Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation

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 remai...

Full description

Bibliographic Details
Published in:Journal of Climate
Main Authors: Harwood, Nathanael, Hall, Richard, DI Capua, Giorgia, Russell, Andrew, Tucker, Allan
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Online Access:https://research.vu.nl/en/publications/7719b3f7-6929-4940-954e-91088500e0a5
https://doi.org/10.1175/JCLI-D-20-0369.1
https://hdl.handle.net/1871.1/7719b3f7-6929-4940-954e-91088500e0a5
http://www.scopus.com/inward/record.url?scp=85103215501&partnerID=8YFLogxK
http://www.scopus.com/inward/citedby.url?scp=85103215501&partnerID=8YFLogxK
id ftvuamstcris:oai:research.vu.nl:publications/7719b3f7-6929-4940-954e-91088500e0a5
record_format openpolar
spelling ftvuamstcris:oai:research.vu.nl:publications/7719b3f7-6929-4940-954e-91088500e0a5 2024-09-15T17:50:55+00:00 Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation Harwood, Nathanael Hall, Richard DI Capua, Giorgia Russell, Andrew Tucker, Allan 2021-03-01 https://research.vu.nl/en/publications/7719b3f7-6929-4940-954e-91088500e0a5 https://doi.org/10.1175/JCLI-D-20-0369.1 https://hdl.handle.net/1871.1/7719b3f7-6929-4940-954e-91088500e0a5 http://www.scopus.com/inward/record.url?scp=85103215501&partnerID=8YFLogxK http://www.scopus.com/inward/citedby.url?scp=85103215501&partnerID=8YFLogxK eng eng https://research.vu.nl/en/publications/7719b3f7-6929-4940-954e-91088500e0a5 info:eu-repo/semantics/openAccess Harwood , N , Hall , R , DI Capua , G , Russell , A & Tucker , A 2021 , ' Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation ' , Journal of Climate , vol. 34 , no. 6 , pp. 2319-2335 . https://doi.org/10.1175/JCLI-D-20-0369.1 Algorithms Arctic Atmospheric circulation Machine learning North Atlantic Ocean Teleconnections /dk/atira/pure/sustainabledevelopmentgoals/life_below_water name=SDG 14 - Life Below Water article 2021 ftvuamstcris https://doi.org/10.1175/JCLI-D-20-0369.1 2024-08-29T00:18:49Z 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. Article in Journal/Newspaper Arctic North Atlantic North Atlantic oscillation Sea ice Vrije Universiteit Amsterdam (VU): Research Portal Journal of Climate 34 6 2319 2335
institution Open Polar
collection Vrije Universiteit Amsterdam (VU): Research Portal
op_collection_id ftvuamstcris
language English
topic Algorithms
Arctic
Atmospheric circulation
Machine learning
North Atlantic Ocean
Teleconnections
/dk/atira/pure/sustainabledevelopmentgoals/life_below_water
name=SDG 14 - Life Below Water
spellingShingle Algorithms
Arctic
Atmospheric circulation
Machine learning
North Atlantic Ocean
Teleconnections
/dk/atira/pure/sustainabledevelopmentgoals/life_below_water
name=SDG 14 - Life Below Water
Harwood, Nathanael
Hall, Richard
DI Capua, Giorgia
Russell, Andrew
Tucker, Allan
Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
topic_facet Algorithms
Arctic
Atmospheric circulation
Machine learning
North Atlantic Ocean
Teleconnections
/dk/atira/pure/sustainabledevelopmentgoals/life_below_water
name=SDG 14 - Life Below Water
description 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.
format Article in Journal/Newspaper
author Harwood, Nathanael
Hall, Richard
DI Capua, Giorgia
Russell, Andrew
Tucker, Allan
author_facet Harwood, Nathanael
Hall, Richard
DI Capua, Giorgia
Russell, Andrew
Tucker, Allan
author_sort Harwood, Nathanael
title Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
title_short Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
title_full Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
title_fullStr Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
title_full_unstemmed Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation
title_sort using bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude north atlantic circulation
publishDate 2021
url https://research.vu.nl/en/publications/7719b3f7-6929-4940-954e-91088500e0a5
https://doi.org/10.1175/JCLI-D-20-0369.1
https://hdl.handle.net/1871.1/7719b3f7-6929-4940-954e-91088500e0a5
http://www.scopus.com/inward/record.url?scp=85103215501&partnerID=8YFLogxK
http://www.scopus.com/inward/citedby.url?scp=85103215501&partnerID=8YFLogxK
genre Arctic
North Atlantic
North Atlantic oscillation
Sea ice
genre_facet Arctic
North Atlantic
North Atlantic oscillation
Sea ice
op_source Harwood , N , Hall , R , DI Capua , G , Russell , A & Tucker , A 2021 , ' Using Bayesian networks to investigate the influence of subseasonal arctic variability on midlatitude North Atlantic circulation ' , Journal of Climate , vol. 34 , no. 6 , pp. 2319-2335 . https://doi.org/10.1175/JCLI-D-20-0369.1
op_relation https://research.vu.nl/en/publications/7719b3f7-6929-4940-954e-91088500e0a5
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.1175/JCLI-D-20-0369.1
container_title Journal of Climate
container_volume 34
container_issue 6
container_start_page 2319
op_container_end_page 2335
_version_ 1810292723045892096