Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

One-third of Southern Ocean productivity is supported by dust deposition

Nature volume 629, pages 603–608 (2024)Cite this article

Subjects

Abstract

Natural iron fertilization of the Southern Ocean by windblown dust has been suggested to enhance biological productivity and modulate the climate1,2,3. Yet, this process has never been quantified across the Southern Ocean and at annual timescales4,5. Here we combined 11 years of nitrate observations from autonomous biogeochemical ocean profiling floats with a Southern Hemisphere dust simulation to empirically derive the relationship between dust-iron deposition and annual net community production (ANCP) in the iron-limited Southern Ocean. Using this relationship, we determined the biological response to dust-iron in the pelagic perennially ice-free Southern Ocean at present and during the last glacial maximum (LGM). We estimate that dust-iron now supports 33% ± 15% of Southern Ocean ANCP. During the LGM, when dust deposition was 5–40-fold higher than today, the contribution of dust to Southern Ocean ANCP was much greater, estimated at 64% ± 13%. We provide quantitative evidence of basin-wide dust-iron fertilization of the Southern Ocean and the potential magnitude of its impact on glacial–interglacial timescales, supporting the idea of the important role of dust in the global carbon cycle and climate6,7,8.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Southern Ocean surface nitrate and dust deposition.
Fig. 2: Seasonal variability of nitrate in the epipelagic zone.
Fig. 3: Annual surface nitrate drawdown (ΔNO3) and ANCP as a function of dust deposition.
Fig. 4: ANCP50m derived from present-day and LGM dust deposition.

Similar content being viewed by others

Data availability

Supporting data used in the analysis are available at https://doi.org/10.5281/zenodo.10374127 (ref. 64). ACCESS-AM2 2015–2019 dust fields are available at https://doi.org/10.5281/zenodo.8303317 (ref. 56). Figures were created in MATLAB and Adobe Illustrator. Source data are provided with this paper.

Code availability

Analysis scripts are available at https://doi.org/10.5281/zenodo.10374127 (ref. 64). The ECHAM6-HAMMOZ model code and all required input data are maintained and made available at https://redmine.hammoz.ethz.ch after signing a software license agreement that can be downloaded from https://redmine.hammoz.ethz.ch/attachments/291/License_ECHAM-HAMMOZ_June2012.pdf.

References

  1. Martin, J. H. Glacial–interglacial CO2 change: the iron hypothesis. Paleoceanography 5, 1–13 (1990).

    Article  ADS  Google Scholar 

  2. Boyd, P. W. et al. A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization. Nature 407, 695–702 (2000).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Martinez-Garcia, A. et al. Iron fertilization of the subantarctic ocean during the last ice age. Science 343, 1347–1350 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Cassar, N. et al. The Southern Ocean biological response to aeolian iron deposition. Science 317, 1067–1070 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Boyd, P. W. & Mackie, D. Comment on the Southern Ocean biological response to aeolian iron deposition. Science 319, 159–159 (2008).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Jickells, T. D. et al. Global iron connections between desert dust, ocean biogeochemistry and climate. Science 308, 67–71 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Lambert, F. et al. Dust fluxes and iron fertilization in Holocene and Last Glacial Maximum climates. Geophys. Res. Lett. 42, 6014–6023 (2015).

    Article  ADS  CAS  Google Scholar 

  8. Emerson, D. Biogenic iron dust: a novel approach to ocean iron fertilization as a means of large scale removal of carbon dioxide from the atmosphere. Front. Mar. Sci. 6, 22 (2019).

  9. Sarmiento, J. L. & Gruber, N. in Ocean Biogeochemical Dynamics Ch. 8 (Princeton Univ. Press, 2006).

  10. Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A. & Weber, T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature 568, 327–335 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Buesseler, K. O. et al. Revisiting carbon flux through the ocean’s twilight zone. Science 316, 567–570 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  12. Mitchell, B. G., Brody, E. A., Holmhansen, O., Mcclain, C. & Bishop, J. Light limitation of phytoplankton biomass and macronutrient utilization in the Southern-Ocean. Limnol. Oceanogr. 36, 1662–1677 (1991).

