Temperature Response from the Change of Surface Heat Flux and Vertical Diffusivity by Urbanization
Bibliographic record
Abstract
A 1-D diffusion model of temperature is employed to understand important features of temperature response to the changes of surface heat flux (SHF) and vertical diffusivity shown in 3-D model simulations. Analytical results show that the temperature response to the SHF change is the convolution of the SHF change and Green’s function (GF). Because the GF is inversely proportional to the square root of diffusion coefficient near the surface, weak/strong diffusivity in the early morning/noontime tends to generate a large/small temperature response by slowing/accelerating heat flow from surface to the atmosphere. The modulation effect of the GF and the convolution effect explain very different temperature responses to the SHF change during each period. Analytical results also show that the temperature response to the change of DF is equal to the convolution of the product of diffusion coefficient change, vertical gradients of reference temperature and the GF. Because the vertical gradient of the GF is negative below 80 m, enhanced/reduced diffusivity would enhance/weaken the urban temperature, if the vertical gradient of reference temperature is negative/positive. Numerical results with typical values of the changes of SHF and diffusivity suggest that the changes of SHF has the dominant contribution to the temperature response.
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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) | 0.001 | 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 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".