Concurrent warming, freshening and cessation of deep convection in the Labrador Sea raised its sea level to a record high
Bibliographic record
Abstract
The Labrador Sea, a major North Atlantic carbon sink and source of ventilated intermediate-depth water masses, is a vital component of the global climate system. Since the 1950s, it has seen significant heat and freshwater content shifts, resulting in arguably the largest full-depth oceanic temperature and salinity changes ever recorded. Here, we quantitatively assess the relative contributions of these changes to sea level variability. Using satellite altimetry in conjunction with profiling Argo float and ship-based hydrographic measurements, we show that between 2017 and 2025 the central Labrador Sea experienced an exceptionally fast sea level rise to record high. Six concurrent factors contributed to this - reduced winter cooling, enhanced summer warming, anomalous freshening, ceased deep convection, reduced deep-water density, and water-column mass gain. The temperature-driven sea level changes are controlled by surface heat fluxes. The salinity effects switched from counterbalancing temperature effect (1948-2015) to reinforcing (2015-2023), making the unprecedented Labrador Sea freshening and feeding it extreme Arctic sea ice losses (with a two-year lag) essential contributors to the 2017-2025 sea level rise.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".