the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jul 2025
Traffic impact modelling in SURFEX-TEB V9.0 model for improved road surface temperature prediction
Abstract. The impact of road traffic on local climate has often been overlooked, being modelled as an aggregated sensible heat flux released into the atmosphere, although it has multiple effects including turbulence, heat from energy inefficiencies of vehicles, tyre friction, snow compaction, and shadowing. These effects can impact the road surface conditions and exacerbate the phenomenon of Urban Heat Island (UHI). This study aims to improve the representation of traffic impacts in the Town Energy Balance (TEB) V9.0 urban climate model. Particular attention has been paid to preserve physical consistency among the parameterisations of tyre friction, turbulence, energy inefficiencies, and radiation impacts of the road traffic within the model. In addition, a method has been developed to model the average engine efficiency of the entire automobile fleet with internal combustion engines (ICEs) using the Worldwide Harmonized Light vehicles Test Cycles (WLTC). The new parameterisations are evaluated using observations from two road weather stations in southern Finland, Nupuri and Palojärvi, which are characterised by clear commuting patterns. To evaluate the new traffic parameterisation, road surface temperature (RST) differences between the two road carriageways is used to isolate the traffic-induced effects from natural factors. The results show that the new parameterisation is able to simulate the traffic-induced impacts on road surface temperatures. In addition, wind-induced impact and rolling friction have been shown to drive traffic effects on RST. Taking explicitly into account the traffic impacts might be better suited to simulate their actual impacts on the local scale.
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RC1: 'Comment on egusphere-2025-2777', Anonymous Referee #1, 29 Jul 2025
This study implemented the 4-wheel vehicle in the TEB model and examined its impact on surface temperature, considering factors such as anthropogenic heat, tire friction, and radiation modification. The study and the model development show great significance. The study can be published with some points being clarified and improved. Please see below comments:
Introduction:
2nd paragraph: Two more previous studies may also be interesting to the authors and provide insights. Chen et al. (2021) studied the 3D vehicle heat impact on the urban thermal environment in Hong Kong by modifying the WRF-SLUMC model, and later studied the impact of electric vehicles. The vehicle heat includes both spatial and temporal information. And consider the components of different vehicle types. Their studies also pointed out the seasonal variation of the vehicle heat impact
1.Chen, X., Yang, J.*, Zhu, R., Wong, M. S., & Ren, C. (2021). Spatiotemporal impact of vehicle heat on urban thermal environment: A case study in Hong Kong. Building and Environment, 205, 108224. https://doi.org/10.1016/j.buildenv.2021.108224
2.Chen, X., & Yang, J.* (2022). Potential benefit of electric vehicles in counteracting future urban warming: a case study of Hong Kong. Sustainable Cities and Society, 104200. https://doi.org/10.1016/j.scs.2022.104200
Modelling strategy:
As mentioned in Sections 2.1 and 2.2, the heterogeneity of the road, wind conditions, and traffic is simplified to use average values. Several point needs further clarification: 1) How does the model consider different vehicle types providing different areas of shading? 2) And how does the model consider different vehicle types with different percentages of the total vehicle amount releasing different anthropogenic heat due to varied energy efficiency? 3) How does the model consider whether there is a vehicle on the street or not at different times throughout the study period?
Methods:
I suggest that authors list the surface energy balance before and after considering the traffic to demonstrate clearly how the traffic related process modifies the surface energy balance and then further changes the estimation of the surface temperature.
Experimental set-up and model configurations:
The estimation from ICCT needs more clarification; it is not clear to me with the current information.
Results:
Fig. 5a and b, are they Delta T or T, or the values subtracted from the observation values? Please make it clear.
Section 5.2:
I would suggest that authors list all simulation cases, including those with different physical processes again, to remind the reader.
The lines in Fig. 7 are not very clear; consider increasing the height-to-width ratio of each sub-figure. The explanation and discussion of Fig. 7 are also not clear and robust enough. For example, the explanation of the less impact from the heat release is not robust and persuasive.
The figure legends in Figs. 7 and 8 are different; the same text is assigned a different color, which is confusing. Besides, for the same text in the two figures, do they indicate the same case? Please also clarify how to calculate the temperature difference (ΔTs). I suggest that the present values directly represent the impact, negative for cooling and positive for warming.
The results shown by Figures 7 and 8 are very interesting and important. However, the current description and discussion are not clear and comprehensive. I would encourage a more straightforward description and discussion of the mechanisms, for instance, including more discussion on the seasonal and diurnal variation.
Please maintain a consistent unit of temperature throughout the entire manuscript. And also consider using different line styles.
The whole description and discussion for Fig. 9 are missing.
Citation: https://doi.org/10.5194/egusphere-2025-2777-RC1 -
RC2: 'Comment on egusphere-2025-2777', Anonymous Referee #2, 06 Aug 2025
This study introduces and evaluates a novel modelling strategy to incorporate traffic-induced impacts into the SURFEX-TEB V9.0 urban climate model. The authors integrate several key parameterizations, including heat release from engine inefficiencies, radiative effects of vehicle bodies, turbulent heat exchange, and surface–tyre interactions. Moreover, the model accounts for the heterogeneity of driving behaviors and vehicle types, thereby enhancing its physical realism. Considering traffic impacts on the local urban climate is both timely and meaningful, and the manuscript is generally well-structured and clearly written. However, I have a few comments and suggestions that may help further clarify and strengthen the study.
1. The current study focuses exclusively on winter. Could the authors elaborate on why summer was not included in the simulations? What differences in traffic-induced impacts would be expected between winter and summer conditions, particularly in terms of radiative processes, boundary layer development, and surface–atmosphere interactions?
2. In the comparison with observations from the two road weather stations in southern Finland (Nupuri and Palojärvi), how were the atmospheric driving conditions specified in the model simulations? Were they based on in-situ measurements, reanalysis products, or another source? This information is important for assessing the reliability of the comparison.
3. According to Figure 5, the traffic-induced temperature difference appears to be lower in the afternoon than in the morning. Could the authors provide a physical explanation for this asymmetry? Is it due to differences in traffic volume, boundary layer stability, or background meteorological conditions?
4. The phrase “During working days wekdays” appears to contain a typographical error. Please clarify or correct this expression.
Citation: https://doi.org/10.5194/egusphere-2025-2777-RC2
Data sets
Datasets, model and scripts for: Traffic impact modeling in SURFEX-TEB V9.0 model for improved road surface temperature prediction Gabriel Colas https://doi.org/10.5281/zenodo.15640054
Model code and software
Datasets, model and scripts for: Traffic impact modeling in SURFEX-TEB V9.0 model for improved road surface temperature prediction Gabriel Colas https://doi.org/10.5281/zenodo.15640054
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