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

海平面上升对地球仪的社会构成挑战。美国未来海平面上升的规划对国家安全、公共卫生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（气候小组关于气候变化中的海洋和冰冻圈的特别报告）总结了两个令人吃惊的事实：（一）最近海平面上升加速是由于格陵兰和南极冰盖的冰损失增加;和（ii）与冰盖不稳定性有关的不确定性来自有限的观测，当前模式中冰盖过程的不完整表示，以及对大气、海洋和冰盖之间复杂相互作用的不断理解。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，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-专业化程度是有效合作的障碍。该项目将通过创建和促进一个称为“Ghub”的集中式网络中心，减少冰盖科学各分支学科之间的障碍，在该中心，冰盖科学各研究领域都可以使用数据集和工具。Ghub可供所有感兴趣的科学家和非专业人员使用。使用Ghub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，有几种推广和教育途径专门针对为普通受众构建冰盖科学，包括来自代表性不足群体的学生。减少近期海平面上升的不确定性的紧迫性依赖于冰盖对气候变化反应的改进建模。预测未来冰盖变化需要冰盖科学多个学科付出巨大努力，包括观测数据、古冰川学（“古”）数据、数值冰盖建模方面的专业知识，以及广泛使用新兴方法来学习从数据中学习，例如机器学习。然而，重大的知识和学科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摄取数据和分析输出，新的和更大的观测数据集没有被建模社区充分利用，并且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者访问。冰盖科学的不同学科方法导致了瓶颈，减缓了对发展中危机的反应。数据生成器和建模者之间的协调对于测试数据驱动的假设，为过去的冰盖变化提供机械解释，以及整合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。解决未优化合作的紧迫问题需要一个新颖的，综合的，跨学科的计划，降低跨冰盖科学的不同方法的障碍。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方法之间的协作效率。我们将开发一个社区建设科学和教育网络基础设施框架，包括模型和数据处理工具，以促进冰盖科学界之间的协调和协同交流。新的网络基础设施将是一座重要的桥梁，将数字冰盖建模社区与过去和现在冰盖状态的快速增长的观测数据集连接起来，最终将改善海平面上升的预测。GHub网络基础设施还将成为组织不同科学团体及时解决紧迫社会需求的模板。该奖项反映了NSF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。

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.