The United Nations world urbanization prospects 2009 report points out, that at present more than 50% of the world’s population is living in urban area, and it is expected that by 2050 this figure will reach 70% [ UN, 2009 ]. Therefore, there is need to pay more attention to the impacts of urban heat island effects on future climate change.

1. Observed contribution of urban heat island effect on warming

The impacts of urban heat island effect on climate change in China and the world have been mostly conducted with meteorological station data for about last 50 years. Both the urban heat island effect and its contribution to climate warming have been calculated. For the quantification of urban heat island effect, one method is that observation stations are divided into degrees in accordance with city population, corresponding to the calculated population changes in different periods. Another method is that differences between the temperature at urban station and rural (countryside) station (or nearby sea surface temperature, SST). A recent study based on remote sensing data shows that heat island effect of the global mean daily temperature is 2.6°C in summer, and 1.4°C in winter [ Zhang et al., 2010 ]. Research on China [ Zhao, 1991 ; Ren et al ., 2008  ;  Jones et al ., 2008 ] indicates that urban heat island effect contributes to climate warming by about 30%. Table 1 summarizes the contribution of urbanization to the warming for some studies in China [ Zhao, 2011 ]. Because many gauge stations in China are located in urban regions, they are influenced by urbanization processes more clearly than before. Urban heat island effect is strengthening climate warming. Early research found that the mean warming was 0.06°C per decade in China for 1951–1989, in which urbanization caused the mean warming of 0.05°C per decade and accounted for 83% of the total warming [ Zhao, 1991 ]. It means that the key warming action was from urban heat island effect during 1950s–1980s. Recent study indicates that temperature increasing was obvious in China for 1961–2004, the mean warming was 0.06–0.09°C per decade up to 0.10°C per decade in some significant areas. The annual mean warming rate due to urban heat island effect was 27%, and 18%–38% for the four seasons. It means that urban heat island effect contributed to 1/5–1/3 of the total warming in China in the last 50 years. Warming due to urban heat island effect in China was significant [ Ren et al., 2008 ]. A new investigation estimated urban heat island effect in comparison with the nearby SST which was not influenced by urbanization. The warming trend in China for 1951–2004 was 0.22°C per decade. At the same time, the warming trend of SST was 0.14°C per decade. Urban heat island effect caused warming of 0.08°C per decade, which was 36% of the total warming. Urban heat island effect in China was more obvious than before due to urban population increase, urban areas extension, social sphere and economy development. The main warming trend in China for the last several decades was from the anthropogenic emissions [ Jones et al., 2008 ]. Summarizing those investigations, the urbanization in China for the last several decades contributed to the warming by 1/5–1/3. The warming caused by urbanization in China cannot be neglected. The warming contributions by urbanization in China are stronger than those in developed countries [ Jones et al ., 2008  ;  Zhao, 2011 ]. It needs to be pointed out that a small warming appeared during 1950s–1980s in China, and the warming by urban heat island effects in China was about 80% of the total. However, it is different for the last 20 years of the 20th century. Although urban heat island effect strengthened in this period, its contribution was not as high as that in 1950s–1980s due to the significantly increased total warming (Table 1 ).

Table 1. Contribution rates of urban heat island effect on warming in China
Author Number of stations Time series (years) Contribution rate (%)
Zhao [1991] 160 1951–1989 (39) 83
Ren et al. [2008] 752 1961–2004 (44) 27
Jones et al. [2008] 728 1951–2004 (54) 36
728 1954–1983 (30) 81
728 1981–2004 (24) 18

2. Earth system model simulation on the impacts of urban heat island on future climate change

Recent studies were concentrated on the impacts of urban heat island on future climate by using climate models such as HadAM4 (UK) and CAM3.5 (USA). The former investigated urban heat island effect between doubled CO2  and the present concentration [ McCarthy et al., 2010 ]. The latter studied urban heat island effect between the IPCC SRES A2 scenario and the present day [ Oleson et al., 2010 ]. The common result is that urban heat island effect on climate may not be static. Therefore, they proposed that climate models should nest urban models.

