Modelling the marine biogeochemical implications of aeolian, sedimentary and riverine iron supply
Bibliographic record
Abstract
Iron is an important nutrient for marine phytoplankton and low concentrations of iron limit phytoplankton growth in around 40% of the surface area of the ocean. Due to the low solubility of iron in the sea, the concentrations of iron are largely dependent on external sources such as atmospheric deposition of iron-containing dust derived from arid areas on land. However, also release of iron from the sediment and the supply of iron from rivers are important external sources of iron to the ocean. In this thesis the role of these external sources in influencing marine biogeochemistry is studied. \nIn a first step, an existing ocean biogeochemical model is used to study the sensitivity of oceanic CO2 uptake to dust deposition. The so-called iron hypothesis suggests that enhanced atmospheric dust deposition to the Southern Ocean during the Last Glacial Maximum around 20,000 years decreased atmospheric CO2 concentrations by increasing phytoplankton growth and export of organically bound carbon to the deep ocean. The first part of the thesis shows that the sensitivity of organic matter export and oceanic CO2 uptake to dust deposition is increased significantly if the impact of iron bioavailability on light harvesting capabilities is explicitly considered. These results also indicate that there is still uncertainty in the biogeochemical response to dust deposition. \nIn the second part of the thesis, a model of the oceanic iron cycle is developed and implemented in the University of Victoria Earth System Climate Model (UVic). This implementation allows iron cycling sensitivity studies in the framework of an earth system model of intermediate complexity. The results show that a precise description of the depth of the sedimentary iron release is necessary to simulate the iron supply from the sediment to the euphotic zone. Scaling the sedimentary iron release with temperature leads to a better agreement of simulated iron concentrations with observations, indicating a possible influence of temperature on the sediment release on the global scale. A test simulation regarding the atmospheric dust deposition shows that neglecting the variability in the solubility of iron in atmospheric dust does not significantly alter iron limitation patterns. However, the assumed global concentration of iron-binding ligands regulates the response to changes in sedimentary release of iron and dust deposition strongly and thus reveals a further major uncertainty in the interaction of the iron cycle with ocean biogeochemistry. \nIn the third part of this thesis, literature data on benthic dissolved iron fluxes, bottom water oxygen concentrations and sedimentary carbon oxidation rates are assembled. The data are analyzed with a diagenetic iron model to derive an empirical transfer function for predicting benthic iron fluxes in dependence on oxygen concentrations and carbon oxidation rates. Employing the empirical function to the UVic-model from the previous chapter leads to a factor of two higher globally averaged iron concentrations in surface waters. Iron fluxes from the sediment could therefore be much larger than previously thought. \nIn the fourth part of this thesis, the empirical transfer function developed in the previous chapter is further tested in the UVic-model. The results show that a riverine supply of iron is necessary as a source of reactive iron to the sediment to balance the release of dissolved iron from the sediment on a global scale. A sensitivity test reveals that export production and oxygen concentrations are highly sensitive to the riverine iron source. This strong sensitivity could play an important role in determining primary production and the extent of low oxygen waters under climate change. \nOverall, this thesis emphasizes the importance of the external sources of iron to the ocean. Dust deposition, sedimentary iron release and riverine iron supply strongly control the dissolved iron concentrations in the ocean. Changes in these external sources can have strong implications for marine biogeochemistry and oceanic CO2 uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".