Improved Extratropical North Atlantic Atmosphere–Ocean Variability with Increasing Ocean Model Resolution
Bibliographic record
Abstract
Abstract North Atlantic atmosphere–ocean variability is assessed in climate model simulations from HighResMIP that have low resolution (LR) or high resolution (HR) in their atmosphere and ocean model components. It is found that some of the LR simulations overestimate the low-frequency variability of subpolar sea surface temperature (SST) anomalies and underestimate its correlation with the NAO compared to ERA5. These deficiencies are significantly reduced in the HR simulations, and it is shown that the improvements are related to a reduction of intrinsic (non-NAO-driven) variability of the subpolar ocean circulation. To understand the cause of the overestimated intrinsic subpolar ocean variability in the LR simulations, a link is demonstrated between the amplitude of the subpolar ocean variability and the mean state of the Labrador–Irminger Seas. Supporting previous studies, the Labrador–Irminger Seas tend to be colder and fresher in the LR simulations compared to the HR simulations and oceanic observations from EN4. This promotes upper-ocean density anomalies in this region to be more salinity-controlled in the LR simulations versus more temperature-controlled in the HR simulations and EN4 observations. It is argued that this causes the excessive subpolar ocean variability in the LR simulations by favoring a positive feedback between subpolar upper-ocean salinity and Atlantic meridional overturning circulation (AMOC) anomalies, rather than a negative feedback between subpolar SST and AMOC anomalies as in the HR simulations. The findings overall suggest that the subpolar ocean mean state impacts the variability of the ocean circulation and SSTs, including their relationship with the atmospheric circulation, in the extratropical North Atlantic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".