CMG: Reconstructing Climate from Tree Ring Data

CMG：从树木年轮数据重建气候

• 批准号: 0934516
• 负责人: Andrew Gelman
• 金额: $ 59.81万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934516&HistoricalAwards=false
• 关键词: CMG; Reconstructing; Climate; Tree; Ring

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project seeks better ways to use tree ring data to infer changes in climate. Annual growth rings in trees have long been used as a primary means of reconstructing climatic conditions over the past two millennia, extending our knowledge of climate beyond the period of the instrumented record. However, tree ring growth depends on factors other than climate, and non-climatic influences must be removed when tree ring data is used for climate reconstruction. Traditional methods for filtering out non-climatic influences can be too extreme, removing some of the climate signal as well as the non-climate noise. The research in this project will improve upon traditional filtering methods using Bayesian hierarchical modeling, a technique in which statistical models will be constructed to specify how tree rings growth is expected to vary in response to both climatic and non-climatic (e.g. tree age) factors. The models will then be combined with tree ring data to produce both climate reconstructions and estimates of the level of uncertainty present in the reconstructions. Additional research will consider the extent to which tree ring-based reconstructions might benefit from changes in the way tree ring data is collected. In particular, the potential benefit from taking additional cores from each tree will be evaluated.Tree ring-based climate reconstructions are an important source of information on the range of natural climate fluctuations, and thus they play an important role in assessing the extent to which recent climate changes are outside the envelope of pre-industrial climate variability. The improvements in tree ring analysis resulting from this research will thus have a broad impact on our understanding of both natural and human-induced climate variation and change. In addition, tree rings are an important source of information regarding the range of past variability in water resources, which is of interest to water managers in the semi-arid southwestern states. Beyond these benefits, the research will develop statistical techniques which will be applicable to a range of scientific problems in which the behavior of a complex system must be inferred from proxy data.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目寻求更好的方法来使用树木年轮数据来推断气候变化。 树木的年轮长期以来一直被用作重建过去两千年气候条件的主要手段，将我们对气候的了解扩展到仪器记录的时期之外。 然而，树木年轮的生长取决于气候以外的因素，当树木年轮数据用于气候重建时，必须去除非气候的影响。 传统的过滤非气候影响的方法可能过于极端，会去除一些气候信号和非气候噪声。 该项目的研究将使用贝叶斯分层模型改进传统的过滤方法，该技术将构建统计模型，以指定树木年轮生长如何响应气候和非气候（例如树木年龄）因素。 这些模型将与树木年轮数据相结合，以产生气候重建和重建中存在的不确定性水平的估计。 更多的研究将考虑基于树轮的重建在多大程度上可能受益于树轮数据收集方式的变化。 特别是，采取额外的核心，从每棵树的潜在效益将进行评估。树木年轮为基础的气候重建是一个重要的信息来源的范围内的自然气候波动，因此，他们发挥了重要作用，在评估的程度，最近的气候变化是在工业化前的气候变率的信封。 因此，这项研究对树木年轮分析的改进将对我们理解自然和人为引起的气候变化和变化产生广泛的影响。 此外，树木年轮是有关水资源过去变化范围的重要信息来源，这对半干旱西南各州的水资源管理人员很有意义。 除了这些好处之外，该研究还将开发适用于一系列科学问题的统计技术，其中复杂系统的行为必须从代理数据中推断出来。