    Article  ADS  Google Scholar 

  13. Martin, J. H., Gordon, R. M. & Fitzwater, S. E. Iron in Antarctic waters. Nature 345, 156–158 (1990).

    Article  ADS  CAS  Google Scholar 

  14. Boyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean. J. Phycol. 38, 844–861 (2002).

    Article  Google Scholar 

  15. DeVries, T., Primeau, F. & Deutsch, C. The sequestration efficiency of the biological pump. Geophys. Res. Lett. https://doi.org/10.1029/2012GL051963 (2012).

  16. Mahowald, N. M. et al. Atmospheric global dust cycle and iron inputs to the ocean. Glob. Biogeochem. Cy. https://doi.org/10.1029/2004GB002402 (2005).

  17. Hamilton, D. S. et al. Earth, wind, fire and pollution: aerosol nutrient sources and impacts on ocean biogeochemistry. Annu. Rev. Mar. Sci. 14, 303–330 (2022).

    Article  ADS  Google Scholar 

  18. Lamy, F. et al. Increased dust deposition in the Pacific Southern Ocean during glacial periods. Science 343, 403–407 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Röthlisberger, R. et al. Ice core evidence for the extent of past atmospheric CO2 change due to iron fertilisation. Geophys. Res. Lett. https://doi.org/10.1029/2004GL020338 (2004).

  20. Muglia, J., Somes, C. J., Nickelsen, L. & Schmittner, A. Combined effects of atmospheric and seafloor iron fluxes to the glacial ocean. Paleoceanography 32, 1204–1218 (2017).

    Article  ADS  Google Scholar 

  21. Saini, H. et al. Southern Ocean ecosystem response to Last Glacial Maximum boundary conditions. Paleoceanogr. Paleoclimatol. 36, e2020PA004075 (2021).

  22. Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993–2005: synthesis and future directions. Science 315, 612–617 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Yoon, J. E. et al. Reviews and syntheses: ocean iron fertilization experiments—past, present and future looking to a future Korean iron fertilization experiment in the Southern Ocean (KIFES) project. Biogeosciences 15, 5847–5889 (2018).

    Article  ADS  CAS  Google Scholar 

  24. Blain, S., Sarthou, G. & Laan, P. Distribution of dissolved iron during the natural iron-fertilization experiment KEOPS (Kerguelen Plateau, Southern Ocean). Deep-Sea Res. II 55, 594–605 (2008).

  25. Kaiser, J., Reuer, M. K., Barnett, B. & Bender, M. L. Marine productivity estimates from continuous O2/Ar ratio measurements by membrane inlet mass spectrometry. Geophys. Res. Lett. https://doi.org/10.1029/2005GL023459 (2005).

  26. Johnson, K. S., Plant, J. N., Dunne, J. P., Talley, L. D. & Sarmiento, J. L. Annual nitrate drawdown observed by SOCCOM profiling floats and the relationship to annual net community production. J. Geophys. Res. Oceans 122, 6668–6683 (2017).

    Article  ADS  Google Scholar 

  27. Krätschmer, S. et al. Simulating glacial dust changes in the Southern Hemisphere using ECHAM6.3-HAM2.3. Clim. Past 18, 67–87 (2022).

    Article  Google Scholar 

  28. Arrigo, K. R., van Dijken, G. L. & Bushinsky, S. Primary production in the Southern Ocean, 1997–2006. J. Geophys. Res. Oceans https://doi.org/10.1029/2007JC004551 (2008).

  29. Tagliabue, A. et al. Surface-water iron supplies in the Southern Ocean sustained by deep winter mixing. Nat Geosci 7, 314–320 (2014).

    Article  ADS  CAS  Google Scholar 

  30. Redfield, A. C. The biological control of chemical factors in the environment. Am. Sci. 46, 205–221 (1958).

    CAS  Google Scholar 

  31. Arteaga, L. A., Pahlow, M., Bushinsky, S. M. & Sarmiento, J. L. Nutrient controls on export production in the Southern Ocean. Glob. Biogeochem. Cy. 33, 942–956 (2019).

    Article  ADS  CAS  Google Scholar 

  32. Huang, Y., Fassbender, A. J. & Bushinsky, S. M. Biogenic carbon pool production maintains the Southern Ocean carbon sink. Proc. Natl Acad. Sci. USA 120, e2217909120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hiscock, W. T. & Millero, F. J. Nutrient and carbon parameters during the Southern Ocean iron experiment (SOFeX). Deep-Sea Res. I 52, 2086–2108 (2005).

