Hierarchy of calibrated global models reveals improved distributions and fluxes of biogeochemical tracers in models with explicit representation of iron
Bibliographic record
Abstract
Abstract Iron is represented in biogeochemical ocean models by a variety of structurally different approaches employing generally poorly constrained empirical parameterizations. Increasing the structural complexity of iron modules also increases computational costs and introduces additional uncertainties, with as yet unclear benefits. In order to demonstrate the benefits of explicitly representing iron, we calibrate a hierarchy of iron modules and evaluate the remaining model-data misfit. The first module includes a complex iron cycle with major processes resolved explicitly, the second module applies iron limitation in primary production using prescribed monthly iron concentration fields, and the third module does not explicitly include iron effects at all. All three modules are embedded into the same circulation model. Models are calibrated against global data sets of NO 3 , PO 4 and O 2 applying a state-of-the-art multi-variable constraint parameter optimization. The model with fully resolved iron cycle is marginally (up to 4.8%) better at representing global distributions of NO 3 , PO 4 and O 2 compared to models with implicit or absent parameterizations of iron. We also found a slow down of global surface nutrient cycling by about 30% and a shift of productivity from the tropics to temperate regions for the explicit iron module. The explicit iron model also reduces the otherwise overestimated volume of suboxic waters, yielding results closer to observations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".