The KPP Trigger of Rapid AMOC Intensification in the Nonlinear Dansgaard‐Oeschger Relaxation Oscillation
Bibliographic record
Abstract
Abstract Millennium time scale Dansgaard‐Oeschger oscillations of glacial climate, clear evidence for the occurrence of which was first provided on the basis of oxygen isotopic data from a Greenland ice core 25 years ago, have recently been shown to arise naturally (without explicit freshwater forcing) in a fully coupled modern climate model description of the interactions between the overturning circulation of the oceans, the atmosphere, and sea ice under maximum glacial conditions. The fast transitions from cold stadial to warm interstadial conditions in a typical D‐O oscillation are characterized by the appearance of an extensive polynya in the stadial sea ice cover of the Irminger Sea south of Denmark Strait that opens due to the onset of intense vertical mixing below the sea ice lid. Through detailed stability analysis of the water column in the region where the polynya first forms, together with analysis of the action of the KPP (Kappa Profile Parameterization) of stratified turbulence employed to represent water column diapycnal diffusivity, the authors show that the opening of the polynya is controlled by this turbulence parameterization. The relative contributions of the different components of the parameterization to polynya opening are investigated in order to better understand the rapid climate change that ensues. The authors furthermore show that the characteristic period of the model predicted Dansgaard‐Oeschger oscillation is also controlled by the detailed nature of this parameterization, a characteristic of the oscillation not previously explained.
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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 | 0.000 | 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) | 0.001 | 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".