Tipping points in ocean and atmosphere circulations
Bibliographic record
Abstract
Abstract. Continued anthropogenic pressures on the Earth system hold the potential to disrupt established circulation patterns in the ocean and atmosphere. In this narrative review, we investigate tipping points in these systems by assessing scientific evidence for feedbacks that may drive self-sustained change beyond critical forcing thresholds, drawing on insights from expert elicitation. The literature provides multiple strands of evidence for oceanic tipping points in the Atlantic Meridional Overturning Circulation (AMOC), the North Atlantic subpolar gyre (SPG), and the Antarctic Overturning Circulation, which may collapse under warmer and “fresher” (i.e. less salty) conditions. A slowdown or collapse of these oceanic circulations would have far-reaching consequences for the rest of the climate system and could lead to strong impacts on human societies and the biosphere. Among the atmospheric circulation systems considered, a few lines of evidence suggest the West African monsoon (WAM) as a tipping system. Its abrupt changes in the past have led to vastly different vegetation states of the Sahara (e.g. “green Sahara” states). Despite multiple potential sources of destabilization, evidence about tipping of the monsoon systems over South America and Asia is limited. Although theoretically possible, there is currently little indication for tipping points in tropical clouds or mid-latitude atmospheric circulations. Similarly, tipping towards a more extreme or persistent state of the El Niño–Southern Oscillation (ENSO) is currently not fully supported by models and observations. While the tipping thresholds for many of these systems are uncertain, tipping could have severe socio-environmental consequences. Stabilizing Earth's climate (along with minimizing other environmental pressures, such as aerosol pollution and ecosystem degradation) is critical for reducing the likelihood of reaching tipping points in the ocean–atmosphere system.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".