    Article  ADS  Google Scholar 

  34. Su, J. Y., Schallenberg, C., Rohr, T., Strutton, P. G. & Phillips, H. E. New estimates of Southern Ocean annual net community production revealed by BGC-Argo floats. Geophys. Res. Lett. 49, e2021GL097372 (2022).

  35. Schallenberg, C., Ross, A. R. S., Davidson, A. B., Stewart, G. M. & Cullen, J. T. Temporal variability of dissolved iron species in the mesopelagic zone at Ocean Station PAPA. J. Mar. Syst. 172, 128–136 (2017).

    Article  Google Scholar 

  36. Moore, J. K. & Braucher, O. Sedimentary and mineral dust sources of dissolved iron to the world ocean. Biogeosciences 5, 631–656 (2008).

    Article  ADS  CAS  Google Scholar 

  37. Twelves, A. G., Goldberg, D. N., Henley, S. F., Mazloff, M. R. & Jones, D. C. Self-shading and meltwater spreading control the transition from light to iron limitation in an Antarctic coastal polynya. J. Geophys. Res. Oceans 126, e2020JC016636 (2021).

  38. Perron, M. M. G. et al. Origin, transport and deposition of aerosol iron to Australian coastal waters. Atmos. Environ. 228, 117432 (2020).

  39. Wiseman, N. A., Moore, J. K., Twining, B. S., Hamilton, D. S. & Mahowald, N. M. Acclimation of phytoplankton Fe:C ratios dampens the biogeochemical response to varying atmospheric deposition of soluble iron. Glob. Biogeochem. Cy. 37, e2022GB007491 (2023).

  40. Tagliabue, A. et al. The interplay between regeneration and scavenging fluxes drives ocean iron cycling. Nat. Commun. 10, 4960 (2019).

  41. Ito, A. et al. Pyrogenic iron: the missing link to high iron solubility in aerosols. Sci. Adv. https://doi.org/10.1126/sciadv.aau7671 (2019).

  42. Tang, W. Y. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  43. Weis, J. et al. Southern Ocean phytoplankton stimulated by wildfire emissions and sustained by iron recycling. Geophys. Res. Lett. 49, e2021GL097538 (2022).

  44. Henley, S. F. et al. Changing biogeochemistry of the Southern Ocean and its ecosystem implications. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00581 (2020).

  45. Sigman, D. M. et al. The Southern Ocean during the ice ages: a review of the Antarctic surface isolation hypothesis, with comparison to the North Pacific. Quat. Sci. Rev. 254, 106732 (2021).

    Article  Google Scholar 

  46. Shoenfelt, E. M., Winckler, G., Lamy, F., Anderson, R. F. & Bostick, B. C. Highly bioavailable dust-borne iron delivered to the Southern Ocean during glacial periods. Proc. Natl Acad. Sci. USA 115, 11180–11185 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Tagliabue, A. et al. How well do global ocean biogeochemistry models simulate dissolved iron distributions? Glob. Biogeochem. Cy. 30, 149–174 (2016).

    Article  ADS  CAS  Google Scholar 

  48. Johnson, K. S. & Coletti, L. J. In situ ultraviolet spectrophotometry for high resolution and long-term monitoring of nitrate, bromide and bisulfide in the ocean. Deep-Sea Res. I 49, 1291–1305 (2002).

    Article  CAS  Google Scholar 

  49. MacIntyre, G. et al. ISUS/SUNA nitrate measurements in networked ocean observing systems. In Proc OCEANS 2009 (IEEE, Biloxi, 2009).

  50. Johnson, K. S. et al. Processing Bio-Argo Nitrate Concentration at the DAC Level (Ifremer, 2018); https://doi.org/10.13155/46121.

  51. OneArgo-Mat: A MATLAB toolbox for accessing and visualizing Argo data v. 1.0.3. Zenodo https://doi.org/10.5281/zenodo.6603689 (2022).

  52. Johnson, K. S. et al. Biogeochemical sensor performance in the SOCCOM profiling float array. J. Geophys. Res. Oceans 122, 6416–6436 (2017).

