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, reducing irradiance. This modifies the profile of radiative energy absorption in the snowpack and therefore its temperature gradient and metamorphism. Perhaps more importantly, it also reduces photochemical reaction rates and the emission of reactive and climatically active molecules to the atmosphere. No study is currently available to quantify the reduction in photochemical rates caused by shrubs buried in snow. Here, we monitor irradiance in the photochemically active range using a filter at 390 ± 125 nm in snow-covered Alnus incana (gray alders) shrubs in the boreal forest near Université Laval and on nearby grassland during a whole winter by placing light sensors at fixed heights in shrubs and on grassland and observed that irradiance in shrubs was greatly reduced. We performed radiative transfer simulations, testing the hypothesis that shrub branches behave as homogeneous absorbers such as soot. At 390 nm, dense shrub branches reduce irradiance similarly to about 140 ppb of soot. Since ice is much more absorbent at longer wavelengths, we also used a filter to monitor the 715–1000 nm wavelengths (effective 760 nm) to investigate the spectral dependence of shrub impacts on radiative energy absorption. Because of competing ice absorption at 760 nm, the effect of shrubs was much weaker than at 390 nm. We calculate that a high branch density will reduce photochemical reaction rates integrated over the whole snowpack by about a factor of 2. This may affect the composition of the lower-Arctic atmosphere in winter and spring in numerous ways, including a lower oxidative capacity, lower level 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 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 | 0.001 |
| 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) | 0.004 | 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".