Intensification and shutdown of deep convection in the Labrador Sea were caused by changes in atmospheric and freshwater dynamics
Bibliographic record
Abstract
Abstract Labrador Sea winter convection forms a cold, fresh and dense water mass, Labrador Sea Water, that sinks to the intermediate and deep layers and spreads across the ocean. Convective mixing undergoes multi-year cycles of intensification (deepening) and relaxation (shoaling), which have been also shown to modulate long-term changes in the atmospheric gas uptake by the sea. Here I analyze Argo float and ship-based observations to document the 2012-2023 convective cycle. I find that the highest winter cooling for the 1994-2023 period was in 2015, while the deepest convection for the 1996-2023 period was in 2018. Convective mixing continued to deepen after 2015 because the 2012-2015 winter mixing events preconditioned the water column to be susceptible to deep convection in three more years. The progressively intensified 2012-2018 winter convections generated the largest and densest class of Labrador Sea Water since 1995. Convection weakened afterwards, rapidly shoaling by 800 m per year in the winters of 2021 and 2023. Distinct processes were responsible for these two convective shutdowns. In 2021, a collapse and an eastward shift of the stratospheric polar vortex, and a weakening and a southwestward shift of the Icelandic Low resulted in extremely low surface cooling and convection depth. In 2023, by contrast, convective shutdown was caused by extensive upper layer freshening originated from extreme Arctic sea-ice melt due to Arctic Amplification of Global Warming.
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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 | 0.001 | 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".