Wang, Difeng; Zhong, Aifen; Gong, Fang; Zhu, Weidong; Fu, Dongyang; Zheng, Zhuoqi; Jingjing, Huang; He, Xianqiang; Bai, Yan (2025): A 1°x1° monthly global sea surface nitrate (SSN) gridded dataset (2003-2023) [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.982482
Always quote citation above when using data! You can download the citation in several formats below.
Published: 2025-05-13 • DOI registered: 2025-06-11
Abstract:
We employed an algorithm for estimating the monthly average sea surface nitrate (SSN) on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1° × 1° grid and the estimated monthly sea surface temperature (SST) and photosynthetically active radiation (PAR) datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and mixed layer depth (MLD) from the Hybrid Coordinate Ocean Model (HYCOM). This dataset contains (1) the predictor variables used to construct the models; (2) the dependent variables used in model development; (3) the local multivariate linear regression models; (4) the global monthly SSN products from 2003 to 2023, generated by local multivariate linear regression models; (5) the validation dataset containing measured and model predictions for 2018-2023. The predictor variables of the method include SST, MLD and PAR. The spatial resolution of the simulated dataset is 1° by 1°. The units of SSN concentration are µmol/l. The relevant data describing paper has been published in the Journal 'Science of the Total Environment' in 2024.
Related to:
Zhong, Aifen; Wang, Difeng; Gong, Fang; Zhu, Weidong; Fu, Dongyang; Zheng, Zhuoqi; Jingjing, Huang; He, Xianqiang; Bai, Yan (2024): Remote sensing estimates of global sea surface nitrate: Methodology and validation. Science of the Total Environment, 950, 175362, https://doi.org/10.1016/j.scitotenv.2024.175362
Funding:
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | File content | Content | Wang, Difeng | |||
| 2 | netCDF file | netCDF | Wang, Difeng | |||
| 3 | netCDF file (File Size) | netCDF (Size) | Bytes | Wang, Difeng | ||
| 4 | netCDF file (Media Type) | netCDF (Type) | Wang, Difeng | |||
| 5 | netCDF file (MD5 Hash) | netCDF (Hash) | Wang, Difeng |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Basic curation (CurationLevelB)
Size:
10 data points
Data
All files referred to in data matrix can be downloaded in one go as ZIP or TAR. Be careful: This download can be very large! To protect our systems from misuse, we require to sign up for an user account before downloading.
| 1 Content | 2 netCDF | 3 netCDF (Size) [Bytes] | 4 netCDF (Type) | 5 netCDF (Hash) |
|---|---|---|---|---|
| Global monthly SSN concentrations from Jan 2003 to Dec 2023 at 1°x1° resolution, estimated using local multivariate linear regression models based on MODIS SST and PAR, and modeled MLD. | global_monthly_sea_surface_nitrate_200301_202312.nc | 77.9 MBytes | application/x-hdf | 498697661ca0749934edc98601f98fc2 |
| Using the training and target datasets, multivariate linear regression was conducted in each 1°x1° grid cell (180x360) to derive local models for SSN, each with its own set of regression coefficients and an intercept term. | local_multivariate_linear_regression_models.nc | 2 MBytes | application/x-hdf | dd8f1ca3698f85f57c625335c3db3619 |
| This dataset provides the target variable. The target variable was derived from the World Ocean Atlas 2018 (WOA18) by averaging nitrate concentrations from the 0-10 m depth range. | target_variable.nc | 3 MBytes | application/x-hdf | 9129ef413dffd8a97de8c037000b4fba |
| Climatological monthly averages of sea surface temperature (SST), photosynthetically active radiation (PAR), and mixed layer depth (MLD) from Jan 2003 to Dec 2017. All variables were resampled and averaged to produce a consistent training dataset for sea surface nitrate (SSN) estimation. | training_dataset.nc | 8.9 MBytes | application/x-hdf | 4812ad927f8ca11b8b8d67c79d785449 |
| This dataset contains in situ SSN measurements from BGC-Argo floats and corresponding predictions from local multivariate linear regression models (2018-2023) for validation purposes. | validation_dataset.nc | 529.9 kBytes | application/x-hdf | 27756cb6d27025b3205021c5d41e6b85 |