Assessing the role of internal variability in the carbon budgets framework
Bibliographic record
Abstract
Carbon budgets are a policy-relevant tool that provides a cap on global total CO2 emissions to limit global mean warming at the desired level, for example, to meet the Paris Agreement target. Internal variability due to natural fluctuations of the climate system affects the temperature and carbon uptake on land and in the ocean. However, uncertainties arising from internal variability have not been quantified in the Transient Climate Response on Cumulative Emissions (TCRE) framework and related carbon budgets. Here we show that even though land carbon uptake exhibits the highest internal variability, most of the uncertainty in TCRE and carbon budgets arises from the temperature component, in concentration-driven simulations. Resulting remaining carbon budgets for 1.5 and 2.0 °C temperature targets differ even up to ±10 PgC (± 36.7 GtCO2; 5-95% range), due to internal variability, which is approximately equivalent to one year of global annual CO2 emissions. Our results suggest that calculating carbon budgets directly from climate models’ output does not introduce significant biases in TCRE and remaining carbon budgets due to internal variability.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".