Data-driven modeling of dissolved iron in the global ocean
| Main Authors: | Nicolas Cassar, Yibin Huang, Alessandro Tagliabue |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2022
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/6385044 |
Daftar Isi:
- Global climatological map of dissolved iron in the global ocean from publication: "Data-driven modeling of dissolved iron in the global ocean" by Huang et al. 2022. File Monthly_dFe.nc (NC_FORMAT_CLASSIC): 1 variable (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: Author: Yibin Huang & Nicolas Cassar Correspond: nicolas.cassar@duke.edu 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. doi:10.3389/fmars.2022.837183”. Creation date: March/20th/2022