Collaborative Research: Frameworks: Ghub as a Community-Driven Data-Model Framework for Ice-Sheet Science

合作研究：框架：Ghub 作为社区驱动的冰盖科学数据模型框架

• 批准号: 2004302
• 负责人: Abani Patra
• 金额: $ 57.45万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004302&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Ghub; Community

Sea level rise is challenging societies around the globe. Planning for future sea level rise in the US is critical for national security, public health, and socioeconomic stability. However, current predictions of sea level rise remain uncertain, because the future behavior of melting ice sheets - a primary cause of sea level rise - is not well understood. A recent United Nations report (IPCC Special Report on the Ocean and Cryosphere in a Changing Climate) summarized two startling facts: (i) Recent sea level rise acceleration is due to increased ice loss from the Greenland and Antarctic ice sheets; and (ii) Uncertainty related to ice-sheet instability arises from limited observations, incomplete representation of ice-sheet processes in current models, and evolving understanding of the complex interactions between the atmosphere, ocean and ice sheets. Improving our ability to forecast the health of ice sheets and hence, predictions of future sea level rise, requires a large, long-lasting collective effort among ice sheet scientists working closely with scientists from the modeling and remote sensing disciplines. One challenge in this collective effort is the range of disciplines and approaches to ice-sheet science - the degree of specialization is an obstacle to efficient collaborative work. This project will lower the barriers among sub-disciplines in ice-sheet science by creating and promoting a centralized web-based hub, called “Ghub,” where datasets and tools will be made accessible to the full range of ice sheet science fields of study. Ghub is accessible to all interested scientists and lay personnel. Use of Ghub includes access to datasets, analysis tools, and cloud computing power, as well as the ability to develop and share new tools within the Ghub environment. Several avenues of outreach and education as part of the Ghub project are specifically aimed at framing ice-sheet science for general audiences, and including students from underrepresented groups.The urgency in reducing uncertainties of near-term sea level rise relies on improved modeling of ice-sheet response to climate change. Predicting future ice-sheet change requires a tremendous effort across a range of disciplines in ice-sheet science including expertise in observational data, paleoglaciology ("paleo") data, numerical ice sheet modeling, and widespread use of emerging methodologies for learning from the data, such as machine learning. However, significant knowledge and disciplinary barriers make collaboration between data and model groups the exception rather than the norm. Most modeling groups write their own tools to ingest data and analyze output, newer and larger observational datasets are not being fully taken advantage of by the modeling community, and paleo data critical for constraining model representation of ice sheet history are largely inaccessible to modelers. The diverse disciplinary approaches to ice-sheet science has led to bottlenecks that slow the response to the developing crisis. Coordination between data generators and modelers is critical for testing data-driven hypotheses, providing mechanistic explanations for past ice-sheet change, and incorporating newly understood physical processes and validating models to improve their predictive ability. Solving the urgent problem of unoptimized collaboration requires a novel, integrated, trans-disciplinary program that lowers barriers across the distinct approaches to ice-sheet science. Fostering collaboration between disciplines will lead to a transformational leap in ice-sheet and sea-level science. To make the leap, we must improve the efficiency in collaboration among traditionally disparate approaches to the problem. We will develop a community-building scientific and educational cyberinfrastructure framework including models and data processing tools, to enable coordination and synergistic exchange between ice-sheet scientific communities. The new cyberinfrastructure will be a significant bridge that connects the numerical ice-sheet modeling community with rapidly growing observational datasets of past and present ice-sheet states that will ultimately improve predictions of sea level rise. The GHub cyberinfrastructure will also be a template for organizing disparate scientific communities to address urgent societal needs in a timely fashion.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.

