To investigate the mitigation potential of cool roofs, an additional set of simulations was conducted for each of the three climate scenarios (including current climate, RCP 4.5, and RCP 8.5). In this group of simulations (white roofs, here abbreviated as WHT), the roof albedo was increased to 0.88, the value of the US EPA Energy Star Solareflect coating material after 3 years of wear and aging (Georgescu et al., 2014). This value is slightly lower than the initial albedo of 0.89, indicating the material's high capability of maintaining high solar reflectance (https://downloads.energystar.gov/bi/qplist/roofs_prod_list.pdf?8ddd-02cf). The cooling benefit of the cool roofs was determined by comparing the urban surface temperature in the WHT simulation with that in the CTR simulation, where the roof albedo was kept as the default value (0.18–0.37), under each climate scenario.

The morphological properties were kept unchanged in the CTR and WHT simulations. Of the 57 selected cities, the average prescribed roof areal fraction in CLM 4.0 is 48.8 %, the average default roof albedo is 0.29, and the average citywide albedo is 0.18. After the deployment of white roofs, the average citywide albedo is increased to 0.47.

To help the reader visualize the changes brought by the cool roofs to the urban landscape, we created two animations using a 3-D point cloud and image mosaic dataset acquired by an unmanned aerial vehicle over an urban neighborhood in Switzerland (http://www.sensefly.com). In Animation Movie S1 in the Supplement, the roof pixels retain their natural reflectance values. In Animation Movie S2, these pixels were replaced with saturation reflectance values to simulate the Solareflect coating material.

It is not possible to directly evaluate the cooling benefit of street vegetation with CLM because vegetation is not explicitly represented in the urban land unit in CLM. Here, we used a simple two-endmember interpolation method to calculate the surface temperature change δT associated with street vegetation as δT=-V×ΔTC, where V is the areal fraction of street vegetation in the urban land unit and ΔTC is the UHI intensity from the CTR run. A negative δT indicates cooling effect, and vice versa. This simple linear method satisfies the two endmembers: at 0 % vegetation, there is no temperature reduction, and the UHI intensity is the original ΔT; at 100 % vegetation, the urban land would be completely converted to the rural landscape, and thus the cooling benefit should totally offset the original ΔT. The calculation assumes that street vegetation consists of native plant species having the same species compositions in the adjacent rural land. In CLM, the average tree-to-grass ratios in the surrounding rural land of selected cities are 1.8, 2.5, and 1.2 for the temperate, continental, and dry climate zones, respectively. These numbers are in line with the real urban forest-to-grass ratio estimated using remote sensing techniques (Myeong et al., 2001; Nowak and Greenfield, 2012). For each of the selected cities, V is the areal fraction of pervious surface in the urban land unit prescribed by the model. The average V value for these cities is 30 %.

This method yields the net cooling of all the biophysical effects associated with street vegetation, including changes in albedo, convection efficiency, evaporation, and storage. It should be noted here that this method may slightly overestimate the vegetation cooling benefit for two reasons. First, the UHIs from the CTR simulations also include anthropogenic heat contribution. According to the offline attribution results, the anthropogenic heat contribution is about 20 % (Fig. 1). Second, street vegetation is inherently not identical to its rural counterparts due to the radiative trapping by urban canyons via the “canyon effect” (Wang, 2014; Ryu et al., 2016). Therefore, the same amount of vegetation inside urban canyons may not induce as much cooling as it would in the adjacent rural land.

We calculated the cooling benefit of green roofs using the diagnostic surface energy balance data as δT=λ01+fΔaK↓+-λ01+f2Rn∗-Qs+QAH(Δf2), with Rn∗=1-aK↓+L↓-1-εL↓-εσTa4,f=λ0ρCpra1+1β,Δf2=-λ0ρCpraΔββ2, where T is the surface temperature, λ0 is the local climate sensitivity (=1/4εσT3), f is the energy redistribution factor, Rn∗ is the apparent net radiation, ρ is the air density, Cp is the specific heat of air at constant pressure, ra is the aerodynamic resistance to heat diffusion, β is the Bowen ratio, a is the surface albedo, K↓ is the incoming solar radiation, L↓ is the incoming longwave radiation, Qs is the stored heat, QAH is the anthropogenic heat release, ε is the surface emissivity, σ is the Stefan–Boltzmann constant, and Ta is the air temperature at the blending height. Equation (2) is obtained by differentiating the surface temperature solution given in Lee et al. (2011).

