Deep water formation in the North Atlantic Ocean in high resolution global coupled climate models
Bibliographic record
Abstract
Abstract. Simulations from seven global coupled climate models performed at high and standard resolution as part of the High Resolution Model Intercomparison Project (HighResMIP) have been analyzed to study the impact of horizontal resolution in both ocean and atmosphere on deep ocean convection in the North Atlantic and to evaluate the robustness of the signal across models. The representation of convection varies strongly among models. Compared to observations from ARGO-floats, most models substantially overestimate deep water formation in the Labrador Sea. In the Greenland Sea, some models overestimate convection while others show too weak convection. In most models, higher ocean resolution leads to increased deep convection in the Labrador Sea and reduced convection in the Greenland Sea. Increasing the atmospheric resolution has only little effect on the deep convection, except in two models, which share the same atmospheric component and show reduced convection. Simulated convection in the Labrador Sea is largely governed by the release of heat from the ocean to the atmosphere. Higher resolution models show stronger surface heat fluxes than the standard resolution models in the convection areas, which promotes the stronger convection in the Labrador Sea. In the Greenland Sea, the connection between high resolution and ocean heat release to the atmosphere is less robust and there is more variation across models in the relation between surface heat fluxes and convection. Simulated freshwater fluxes have less impact than surface heat fluxes on convection in both the Greenland and Labrador Sea and this result is insensitive to model resolution. is not robust across models. The mean strength of the Labrador Sea convection is important for the mean Atlantic Meridional Overturning Circulation (AMOC) and in around half of the models the variability of Labrador Sea convection is a significant contributor to the variability of the AMOC.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".