Changes of decadal SST Variations in the subpolar North Atlantic under strong CO2 forcing as an indicator for the ocean circulation’s contribution to Atlantic Multidecadal Variability
Bibliographic record
Abstract
The question, whether ocean dynamics are relevant for basin-scale North Atlantic decadal temperature variability is subject of ongoing discussions. Here, we analyze a set of simulations with a single climate model, consisting of a 2000-year pre-industrial control experiment, a 100-member historical ensemble, and a 100-member ensemble forced with an incremental CO2 increase by 1%/year. Compared to previous approaches, our setup offers the following advantages: First, the large ensemble size allows to robustly separate internally and externally forced variability and to robustly detect statistical links between different quantities. Second, the availability of different scenarios allows to investigate the role of the background state for drivers of the variability. We find strong evidence that ocean dynamics, particularly ocean heat transport variations, form an important contribution to generate the Atlantic Multidecadal Variability (AMV) in the Max Planck Institute Earth System Model (MPI- ESM). Particularly the Northwest North Atlantic is substantially affected by ocean circulation for the historical and pre-industrial simulations. Anomalies of the Labrador Sea deep ocean density precede a change of the Atlantic Meridional Overturning Circulation (AMOC) and heat advection to the region south of Greenland. Under strong CO2 forcing the AMV-SST regression pattern shows crucial changes: SST variability in the north western part of the North Atlantic is strongly reduced, so that the AMV pattern in this scenario is dominated by the low-latitude branch. We found a connection to changes in the deep water formation, that cause a strong reduction of the mean AMOC and its variability. Consequently, ocean heat transport convergence becomes less important for the SST variability south of Greenland.
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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".