Feedbacks in Emission-Driven and Concentration-Driven Global Carbon Budgets
Bibliographic record
Abstract
Abstract Emissions of CO2 into the atmosphere affect the carbon budgets of the land and ocean as biogeochemical processes react to increased CO2 concentrations. Biogeochemical processes also react to changes in temperature and other climate parameters. This behavior is characterized in terms of carbon–concentration and carbon–climate feedback parameters. The results of this study include 1) the extension of the direct carbon feedback formalism of Boer and Arora to include results from radiatively coupled simulations, as well as those from the biogeochemically coupled and fully coupled simulations used in earlier analyses; 2) a brief analysis of the relationship between this formalism and the integrated feedback formalism of Friedlingstein et al.; 3) the feedback analysis of simulations based on each of the representative concentration pathways (RCPs) RCP2.6, RCP4.5, and RCP8.5; 4) a comparison of the effects of specifying atmospheric CO2 concentrations or CO2 emissions; and 5) the quantification of the relative importance of the two feedback mechanisms in terms of their cumulative contribution to the change in atmospheric CO2. Feedback results are broadly in agreement with earlier studies in that carbon–concentration feedback is negative for the atmosphere and carbon–climate feedback is positive. However, the magnitude and evolution of feedback behavior depends on the formalism employed, the scenario considered, and the specification of CO2 from emissions or as atmospheric concentrations. Both feedback parameters can differ by factors of two or more, depending on the scenario and on the specification of CO2 emissions or concentrations. While feedback results are qualitatively useful and illustrative of carbon budget behavior, they apply quantitatively to particular scenarios and cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".