Simulated and Observed Transport Estimates Across the Overturning in the Subpolar North Atlantic Program (OSNAP) Section
Bibliographic record
Abstract
Abstract. A comparison of simulated and observed overturning transports and related properties across the Overturning in the Subpolar North Atlantic Program (OSNAP) sections for the 2014–2022 period is presented, considering both depth and density space transports. The effort was motivated by the observational transport estimates at both OSNAP-West (OW) and OSNAP-East (OE) sections which show a minor role for the Labrador Sea (LS) in setting the mean and variability of the overturning in the subpolar North Atlantic. There are 9 participating groups from around the world, contributing a total of 18 ocean – sea simulations with 6 different ocean models. The simulations use a common set of interannually-varying atmospheric forcing datasets. The horizontal resolutions of the simulations range from nominal 1° to eddy-resolving resolutions of 0.1°–0.05°. While there are many differences between the simulations and observations as well as among the individual simulations in terms of transport properties, the simulations show significantly larger transports at OE than at OW in general agreement with the observations. Analyzing overturning circulations in both depth and density space together provides a more complete picture of the overturning properties and features. This analysis also reveals that, in both the simulations and observations, northward and southward flows substantially cancel each other, producing much smaller residual (total) transports. Such cancellations tend to be much more prominent in depth space than in density space. In general, the observed transport features are captured better at OE than OW. The simulations generally show larger (smaller) transports with positive (negative) temperature and salinity biases in the upper ocean near the OSNAP sections, but with no such relationship with density biases. In high-resolution simulations, the transport profiles agree better with the observations in general, but challenges remain in some other metrics considered in our analysis. When transports are calculated using a density referenced to 2000-m depth, rather than the ocean surface, the relative contributions of transports at OW increase modestly.
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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 | 0.000 | 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".