Urban Surface Energy Balance Models: Model Characteristics and Methodology for a Comparison Study - Meteorological and Air Quality Models for Urban Areas - page 86

phenomenon which is clearly attributable to human activity is the urban heat island
(UHI). Urban areas often are several degrees warmer than the surrounding country-
side, particularly at night under clear, calm conditions (Oke, 1973; Grimmond,
2007). Enhanced urban temperatures affect energy demand, air pollution concen-
trations and chemistry, water use, and human comfort, and have implications for
human health and well being. The development of sustainable cities over the next
century requires a clear understanding of how urban areas influence the local cli-
mate and increasingly, surface energy balance models are being used as tools in
urban design and performance evaluation (e.g. Hacker et al., 2004). A consequence
of warm surface temperatures is that air is more vigorously mixed upward, hence air
pollution dispersion modelling benefits from better representation of the urban sur-
face energy balance. Moreover, as atmospheric boundary layer motions (for heights
less than 1-2 km) are very sensitive to the surface energy balance, an improved
understanding of urban surface-atmosphere exchanges will better allow the impact
of cities on regional scale weather systems to be determined (Taha, 1999; Bornstein
and Lin, 2000).
The fundamental processes that need to be modelled are the surface-atmosphere
exchanges of heat, mass and momentum at the local-scale. In cities these exchanges
are altered by the materials and morphology of the urban environment, human
behaviour, and the addition of anthropogenic heat flux (Q F ) to the available energy:

Q ∗ + Q F = Q H + Q E + Q S

where Q ∗ is net all wave radiation, Q H is the turbulent sensible heat flux, Q E is
the turbulent latent heat flux and Q S is the net heat storage flux associated with

heating/cooling of this mass (gas, liquids, and solids).
Recently there has been a rapid increase in the number of land-atmosphere
exchange models that explicitly parameterize urban surfaces (see reviews of Brown,
2001; Best, 2006; Masson, 2006; Martilli, 2007; Lee and Park, 2008). These
have been developed with the aim of predicting temperatures at different spatial
scales, guiding more energy efficient design and construction, and improving air
quality, meteorological and regional climate forecasting. The models differ sig-
nificantly in the exchanges they explicitly consider and in the approach taken to
modelling each flux. Applications and evaluations illustrate that the inclusion of
even simple urban surface parameterizations leads to improved temperature pre-
dictions (e.g. Taha, 1999). However, while evaluations of individual models have
been undertaken, there has been no systematic evaluation addressing questions
such as:

• Do the models produce physically realistic simulations of urban heat exchange?

• How complex do parameterizations of heat exchange need to be to simulate phys-

ically realistic fluxes and temperatures?

• What are the costs (processing time, data requirements) versus the benefits

(improvements in model prediction) between different types of models?