Implications of CO<sub>2</sub> fertilization for future climate change in a coupled climate–carbon model
Bibliographic record
Abstract
Abstract The terrestrial carbon cycle plays a critical role in determining levels of atmospheric CO 2 that result from anthropogenic carbon emissions. Elevated atmospheric CO 2 is thought to stimulate terrestrial carbon uptake, through the process of CO 2 fertilization of vegetation productivity. This negative carbon cycle feedback results in reduced atmospheric CO 2 growth, and has likely accounted for a substantial portion of the historical terrestrial carbon sink. However, the future strength of CO 2 fertilization in response to continued carbon emissions and atmospheric CO 2 rise is highly uncertain. In this paper, the ramifications of CO 2 fertilization in simulations of future climate change are explored, using an intermediate complexity coupled climate–carbon model. It is shown that the absence of future CO 2 fertilization results in substantially higher future CO 2 levels in the atmosphere, as this removes the dominant contributor to future terrestrial carbon uptake in the model. As a result, climate changes are larger, though the radiative effect of higher CO 2 on surface temperatures in the model is offset by about 30% due to reduced positive dynamic vegetation feedbacks; that is, the removal of CO 2 fertilization results in less vegetation expansion in the model, which would otherwise constitute an important positive surface albedo‐temperature feedback. However, the effect of larger climate changes has other important implications for the carbon cycle – notably to further weaken remaining carbon sinks in the model. As a result, positive climate–carbon cycle feedbacks are larger when CO 2 fertilization is absent. This creates an interesting synergism of terrestrial carbon cycle feedbacks, whereby positive (climate–carbon cycle) feedbacks are amplified when a negative (CO 2 fertilization) feedback is removed.
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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".