Abrupt shifts in Subpolar Gyre deep convection under stable climate conditions
Bibliographic record
Abstract
The potential collapse of North Atlantic subpolar gyre (SPG) deep convection under global warming has emerged as an increasingly important research topic and a significant source of public concern in the context of climate risk. Although both conceptual and coupled climate models have indicated the possibility of abrupt changes in SPG circulation, a comprehensive understanding of the mechanisms behind the convection shutdown remains incomplete, despite existing dynamical interpretations. Preindustrial control simulations from coupled climate models, designed to simulate a stable preindustrial climate state over time periods of the order of 10^3 years, have been shown to provide meaningful insights about the behavior of SPG in absence of anthropogenic global warming. In this study, we investigate the potential collapse of SPG deep convection in the preindustrial control simulation of six models that contribute to the Climate Model Intercomparison Project 6 (CMIP6). By analyzing the time series of mixed layer depth, we select events of winter SPG shallow convection with return period exceeding 50 years. The temporal evolution of the SPG states leading to convection shutdown exhibits common features across different model simulations. Notably, a positive sea surface temperature anomaly emerges in the SPG region the year before the event, coupled with a strong and persistent negative phase of the North Atlantic Oscillation which is followed by an abrupt freshwater release in the Labrador sea. Defining a causal chain, as aimed in this work, could be valuable for spoiling the major feedback mechanisms involved in the process as well as for detecting dynamical early warning signals, with a possible improvement in the predictability of such convection collapse events. Future steps include testing this hypothesis in forced simulations to explore parallels between the autonomous (preindustrial) and non-autonomous cases.
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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) | 0.000 | 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 | 0.000 |
| 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".