A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change
A Bayesian hierarchical model for reconstructing relative sea level: from raw data to rates of change
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DOI：
10.5194/cp-12-525-2016
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发表时间：
2016-01-01
影响因子：
4.3
通讯作者：
Parnell, Andrew C.
中科院分区：
文献类型：
--
作者：
Cahill, Niamh;Kemp, Andrew C.;Parnell, Andrew C.
本文提出了一个贝叶斯层次模型，用于重建具有量化不确定性的相对海平面（RSL）变化的连续和动态演变。重建是根据盐沼沉积物年代岩心中保存的生物（有孔虫）和地球化学（三角洲C-13）海平面指标进行的。我们的模型由三个模块组成：(1)一个新的贝叶斯转移（B-TF）函数，用于将生物指标校准为潮汐高程，该函数足够灵活，可以正式容纳其他代理；(2)利用Bchron年龄-深度模型开发的现有年代学，以及(3)现有的用于估算海平面变化率的误差-变量积分高斯过程（EIV-IGP）模型。我们利用有孔虫开发了一种新的B-TF，并将其与广泛使用的加权平均传递函数（WA-TF）的结果进行了比较。在B-TF模型中正式加入第二个代理可以减小垂直不确定性，并提高重建RSL的精度。与WA-TF相比，多代理B-TF的垂直不确定性平均小28%。当与历史验潮仪测量值进行评估时，多代理B-TF最准确地重建了仪器记录中观测到的RSL变化（均方误差= 0.003 m(2)）。贝叶斯层次模型为重建和分析海平面随时间的变化提供了一个单一的、统一的框架。该方法适用于利用生物代用物重建其他古环境变量（如温度）。
We present a Bayesian hierarchical model for reconstructing the continuous and dynamic evolution of relative sea-level (RSL) change with quantified uncertainty. The reconstruction is produced from biological (foraminifera) and geochemical (delta C-13) sea-level indicators preserved in dated cores of salt-marsh sediment. Our model is comprised of three modules: (1) a new Bayesian transfer (B-TF) function for the calibration of biological indicators into tidal elevation, which is flexible enough to formally accommodate additional proxies; (2) an existing chronology developed using the Bchron age-depth model, and (3) an existing Errors-In-Variables integrated Gaussian process (EIV-IGP) model for estimating rates of sea-level change. Our approach is illustrated using a case study of Common Era sea-level variability from New Jersey, USA We develop a new B-TF using foraminifera, with and without the additional (delta C-13) proxy and compare our results to those from a widely used weighted-averaging transfer function (WA-TF). The formal incorporation of a second proxy into the B-TF model results in smaller vertical uncertainties and improved accuracy for reconstructed RSL. The vertical uncertainty from the multi-proxy B-TF is similar to 28% smaller on average compared to the WA-TF. When evaluated against historic tide-gauge measurements, the multi-proxy B-TF most accurately reconstructs the RSL changes observed in the instrumental record (mean square error = 0.003 m(2)). The Bayesian hierarchical model provides a single, unifying framework for reconstructing and analyzing sea-level change through time. This approach is suitable for reconstructing other paleoenvironmental variables (e.g., temperature) using biological proxies.