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: 2002518
• 负责人: Judson Partin
• 金额: $ 5.44万
• 依托单位: University of Texas at Austin
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002518&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.

该项目旨在采用大数据方法，利用新兴的古气候数据标准，以解决气候动态中的两个基本问题：（Q1）氢化气候突然变化的空间范围； （Q2）过去温度变化的知识如何有助于减少二十一世纪气候预测的传播。潜在的更广泛的影响包括全球古气候社区的大数据分析中的建筑能力。所有分析都将作为开源计算叙事共享，以在早期研究人员的古数据科学中传播最佳实践，并为将来的这种分析提供模板。特别是，研究人员将利用近期的古气候数据合成工作，以及在探索时间表的分析中的分析中，以探索Palesental paleore nistrim forlocenal hollocenal hallocenal hallocenal hallocenal hallocenal hallocenal hallocenal hallocenal hallocenal hollocen hallocenal holacemen halloceal instrim holoceen solace inth holoceen hallocen holac holac instrim。拟议的工作还将将古气候数据与耦合模型对比项目（CMIP6）合奏的未来气候预测联系起来。 该项目将通过基于工作流程和分析的三个年度研讨会为早期的职业科学家提供培训，并根据本研究的一部分开发了工作流程和方法。此外，该项目将支持一位在古气候，数据科学和气候建模之间的交叉点工作的研究生。其他更广泛的影响包括通过年轻研究人员计划和知识跨学科数据科学中心进行外展活动，以使高中生和数据科学领域的研究生揭露跨学科研究。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子和更广泛的影响，可以通过评估来进行评估，以审查Criteria。