The effectiveness of agricultural carbon dioxide removal using the University of Victoria Earth System Climate Model
Bibliographic record
Abstract
Abstract. A growing body of evidence suggests that to achieve the temperature goals of the Paris Agreement, carbon dioxide removal (CDR) will likely be required in addition to massive carbon dioxide (CO2) emissions reductions. Nature-based CDR, which includes a range of strategies to enhance carbon storage in natural and managed land reservoirs, such as agricultural lands, could play an important role in efforts to limit climate warming to well below 2 °C above preindustrial levels. However, there remains a substantial knowledge gap on how the climate will respond to CDR when the removed carbon remains in the active carbon cycle. This study uses an intermediate-complexity climate model to perform simulations of agricultural CDR via soil carbon sequestration at rates reflecting realistic costs under three future emissions scenarios. We found that plausible levels of agricultural CDR reduced CO2 concentration by 5–19 ppm and global surface air temperature by 0.02–0.10 °C by the end of the century. This temperature decrease was non-linear with respect to cumulative removals, as the removed carbon remained part of the active carbon cycle, lessening the climate benefit than if it was removed permanently. In low-emissions scenarios, a given amount of CDR was found to be more effective at reducing surface air temperature and less effective at reducing atmospheric CO2, compared to high-emissions scenarios. This was due to a proportionally larger impact of CDR on radiative balance at lower atmospheric CO2 and reduced weakening of the carbon sinks at lower atmospheric CO2. CDR was substantially more effective when implemented at a higher rate, as CDR results in a proportionally larger difference in a climate with lower cumulative air fraction of CO2. Land and soil carbon responses were driven by the scenario-dependent balances between the impacts of CDR on primary productivity from CO2 fertilization and the impacts on soil respiration from increased soil carbon availability and global temperatures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".