Response of Atlantic Meridional Overturning circulation to variability in surface wind stress on different scales
Bibliographic record
Abstract
<!--!introduction!--> The Atlantic Meridional Overturning Circulation (AMOC) trends on decadal time scales and larger are often masked by large interannual and seasonal variability. This variability and thus uncertainty in AMOC estimates on these scales comes from atmospheric synoptic eddies and mesoscale processes. In this study, we investigate AMOC sensitivity to synoptic and higher frequency variability in atmospheric winds. We performed the suite of sensitivity experiments with 1/12° regional configuration of MITgcm where we apply low-pass filtering to surface winds to extract variability lower than 2 days and 10 days thus capturing the impact of mesoscale variability and atmospheric cyclones respectively on the circulation in the subpolar North Atlantic. Larger magnitude of the surface winds primarily has local effects on the ocean state enhancing ocean heat loss, decreasing upper ocean temperatures, strengthening subpolar gyre, deepening the mixed layer in the Labrador Sea, and increasing the magnitude of AMOC in the lower limb. When we apply scaling factor to filtered winds so the overall time integrated energy input into every grid cell is the same as in our control experiment and the only things that changes is wind variability, we see both local and remote effects on the oceanic circulation. Larger variability of surface winds leads to weaker subpolar gyre and reduced heat loss in the Labrador Sea that later leads to the warming of surface waters in the Irminger Sea. We also further separate these effects into the ones coming from the impact of winds on buoyancy and momentum fluxes.
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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 | 0.002 |
| 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) | 0.002 | 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".