气候变化领域集成服务门户(CCPortal): Exploring North Atlantic and north pacific decadal climate prediction using self-organizing maps

CCPortal

DOI	10.1175/JCLI-D-20-0017.1
	Exploring North Atlantic and north pacific decadal climate prediction using self-organizing maps
	Gu Q.; Gervais M.
发表日期	2021
ISSN	0894-8755
起始页码	123
结束页码	141
卷号	34 期号:1
英文摘要	Decadal climate prediction can provide invaluable information for decisions made by government agencies and industry. Modes of internal variability of the ocean play an important role in determining the climate on decadal time scales. This study explores the possibility of using self-organizing maps (SOMs) to identify decadal climate variability, measure theoretical decadal predictability, and conduct decadal predictions of internal climate variability within a long control simulation. SOM is applied to an 11-yr running-mean winter sea surface temperature (SST) in the North Pacific and North Atlantic Oceans within the Community Earth System Model 1850 preindustrial simulation to identify patterns of internal variability in SSTs. Transition probability tables are calculated to identify preferred paths through the SOM with time. Results show both persistence and preferred evolutions of SST depending on the initial SST pattern. This method also provides a measure of the predictability of these SST patterns, with the North Atlantic being predictable at longer lead times than the North Pacific. In addition, decadal SST predictions using persistence, a first-order Markov chain, and lagged transition probabilities are conducted. The lagged transition probability predictions have a reemergence of prediction skill around lag 15 for both domains. Although the prediction skill is very low, it does imply that the SOM has the ability to predict some aspects of the internal variability of the system beyond 10 years. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Climatology; Conformal mapping; Forecasting; Markov chains; Self organizing maps; Surface waters; Decadal climate variability; Government agencies; Internal climate variability; Internal variability; North Atlantic Ocean; Sea surface temperature (SST); Self organizing maps(soms); Transition probabilities; Oceanography; climate change; climate prediction; machine learning; Markov chain; Pacific Decadal Oscillation; sea surface temperature; self organizing map; Atlantic Ocean; Atlantic Ocean (North); Pacific Ocean; Pacific Ocean (North)
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/170980
作者单位	Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, United States; Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, United States; Lamont-Doherty Earth Observatory, Columbia University, New York, NY, United States
推荐引用方式 GB/T 7714	Gu Q.,Gervais M.. Exploring North Atlantic and north pacific decadal climate prediction using self-organizing maps[J],2021,34(1).
APA	Gu Q.,&Gervais M..(2021).Exploring North Atlantic and north pacific decadal climate prediction using self-organizing maps.Journal of Climate,34(1).
MLA	Gu Q.,et al."Exploring North Atlantic and north pacific decadal climate prediction using self-organizing maps".Journal of Climate 34.1(2021).