Drivers of UHI spatial variations

We analysed the annual mean ΔT measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board of the Aqua satellite from 2003 to 2013 for 39 cities across Mainland China (Fig. 1a). Being a proxy for heat release from anthropogenic sources and from solar energy stored in buildings, population is a predictor frequently used to explain city-to-city variations in the nighttime UHI intensity observed by satellites4,15,16,17 and by weather stations2. Our results indicate that, in contrast to studies reported for other regions of the world4,15, city population is actually a poor predictor of the nighttime ΔT variations among these cities (Fig. 1b). Shanghai, located in southeast China and the largest city we analysed (population 14 million), shows a weak surface UHI (1.5 K), whereas Hami, a small city (population 0.45 million) in northern China, exhibits one of the strongest surface UHIs (5.0 K). The overall correlation between population and nighttime ΔT is not statistically significant (P=0.17).

Figure 1: Nighttime MODIS surface urban heat island intensity in mainland China from 2003 to 2013. (a) Spatial variation of the annual-mean nighttime MODIS-derived surface Urban Heat Island (UHI) across mainland China (K). (b) Surface UHI intensity relationship with population. (c) Surface UHI dependence on urban–rural AOD difference. Red, blue and black circles indicate large (population >7 million), medium (3–7 million) and small cities (<3 million), respectively. The two thick lines in a mark the boundary of three Köppen–Geiger climate zones (humid, semi-humid and semi-arid from south to north). Lines in b,c are linear regression with regression statistics noted. Errors on the regression parameters are 95% confidence bounds. Full size image

Another unusual feature is the diurnal variations of the surface UHI. The MODIS-derived nighttime ΔT (3.4±0.2 K, mean±1 s.e.) is higher than the daytime value (2.1±0.3 K; P<0.001). The diurnal contrast is especially striking for cities in the semi-arid climate, where the mean nighttime ΔT is 4.0±0.4 K, but the daytime ΔT is only 0.3±0.5 K (P<0.001; Fig. 2c,f). The diurnal patterns in China differ from those observed by satellites for North America18, Europe19,20, South America20 and Oceania20 where the daytime surface UHI is stronger than the nighttime UHI and where the UHI of semi-arid cities is generally weak at night4,17,18. Our results are broadly consistent with the UHI spatial pattern documented for China in a previous study using a shorter MODIS time series21, although our UHI intensity is generally greater, because we only used pure urban and rural pixels to calculate ΔT.

Figure 2: Urban Heat Island attribution and comparison with satellite observation. Daytime Urban Heat Island (UHI) attribution in (a) humid region, (b) semi-humid region and (c) semi-arid region. Nighttime UHI attribution in (d) humid region, (e) semi-humid region and (f) semi-arid region. The red bar denotes the MODIS-derived UHI. The green bar denotes the UHI intensity determined online by the CLM model. The blue bar denotes the intensity calculated offline as the sum of the component contributions (changes in albedo as part of the radiation term (yellow bar), efficiency of sensible heat convection (magenta bar), evaporation (black bar), heat storage (orange bar) and anthropogenic heat release (pink bar) equation (2)) but excludes the haze contribution. Error bars are ±1 s.e. Full size image

One explanation for these unique surface UHI patterns is related to haze pollution. We find that the spatial variations of the annual mean nighttime ΔT are significantly correlated with the difference in AOD between urban areas and the adjacent rural land (Fig. 1c; ΔAOD, urban AOD minus rural AOD; P<0.01). Significantly positive correlation is also found between the summer nighttime ΔT and ΔAOD (P<0.01). Cities having a thicker haze layer than the surrounding rural environment tend to display a stronger UHI. We use ΔAOD, because ΔT is also a perturbation signal in reference to the rural background. The AOD itself is not a good predictor of the ΔT variations (P=0.48). Only after controlling for ΔAOD does ΔT show significant dependence on population (P<0.01).

