Albedo‐Induced Global Warming Potential Following Disturbances in Global Temperate and Boreal Forests
Bibliographic record
Abstract
Abstract Forest disturbances can result in very different canopies that carry elevated albedo, thus causing substantial cooling effects on the climate. Unfortunately, the resulting dynamic global warming potential from altered albedo (GWPΔα) is poorly understood. We examined and modeled the changes in albedo over time after disturbances (i.e., forest age) by forest type, disturbance type and geographic location using direct measurements from 107 sites in temperate and boreal regions. Albedo in undisturbed forests was used as the reference to calculate albedo changes (Δα) and GWPΔα after a disturbance. We found that age is a significant factor for predicting albedo amid the obvious regulations from forest type and geographic locations. We found the strongest cooling GWPΔα in the first 10 years after a disturbance, but it decreased rapidly with time. The changes in GWPΔα were very different from the chronosequence of net ecosystem production (NEP). In the first decade after disturbances, GWPΔα was negative (i.e., cooling) and surprisingly larger in magnitude, with an average of −0.609 kg CO2 m−2 yr−1, compared to NEP of −0.166 kg CO2 m−2 yr−1. Albedo continued to decrease and approached pre‐disturbance levels until around 50 years, resulting in a nearly zero GWPΔα. This research illustrates that many forests in temperate and boreal regions can be considered significant cooling agents by taking into account the high albedo of young forests following disturbances.
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 | 0.000 |
| 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 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".