# Evaluation of snow conditions modelling for various LCZ types

Gabriel Colas, Valéry Masson

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Gabriel Colas, Valéry Masson. Evaluation of snow conditions modelling for various LCZ types. Urban Climate, 2026, 65, pp.102767. 10.1016/j.uclim.2025.102767. hal-05454832

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# Evaluation of snow conditions modelling for various LCZ types

Gabriel Colas *, Valéry Masson

Météo-France, CNRS, Univ. Toulouse, CNRM, Toulouse, France

## ARTICLE INFO

**Keywords:**

Urban climate

Cold cities

Snow cover simulations

Urban-PLUMBER

## ABSTRACT

Snow conditions in cities have received little attention, while they were extensively studied on natural surfaces with several model intercomparison projects. This short article explores how improvements in snow parameterisations lead to a better simulation of the energy balance for various LCZ types. Thanks to urban sites instrumented by tower fluxes gathered by the Urban-PLUMBER intercomparison, we have been able to test the Town Energy Balance (TEB) urban canopy model behaviour, consistency, and performance on 9 cold winter climate urban sites located on America, Europe and Asia. TEB has recently been upgraded with a multilayer snow model on the road surface. This, associated with a new simple snow removal parameterisation improves the model simulations, especially on the overall town albedo and the subsequent net radiation and energy fluxes. This comparison on 9 urban sites of various densities also suggests that in dense neighbourhoods (LCZ2), the extension and duration of the snow mantel on roofs are too large. It would be necessary to take into account the impact of pitched roofs on snow shedding. Other effects as snow darkening and or improved parameterisation of the snow removal may also be studied to help to overcome the current model limitations.

## 1. Introduction

Winter conditions have a strong influence over urban energy balance and hydrology. A pronounced UHI can be observed in urban areas (Hinkel et al., 2003; Malevich and Klink, 2011). But the processes driving UHI are different in winter than in summer, with anthropogenic heat from building consumption or traffic being the most important factors (Bohnenstengel et al., 2013; Varentsov et al., 2018). The presence of snow cover and of its associated processes have strong impacts on the urban energy balance and on the hydrology. Snow accumulation on impervious surfaces increases surface albedo (Mori and Sato, 2015), insulates the underlying materials (Malevich and Klink, 2011), masks the influence of the built and vegetated fractions (Järvi et al., 2017), and acts as a seasonal water reservoir (Eimers and McDonald, 2015). The increased albedo has a direct impact on the surface energy balance with decreased net shortwave and longwave radiation (Baker et al., 1992; Mori and Sato, 2015; Karsisto et al., 2016) and sensible heat flux up to 85% on roofs according to Mori and Sato (2015) in Sapporo city in Japan. According to Lemonsu et al. (2008), the snow cover modifies both latent and sensible heat fluxes. These effects lead to a decrease in surface air temperature in urban areas, especially in maximum daily temperatures (Mori and Sato, 2015). It is in agreement with studies in non-urban areas where an important decrease in surface air temperature is observed with snow-covered grounds (Mote, 2008; Fernández-Castillo et al., 2025).

However, anthropogenic activities, such as snow removal, create spatially heterogeneous snow cover and reduces the albedo by revealing the underlying roads' pavement (Swenson and Lawrence, 2012; Karsisto et al., 2016). In Malevich and Klink (2011), observations show that the snow cover increases the urban heat island during the day and at night with an increase of 0.5 °C and

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: gabriel.colas@meteo.fr (G. Colas).</footnote>
1 °C, respectively. This is attributed to the higher albedo in rural areas than in cities due to darkening snow and snow removal from anthropogenic activities.

Urban canopy models are essential tools to simulate the strong interaction between human-made environments and meteorological conditions. However according to Giridharan and Kolokotroni (2009) due to the lack of consequences for human health, winter urban climate modelling has received little attention. Several urban canopy models do not simulate the snow cover accumulation on artificial surfaces or employ simplified snow schemes and snow cover parameterisations (Lipson et al., 2024). For example, snow cover accumulation is not modelled in the ASLUM (Wang et al., 2011), SNUUCM (Wang and Zender, 2011) or TERRA (Wouters et al., 2015) models. In addition, models like SUEWS, ECLand-U or Lodz-SUEB simulate the snow cover accumulation with simple one-layer models. The findings of Colas et al. (2025) demonstrate that the multilayer snow model implemented in the TEB model improves the simulation of the artificial surface temperature below the snow mantle and the snow melting process. CLMU (Lawrence et al., 2019) and JULES include multilayer snow model; however, they lack urban-specific adaptations, such as snow removal parameterisation (Karsisto et al., 2016) or adjustments for snow-darkening caused by anthropogenic activities (Zhang et al., 2022). Some models take into account a parameterisation of snow removal operation, such as snow cover curves dependent on snow water equivalent (SWE) from Dutra et al. (2010) for impervious surfaces as in SUEWS and ECLand-U. Snow conditions simulated by urban canopy models have been sparsely evaluated, with the exception of Lemonsu et al. (2010) on Montreal, Canada, which allowed to improve the snow darkening effect and subsequent snow evolution through winter and early spring. To the authors' knowledge, no major studies exist on the subject. In comparison, for natural environments, the majority of land surface schemes have a fully coupled snow model inside which has been evaluated through several major intercomparison projects (Etchevers et al., 2004; Essery et al., 2009; Rutter et al., 2009; Krinner et al., 2018). Two decades ago, many models already used multilayer snow models (Etchevers et al., 2004) and already included the impact of the surrounding conditions like forests on the snowpack evolution Rutter et al. (2009).

