Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns

Abstract Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Our study is based on an extensive gridded dataset of winter salinity in the upper 50 m layer of the Arctic Ocean for the periods 1950–1993 and 2007–2012, obtained from ~20 000 profil...

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Published in:Annals of Glaciology
Main Authors: Cherniavskaia, Ekaterina A., Sudakov, Ivan, Golden, Kenneth M., Strong, Courtenay, Timokhov, Leonid A.
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press (CUP) 2018
Subjects:
Online Access:http://dx.doi.org/10.1017/aog.2018.10
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305518000101
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spelling crcambridgeupr:10.1017/aog.2018.10 2024-06-09T07:38:30+00:00 Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns Cherniavskaia, Ekaterina A. Sudakov, Ivan Golden, Kenneth M. Strong, Courtenay Timokhov, Leonid A. 2018 http://dx.doi.org/10.1017/aog.2018.10 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305518000101 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Annals of Glaciology volume 59, issue 76pt2, page 83-100 ISSN 0260-3055 1727-5644 journal-article 2018 crcambridgeupr https://doi.org/10.1017/aog.2018.10 2024-05-15T13:10:23Z Abstract Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Our study is based on an extensive gridded dataset of winter salinity in the upper 50 m layer of the Arctic Ocean for the periods 1950–1993 and 2007–2012, obtained from ~20 000 profiles. We investigate the interannual variability of the salinity fields, identify predominant patterns of anomalous behavior and leading modes of variability, and develop a statistical model for the prediction of surface-layer salinity. The statistical model is based on linear regression equations linking the principal components of surface-layer salinity obtained through empirical orthogonal function decomposition with environmental factors, such as atmospheric circulation, river runoff, ice processes and water exchange with neighboring oceans. Using this model, we obtain prognostic fields of the surface-layer salinity for the winter period 2013–2014. The prognostic fields generated by the model show tendencies of surface-layer salinification, which were also observed in previous years. Although the used data are proprietary and have gaps, they provide the most spatiotemporally detailed observational resource for studying multidecadal variations in basin-wide Arctic salinity. Thus, there is community value in the identification, dissemination and modeling of the principal modes of variability in this salinity record. Article in Journal/Newspaper Annals of Glaciology Arctic Arctic Ocean Cambridge University Press Arctic Arctic Ocean Annals of Glaciology 59 76pt2 83 100
institution Open Polar
collection Cambridge University Press
op_collection_id crcambridgeupr
language English
description Abstract Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Our study is based on an extensive gridded dataset of winter salinity in the upper 50 m layer of the Arctic Ocean for the periods 1950–1993 and 2007–2012, obtained from ~20 000 profiles. We investigate the interannual variability of the salinity fields, identify predominant patterns of anomalous behavior and leading modes of variability, and develop a statistical model for the prediction of surface-layer salinity. The statistical model is based on linear regression equations linking the principal components of surface-layer salinity obtained through empirical orthogonal function decomposition with environmental factors, such as atmospheric circulation, river runoff, ice processes and water exchange with neighboring oceans. Using this model, we obtain prognostic fields of the surface-layer salinity for the winter period 2013–2014. The prognostic fields generated by the model show tendencies of surface-layer salinification, which were also observed in previous years. Although the used data are proprietary and have gaps, they provide the most spatiotemporally detailed observational resource for studying multidecadal variations in basin-wide Arctic salinity. Thus, there is community value in the identification, dissemination and modeling of the principal modes of variability in this salinity record.
format Article in Journal/Newspaper
author Cherniavskaia, Ekaterina A.
Sudakov, Ivan
Golden, Kenneth M.
Strong, Courtenay
Timokhov, Leonid A.
spellingShingle Cherniavskaia, Ekaterina A.
Sudakov, Ivan
Golden, Kenneth M.
Strong, Courtenay
Timokhov, Leonid A.
Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns
author_facet Cherniavskaia, Ekaterina A.
Sudakov, Ivan
Golden, Kenneth M.
Strong, Courtenay
Timokhov, Leonid A.
author_sort Cherniavskaia, Ekaterina A.
title Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns
title_short Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns
title_full Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns
title_fullStr Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns
title_full_unstemmed Observed winter salinity fields in the surface layer of the Arctic Ocean and statistical approaches to predicting large-scale anomalies and patterns
title_sort observed winter salinity fields in the surface layer of the arctic ocean and statistical approaches to predicting large-scale anomalies and patterns
publisher Cambridge University Press (CUP)
publishDate 2018
url http://dx.doi.org/10.1017/aog.2018.10
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305518000101
geographic Arctic
Arctic Ocean
geographic_facet Arctic
Arctic Ocean
genre Annals of Glaciology
Arctic
Arctic Ocean
genre_facet Annals of Glaciology
Arctic
Arctic Ocean
op_source Annals of Glaciology
volume 59, issue 76pt2, page 83-100
ISSN 0260-3055 1727-5644
op_rights http://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.1017/aog.2018.10
container_title Annals of Glaciology
container_volume 59
container_issue 76pt2
container_start_page 83
op_container_end_page 100
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