Climate engineering and the risk of rapid climate change
Bibliographic record
Abstract
Recent research has highlighted risks associated with the use of climate engineering as a method of stabilizing global temperatures, including the possibility of rapid climate warming in the case of abrupt removal of engineered radiative forcing. In this study, we have used a simple climate model to estimate the likely range of temperature changes associated with implementation and removal of climate engineering. In the absence of climate engineering, maximum annual rates of warming ranged from 0.015 to 0.07 °C/year, depending on the model's climate sensitivity. Climate engineering resulted in much higher rates of warming, with the temperature change in the year following the removal of climate engineering ranging from 0.13 to 0.76 °C. High rates of temperature change were sustained for two decades following the removal of climate engineering; rates of change of 0.5 (0.3,0.1) °C/decade were exceeded over a 20 year period with 15% (75%, 100%) likelihood. Many ecosystems could be negatively affected by these rates of temperature change; our results suggest that climate engineering in the absence of deep emissions cuts could arguably constitute increased risk of dangerous anthropogenic interference in the climate system under the criteria laid out in the United Nations Framework Convention on Climate Change.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".