Uncertainty in optimal fingerprinting is underestimated
Bibliographic record
Abstract
Abstract Detection and attribution analyses of climate change are crucial in determining whether the observed changes in a climate variable are attributable to human influence. A commonly used method for these analyses is optimal fingerprinting, which regresses observed climate variables on the signals, climate model simulated responses under external forcings. The method scales the simulated response under each external forcing by a scaling factor to best match the observations. The method relies critically on the confidence intervals for the scaling factors. The coverage rate, the relative frequency a confidence interval containing the unknown true value of the corresponding scaling factor, in the prevailing practice has been noted to be lower than desired. The mechanism of this under-coverage and its impacts on detection and attribution analyses, however, have not been investigated. Here we show that the under-coverage is due to insufficient consideration of the uncertainty in estimating the natural variability when fitting the regression and making statistical inferences. The implication is that the ranges of uncertainties in important quantities such as attributable anthropogenic warming and climate sensitivity based on the optimal fingerprinting technique should be wider than what has been believed, especially when the signals are weak. As a remedy, we propose a calibration method to correct this bias in the coverage rate of the confidence levels. Its effectiveness is demonstrated in a simulation study with known ground truth. The use of a large sample of climate model simulations to estimate the natural variability helps to reduce the uncertainty of the scaling factor estimates, and the calibrated confidence intervals provide more valid uncertainty quantification than the uncalibrated. An application to detection and attribution of changes in mean temperature at the global, continental, and subcontinental scale demonstrates that weaker detection and attribution conclusions are obtained with calibrated confidence intervals.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; both teacher heads agree on what is shown here.
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".