Resolution Dependence of Atmosphere–Ocean Interactions and Water Mass Transformation in the North Atlantic
Bibliographic record
Abstract
Abstract Water mass transformation (WMT) in the North Atlantic plays a key role in driving the Atlantic Meridional Overturning Circulation (AMOC) and its variability. Here, we analyze subpolar North Atlantic WMT in high‐ and low‐resolution versions of the Community Earth System Model version 1 (CESM1) and investigate whether differences in resolution and climatological WMT impact low‐frequency AMOC variability and the atmospheric response to this variability. We find that high‐resolution simulations reproduce the WMT found in a reanalysis‐forced high‐resolution ocean simulation more accurately than low‐resolution simulations. We also find that the low‐resolution 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 North Atlantic Oscillation‐like sea‐level pressure pattern leads AMOC changes. However, the high‐resolution simulation shows a pronounced atmospheric response to the AMOC variability not found in the low‐resolution version. 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 may be similar in nature.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".