Zhenxia Liu, Pei Du, Zengjie Wang, Binru Zhao, Wen Luo, Zhaoyuan Yu, Linwang Yuan. Coastal phytoplankton blooms and multivariate analysis with meteorological factors and climate oscillation signals in Western North Pacific[J]. Acta Oceanologica Sinica.
Citation:
Zhenxia Liu, Pei Du, Zengjie Wang, Binru Zhao, Wen Luo, Zhaoyuan Yu, Linwang Yuan. Coastal phytoplankton blooms and multivariate analysis with meteorological factors and climate oscillation signals in Western North Pacific[J]. Acta Oceanologica Sinica.
Zhenxia Liu, Pei Du, Zengjie Wang, Binru Zhao, Wen Luo, Zhaoyuan Yu, Linwang Yuan. Coastal phytoplankton blooms and multivariate analysis with meteorological factors and climate oscillation signals in Western North Pacific[J]. Acta Oceanologica Sinica.
Citation:
Zhenxia Liu, Pei Du, Zengjie Wang, Binru Zhao, Wen Luo, Zhaoyuan Yu, Linwang Yuan. Coastal phytoplankton blooms and multivariate analysis with meteorological factors and climate oscillation signals in Western North Pacific[J]. Acta Oceanologica Sinica.
School of Environment, Nanjing Normal University, Nanjing 210023, China
2.
School of Geography, Nanjing Normal University, Nanjing, 210023, China
3.
Key Laboratory of VGE (Ministry of Education), Nanjing Normal University, Nanjing 210023, China
4.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
Funds:
The National Natural Science Foundation of China under contract Nos 42230406, 42130103 and 42376223.
Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hybrid statistical-dynamical models. However, these relationships have rarely been investigated across different temporal scales. This study employs wavelet transform coherence and multiple wavelet coherence to examine the multiscale and multivariate relationships between phytoplankton blooms, meteorological factors, and climate oscillations in eight large marine ecosystems of the Western North Pacific. The results reveal that all phytoplankton blooms in the studied ecosystems exhibit significant annual oscillations, while seasonal climate patterns demonstrate either unimodal or bimodal distributions. A comparison of the wavelet transform coherence and multiple wavelet coherence results indicates that meteorological factors primarily drive short-period variations in phytoplankton blooms, whereas climate oscillations exert more influence on long-term changes. The explanation of phytoplankton blooms increases as the driver factors increase, but there is also some decreasing due to the collinearity between different factors. The sea-air temperature difference emerges as the most significant driving factor, with its mechanisms varying across marine ecosystems: one type influences mixed-layer depth, while the other arises from interspecific differences in temperature sensitivity. Furthermore, the results underscore the importance of integrating non-dominant large-scale circulation indices with predominant meteorological factors for a more comprehensive understanding.
Figure 1. Average value of BI in the study area from January 2003 to December 2020 and BI original time series—South China Sea (SCS), Sulu-Celebes Sea (SS), East China Sea (ECS), Yellow Sea (YS), Kuroshio Current (KC), Sea of Japan (SJ), Sea of Okhotsk (SO), Oyashio Current (OC).
Figure 2. Continuous wavelet transforms of the BI in eight Western North Pacific LMEs. The period is measured in months. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. Thick contours denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. The color denotes the strength of wavelet power.
Figure 3. Box-plots of month BI over the study period on eight LMEs. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. The midline in the boxplots is the median, the upper and lower boundaries of the box are the interquartile range, and the red crosses are the outliers.
Figure 4. Wavelet transform coherence between BI and meteorological factors. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. The period is measured in months. Each subplot shows the wavelet transform coherence between Bloom Intensity in a single LMEs and the individual meteorological factors that best explain Bloom Intensity variation in that LMEs. Thick contours denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. Small arrows denote the relative phase relationship (in-phase, arrows point right; anti-phase, arrows point left). The color denotes the strength of coherence.
Figure 5. Wavelet transform coherence between BI series and climate oscillation signals. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. The period is measured in months. Each subplot shows the wavelet transform coherence between Bloom Intensity in a single LMEs and the individual climate oscillation signal that best explains Bloom Intensity variation in that LMEs. Thick contours denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. Small arrows denote the relative phase relationship (in-phase, arrows point right; anti-phase, arrows point left). The color denotes the strength of coherence.
Figure 6. Two-factor multiple wavelet coherence. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. The period is measured in months. Each subplot shows the multiple wavelet coherence between Bloom Intensity in a single LMEs and the best combination of two factors. Thick contours denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. The color denotes the strength of coherence.
Figure 7. Three-factor multiple wavelet coherence. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. The period is measured in months. Each subplot shows the multiple wavelet coherence between bloom intensity in a single LMEs and the best combination of three factors. Thick contours denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. The color denotes the strength of coherence.
Figure 8. Meteorological factors/climate oscillation signals combination multiple wavelet coherence. (a) South China Sea; (b) Sulu-Celebes Sea; (c) East China Sea; (d) Yellow Sea; (e) Kuroshio Current; (f) Sea of Japan; (g) Oyashio Current; (h) Sea of Okhotsk. The period is measured in months. Each subplot shows the multiple wavelet coherence between Bloom Intensity in a single LMEs and the best meteorological factors/climate oscillation signals combination. Thick contours denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. The color denotes the strength of coherence.
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