Inter-annual Variability in the Subpolar Overturning Circulation: A Sensitivity Analysis
Bibliographic record
Abstract
We employ multi-ensemble Met Office Decadal Prediction System hindcasts to analyse the impact of atmospheric winds and North Atlantic Oscillation (NAO) phases on the overturning circulation in the North Atlantic Ocean. A positive NAO phase is generally associated with an anomalously strong and/or northward shifted jet stream in the North Atlantic, and the vice-versa is true for a negative NAO phase. As a consequence of relatively strong winds, oceans tend to lose more heat to the atmosphere in winter in many parts of the subpolar North Atlantic Ocean. This process is expected to create negative anomalies in sea surface temperature and generate more dense water on the ocean surface at high latitudes resulting in a strengthening in the overturning circulation. Here, we examine the sensitivity of the overturning circulation to NAO phases in multi-ensemble decadal hindcasts to understand how the interior ocean responds to different NAO phases. For this purpose, we analyse the changes in east-west density contrasts, upper ocean heat content, mixed-layer depth, meridional heat and salt transport in different oceanic regions, i.e. Labrador Sea, Irminger Sea and Nordic Seas. In particular, we perform a linear regression analysis for the above-mentioned diagnostics and NAO indices to assess how sensitive the upper ocean is to changes in the atmospheric state. We further compare our results against reanalysis data and in-situ observations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".