Quantified positive radiative forcing at a greening Canadian boreal-Arctic transition over the last four decades
Bibliographic record
Abstract
Climate warming in northern and Arctic regions drives vegetation growth and shifts species distribution. In northern Quebec's Boreal-Arctic transition (forest-tundra ecotone), this is seen in the replacement of lichen by shrubs, primarily dwarf birch. These changes impact surface albedo, contributing to climate forcings with broad consequences. This study measures vegetation changes in Tasiapik valley near Umiujaq, Quebec, using a combination of (1) hyperspectral data (347–2400 nm) collected from 62 vegetation assemblages, including lichen, dwarf birch, willow, and spruce, to calculate broadband albedo, and (2) remote sensing data from Landsat satellites over 1984–2023. By combining these data, the proportion of vegetation type for each pixel was determined at the beginning and end of the 40-year period. The areal coverage of six main vegetation types was quantified over the 9.25 km 2 valley. The most significant change was lichen replacement by dwarf birch with lichen understory, leading to an albedo reduction from 0.233 to 0.168 and a summer shortwave forcing of 11.17 W m −2 . At the valley scale, the spatially-averaged summer forcing was 2.16 W m −2 when considering all observed vegetation changes. These values, lower than those in previous Norwegian studies, highlight the spatial variability of shortwave forcing due to lichen replacement. We observed that the vegetation change producing the greatest positive radiative forcing also caused the strongest greening. This suggests that Landsat-based greening may be used as a proxy for surface albedo change on an Arctic scale. This unique combination of ground and satellite data allows quantification of a direct, first-order effect of Arctic shrubification. • Arctic vegetation changes induce changes in albedo which feed back on climate. • This study quantifies albedo and vegetation changes at a valley scale in Northern Quebec . • We combined field hyperspectral data and robust satellite remote sensing. • The main vegetation change is lichen replacement by dwarf birch. • Over the past 40 years, the valley-scale forcing is 2.16 W m −2 .
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".