Assessment of infrastructure-based reductions of future heat wave intensity with advanced mesoscale modelling
Bibliographic record
Abstract
Future urban climates are likely to warm substantively in coming decades as a result of climate change, and greater heat wave severity is anticipated. Moreover, the urban heat island contributes additional heat, especially during evening and night. Infrastructure-based heat reduction strategies can reduce canopy air temperatures during daytime, and to some extent at night. These strategies also have several additional effects beyond air temperature reduction. Here, we apply an early coupling of the WRF mesoscale model with the BEP-Tree urban canopy model to simulate extreme heat events representative of both contemporary and projected future climates for the metropolitan region of Toronto, Canada. Urban and non-urban land cover is derived using the state-of-the-art LCZ Generator methodology. Subsequently, the effectiveness of heat mitigation strategies, including highly reflective surfaces and vegetation, is quantified for the future scenario in the context of the increase in heat wave intensity. Specifically, the neighbourhood- and city-scale climate impacts of street trees across the diurnal cycle are quantified, and the diurnal progression of their local climate effects is discussed with reference to their modifications to multiple physical processes in the canopy. Effects of all heat mitigation strategies on canopy climate, building energy use, and thermal comfort indices are evaluated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".