Subpolar North Atlantic water mass transformation and overturning in eddying and non-eddying simulations
Bibliographic record
Abstract
Buoyancy forcing in the subpolar North Atlantic Ocean (SPNA) is an important driver of the Atlantic Meridional Overturning Circulation (AMOC). To advance understanding of the mechanisms connecting the two processes and their relative importance in sub-basins within the SPNA, we apply the Water Mass Transformation Framework to a matched-pair of forced, ocean-sea ice simulations configured at eddying and non-eddying resolution. Within the first decade of simulation, the non-eddying simulation produces a weak AMOC, ~11 Sv at 26.5°N, while AMOC in the eddying simulation is more realistic and is thus analyzed for comparison. Surface water mass transformation and boundary transport are calculated in both density and temperature-salinity coordinates in three separate deep water formation regions, the Iceland Basin, the Irminger Sea and Labrador Sea, during the first decade of both simulations. We identify strong, ~1 g kg−3, surface freshening in the Irminger and Labrador seas in the non-eddying simulation during the AMOC decline. This freshening significantly reduces the density of the surface outcrops where surface water mass formation occurs, essentially removing this contribution to deep water formation. Concurrently, boundary transports in these two regions are warmer and saltier in the non-eddying simulation compared to the eddying simulation where the density of surface water mass formation is stable. The warming and salinification of boundary transports in the non-eddying simulation is interpreted as an obstacle to deep water formation. Labrador Sea surface water mass transformation is important, despite its small contribution to buoyancy overturning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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".