Explicit silicate cycling in the Kiel Marine Biogeochemistry Model,version 3 (KMBM3) embedded in the UVic ESCM version 2.9
Bibliographic record
Abstract
Abstract. We describe and test a new model of biological marine silicate cycling, implemented in the University of Victoria Earth System Climate Model (UVic ESCM) version 2.9. This new model adds diatoms, which are a key aspect of the biological carbon pump, to an existing ecosystem model. The new model performs well against important ocean biogeochemical indicators and captures the large-scale features of the marine silica cycle. Furthermore it is computationally efficient, allowing both fully-coupled, long-timescale transient simulations, as well as "offline" transport matrix spinups. We assess the fully-coupled model against modern ocean observations, the historical record since 1960, and a business-as-usual atmospheric CO2 forcing to the year 2300. The model simulates a global decline in net primary production (NPP) of 1.3 % having occurred since the 1960s, with the strongest declines in the tropics, northern mid-latitudes, and Southern Ocean. The simulated global decline in NPP reverses after the year 2100 (forced by the extended RCP CO2 concentration scenario), and NPP returns to pre-industrial rates by 2300. This recovery is dominated by increasing primary production in the Southern Ocean, mostly by calcifying phytoplankton. Large increases in calcifying phytoplankton in the Southern Ocean offset a decline in the low latitudes, producing a global net calcite export in 2300 that varies only slightly from pre-industrial rates. Diatoms migrate southward in our simulations, following the receding Antarctic ice front, but are out-competed by calcifiers across most of their pre-industrial Southern Ocean habitat. Global opal export production thus drops to 50 % of its pre-industrial value by 2300. Model nutrients phosphate, silicate, and nitrate build up along the Southern Ocean particle export pathway, but dissolved iron (for which ocean sources are held constant) increases in the upper ocean. This different behaviour of iron is attributed to a reduction of low-latitude NPP (and consequently, a reduction in both uptake and export and particle, including calcite, scavenging), an increase in seawater temperatures (raising the solubility of particle forms), and stratification that "traps" the iron near the surface. These results are meant to serve as a baseline for sensitivity assessments to be undertaken with this model in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".