气候变化领域集成服务门户(CCPortal): Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature

CCPortal

DOI	10.1029/2019MS001654
	Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature
	Falasca F.; Bracco A.; Nenes A.; Fountalis I.
发表日期	2019
ISSN	19422466
起始页码	1479
结束页码	1515
卷号	11 期号:6
英文摘要	A framework for analyzing and benchmarking climate model outputs is built upon δ-MAPS, a recently developed complex network analysis method. The framework allows for the possibility of highlighting quantifiable topological differences across data sets, capturing the magnitude of interactions including lagged relationships and quantifying the modeled internal variability, changes in domains properties and in their connections over space and time. A set of four metrics is proposed to assess and compare the modeled domains shapes, strengths, and connectivity patterns. δ-MAPS is applied to investigate the topological properties of sea surface temperature from observational data sets and in a subset of the Community Earth System Model (CESM) Large Ensemble focusing on the past 35 years and over the 20th and 21st centuries. Model ensemble members are mapped in a reduced metric space to quantify internal variability and average model error. It is found that network properties are on average robust whenever individual member or ensemble trends are removed. The assessment identifies biases in the CESM representation of the connectivity patterns that stem from too strong autocorrelations of domains signals and from the overestimation of the El Niño–Southern Oscillation amplitude and its thermodynamic feedback onto the tropical band in most members. ©2019. The Authors.
英文关键词	climate variability; ENSO; future projections; model comparison; model validation; network analysis
语种	英语
scopus关键词	Atmospheric pressure; Atmospheric temperature; Climatology; Complex networks; Electric network analysis; Oceanography; Optical projectors; Submarine geophysics; Surface properties; Surface waters; Topology; Climate variability; ENSO; Future projections; Model comparison; Model validation; Climate models; autocorrelation; benchmarking; climate variation; connectivity; El Nino-Southern Oscillation; future prospect; model validation; network analysis; sea surface temperature; trend analysis
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156905
作者单位	School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, United States; Institute of Geosciences and Earth Resources, National Research Council of Italy, Pisa, Italy; ENAC, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras, Greece; Institute for Environmental Research and Sustainable Development, National Observatory of Athens, P. Penteli, Greece; College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
推荐引用方式 GB/T 7714	Falasca F.,Bracco A.,Nenes A.,et al. Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature[J],2019,11(6).
APA	Falasca F.,Bracco A.,Nenes A.,&Fountalis I..(2019).Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature.Journal of Advances in Modeling Earth Systems,11(6).
MLA	Falasca F.,et al."Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature".Journal of Advances in Modeling Earth Systems 11.6(2019).