Published March 25, 2022 | Version 2
Journal article Open

Data-driven modeling of dissolved iron in the global ocean

  • 1. Duke University
  • 2. University of Liverpool



     1 variables (excluding dimension variables):

        double dFe_RF [Longitude, Latitude, Depth, Month]   
            units: nmol L-1
            FillValue: NaN
            long_name: Monthly dissolved iron simulated from random forest algorithm
            coordinates: [Longitude, Latitude, Depth, Month]

     4 dimensions:

        Longitude  Size:357
            units: degree_north
            long_name: Longitude

        Latitude  Size:147
            units: degree_east
            long_name: Latitude

        Depth  Size:31
            units: meter
            long_name: Depth

        Month  Size:13
           Units: "Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec",  
                      "Annuual mean"            
           long_name: Month

4 global attributes:
        Authors: Yibin Huang & Nicolas Cassar
        Corresponding author:
        Request_for_citation: If you use these data in publications or presentations, please cite:
        “Huang, Y., Tagliabue, A., & Cassar, N. (2022). Data-driven modeling of dissolved iron in
        the global ocean. Frontiers in Marine Science”.
  Creation date: Aug/4th/2022

Updated record:  the updated version entitled "Month_dFe_V2" interpolates to a greater degree, thereby filling missing values in some coastal and open ocean regions.


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