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Record W2525540518

Modelling the marine biogeochemical implications of aeolian, sedimentary and riverine iron supply

2015· dissertation· en· W2525540518 on OpenAlexfundno aff
Levin Nickelsen

Bibliographic record

VenueHelmholtz Centre for Ocean Research Kiel (GEOMAR) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftEuropean CommissionUniversity of VictoriaMcGill University
KeywordsBiogeochemical cycleIron fertilizationPhytoplanktonDeposition (geology)BiogeochemistryEnvironmental scienceAeolian processesOceanographySedimentChemical oceanographyCarbon cycleEnvironmental chemistryEarth scienceNutrientGeologyEcosystemChemistryEcologyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.043
GPT teacher head0.347
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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