Collaborative Research: A Big Data Approach to Fundamental Paleoclimate Questions

合作研究：解决基本古气候问题的大数据方法

• 批准号: 2002556
• 负责人: Julien Emile-Geay
• 金额: $ 39.81万
• 依托单位: University of Southern California
• 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: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002556&HistoricalAwards=false
• 关键词: Collaborative; Research; Big; Data; Approach

This project seeks to apply a Big Data approach, harnessing emerging standards for paleoclimate data, to address two fundamental questions in climate dynamics: (Q1) the spatial extent of abrupt changes in hydroclimate; and (Q2) How can knowledge of past temperature variations help reduce the spread of twenty-first century climate projections. The potential Broader Impacts include building capacity in Big Data analysis in the global paleoclimate community. All analyses will be shared as open-source computational narratives to disseminate best practices in paleo data science among early-career researchers, and to provide a template for future analyses of this kind.Specifically, the researchers will leverage recent paleoclimate data synthesis efforts, as well as advances in the analysis of chronologically uncertain timeseries, to explore fundamental questions in Holocene paleoclimatology. The proposed work will also link paleoclimate data to future climate projections from the Coupled Model Intercomparison Project (CMIP6) ensemble. The project will provide training to early career scientists through three yearly workshops in data management and analysis based on workflows and methodology developed as part of this research. Additionally, the project will support one graduate student working at the intersection between paleoclimatology, data science, and climate modeling. Other Broader Impacts include outreach through the Young Researchers program and Center for Knowledge-Powered Interdisciplinary Data Science to expose high-school students and graduate students in data science to interdisciplinary research.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.

该项目旨在应用大数据方法，利用古气候数据的新兴标准，解决气候动力学中的两个基本问题：(Q1)水文气候突变的空间范围； (Q2) 了解过去的温度变化如何有助于减少二十一世纪气候预测的传播。潜在的更广泛影响包括全球古气候界大数据分析能力建设。所有分析都将作为开源计算叙述进行共享，以向早期职业研究人员传播古数据科学的最佳实践，并为未来的此类分析提供模板。具体来说，研究人员将利用最近的古气候数据合成工作以及时间序列不确定时间序列分析方面的进展，探索全新世古气候学的基本问题。拟议的工作还将把古气候数据与耦合模型比对项目（CMIP6）整体的未来气候预测联系起来。 该项目将根据本研究开发的工作流程和方法，通过每年三次的数据管理和分析研讨会，为早期职业科学家提供培训。此外，该项目还将支持一名在古气候学、数据科学和气候建模交叉领域工作的研究生。其他更广泛的影响包括通过青年研究人员计划和知识驱动的跨学科数据科学中心进行推广，让数据科学领域的高中生和研究生接触跨学科研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。