CMG Collaborative Research: Efficient high dimensional Bayesian methods for climate field reconstruction

CMG 合作研究：气候场重建的高效高维贝叶斯方法

• 批准号: 1025465
• 负责人: Balakanapathy Rajaratnam
• 金额: $ 35.46万
• 依托单位: Stanford University
• 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 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025465&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Efficient; dimensional

Paleoclimate reconstructions aim to recreate past climates and are critical in assessing how modern day temperatures (and other climate variables) are anomalous in a millennial context. Most of the methods available in the literature give consolidated estimates of climate variables over the past millennium and beyond. It is widely recognized that changes in climate differ by spatial location; for example, estimated changes in temperature, and variability thereof, between the tropics and the polar regions are different. Obtaining a detailed understanding of such phenomena requires developing methodology for paleoclimate reconstructions that are spatially disaggregated, with uncertainty quantifications of estimated past climate. Spatially disaggregate paleoclimate reconstructions are fundamentally high dimensional statistical problems, with unique challenges imposed by the geosciences context. In particular, paleoclimate reconstruction rely on relating climate variables to proxy variables (such as tree rings, ice cores etc..). The project will lay the theoretical foundation for high dimensional paleoclimate reconstructions using modern day statistical methods. As a concrete application the project will reconstruct global past climates at a spatially disaggregated levels for the past millennium, and also attach confidence statements to these reconstructions. The proposed work entails a collaboration between the mathematical and geo-sciences to solve a scientific problem at the interface of both fields. The methods developed during the project also have broad applications in other fields, such as genomics, where relationships between genes in a high dimensional context are often studied.Climate field reconstructions are inherently multivariate inference problems (e.g., spatial data on a large grid are required), rely on noisy input data, and are often so high-dimensional that the data dimension is close to or exceeds the sample size, resulting in ill-posed or rank-deficient estimation problems. In this context high dimensional mean and covariance estimation is often at the center of the inferential problem. Furthermore, meaningful solutions to such problems require a reliable knowledge of the uncertainty in estimated model parameters. The need for a rigorous quantification of uncertainties has recently spurred much interest in Bayesian methods for climate reconstruction, usually based on Markov Chain Monte Carlo (MCMC) sampling techniques. The latter pose two major issues, as: (1) there is no guarantee that posterior samples are generated from the required distribution (convergence issue); (2) they rely on computationally-heavy algorithms which limit their applicability (efficiency, scalability and applicability). We propose to overcome both limitations by using a flexible but high dimensional Bayesian approach that leads to closed-form solutions for posterior quantities, hence alleviating much of the computational burden. Convergence issues will be addressed first, and dimensionality reduction will subsequently be implemented by exploiting the rich theory of Markov Random fields. The outcome of the theoretical component of the project will be the construction of novel and sound, efficient high-dimensional Bayesian algorithms tailor-made for climate field reconstruction problems. These new statistical tools will then be applied to the reconstruction of global and regional temperature fields from heterogeneous geological proxies (tree rings, ice cores, speleothems, corals, sediments) over the Common Era, and new analysis of instrumental surface temperature and sea-level pressure. The solutions will be accompanied by credible intervals, and promises to yield new insights into natural climate variability at global scales.

古气候重建旨在重现过去的气候，对于评估现代温度（和其他气候变量）在千年背景下的异常情况至关重要。文献中现有的大多数方法都对过去一千年及以后的气候变量作出了综合估计。人们普遍认识到，气候变化因空间位置而异;例如，热带地区和极地地区的估计温度变化及其变异性是不同的。要详细了解这种现象，需要制定方法，在空间上分解的古气候重建，与不确定性量化的估计过去的气候。空间非聚集古气候重建从根本上说是高维统计问题，与地球科学的背景下所施加的独特的挑战。特别是，古气候重建依赖于将气候变量与代理变量（如树木年轮，冰芯等）相关联。该项目将为利用现代统计方法进行高维古气候重建奠定理论基础。作为一个具体的应用，该项目将重建全球过去的气候在空间上分解的水平，为过去的千年，并附上这些重建的信心声明。拟议的工作需要数学和地球科学之间的合作，以解决这两个领域之间的科学问题。在该项目期间开发的方法在其他领域也有广泛的应用，例如基因组学，其中经常研究高维背景下基因之间的关系。气候场重建本质上是多变量推理问题（例如，需要大网格上的空间数据），依赖于有噪声的输入数据，并且通常是如此高维以至于数据维度接近或超过样本大小，从而导致不适定或秩不足估计问题。在这种情况下，高维均值和协方差估计往往是推理问题的中心。此外，这些问题的有意义的解决方案，需要一个可靠的知识，估计模型参数的不确定性。对不确定性进行严格量化的需求最近引起了人们对贝叶斯气候重建方法的极大兴趣，该方法通常基于马尔可夫链蒙特卡罗（MCMC）采样技术。后者提出了两个主要问题，因为：（1）不能保证后验样本是从所需的分布中生成的（收敛问题）;（2）它们依赖于计算量大的算法，这限制了它们的适用性（效率，可扩展性和适用性）。我们建议通过使用灵活但高维的贝叶斯方法来克服这两个限制，该方法导致后验量的封闭形式的解决方案，从而减轻了大部分的计算负担。收敛问题将首先得到解决，随后将通过利用马尔可夫随机场的丰富理论来实现降维。该项目理论部分的成果将是为气候场重建问题量身定制的新颖、合理、高效的高维贝叶斯算法的构建。然后，这些新的统计工具将被应用于从共同时代的异质地质代理（树木年轮，冰芯，洞穴沉积物，珊瑚，沉积物）重建全球和区域温度场，并对仪器表面温度和海平面压力进行新的分析。这些解决方案将伴随着可信的时间间隔，并有望在全球范围内对自然气候变化产生新的见解。