Ocean Clustering

Datasets, algorithms, and code to develop clustering methods for the global ocean

This project aim to create a internally consistent dataset observed and modelled ocean properties to be used when developing and testing geophysical clustering approaches. We will provide both data and code to perform the analysis. The dataset consists of monthly climologies resampled to the same 1/2° grid, both gridded in netcdf format and tabulated as hdf5 and csv files. The tabulated data are also provided subsampled by including every 2nd, 4th or 8th datapoint. These files are much smaller to download and work with.

The effort is done under the auspice of the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES), which seeks to develop and apply quantitative models of the structure and function of marine microbial communities at seasonal and basin scales.

Included Parameters

• Chlorophyll (Chl), PAR, Kd490, Euphotic Depth
• remotely sensed reflectances (Rrs412, Rrs443, Rrs490, Rrs510, Rrs555, Rrs670)
• Sea Surface Temperature (SST), Mixed Layer Depth (MLD)
• Wind Speed (wind), Eddy Kinetik Energy (EKE), Bathymetry

Remote Sensing Data

These were downloaded from sources listed below and regridded using ... (?)

Monthly climatologies (v0.2.5 alpha)

Resolution	Gridded	Pandas/hdf5	Comma separated
1/2°	Download cdf	Download hdf	Download csv
1°		Download hdf	Download csv
2°		Download hdf	Download csv
4°		Download hdf	Download csv

Netcdf files are identical to the 0.2 version but the csv and tab files includes invalid data. Just remove all NaN's if needed. There are many ways to access the file contents -- here are a few examples:

Using Python & csv:

pd.read_hdf("https://rsg.pml.ac.uk/shared_files/brj/CBIOMES_ecoregions/ver_0_2_5/tabulated_geospatial_montly_clim_045_090_ver_0_2_5.csv").dropna(inplace=True)

Using Julia & csv:

using CSV;
CSV.read(download("https://rsg.pml.ac.uk/shared_files/brj/CBIOMES_ecoregions/ver_0_2_5/tabulated_geospatial_montly_clim_045_090_ver_0_2_5.csv"))

Using Julia & nectdf:

path="https://rsg.pml.ac.uk/shared_files/brj/CBIOMES_ecoregions/ver_0_2_5"
file="gridded_geospatial_montly_clim_360_720_ver_0_2.nc"
run(`wget $root/$file`)

Using NCDatasets
Dataset(file)

Data Sources

Variable	Source	DOI
SST	ESA SST-CCI L4	10.5285/62c0f97b1eac4e0197a674870afe1ee6
Chl	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
PAR	NASA MODIS L3	10.5067/AQUA/MODIS/L3M/PAR/2018
Kd490	NASA MODIS L3	10.5067/AQUA/MODIS/L3M/KD/2018
euphotic_depth	NASA MODIS L3	10.5067/AQUA/MODIS/L3M/ZLEE/2018
mld	NOAA PMEL MIMOC	10.1002/jgrc.20122
wind	NOAA NESDIS2	10.1029/2006GL027086
EKE	OSCAR1	10.5067/OSCAR-03D01
bathymetry	NOAA ETOPO1	10.7289/V5C8276M
Rrs412	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs443	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs490	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs510	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs555	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs670	ESA OC-CCI L4	10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
---	---	---

¹ Calculated from geostrophic velocities ² Blended Sea Winds from URL

Model Monthly Climatologies (v0.2.6 alpha)

These were downloaded from the source listed below and regridded using the gcmfaces toolbox plus recipes from OceanColorData.jl (these notebooks) and those included here in DataSet From DARWIN/.

Resolution	Gridded	Pandas/hdf5	Comma separated
1/2°	Download cdf	Download hdf	Download csv
1°		Download hdf	Download csv
2°		Download hdf	Download csv
4°		Download hdf	Download csv

Rrs values have been fixed.

Source: DOI, URL, Documentation

Standard Classification Estimates

Download cdf

There are still some issues with the hdf and csv files.

Included Parameters: Longhurst, optical_water_classes