The role of biota in the Southern Ocean carbon cycle
Boyd, P.W.; Arrigo, K.R.; Ardyna, M.; Halfter, S.; Hückstädt, L.; Kuhn, A.M.; Lannuzel, D.; Neukermans, G.; Novaglio, C.; Shadwick, E.H.; Swart, S.; Thomalla, S.J. (2024). The role of biota in the Southern Ocean carbon cycle. Nat. Rev. Earth Environ. 5: 390–408. https://dx.doi.org/10.1038/s43017-024-00531-3 In: Nature Reviews Earth & Environment. Springer Nature: London. e-ISSN 2662-138X, more |  |
| Authors | | Top | - Boyd, P.W.
- Arrigo, K.R.
- Ardyna, M.
- Halfter, S.
| - Hückstädt, L.
- Kuhn, A.M.
- Lannuzel, D.
- Neukermans, G., more
| - Novaglio, C.
- Shadwick, E.H.
- Swart, S.
- Thomalla, S.J.
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| Abstract | The Southern Ocean, although relatively understudied owing to its harsh environment and geographical isolation, has been shown to contribute substantially to processes that drive the global carbon cycle. For example, phytoplankton photosynthesis transforms carbon dioxide into new particles and dissolved organic carbon. The magnitude of these transformations depends on the unique oceanographic and biogeochemical properties of the Southern Ocean. In this Review, we synthesize observations of biologically mediated carbon flows derived from the expanded observational network provided by remote-sensing and autonomous platforms. These observations reveal patterns in the magnitude of net primary production, including under-ice blooms and subsurface chlorophyll maxima. Basin-scale annual estimates of the planktonic contribution to the Southern Ocean carbon cycle can also be calculated, indicating that the export of biogenic particles and dissolved organic carbon to depth accounts for 20–30% (around 3 Gt yr–1) of the global export flux. This flux partially compensates for carbon dioxide outgassing following upwelling, making the Southern Ocean a 0.4–0.7 Gt C yr–1 sink. This export flux is surprisingly large given that phytoplankton are iron-limited with low productivity in more than 80% of the Southern Ocean. Solving such enigmas will require the development of four-dimensional regional observatories and the use of data-assimilation and machine-learning techniques to integrate datasets. |
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