The Bowen ratio of green roofs was the average Bowen ratio value for grassland calculated at the subgrid level by the CLM for the grass plant functional type with its own soil column (Schultz et al., 2016). This implies that the water-conserving native grass is used for green roofs. The citywide Bowen ratio change was the area-weighted average of the green roof Bowen ratio and the Bowen ratio of other surface components in the urban land unit. The Bowen ratio change (Δβ) was the difference in Bowen ratio between urban land units with and without green roof installation.

The green roofs were assigned the average warm-season grassland albedo value of 0.20 (Bonan, 2008). The citywide albedo was the area-weighted average of the green roof albedo and the albedo of other surface components in the urban land unit. The albedo change (Δa) was the difference between the city with and without green roof installation. The average Δa of the 57 cities was 0.01.

The change in surface roughness induced by green roofs is minimal and thus was omitted in this analysis.

The same offline method was used to calculate the effect of reflective pavement as δT=-λ01+fΔaK↓, where Δa is the citywide albedo change associated with the use of reflective pavement. Only the impervious surface in each urban land unit is considered for conversion into reflective pavement. We increased the pavement albedo from the default value of 0.13 to 0.25 as recommended by Akbari et al. (2012). The areal fraction of pavement was on average 20.2 %. The average citywide albedo of the 57 cities was increased by 0.04. In order to further confirm the validity of the offline method, we conducted another side simulation to directly simulate the cooling benefits of reflective pavements under current climate. In this simulation, the albedos of impervious surface in each urban land unit of the 57 cities were raised to 0.25.

Adoption of the bright, reflective Solareflect roof material transforms almost all the cities into cold islands in both the current and the future climates, as indicated by the negative daytime ΔT (Figs. 3c and 4c). In other words, these cities become isolated “white oases” surrounded by a hot landscape. Under the RCP4.5 scenario, the average daytime surface temperature reduction in the WHT simulation is 6.5 ± 0.3 K for the period 2071–2100, in reference to the CTR simulation for the same period (Table S1 in the Supplement). There is a discernible spatial pattern in the oasis effect, which follows the climatological wetness gradient across the continent (Fig. 4c). The strongest oases are located in the dry region where the urban land is 6.2 ± 0.4 K cooler than the surrounding rural land (Figs. 3c and 4c). For comparison, the average oasis effect for the cities in the humid temperate region is -2.6 ± 0.5 K. The average ΔT of all the cities is -3.4 ± 0.3 K, enough to offset 80 % of the GHG warming. The cooling benefit of reflective roofs is similar under the RCP8.5 scenario (Table S1, Fig. 3c).

Our results indicate a stronger cooling effect of reflective roofs than those estimated by previous research. Studies using the WRF model showed a cooling effect of 0.2–2.0 K in screen-height air temperature with reflective roof materials of different albedos (Georgescu et al., 2014; Li et al., 2014; Stone et al., 2014). A case study of the California South Coast Air Basin area using a mesoscale model coupled with an urban airshed model showed that a citywide albedo increase by 0.30 can reduce the air temperature by up to 4.5 K in the mid-afternoon (Taha et al., 1997). However, Rosenzweig et al. (2009) reported very modest cooling effects (0.3–0.6 K) from cool roofs with albedo of 0.5 in New York City using a regional climate model. Studies using global climate models also report local cooling effects, ranging from a global average of 0.02 K (Jacobson and Ten Hoeve, 2012) to 0.4 K (Oleson et al., 2010c) decrease in air temperature with different prescribed cool roof albedos. Apparently, these estimates vary with the choice of cool roof albedo as well as the roof space available for cool roof installation (Akbari et al., 2012; Rosenzweig et al., 2009).

