Impacts of Urban Albedo Increase on Local Air Temperature at Daily–Annual Time Scales: Model Results and Synthesis of Previous Work
Bibliographic record
Abstract
Abstract The authors combine urban and soil–vegetation surface parameterization schemes with one-dimensional (1D) boundary layer mixing and radiation parameterizations to estimate the maximum impact of increased surface albedo on urban air temperatures. The combined model is evaluated with measurements from an urban neighborhood in Basel, Switzerland, and the importance of surface–atmosphere model coupling is demonstrated. Impacts of extensive albedo increases in two Chicago, Illinois, neighborhoods are modeled. Clear-sky summertime reductions of diurnal maximum air temperature for the residential neighborhood (λp = 0.33) are −1.1°, −1.5°, and −3.6°C for uniform roof albedo increases of 0.19, 0.26, and 0.59, respectively; reductions are about 40% larger for the downtown core (λp = 0.53). Realistic impacts will be smaller because the 1D modeling approach ignores advection; a lake-breeze scenario is modeled and temperature reductions decline by 80%. Assuming no advection, the analysis is extended to seasonal and annual time scales in the residential neighborhood. Yearly average temperature decreases for a 0.59 roof albedo increase are about −1°C, with summer (winter) reductions about 60% larger (smaller). Annual cooling degree-day decreases are approximately offset by heating degree-day increases and the frequency of very hot days is reduced. Despite the variability of modeling approaches and scenarios in the literature, a consistent range of air temperature sensitivity to albedo is emerging; a 0.10 average increase in neighborhood albedo (a 0.40 roof albedo increase for λp = 0.25) generates a diurnal maximum air temperature reduction of approximately 0.5°C for “ideal” conditions, that is, a typical clear-sky midlatitude summer day.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".