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Semi‐idealised urban heat advection simulations using the WRF mesoscale model

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<mark>Journal publication date</mark>15/03/2019
<mark>Journal</mark>International Journal of Climatology
Issue number3
Number of pages14
Pages (from-to)1345-1358
Publication StatusPublished
Early online date8/10/18
<mark>Original language</mark>English


Urban heat advection (UHA) can extend the climatic impact of a city into the surrounding countryside. This may lead to an intensification of already well‐documented Urban Heat Island (UHI) impacts on health and infrastructure, and challenge the representativeness of long‐term reference temperature records taken near urban areas. However, previous UHA studies have been unable to accurately quantify surface‐level UHA due to challenges arising from complex urban land‐use patterns. To address this, the numerical Weather Research and Forecasting (WRF) mesoscale model coupled with the Building Energy Parameterization urban canopy scheme is used to simulate meteorological fields for idealised land‐use cases. Hypothetical square cities (up to 32 km in size) are simulated for a year's period. A time‐mean 2 m temperature field (representing the canopy UHI) shows that the mean UHI intensity (up to 4.3oC (s.d. 1.7 oC), wind speed < 3.9 m s‐1) is linearly related to the logarithm of city size. This finding, entirely derived from numerical modelling, is consistent with the log‐linear relationships previously found in the observational data of many cities in the world. A UHA methodology was then applied to the temperature fields to separate UHA from the UHI, with up to 2.9oC (s.d. 1.7oC) of UHA found downwind of the largest city size. For this hypothetical city size, an UHA intensity of 0.5oC is found up to 24 km downwind from the urban boundary. In addition, the UHA‐distance profiles along the central horizontal transect for various urban sizes are found to follow a scaling rule as a good approximation. As a result, the findings of this paper can be used as a starting point for climate impact assessments for areas surrounding urban areas without the need for complex, computation‐intensive simulations.