What explains the proportionality of global warming to cumulative carbon emissions?
Bibliographic record
Abstract
The constant ratio of global warming to cumulative CO2 emissions underpins the use of remaining carbon budgets as policy tools, and the need to reach net zero CO2 emissions to stabilize global mean temperature. One requirement for this proportionality is that the temperature response to a pulse emission of CO2 is independent of the background emissions scenario, and this property has been explained by a balance between the logarithmic dependence of radiative forcing on CO2 concentration, and the saturation of CO2 sinks at higher CO2 levels. Several studies have argued that this proportionality also arises because heat and carbon are mixed into the ocean by similar physical processes, and this argument was echoed in the Intergovernmental Panel on Climate Change Sixth Assessment Report. However, contrary to this hypothesis, atmosphere-ocean fluxes of heat and carbon evolve very differently to each other in abrupt CO2 increase experiments in five earth system models, and changes in the atmosphere, ocean and land carbon pools all contribute to making warming proportional to cumulative emissions. Moreover, an analytical model only exhibits proportional heat and carbon fluxes and proportional warming to cumulative emissions if the land and atmosphere carbon pools are neglected, among other unrealistic assumptions. These results strongly suggest that this proportionality is not amenable to a simple physical explanation, but rather arises because of the complex interplay of multiple physical and biogeochemical processes.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".