Revisiting “nutrient trapping” in global coupled biogeochemical ocean circulation models
Bibliographic record
Abstract
We analyze an extensive set of global coupled biogeochemical ocean circulation models. The focus is on the equatorial Pacific. In all simulations, which are consistent with observed standing stocks of relevant biogeochemical species at the surface, we find spuriously enhanced (reduced) macronutrient (oxygen) concentrations in the deep eastern equatorial Pacific. This modeling problem, apparently endemic to global coupled biogeochemical ocean circulation models, was coined “nutrient trapping” by Najjar et al. (1992). In contrast to Aumont et al. (1999), we argue that “nutrient trapping” is still a persistent problem, even in eddy‐permitting models and, further, that the scale of the problem retards model projections of nitrogen cycling. In line with previous work, our results indicate that a deficient circulation is at the core of the problem rather than an admittedly poor quantitative understanding of biogeochemical cycles. More specifically, we present indications that “nutrient trapping” in models is a result of a spuriously damped Equatorial Intermediate (zonal) Current System and Equatorial Deep Jets—phenomenon which await a comprehensive understanding and have, to date, not been successfully simulated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".