气候变化领域集成服务门户(CCPortal): Indicator Patterns of Forced Change Learned by an Artificial Neural Network

CCPortal

DOI	10.1029/2020MS002195
	Indicator Patterns of Forced Change Learned by an Artificial Neural Network
	Barnes E.A.; Toms B.; Hurrell J.W.; Ebert-Uphoff I.; Anderson C.; Anderson D.
发表日期	2020
ISSN	19422466
卷号	12 期号:9
英文摘要	Many problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual-mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal-to-noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change. © 2020. The Authors.
语种	英语
scopus关键词	Backpropagation; Climate models; Forecasting; Multilayer neural networks; Signal to noise ratio; Visualization; Annual mean temperatures; Climate model simulations; Indicator patterns; Internal climate variability; Model Differences; Multi-linear regression; Network visualization; Visualization tools; Climate change; artificial neural network; climate change; climate modeling; signal-to-noise ratio
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/156645
作者单位	Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States; Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United States; Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States; Department of Computer Science, Colorado State University, Fort Collins, CO, United States; Pattern Exploration LLC, Fort Collins, CO, United States
推荐引用方式 GB/T 7714	Barnes E.A.,Toms B.,Hurrell J.W.,et al. Indicator Patterns of Forced Change Learned by an Artificial Neural Network[J],2020,12(9).
APA	Barnes E.A.,Toms B.,Hurrell J.W.,Ebert-Uphoff I.,Anderson C.,&Anderson D..(2020).Indicator Patterns of Forced Change Learned by an Artificial Neural Network.Journal of Advances in Modeling Earth Systems,12(9).
MLA	Barnes E.A.,et al."Indicator Patterns of Forced Change Learned by an Artificial Neural Network".Journal of Advances in Modeling Earth Systems 12.9(2020).