Changing Spatial Patterns of Deep Convection in the Subpolar North Atlantic
Bibliographic record
Abstract
Abstract Deep convection and associated deep water formation are key processes for climate variability, since they impact the oceanic uptake of heat and trace gases and alter the structure and strength of the global overturning circulation. For long, deep convection in the subpolar North Atlantic was thought to be confined to the central Labrador Sea in the western subpolar gyre (SPG). However, there is increasing observational evidence that deep convection also has occurred in the eastern SPG south of Cape Farewell and in the Irminger Sea, in particular, in 2015–2018. Here we assess this recent event in the context of the temporal evolution of spatial deep convection patterns in the SPG since the mid‐twentieth century, using realistic eddy‐rich ocean model simulations. These reveal a large interannual variability with changing contributions of the eastern SPG to the total deep convection volume. Notably, in the late 1980s to early 1990s, the period with highest deep convection intensity in the Labrador Sea related to a persistent positive phase of the North Atlantic Oscillation, the relative contribution of the eastern SPG was small. In contrast, in 2015–2018, deep convection occurred with an unprecedented large relative contribution of the eastern SPG. This is partly linked to a smaller north‐westward extent of deep convection in the Labrador Sea compared to previous periods of intensified deep convection, and may be a first fingerprint of freshening trends in the Labrador Sea potentially associated with enhanced Greenland melting and the oceanic advection of the 2012–2016 eastern North Atlantic fresh anomaly.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".