The Irminger Sea – air-ice-ocean interactions in a 5 km coupled simulation with ICON-ESM
Bibliographic record
Abstract
Recent observations suggest that deep convection and water mass transformation in the Irminger Sea southeast of Greenland, together with overflows from the Nordic Seas, may be more important for the variability of the Atlantic meridional overturning circulation (AMOC) than the Labrador Sea. The preconditioning for and triggering of deep convection in the Irminger Sea is strongly associated with topography-induced mesoscale wind phenomena, such as Greenland tip jets, katabatic winds and marine cold air outbreaks. However, the resolution of current coupled climate models is too coarse to capture all the properties of these wind systems or to capture them at all. Here we explore the air-ice-ocean interactions induced by mesoscale wind phenomena in the Irminger Sea in a 1-year global coupled 5km simulation with ICON-ESM. The model is able to capture the complex interactions of the wind field and the ocean. We find that strong downward katabatic winds cause substantial heat loss from the Irminger Sea in addition to Greenland tip jets. The outflowing katabatic winds form narrow streaks of cold air that extend across the entire Irminger basin from southeast Greenland to Iceland. In addition, cold air outbreaks from the sea ice lead to the genesis of mesoscale cyclones, which in turn can cause Greenland tip jets before moving off to the east. All these wind phenomena cause substantial heat loss that preconditions the ocean for deep convection. If these wind systems are not resolved, the water mass transformation in the Irminger Sea could be too weak, contributing to why the Labrador Sea dominates AMOC variability in models. We conclude that resolving these mesoscale wind systems in an Earth system model could have significant implications for deep convection and water mass transformation in the Irminger Sea, and thus for AMOC variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".