Global estimates of marine gross primary production based on machine‐learning upscaling of field observations

4 variables (excluding dimension variables):

double GPP_LD_MLD_RF[Lon,Lat,Month]   
         units: mmol O2 m-2 d-1
         fill value: NaN
         long_name: Monthly mixed-layer integration of gross primary production trained from the
                             dataset determined by the light-dark bottle incubation using Random Forest
                             algorithm
        coordinates: [Longitude, Latitude Month]

double GPP_LD_ZEU_RF[Lon,Lat,Month]   

            units: mmol O2 m-2 d-1
            fill value: NaN
            long_name: Monthly euphotic-zone integration of gross primary production trained from
                                the dataset determined by the light-dark bottle incubation using Random
                                 Forest algorithm
            coordinates: [Longitude, Latitude Month]

double GPP_Triple_MLD_RF[Lon,Lat,Month]   
            units: mmol mmol O2 m-2 d-1
            fillvalue: NaN
            long_name: Monthly mixed-layer integration of gross primary production trained from
                                the dataset determined by the triple isotopes of dissolved oxygen using
                                Random Forest algorithm
            coordinates: [Longitude, Latitude Month]
        
double GPP_Triple_ZEU_RF[Lon,Lat,Month]   
            units: mmol O2 m-2 d-1
            fill value: NaN
            long_name: Monthly euphotic-zone integration of gross primary production trained from
                                 the dataset determined by the triple isotopes of dissolved oxygen using
                               Random Forest algorithm

3 dimensions:

        Lon  Size:181
            units: degree_north
            long_name: Longitude

        Lat  Size:91
            units: degree_east
            long_name: Latitude

        Month  Size:13
            units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean
            long_name: Month

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., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of
                                     marine gross primary production based on machine‐learning upscaling of
                                     field observations. Global Biogeochemical Cycles, 35, e2020GB006718.
                                     https://doi.org/10.1029/2020GB006718
 
Creation date: Dec/6th/2021