Tundra shrub effects on growing season energy and carbon dioxide exchange
Bibliographic record
Abstract
Increased shrub cover on the Arctic tundra is expected to impact ecosystem-atmosphere exchanges of carbon and energy resulting in feedbacks to the climate system, yet few direct measurements of shrub tundra-atmosphere exchanges are available to corroborate expectations. Here we present energy and carbon dioxide (CO 2 ) fluxes measured using the eddy covariance technique over six growing seasons at three closely located tundra sites in Canada’s Low Arctic. The sites are dominated by the tundra shrub Betula glandulosa , but percent cover varies from 17%–60% and average shrub height ranges from 18–59 cm among sites. The site with greatest percent cover and height had greater snow accumulation, but contrary to some expectations, it had similar late-winter albedo and snow melt dates compared to the other two sites. Immediately after snowmelt latent heat fluxes increased more slowly at this site compared to the others. Yet by the end of the growing season there was little difference in cumulative latent heat flux among the sites, suggesting evapotranspiration was not increased with greater shrub cover. In contrast, lower albedo and less soil thaw contributed to greater summer sensible heat flux at the site with greatest shrub cover, resulting in greater total atmospheric heating. Net ecosystem exchange of CO 2 revealed the potential for enhanced carbon cycling rates under greater shrub cover. Spring CO 2 emissions were greatest at the site with greatest percent cover of shrubs, as was summer net uptake of CO 2 . The seasonal net sink for CO 2 was ~2 times larger at the site with the greatest shrub cover compared to the site with the least shrub cover. These results largely agree with expectations that the growing season feedback to the atmosphere arising from shrub expansion in the Arctic has the potential to be negative for CO 2 fluxes but positive for turbulent energy fluxes.
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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 | 0.000 |
| 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) | 0.002 | 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".