Thus, this study attempts to find evidence to answer 3 research questions: (1) Is it relevant to include a better parameterisation of snow in urban canopy models? (2) What are the snow-related processes that have the greatest impact on model performance? (3) Does the urban canopy model accurately represent the evolution of albedo, which would lead to possible positive implications for both weather forecasts and climate change studies? The Town Energy Balance (TEB) is chosen for this study. It has received improvement in snow modelling on impervious surfaces from Colas et al. (2025). In this new version, TEB includes on the road surface a multilayer snow model named Explicit Snow (ES), also used with land surface models (Decharme et al., 2016), an ice reservoir and a new parameterisation of snow removal activities. This study evaluate these improvements, mostly validated on open roads, for various urban neighbourhoods. We follow the validation methodology from the extensive urban canopy model intercomparison project Urban-PLUMBER (Lipson et al., 2024). TEB is configured and launched on the sites subject to snowfall and compared with the observations. In Section 2, the experimental set-up, the sites, and the model configurations are presented. Then in Section 3, the results of the model are evaluated and analysed. Section 4 concludes.

## 2. Methodology

### 2.1. Description of the sites

One way to evaluate urban climate models is through the analysis of the town aggregated fluxes, which are calculated across all models as in PILPS-Urban (Grimmond et al., 2011) and Urban-PLUMBER (Lipson et al., 2024) intercomparison projects. A decade ago, PILPS-Urban has allowed the modellers to address some of the deficiencies, especially in mid-complexity models, resulting in improved performance. Today, the collection of urban sites in the Urban-PLUMBER dataset is the reference for both multisite and multimodel urban environmental simulation. Jongen et al. (2024) have already identified persistent challenges thanks to this project: urban climate models exhibit substantial heterogeneities in their hydrological budgets. Following this track, Urban-PLUMBER is used in this study to assess its relevance in studying the cold urban climate and to evaluate the simulations of the TEB urban canopy model.

In Urban-PLUMBER, of the 20 sites collected by the initiative, 8 are in cold or continental climates according to the Köppen-Geiger climate classification, and snow fall occurs for 9 of them. Nevertheless, Lipson et al. (2022) admit that there is a lack of site representativity for very cold climates like, for example, Tromsø with a snow season duration between 190–200 days and an average temperature of 2.9 °C in 2010 (Vikhamar-Schuler et al., 2010). Mostly made up of high-quality flux tower measurements provided in 30- or 60-minutes periods, the analysis period is used to validate the urban climate simulations. The upwelling radiation fluxes (SW† and LW†), sensible (Qh) and latent (Qle) heat fluxes measurements from the flux tower are not gap-filled and used to evaluate the models, while the other measurements are gap-filled. The Urban-PLUMBER dataset provides land-use types fractions at each sites, as recommended by Grimmond et al. (2011), calculated to focus on the evaluation of turbulent fluxes. At several sites, to capture the real eddy covariance footprint of the environment (Lipson et al., 2022), entire sectors are excluded from ground fraction calculations depending on the dominant wind direction.

For this study, at Vancouver (CA-Sunset) and both Helsinki sites (FI-Torni, FI-Kumpula) sites, the ground fraction calculations are adapted to compare the radiation fluxes measured from radiometer with a field of view of 360° with the simulations. In addition, the Minneapolis site is removed from albedo and radiation fluxes comparisons since the radiometers are installed at a turf grass site at 2-metre height and several hundred metres from the tower. SW† measurements in the sensor field of view are not representative of an urban site.
## 2.2. Model setup

The Town Energy Balance physical model (TEB) is forced directly by the Urban-PLUMBER dataset without additional processing of the downward shortwave (SW↓) and longwave radiation (LW↓), air temperature, specific humidity, air pressure, wind direction and speed, rainfall and snowfall rates. TEB is launched on a continuous simulation composed of a one-year spinnup, and of the whole validation period with 30-minutes or hourly outputs depending on the site. TEB, like the other models evaluated in Lipson et al. (2024), is configured to represent and simulate local urban conditions. As advection is not modelled directly in the model, TEB simulates the local environment around the tower fluxes as a single vertical profile and is forced by the tower fluxes measurements taken at several tens of metres high.

