Scenario Dependence of Biogeochemical and Biogeophysical Effects of Reforestation 
Bibliographic record
Abstract
Reforestation is a widely considered nature-based method for climate mitigation. The net effect of reforestation on the climate system has two components: i) biogeochemical and ii) biogeophysical effects. The biogeochemical effect of reforestation involves the radiative cooling from the reduction in atmospheric CO2 concentration due to additional carbon storage on land. The biogeophysical effects are due to the changes in energy and moisture balances at the surface associated with reforestation. For example, the changes in land surface albedo due to reforestation modifies the surface energy balance, and consequently, affects the climate response. We hypothesize that both the biogeochemical and biogeophysical effects of reforestation are scenario dependent. The scenario dependence of biogeochemical effects could arise from different amount of additional carbon storage on land in different scenarios (larger CO2 fertilization in higher emission scenarios could lead to larger storage of carbon on land), while differences in the climate feedbacks such as the snow albedo feedback could result in scenario dependence of biogeophysical effects. In this study, we investigate the scenario dependence of biogeochemical and biogeophysical effects of reforestation by performing three sets of simulations with an Earth system model of intermediate complexity. The first set are baseline scenarios in which fossil fuel emissions, non-CO2 greenhouse gas forcing and aerosol forcing prescribed from different SSP scenarios with land use change fixed at 2020 values. The second and third sets involve emission and concentration driven reforestation experiments (each implemented with different SSP scenarios) designed for separating the biogeochemical and biogeophysical effects of reforestation.   We find that biogeochemical effects show strong scenario dependence (Figure 1). Further, biogeochemical effects do not increase monotonically, despite the increase in additional carbon storage on land with the increase in background emissions. The non-monotonic behavior of the biogeochemical cooling effects is because of the logarithmic dependence of radiative forcing on atmospheric CO2 concentration and the saturation of the land carbon sink at higher emission levels. Biogeophysical effects are also non-monotonic in response to the increase in background emissions, however, they exhibit less scenario dependence than biogeochemical effects (Figure 1). Our results show that the effectiveness of reforestation for climate mitigation declines under high emission scenarios. Therefore, immediate cessation of fossil fuel emissions not only stabilizes the climate but also enhances the climate mitigation potential through reforestation. Figure 1. The a) net, b) biogeochemical and c) biogeophysical effects of reforestation in five different SSP Scenarios.
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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 | 0.001 |
| 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".