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.

研究团队在该协作提案中旨在从古代理数据中产生区域际阶段的气候预测。 通常，研究人员将改善现有的分析方法，并开发新的统计方法，以重建过去的气候，从异质地质代理（树环，冰核，冰核，辣椒，珊瑚，沉积物），特别强调了过去两个千年的区域变化。具体而言，研究团队将使用正则期望最大化算法（Regem）算法来利用不同气候变量或代理之间空间的线性协方差来估算缺失值并估算气候统计。目的是直接解决数据中的不确定性，尤其是围绕一个系列中丢失的数据。 这对于古气候数据集很重要，因为空间或时间系列可能不是连续的。更广泛的影响涉及及时整合统计和气候学的训练跨学科边界的培训。