Simulating AMOC tipping driven by internal climate variability with a rare event algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study investigates the possibility of Atlantic Meridional Overturning Circulation (AMOC) noise-induced tipping solely driven by internal climate variability without applying external forcing that alter the radiative forcing or the North Atlantic freshwater budget. We address this hypothesis by applying a rare event algorithm to ensemble simulations of present-day climate with an intermediate complexity climate model. The algorithm successfully identifies trajectories leading to abrupt AMOC slowdowns, which are unprecedented in a 2000-year control run. Part of these AMOC weakened states lead to collapsed state without evidence of AMOC recovery on multi-centennial time scales. The temperature and Northern Hemisphere jet stream responses to these internally-induced AMOC slowdowns show strong similarities with those found in externally forced AMOC slowdowns in state-of-the-art climate models. The AMOC slowdown seems to be initially driven by Ekman transport due to westerly wind stress anomalies in the North Atlantic and subsequently sustained by a complete collapse of the oceanic convection in the Labrador Sea. These results demonstrate that transitions to a collapsed AMOC state purely due to internal variability in a model simulation of present-day climate are rare but theoretically possible. Additionally, these results show that rare event algorithms are a tool of valuable and general interest to study tipping points since they introduce the possibility of collecting a large number of tipping events that cannot be sampled using traditional approaches. This opens the possibility of identifying the mechanisms driving tipping events in complex systems in which little a-priori knowledge is available.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it