For each site, a restrictive amount of physiographic parameter is chosen, which corresponds to the “baseline experiment” from Lipson et al. (2024), such as building height, road, building, bare ground, low vegetation, and tree as ground fractions. They allow us to calculate the average fluxes from heterogeneous land uses.

The parameterisations and physical configuration of TEB are identical for all sites. TEB is launched with 3 different snow configurations; first with a one-layer snow model (Ref) (Masson, 2000), then with the Explicit Snow (ES) model and finally with ES in combination with the snow removal parameterisation for urban environment of Colas et al. (2025) (ES + removal). The snow removal parameterisation is the same at each site: each 6 h of continuous snow cover on the road surface, 75% of the road is cleared from the snow and piled up on the remaining 25%. The entire data set to study the climate of cold cities with URBAN-Plumber, with additional observations, TEB configuration files, TEB modified code, sites descriptions, and scripts to reproduce the figures are available in the Zenodo repository of Colas (2025).

## 2.3. Parameterisation of albedo

In this study the original spectral albedo calculation in ES for natural surfaces, as described in Decharme et al. (2016), is used, instead of the parameterisation of the snow albedo for ES in Colas et al. (2025) derived from Lemonsu et al. (2010). The latter parameterisation was expected to be in better agreement with Lemonsu et al. (2008) and Zhang et al. (2022), which explain that in cities, the contamination of snow by particles from anthropogenic activities leads to darker snow. However, it underestimates the snow darkening, and decreases the snow albedo slower than the newer ES original spectral albedo method. In the future, snow albedo calculations should be further improved to accurately represent snow darkening in urban areas.

# 3. Improved snow simulations on Urban-PLUMBER sites

## 3.1. Albedo and shortwave fluxes

The analysis of the urban climate of cold cities in winter and spring uncovers the significant impact of snowfall. The snow cover increases on average the observed town albedo as shown in Fig. 1 and the upwelling solar fluxes (SW↑). As snow periods are the primary focus of this study, subsets that range from the first to the last snow flake that hold on the ground for the entire season are extracted to calculate the albedo scores, as well as the fluxes performances in Fig. 2. As mentioned above, the comparison on albedo and radiative fluxes cannot be done for US-Minneapolis.

The changes in the town albedo are more significant for sites with more substantial snowfall, such as those of Finland and Poland, compared to the Korean sites. At the FI-Torni and FI-Kumpula sites, during the snow melting period ranging from late February to April, the decreasing values of albedos are partially reproduced by the ES model as shown in Fig. 1 with slight decreasing values. On several sites as KR-Ochang, KR-Jungnang, and US-Baltimore, albedo performances seem even to be in favour of the simple snow model, also supported by the upwelling solar fluxes (SW↑) performance on Fig. 2. On Fig. 3, on the absorbed solar fluxes, there is also no clear improvement on the period selected, with sometimes increased or decreased values. It is mainly attributed to the behaviour differences between the two albedo schemes.

The two pairs of sites, FI-Torni/FI-Kumpula and PL-Lipowa/PL-Narutowicza are subject to the same snowfall and to almost similar atmospheric conditions, but they have different observed albedo. Indeed, land-use type plays an important role in winter, in particular on the town albedo, as pointed out also by Karsisto et al. (2016) and Swenson and Lawrence (2012). FI-Kumpula site is influenced by the woods around the hill, where anthropogenic impacts are smaller. A similar drop of performance happen at FI-Kumpula around mid-February with a fall of simulated upwelling solar fluxes (SW↑). At this site, the albedo is not overestimated but underestimated. This behaviour may be caused by the lack of snow parameterisation on tree branches.

ES with the snow removal parameterisation has a bigger impact on the upwelling solar fluxes (SW↑) as shown in Fig. 2 and corroborated by Fig. 3. At FI-Torni and PL-Narutowicza (both LCZ2), the parameterisation of snow removal leads to a significant increase of the performance (Table 1).

The number of instrumented sites allows to analyse the behaviour of the model on several types of Local Climate Zones (LCZ) (Table 1). The first analysis is that overall, the Explicit Snow Scheme alone only, at best, marginally improves the simulation of albedo, with the exception of LCZ5, possibly due to the influence of trees and albedo parameterisation changes, as noted above. The addition of the removal parameterisation improves simulations for all LCZ. One also observe that the model is best for low-rise and large low-rise buildings setups (LCZ3, LCZ6 and LCZ8), independently of the building density. On the contrary, the model performs worse for the LCZ2, despite the aforementioned improvement, with albedo staying too high for both LCZ2 sites (Fig. 1).
<caption>Table 1</caption>

Root Mean Square Error of town albedo for the three simulations (Ref: Reference, ES: with Explicit Snow scheme, ES+removal: with explicit snow scheme and removal of snow) for each of the LCZ types: LCZ2 (FI-Torni and PL-Narutowicza), LCZ3 (KR-Jungnang), LCZ5 (PL-Lipowa et FI-Kumpula), LCZ6 (CA-Sunset et US-Baltimore) and LCZ8 (KR-Ochang).

