Recommended temperature metrics for carbon budget estimates, model evaluation and climate policy
Bibliographic record
Abstract
Recent estimates of the amount of carbon dioxide that can still be emitted while achieving the Paris Agreement temperature goals are larger than previously thought. One potential reason for these larger estimates may be the different temperature metrics used to estimate the observed global mean warming for the historical period, as they affect the size of the remaining carbon budget. Here we explain the reasons behind these remaining carbon budget increases, and discuss how methodological choices of the global mean temperature metric and the reference period influence estimates of the remaining carbon budget. We argue that the choice of the temperature metric should depend on the domain of application. For scientific estimates of total or remaining carbon budgets, globally averaged surface air temperature estimates should be used consistently for the past and the future. However, when used to inform the achievement of the Paris Agreement goal, a temperature metric consistent with the science that was underlying and directly informed the Paris Agreement should be applied. The resulting remaining carbon budgets should be calculated using the appropriate metric or adjusted to reflect these differences among temperature metrics. Transparency and understanding of the implications of such choices are crucial to providing useful information that can bridge the science–policy gap. Different methods for determining global mean temperature change are preferable for different purposes, such as estimating carbon budgets versus consistency with the achievement of the Paris Agreement, argues a Perspective that outlines methodological choices and recommends decision pathways.
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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 | 0.014 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".