Identifying when thresholds from the Paris Agreement are breached: the minmax average, a novel smoothing approach
Bibliographic record
Abstract
Abstract Identifying when a given threshold has been breached in the global temperature record has become of crucial importance since the Paris Agreement. However there is no formally agreed methodology for this. In this work we show why local smoothing methodologies like the moving average and other climate modeling based approaches are fundamentally ill-suited for this specific purpose, and propose a better one, that we call the minmax average. It has strong links with the isotonic regression, is conceptually simple and is arguably closer to the intuitive meaning of “breaching the threshold” in the climate discourse, all favorable features for acceptability. When applied to the global mean surface temperature anomaly (GMSTA) record from Berkeley Earth, we obtain the following conclusions. First, the rate of increase has been ∼+0.25°C per decade since 1995. Second, based on this new estimate alone, we should plausibly expect the GMSTA to reach 1.49°C in 2023 and not go below that on average in the medium-term future. When taking into account the record temperatures of the second half of 2023, not having breached the 1.5°C threshold already in July 2023 is only possible with record long and/or deep La Niña in the following years.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".