• 负责人: Jason Briner
• 金额: $ 27.88万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837544&HistoricalAwards=false
• 关键词: EAGER; Exploring; community; driven; data

In the wake of devastating flooding related to recent hurricane strikes on densely inhabited areas, the potential impact of sea level rise has never been more evident. Rising sea level is challenging societies around the globe, but current predictions of future sea level rise contain uncertainty. Greenland's ice, if fully melted, would raise global sea levels more than 7 m. However, at present, we do not have data or models that allow for a definitive consensus view of Greenland Ice Sheet vulnerability to climate change, and our sea level predictions are only as good as our ability to model Greenland Ice Sheet change using computer simulations and the data driving those simulations. Vast datasets of observations and increasingly sophisticated computer models are being employed to solve this problem. Yet, significant knowledge and disciplinary barriers make collaboration between data and model groups the exception rather than the norm. The PIs propose to establish a community-building scientific and educational cyber hub to enable seamless access to data, and coordination and synergy between different scientific disciplines. The overarching goal is to increase the accuracy of predictions of the Greenland Ice Sheet contribution to sea level rise by 2050, 2100 and beyond. The project will support two female PIs (one of which is a young investigator) and partial funding for two graduate students.Observational datasets of Greenland Ice Sheet change, both from the satellite era and the recent geologic past, are rapidly expanding. Some of these observations have been used for calibration and validation of ice sheet models thereby improving the physical understanding of ice sheet behavior and estimates of future sea level rise. But substantial data-model gaps remain due to the knowledge barrier of understanding and using satellite- and paleo-data and the lack of a standard framework for using available observational datasets in ice sheet modeling experiments. There is significant potential to generate a long-lasting cyberinfrastructure framework with ice sheet data, software tools, online cloud-based execution and educational materials. When combined, this would lead to rapid progress in improving ice sheet modeling capability and decreasing uncertainty in sea level rise forecasting. The PIs will bring together experts in ice sheet observation, data analysis and modeling to guide the creation of a community hub that will enable two-way communication between data generators and modelers. The PIs will pilot software tools necessary to facilitate interoperability among the various data sets and modeling tools and investigate new metrics for model-data intercomparison and model assessment.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.

在与最近对居住地区的飓风袭击有关的毁灭性洪水之后，海平面上升的潜在影响从未如此明显。海平面上升是全球范围内挑战的社会，但是目前对未来海平面上升的预测包含不确定性。格陵兰的冰，如果完全融化，将使全球海平面高7 m以上。但是，目前，我们没有数据或模型可以使格陵兰冰板脆弱性具有确定的共识观点，而我们的海平面预测仅与我们使用计算机模拟和驱动这些模拟的数据对Greenland Ice Plage进行建模更改的能力一样好。正在采用大量的观测数据集和越来越复杂的计算机模型来解决此问题。然而，重要的知识和纪律障碍使数据和模型组之间的合作是例外而不是规范。 PIS建议建立一个社区建设的科学和教育网络枢纽，以无缝获取数据，以及不同科学学科之间的协调和协同作用。总体目标是提高格陵兰冰盖对海平面上升的预测的准确性，到2050年，2100年及以后。该项目将支持两名女性PI（其中一名是年轻的研究者）和两名研究生的部分资金。从卫星时代和最近的地质过去，格陵兰冰盖变化的观察数据集正在迅速扩大。其中一些观察结果已用于校准和验证冰盖模型，从而提高了对冰盖行为的物理理解以及对未来海平面上升的估计。但是，由于了解和使用卫星和古数据的知识障碍以及缺乏用于在冰盖建模实验中使用可用的观测数据集的标准框架，因此仍然存在大量的数据模型差距。具有冰盖数据，软件工具，基于云的执行和教育材料的持久网络基础结构框架具有巨大的潜力。合并后，这将导致在改善冰盖建模能力和降低海平面上升预测的不确定性方面的快速进步。 PI将汇集冰盖观察，数据分析和建模方面的专家，以指导创建社区中心，该枢纽将在数据生成器和建模者之间进行双向通信。 PI将驾驶各种数据集和建模工具之间的互操作性的必要必要的软件工具，并调查模型数据对比和模型评估的新指标。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。