Collaborative Research: P2C2--Multiproxy Reconstructions as A Missing-Data Problem: New Techniques and their Application to Regional Climates of the Past Millennium

合作研究：P2C2——作为缺失数据问题的多代理重建：新技术及其在过去千年区域气候中的应用

• 批准号: 1003614
• 负责人: Tapio Schneider
• 金额: $ 8.91万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-15 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1003614&HistoricalAwards=false
• 关键词: Collaborative; Research; P2C2; Multiproxy; Reconstructions

The research team, in this collaborative proposal, aims to produce regional decadal-scale climate forecasts from paleo proxy data. Generally, the researchers will improve existing analytical methods and develop new statistical methods for the reconstruction of past climates from heterogeneous geological proxies (tree rings, ice cores, speleothems, corals, sediments), with particular emphasis on regional variability over the past two millennia. Specifically, the research team will use the regularized expectation-maximization algorithm (RegEM) algorithm to exploit linear covariation in space among different climate variables or proxies to impute missing values and estimate climate statistics. The goal is to directly address uncertainties in data, especially surrounding missing data within a series. This is important for paleoclimate data sets since the spatial or temporal series may not be continuous.The broader impacts involve the training of students across disciplinary boundaries in a timely integration of statistics and climatology.

在这项合作提案中，研究小组的目标是根据古代理数据生成区域十年尺度的气候预测。 一般来说，研究人员将改进现有的分析方法并开发新的统计方法，用于根据异质地质代理（树木年轮、冰芯、洞穴、珊瑚、沉积物）重建过去的气候，特别强调过去两千年的区域变化。具体来说，研究团队将使用正则化期望最大化算法（RegEM）算法来利用不同气候变量或代理之间空间的线性协变来估算缺失值并估计气候统计数据。目标是直接解决数据中的不确定性，尤其是一系列数据中缺失的数据。 这对于古气候数据集很重要，因为空间或时间序列可能不是连续的。更广泛的影响包括对跨学科边界的学生进行及时整合统计学和气候学的培训。