AMOC recovery in a multi-centennial scenario using a coupled atmosphere-ocean-ice sheet model
Bibliographic record
Abstract
Future global warming will affect ocean conditions by different mechanisms. One mechanism is the melting of the Greenland Ice Sheet (GIS), which may lead to a freshening of regions of deep water formation and eventually contribute to a possible slowdown of the Atlantic Meridional Overturning Circulation (AMOC). We simulate the two Coupled Model Intercomparison Project (CMIP) scenarios RCP4.5 and RCP8.5, to assess the effects of melt-induced fresh water on the AMOC. We use a newly developed coupled multi-resolution atmosphere-ocean-ice sheet model with high resolution at the coasts resolving the complex ocean dynamics. Our results show an AMOC recovery for both scenarios in simulations run with and without an included ice sheet model. We find that the ice sheet is not only acting as a source of freshwater to the ocean but also as a sink. This leads to local storage and redistribution of freshwater and largely compensates for the meltwater release. This physical consistency is missing in climate models without dynamic ice sheets. Therefore, we argue that freshwater hosing experiments should be assessed critically, as they might overestimate the North Atlantic freshening, induced by ice sheet melting. Because of the compensating effect, we find little effect of the included ice sheet model on the AMOC. Our results show a main freshwater release in West Greenland. There, the freshwater might be trapped in the Labrador Current and transported away from regions of deep water formation. Our results show an AMOC recovery, starting within the first half of the 22nd century. We assume the increase in net evaporation over the Atlantic and the resulting increase in ocean salinity, to be the main driver of this recovery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".