Data Products

SOCCO makes its datasets, model outputs and code openly available through our Zenodo community. Each link below points to the latest version of the dataset.

Global, satellite-derived estimates of ocean net primary production (the rate at which phytoplankton turn carbon dioxide into organic carbon), on a 25 km grid at 8-day resolution. Updated as new satellite years become available.

• Multi-model net primary production (six algorithms)
  Combined product from the Eppley-VGPM, Behrenfeld-VGPM, Behrenfeld-CbPM, Westberry-CbPM, Lee-AbPM and Silsbe-CAFE algorithms. Described in Ryan-Keogh et al. (2023), Earth System Science Data.
  Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Moalusi, T. · Zenodo: https://doi.org/10.5281/zenodo.7849934
• Net primary production from the Lee-AbPM algorithm
  Single-algorithm product, extended each year with the latest satellite data.
  Ryan-Keogh, T. J. · Zenodo: https://doi.org/10.5281/zenodo.10014029
• Carbon-based models (CbPM and CAFE), Hadley mixed-layer depths (0.125 criterion)
  Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE estimates using EN4 mixed-layer depths. Described in Ryan-Keogh et al. (2023).
  Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Moalusi, T. · Zenodo: https://doi.org/10.5281/zenodo.7858589
• Carbon-based models (CbPM and CAFE), HYCOM mixed-layer depths (0.125 criterion)
  As above, using HYCOM mixed-layer depths with a 0.125 kg m^-3 density criterion. Described in Ryan-Keogh et al. (2023).
  Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Moalusi, T. · Zenodo: https://doi.org/10.5281/zenodo.7861157
• Carbon-based models (CbPM and CAFE), HYCOM mixed-layer depths (0.030 criterion)
  As above, using HYCOM mixed-layer depths with a 0.030 kg m^-3 density criterion. Described in Ryan-Keogh et al. (2023).
  Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Moalusi, T. · Zenodo: https://doi.org/10.5281/zenodo.7860490

Timing, duration and intensity of phytoplankton blooms across the global ocean, calculated from ESA Ocean Colour CCI satellite data using three detection methods (threshold, cumulative sum and rate of change).

• Global phytoplankton phenological indices, 4 km resolution
  Highest-resolution version of the global bloom phenology dataset. Described in Nicholson et al. (2025), Earth System Science Data.
  Nicholson, S.-A., Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Smith, M. · Zenodo: https://doi.org/10.5281/zenodo.8402931
• Global phytoplankton phenological indices, 9 km resolution
  Medium-resolution version. Described in Nicholson et al. (2025).
  Nicholson, S.-A., Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Smith, M. · Zenodo: https://doi.org/10.5281/zenodo.8402846
• Global phytoplankton phenological indices, 25 km resolution
  Coarse-resolution version, suited to global and long-term analyses. Described in Nicholson et al. (2025).
  Nicholson, S.-A., Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Smith, M. · Zenodo: https://doi.org/10.5281/zenodo.8402822
• Climate-driven change to phytoplankton blooms: CMIP6 phenology outputs
  Bloom phenology metrics from four CMIP6 Earth system models (CNRM-ESM2-1, MPI-ESM1-2-LR, NorESM2-LM and NorESM2-MM).
  Nicholson, S.-A., Ryan-Keogh, T. J. & Thomalla, S. J. · Zenodo: https://doi.org/10.5281/zenodo.10183946
• Climate-driven reorganisation of global phytoplankton bloom phenology
  Data and code behind a new global analysis of how bloom timing is shifting.
  Nicholson, S.-A., Ryan-Keogh, T. J., Thomalla, S. J. & Chang, N. · Zenodo: https://doi.org/10.5281/zenodo.21918805

Outputs from BIOPERIANT12, SOCCO's 1/12° coupled physics–biogeochemistry model of the Southern Ocean.

• BIOPERIANT12 model data
  Model inputs and outputs used to evaluate the BIOPERIANT12 configuration. Described in Chang et al. (2025), Geoscientific Model Development.
  Chang, N., Nicholson, S.-A., du Plessis, M., Lebehot, A. D., Mashifane, T., Moalusi, T. C., et al. · Zenodo: https://doi.org/10.5281/zenodo.13919282
• Heat and carbon characteristics of modelled mesoscale eddies in the South Atlantic
  Eddy-resolving outputs from the BIOPERIANT12 model. Described in Smith et al. (2023), JGR Oceans.
  Smith, T. G., Nicholson, S.-A., Engelbrecht, F. A., Chang, N., Mongwe, N. P. & Monteiro, P. M. S. · Zenodo: https://doi.org/10.5281/zenodo.8211596

Datasets that underpin the figures and results in SOCCO papers, so others can reproduce and build on our work.

