The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
How well do global ocean biogeochemistry models simulate dissolved iron distributions?
Open accessAbstract
Numerical models of ocean biogeochemistry are relied upon to make projections about the impact of climate change on marine resources and test hypotheses regarding the drivers of past changes in climate and ecosystems. In large areas of the ocean, iron availability regulates the functioning of marine ecosystems and hence the ocean carbon cycle. Accordingly, our ability to quantify the drivers and impacts of fluctuations in ocean ecosystems and carbon cycling in space and time relies on first achieving an appropriate representation of the modern marine iron cycle in models. When the iron distributions from 13 global ocean biogeochemistry models are compared against the latest oceanic sections from the GEOTRACES program, we find that all models struggle to reproduce many aspects of the observed spatial patterns. Models that reflect the emerging evidence for multiple iron sources or subtleties of its internal cycling perform much better in capturing observed features than their simpler contemporaries, particularly in the ocean interior. We show that the substantial uncertainty in the input fluxes of iron results in a very wide range of residence times across models, which has implications for the response of ecosystems and global carbon cycling to perturbations. Given this large uncertainty, iron fertilization experiments based on any single current generation model should be interpreted with caution. Improvements to how such models represent iron scavenging and also biological cycling are needed to raise confidence in their projections of global biogeochemical change in the ocean.
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Cites
Global Iron Connections Between Desert Dust, Ocean Biogeochemistry, and Climate
Jickells, T. D., An, Z. S., Andersen, K. K., Baker, A. R., Bergametti, G., Brooks, N., Cao, J. J., Boyd, P. W., Duce, R. A., Hunter, K. A., et al. (2005) · Science · doi:10.1126/science.1105959 · Cited by 2,881
Self-citationProcesses and patterns of oceanic nutrient limitation
Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., Galbraith, E. D., Geider, R. J., Guieu, C., Jaccard, S. L., et al. (2013) · Nature Geoscience · doi:10.1038/ngeo1765 · Cited by 2,236
Self-citationMultiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., et al. (2013) · Biogeosciences · doi:10.5194/bg-10-6225-2013 · Cited by 1,649
Self-citationDunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., et al. (2012) · Journal of Climate · doi:10.1175/jcli-d-11-00560.1 · Cited by 1,349
Self-citationThe biogeochemical cycle of iron in the ocean
Boyd, P. W., & Ellwood, M. J. (2010) · Nature Geoscience · doi:10.1038/ngeo964 · Cited by 992
What controls dissolved iron concentrations in the world ocean?
Johnson, K. S., Gordon, R. M., & Coale, K. H. (1997) · Marine Chemistry · doi:10.1016/s0304-4203(97)00043-1 · Cited by 843
PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., & Gehlen, M. (2015) · Geoscientific Model Development · doi:10.5194/gmd-8-2465-2015 · Cited by 842
Self-citationInterrelated influence of iron, light and cell size on marine phytoplankton growth
Sunda, W. G., & Huntsman, S. A. (1997) · Nature · doi:10.1038/37093 · Cited by 759
The role of trace metals in photosynthetic electron transport in O2-evolving organisms
Raven, J. A., Evans, M. C. W., & Korb, R. E. (1999) · Photosynthesis Research · doi:10.1023/a:1006282714942 · Cited by 697
Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke, W., et al. (2013) · Journal of Climate · doi:10.1175/jcli-d-12-00150.1 · Cited by 662
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Cited by
Kwiatkowski, L., Torres, O., Bopp, L., Aumont, O., Chamberlain, M., Christian, J. R., Dunne, J. P., Gehlen, M., Ilyina, T., John, J. G., et al. (2020) · Biogeosciences · doi:10.5194/bg-17-3439-2020 · Cited by 897
“The accurate simulation of many of the biogeochemical tracers upon which net primary production (NPP) depends (e.g. the distribution of iron; Tagliabue et al., 2016) represents a significant and ongoing challenge to ESMs (Séférian et al., 2020).”
BackgroundSelf-citationClimate, ecosystems, and planetary futures: The challenge to predict life in Earth system models
Bonan, G. B., & Doney, S. C. (2018) · Science · doi:10.1126/science.aam8328 · Cited by 698
CMIP6 Simulations With the CMCC Earth System Model (CMCC‐ESM2)
Lovato, T., Peano, D., Butenschön, M., Materia, S., Iovino, D., Scoccimarro, E., Fogli, P. G., Cherchi, A., Bellucci, A., Gualdi, S., et al. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2021ms002814 · Cited by 426
Aerosol trace metal leaching and impacts on marine microorganisms
Mahowald, N. M., Hamilton, D. S., Mackey, K. R. M., Moore, J. K., Baker, A. R., Scanza, R. A., & Zhang, Y. (2018) · Nature Communications · doi:10.1038/s41467-018-04970-7 · Cited by 330
Self-citationIron persistence in a distal hydrothermal plume supported by dissolved–particulate exchange
Fitzsimmons, J. N., John, S. G., Marsay, C. M., Hoffman, C. L., Nicholas, S. L., Toner, B. M., German, C. R., & Sherrell, R. M. (2017) · Nature Geoscience · doi:10.1038/ngeo2900 · Cited by 318
Metadata
- Authors
- Tagliabue, A., Aumont, O., DeAth, R., Dunne, J. P., Dutkiewicz, S., Galbraith, E., Misumi, K., Moore, J. K., Ridgwell, A., Sherman, E., Stock, C., Vichi, M., Völker, C., & Yool, A.
- Venue
- Global Biogeochemical Cycles
- Publisher
- American Geophysical Union (AGU)
- Published
- 2016-02
- Volume
- 30
- Issue
- 2
- Pages
- 149-174
- ISSN
- 0886-6236 (ISSN-L)
- 1944-9224
- OpenAlex
- W2228781294
- Cited by
- 371
- References
- 80
- FWCI
- 19.67
- Type
- journal article
- Language
- en
- Open access
- green
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- Tagliabue, A., Aumont, O., DeAth, R., Dunne, J. P., Dutkiewicz, S., Galbraith, E., Misumi, K., Moore, J. K., Ridgwell, A., Sherman, E., Stock, C., Vichi, M., Völker, C., & Yool, A. (2016). How well do global ocean biogeochemistry models simulate dissolved iron distributions? Global Biogeochemical Cycles. https://doi.org/10.1002/2015gb005289
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
