Surface-atmosphere energy exchanges and their effects on surface climate and atmospheric boundary layer characteristics in the forest-tundra ecotone in northwestern Canada
Bibliographic record
Abstract
The circumpolar forest-tundra ecotone is experiencing rapid changes in vegetation composition and structure. Collectively, these changes modify surface-atmosphere energy exchanges and thus characteristics of the atmospheric boundary layer (ABL). Here, we characterize differences in bulk surface properties and resulting energy balance components using multi-year eddy covariance and supporting measurements made at a mineral upland tundra and a nearby subarctic woodland site between 2013 and 2022. The two sites are characteristic of the forest-tundra ecotone of northwestern Canada. A mixed-layer slab model, in combination with radiosonde observations, was used to gain more insights into differences in ABL characteristics. Compared to the tundra, the tree cover of the woodland led to an enhanced ability to transfer heat into the atmosphere, a higher resistance to evapotranspiration and a stronger coupling between surface and atmosphere. Sensible heat flux (H) was generally higher at the woodland than at the tundra. The largest difference in daily mean H was observed in late winter and spring when the albedo of the snow-covered landscape at the woodland was reduced by 45% compared to the tundra. Both sites experienced similar latent heat flux (LE) throughout the year. At the woodland, modelled afternoon air temperature in the mixed layer was up to 9 °C higher in spring and up to 3 °C higher in summer compared to the tundra. In accordance with model results, radiosonde observations indicated a deeper ABL and higher air temperature at the woodland. The presence of trees in the southern part of the forest-tundra ecotone increases air temperature throughout the year and has a drying effect in spring. Consequently, vegetation shifts in the forest-tundra ecotone can be expected to modify local surface climate change patterns, which must be considered when assessing climate change impacts in the region.
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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) | 0.000 | 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".