    Article  ADS  Google Scholar 

  53. Bi, D. H. et al. Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model. J. South. Hemisph. Earth Syst. Sci. 70, 225–251 (2020).

    Article  Google Scholar 

  54. Fiddes, S. L., Protat, A., Mallet, M. D., Alexander, S. P. & Woodhouse, M. T. Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right. Atmos. Chem. Phys. 22, 14603–14630 (2022).

    Article  ADS  CAS  Google Scholar 

  55. Woodward, S. Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model. J. Geophys. Res. Atmos. 106, 18155–18166 (2001).

    Article  ADS  CAS  Google Scholar 

  56. Fiddes, S. L. ACCESS-AM2 dust fields 2015-2019 [data set]. Zenodo https://doi.org/10.5281/zenodo.8303317 (2023).

  57. Stevens, B. et al. Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst. 5, 146–172 (2013).

    Article  ADS  Google Scholar 

  58. Schultz, M. G. et al. The chemistry-climate model ECHAM6.3-HAM2.3-MOZ1.0. Geosci. Model. Dev. 11, 1695–1723 (2018).

    Article  ADS  CAS  Google Scholar 

  59. ECHAM6-HAMMOZ model data (HAMMOZ, accessed 14 September 2020); https://redmine.hammoz.ethz.ch/projects/hammoz/repository/1/show/echam6-hammoz/branches/tanja.

  60. Basterretxea, G., Font-Muñoz, J. S., Hernández-Carrasco, I. & Sañudo-Wilhelmy, S. A. Global variability of high-nutrient low-chlorophyll regions using neural networks and wavelet coherence analysis. Ocean Sci. 19, 973–990 (2023).

    Article  ADS  CAS  Google Scholar 

  61. Verdy, A. & Mazloff, M. R. A data assimilating model for estimating Southern Ocean biogeochemistry. J. Geophys. Res. Oceans 122, 6968–6988 (2017).

    Article  ADS  CAS  Google Scholar 

  62. Holte, J. & Talley, L. A new algorithm for finding mixed layer depths with applications to Argo data and subantarctic mode water formation. J. Atmos. Ocean Tech. 26, 1920–1939 (2009).

    Article  Google Scholar 

  63. Krumhardt, K. M., Long, M. C., Sylvester, Z. T. & Petrik, C. M. Climate drivers of Southern Ocean phytoplankton community composition and potential impacts on higher trophic levels. Front. Mar. Sci. 9, 916140 (2022).

  64. Weis, J. Code repository for “One-third of Southern Ocean productivity is supported by dust deposition”. Zenodo https://doi.org/10.5281/zenodo.10374127 (2024).

  65. Frank, M. et al. Similar glacial and interglacial export bioproductivity in the Atlantic sector of the Southern Ocean: multiproxy evidence and implications for atmospheric CO2. Paleoceanogr. Paleoclimatol. 15, 642–658 (2000).

  66. Nürnberg, C. C., Bohrmann, G., Frank, M. & Schlüter, M. Barium accumulation in the Atlantic sector of the Southern Ocean—results from 190,000 year records. Paleoceanogr. Paleoclimatol. 12, 594–603 (1997).

  67. Lamy, F. et al. Increased dust deposition in the Pacific Southern Ocean during glacial periods. Science 343, 403–407 (2014)https://doi.org/10.1126/science.1245424.

  68. Toyos, M. H. et al. Concentration, accumulation rates, Th fluxes, focusing factors and productivity proxies on core PS97/093-2 over the past 400,000 years. PANGAEA https://doi.org/10.1594/PANGAEA.934588 (2021).

  69. Thöle, L. M. et al. Glacial–interglacial dust and export production records from the Southern Indian Ocean. Earth Planet. Sci. Lett. 525, 115716, (2019).

