Incorporating changes in albedo in estimating the climate mitigation benefits of land use change projects
Bibliographic record
Abstract
Abstract. Some climate scientists are questioning whether the practice of converting of non-forest lands to forest land (afforestation or reforestation) is an effective climate change mitigation option. The discussion focuses particularly on areas where the new forest is primarily coniferous and there is significant amount of snow since the increased climate forcing due to the change in albedo may counteract the decreased climate forcing due to carbon dioxide removal. In this paper, we develop a stand-based model that combines changes in surface albedo, solar radiation, latitude, cloud cover and carbon sequestration. As well, we develop a procedure to convert carbon stock changes to equivalent climatic forcing or climatic forcing to equivalent carbon stock changes. Using the model, we investigate the sensitivity of combined affects of changes in surface albedo and carbon stock changes to model parameters. The model is sensitive to amount of cloud, atmospheric absorption, timing of canopy closure, carbon sequestration rate among other factors. The sensitivity of the model is investigated at one Canadian site, and then the model is tested at numerous sites across Canada. In general, we find that the change in albedo reduces the carbon sequestration benefits by approximately 30% over 100 years, but this is not drastic enough to suggest that one should not use afforestation or reforestation as a climate change mitigation option. This occurs because the forests grow in places where there is significant amount of cloud in winter. As well, variations in sequestration rate seem to be counterbalanced by the amount and timing of canopy closure. We close by speculating that the effects of albedo may also be significant in locations at lower latitudes, where there are less clouds, and where there are extended dry seasons. These conditions make grasses light coloured and when irrigated crops, dark forests or other vegetation such as biofuels replace the grasses, the change in carbon stocks may not compensate for the darkening of the surface.
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 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".