• Basin-scale changes in net primary production from the biogeochemical-Argo array
  Data for figures in a study using BGC-Argo floats, used with notebooks on GitHub.
  Ryan-Keogh, T. J. & Thomalla, S. J. · Zenodo: https://doi.org/10.5281/zenodo.20155125
• Glider dissipation measurements in the Southern Ocean (summers 2017–2019)
  Turbulence microstructure data from Slocum glider missions.
  Giddy, I., Fer, I. & Nicholson, S.-A. · Zenodo: https://doi.org/10.5281/zenodo.14981744
• Global declines in net primary production underestimated by climate models
  Data used with the accompanying notebooks on GitHub. Described in Ryan-Keogh et al. (2025), Communications Earth & Environment.
  Ryan-Keogh, T. J., Tagliabue, A. & Thomalla, S. J. · Zenodo: https://doi.org/10.5281/zenodo.14185536
• Southern Ocean summer warming is regulated by storm-driven mixing
  Data used to produce the paper's figures. Described in du Plessis et al. (2025), Nature Geoscience.
  du Plessis, M., Nicholson, S.-A., Monteiro, P. M. S., Swart, S., Giddy, I. & Prend, C. · Zenodo: https://doi.org/10.5281/zenodo.12779501
• Storms and air–sea CO₂ fluxes in the sub-Antarctic Southern Ocean
  Data used to produce the paper's figures. Described in Toolsee et al. (2024), Geophysical Research Letters.
  Toolsee, T., Nicholson, S.-A. & Monteiro, P. M. S. · Zenodo: https://doi.org/10.5281/zenodo.10389977
• Widespread changes in Southern Ocean phytoplankton blooms linked to climate drivers
  Bloom phenology metrics from Ocean Colour CCI v6.0. Described in Thomalla et al. (2023), Nature Climate Change.
  Thomalla, S. J., Nicholson, S.-A., Ryan-Keogh, T. J. & Smith, M. · Zenodo: https://doi.org/10.5281/zenodo.8087124
• Drivers of in situ fluorescence quantum yield variability in the Southern Ocean
  Datasets accompanying the paper. Described in Ryan-Keogh et al. (2023), Limnology and Oceanography.
  Ryan-Keogh, T. J., Bone, E., Thomalla, S. J., Lain, L., Smith, M., Bernard, S., et al. · Zenodo: https://doi.org/10.5281/zenodo.6352197
• Early winter barium excess in the Southern Indian Ocean (GEOTRACES GIPr07)
  Datasets accompanying the paper. Described in van Horsten et al. (2022), Biogeosciences.
  van Horsten, N. R., Planquette, H., Sarthou, G., Ryan-Keogh, T. J., Lemaitre, N., Mtshali, T. N., et al. · Zenodo: https://doi.org/10.5281/zenodo.6583337
• Phytoplankton photophysiology in the Atlantic Southern Ocean
  Datasets accompanying the paper. Described in Singh et al. (2022), Frontiers in Marine Science.
  Singh, A., Thomalla, S. J., Fietz, S. & Ryan‐Keogh, T. J. · Zenodo: https://doi.org/10.5281/zenodo.6411649
• Photophysiological response of autumn phytoplankton in the Antarctic sea-ice zone
  Datasets accompanying the paper. Described in Singh et al. (2023), Biogeosciences.
  Singh, A., Fietz, S., Thomalla, S. J., Sanchez, N., Ardelan, M. V., Moreau, S., et al. · Zenodo: https://doi.org/10.5281/zenodo.6322942
• OSSE dataset: sensitivity of pCO₂ reconstructions to sampling scales
  Simulated sampling experiments in a Southern Ocean sub-domain. Described in Djeutchouang et al. (2022), Biogeosciences.
  Djeutchouang, L. M., Chang, N., Gregor, L., Vichi, M. & Monteiro, P. M. S. · Zenodo: https://doi.org/10.5281/zenodo.5788735
• Sensitivity of the Southern Ocean biological carbon pump to climate change
  Glider data used for a figure on spring bloom initiation, first published in Thomalla et al. (2015). Described in Thomalla et al. (2015), ICES Journal of Marine Science.
  Thomalla, S. J. & Ryan-Keogh, T. J. · Zenodo: https://doi.org/10.5281/zenodo.7108116

Analysis code released alongside our datasets.

• Code for "Satellite-derived global-ocean phytoplankton phenology indices"
  Python code used to produce the paper's figures (also on GitHub). Described in Nicholson et al. (2025).
  Nicholson, S.-A., Ryan-Keogh, T. J., Thomalla, S. J., Chang, N. & Smith, M. · Zenodo: https://doi.org/10.5281/zenodo.15323391
• Python routines for "Sea-ice impacts inter-annual variability of phytoplankton bloom characteristics"
  Analysis code for the Weddell Sea sea-ice study (also on GitHub). Described in Giddy et al. (2023), Geophysical Research Letters.
  Giddy, I. S., Nicholson, S.-A., Queste, B. Y. & Thomalla, S. · Zenodo: https://doi.org/10.5281/zenodo.5655923