Expert judgments about transient climate response to alternative future trajectories of radiative forcing
Bibliographic record
Abstract
There is uncertainty about the response of the climate system to future trajectories of radiative forcing. To quantify this uncertainty we conducted face-to-face interviews with 14 leading climate scientists, using formal methods of expert elicitation. We structured the interviews around three scenarios of radiative forcing stabilizing at different levels. All experts ranked “cloud radiative feedbacks” as contributing most to their uncertainty about future global mean temperature change, irrespective of the specified level of radiative forcing. The experts disagreed about the relative contribution of other physical processes to their uncertainty about future temperature change. For a forcing trajectory that stabilized at 7 Wm-2 in 2200, 13 of the 14 experts judged the probability that the climate system would undergo, or be irrevocably committed to, a “basic state change” as ≥0.5. The width and median values of the probability distributions elicited from the different experts for future global mean temperature change under the specified forcing trajectories vary considerably. Even for a moderate increase in forcing by the year 2050, the medians of the elicited distributions of temperature change relative to 2000 range from 0.8–1.8 °C, and some of the interquartile ranges do not overlap. Ten of the 14 experts estimated that the probability that equilibrium climate sensitivity exceeds 4.5 °C is > 0.17, our interpretation of the upper limit of the “likely” range given by the Intergovernmental Panel on Climate Change. Finally, most experts anticipated that over the next 20 years research will be able to achieve only modest reductions in their degree of uncertainty.
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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 | 0.042 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".