Is There Sufficient Information to Reliably Estimate Return Periods for Very Rare Heat Extremes in Event Attribution?
Bibliographic record
Abstract
Abstract Accuracy and uncertainty of the probability estimates associated with extreme events of long return periods in a typical event attribution context are not well understood, making it difficult to interpret the meaning of event attribution results. This study evaluates the effectiveness of approaches used in event attribution studies for estimating the return periods of hot extremes based on large samples from large ensembles of climate model simulations. We found that, even with large sample sizes, annual maxima of temperature extremes do not fit well to a generalized extreme value (GEV) distribution in the far right tail, leading to an overestimation of return period. However, the maxima of multi‐year blocks generally fit well to a GEV distribution, improving the accuracy of exceedance probability estimates for very rare events. For events with shorter return periods, such as those spanning tens of years or less, fitting annual maxima to a GEV distribution can generally still provide robust estimates. Additionally, estimates based on data samples of similar lengths to observational records are less reliable due to limited sample sizes. These findings highlight the need for caution when interpreting event attribution results for events with long return periods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".