Extinction of the northern oceanic deep convection in an ensemble of climate model simulations of the 20th and 21st centuries
Bibliographic record
Abstract
We study the variability and the evolution of oceanic deep convection in the northern North Atlantic and the Nordic Seas from 1850 to 2100 using an ensemble of 12 climate model simulations with EC-Earth. During the historical period, the model shows a realistic localization of the main sites of deep convection, with the Labrador Sea accounting for most of the deep convective mixing in the northern hemisphere. Labrador convection is partly driven by the NAO (correlation of 0.6) and controls part of the variability of the AMOC at the decadal time scale (correlation of 0.6 when convection leads by 3–4 years). Deep convective activity in the Labrador Sea starts to decline and to become shallower in the beginning of the twentieth century. The decline is primarily caused by a decrease of the sensible heat loss to the atmosphere in winter resulting from increasingly warm atmospheric conditions. It occurs stepwise and is mainly the consequence of two severe drops in deep convective activity during the 1920s and the 1990s. These two events can both be linked to the low-frequency variability of the NAO. A warming of the sub-surface, resulting from reduced convective mixing, combines with an increasing influx of freshwater from the Nordic Seas to rapidly strengthen the surface stratification and prevent any possible resurgence of deep convection in the Labrador Sea after the 2020s. Deep convection in the Greenland Sea starts to decline in the 2020s, until complete extinction in 2100. As a response to the extinction of deep convection in the Labrador and Greenland Seas, the AMOC undergoes a linear decline at a rate of about −0.3 Sv per decade during the twenty-first century.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".