Dynamic wavelet correlation analysis for multivariate climate time series
Abstract The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale recons...
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Online Access: | http://dx.doi.org/10.1038/s41598-020-77767-8 http://www.nature.com/articles/s41598-020-77767-8.pdf http://www.nature.com/articles/s41598-020-77767-8 |
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crspringernat:10.1038/s41598-020-77767-8 2023-05-15T17:28:22+02:00 Dynamic wavelet correlation analysis for multivariate climate time series Polanco-Martínez, Josué M. Fernández-Macho, Javier Medina-Elizalde, Martín 2020 http://dx.doi.org/10.1038/s41598-020-77767-8 http://www.nature.com/articles/s41598-020-77767-8.pdf http://www.nature.com/articles/s41598-020-77767-8 en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Reports volume 10, issue 1 ISSN 2045-2322 Multidisciplinary journal-article 2020 crspringernat https://doi.org/10.1038/s41598-020-77767-8 2022-01-04T15:07:09Z Abstract The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context. Article in Journal/Newspaper North Atlantic Springer Nature (via Crossref) Scientific Reports 10 1 |
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Springer Nature (via Crossref) |
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English |
topic |
Multidisciplinary |
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Multidisciplinary Polanco-Martínez, Josué M. Fernández-Macho, Javier Medina-Elizalde, Martín Dynamic wavelet correlation analysis for multivariate climate time series |
topic_facet |
Multidisciplinary |
description |
Abstract The wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context. |
format |
Article in Journal/Newspaper |
author |
Polanco-Martínez, Josué M. Fernández-Macho, Javier Medina-Elizalde, Martín |
author_facet |
Polanco-Martínez, Josué M. Fernández-Macho, Javier Medina-Elizalde, Martín |
author_sort |
Polanco-Martínez, Josué M. |
title |
Dynamic wavelet correlation analysis for multivariate climate time series |
title_short |
Dynamic wavelet correlation analysis for multivariate climate time series |
title_full |
Dynamic wavelet correlation analysis for multivariate climate time series |
title_fullStr |
Dynamic wavelet correlation analysis for multivariate climate time series |
title_full_unstemmed |
Dynamic wavelet correlation analysis for multivariate climate time series |
title_sort |
dynamic wavelet correlation analysis for multivariate climate time series |
publisher |
Springer Science and Business Media LLC |
publishDate |
2020 |
url |
http://dx.doi.org/10.1038/s41598-020-77767-8 http://www.nature.com/articles/s41598-020-77767-8.pdf http://www.nature.com/articles/s41598-020-77767-8 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Scientific Reports volume 10, issue 1 ISSN 2045-2322 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-020-77767-8 |
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Scientific Reports |
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10 |
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1 |
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1766120992283820032 |