A global model of the marine ecosystem for long‐term simulations: Sensitivity to ocean mixing, buoyancy forcing, particle sinking, and dissolved organic matter cycling
Bibliographic record
Abstract
A new model of the marine ecosystem coupled into a global Earth System Climate Model suitable for long‐term (multimillennial timescale) simulations is presented. The model is based on nitrate as the sole limiting nutrient. Prognostic equations for nutrients, phytoplankton, zooplankton, and detritus are solved online in the three‐dimensional ocean circulation model component. Experiments with different parameterizations of vertical mixing, including a scheme of tidally driven mixing, changes in buoyancy forcing in the Southern Ocean, different particle sinking velocities, and the inclusion of dissolved organic matter are performed, and the results are compared with observations. The results reemphasize the roles of Southern Ocean freshwater forcing and diapycnal mixing in the low‐latitude pycnocline in setting the global deep water circulation and properties. The influence of high mixing in the Southern Ocean as inferred from observations is much more limited. The deep water circulation also has a strong influence on the marine ecosystem and nutrient distributions. We demonstrate that larger values of vertical diffusion lead to a shallower nutricline due to increased upwelling. Export production and nutrient distributions respond sensitively to changes in mixing and to the ratio of particle sinking to remineralization in the upper ocean. The best fits to global measurements of temperature, salinity, deep ocean radiocarbon, mixed layer depth, nutrients, and chlorophyll are obtained for values of vertical mixing in the pycnocline of around 0.2–0.3 × 10 −4 m 2 /s and for e‐folding depth for particle remineralization of 100–200 m. A simple parameterization of dissolved organic matter dynamics increases primary production and nutrient concentrations in the upper ocean and improves chlorophyll distributions in the subtropical gyres but has no discernible influence on particulate export fluxes. Remaining model deficiencies are identified, and strategies for future model improvement are outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".