气候变化领域集成服务门户(CCPortal): Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation

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DOI	10.1175/JCLI-D-20-0369.1
	Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
	Harwood N.; Hall R.; Capua G.D.I.; Russell A.; Tucker A.
发表日期	2021
ISSN	08948755
起始页码	2319
结束页码	2335
卷号	34 期号:6
英文摘要	Recent enhanced warming and sea ice depletion in the Arctic have been put forward as potential drivers of severe weather in the midlatitudes. Evidence of a link between Arctic warming and midlatitude atmospheric circulation is growing, but the role of Arctic processes relative to other drivers remains unknown. Arctic-midlatitude connections in the North Atlantic region are particularly complex but important due to the frequent occurrence of severe winters in recent decades. Here, dynamic Bayesian networks with hidden variables are introduced to the field to assess their suitability for teleconnection analyses. Climate networks are constructed to analyze North Atlantic circulation variability at 5-day to monthly time scales during the winter months of the years 1981-2018. The inclusion of a number of Arctic, midlatitude, and tropical variables allows for an investigation into the relative role of Arctic influence compared to internal atmospheric variability and other remote drivers. A robust covariability between regions of amplified Arctic warming and two definitions of midlatitude circulation is found to occur entirely within winter at submonthly time scales. Hidden variables incorporated in networks represent two distinct modes of stratospheric polar vortex variability, capturing a periodic shift between average conditions and slower anomalous flow. The influence of the Barents-Kara Seas region on the North Atlantic Oscillation is found to be the strongest link at 5- and 10-day averages, while the stratospheric polar vortex strongly influences jet variability on monthly time scales. © 2021 American Meteorological Society
英文关键词	Algorithms; Arctic; Atmospheric circulation; Machine learning; North Atlantic Ocean; Teleconnections
语种	英语
scopus关键词	Atmospheric pressure; Oceanography; Sea ice; Time measurement; Vortex flow; Atmospheric circulation; Atmospheric variability; Dynamic Bayesian networks; Hidden variable; North Atlantic oscillations; Severe weather; Stratospheric polar vortex; Teleconnections; Bayesian networks; air-sea interaction; atmospheric circulation; Bayesian analysis; machine learning; North Atlantic Oscillation; polar vortex; sea ice; seasonal variation; stratosphere; teleconnection; Arctic Ocean; Atlantic Ocean; Atlantic Ocean (North); Barents Sea; Kara Sea
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/178660
作者单位	Institute of Environment, Health and Societies, Brunel University London, Uxbridge, United Kingdom; School of Geography, University of Lincoln, Lincoln, United Kingdom; Potsdam Institute for Climate Impact Research, Potsdam, Germany; Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Committee on Climate Change, London, United Kingdom; Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom; Department of Computer Science, Brunel University London, Uxbridge, United Kingdom
推荐引用方式 GB/T 7714	Harwood N.,Hall R.,Capua G.D.I.,et al. Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation[J],2021,34(6).
APA	Harwood N.,Hall R.,Capua G.D.I.,Russell A.,&Tucker A..(2021).Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation.Journal of Climate,34(6).
MLA	Harwood N.,et al."Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation".Journal of Climate 34.6(2021).