Climate oscillations and their impact on phytoplankton - Phytoplankton Carbon Fixation

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Research Background

The interrelationship between marine ecosystems and global climate change is currently a hot topic of research. Phytoplankton, as the primary producers in marine ecosystems, are generally considered excellent indicators of environmental and climatic changes in the ocean. The accumulation of long-term remote sensing datasets provides a reliable data source for studying the multiscale ecological responses of the ocean to physical environmental changes. In this study, by integrating multi-year water color data, microwave data, and in situ measurements, we systematically investigated the multiscale variations in phytoplankton chlorophyll concentration and algal bloom occurrence time in the Northwest Pacific and Northeast Indian Ocean. We explored the factors driving these multiscale variations and focused on analyzing the impact of short-term climate oscillations on phytoplankton. The study aimed to gain a deeper understanding of the ecosystem dynamics and its coupling with physical processes in these marine regions, laying a foundation for further understanding how global warming will affect marine ecosystems in the future.

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Figures

Fig1 Monotonic Trend in Chlorophyll-a Concentration from 1997 to 2011 (p<0.05)
Fig 2 Climatological Monthly Average of Chlorophyll-a Concentration in the South China Sea (1998-2017)
Fig 3 Seasonal Vertical Distribution of Chlorophyll-a Concentration in the Arabian Sea

Scientific Progress

We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the three-dimensional (3D) structure of chlorophyll a (Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. We can use the trained RF-Model combined with the monthly reanalysis and satellite data to produce the monthly 3D Chla product in the NIO. The product has a spatial resolution of 1/4°×1/4° and 32 vertical levels ranging from 0 to 200 m.

The predictors are the 18 input variables (day of the year, longitude, latitude, Rrs at 412, 443, 490, 560, and 665 nm, SST, Kd490, PAR, SLA, wind (u and v components), temperature, salinity, MLD, and depth), which can be divided into three parts:(1) the temporal component, i.e., day of the year. (2) the spatial component, i.e., longitude and latitude; (3) the sea-surface component, i.e., Rrs at 412, 443, 490, 560, and 665 nm, SST, Kd490, PAR, SLA, and wind (u and v components); and (4) the vertical component, i.e., temperature, salinity, MLD, and depth. These training and validation datasets are used to train the RF-Model, adjust its hyperparameters during training, and test the performance of the trained RF. The RF-Model is trained and evaluated using a large database including 9,738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1,948 Chla profiles) agree fairly well, with R2 =0.962, root-mean-square error (RSME) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-Model is more accurate and robust, compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-Model can be used to develop long-time series products, to understand the variability of 3D Chla in future climate change scenarios. At the same time, this study provides an invaluable data source on the vertical structure of Chla in the NIO. A major application of the RF-Chla profiles is to obtain the 3D phytoplankton structure and phytoplankton biomass with high vertical resolution. It is a critical step to improve the characterization and quantification of carbon fluxes in the NIO.

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References

1. Bai, Yan, Xianqiang He, Delu Pan, Chen-Tung Arthur Chen, Yan Kang, Xiaoyan Chen, and Wei-Jun Cai. 2014. “Summertime Changjiang River Plume Variation during 1998–2010.” Journal of Geophysical Research: Oceans 119 (9): 6238–57. https://doi.org/10.1002/2014JC009866.
2. Chen, Cheng, Zhihua Mao, Fuping Tang, Guoqi Han, and Yazhou Jiang. 2017. “Declining Riverine Sediment Input Impact on Spring Phytoplankton Bloom off the Yangtze River Estuary from 17-Year Satellite Observation.” Continental Shelf Research 135 (March): 86–91. https://doi.org/10.1016/j.csr.2017.01.012.
3. Chen, Jianyu, Delu Pan, Mingliang Liu, Zhihua Mao, Qiankun Zhu, Ninghua Chen, Xiaoyu Zhang, and Bangyi Tao. 2017. “Relationships Between Long-Term Trend of Satellite-Derived Chlorophyll-a and Hypoxia Off the Changjiang Estuary.” Estuaries and Coasts 40 (4): 1055–65. https://doi.org/10.1007/s12237-016-0203-0.
4. Chen, Xiaoyan, Delu Pan, Yan Bai, Xianqiang He, Chen-Tung Arthur Chen, and Zengzhou Hao. 2013. “Episodic Phytoplankton Bloom Events in the Bay of Bengal Triggered by Multiple Forcings.” Deep Sea Research Part I: Oceanographic Research Papers 73 (March): 17–30. https://doi.org/10.1016/j.dsr.2012.11.011.
5. Chen, Xiaoyan, Delu Pan, Xianqiang He, Yan Bai, and Difeng Wang. 2012. “Upper Ocean Responses to Category 5 Typhoon Megi in the Western North Pacific.” Acta Oceanologica Sinica, no. 1: 51–58. https://doi.org/10.1007/s13131-012-0175-2.
6. Dang, Xiaoyan, Yan Bai, Fang Gong, Xiaoyan Chen, Qiankun Zhu, Haiqing Huang, and Xianqiang He. 2022. “Different Responses of Phytoplankton to the ENSO in Two Upwelling Systems of the South China Sea.” Estuaries and Coasts 45 (2): 485–500. https://doi.org/10.1007/s12237-021-00987-2.
7. Dang, Xiaoyan, Xiaoyan Chen, Yan Bai, Xianqiang He, Chen-Tung Arthur Chen, Teng Li, Delu Pan, and Zhenke Zhang. 2020. “Impact of ENSO Events on Phytoplankton over the Sulu Ridge.” Marine Environmental Research 157 (May): 104934. https://doi.org/10.1016/j.marenvres.2020.104934.
8. He, X., Y. Bai, D. Pan, C.-T. A. Chen, Q. Cheng, D. Wang, and F. Gong. 2013. “Satellite Views of the Seasonal and Interannual Variability of Phytoplankton Blooms in the Eastern China Seas over the Past 14 Yr (1998-2011).” Biogeosciences 10 (7): 4721–39. https://doi.org/10.5194/bg-10-4721-2013.
9. He, Xianqiang, Delu Pan, Yan Bai, Tianyu Wang, Chen-Tung Arthur Chen, Qiankun Zhu, Zengzhou Hao, and Fang Gong. 2017. “Recent Changes of Global Ocean Transparency Observed by SeaWiFS.” Continental Shelf Research 143 (July): 159–66. https://doi.org/10.1016/j.csr.2016.09.011.
10. Hu, Qiwei, Xiaoyan Chen, Xianqiang He, Yan Bai, Fang Gong, Qiankun Zhu, and Delu Pan. 2021. “Effect of El Niño-Related Warming on Phytoplankton’s Vertical Distribution in the Arabian Sea.” Journal of Geophysical Research: Oceans 126 (11): e2021JC017882. https://doi.org/10.1029/2021JC017882.
11. Hu, Qiwei, Xiaoyan Chen, Wanyi Huang, and Fenghua Zhou. 2021. “Phytoplankton Bloom Triggered by Eddy-Wind Interaction in the Upwelling Region East of Hainan Island.” Journal of Marine Systems 214 (February): 103470. https://doi.org/10.1016/j.jmarsys.2020.103470.

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Research Background

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Scientific Progress

References

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