Key roles for atmospheric feedback and mesoscale eddies in modelling the AMOC response to enhanced Greenland runoff
Bibliographic record
Abstract
<!--!introduction!--> To understand and project the implications of enhanced Greenland Ice Sheet mass loss and potential Atlantic Meridional Overturning Circulation weakening it is necessary to determine and overcome challenges in simulating their complex linkages. We discuss the role of the ocean mean state, subpolar gyre circulation, mesoscale eddies and atmospheric coupling in shaping the response of the subpolar North Atlantic Ocean to enhanced Greenland runoff. A suite of eight dedicated 60 to 100-year long model experiments with and without atmospheric coupling, with eddy processes parameterized and explicitly simulated, and with regular and significantly enlarged Greenland runoff is presented. The important role of an interactive atmosphere stands out as being crucial for limiting the AMOC weakening because its response to ocean changes enables a compensating temperature feedback. Further, explicitly simulating mesoscale dynamics yields a more realistic distribution path of the meltwater along the North American coast and into the wider North Atlantic with implications for coastal sea-level rise projections. Underestimating eddy activity in the Labrador Sea may lead to too little or too slow entrainment of meltwater and lack of stratification in the deep convection regions. In this respect we demonstrate where eddy parameterization works quite successfully and where only high resolution (>1/12˚) yields a realistic ocean response. This underlines the necessity to advance scale-aware eddy parameterizations for next-generation climate models. Paper published in Ocean Science 02/2023: https://os.copernicus.org/articles/19/141/2023/
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".