DOI: 10.5194/cp-12-525-2016
Scopus记录号: 2-s2.0-84959359150
论文题名: A Bayesian hierarchical model for reconstructing relative sea level: From raw data to rates of change
作者: Cahill N. ; Kemp A.C. ; Horton B.P. ; Parnell A.C.
刊名: Climate of the Past
ISSN: 18149324
出版年: 2016
卷: 12, 期: 2 起始页码: 525
结束页码: 542
语种: 英语
Scopus关键词: Bayesian analysis
; bioindicator
; calibration
; paleoenvironment
; proxy climate record
; reconstruction
; saltmarsh
; sea level change
; tidal cycle
; Atlantic Coast [North America]
; Atlantic Coast [United States]
; New Jersey
; United States
; Foraminifera
英文摘要: 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 (Í 13C) 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 (Í 13C) 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 - 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 = 0.003m2). 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. © Author(s) 2016. CC Attribution 3.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/49051
Appears in Collections: 气候变化与战略
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Recommended Citation:
Cahill N.,Kemp A.C.,Horton B.P.,et al. A Bayesian hierarchical model for reconstructing relative sea level: From raw data to rates of change[J]. Climate of the Past,2016-01-01,12(2)