4. R ESULTS AND DISCUSSION
4.3 M ETHODOLOGY DISCUSSION
4.3.1 T
HE1-D
BULK URBAN CANOPY MODELAs previously described in the methodology section, the 1-D bulk urban canopy model is a semi-empirical urban canopy parametrization (SURY) for atmospheric modelling, where urban canopy parameters are translated into bulk parameters. The bulk albedo approximation avoids explicit numerical computation of the complex trapping of radiation in urban canyons, and therefore reduces the computational demand compared to other canyon radiation models (Wouters et al., 2016).
The model performance strongly depends on the temperature quantity considered. Consequently, it is important to keep in mind that parameter settings that improve the UHI effects can lead to worse absolute temperatures, and the other way around. This is also the case for day-time temperatures vs. night-time temperatures, and land-surface temperatures vs. air temperatures (Wouters et al., 2016).
Wouters et al. (2016) investigated the model sensitivity of SURY for urban canopy parameter ranges in the local climate zones of compact low-rise and compact mid-rise cities in Stewart and Oke (2012).
They found that the change in response and performance to alterations in urban canopy parameters are generally restricted to the urban areas and do not affect the rural areas. An intermediate sensitivity was
found for the building height and height-to-width ratio, and roof fraction displayed the lowest sensitivity.
For both surface and atmospheric UHIs, the albedo sensitivity is high, although slightly lower than the sensitivity for thermal parameters during daytime. At night-time, the sensitivity is somewhat lower than for AHE, height-to-width ratio and roof fraction. Some of the model errors exceed the model sensitivity range with regard to uncertainty of the urban canopy parameters. This emphasizes that the majority of the model uncertainty may result from deficiencies in the land-surface module and other aspects of the coupled atmospheric model, and not a result of urban canopy parameter uncertainty (Wouters et al., 2016).
SURY allows for the verification of consistency between urban canopy parameters and bulk parameters, resulting in increased precision and comparison consistency in climate assessments for urban-climate research. However, the semi-explicit nature of SURY implies some limitations for complex physical urban processes. Specifically, SURY does not resolve the full heterogeneity of the urban canopy, and micro-scale dynamic and physical processes and features are therefore not represented. Instead, it assumes a homogeneous surface temperature of the urban canopy. Consequently, the model does not represent the varying temperatures among the different elements in the urban canopy resulting from shadowing and thermal and radiative properties (Wouters et al., 2016).
The 1-D atmospheric layer model is also based on many simplifications and assumptions. The temperature change with time is calculated using K-theory for turbulence and a simple relaxation time approach for advection (Krayenhoff and Voogt, 2010). For details see Appendix A1.10. The turbulent eddy diffusion coefficient for temperature is calculated assuming close to neutral atmospheric conditions and is dependent on the boundary layer height (assumed to be a time dependent input to the model) and the thermal roughness length. The assumption of near neutral conditions will possibly underestimate turbulence during strong convection. However, this simplification is in most cases of less importance than the choice of relaxation time for the advective processes. By using the displacement height and aerodynamical roughness of the city, the windspeed can be estimated using the rural wind speeds at the top of the atmospheric model (assumed to be a time dependent input to the model) and calculated for each model layer by assuming a logarithmic wind profile. Details of the calculations are given in Appendix A1.10 (Sorteberg, 2017).
4.3.2 D
EGREE-
DAYSThe degree-day approach is considered only as an approximation method, as many simplifying assumptions must be made. One of these assumptions is the use of average conditions of parameters such as internal temperatures, causal gains and air infiltrations rates (CIBSE, 2006). According to
Antunes et al. (2015), there are three main criticisms that should be emphasized when using degree days:
1) The use of outdoor ambient temperatures is by far the biggest limitation. With respect to this study, the temperature of the urban layer was used, which is likely an overestimate of the real temperature of the air that affects the buildings. 2) The way the degree-days methodology is calculated assumes steady state conditions, where each degree rise would result in an equal indoor temperature rise. There will also be a lot of other factors affecting the indoor temperature, such as fresh air loads and window shading.
3) Comfort levels experienced by building residents will differ from one region to the next, as buildings have varying levels of insulation and cooling technologies. Additionally, perceptions of thermal comfort are evaluated independent of age, health and activity levels, and it is false to assume that these factors will remain constant over time. As global warming drives the local urban temperatures up, the population is likely to acclimatize and adapt to the new temperature regimen. Consequently, people’s perception of thermal comfort will change and thereby affect the BPT.
Additionally, calculating degree-days using hourly temperatures does not imply that the hourly energy consumption can be accurately estimated. When performing a building energy analysis, summation of degree-days over a sufficient period of time is necessary to produce outputs of any real value.
The key to a credible cooling degree-day energy assessment lies in the definition of base temperature.
Base temperatures are calculated from the balance point temperature (BPT) and it considers the building size, configuration and available cooling technology for the region in question. As the base temperature is this study is not site specific, some uncertainty can be expected. Moreover, building size and configuration have not been assumed, leading to increased uncertainty. An additional problem for cooling energy calculations is that this needs to be defined specifically for each different type of cooling system. Personal preferences, acclimatization and specific building characteristics can also lead to variations in base temperatures, and these properties are likely to vary between the cities in this study.
This lack of objectivity means that it is not surprising to find a wide range of base temperatures around the world (Antunes et al., 2015).
4.3.3 E
NERGY CONSUMPTIONThe method for calculating cooling energy loads and consumption is a simplification that is based on a lot of assumptions, and caution should therefore be exercised when interpreting results. Although the base temperatures are calculated from the balance point temperature (BPT), which take into account building size, configuration and available cooling technology, parameters such as solar gains, internal gains, fresh air loads and humidity of the indoor air are building and climate specific. As this study aims to compare the weather-related energy demands of idealized buildings in different climate zones,
building-specific parameters have been overlooked, and a more comprehensive assessment of the energy demand should therefore be applied for specific sites and buildings.
The temperature dependency of the COP has been considered, but the fact that the COP will vary with the diurnal temperature cycle is overlooked. The study does not contemplate overnight cooling effects, which in theory can be incorporated into the base temperature. Yet, the energy consumption calculations account for occupancy periods. However, as many unknown factors are related to these issues, it is hard to establish a number of hours representative for both commercial and residential buildings across cultures, regions and climates.