<table><thead><tr><th></th><th>LCZ2</th><th>LCZ3</th><th>LCZ5</th><th>LCZ6</th><th>LCZ8</th></tr></thead><tbody><tr><th>Ref</th><td>0.27</td><td>0.11</td><td>0.15</td><td>0.11</td><td>0.15</td></tr><tr><th>ES</th><td>0.28</td><td>0.10</td><td>0.12</td><td>0.10</td><td>0.15</td></tr><tr><th>ES + removal</th><td>0.22</td><td>0.09</td><td>0.11</td><td>0.09</td><td>0.10</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 1. Monthly average town albedo observed and simulated by TEB with a one-layer snow model (Ref), with the explicit snow model (ES) and with the explicit snow model with the snow removal parameterisation (ES + removal) at Urban-PLUMBER sites. Filled areas are the standard deviation of the albedo observed in black and simulated by ES + removal in orange for each month.</figcaption></figure>

This is suggesting that crucial processes may be still lacking for these densely built city centre sites, where the building density is large. In PL-Narutowicza, there is a mix of flat and pitched and dark roofs and in FI-Torni the building covers 37% of the neighbourhood, and roofs are pitched and black. Photograph analysis (not shown) indicates that snow mantel can be absent on roofs while it is present on roads. The absence of snow mantel on roofs is probably due to fall of the pack by combination of the slope and heat flux from the building at the base of the snow. These processes should be incorporated in the model. On LCZ8 and LCZ3 sites, the roofs are flat, and snow mantel may stay longer, as simulated by TEB. The low density sites (LCZ5 and LCZ6) may have less influence of roofs on the overall albedo. In addition, other processes may also be missing, especially those influencing snow darkening, that could be stronger in city centres (pollution due to traffic on snow, incorporation of gravel during removal, or aerosols particles), that would add up to the effect of snow mantel evolution on roofs.

## 3.2. Other terms of the energy balance

There is a faint improvement over the sensible heat fluxes with ES and ES + removal for most of the sites with the best improvement over the US-Minneapolis site (Fig. 2). For FI-Torni, FI-Kumpula and PL-Narutowicza, the turbulent fluxes differences over the snowpack between the model configurations are visible from mid-February to the end of the snow season. This period is associated with stronger downwelling solar fluxes (SW¹) and may be the major driver as found by Ho and Valeo (2005) at Calgary. The ES + removal parameterisation tends to increase the sensible and latent heat flux over the other snow parameterisations. These values are closer to the observed values at the FI-Torni site and compensate the negative bias, as seen on Fig. 3. For the other sites, depending on the conditions, it increases the positive bias or decreases the negative bias. Fig. 2 shows a performance degradation on latent heat flux, with the new parameterisations. It is stronger on sites with the most snowfall, such as for FI-Torni and FI-Kumpula. On average the snow removal parameterisation decrease the most the performances, but this pattern is unique to the latent flux,
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Taylor diagram for each aggregated town flux. The three configurations of TEB are drawn for each site (Ref, ES and ES + removal). The star is the observation. Closer values from the observation point means better performances.</figcaption></figure>

with no other performances degradations over the other fluxes. However, the differences between the latent heat flux modelled and observed are small compared to the other fluxes as shown in Fig. 3.

In addition, ES only parameterisation decreases the upwelling infrared radiation fluxes $LW_1$, while ES + removal parameterisation is almost equivalent to the reference simulation. On the one hand, ES only parameterisation tends to decrease the performance on the $LW_1$ fluxes, contradictorily to the increased performance on the snow surface temperature for open road (Colas et al., 2025). Indeed, for open roads, ES shows more relevant values of snow surface temperature, whereas the simple snow model follows wrongly the air temperature. On the other hand, the performance of ES + removal and the reference configurations lead to similar results. But this may come from compensating errors in the reference simulation. First, the snow surface temperature of the simple snow model is too warm but compensates the overall cold bias from the town surface temperature modelled. Second, the average values between the road and snow surface temperature lead to the same upwelling infrared radiation fluxes $LW_1$. However, ES + removal configuration is more physically sound.

