Impact of shrub branches on the shortwave vertical irradiance profile in snow
Bibliographic record
Abstract
Abstract. In the Arctic, shrubs are expanding and are covered by snow most of the year. Shrub branches buried in snow absorb solar radiation and therefore reduce irradiance. This reduces photochemical reaction rates and the emission of reactive and climatically active molecules to the atmosphere. Here we monitored irradiance at selected wavelengths using filters at 390±125 nm and >715 nm in snow-covered Alnus incana (gray alders) shrubs in the boreal forest near Laval University and on nearby grassland during a whole winter by placing light sensors at fixed heights in shrubs and on grassland. Irradiance in shrubs was greatly reduced at 390 nm and much less at 760 nm, where ice is much more absorbent. We performed radiative transfer simulations, testing the hypothesis that shrub branches behave as homogeneous absorbers such as soot. At 390 nm, dense shrub branches are found to reduce irradiance similarly to about 140 ppb of soot. For the >715 nm wavelengths, insufficient data and the greater ice absorption do not allow accurate conclusions. Noting that photochemically active radiation is mostly in the near UV and blue, we calculate that a high branch density will reduce photochemical reaction rates integrated over the whole snowpack by about a factor of two. This may affect the composition of the lower Arctic atmosphere in winter and spring in numerous ways, including a lower oxidative capacity, lower levels of nitrogen oxides and modified secondary aerosol production. Climatic effects are expected from these compositional changes.
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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) | 0.001 | 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".