There are three reasons why we observe a stronger cooling effect compared to previous studies. First, the albedo of the cool roof in our study (0.88) is at the higher end of the range (0.3–0.9) used in previous modeling studies. Second, we define ΔT using radiative surface temperature rather than screen-height air temperature. The radiative surface temperature is more sensitive to the surface radiation balance than the air temperature. Using a mesoscale weather model, Li et al. (2014) showed that, when measured by radiative surface temperature, the cooling effect of cool roofs was 3–4 K, which is much higher than that of 0.5 K measured by 2 m air temperature. These results are consistent with observational studies using radiative surface temperature. Using LANDSAT satellite albedo and radiative temperature observations, Mackey et al. (2012) detected a surface cooling effect of 5.0 K with an albedo increase of 0.16. Gaffin et al. (2012) tested three generic white membranes in New York City and found that the radiative temperature of the white surfaces was on average 23.6 K lower than a black surface during peak sunlight times. Third, our analysis and results are restricted to midday hours (13:00 local time) and summer months (June–August), rather than the annual mean values as reported in previous studies. The rationale behind this restriction is that heat stress mitigation is a more pressing need in the summer (Rosenzweig et al., 2009). Because surface incoming solar radiation peaks in the summer noon time, the reflected solar radiation by cool roofs would peak as well. The cooling effect induced by cool roofs increases as the reflected solar radiation increases. Therefore, the reductions in ΔT reported here are likely the maximum potential of urban heat mitigation by cool roofs.

Our results are based on the albedo value of roof coating material after 3 years of use. The long-term weathering and aging problem of the reflective material has not been taken into account. Although the albedo of the Solareflect coating material decreases only slightly from 0.89 to 0.88 after 3 years, its long-term performance of retaining solar reflectance is unknown. In addition, none of the current roofing materials can last 96 years (2005–2100) while preserving high albedos. Re-coating or reroofing seems necessary for long-term use. Therefore, our results should be interpreted as an upper bound of the cooling potential of the cool roof strategy.

Street vegetation is less effective than cool roofs in terms of temperature reduction. The daytime surface temperature reduction is 1.3 ± 0.2 K in the temperate climate and 0.3 ± 0.1 K in the dry climate under the RCP4.5 scenario. The cooling under the RCP8.5 scenario is 1.1 ± 0.1 K in the temperate climate and 0.3 ± 0.1 K in the dry climate (Table S1). The cooling effect is weaker in the dry climate compared to that in the temperate climate. One factor that limits the mitigation potential is the area available for vegetation planting. In the model domain, the pervious surface that can be converted to vegetation cover occupies 25 to 45 % of the urban land. Our calculation assumes that street vegetation consists of native species that have adapted to local soil moisture conditions and can grow without additional water supply. Enhanced cooling brought by irrigation is not considered.

As mentioned previously, our method for calculating the cooling potential of street vegetation may slightly overestimate the cooling benefit because of the anthropogenic heat contribution to the UHI and the radiative trapping effects by urban canyons. Our offline attribution results show that anthropogenic heat contribution is about 20 % (Fig. 1). Ryu et al. (2016) showed that the trees in the urban canyon decreased the sensible heat flux and increased the latent heat flux by roughly a similar amount. Another potential source of uncertainty to this method is the street vegetation composition. Cities that have different vegetation composition from their surrounding rural landscapes may generate slightly different cooling than predicted here. However, integrating all these considerations into our results would not change the point that street vegetation reduces urban surface temperature less effectively in comparison to cool roofs.

The cooling effects of street vegetation estimated in this study are generally consistent with previous studies. Rosenzweig et al. (2009) found that planting trees in open spaces and along streets reduces the mid-afternoon air temperature by 0.2–0.6 K in neighborhoods of New York City. Luley and Bond (2002) reported that in their maximum scenario, in which all urban grass is replaced by trees, an up to 1.0 K reduction in air temperature is expected in Manhattan, New York City, on a summer afternoon. By forcing a mesoscale weather forecast model with atmospheric conditions generated by a global climate model, Stone et al. (2014) found that, under the business-as-usual climate change scenario, increasing green vegetation in urban public areas reduces the urban daily average air temperature by 0.1–0.3 K. Consistent with our results, they also reported a weaker vegetation cooling effect over a city in the dry climate compared to the cities in the temperate climate. In extreme scenarios, in which the urban center of Atlanta, Georgia, is fully substituted by forest, Stone et al. (2013) found a reduction in air temperature of 0.5–1.5 K. Generally, the cooling benefit varies with different cities because of the area available for tree planting. Our estimates fall at the higher end of the range reported previously, partly because the temperature difference is measured by surface radiative temperature.