Thus, the winter parameterisation improvements lead to mixed results on the turbulent fluxes. On the infrared fluxes, even if the scores are similar, the model analysis shows that the ES + removal configuration is more accurate on other variables like on the surface variables. In contrary, upwelling solar fluxes and albedo simulations are significantly different between the snow parameterisations.

### 3.3. Summary of the results

The improvement in road winter conditions in the TEB model leads to consistent conclusions among all experimental sites on the albedo: (1) The multilayer snow model reduces the albedo during the snowmelt period, which is consistent with the comments of Best et al. (2011) and Colas et al. (2025). (2) Snow removal parameterisation reduces the simulated albedo even more and plays an important role in the town albedo as stated by Dutra et al. (2010). (3) Even with the new snow processes on the road, the simulated albedo is still higher than observed, except at the FI-Kumpula site, for most LCZ types, especially for the FI-Torni site.
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Monthly averaged bias on the nine observation sites of shortwave, longwave, sensible and latent heat fluxes for the Reference (Ref), Explicit Snow (ES) and Explicit Snow with removal parameterisation (ES + removal) simulations. Snow depth simulated for each simulation is also indicated (scale vary between sites). Radiation fluxes scores are not available for US-Minneapolis.</figcaption></figure>

and PL-Narutowicza city center neighbourhoods (LCZ2). In addition, upwelling solar fluxes (SW†) are improved on average by the improved snow representation with the snow removal parameterisation in TEB suggesting that taking into account anthropogenic impacts is relevant.

## 4. Conclusion

In this work, a thorough evaluation of different snow parameterisations from simple to mid-complexity level have been carried out on urban environments. The results of this study suggest that the use of a multilayer snow model (Explicit Snow) paired with a snow removal parameterisation on the road contributes to improve the simulation of winter conditions in cities impacted by long-lasting snow cover. In particular, the upwelling solar radiation flux (and the albedo) and the upwelling longwave fluxes (and hence the overall urban surface temperature) simulations are closer to the observed values, while influence on other fluxes is more limited.

However, these parameterisations have shown limits and suggest new orientations to further improve the representation of the snowpack in cities. For instance, simulations performances decrease the most when downward solar radiation increase like from mid-February to the end of the snow season at the Finnish sites. It suggests that improving the town albedo simulation could have significant impacts on the results. For all sites where road fractions are significant, factors such as snow cover duration, melting water, snow darkening and snow piles are a joint object of study to improve the representation of the town albedo. For LCZ2 dense city centers, a representation of the impact of pitched roofs on the snow pack, and subsequent fall of the snowpack, should be explored.

Modelling cold cities is specifically relevant for climate change studies, as the average temperature tends to increase more in northern countries than in the rest of the world, such as in Finland *Ruosteenoja* and *Jylhä* (2021). It could support initiative to understand the interactions between urban areas and regional climate change (Langendijk et al., 2024). In addition, improving winter urban conditions would help improve weather forecasts that suffer from the too high albedo values provided by urban climate models. Cities with intense snowfall are likely to benefit the most from these improvements.

### CRediT authorship contribution statement

**Gabriel Colas:** Writing – Original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. **Valéry Masson:** Writing – Review & Validation, Supervision, Software, Methodology.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Data availability

The entire dataset to study the climate of cold cities with URBAN-Plumber with additional observations, TEB configuration files, TEB modified code, and scripts to reproduce the figures are available.

## References

Baker, D.G., Ruschy, D.L., Skaggs, R.H., Wall, D.B., 1992. Air temperature and radiation depressions associated with a snow cover. J. Appl. Meteorol. Clim. 31 (3), 247–254. http://dx.doi.org/10.1175/1520-0450(1992)031<0247:ATARDA>2.0.CO;2

Best, M.J., Pryor, M., Clark, D.B., Rooney, G.G., Essery, R.L.H., Ménard, C.B., Edwards, J.M., Hendry, M.A., Porson, A., Gedney, N., Mercado, L.M., Sitch, S., Blyth, E., Boucher, O., Cox, P.M., Grimmond, C.S.B., Harding, R.J., 2011. The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes. Geosci. Model. Dev. 4 (3), 677–699. http://dx.doi.org/10.5194/gmd-4-677-2011.

Bohnenstengel, S.I., Hamilton, I., Davies, M., Belcher, S.E., 2013. Impact of anthropogenic heat emissions on London's temperatures. Q. J. R. Meteorol. Soc. 140 (679), 687–698. http://dx.doi.org/10.1002/qj.2144.

Colas, G., 2025. Urban-PLUMBER: Dataset and code to study the climate of cold cities with snow with the model TEB [code and dataset]. URL https://doi.org/10.5281/zenodo.15789893.

Colas, G., Masson, V., Bouttier, F., Bouilloud, L., Pavan, L., Karsisto, V., 2025. Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice. Geosci. Model. Dev. 18 (11), 3453–3472. http://dx.doi.org/10.5194/gmd-18-3453-2025.

