Quantifying the impacts of large-scale implementation of white roofs on the climate of Montreal Island through integrated super-resolution modeling
Bibliographic record
Abstract
• Large-scale implementation of white roofs indicates different responses for urban and non-urban fractions over Montreal island • Significant cooling noted for urban fractions, while warming is noted for some non-urban fractions • Warming of non-urban fraction is linked primarily with local winds such as land and sea breezes • Integrated high-resolution modelling is critical for better quantification of urban heat mitigation strategies The net impact of large-scale implementation of white roofs, one of the many urban heat mitigation strategies widely being adopted, on the urban climate is investigated. This is achieved through super-resolution (250 m) regional climate model simulations, with and without white roofs, for summer, for the island of Montreal, Canada. Being surrounded by water bodies, local winds such as land and sea breezes are important for the island, which are captured by the super-resolution climate simulation along with other climate characteristics. Grid-averaged surface and 2 m temperature results suggest general cooling with white roof implementation. However, analysis shows warming for the non-urban fractions in different parts of the island as a result of the complex interactions between various aspects of the regional climate system. During daytime, this warming, particularly noted for the west and central parts of the island, is associated with reduced latent heat flux in the white roof simulation, caused by reduced surface energy, weakening of sea breeze circulation, and decreased soil moisture from reduced convective precipitation. During nighttime, the white roof enhanced land breeze also warms the non-urban fractions of low-density urban zones in the eastern and western parts of the island, through advection of relatively warmer air from nearby urban areas. The new insights on the adverse effect of large-scale white roof implementation, primarily triggered by changes in local/non-synoptic winds, highlight the need for integrated modelling in quantifying the net impact of urban heat mitigation strategies, which is crucial for providing equitable benefits to all regions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".