A robust empirical seasonal prediction of winter NAO and surface climate

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how a...

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Published in:Scientific Reports
Main Authors: Wang, L., Ting, M., Kushner, P. J.
Format: Text
Language:English
Published: Nature Publishing Group UK 2017
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428292/
http://www.ncbi.nlm.nih.gov/pubmed/28325893
https://doi.org/10.1038/s41598-017-00353-y
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spelling ftpubmed:oai:pubmedcentral.nih.gov:5428292 2023-05-15T17:33:32+02:00 A robust empirical seasonal prediction of winter NAO and surface climate Wang, L. Ting, M. Kushner, P. J. 2017-03-21 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428292/ http://www.ncbi.nlm.nih.gov/pubmed/28325893 https://doi.org/10.1038/s41598-017-00353-y en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428292/ http://www.ncbi.nlm.nih.gov/pubmed/28325893 http://dx.doi.org/10.1038/s41598-017-00353-y © The Author(s) 2017 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ CC-BY Article Text 2017 ftpubmed https://doi.org/10.1038/s41598-017-00353-y 2017-05-21T00:17:44Z A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America. Text North Atlantic North Atlantic oscillation Sea ice PubMed Central (PMC) Scientific Reports 7 1
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Article
spellingShingle Article
Wang, L.
Ting, M.
Kushner, P. J.
A robust empirical seasonal prediction of winter NAO and surface climate
topic_facet Article
description A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America.
format Text
author Wang, L.
Ting, M.
Kushner, P. J.
author_facet Wang, L.
Ting, M.
Kushner, P. J.
author_sort Wang, L.
title A robust empirical seasonal prediction of winter NAO and surface climate
title_short A robust empirical seasonal prediction of winter NAO and surface climate
title_full A robust empirical seasonal prediction of winter NAO and surface climate
title_fullStr A robust empirical seasonal prediction of winter NAO and surface climate
title_full_unstemmed A robust empirical seasonal prediction of winter NAO and surface climate
title_sort robust empirical seasonal prediction of winter nao and surface climate
publisher Nature Publishing Group UK
publishDate 2017
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428292/
http://www.ncbi.nlm.nih.gov/pubmed/28325893
https://doi.org/10.1038/s41598-017-00353-y
genre North Atlantic
North Atlantic oscillation
Sea ice
genre_facet North Atlantic
North Atlantic oscillation
Sea ice
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428292/
http://www.ncbi.nlm.nih.gov/pubmed/28325893
http://dx.doi.org/10.1038/s41598-017-00353-y
op_rights © The Author(s) 2017
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
op_rightsnorm CC-BY
op_doi https://doi.org/10.1038/s41598-017-00353-y
container_title Scientific Reports
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