Download references

Acknowledgements

We would like to acknowledge the Argo Program, which is part of the Global Ocean Observing System (https://www.seanoe.org/data/00311/42182/), the Southern Ocean Observing System (SOOS) and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project funded by the National Science Foundation, Division of Polar Programs (NSF PLR-1425989 and OPP-1936222), supplemented by NASA. The BGC-Argo data were collected and made freely available by the International Argo Program and the national programmes that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (project jk72), which is supported by the Australian Government. This research was partially funded by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP190103504). A.R.B. and S.L.F. are supported by the Australian Antarctic Program Partnership (AAPP) as part of the Antarctic Science Collaboration Initiative (ASCI000002). J.W. and P.G.S. are also supported by the Australian Research Council Centre of Excellence for Climate Extremes (CLEX, CE170100023). We thank S. Krätschmer for providing the LGM dust deposition simulations used in this study. We thank M. Mazloff and T. Rohr for their valuable insights and constructive feedback.

Author information

Authors and Affiliations

  1. Institute for Marine and Antarctic Studies (IMAS), University of Tasmania, Hobart, Tasmania, Australia

    Jakob Weis, Zanna Chase, Peter G. Strutton, Andrew R. Bowie & Sonya L. Fiddes

  2. Australian Research Council Centre of Excellence for Climate Extremes (CLEX), University of Tasmania, Hobart, Tasmania, Australia

    Jakob Weis, Peter G. Strutton & Sonya L. Fiddes

  3. Australian Research Council Centre for Excellence in Antarctic Science (ACEAS), University of Tasmania, Hobart, Tasmania, Australia

    Zanna Chase & Peter G. Strutton

  4. Australian Antarctic Program Partnership (AAPP), University of Tasmania, Hobart, Tasmania, Australia

    Christina Schallenberg, Andrew R. Bowie & Sonya L. Fiddes

  5. Environment, CSIRO, Hobart, Tasmania, Australia

    Christina Schallenberg

Authors
  1. Jakob Weis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zanna Chase
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christina Schallenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter G. Strutton
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew R. Bowie
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sonya L. Fiddes
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.W., Z.C., C.S., P.G.S. and A.R.B. conceived the study. J.W. conducted the analysis and wrote the manuscript with contributions from all co-authors. S.L.F. provided ACCESS dust deposition model outputs. All authors contributed to the interpretation of the results.

Corresponding author

Correspondence to Jakob Weis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Mathis Hain and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Regional extents of the 50 dust regimes.

The 50 dust regimes (R1–R50) defined in this study from low dust (top left) to high dust deposition (bottom right) and 2012–2022 BGC-Argo nitrate observations included in each regime. Lower and upper annual mean dust flux limits (mg m−2 d−1) delineating each regime are indicated in the titles. Note that dust regime boundaries are partially overlapping. Dust limits increase exponentially from low-dust to high-dust regimes, due to the exponential decline of dust with distance from the source regions (see Fig. 1b), ensuring that regimes are similar in regional extent and number of float observations.

Source Data

Extended Data Fig. 2 Monthly nitrate climatologies of the 50 dust regimes.

Monthly 0–200 m nitrate climatologies calculated from float observations in each of the 50 dust regimes (Extended Data Fig. 1). Plotted on the x-axis is the difference in nitrate concentration relative to the winter surface nitrate maximum, illustrating the seasonal nitrate depletion in the epipelagic zone. The horizontal grey bar above each panel indicates the 50-m-averaged maximum seasonal nitrate difference between the winter maximum to the summer minimum (solid profiles, drawdown values are indicated in the title).

Source Data

Extended Data Fig. 3 Surface nitrate seasonality in each dust regime.

a, 50-m-mean surface nitrate depletion between the seasonal surface nitrate maximum (triangles) and minimum (circles) in each of the 50 dust regimes (Extended Data Fig. 1). b, Histogram of the winter nitrate maximum (blue bars) and summer minimum months (red bars), defining the start and end of the productive period, in each dust regime. In >80% of the regimes, the productive period begins in August or September (41 out of 50) and ends between January and March (44 out of 50).

Source Data

Extended Data Fig. 4 Fe:C ratios derived from net community production and dust-derived soluble iron fluxes.

200-m-integrated net community production (NCP200m = ANCP200m divided by the productive period length) regressed against dust deposition fluxes (lower x-axis) in each of the 50 dust regimes. Southern Ocean basin-averaged Fe:C uptake ratios, indicated in the key, were inferred from the inverse of the regression slope (dotted line) and bioavailable soluble iron (sFe) fluxes (upper x-axis, derived from dust using 3.5 weight-% dust-iron content16 and 5–15% fractional iron solubility38). Observations exceeding 7 mg dust m-2 d-1 (open markers) were excluded from the regression and the Fe:C calculation due to the assumed limitation of productivity by iron-scavenging and self-shading on NCP under high dust loads. See the methods for further information.

