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Ocean Science An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/os-2017-11
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-2017-11
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 Apr 2017

28 Apr 2017

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This preprint was under review for the journal OS but the revision was not accepted.

Seasonal to interannualvariability of Chlorophyll-a and sea surface temperature in the Yellow Sea using MODIS satellite datasets

Chunli Liu1, Qiwei Sun2,3, Sufen Wang2, Qianguo Xing4, Lixin Zhu1, and Zhenlin Liang1 Chunli Liu et al.
  • 1Marine college, Shandong University (Weihai), 264209, China
  • 2The State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, 510301, China
  • 3University of Chinese Academ y of Sciences, 100049, China
  • 4Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, 264003, China

Abstract. The spatial and temporal variability of Chlorophyll-a concentration (CHL) and sea surface temperature (SST) in the Yellow Sea (YS) were examined using Empirical Orthogonal Function (EOF) analysis, which was based on the monthly, cloud-free Data INterpolating Empirical Orthogonal Function (DINEOF) reconstruction datasets for 2003–2015. The variability and oscillation periods on an inter-annual timescale were also confirmed using the Morlet wavelet transform and wavelet coherence analyses. At a seasonal time scale, the CHL EOF1 mode was dominated by a seasonal cycle of a spring and a fall bloom, with a spatial distribution that was modified by the strong mixing of the water column of the Yellow Sea Cold Warm Mass (YSCWM) that facilitated nutrient delivery from the ocean bottom. The EOF2 mode was likely associated with a winter bloom in the southern region, where it was affected by the Yellow Sea Warm Current (YSWC) that moved from southeast to north in winter. The SST EOF1 explained 99 % of the variance in total variabilities, which was dominated by an obvious seasonal cycle (in response to net surface heat flux) that was inversely proportional to the water depth. At the inter-annual scale, the wavelet power spectrum and global power spectrum of CHL and SST showed significant similar periods of variations. The dominant periods for both spectra were 2–4 years during 2003–2015. A significant negative cross-correlation existed between CHL and SST, with the largest correlation coefficient at time lags of 4 months. The wavelet coherence further identified a negative relationship that was significant statistically between CHL and SST during 2008–2015, with periods of 1.5–3 years. These results provided insight into how CHL might vary with SST in the future.

Chunli Liu et al.

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Chunli Liu et al.

Chunli Liu et al.

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