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 CO2 that result from anthropogenic carbon emissions. Elevated atmospheric CO2 is thought to stimulate terrestrial carbon uptake, through the process of CO2 fertilization of vegetation productivity. This negative carbon cycle feedback results in reduced atmospheric CO2 growth, and has likely accounted for a substantial portion of the historical terrestrial carbon sink. However, the future strength of CO2 fertilization in response to continued carbon emissions and atmospheric CO2 rise is highly uncertain. In this paper, the ramifications of CO2 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 CO2 fertilization results in substantially higher future CO2 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 CO2 on surface temperatures in the model is offset by about 30% due to reduced positive dynamic vegetation feedbacks; that is, the removal of CO2 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 CO2 fertilization is absent. This creates an interesting synergism of terrestrial carbon cycle feedbacks, whereby positive (climate–carbon cycle) feedbacks are amplified when a negative (CO2 fertilization) feedback is removed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".