气候变化领域集成服务门户(CCPortal): El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles

CCPortal

DOI	10.1029/2019JC015947
	El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles
	Houghton I.A.; Wilson J.D.
发表日期	2020
ISSN	21699275
卷号	125 期号:9
英文摘要	Variability in the El Niño-Southern Oscillation (ENSO) has global impacts on seasonal temperatures and rainfall. Current detection methods for extreme phases, which occur with irregular periodicity, rely upon sea surface temperature anomalies within a strictly defined geographic region of the Pacific Ocean. However, under changing climate conditions and ocean warming, these historically motivated indicators may not be reliable into the future. In this work, we demonstrate the power of data clustering as a robust, automatic way to detect anomalies in climate patterns. Ocean temperature profiles from Argo floats are partitioned into similar groups utilizing unsupervised machine learning methods. The automatically identified groups of measurements represent spatially coherent, large-scale water masses in the Pacific, despite no inclusion of geospatial information in the clustering task. Further, spatiotemporal dynamics of the clusters are strongly indicative of El Niño events, the east Pacific warming phase of ENSO. The fitting of a cluster model on a collection of ocean profiles identifies changes in the vertical structure of the temperature profiles through reassignment to a different group, concisely capturing physical changes to the water column during an El Niño event, such as thermocline tilting. Clustering proves to be an effective tool for analysis of the irregularly sampled (in space and time) data from Argo floats and may serve as a novel approach for detecting anomalies given the freedom from thresholding decisions. Unsupervised machine learning could be particularly valuable due to its ability to identify patterns in data sets without user-imposed expectations, facilitating further discovery of anomaly indicators. ©2020. American Geophysical Union. All Rights Reserved.
英文关键词	Argo floats; data clustering; ENSO dynamics
语种	英语
来源期刊	Journal of Geophysical Research: Oceans
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/186701
作者单位	The Data Institute, University of San Francisco, San Francisco, CA, United States; Department of Mathematics and Statistics, University of San Francisco, San Francisco, CA, United States
推荐引用方式 GB/T 7714	Houghton I.A.,Wilson J.D.. El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles[J],2020,125(9).
APA	Houghton I.A.,&Wilson J.D..(2020).El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles.Journal of Geophysical Research: Oceans,125(9).
MLA	Houghton I.A.,et al."El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles".Journal of Geophysical Research: Oceans 125.9(2020).