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，该研究小组的目标是从古代用数据中产生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，用于从异质地质代理（树木年轮，冰芯，洞穴沉积物，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。具体而言，研究小组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的空间线性协变来估算缺失值和估计气候统计数据。其目标是直接解决数据中的不确定性，特别是围绕一个系列中的缺失数据。 这对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，更广泛的影响涉及对跨学科的学生进行及时整合统计学和气候学的培训。