Deep convection variability in the Labrador versus Irminger Sea over the last decades
Bibliographic record
Abstract
Deep convection in the subpolar North Atlantic has been suggested to be a key process impacting the strength and variability of the Atlantic Meridional Overturning Circulation as well as the ocean’s uptake and deep storage of heat and anthropogenic CO2. However, the spatial pattern and strength of deep convection are subject to variability on interannual-to-decadal timescales and despite intense research in the field the nature of this variability is not fully understood. In this work, we employ a hindcast simulation with the eddy-rich (1/20°) ocean/sea-ice model configuration VIKING20X to analyze the variability of deep convection in the subpolar North Atlantic over the last decades (1980-2018). A special focus is set on mixed layer depth (MLD) pattern and deep water formation characteristics in the Labrador versus Irminger Sea. We show that, in agreement with observations, the VIKING20X hindcast captures strong convection events with particularly deep MLDs in the winters of the early 1980s, late 1980s and early 1990s, as well as in recent years. Yet, there are large differences in the spatial pattern of the deep convection events, as well as in the volume and thermohaline properties of the newly formed deep water. Most notably, in recent years deep convection intensity, and in particular its spatial extent, increased in the Irminger Sea and decreased in the Labrador Sea compared to the late 1980s and early 1990s. We finally discuss potential drivers of the simulated changes, thereby contrasting the relative importance of wintertime atmosphere-ocean buoyancy fluxes and oceanic preconditioning.
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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".