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: 2004826
• 负责人: Jason Briner
• 金额: $ 352.29万
• 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: 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=2004826&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.

海平面上升是挑战地球的挑战。但是，当前的海平面预测仍然不确定，因为融化冰盖的未来行为是海平面的主要原因（IPCC气候）总结了两个令人震惊的事实：（i）最近的海平面上升加速度是由于格陵兰和南极冰的增加而引起的；并了解复杂的互动，大气E，海洋和冰盖，改善了我们的冰的健康，因此可以预测未来的海平面上升集体的集体一个。基于所有感兴趣的科学家和外行人员都可以访问B的“ GHUB”。随着GHUB设想中的新工具的奉献精神，作为GHUB项目的一部分，旨在为包括不足的学生包括在内的冰上科学。在改进的冰模型上，人们对气候变化的反应需要在冰格科学的一系列学科中做出巨大的努力但是，从机器学习等数据中学习的Hodologies。冰盖历史的生态数据的关键数据对建模者来说是无法访问的。并验证了他们的预测能力。对于这个问题，我们将开发一些包括模型和数据处理工具的ERINFRASTRUCTURE框架，新的Cyber​​inFrastructure将是一座重要的桥梁，将数值的冰单建模社区与过去和现在的冰单的观察者TS结合在一起，以最终改善预测海平面上升，以及时的方式来解决需求。

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

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

GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework for evaluating glacier velocity products derived from optical satellite image feature tracking

GLAcier 特征跟踪测试套件 (GLAFT)：一个基于统计和物理的框架，用于评估源自光学卫星图像特征跟踪的冰川速度产品

• DOI: 10.5194/tc-17-4063-2023
• 发表时间: 2023
• 期刊: The Cryosphere
• 作者: Zheng, Whyjay;Bhushan, Shashank;Van Wyk De Vries, Maximillian;Kochtitzky, William;Shean, David;Copland, Luke;Dow, Christine;Jones-Ivey, Renette;Pérez, Fernando
• 通讯作者: Pérez, Fernando