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

在这项合作提案中，研究小组的目标是从古代理数据中产生区域十年尺度的气候预测。一般而言，研究人员将改进现有的分析方法，并开发新的统计方法，根据不同的地质指标(树轮、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异性。具体地说，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计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，特别是围绕一系列缺失数据的不确定性。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。更广泛的影响涉及到对学生进行跨学科边界的培训，使其及时整合统计学和气候学。

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.