DOI: 10.1175/JCLI-D-11-00387.1
Scopus记录号: 2-s2.0-84867228060
论文题名: Causal discovery for climate research using graphical models
作者: Ebert-Uphoff I. ; Deng Y.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 17 起始页码: 5648
结束页码: 5665
语种: 英语
Scopus关键词: Boreal winters
; Causal relationships
; Cause-effect relationships
; Climate research
; Climate scientists
; Constraint-based
; Correlation analysis
; Dynamical process
; Eastern pacific
; GraphicaL model
; Independence graphs
; Information flows
; Low frequency
; Low frequency variability
; Low-frequency modes
; North Atlantic oscillations
; Pacific-north america
; Statistical datas
; Statistical techniques
; Structure-learning
; Teleconnections
; Temporal sequencing
; Time-scales
; Western Pacific
; Atmospheric pressure
; Data mining
; Flow graphs
; Graphic methods
; Wind effects
; Bayesian networks
; atmospheric dynamics
; climate modeling
; climate oscillation
; data mining
; geostatistics
; graphical method
; network analysis
; spatiotemporal analysis
; teleconnection
英文摘要: Causal discovery seeks to recover cause-effect relationships from statistical data using graphical models. One goal of this paper is to provide an accessible introduction to causal discovery methods for climate scientists, with a focus on constraint-based structure learning. Second, in a detailed case study constraintbased structure learning is applied to derive hypotheses of causal relationships between four prominent modes of atmospheric low-frequency variability in boreal winter including the Western Pacific Oscillation (WPO), Eastern Pacific Oscillation (EPO), Pacific-North America (PNA) pattern, and North Atlantic Oscillation (NAO). The results are shown in the form of static and temporal independence graphs also known as Bayesian Networks. It is found that WPO and EPO are nearly indistinguishable from the cause- effect perspective as strong simultaneous coupling is identified between the two. In addition, changes in the state of EPO (NAO) may cause changes in the state of NAO (PNA) approximately 18 (3-6) days later. These results are not only consistent with previous findings on dynamical processes connecting different low-frequency modes (e.g., interaction between synoptic and low-frequency eddies) but also provide the basis for formulating new hypotheses regarding the time scale and temporal sequencing of dynamical processes responsible for these connections. Last, the authors propose to use structure learning for climate networks, which are currently based primarily on correlation analysis. While correlation-based climate networks focus on similarity between nodes, independence graphs would provide an alternative viewpoint by focusing on information flow in the network. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52231
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States; School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, United States
Recommended Citation:
Ebert-Uphoff I.,Deng Y.. Causal discovery for climate research using graphical models[J]. Journal of Climate,2012-01-01,25(17)