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: 1025444
• 负责人: Lorraine Lisiecki
• 金额: $ 15.7万
• 依托单位: University of California-Santa Barbara
• 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: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025444&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.

智力优势：地层对齐是将长期海洋气候记录（105- 107年）放置在一个共同的年龄模型上的主要方法。然而，目前还没有量化与这些对齐相关的不确定性的技术。该项目将建立概率模型的自动地层对齐算法的古气候记录作为一种手段，表征这种不确定性。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（来自地层对齐）经常被用来评估气候系统内的因果关系。因此，本研究还将评估对准不确定性对这些评估的影响。此外，将通过轨道调整为年龄模型开发创建概率算法。这项工作提高了古气候年龄模型的准确性和误差估计，将改善对气候系统的估计？辐射强迫变化的敏感性。由PI L. Lisiecki使用动态规划来根据用户定义的参数设置找到古气候记录的最佳对齐，并在没有不确定性分析的情况下产生一个最佳拟合对齐。新版本将为用户提供与其概率成比例的路线抽样，并将提供路线中每个点的估计相对年龄的误差条。具体来说，该项目将开发两个概率版本的对齐算法（成对和多个）的形式（对和配置文件）隐马尔可夫模型（HMM）和开发一个概率HMM创建轨道调整的年龄模型的古气候数据。年龄模型开发的算法将纳入从成对和剖面HMM算法中获得的关于沉积速率变异性的知识。所有这三种算法将被应用于创建一个新的堆栈模型的海底#948; 18 O记录（全球气候的代理）的不确定性估计，其中包括数据噪声，对齐的不确定性和年龄模型的不确定性。这种“概率叠加”在科学上很重要，因为它将为过去气候变化的广泛使用的测量提供不确定性估计。 该项目还旨在开发统计方法，以表征地层对齐和轨道调整的后验分布的形状。这种对齐问题是在一个大类的离散高维问题，往往具有复杂的多模态的解决方案空间，这是难以表征。到目前为止，这些空间的特征仅限于点估计和这些高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空间中的多模式集群，并将其描述为使用后验分布的直接样本和每个采样值的概率的特定概率模型。鉴于点估计和置信限在这种高维空间中的有限效用，后验空间的这些概率特征将大大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。新的软件和带有不确定性分析的#948; 18 O堆栈将发布在NOAA NCDC网站上，在Lisiecki的个人网站上，Brown CCMB网络服务器上。新软件将改进地层对齐和对它们的不确定性的估计，最终将导致更好地了解气候系统和更好地预测气候变化。对齐问题是离散高维推理中的许多问题之一，包括：RNA二级结构的预测;灵长类基因组中片段重复的表征;以及语言学中的随机上下文无关语法。这项关于离散高维后验空间特征的工作将对所有其他领域及其他领域产生直接影响。该建议将培养本科生和研究生以及地层学和数理统计方面的博士后。该提案还将通过在职业生涯开始时支持女性PI来扩大代表性不足群体的参与。