CMG Collaborative Research: Probabilistic Stratigraphic Alignment and Dating of Paleoclimate Data

CMG 合作研究：概率地层排列和古气候数据测年

• 批准号: 1025438
• 负责人: Charles Lawrence
• 金额: $ 60.44万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025438&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Probabilistic; Stratigraphic

Intellectual Merit: Stratigraphic alignment is the primary way in which long marine climate records (105-107years) are placed on a common age model. However, currently there are no techniques for quantifying the uncertainty associated with these alignments. This project will build probabilistic models of an automated stratigraphic alignment algorithm for paleoclimate records as a means of characterizing this uncertainty. The development of this uncertainty analysis is important because the relative timing of climate responses (derived from stratigraphic alignment) is frequently used to evaluate causal relationships within the climate system. Therefore, this study will also assess the effects of alignment uncertainty on these evaluations. Additionally, a probabilistic algorithm will be created for age model development through orbital tuning. The improved accuracy and error estimates for paleoclimate age models that result from this work will improve estimates of the climate system?s sensitivity to changes in radiative forcing. The original software developed by PI L. Lisiecki uses dynamic programming to find the optimal alignment of paleoclimate records based on user-defined parameter settings and produces one best-fit alignment with no uncertainty analysis. The new version will provide users with alignments sampled in proportion to their probability and will provide error bars for the estimated relative ages at each point in the alignment. Specifically, this project will develop two probabilistic versions of the alignment algorithm (pairwise and multiple) in the form of (pair and profile) Hidden Markov models (HMM) and develop a probabilistic HMM for creating orbitally tuned age models for paleoclimate data. The algorithm for age model development will incorporate knowledge gained about sedimentation rate variability from the pair and profile HMM algorithms. All three algorithms will be applied to create a new stack model of benthic δ18O records (a proxy for global climate) with uncertainty estimates which include data noise, alignment uncertainty and age model uncertainty. This "probabilistic stack" is scientifically important because it will yield uncertainty estimates for a widely used measure of past climate change. This project also aims to develop statistical methods to characterize the shapes of the posterior distributions of stratigraphic alignments and orbital tuning. This alignment problem is in a large class of discrete high dimensional problems that often have complex multimodal solution spaces which are difficult to characterize. To date the characterization of these spaces has been limited to a point estimate(s) and Bayesian confidence limits around these high-D estimates. In this project novel methods will be developed for the identification of clusters from multiple modes in these high-D spaces and characterize them as specific probabilistic models using both direct samples from the posterior distribution and the probabilities of each sampled value. Given the limited utility of point estimates and confidence limits in such high-D spaces, these probabilistic characterizations of posterior spaces will greatly improve the ability to describe such posterior spaces. Broader Impacts: The current version of the alignment software developed by PI Lisiecki has been downloaded by users in many different countries and applied to a wide variety of data in many publications. The new software and δ18O stack with uncertainty analysis will be posted on the NOAA NCDC website, on Lisiecki's personal website, the Brown CCMB web server. The new software will improve stratigraphic alignments and estimation of their uncertainty, which ultimately will lead to a better understanding of the climate system and better climate change predictions. The alignment problem is one of many problems in discrete high-D inference, including: the prediction of RNA secondary structures; the characterization of segmental duplications in primate genomes; and stochastic context free grammars in linguistics. This work on the characterization of discrete high-D posterior spaces will have a direct impact in all of these other areas and beyond. This proposal will train undergraduate and graduate students and a post-doc in both stratigraphy and mathematical statistics. This proposal will also broaden participation of under-represented groups by supporting a female PI at the start of her career.

知识价值：地层排列是将长期海洋气候记录（105-107年）放在一个共同年龄模型上的主要方法。然而，目前还没有量化与这些排列相关的不确定性的技术。该项目将为古气候记录建立自动地层排列算法的概率模型，作为表征这种不确定性的一种手段。这种不确定性分析的发展是重要的，因为气候响应的相对时间（来自地层排列）经常用于评估气候系统内的因果关系。因此，本研究还将评估对齐不确定性对这些评估的影响。此外，将通过轨道调谐创建一个概率算法用于年龄模型的开发。这项工作提高了古气候年龄模型的精度和误差估计，将改善对气候系统的估计。S对辐射强迫变化的敏感性。由PI L. Lisiecki开发的原始软件使用动态规划来找到基于用户自定义参数设置的古气候记录的最佳对齐，并产生一个最适合的对齐，没有不确定性分析。新版本将为用户提供按概率比例采样的对齐，并将为对齐中每个点的估计相对年龄提供误差条。具体来说，该项目将以（对和剖面）隐马尔可夫模型（HMM）的形式开发两种概率版本的对齐算法（成对和多重），并开发一种概率HMM，用于创建古气候数据的轨道调整年龄模型。年龄模型开发的算法将结合从配对和剖面HMM算法中获得的关于沉积速率变异性的知识。这三种算法将被应用于创建一个新的底栖生物堆栈模型。18O记录（全球气候的代表）具有不确定性估计，包括数据噪声、排列不确定性和年龄模式不确定性。这种“概率叠加”在科学上很重要，因为它将为一种广泛使用的过去气候变化测量方法提供不确定性估计。本项目还旨在发展统计方法，以表征地层排列和轨道调谐的后验分布的形状。该对准问题是一类较大的离散高维问题，通常具有复杂的多模态解空间，难以表征。迄今为止，这些空间的表征仅限于点估计和围绕这些高d估计的贝叶斯置信限。在这个项目中，将开发新的方法来识别这些高d空间中来自多个模式的集群，并使用后验分布的直接样本和每个采样值的概率将它们表征为特定的概率模型。考虑到在这种高d空间中点估计和置信限的有限效用，这些后验空间的概率特征将极大地提高描述这种后验空间的能力。更广泛的影响：PI Lisiecki开发的校准软件的当前版本已被许多不同国家的用户下载，并应用于许多出版物中的各种数据。新软件和&amp；#948；18O堆栈和不确定性分析将发布在NOAA NCDC网站上，在Lisiecki的个人网站，布朗CCMB网络服务器上。新的软件将改进地层排列和对其不确定性的估计，这最终将导致对气候系统的更好理解和更好的气候变化预测。定位问题是离散高d推理中的众多问题之一，包括：RNA二级结构的预测；灵长类动物基因组片段重复的特征分析语言学中的随机上下文无关语法。这项关于离散高d后空间表征的工作将对所有这些其他领域以及其他领域产生直接影响。该计划将培养地层学和数理统计方面的本科生、研究生和博士后。这项建议还将通过在女性PI开始其职业生涯时提供支持，扩大代表性不足群体的参与。