There is no evidence of haze enhancement on the daytime ΔT. The correlation between the annual daytime ΔT and ΔAOD is poor (P=0.43; Fig. 3). Repeating the correlation analysis for the summer season reveals similarly poor correlation (P=0.50). Instead, the most important factors explaining the daytime ΔT variations are population, urban–rural difference in normalized difference in vegetation index and cropland fraction of the rural background (P-values<0.001). Annual mean precipitation exerts a strong control on the daytime ΔT in North America (P<0.001; ref. 3) but a weak control in China (P=0.06). This regional difference can be explained by the fact that cropland is a more prominent non-urban land cover in China than in North America. Irrigation is commonplace in China, with 48% of the farmland receiving water from irrigation in addition to water supplied by rain22. Domesticated plants supported by irrigation water do not behave in the same way as natural ecosystems in terms of surface energy exchanges. After excluding cities whose adjacent rural area consists of >50% cropland pixels, precipitation becomes a significant controlling factor (linear correlation=0.57, P<0.01, number of observations=21). The UHI dependence on precipitation and irrigation highlights the important role of the rural background environment in calculating ΔT.

Figure 3: Variance of the annual mean daytime and nighttime ΔT explained by different biophysical drivers. ΔAOD, difference in aerosol optical depth; ΔNDVI, urban-rural difference in normalized difference in vegetation index; P, population; ΔWSA, difference in white sky albedo; CF, crop fraction; MAP, annual mean precipitation; MAT, annual mean air temperature. Positive correlations are shown in the upper panel and negative correlations in the lower panels. Confidence levels are denoted by *P<0.01 and **P<0.001. Full size image

Attribution of the haze effect

Figure 1c can be viewed as empirical evidence supporting the long-held hypothesis that urban haze pollution is a contributor that intensifies the UHI2. We now make a quantitative attribution of the haze contribution to the nighttime ΔT by combining climate model calculations with analysis of surface longwave radiation observations. The surface radiation data are used in conjunction with the surface longwave radiation calculated by an atmospheric data assimilation model, to obtain the sensitivity of L ↓ to AOD, that is, the amount of enhancement in L ↓ in response to a unit increase in AOD (Methods). Here, L ↓ is the downward longwave radiation received by the surface including emissions and scattering of air molecules and aerosols. In the climate model, the urban land is parameterized as a separate land unit at the subgrid level. We force the model with an assimilated atmosphere and save the surface energy balance variables of urban and non-urban subgrid land units for offline UHI attribution4. The attribution method separates the contributions of external radiative forcing, energy redistribution via aerodynamic resistance-associated sensible heat convection and energy redistribution via evaporation23.

In this framework, the aerosol effect is an external forcing similar to anthropogenic heat release and to changes in the surface shortwave radiation arising from the urban–rural surface albedo difference, and can be expressed as,

where (ΔT) h is haze contribution to the UHI intensity, λ 0 (≈0.20 K m2 W−1) is the local intrinsic climate sensitivity, f is a dimensionless energy redistribution factor and ΔL ↓ is the urban–rural contrast in L ↓ calculated as the product of the satellite-observed ΔAOD and the longwave radiation sensitivity to AOD. Our AOD sensitivity values (Table 1) fall in the range of those calculated with radiative transfer models24,25,26,27. According to the observations of a ground-based aerosol remote-sensing network28, the aerosol Ångström exponent is smaller in the semi-arid northwest Chinese cities, indicating larger particle sizes, than in cities in the humid central and eastern China. The sensitivity for the semi-arid climate zone is much higher than for the humid climate zone, confirming a stronger longwave radiative forcing of coarser particles11,25. Our estimates of ΔL ↓ , ∼1.1 and 8.0 W m−2 for the cities in the humid and semi-arid climate zone, respectively, are lower than those reported from paired observations at urban and rural sites29,30, because we did not consider the L ↓ enhancement caused by a warmer urban boundary layer31 and emissions from urban canopy walls32. In the model domain, L ↓ represents the downward longwave radiation incident on a reference plane above the urban canopy, which is the first model grid height.

Table 1 Estimate of the haze contribution to the night-time UHI for three climate zones. Full size table

We estimate that the haze contribution to the nighttime ΔT is 0.70±0.26 K (mean±1 s.e.) for the semi-arid cities and is small for the other two groups of cities (Table 1). The larger (ΔT) h in semi-arid climate is a result of less efficient energy redistribution (smaller f-values due to larger aerodynamic resistance and Bowen ratio; equation (5)) between the land and the atmosphere, a larger urban–rural contrast in pollution level and a stronger longwave radiative forcing of coarser aerosols.

