Spiciness of the subpolar North Atlantic affects the response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 models
Bibliographic record
Abstract
Climate models are a valuable tool to study the interaction between ocean and atmosphere. Nevertheless, they are known to suffer from various biases and uncertainties. In the subpolar North Atlantic typical biases among models from the Coupled Model Intercomparison Project phase 6 (CMIP6) are found in the mean surface temperature and salinity, and in the mean sea ice concentration. These biases will affect the air-sea interaction.In this study, we are investigating the diversity of CMIP6 models with respect to their response of the Atlantic Meridional Overturning Circulation (AMOC) to the North Atlantic Oscillation (NAO) in pre-industrial control experiments. This response is sensitive to the mean spiciness of the North Atlantic. Thus, we focus on two categories of models: Models that are spicy (warm-salty) and models that are minty (cold-fresh) within the subpolar gyre of the North Atlantic. Spicy models tend to have a lower sea ice cover in the Labrador Sea (LS) and larger LS heat loss during a positive NAO, compared to minty models. Also, spicy models have a weaker stratification in the LS. Sub-surface density changes 1 to 3 years after the NAO are larger in the spicy models and establish a zonal density gradient that can cause a stronger delayed AMOC response that is also more coherent across latitudes.Although some metrics seem to be more realistic in the spicy models, other characteristics seem less realistic compared to the minty models, like the mixed layer depth relative importance between the eastern and the western subpolar North Atlantic. This could be a sign for how some mean states or processes might be right for the wrong reasons and stresses the need for model improvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".