DOI: 10.5281/zenodo.16957343
title: Dataset1 for the submitted manuscript entitled "Variability of Particulate Organic Carbon Stock in the South China Sea with reference to Profile Structures"

description: This dataset contains 

'Supporting Information S1.xlsx' for the submitted manuscript entitled "Variability of Particulate Organic Carbon Stock in the South China Sea with reference to Profile Structures". All the data presented as figures and tables in this manuscript and the Supporting Information S1 coule be found here. 

cp660 in the northern SCS.zip: Using field observation data, we developed and evaluated satellite-based inversion approaches for estimating the three-dimensional distribution of cp660 in the upper layer of the SCS. Three methods were proposed: an empirical constant approach for cp660 profiles following an exponential decay structure; a Bayesian optimization algorithm for Gauss-like profiles; and a machine learning model applicable to both profile types. Evaluation results demonstrated that all three methods effectively captured the spatiotemporal variability of the cp660 vertical structure in this region. Using the above algorithms, we generated monthly three-dimensional distribution of cp660 for the northern SCS spanning 2003–2023. 

Climatological_monthly_NPP_POC FLUX_micro_phytoplankton_Fraction.mat: This dataset contains the NPP, fraction of micro-phytoplankton, mixing layer depth, euphotic layer depth, cp660 profile(for POC stock calculation), and the POC flux at euphotic and 100 m depth, required for food web model calculations in the northern South China Sea. All inputs and results are climatological monthly data. For satellite derived climatological monthly NPP dataset, we obtained it following the method proposed by Song et al. (2023). For climatological monthly fraction of micro-phytoplankton dataset, we obtained it following the particle backscattering-based method in Li et al. (2021).



Song, L., Lee, Z., Shang, S., Huang, B., Wu, J., Wu, Z., . . . Liu, X. (2023). On the Spatial and Temporal Variations of Primary Production in the South China Sea. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14. doi:10.1109/TGRS.2023.3241209

Li, T., Bai, Y., He, X., Tao, B., Chen, X., Gong, F., & Wang, T. (2021). Phytoplankton size classes changed oppositely over shelf and basin areas of the South China Sea during 2003–2018. Progress in Oceanography, 191, 102496. doi:https://doi.org/10.1016/j.pocean.2020.102496


SCSdepth9km.mat: This file gives the topography (water depth) data used in research, which is generated from the ETOPO1 dataset.

SCSLatLon.mat: This file gives the latitude and longitude data used in research.

Satellite derived Chla and Rrs data are included in Chla_monthly_9km.7z, Rrs_555_Monthly_9km.7z, Rrs_443_monthly_9km.7z and Rrs_555_daily_9km.7z, Rrs_443_monthly_9km.7z, Chla_daily_9km.7z.

files with 'MLD' in filenames are the mixed layer depth data used in research.

 

HYCOM derived temperature and salinity profiles dataset are also presented here. 

Please note: the 0-15m salinity profiles dataset is avaiable at 10.5281/zenodo.17905013. 

 

Satellite-derived monthly and daily sea surface Chla (mg m-3) and Rrs (sr-1) data are original available at https://oceancolor.gsfc.nasa.gov. 

HYCOM-derived profile seawater salinity (psu) and temperature (℃) data are original from the GEE platform (https://code.earthengine.google.com/). 

Monthly and 8 days mixed layer depth (MLD, m) are original from https://orca.science.oregonstate.edu/2160.by.4320.monthly.hdf.mld030.hycom.php. 

Topography (water depth) data are original  from https://www.ngdc.noaa.gov/mgg/global/global.html.