#urban_heat_islands
#physical_mechanisms
The 2014 study by Xuhui Lee from Nanjing University of Information Science and Technology, China, and coauthors investigated the drivers of urban heat island effect using 65 cities across North America.
Usually, urban heat island effect - expressed as the temperature difference between urban and rural areas, $\Delta$T - is considered to be primarily created by the reduced evaporative cooling over the urban surface.
However, Lee and coauthors showed that variations in $\Delta$T, across space, are rather created by variations in convective efficiency, which is determined by aerodynamic resistance, or surface roughness. The cities in humid regions are aerodynamically smoother than their surrounding rural areas, resulting in less efficient transfer of heat to the lower atmosphere. The cities in dry regions, on the country, are aerodynamically rougher than their surrounding rural areas, resulting in more efficient transfer of heat to the lower atmosphere. Therefore, the urban heat island effect is more important for the humid cities, and albedo management is an important means to mitigate urban heat island effect.
Background
The contributors to urban heat island effect includes (1) reduced evaporative cooling, (2) waste heat from human activities, (3) (sometimes) changed albedo, (4) daytime heat storage and nighttime heat release by buildlings, which is usually greater than by vegetation and soil, (5) energy redistribution through convection between the land surface and the atmospheric boundary layer. Among these, (1) and (5) are natural factors, and (2)(3)(4) are morphological and anthropogenic aspects of the city itself.
Method
The urban heat island effect was quantified in two ways. First, via the NASA MODIS satellite land surface temperature (LST) from 2003 to 2012 - note this is different from air temperature. Second, via a climate model (the Community Earth System Model) run at 0.23 x 0.31 degrees resolution to reflect individual cities from 1972 to 2004.
For the MODIS data, nine urban pixels (3x3) were selected from the city center, and were paired with 1-3 patches of 3x3 to 7x7 pixels in the surrounding rural land. Water pixels were excluded. [Isn't this too few?]
The climate model does not have a urban core definition, because the urban core and the rural areas are different land units, not spatially distinguished, in the same grid cell. [Now this clearly suggests potential biases in the attribution results due to model deficiencies. The study also used LST, and the findings would probably be less evident if air temperature were used]
Partition of the contributions from terms (1)-(5) were achieved via the method described in ref. 19. The partitioning equation is shown in the paper, and appears to depend on land surface model parameters. Aerodynamic resistance is calculated as a function of the thermal gradient between the temperature at the surface and at reference height, and the sensible heat flux.
Results
1. Simple regression across space between factors and the MODIS LST shows that annual mean nighttime $\Delta$T is positively correlated with the logarithm of population, but not significantly correlated with the climate (precipitation, solar radiation, temperature). On the other hand, the annual mean dayttime $\Delta$T is strongly positively correlated with precipitation, and weakly correlated with population.
Geographically, the visual effect is that the nighttime $\Delta$T does not differ much between the east and west, but the daytime $\Delta$T is clearly bigger for the eastern cities (Fig. 1).
These findings are also well-supported by past studies (refs 14,15,16,17)
[This result seems to suggest that nighttime $\Delta$T is mainly controlled by anthropogenic heat release, whereas daytime mainly by the natural evaporative and convective factors.]
2. The attribution plot, Fig. 2, shows that
(1) nighttime $\Delta$T is dominated by the evaporative term and the building storage term.
(2) for dayttime $\Delta$T, the evaporative term always contributed positively. The convective term contributed most positively in the southeastern U.S., moderately positively in the northern temperate zones, and negatively in the southeastern U.S. Interestingly, the radiation term (albedo) is negative in the southeastern U.S., suggesting that the cities increased the albedo of the surface and reflected more sunlight, but positive elsewhere.
3. Fig. 3 further shows the spatial correlations between the individual terms and precipitation. It is clear that the positive correlation between daytime $\Delta$T and precipitation is contributed mainly by the convection term. In fact, the covariance analysis shows that the convection term explains 200% of the covariance, whereas the other terms together explains -100%.
In rural lands in the humid areas, the aerodynamic roughness is greater, meaning lower aerodynamic resistance (39 s m-1) than the urban counterpart (62 s m-1). Lower aerodynamic resistance means greater convective efficiency.
In past studies, the urban cool island effect in dry regions was explained by the evaporative cooling of urban greenspace (refs 15, 16, 17). However, in the current results, a lot of the cooled pixels are vegetation free.
4. Fig. 4 differs from Figs. 1-3 by showing the temporal sensitivity of dayttime and nighttime $\Delta$T to precipitation. However, there is conflict between the MODIS data and the climate model with regard to whether the dayttime $\Delta$T in the majority of the cities is positively or negatively correlated with precipitation. The conflict may be due to the short length of the MODIS data, or the bias of the climate model. Nonetheless, the precipitation-induced cooling effect becomes more evident as annual mean precipitation increases, i.e. towards the east. This is clearly induced by evaporative cooling.