Decharme, B., Brun, E., Boone, A., Delire, C., Le Moigne, P., Morin, S., 2016. Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model. Cryosphere 10 (2), 853–877. http://dx.doi.org/10.5194/tc-10-853-2016.

Dutra, E., Balsamo, G., Viterbo, P., Miranda, P.M.A., Beljaars, A., Schär, C., Elder, K., 2010. An Improved Snow Scheme for the ECMWF Land Surface Model: Description and Offline Validation in: Journal of Hydrometeorology Volume 11 Issue 4 (2010). J. Hydrometeorol. 11 (4), 899–916. http://dx.doi.org/10.1175/2010JHM1249.1.

Eimers, M.C., McDonald, E.C., 2015. Hydrologic changes resulting from urban cover in seasonally snow-covered catchments. Hydrol. Process. 29 (6), 1280–1288. http://dx.doi.org/10.1002/hyp.10250.

Essery, R., Rutter, N., Pomeroy, J., Baxter, R., Stähli, M., Gustafsson, D., Barr, A., Bartlett, P., Elder, K., 2009. SNOWMIP2: An evaluation of forest snow process simulations. Bull. Am. Meteorol. Soc. 90 (8), 1120–1136. http://dx.doi.org/10.1175/2009BAMS2629.1.

Etchevers, P., Martin, E., Brown, R., Fierz, C., Lejeune, Y., Bazile, E., Boone, A., Dai, Y.-J., Essery, R., Fernandez, A., et al., 2004. Validation of the energy budget of an alpine snowpack simulated by several snow models (snow MIP project). Ann. Glaciol. 38, 150–158. http://dx.doi.org/10.3189/172756404781814825.

Fernández-Castillo, P., Román-Cascón, C., Yagüe, C., 2025. Impact of snow cover on the surface energy balance and its contribution to the extreme cold wave in Spain after snowstorm filomena. Atmos. Res. 320, 108015. http://dx.doi.org/10.1016/j.atmosres.2025.108015.

Giridharan, R., Kolokotroni, M., 2009. Urban heat island characteristics in London during winter. Sol. Energy 83 (9), 1668–1682. http://dx.doi.org/10.1016/j.solener.2009.06.007.

Grimmond, C.S.B., Blackett, M., Best, M.J., Baik, J.-J., Belcher, S.E., Beringer, J., Bohnenstengel, S.I., Calmet, I., Chen, F., Coutts, A., Dandou, A., Fortuniak, K., Gouvea, M.L., Hamdi, R., Hendry, M., Kanda, M., Kawai, T., Kawamoto, Y., Kondo, H., Krayenhoff, E.S., Lee, S.-H., Loridan, T., Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson, A., Ryu, Y.-H., Salamanca, F., Steeneveld, G., Tombrou, M., Voogt, J.A., Young, D.T., Zhang, N., 2011. Initial results from Phase 2 of the international urban energy balance model comparison. Int. J. Climatol. 31 (2), 244–272. http://dx.doi.org/10.1002/joc.2227.

Hinkel, K.M., Nelson, F.E., Klene, A.E., Bell, J.H., 2003. The urban heat island in winter at barrow, alaska. Int. J. Climatol. 23 (15), 1889–1905. http://dx.doi.org/10.1002/joc.971.

Ho, C.L.I., Valeo, C., 2005. Observations of urban snow properties in calgary, Canada. Hydrol. Process. 19 (2), 459–473. http://dx.doi.org/10.1002/hyp.5544.

Järvi, L., Grimmond, C.S.B., McFadden, J.P., Christen, A., Strachan, I.B., Taka, M., Warsta, L., Heimann, M., 2017. Warming effects on the urban hydrology in cold climate regions. Sci. Rep. 7 (1), 5833. http://dx.doi.org/10.1038/s41598-017-05733-y.

Jongen, H.J., Lipson, M., Teuling, A.J., Grimmond, S., Baik, J.-J., Best, M., Demuzere, M., Fortuniak, K., Huang, Y., De Kauwe, M.G., Li, R., McNorton, J., Meili, N., Oleson, K., Park, S.-B., Sun, T., Tsigirnkakis, A., Varentsov, M., Wang, C., Wang, Z.-H., Steeneveld, G.J., 2024. The water balance representation in urban-PLUMBER land surface models. J. Adv. Model. Earth Syst. 16 (10), http://dx.doi.org/10.1029/2024MS004231, e2024MS004231.

Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C.S.B., Oleson, K.W., Kouznetsov, R., Masson, V., Järvi, L., 2016. Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki. Q. J. R. Meteorol. Soc. 142 (694), 401–417. http://dx.doi.org/10.1002/qj.2659.

Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C.B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M.S., Schaedler, G., Semenov, V., Smirnova, T.G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., Zhu, D., 2018. ESM-snowmip: assessing snow models and quantifying snow-related climate feedbacks. Geosci. Model. Dev. 11 (12), 5027–5049. http://dx.doi.org/10.5194/gmd-11-5027-2018.

Langendijk, G.S., Halenka, T., Hoffmann, P., Adinolfi, M., Aldama Campino, A., Asselin, O., Bastin, S., Bechtel, B., Belda, M., Bushenkova, A., Campanale, A., Chun, K.P., Constantinidou, K., Coppola, E., Demuzere, M., Doan, Q.-V., Evans, J., Feldmann, H., Fernandez, J., Fita, L., Hadjinicolaou, P., Hamdi, R., Hundhausen, M., Grawe, D., Johannsen, F., Milovac, J., Katragkou, E., Kerroumi, N.E.I., Kotlarski, S., Le Roy, B., Lemonsu, A., Lennard, C., Lipson, M., Mandal, S., Muñoz Pabón, L.E., Pavlidis, V., Pietikäinen, J.-P., Raffa, M., Raluy-López, E., Rechid, D., Ito, R., Schulz, J.-P., Soares, P.M., Takane, Y., Teichmann, C., Thatcher, M., Top, S., Van Schaeybroeck, B., Wang, F., Yuan, J., 2024. Towards better understanding the urban environment and its interactions with regional climate change - the WCRP CORDEX flagship pilot study URB-RCC. Urban Clim. 58, 102165. http://dx.doi.org/10.1016/j.uclim.2024.102165.

Lawrence, D.M., Fisher, R.A., Koven, C.D., Oleson, K.W., Swenson, S.C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P.J., Li, F., Li, H., Lombardozzi, D., Riley, W.J., Sacks, W.J., Shi, M., Vertenstein, M., Wieder, W.R., Xu, C., Ali, A.A., Badger, A.M., Bisht, G., van den Broeke, M., Brunke, M.A., Burns, S.P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J.B., Flanner, M., Fox, A.M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L.R., Lipscomb, W.H., Lu, Y., Pandey, A., Pelletier, J.D., Perket, J., Randerson, J.T., Ricciuto, D.M., Sanderson, B.M., Slater, A., Subin, Z.M., Tang, J., Thomas, R.Q., Val Martin, M., Zeng, X., 2019. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11 (12), 4245–4287. http://dx.doi.org/10.1029/2018MS001583.

Lemonsu, A., Bélair, S., Mailhot, J., Benjamin, M., Morneau, G., Harvey, B., Chagnon, F., Jean, M., Voogt, J., 2008. Overview and first results of the montreal urban snow experiment 2005. J. Appl. Meteorol. Clim. 47 (1), 59–75. http://dx.doi.org/10.1175/2007JAMC1639.1.

Lemonsu, A., Bélair, S., Mailhot, J., Leroyer, S., 2010. Evaluation of the town energy balance model in cold and snowy conditions during the montreal urban snow experiment 2005. J. Appl. Meteorol. Clim. 49 (3), 346–362. http://dx.doi.org/10.1175/2009JAMC2131.1.
Lipson, M.J., Grimmond, S., Best, M., Abramowitz, G., Coutts, A., Tapper, N., Baik, J.-J., Beyers, M., Blunn, L., Boussetta, S., Bou-Zeid, E., De Kauwe, M.G., de Munck, C., Demuzere, M., Fatichi, S., Fortuniak, K., Han, B.-S., Hendry, M.A., Kikegawa, Y., Kondo, H., Lee, D.-I., Lee, S.-H., Lemonsu, A., Machado, T., Manoli, G., Martilli, A., Masson, V., McNorton, J., Meili, N., Meyer, D., Nice, K.A., Oleson, K.W., Park, S.-B., Roth, M., Schoetter, R., Simón-Moral, A., Steeneveld, G.-J., Sun, T., Takane, Y., Thatcher, M., Tsiringakis, A., Varentsov, M., Wang, C., Wang, Z.-H., Pitman, A.J., 2024. Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results. Q. J. R. Meteorol. Soc. 150 (758), 126–169. http://dx.doi.org/10.1002/qj.4589.

Lipson, M., Grimmond, S., Best, M., Chow, W.T.L., Christen, A., Chrysoulakis, N., Coutts, A., Crawford, B., Earl, S., Evans, J., Fortuniak, K., Heusinkveld, B.G., Hong, J.-W., Hong, J., Järvi, L., Jo, S., Kim, Y.-H., Kotthaus, S., Lee, K., Masson, V., McFadden, J.P., Michels, O., Pawlak, W., Roth, M., Sugawara, H., Tapper, N., Velasco, E., Ward, H.C., 2022. Harmonized gap-filled datasets from 20 urban flux tower sites. Earth Syst. Sci. Data 14 (11), 5157–5178. http://dx.doi.org/10.5194/essd-14-5157-2022.

