EXPERT ASSESSMENT OF UNCERTAINTIES IN DETECTION AND ATTRIBUTION OF CLIMATE CHANGE
Bibliographic record
Abstract
The problem of detection of climate change and attribution of causes of change has been formalized as a series of discrete probability judgements in an expert elicitation protocol. Here results are presented from the protocol for 19 experts, highlighting areas of convergence and divergence among experts. There is broad agreement among the experts that the global mean surface air temperature, vertical pattern of temperature change, geographical pattern of temperature change, and changes in diurnal temperature are the important lines of evidence for climate change detection and attribution. For the global mean and vertical pattern lines of evidence, the majority of experts (90%) reject the null hypothesis (no climate change) at the 5% significance level, thereby lending strong support to detection of climate change. For these lines of evidence the median probability of detection at the 5% significance level across experts exceeds 0.9. For the geographical pattern and diurnal cycle lines of evidence, there is far less agreement and fewer than half the experts support detection at even the 10% level of significance. On attribution there is a broad consensus that greenhouse forcing is responsible for about half the warming in global mean temperature in the past century. This result is fairly robust to uncertainties assessed in the relevant forcings by this set of experts. For the other lines of evidence, greenhouse forcing makes smaller fractional contributions with more spread among expert assessments. The near consensus of the experts on detection of climate change and attribution to greenhouse gases rests on the evidence of change in global mean surface air temperature. For the other lines of evidence, there is either significant expert disagreement on detection (the geographical pattern and diurnal cycle), or attribution of change is predominantly to causes other than greenhouse gas forcing (the vertical pattern).
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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 | 0.448 | 0.597 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".