Dynamic wavelet correlation analysis for multivariate climate time series

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 c...

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Bibliographic Details
Published in:Scientific Reports
Main Authors: Polanco-Martínez, J.M., Fernández Macho, Francisco Javier, Medina-Elizalde, M.
Format: Article in Journal/Newspaper
Language:English
Published: Scientific Reports 2020
Subjects:
article
climate
correlation analysis
human
oscillation
time series analysis
Moses
North Atlantic
Online Access:http://hdl.handle.net/10810/51507
https://doi.org/10.1038/s41598-020-77767-8
Description
Summary: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 J.M.P.M was funded by the PIC 444/18 – EU Interreg project MOSES (EAPA 224/2016), FEDER funds and the SEPE (Spanish Public Service of Employment). J.F.M. acknowledges research funding received from UPV/EHU Econometrics Research Group (Basque Government Dpt. of Education grant IT-1359-19) and Spanish Ministry of Economy and Business (grant MTM2016-74931-P).

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