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Authigenic mineral phases as a driver of the upper-ocean iron cycle

Nature volume 620, pages 104–109 (2023)Cite this article

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Abstract

Iron is important in regulating the ocean carbon cycle1. Although several dissolved and particulate species participate in oceanic iron cycling, current understanding emphasizes the importance of complexation by organic ligands in stabilizing oceanic dissolved iron concentrations2,3,4,5,6. However, it is difficult to reconcile this view of ligands as a primary control on dissolved iron cycling with the observed size partitioning of dissolved iron species, inefficient dissolved iron regeneration at depth or the potential importance of authigenic iron phases in particulate iron observational datasets7,8,9,10,11,12. Here we present a new dissolved iron, ligand and particulate iron seasonal dataset from the Bermuda Atlantic Time-series Study (BATS) region. We find that upper-ocean dissolved iron dynamics were decoupled from those of ligands, which necessitates a process by which dissolved iron escapes ligand stabilization to generate a reservoir of authigenic iron particles that settle to depth. When this ‘colloidal shunt’ mechanism was implemented in a global-scale biogeochemical model, it reproduced both seasonal iron-cycle dynamics observations and independent global datasets when previous models failed13,14,15. Overall, we argue that the turnover of authigenic particulate iron phases must be considered alongside biological activity and ligands in controlling ocean-dissolved iron distributions and the coupling between dissolved and particulate iron pools.

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Fig. 1: Seasonal evolution of DFe.
Fig. 2: Observations and modelling of DFe and ligand dynamics.
Fig. 3: Seasonal evolution of PFe phases.
Fig. 4: An integrated view of the ocean iron cycle.

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Data availability

Oceanographic data collected and analysed in this study are available at https://www.bco-dmo.org/project/822807 and https://www.bco-dmo.org/dataset/888772.

Code availability

Model code is available at https://github.com/atagliab/PISCES-BAIT and output at https://doi.org/10.5281/zenodo.7378193.

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Acknowledgements

We thank the captains and crews of RV Atlantic Explorer and RV Endeavor and the BATS programme team for their invaluable assistance during the four project cruises. O. Antipova provided assistance in synchrotron data collection and analysis and S. Burns provided assistance with sampling at sea. The model simulations were undertaken on Barkla, part of the High Performance Computing facilities at the University of Liverpool, UK. A.T. and D.K. were supported by NERC award NE/S013547/1; P.S. and B.S. were supported by NSF award OCE-1829833; B.S.T., D.C.O. and L.E.S. were supported by NSF award OCE-1829819; K.N.B. and S.C. were supported by NSF award OCE-1829777; R.J. was supported by NSF award OCE-1829844. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under contract no. DE-AC02-06CH11357.

Author information

Author notes

  1. Daniela König

    Present address: Department of Oceanography, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu, HI, USA

Authors and Affiliations

  1. School of Environmental Sciences, University of Liverpool, Liverpool, UK

    Alessandro Tagliabue & Daniela König

  2. College of Marine Science, University of South Florida, St. Petersburg, FL, USA

    Kristen N. Buck & Salvatore Caprara

  3. College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA

    Kristen N. Buck

  4. Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA

    Laura E. Sofen & Benjamin S. Twining

  5. LOCEAN, IRD-CNRS-Sorbonne Université-MNHN, IPSL, Paris, France

    Olivier Aumont

  6. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

    Philip W. Boyd

  7. School of Earth and Environment, University of Leeds, Leeds, UK

    William B. Homoky

  8. Bermuda Institute of Ocean Sciences, St. George’s, Bermuda

    Rod Johnson

  9. Skidaway Institute of Oceanography, University of Georgia, Department of Marine Sciences, Savannah, GA, USA

    Daniel C. Ohnemus

  10. Department of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA, USA

    Bettina Sohst & Peter Sedwick

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Contributions

The overarching BAIT programme was conceptualized by P.S., K.N.B., R.J., A.T., D.C.O. and B.S.T. Field and laboratory work was conducted by K.N.B., S.C., R.J., D.C.O., L.E.S., B.S., P.S., A.T. and B.S.T. This study was designed and led by A.T., alongside K.N.B., L.E.S. and B.S.T., with further contributions from O.A., P.W.B., W.B.H. and P.S. Analysis of dissolved iron, ligands and particles was performed by P.S. and B.S., K.N.B. and S.C., and L.E.S. and B.S.T., respectively. Modelling work was undertaken by A.T. Data synthesis and model-data comparisons were conducted by A.T., D.K. and L.E.S. A.T. led the drafting of the manuscript, with input from all co-authors.

