Seasonal patterns and drivers of sub-canopy cooling of hemiboreal forests in eastern Canada and beyond
Bibliographic record
Abstract
Microclimatic conditions below and above forest canopies can vary substantially and depend on forest structure and composition. Better understanding seasonal dynamics and drivers of microclimatic variations in forests is crucial to estimate ecological climate change impacts, particularly during times of heat stress. Here, we analyse two years of forest microclimate and surface energy balance observations in a mixedwood forest in eastern Canada to quantify sub-canopy cooling and to identify its drivers. Air temperature and humidity profiles from the forest floor through the forest canopy into the surface layer were measured at an eddy covariance flux tower alongside net radiation and turbulent fluxes of sensible and latent heat during an anomalous wet year (2023) and an anomalous dry year (2024). We observe a small midday sub-canopy warming effect of about 0.2 to 0.5 C in April and May during and shortly after snowmelt when large Bowen ratios of >2 are observed. However, during the remaining months we observe middy sub-canopy cooling peaking at a median of about +1 C in September in the wet year of 2023 and at +0.7 C in July of the dry year of 2024 when Bowen ratios were about 0.5. Boosted regression trees will be used to determine how canopy properties and energy exchange across the soil-vegetation-atmosphere continuum contributes to these microclimatic dynamics. Furthermore, we will extend this analysis to nine other forest sites across North America including evergreen needleleaf, deciduous broadleaf, and mixed forests as well as a wooden savannah ecosystem. Our findings will improve our understanding of how seasonal dynamics in sub-canopy cooling and warming can alleviate or exacerbate heat stress for sub-canopy plant communities in the light of a rapidly changing climate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".