| 项目编号: | 1602920
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| 项目名称: | Collaborative Research: P2C2--Derivation of Ensemble and Joint-Variable Climate Field Reconstructions of the Common Era Using New Random Field Methods |
| 作者: | Jason Smerdon
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| 承担单位: | Columbia University
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| 批准年: | 2016
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| 开始日期: | 2016-11-01
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| 结束日期: | 2019-10-31
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| 资助金额: | 157085
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Atmospheric and Geospace Sciences
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| 英文关键词: | dependent spatio-temporal random field
; climate field reconstruction
; cfr
; new cutting-edge study
; single coherent reconstruction
; bayesian hierarchical model
; scientific research
; climate model
; large-scale cfr methodology
; reconstruction methodology
; climate variability
; last several millennium
; interdisciplinary research
; cfr method
; novel statistical research
; climate scientist
; available reconstruction
; climate dynamics
; powerful cfr methodology
; climate science
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| 英文摘要: | The project generally aims to explore climate field reconstructions (CFRs) to target spatial patterns of climate variability that may aid in more artful characterizations of climate dynamics than the more widely available reconstructions of single indices (e.g. Northern Hemisphere means). An increasing number of CFRs are emerging that span the last several millennia over regional and global spatial scales. This situation allows for an extensive evaluation of different CFRs and new cutting-edge studies to improve CFR methods. The research involves novel statistical research in an area of climate science that presents important statistical challenges, thereby fostering potential intellectual advancement across the fields of math and physical science.
The specific goal of this project is to provide a rigorous and comprehensive statistical assessment of CFRs by pursuing a nonparametric approach to jointly evaluate the first and second moments of two dependent spatio-temporal random fields based on functional data analysis. Bayesian hierarchical models that incorporate the skill assessment of each climate field reconstruction (CFR) are expected to integrate the strengths of individual CFRs and climate models into a single coherent reconstruction.
These developments will significantly benefit the understanding of the spatio-temporal characteristics of different CFRs and the advancement of new and powerful CFR methodologies. Formal statistical tests will specifically be developed to determine the difference between two CFRs in terms of their first and second moments jointly, or of their eigenvalues and eigenfunctions jointly. The tests will yield a systematic assessment of the discrepancies across widely employed CFRs, which will be in turn used to integrate different CFRs.
Multivariate spatial copula models will also be developed that could account for non-stationary teleconnections to reconstruct the spatially varying bivariate distribution of temperatures and precipitation given proxy data. No large-scale CFR methodology has attempted to account for both teleconnection non-stationarity and the multivariate nature of climate and proxies, making the inclusion of these features into a reconstruction methodology a potential major advance.
The project will foster fundamental collaborations between statisticians and climate scientists thereby laying the foundation for more interdisciplinary research. The project will engage undergraduate students in many aspect of the scientific research. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/90810
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Jason Smerdon. Collaborative Research: P2C2--Derivation of Ensemble and Joint-Variable Climate Field Reconstructions of the Common Era Using New Random Field Methods. 2016-01-01.
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