Malevich, S.B., Klink, K., 2011. Relationships between snow and the wintertime minneapolis urban heat island. J. Appl. Meteorol. Clim. 50 (9), 1884–1894. http://dx.doi.org/10.1175/JAMC-D-11-05.1.

Masson, V., 2000. A physically-based scheme for the urban energy budget in atmospheric models. Bound. Layer Meteorol. 94, 357–397. http://dx.doi.org/10.1023/A:1002463829265.

Mori, K., Sato, T., 2015. Evaluating the role of snow cover in urban canopy layer on the urban heat island in sapporo, japan with a regional climate model. J. Meteorol. Soc. Jpn. 93 (5), 581–592. http://dx.doi.org/10.2151/jmsj.2015-039.

Mote, T.L., 2008. On the role of snow cover in depressing air temperature. J. Appl. Meteorol. Clim. 47 (7), 2008–2022. http://dx.doi.org/10.1175/2007JAMC1823.1.

Ruosteenoja, K., Jylhä, K., 2021. Projected climate change in Finland during the 21st century calculated from CMIP6 model simulations. Geophysica 56, URL https://www.geophysica.fi/article/id-56-3-ruosteenoja/.

Rutter, N., Essery, R., Pomeroy, J., Altimir, N., Andreadis, K., Baker, I., Barr, A., Bartlett, P., Boone, A., Deng, H., Douville, H., Dutra, E., Elder, K., Ellis, C., Feng, X., Gelfan, A., Goodbody, A., Gusev, Y., Gustafsson, D., Hellström, R., Hirabayashi, Y., Hirota, T., Jonas, T., Koren, V., Kuragina, A., Lettenmaier, D., Li, W.-P., Luce, C., Martin, E., Nasonova, O., Pumpanen, J., Pyles, R.D., Samuelsson, P., Sandells, M., Schädler, G., Shmakin, A., Smirnova, T.G., Stähli, M., Stöckli, R., Strasser, U., Su, H., Suzuki, K., Takata, K., Tanaka, K., Thompson, E., Vesala, T., Viterbo, P., Wiltshire, A., Xia, K., Xue, Y., Yamazaki, T., 2009. Evaluation of forest snow processes models (SnowMIP2). J. Geophys. Res.: Atmospheres 114 (D6), http://dx.doi.org/10.1029/2008JD011063.

Swenson, S.C., Lawrence, D.M., 2012. A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance. J. Geophys. Res.: Atmospheres 117 (D21), D21107. http://dx.doi.org/10.1029/2012JD018178.

Varentsov, M., Konstantinov, P., Baklanov, A., Esau, I., Miles, V., Davy, R., 2018. Anthropogenic and natural drivers of a strong winter urban heat island in a typical Arctic city. Atmos. Chem. Phys. 18 (23), 17573–17587. http://dx.doi.org/10.5194/acp-18-17573-2018.

Vikhamar-Schuler, D., Hanssen-Bauer, I., Førland, E., 2010. Long-term climate trends of Finnmarksvidda, Northern-Norway. Technical Report, Norwegian Meteorological Institute, URL https://www.met.no/publikasjoner/met-report/met-report-2010/_/attachment/download/822ad8c7-a542-42a8-aa8e-d826a16b168a:0be8eef9ad5abde5a9085313a97c6033013e26a4/MET-report-06-2010.pdf.

Wang, Y.P., Kowalczyk, E., Leuning, R., Abramowitz, G., Raupach, M.R., Pak, B., van Gorsel, E., Luhar, A., 2011. Diagnosing errors in a land surface model (CABLE) in the time and frequency domains. J. Geophys. Res.: Biogeosciences 116 (G1), G01034. http://dx.doi.org/10.1029/2010JG001385.

Wang, X., Zender, C.S., 2011. Arctic and antarctic diurnal and seasonal variations of snow albedo from multiyear baseline surface radiation network measurements. J. Geophys. Res.: Earth Surf. 116 (F3), F03008. http://dx.doi.org/10.1029/2010JF001864.

Wouters, H., Demuzere, M., Ridder, K.D., van Lipzig, N.P.M., 2015. The impact of impervious water-storage parametrization on urban climate modelling. Urban Clim. 11, 24–50. http://dx.doi.org/10.1016/j.uclim.2014.11.005.

Zhang, F., Zhang, L., Pan, M., Zhong, X., Zhao, E., Wang, Y., Du, C., 2022. Black carbon and mineral dust in snow cover across a typical city of Northeast China. Sci. Total Environ. 807, 150397. http://dx.doi.org/10.1016/j.scitotenv.2021.150397.