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

最近，飓风袭击了人口密集的地区，随之而来的是毁灭性的洪水，海平面上升的潜在影响从未如此明显。海平面上升正在挑战全球社会，但目前对未来海平面上升的预测包含不确定性。格陵兰岛的冰如果完全融化，将使全球海平面上升7米以上。然而，目前，我们还没有数据或模型，可以对格陵兰冰盖对气候变化的脆弱性达成明确的共识，我们的海平面预测仅与我们利用计算机模拟和驱动这些模拟的数据模拟格陵兰冰盖变化的能力一样好。大量的观测数据集和日益复杂的计算机模型正在被用来解决这个问题。然而，重要的知识和学科障碍使得数据和模型组之间的协作成为例外，而不是常态。这些计划建议建立一个以社区为基础的科学和教育网络枢纽，以实现数据的无缝访问，以及不同科学学科之间的协调和协同。总体目标是提高预测2050年、2100年及以后格陵兰冰盖对海平面上升的影响的准确性。该项目将资助两名女性pi（其中一名是年轻研究员），并为两名研究生提供部分资助。格陵兰冰盖变化的观测数据集，无论是从卫星时代还是最近的地质历史，都在迅速扩大。其中一些观测结果已用于冰盖模式的校准和验证，从而提高了对冰盖行为的物理认识和对未来海平面上升的估计。但是，由于在理解和使用卫星和古数据方面存在知识障碍，以及缺乏在冰盖模拟实验中使用现有观测数据集的标准框架，数据模型方面仍然存在巨大差距。利用冰盖数据、软件工具、基于云的在线执行和教育材料，建立一个持久的网络基础设施框架具有巨大的潜力。如果结合起来，这将导致在改善冰盖模拟能力和减少海平面上升预测的不确定性方面取得快速进展。pi将汇集冰盖观测、数据分析和建模方面的专家，指导建立一个社区中心，使数据生成器和建模者之间能够双向通信。pi将试点必要的软件工具，以促进各种数据集和建模工具之间的互操作性，并研究模型-数据相互比较和模型评估的新度量。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。

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.