Bayesian Inference for Palaeoclimate with time Uncertainty and Stochastic Volatility
Bayesian Inference for Palaeoclimate with time Uncertainty and Stochastic Volatility
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古气候随时间不确定性和随机波动的贝叶斯推断
DOI：
10.1111/rssc.12065
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Parnell A
中科院分区：
文献类型：
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作者：
Parnell A
我们提出并拟合了一个贝叶斯模型来推断几千年来的古气候。我们使用的数据来自于从沉积物岩心中提取的古代花粉计数，以及提供（不确定的）年龄的放射性碳年代。当与现代花粉-气候数据集相结合时，我们可以将古代花粉校准为古代气候。我们使用正态-逆高斯过程来模拟古气候随时间的随机波动，并提出了一种新的模块化马尔可夫链Monte链算法来实现快速计算。我们以北爱尔兰的一个案例研究来说明我们的方法，并提供了一个R包Bclim，供其他站点使用。
We propose and fit a Bayesian model to infer palaeoclimate over several thousand years. The data that we use arise as ancient pollen counts taken from sediment cores together with radiocarbon dates which provide (uncertain) ages. When combined with a modern pollen–climate data set, we can calibrate ancient pollen into ancient climate. We use a normal–inverse Gaussian process prior to model the stochastic volatility of palaeoclimate over time, and we present a novel modularized Markov chain Monte Chain algorithm to enable fast computation. We illustrate our approach with a case-study from Sluggan Moss, Northern Ireland, and provide an R package, Bclim, for use at other sites.