海平面上升正对全球社会构成挑战。对美国未来海平面上升的规划对国家安全、公共健康和社会经济稳定至关重要。然而，目前对海平面上升的预测仍然不确定，因为冰盖融化的未来行为--海平面上升的主要原因--还没有得到很好的理解。最近的一份联合国报告(气专委关于气候变化中的海洋和冰冻圈的特别报告)概述了两个令人震惊的事实：(1)最近海平面上升加速是由于格陵兰和南极冰盖的冰流失增加；(2)与冰盖不稳定有关的不确定性源于有限的观测、现有模型对冰盖过程的不完全描述以及对大气、海洋和冰盖之间复杂相互作用的不断加深的理解。提高我们预测冰盖健康状况的能力，从而预测未来海平面上升的能力，需要冰盖科学家与建模和遥感学科的科学家密切合作，进行大规模、长期的集体努力。这一集体努力的一个挑战是冰盖科学的学科和方法的范围--专业化程度是高效协作工作的障碍。该项目将通过创建和促进一个名为“GHub”的集中式网络中心来降低冰盖科学各分支学科之间的壁垒，在该中心，数据集和工具将可供所有冰盖科学研究领域使用。GHub对所有感兴趣的科学家和非专业人员都是开放的。GHub的使用包括访问数据集、分析工具和云计算能力，以及在GHub环境中开发和共享新工具的能力。作为GHub项目的一部分，有几种推广和教育的途径，其具体目的是为普通受众和来自代表性不足群体的学生制定冰盖科学框架。减少近期海平面上升不确定性的迫切性取决于改进冰盖应对气候变化的模型。预测未来的冰盖变化需要在冰盖科学的一系列学科中做出巨大的努力，包括在观测数据、古冰川学(“古”)数据、数值冰盖建模方面的专业知识，以及广泛使用新兴的数据学习方法，如机器学习。然而，大量的知识和纪律障碍使数据和模型组之间的合作成为例外，而不是常态。大多数建模小组编写自己的工具来获取数据和分析输出，较新的和更大的观测数据集没有得到建模社区的充分利用，对于约束冰盖历史的模型表示至关重要的古数据在很大程度上无法被建模人员访问。对冰盖科学的不同学科方法导致了瓶颈，减缓了对不断发展的危机的反应。数据生成器和建模器之间的协调对于测试数据驱动的假说、为过去的冰盖变化提供机械解释、纳入新理解的物理过程和验证模型以提高其预测能力至关重要。解决非优化协作的紧迫问题需要一个新颖的、集成的、跨学科的计划，以降低冰盖科学不同方法的障碍。促进学科之间的合作将导致冰盖和海平面科学的变革性飞跃。为了实现这一飞跃，我们必须提高传统上不同的解决问题的方法之间的合作效率。我们将开发包括模型和数据处理工具在内的社区建设科学和教育网络基础设施框架，以实现冰盖科学界之间的协调和协同交流。新的网络基础设施将成为连接数值冰盖模型界与快速增长的过去和现在冰盖状态观测数据集的重要桥梁，这些数据集最终将改善对海平面上升的预测。GHub网络基础设施也将是一个模板，用于组织不同的科学界及时解决紧迫的社会需求。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和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

GHub : Building a glaciology gateway to unify a community

GHub：建立一个冰川学门户来统一社区

• DOI: 10.1002/cpe.6130
• 发表时间: 2020
• 期刊: Concurrency and Computation: Practice and Experience
• 作者: Sperhac, Jeanette M.;Poinar, Kristin;Jones‐Ivey, Renette;Briner, Jason;Csatho, Beata;Nowicki, Sophie;Simon, Erika;Larour, Eric;Quinn, Justin;Patra, Abani
• 通讯作者: Patra, Abani

Workflows for Construction of Spatio-Temporal Probabilistic Maps for Volcanic Hazard Assessment

火山灾害评估时空概率图构建工作流程

• DOI: 10.3389/feart.2021.744655
• 发表时间: 2022
• 期刊: Frontiers in Earth Science
• 影响因子: 2.9
• 作者: Jones-Ivey, Renette;Patra, Abani;Bursik, Marcus
• 通讯作者: Bursik, Marcus

Firn aquifer water discharges into crevasses across Southeast Greenland

• DOI: 10.1017/jog.2023.25
• 发表时间: 2023-05
• 期刊: Journal of Glaciology
• 影响因子: 3.4
• 作者: Eric Cicero;K. Poinar;R. Jones-Ivey;A. Petty;Jeanette M. Sperhac;A. Patra;J. Briner