Quantifying the Limits of a Linear Temperature Response to Cumulative CO2 Emissions
Bibliographic record
Abstract
Abstract Recent studies have shown that the transient climate response to cumulative carbon emissions (TCRE) of the global temperature can be well approximated by a constant value for cumulative emissions up to about 2 TtC. However, there has been little attention given in the literature to how the TCRE varies across the range of emissions rates represented by the current RCP emissions scenarios. The authors use an ensemble of simulations generated using the University of Victoria Earth System Climate Model to quantify how the temperature response to cumulative emissions varies as a function of both the total magnitude and the rate of CO2 emissions. This study shows that the 500-yr response to a pulse CO2 emission (1.81°C TtC−1) does not depend on the magnitude of cumulative emissions up to 3 TtC. The TCRE (1.66°C TtC−1), which relates to the short-term response, is relatively insensitive to constant-rate emissions up to 30 GtC yr−1. This experiment shows that the formal way of estimating the TCRE—that is, at the point of CO2 doubling in an idealized scenario with a 1% yr−1 increase of the atmospheric concentration—is a highly robust measure. The authors conclude that the TCRE provides a good estimate of the temperature response to CO2 emissions in RCP scenarios 2.6, 4.5, and 6, whereas a constant TCRE value significantly overestimates the temperature response to CO2 emissions in RCP8.5.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".