Source Data

Extended Data Fig. 5 Covariance analysis between ANCP and mixing, latitude and temperature.

Linear regressions of ANCP50m (black, left y-axis) and ANCP200m (blue, right y-axis) against, a, seasonal mean mixed layer depths, b, the seasonal shoaling of the mixed layer, c, latitude and, d, seasonal mean temperatures (50 and 200-m-averaged). R2 and p-values are indicated in the key. ANCP increases northwards, whereas insolation decreases northwards during the productive period (austral spring and summer). Furthermore, the insolation difference in the observed latitude range is minor, which precludes a direct influence of light on the observed increase in ANCP. Temperature limitation factors (Tlim, box next to panel e) were calculated to estimate the maximum possible temperature-induced increase in productivity across dust regimes (see methods), indicating that temperature differences can only account for a minor fraction of the observed ANCP increase.

Source Data

Extended Data Fig. 6 Latitudinally binned 200-m-integrated ANCP.

ANCP200m averaged over 5° latitudinal bins from this study (yellow markers, mapped on the right) compared against corresponding literature values26,31 (black markers). Each set of markers refers to the same 5° latitude bin indicated by the x-axis ticks. Oxygen-derived ANCP estimates from Arteaga, et al.31 were calculated based on respiration rates integrated from 100 to 500 m. Regions accounted for in the latitudinally binned and basin-integrated estimates reported in the main text, highlighted in colour, exclude the sea ice zone, shelf regions and high dust regions ( > 7 mg dust m−2 d−1) and cover 76 million km2. High dust regions were excluded due to the uncertainty associated with the decline of ANCP200m in these regions (see Fig. 3).

Source Data

Extended Data Fig. 7 Maximum ANCP supportable by the winter nitrate inventory.

Upper ANCP limit (ANCPmax in equation 6), derived from the 50-m-integrated winter nitrate inventory at the start of the productive period (August/September climatology, 2013–2021 B-SOSE61). These values are considered to be the upper limit of ANCP50m before productivity is limited by nitrate and cannot be further sustained by dust-iron addition (see Methods). ANCPmax values were used to cap present-day and LGM ANCP estimates in high-dust and low-nitrate regions, respectively. Adjusted regions are indicated by red and white dots in Fig. 4.

Source Data

Extended Data Fig. 8 ECHAM6.3 simulated LGM dust deposition fluxes and difference relative to present-day fluxes.

a, Southern Ocean dust deposition fluxes during the Last Glacial Maximum (LGM) obtained from the ECHAM6.3-HAM2.3 coupled atmosphere-aerosol model27. Markers indicate sediment core locations referred to in Extended Data Table 1. b, LGM dust fluxes divided by ACCESS-AM2 present-day dust fluxes mapped in Fig. 1b. Across the pelagic ice-free Southern Ocean, ECHAM LGM dust fluxes are, on average, by a factor of 11.4 ± 6.6 higher than ACCESS present-day dust fluxes.

Source Data

Extended Data Fig. 9 Cumulative areas impacted by dust, nitrate limitation and high dust loads.

The grey shades indicate the cumulative area of, a, present-day and, b, LGM dust influence on the Southern Ocean with decreasing dust deposition, illustrating the expansion of dust from the source regions. The total area of the study region, the pelagic ice-free Southern Ocean south of 30° S, is 78 million km2. The overlayed blue, red and yellow shades indicate, respectively, the cumulative area that is impacted by nitrate limitation, high dust loads (> 7 mg m-2 d-1), or both. Therefore, the blue and yellow shade combined represent high-dust regions and the red and yellow shade combined represent nitrate-limited regions.

Source Data

Extended Data Table 1 Glacial–interglacial productivity variability comparison
Full size table

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weis, J., Chase, Z., Schallenberg, C. et al. One-third of Southern Ocean productivity is supported by dust deposition. Nature 629, 603–608 (2024). https://doi.org/10.1038/s41586-024-07366-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-024-07366-4

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene
pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy