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Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China
A time series of satellite data on Chlorophyll-a concentration (Chl-a) that used ocean color was studied to determine mechanisms of phytoplankton variation in recent decade in the Yellow Sea, China during 2003–2015. The variability patterns on seasonal and inter-annual oscillation periods were confi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6684081/ https://www.ncbi.nlm.nih.gov/pubmed/31386653 http://dx.doi.org/10.1371/journal.pone.0220058 |
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author | Liu, Chunli Sun, Qiwei Xing, Qianguo Wang, Sufen Tang, Danling Zhu, Donghe Xing, Xiang |
author_facet | Liu, Chunli Sun, Qiwei Xing, Qianguo Wang, Sufen Tang, Danling Zhu, Donghe Xing, Xiang |
author_sort | Liu, Chunli |
collection | PubMed |
description | A time series of satellite data on Chlorophyll-a concentration (Chl-a) that used ocean color was studied to determine mechanisms of phytoplankton variation in recent decade in the Yellow Sea, China during 2003–2015. The variability patterns on seasonal and inter-annual oscillation periods were confirmed using the Empirical Orthogonal Function (EOF), and Morlet wavelet transform analyses, respectively. The first EOF mode for Chl-a was dominated by obvious spring and fall blooms in a spatial pattern that was related to the strong mixing of the water masses from the Yellow Sea Cold Warm Mass (YSCWM) and the Yellow Sea Warm Current (YSWC) in winter. The second EOF mode for Chl-a showed an opposite spatial pattern between the northern and southern regions. The temporal coefficient showed differences in the timing of blooms. On an inter-annual scale, Chl-a indicated variation at periods of 2–4 years during 2003–2015. Chl-a showed a significantly negative correlation with the sea surface temperature (r = -0.21, p<0.01), with time lags of 4 months (Chl-a ahead). Chl-a was coupled with El Niño Southern Oscillation (ENSO) events, with a positive correlation (r = 0.46, p<0.01) at a lag of 3–5 months (ENSO ahead). The present study demonstrated that the variation in phytoplankton biomass was controlled primarily by water mass seasonally, and it was influenced by ENSO events on an inter-annual scale. |
format | Online Article Text |
id | pubmed-6684081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66840812019-08-15 Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China Liu, Chunli Sun, Qiwei Xing, Qianguo Wang, Sufen Tang, Danling Zhu, Donghe Xing, Xiang PLoS One Research Article A time series of satellite data on Chlorophyll-a concentration (Chl-a) that used ocean color was studied to determine mechanisms of phytoplankton variation in recent decade in the Yellow Sea, China during 2003–2015. The variability patterns on seasonal and inter-annual oscillation periods were confirmed using the Empirical Orthogonal Function (EOF), and Morlet wavelet transform analyses, respectively. The first EOF mode for Chl-a was dominated by obvious spring and fall blooms in a spatial pattern that was related to the strong mixing of the water masses from the Yellow Sea Cold Warm Mass (YSCWM) and the Yellow Sea Warm Current (YSWC) in winter. The second EOF mode for Chl-a showed an opposite spatial pattern between the northern and southern regions. The temporal coefficient showed differences in the timing of blooms. On an inter-annual scale, Chl-a indicated variation at periods of 2–4 years during 2003–2015. Chl-a showed a significantly negative correlation with the sea surface temperature (r = -0.21, p<0.01), with time lags of 4 months (Chl-a ahead). Chl-a was coupled with El Niño Southern Oscillation (ENSO) events, with a positive correlation (r = 0.46, p<0.01) at a lag of 3–5 months (ENSO ahead). The present study demonstrated that the variation in phytoplankton biomass was controlled primarily by water mass seasonally, and it was influenced by ENSO events on an inter-annual scale. Public Library of Science 2019-08-06 /pmc/articles/PMC6684081/ /pubmed/31386653 http://dx.doi.org/10.1371/journal.pone.0220058 Text en © 2019 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Liu, Chunli Sun, Qiwei Xing, Qianguo Wang, Sufen Tang, Danling Zhu, Donghe Xing, Xiang Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China |
title | Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China |
title_full | Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China |
title_fullStr | Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China |
title_full_unstemmed | Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China |
title_short | Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China |
title_sort | variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the yellow sea, china |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6684081/ https://www.ncbi.nlm.nih.gov/pubmed/31386653 http://dx.doi.org/10.1371/journal.pone.0220058 |
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