A dynamic marine iron cycle module coupled to the University of Victoria Earth System Model: the Kiel Marine Biogeochemical Model 2 for UVic 2.9
Bibliographic record
Abstract
Abstract. Marine biological production as well as the associated biotic uptake of carbon in many ocean regions depends on the availability of nutrients in the euphotic zone. While large areas are limited by nitrogen and/or phosphorus, the micronutrient iron is considered the main limiting nutrient in the North Pacific, equatorial Pacific and Southern Ocean. Changes in iron availability via changes in atmospheric dust input are discussed to play an important role in glacial–interglacial cycles via climate feedbacks caused by changes in biological ocean carbon sequestration. Although many aspects of the iron cycle remain unknown, its incorporation into marine biogeochemical models is needed to test our current understanding and better constrain its role in the Earth system. In the University of Victoria Earth System Climate Model (UVic) iron limitation in the ocean was, until now, simulated pragmatically with an iron concentration masking scheme that did not allow a consistent interactive response to perturbations of ocean biogeochemistry or iron cycling sensitivity studies. Here, we replace the iron masking scheme with a dynamic iron cycle and compare the results to available observations and the previous marine biogeochemical model. Sensitivity studies are also conducted with the new model to test the sensitivity of the model to parameterized iron ligand concentrations, the importance of considering the variable solubility of iron in dust deposition, the importance of considering high-resolution bathymetry for the sediment release of iron, the effect of scaling the sedimentary iron release with temperature and the sensitivity of the iron cycle to a climate change scenario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".