Simulating spontaneous AMOC collapses with a Rare Event Algorithm
Bibliographic record
Abstract
Understanding the stability of the Atlantic Meridional Overturning Circulation (AMOC) and its future development under anthropogenic forcing is of key importance for advancing climate science. Previous studies have explored the stability of the AMOC by applying external perturbations in climate models, such as freshwater hosing to the North Atlantic Ocean. However, if the system is close to losing stability, the tipping of the AMOC may also spontaneously occur via internal coupled atmosphere-ocean variability. Here, we address this hypothesis - using an innovative approach - by studying the nature of a spontaneous collapse of the AMOC in an intermediate complexity climate model (PlaSIM coupled to the LSG ocean) featuring - under pre-industrial conditions - an apparently stable state. Excluding all possible external forcing elements (for example green-house gasses increase, water hosing, radiative forcing anomalies), significant AMOC slowdowns and collapses can be treated as extreme events solely driven by the chaotic internal atmospheric variability. Facing this problem, we look for extreme AMOC slowdowns by applying a Rare Event Algorithm (Ragone, Wouters and Bouchet, 2018), which - via a selective cloning of the most interesting model trajectories - allows a faster exploration of the model phase space in the direction of an AMOC decrease.After exploring the parameters of the rare event algorithm, we find a regime in which PLASIM/LSG shows an abrupt AMOC slowdown over a 20-years period to a substantially weakened state, which is unprecedented in the pre-industrial run. Stability analysis reveals that part of these slowdown states are actually collapsed, i.e. states around a much lower value of the AMOC that do not recover to previous values.This approach also enables us to isolate the atmospheric processes driving the AMOC slowdown, from the climate response to the weakened AMOC state. Interestingly, we find that the climatic response to internally-induced AMOC slowdowns shows strong similarities with the responses to externally forced AMOC slowdowns in state-of-the-art climate models for what concerns temperature, wind, and precipitation changes. Looking at the mechanisms causing the AMOC weakening, instead, we find that zonal wind stress over the North Atlantic is the main driver of the AMOC slowdown, via changes in Ekman transport that affect salinity and deep convection in the Labrador sea. In this climate model, the repeated occurrence of this circulation anomaly for a few decades is sufficient to drive an AMOC collapse without possibility of recovery on multi-centennial time scales.Overall, these results show that the methodology proposed here can be generally useful for other studies in Tipping Points since it introduces the possibility of collecting a large number of critical events that cannot be sampled using traditional approaches.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".