In the semi-arid climate zone in China, both the urban and its adjacent rural area are affected by coarse mineral particles transported from the Taklimakan Desert and the Gobi Desert33. In the urban environment, road fugitive dust, construction-derived dust, and domestic heating and cooking are additional sources of coarse mode aerosols, explaining why the ratio of PM 10 to PM 2.5 concentration is greater in the semi-arid cities in Northwest China than in the humid cities in South China34,35,36. The urban–rural AOD difference observed here appears to result from these urban anthropogenic sources.

The haze contribution to the daytime ΔT is uncertain because of the opposing effects aerosols have on the surface shortwave and longwave radiation11, but the lack of correlation between the daytime ΔT and ΔAOD (Fig. 3) suggests that it may be negligible. This inference is consistent with the model results. The daytime ΔT determined online by the Community Land Model (CLM) model, denoted as CLM in Fig. 2, is in good agreement with the MODIS-derived values (denoted as MODIS) for the cities in humid and semi-humid climates (Fig. 2a,b). For the cities in semi-arid climate, the model online result overestimates the observation, but the offline diagnostic calculation (denoted as Calculated), which is the sum of all the terms in equation (2), shows a good agreement (Fig. 2c). The overall agreement leaves little room for an additional contribution due to the haze effect, implying that the relative reduction of the shortwave radiation in the city in reference to the rural background is roughly equal to the relative enhancement of the longwave radiation. This offsetting effect of aerosols on radiation has been reported previously by ref. 37 and the year-long observations at an urban–rural site pair support this interpretation29. Comparison of the model results and the MODIS observation for the summer also reveals very good agreement for the daytime.

According to the attribution diagnostics, the main contributor to the daytime UHI in the humid climate is the reduction in sensible heat convection efficiency of the urban land, not a reduction in evaporation. In the semi-arid climate, the role of convection is reversed, contributing to a cooling signal. These results are in agreement with those obtained previously for cities in North America4.

In contrast to the daytime results, the modelled nighttime ΔT is too low in comparison with the MODIS observations (Fig. 2d–f), even though the same model is able to reproduce the observations in North America. The atmosphere in the model domain is free of haze pollution. With the inclusion of the haze contributions calculated offline (Table 1), the modeled ΔT is still biased low. One reason for the low bias is that the model scheme does not have a complete accounting of all sources of anthropogenic heat release in the urban land38. The anthropogenic heat flux is an important contributor to the nighttime UHI21. Another possibility is that equation (1) has omitted a dynamic mechanism associated with the haze pollution. Aerosols are known to warm the atmosphere12,39, potentially making the boundary layer air above the urban land more stable than that above the rural land and thus reducing the efficiency of energy dissipation from the urban surface to the atmosphere. The end result is an amplified UHI intensity. It appears that this stability mechanism is especially strong in the humid climate, as suggested by the large model bias error (Fig. 2d), although a definite answer will require an improved anthropogenic heat parameterization for China.

Our study implies that abatement of haze pollution has a co-benefit of reducing heat stress on urban residents. The UHI intensity is a perturbation signal in reference to the rural background temperature. A complete assessment of the haze effect must recognize that the rural atmosphere is also changing. The MODIS observation indicates that the rural AOD in China is 0.20 and 0.53, greater than that in North America in the semi-arid and the humid climate zones, respectively. The data in Table 1 suggest that the rural land in China may be receiving ∼15 W m−2 more longwave radiation energy at night than under haze-free conditions. Our subgrid energy balance analysis is not suited for quantifying the impact of the rural background change, because the change signal is regional in scale. The nighttime temperature in China increased at 0.47 K per decade from 1979 to 2012 (ref. 40), which is roughly twice the global mean temperature trend for the same time period41. We hypothesize that haze pollution has contributed to the accelerated warming in China. If the hypothesis is proved valid, pollution abatement should also relieve heat stress on rural populations.