A new framework for understanding and quantifying uncertainties in the remaining carbon budget
Bibliographic record
Abstract
The remaining carbon budget quantifies the allowable future CO2 emissions to keep global mean warming below a desiredlevel. Carbon budget estimates are subject to uncertainty in the Transient Climate Response to Cumulative CO2 Emissions (TCRE), which measures the warming resulting from a given total amount of CO2 emitted. Moreover, other sources of uncertainty linked to non-CO2 emissions have been shown to also strongly affect estimates of the remaining carbon budget. Here we present a new framework that estimates the TCRE using geophysical constraints derived from observations, and integrates the effect of geophysical and socioeconomic pathway uncertainties on the distribution of the remaining carbon budget. We estimate a median TCRE of 0.40 °C and likely range of 0.3 to 0.5 °C (17-83%) per 1000 GtCO2 emitted. Our 1.5 °C remaining carbon budget has a median value of 710 GtCO2 from 2020 onwards, with a range of 470 to 960 GtCO2, (for a 67% to 33% chance of not exceeding the target). Uncertainty in the amount of current warming from non-CO2 forcing is the dominant geophysical contributor to the spread in both the TCRE and remaining carbon budget estimates. The remaining carbon budget distribution is also strongly affected by current and future mitigation decisions, where the range of non-CO2forcing across scenarios has the potential to increase or decrease the median 1.5 °C remaining carbon budget by 740 GtCO2.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".