利用冰芯资料重建末次盛冰期南极冰盖形状

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-09-18 DOI:10.1093/jrsssc/qlad078
Fiona E Turner, Caitlin E Buck, Julie M Jones, Louise C Sime, Irene Malmierca Vallet, Richard D Wilkinson
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引用次数: 0

摘要

南极冰盖(AIS)是地球上最大的冷冻水储存库;了解它在过去是如何变化的,可以让我们更好地预测它和海平面可能会如何变化。在这里,我们使用以前的AIS重建,来自冰芯的水同位素比率,以及冰盖形状和同位素比率之间关系的模拟器预测来创建末次盛冰期AIS模型。我们开发了一个先验分布,该分布捕获了有关AIS的专家意见,生成了一个设计的潜在形状集合,通过气候模型HadCM3运行这些集合,并训练了一个高斯过程模拟器来模拟冰盖形状和同位素比率之间的联系。为了使分析在计算上易于处理,我们开发了一种优先主成分方法,该方法允许我们以一种方式减少问题的维度,这种方式说明了我们在重建中放置的不同重要性,允许我们创建反映先前不确定性的基础。我们使用马尔科夫链蒙特卡罗从后验分布中取样,发现HadCM3预测的同位素比率与冰芯观测值密切匹配的形状。后验分布使我们能够量化重建形状的不确定性,这是其他分析中缺少的一个特征。
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Reconstructing the Antarctic ice-sheet shape at the Last Glacial Maximum using ice-core data
Abstract The Antarctic ice sheet (AIS) is the Earth’s largest store of frozen water; understanding how it changed in the past allows us to improve projections of how it, and sea levels, may change. Here, we use previous AIS reconstructions, water isotope ratios from ice cores, and simulator predictions of the relationship between the ice-sheet shape and isotope ratios to create a model of the AIS at the Last Glacial Maximum. We develop a prior distribution that captures expert opinion about the AIS, generate a designed ensemble of potential shapes, run these through the climate model HadCM3, and train a Gaussian process emulator of the link between ice-sheet shape and isotope ratios. To make the analysis computationally tractable, we develop a preferential principal component method that allows us to reduce the dimension of the problem in a way that accounts for the differing importance we place in reconstructions, allowing us to create a basis that reflects prior uncertainty. We use Markov chain Monte Carlo to sample from the posterior distribution, finding shapes for which HadCM3 predicts isotope ratios closely matching observations from ice cores. The posterior distribution allows us to quantify the uncertainty in the reconstructed shape, a feature missing in other analyses.
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
期刊最新文献
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