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Correspondence to Alessandro Tagliabue.

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Nature thanks Kazuhiro Misumi, Brandy Toner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Seasonal evolution of total and stronger ligands.

Observed and modelled total (black symbols) and stronger (red symbols) ligand concentrations (nM). Black lines are model solutions at the BATS site from the PISCES-Quota model, with varying total ligands derived from DOC (using 0.09, 0.08 and 0.07 nM LT µM DOC−1). Blue lines represent model solutions from PISCES-Quota-Fe, with either prognostic stronger ligands (solid line) or DOC-derived total weaker ligands (dashed line, using 0.09 nM LT µM DOC−1).

Extended Data Fig. 2 Seasonal evolution of excess ligands.

Observed and modelled excess total (black symbols) and strong (red symbols) ligands (both in nM). Solid and dashed black lines are model solutions at the BATS site from the PISCES-Quota model, with varying total ligands derived from DOC (using 0.09, 0.08 and 0.07 nM LT (µM DOC)−1) or prognostic stronger ligands (thin black lines). Blue lines represent model solutions from PISCES-Quota-Fe, with either prognostic stronger ligands (solid line) or DOC-derived total ligands (dashed line, using 0.09 nM LT (µM DOC)−1). Values less than zero are when DFe concentrations exceed the concentrations of either L1 or LT. Only the PISCES-Quota-Fe model is able to generate the observed large excess ligand pools.

Extended Data Fig. 3 Variations in the seasonal evolution of dissolved iron.

DFe data and model solutions at the BATS site. Red crosses are DFe data for each voyage for three stations in the BATS region. All black lines are model solutions at the BATS site from the PISCES-Quota model, with total ligands derived from DOC (using 0.09 nM LT µM DOC−1) but with varying strengths of scavenging of free Fe by lithogenic particles. Blue lines represent model solutions from the new PISCES-Quota-Fe model, with either prognostic stronger ligands (solid line) or DOC-derived total ligands (dashed line, using 0.09 nM LT (µM DOC)−1). In red, we also compare the default PISCES-Quota (solid line, with total ligands derived from DOC using 0.09 nM LT µM DOC−1) and PISCES standard (dashed line) models. This demonstrates that there is little difference in the model–data mismatch in the seasonal evolution of DFe between PISCES-Quota and the standard PISCES model.

Extended Data Fig. 4 Global model–data comparison of dissolved iron.

Observed and modelled dissolved iron (nM) for ten GEOTRACES sections for PISCES-Quota-Fe and PISCES-Quota. Observations and models are binned onto the same vertical grid.

Extended Data Fig. 5 Model performance for biogeochemical metrics.

Plots showing the difference in performance between PISCES-Quota and PISCES-Quota-Fe for a suite of biogeochemical diagnostics. Average upper 100 m NO3 and PO4 are in mmol m−3, average 200–600 m O2 is in mmol m−3, total chlorophyll (T-Chl) at the surface (summed across the picophytoplankton, nanophytoplankton and diatoms) is in mg m−3 and carbon export at 100 m is in mol m−2 year−1. It can be seen that the new PISCES-Quota-Fe model does not substantially alter the biogeochemical mean state of the model.

Extended Data Fig. 6 Iron cycle fluxes in the Atlantic and Pacific oceans.

Proportional contributions of different processes to total DFe supply and removal fluxes along two example sections in the Atlantic (20° W) and Pacific (150° W) oceans from the PISCES-Quota-Fe model with prognostic strong ligands.

Extended Data Table 1 Model–observations statistical assessment
Full size table

Supplementary information

Supplementary Table 1

Previous iron-cycle-process studies. A summary of available measurements of the ocean iron cycle from time series stations and process studies that collected temporal observations. We provide the name and broad location and year of the study, the seasonal sampling frequency, depths and whether there was concurrent sampling of DFe, PFe, total ligands (Ltot), strong and weak ligands (L1 and L2) and PFe phases (lithogenic and biogenic). The current study, in the top row, is the only one to provide such data across all seasons and iron parameters.

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Tagliabue, A., Buck, K.N., Sofen, L.E. et al. Authigenic mineral phases as a driver of the upper-ocean iron cycle. Nature 620, 104–109 (2023). https://doi.org/10.1038/s41586-023-06210-5

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