Kostadinov, Tihomir Sabinov; Robertson-Lain, Lisl; Kong, Christina Eunjin; Zhang, Xiaodong; Maritorena, Stéphane; Bernard, Stewart; Loisel, Hubert; Jorge, Daniel S F; Kochetkova, Ekaterina; Roy, Shovonlal; Jönsson, Bror; Martinez-Vicente, Victor; Sathyendranath, Shubha (2022): Particle Size Distribution and Size-partitioned Phytoplankton Carbon Using a Two-Component Coated-Spheres Bio-optical Model: Monthly Global 4 km Imagery Based on the OC-CCI v5.0 Merged Ocean Color Satellite Data Set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.939863

Monthly global 4km satellite products spanning September 1997 to December 2020. The data contains Particle Size Distribution (PSD) parameters of an assumed power-law PSD, absolute and fractional size-partitioned phytoplankton carbon and associated variables such as particulate organic carbon (POC) and Chlorophyll-a as derived from the PSD algorithm. The retrieval is based on a backscattering bio-optical model using two particle populations and coated spheres for phytoplankton inherent optical properties (IOP) modeling, and a retrieval using spectral angle mapping (SAM - where satellite spectra are classified using a comparison to a collection of modeled end-member spectra, by treating spectra as vectors and using their dot product). Partial uncertainties are given as standard deviation and are estimated using a combination of Monte Carlo simulations and analytical error propagation. An empirical tuning factor is given for attaining more realistic estimated model concentrations of POC and Chlorophyll-a. The tuning factor is multiplicative, to be applied in linear space. This tuning factor has not been applied to the monthly data, users can choose whether or not to apply it to absolute carbon and Chlorophyll-a concentrations. The factor does not affect retrievals of fractional contributions of phytoplankton size classes to total phytoplankton carbon. Monthly climatologies files and an overall climatology file are also provided, and in those files, both untuned (tuning factor not applied) and tuned (tuning factor applied) variables are provided, for user convenience. Input remote-sensing reflectance data are v5.0 of the Ocean Colour -Climate Change Initiative (OC-CCI) of the European Space Agency. The OC-CCI general reference is Sathyendranath et al. (2019; doi:10.3390/s19194285), and for v5.0 of the dataset, the reference is Sathyendranath et al. (2021; doi:10.5285/1dbe7a109c0244aaad713e078fd3059a). More detailed metadata, including geospatial metadata, are given in the netCDF files. Variable names should be self-explanatory. Quick browse images are provided as well. Coastlines in these quick browse images are from v2.3.7 of the GSHHS data set - see Wessel and Smith (1996) (doi:10.1029/96JB00104). Modeling and data processing was done in MATLAB ®.

Associated manuscript to this data set: Kostadinov et al. (2023; doi:10.5194/os-19-703-2023). See full citation in the "Related to" section. This project is supported by NASA grant #80NSSC19K0297. We acknowledge Olaf Hansen, Harish Vedantham, Marco Bellacicco, Salvatore Marullo, Irina Marinov, Ivona Cetinic, Giorgio Dall'Olmo and David Desailly for various help/useful discussions they've provided. We further acknowledge ESA, PML and the OC-CCI team and data and algorithm contributors, as well as the ESA BICEP Project team and BICEP data contributors, in-situ PSD validation data contributors as given in Kostadinov et al. (2009; doi:10.1029/2009JC005303) and David Siegel/ UCSB ERI / Plumes and Blooms Project team and NASA EXPORTS teams. We acknowledge Giorgio Dall'Olmo, Emmanuele Organelli and the AMT26 cruise team for their in-situ PSD data (doi:10/cwbj). We further acknowledge all in-situ data contributors to the BICEP/POCO projects compilations of POC and pico-phytoplankton carbon data sets. The modeling and processing is done using the sinusoidal projection (one of the projections provided by OC-CCI), but data here is presented in equidistant cylindrical projection (geographic, unprojected lat/lon) for user convenience. Erik Fields, ESA, BEAM (Brockmann Consult GmbH) and NASA are acknowledged for the re-projection algorithm.

Further details of algorithm and data, and further acknowledgements are given in the associated manuscript Kostadinov et al. (2023; doi:10.5194/os-19-703-2023), (full citation given above).

Scientific code associated with the PSD/phyto C algorithm development, as well as the operational code used to generate this data set, are available at: Kostadinov et al. (2022; doi:10.5281/zenodo.6354654).

Disclaimer: This is an experimental, research data set. No warranty or guarantee of any kind is given, express or implied, of fitness for any purpose or of any level of accuracy. Under no circumstances shall the authors or their institutions be liable to anyone for direct, indirect, incidental, consequential, special, exemplary, or any other kind of damages (however caused and on any theory of liability, and including damages incurred by third parties), arising from or relating to this data set, or user's use, inability to use, or misuse of the data set, or errors of the data set. The data set is not guaranteed to be error-free, and is not meant to be used in any mission-critical applications. Use at your own risk. NASA, ESA or other institutions have not formally or informally reviewed these data, and views and opinions expressed here are those of the author(s) and do not necessarily reflect those of NASA or ESA or the author's institutions. Corresponding author: Tihomir S. Kostadinov - tkostadinov@csusm.edu