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Strong contributions of local background climate to urban heat islands


The urban heat island (UHI), a common phenomenon in which surface temperatures are higher in urban areas than in surrounding rural areas, represents one of the most significant human-induced changes to Earth’s surface climate1,2. Even though they are localized hotspots in the landscape, UHIs have a profound impact on the lives of urban residents, who comprise more than half of the world’s population3. A barrier to UHI mitigation is the lack of quantitative attribution of the various contributions to UHI intensity4 (expressed as the temperature difference between urban and rural areas, ΔT). A common perception is that reduction in evaporative cooling in urban land is the dominant driver of ΔT (ref. 5). Here we use a climate model to show that, for cities across North America, geographic variations in daytime ΔT are largely explained by variations in the efficiency with which urban and rural areas convect heat to the lower atmosphere. If urban areas are aerodynamically smoother than surrounding rural areas, urban heat dissipation is relatively less efficient and urban warming occurs (and vice versa). This convection effect depends on the local background climate, increasing daytime ΔT by 3.0 ± 0.3 kelvin (mean and standard error) in humid climates but decreasing ΔT by 1.5 ± 0.2 kelvin in dry climates. In the humid eastern United States, there is evidence of higher ΔT in drier years. These relationships imply that UHIs will exacerbate heatwave stress on human health in wet climates where high temperature effects are already compounded by high air humidity6,7 and in drier years when positive temperature anomalies may be reinforced by a precipitation–temperature feedback8. Our results support albedo management as a viable means of reducing ΔT on large scales9,10.

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Figure 1: Precipitation and population influences on MODIS-derived annual-mean UHI intensity.
Figure 2: Attribution of UHI intensity in three Köppen–Geiger climate zones.
Figure 3: Relationship between model-predicted daytime ΔT and precipitation among the cities.
Figure 4: Temporal sensitivity of UHI intensity to precipitation.


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This research was supported by the Ministry of Education of China (grant PCSIRT), the Yale Climate and Energy Institute, the Yale Institute of Biospheric Studies, and a Yale University Graduate Fellowship. K.O. acknowledges support from NASA grant NNX10AK79G (the SIMMER project) and the NCAR WCIASP. NCAR is sponsored by the US National Science Foundation. The model simulations were supported by the Yale University Faculty of Arts and Sciences High Performance Computing Center.

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Authors and Affiliations



X.L. designed the research. L.Z. carried out the model simulation and data analysis. R.B.S. contributed ideas to the research design. K.O. contributed ideas to the model simulation. X.L. and L.Z. drafted the manuscript.

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Correspondence to Xuhui Lee.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Precipitation and population influences on MODIS-derived annual mean UHI intensity.

a, Dependence of daytime UHI on population size (r = 0.27, P = 0.027). b, Dependence of night-time UHI on precipitation (r = 0.05, P = 0.70). Red, green and blue symbols denote cities with annual mean precipitations less than 500 mm, between 500 and 1,100 mm, and over 1,100 mm, respectively. The solid line in a is the linear regression fit to the data. Parameter bounds for the regression slope are the 95% confidence interval.

Extended Data Figure 2 Time series of MODIS and model-predicted daytime ΔT and annual precipitation.

a, Billings, Montana. b, Richmond, Virginia.

Extended Data Figure 3 Relationship between interannual variations in model-predicted daytime ΔT and precipitation.

a, Correlation of ΔT and the individual biophysical components with annual precipitation at Billings, Montana. b, Same as in a except for Richmond, Virginia. c, ΔT–precipitation temporal covariance explained by different biophysical factors at Billings, Montana. d, Same as in c except for Richmond, Virginia. Lines are best linear regression fits to the data points. Parameter bounds for the regression slope are the 95% confidence interval.

Extended Data Figure 4 Albedo influence on annual mean night-time UHI intensity.

a, Dependence of night-time MODIS-derived UHI on white-sky albedo difference (that is, urban albedo minus rural albedo; r = −0.60, P < 0.001). b, Dependence of night-time modelled UHI on modelled albedo difference (r = −0.56, P < 0.001 excluding four outliers; r = −0.18, P = 0.16 with all data points). The four outliers in the upper right corner of b are coastal cities (Olympia, Washington; Seattle, Washington; Salem, Oregon; Vancouver, British Columbia) that have high biases of the modelled ΔT compared to the MODIS ΔT. Lines are linear regression fits to the data. Parameter bounds for the regression slope are the 95% confidence interval.

Extended Data Table 1 Urban parameters of a city pair in CLM
Extended Data Table 2 Size statistics for selected cities in the United States and in Canada

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Zhao, L., Lee, X., Smith, R. et al. Strong contributions of local background climate to urban heat islands. Nature 511, 216–219 (2014).

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