气候变化领域集成服务门户(CCPortal): Statistical modeling of rates and trends in Holocene relative sea level

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DOI	10.1016/j.quascirev.2018.10.032
	Statistical modeling of rates and trends in Holocene relative sea level
	Ashe E.L.; Cahill N.; Hay C.; Khan N.S.; Kemp A.; Engelhart S.E.; Horton B.P.; Parnell A.C.; Kopp R.E.
发表日期	2019
ISSN	0277-3791
起始页码	58
结束页码	77
卷号	204
英文摘要	Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatio-temporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical framework. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, we recommend non-parametric approaches for modeling temporal and spatio-temporal RSL. © 2018 Elsevier Ltd
英文关键词	Hierarchical statistical modeling; RSL; Sea level
语种	英语
scopus关键词	Geochronology; Sea level; Statistics; Correlation structure; Nonparametric approaches; Spatial and temporal variability; Spatio-temporal models; Spatiotemporal variability; Statistical framework; Statistical modeling; Statistical treatment; Uncertainty analysis; Holocene; modeling; sea level change; spatial variation; temporal variation; trend analysis
来源期刊	Quaternary Science Reviews
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/152053
作者单位	Department of Statistics and Biostatistics, Rutgers University, New Brunswick, NJ, United States; Department of Earth & Planetary Sciences, Rutgers University, New Brunswick, NJ, United States; Institute of Earth, Ocean & Atmospheric Sciences, Rutgers University, New Brunswick, NJ, United States; School of Mathematics and Statistics, Maynooth University, Kildare, Ireland; Department of Earth and Planetary Sciences, Boston College, Chestnut Hill, MA, United States; Asian School of the Environment, Nanyang Technological University, Singapore; Department of Earth and Ocean Sciences, Tufts University, Medford, MA, United States; Department of Geosciences, University of Rhode Island, Kingston, RI, United States; Earth Observatory of Singapore, Nanyang Technological University, Singapore; Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, United States; Hamilton Institute, Insight Centre for Data Analytics, Maynooth University, Kildare, Ireland
推荐引用方式 GB/T 7714	Ashe E.L.,Cahill N.,Hay C.,et al. Statistical modeling of rates and trends in Holocene relative sea level[J],2019,204.
APA	Ashe E.L..,Cahill N..,Hay C..,Khan N.S..,Kemp A..,...&Kopp R.E..(2019).Statistical modeling of rates and trends in Holocene relative sea level.Quaternary Science Reviews,204.
MLA	Ashe E.L.,et al."Statistical modeling of rates and trends in Holocene relative sea level".Quaternary Science Reviews 204(2019).