Green roofs generate modest cooling. The average daytime surface temperature reduction is 1.6 ± 0.2 K for the 57 cities under the RCP4.5 and RCP8.5 scenarios (Fig. S1c and g; Table S1). According to our offline UHI attribution, the albedo effect of green roofs is negligible (Table S1). Even though the albedo of green roofs (0.20, the average warm-season grassland albedo; Bonan, 2008) is lower than the average default roof value (0.29), the citywide albedo is decreased only slightly (by 0.01) in comparison to the CTR simulations. This is in sharp contrast to the WHT simulations where the citywide albedo is increased by an average amount of 0.29. Therefore, the contribution of green roofs to the temperature reduction comes from enhanced evaporation. Once again, we assume in our calculation that water-conserving native grass is planted on the roofs to minimize irrigation demand; this is a fair assumption because water will be a scarce resource in future cities (McDonald et al., 2011). Cities that can afford rooftop irrigation (Georgescu et al., 2014; Bass et al., 2003) are expected to gain more cooling than predicted here.

These results are comparable with the green roof cooling effects reported by previous studies. Building-scale observational studies have shown a relatively large range of temperature reduction. A case study evaluating the “green” and “white” policy in Chicago, Illinois, found that green roofs can reduce the building-top temperature by 0.3–2.6 K according to the LANDSAT satellite observations (Mackey et al., 2012). Ismail et al. (2011) used an experimental approach on a test building in Malaysia and showed a 4.6 K reduction in the surface temperature around noontime between a green roof and a black bare roof. Mesoscale modeling studies have also shown cooling estimates ranging from 0.2 to 0.8 K if measured by air temperature (Rosenzweig et al., 2009; Stone et al., 2014) and 3 to 4 K if measured by surface temperature (Li et al., 2014). All these results, together with our study, confirm that a major factor in determining the mitigation potential of green roof is water availability.

Our results favor cool roofs as the preferred method for urban heat mitigation. Although empirical data are not yet available to validate the white oasis effect, some lessons can be drawn from studies of green oases. Local cooling has been observed for green oases as small as several hectares in area (Kai et al., 1997). A typical green oasis effect ranges from -1 to -7 K (Potchter et al., 2008), comparable in magnitude to the white oasis effect reported here. The strongest green oasis effect is also found in dry climates (Potchter et al., 2008). In the case of green oases, surface cooling by evaporation typically results in a stable inversion air layer above the ground, which would severely limit air pollution dispersion and worsen air quality. In contrast, unstable lapse conditions generally prevail over white oases (Fig. S2). In other words, implementation of cool roofs may decrease the dispersion capacity of urban air due to a reduction in the mixed layer depth (Georgescu et al., 2012), but not nearly to the extent of a stable stratification brought by the green oasis effect.

An explanation to this phenomenon can be achieved using the big-leaf model (Sellers et al., 1996) solution of surface temperature shown below: Ts-Ta=ra+rcρdcp⋅γRn-G-ρdcpD/(ra+rc)Δ+γ(ra+rc)/ra, where Ts is the surface temperature, Ta is the air temperature at a reference height, ra is the aerodynamic resistance, and rc is the surface resistance; Rn is the net radiation; G is the storage heat; D is the water vapor pressure deficit; Δ is the slope of the saturation vapor pressure curve; and γ is the psychrometric constant. Equation (7) shows that the sign of (Ts-Ta) is determined by the sign of the second numerator on the right-hand side,-ρdcpD/(ra+rc). In green oases in dry climates, D is a large number and the surface resistance is small due to irrigation, with both factors combined yielding a negative sign of the numerator of the term on the right side of the equation. Therefore, an inversion (Ts<Ta) typically prevails in the surface layer over a green oasis. Over white oases, the available energy (Rn-G) is reduced somewhat, but, because the surface resistance is very large in urban land, the numerator of the term on the right-hand side of Eq. (7) stays positive. Therefore, unstable lapse conditions (Ts>Ta) should be expected over white oases.