Detecting climate milestones on the path to climate stabilization
Bibliographic record
Abstract
The era of anthropogenic climate change can be described by defined climate milestones. These milestones mark changes in the historic trajectory of change, and include peak greenhouse gas emissions, peak CO2 concentration, deceleration of warming, net-zero emissions, and a transition to global cooling. However, given internal variability in the Earth system and measurement uncertainty, definitively saying that a milestone has passed requires rigour, with the statistical illusion of the 2010s global warming hiatus being a recent cautionary tale of the need for robust methods.Here we use CMIP6 simulations of peak-and-decline scenarios to examine the time needed to robustly detect three climate milestones: 1) the slowdown of global warming; 2) the end of global surface temperature increase; and 3) peak concentration of CO2. To detect these climate milestones we use a modified version of the Monte-Carlo based method of Rahmstorf et al. 2017, developed to test whether the global warming hiatus was an illusion. The method has been modified to account for auto-correlated noise characteristic of the climate system.We estimate that it will take 40 to 60 years after a simulated slowdown in warming rate, to robustly detect the signal in the global average temperature record. Detecting when warming has stopped will also be difficult and for the one peak-and-decline scenario that has model simulations extended to the year 2300, it takes until the mid 22nd century to have enough data to conclude that warming has stopped. Detecting that CO2 concentration has peaked is far easier, and a drop in CO2 concentration of 3 ppm is consistent with a greater than 99% chance that CO2 has peaked in all scenarios examined. Overall it is sobering that even under aggressive mitigation scenarios a conclusive end to global warming is at the very outer edge of the living future, with only a small number of the very youngest children alive today likely to witness detection of the end of global warming.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".