Southern Ocean Ecosystem Response to Last Glacial Maximum Boundary Conditions
Bibliographic record
Abstract
Abstract Phytoplankton exert a significant control on the marine carbon cycle and can thus impact atmospheric CO 2 concentration. Here we use a new ecosystem model to analyze the response of diatoms and coccolithophores in the Southern Ocean to Last Glacial Maximum (LGM) climate conditions, and changes in aeolian iron (Fe) input in the Southern Ocean. We find that LGM climate conditions without changes in Fe input lead to a large increase in diatoms north of the winter sea ice edge in the South Atlantic (19%) and the South Pacific (26%), and a 31% and 9% increase within the seasonal sea‐ice zone in the South Atlantic and Indian oceans, respectively, while diatoms decrease in the Ross and Weddell Seas, and in the South Pacific (62%) south of the winter sea ice edge. Coccolithophores increase by 11% in the South West Atlantic near 45°S but are outcompeted by diatoms within the seasonal sea‐ice zone, where they decrease by 21%. Overall, this results in a 11% decrease in Southern Ocean net primary productivity (NPP) and a 2.4% decrease in export production (EP). A series of sensitivity experiments with different aeolian Fe input are compared to available paleo‐proxy records. The best fit is obtained for a simulation forced with dust fluxes from Lambert et al. (2015), https://doi.org/10.1002/2015gl064250 and reduced Antarctic Bottom Water formation in the Weddell Sea. The 78% increase in aeolian Fe input in the Southern Ocean in this simulation increases the Southern Ocean EP by 4.4%, while NPP remains 8.7% weaker compared to preindustrial.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".