globalchange  > 过去全球变化的重建
DOI: 10.2172/1209103
报告号: DOE-GTRC--07143
报告题名:
Validation and quantification of uncertainty in coupled climate models using network analysis
作者: Bracco, Annalisa
出版年: 2015
发表日期: 2015-08-10
总页数: 12
国家: 美国
语种: 英语
英文关键词: Network Analysis
中文主题词: 地表温度 ; 海表温度
主题词: SURFACE TEMPERATURE ; SEA SURFACE TEMPERATURE
英文摘要: We developed a fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. This approach can be applied to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation, and is substantially new within the available network methodologies developed for climate studies. At the first layer, gridded climate data are used to identify ‘‘areas’’, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. The robustness of the method (i.e. the ability to separate between topological distinct fields, while identifying correctly similarities) has been extensively tested. It has been proved that it provides a reliable, fast framework for comparing and ranking the ability of climate models of reproducing observed climate patterns and their connectivity. We further developed the methodology to account for lags in the connectivity between climate patterns and refined our area identification algorithm to account for autocorrelation in the data. The new methodology based on complex network analysis has been applied to state-of-the-art climate model simulations that participated to the last IPCC (International Panel for Climate Change) assessment to verify their performances, quantify uncertainties, and uncover changes in global linkages between past and future projections. Network properties of modeled sea surface temperature and rainfall over 1956–2005 have been constrained towards observations or reanalysis data sets, and their differences quantified using two metrics. Projected changes from 2051 to 2300 under the scenario with the highest representative and extended concentration pathways (RCP8.5 and ECP8.5) have then been determined. The network of models capable of reproducing well major climate modes in the recent past, changes little during this century. In contrast, among those models the uncertainties in the projections after 2100 remain substantial, and primarily associated with divergences in the representation of the modes of variability, particularly of the El NiĂąo Southern Oscillation (ENSO), and their connectivity, and therefore with their intrinsic predictability, more so than with differences in the mean state evolution. Additionally, we evaluated the relation between the size and the ‘strength’ of the area identified by the network analysis as corresponding to ENSO noting that only a small subset of models can reproduce realistically the observations.
URL: http://www.osti.gov/scitech/servlets/purl/1209103
Citation statistics:
资源类型: 研究报告
标识符: http://119.78.100.158/handle/2HF3EXSE/41801
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

Files in This Item: Download All
File Name/ File Size Content Type Version Access License
1209103.pdf(1052KB)研究报告--开放获取View Download

Recommended Citation:
Bracco, Annalisa. Validation and quantification of uncertainty in coupled climate models using network analysis. 2015-01-01.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Bracco, Annalisa]'s Articles
百度学术
Similar articles in Baidu Scholar
[Bracco, Annalisa]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Bracco, Annalisa]‘s Articles
Related Copyright Policies
Null
收藏/分享
文件名: 1209103.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.