AMOC and water-mass transformation in high- and low-resolution models: Climatology and variability
Bibliographic record
Abstract
Water-mass transformation in the North Atlantic plays an important role in the Atlantic Meridional Overturning Circulation (AMOC) and its variability. Here we analyze subpolar North Atlantic water-mass transformation in high- and low-resolution versions of the Community Earth System Model (CESM1) and investigate whether differences in resolution and climatological water-mass transformation impact low-frequency AMOC variability. We find that high-resolution simulations reproduce the water-mass transformation found in a reanalysis-forced high-resolution ocean simulation more accurately than low-resolution simulations. We also find that the low-resolution CESM1 simulations, including one forced with the same atmospheric reanalysis data, have larger biases in surface heat fluxes, sea-surface temperatures and salinities compared to the high-resolution simulations. Despite these major climatological differences, the mechanisms of low-frequency AMOC variability are similar in the high- and low-resolution versions of CESM1. The Labrador Sea WMT plays a major role in driving AMOC variability, and a similar NAO-like sea-level pressure pattern leads AMOC changes. However, the high-resolution simulation shows a more pronounced atmospheric response to the AMOC variability. The consistent role of Labrador Sea WMT in low-frequency AMOC variability across high- and low-resolution coupled simulations, including a simulation which accurately reproduces the WMT found in an atmospheric reanalysis-forced high-resolution ocean simulation, suggests that the mechanisms are similar in the real world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".