Climate between urban and rural areas were contrasted by using the earth system model CCSM4 of CMIP5 (Coupled Model Intercomparison Project phase 5) with several scenarios [ Oleson, 2012 ]. The CCSM4 is an earth system model with land model (CLM4) which includes the parameterizations of urban surface types. Based on it, an urbanization model is nested to CLM4, named CLMU. The urbanization model is a three-dimensional model with parameterization processes, i.e., heights of roofs, sunlit walls, and shaded walls of buildings, as well as pervious land surface (such as grasslands of residential lawns and parks) and impervious (such as roads, parking lots and sidewalks) canyon floor. The urbanization model has 15 layers for temperature and hydrological cycle in vertical direction, and up to 5 additional layers for snow based on snow depth. Urban areas consider land features including building height and street width. According to the CMIP5 experiment design [ Taylor et al., 2012 ], the CCSM4 runs the historical experiments with both anthropogenic and natural forcings for 1850–2005, and three RCP (Representative Concentration Pathway) experiments (RCP8.5, RCP4.5 and RCP2.6). Five ensembles have been calculated for each experiment, respectively. The definition of urban heat island effect is the difference of surface air temperature between urban and rural regions. The simulated results indicate that: 1) Urban heat island effect in the 20th century caused the averaged urban warming by about 1.4°C compared with rural regions. 2) An obvious warming in the global, land, urban and rural regions will very likely appear for three periods of 2011–2030, 2046–2065, and 2080–2099 as projected by the model with RCPs, respectively. The model with RCP8.5 projects a global mean warming of 0.66°C, 1.91°C and 3.48°C for three periods relative to 1986–2005, respectively. A lower warming for RCP4.5 and RCP2.6 is noted. The global land warming is prominent. 3) Impacts of urban heat island on future mean temperature, maximum and minimum temperatures will continue to have a warming effect. 4) Differences of temperature between urban and rural areas for the three periods in RCP8.5 are more significant than in RCP2.6 and RCP4.5. Urban heat island effect decreases as time goes by due to more warming in rural areas than in urban areas (Table 2 ).

Table 2. Ensemble mean global (GL), land, urban (U), and rural (R) 2-m air temperature, and urban minus rural temperature for 1850–1869 and 1986–2005 from the history simulations, and those results for three future time periods from RCP8.5, RCP4.5, and RCP2.6 simulations [Oleson, 2012 ] (unit: °C)
Scenario Period 2-m air temperature Urban minus rural
GL Land U R Ta Tmax Tmin
History 1850–1869 13.34 7.39 17.51 16.06 1.45 0.56 2.02
1986–2005 14.30 8.56 18.35 16.93 1.42 0.55 1.98
RCP8.5 2011–2030 0.66 0.84 0.72 0.73 1.41 0.53 1.96
2046–2065 1.91 2.48 2.12 2.16 1.38 0.48 1.93
2080–2099 3.48 4.48 3.75 3.82 1.35 0.42 1.91
RCP4.5 2011–2030 0.58 0.75 0.65 0.65 1.42 0.53 1.98
2046–2065 1.29 1.65 1.44 1.43 1.43 0.53 2.00
2080–2099 1.62 2.03 1.76 1.75 1.43 0.49 2.00
RCP2.6 2011–2030 0.60 0.77 0.68 0.68 1.42 0.53 1.97
2046–2065 0.88 1.12 0.97 0.98 1.41 0.52 1.98
2080–2099 0.85 1.04 0.91 0.91 1.42 0.53 1.97

Note: Projections are relative to 1986–2005. Ta , Tmax , and Tmin  are daily mean, daily maximum and minimum temperatures, respectively

Further calculations focused on heat island effects in 11 regions of the world for December to February (DJF) and June to August (JJA) in the present day (1986–2005) and 2080–2099 relative to 1986–2005 as projected by the model with RCP8.5, respectively (Table 3 ). It is found that: 1) Whether in DJF or JJA of the present day, urban heat island has a warming effect as simulated by the model. In DJF for example, the lowest warming is in East Africa (0.94°C), the highest warming is in Central Asia (2.26°C). Results for East Asia which are related to China show a warming of 1.93°C in DJF and 1.29°C in JJA. 2) The model with RCP8.5 projects a significant warming in 11 regions for 2080–2099. The warming is 2.68–4.43°C in DJF and 3.20–4.87°C in JJA. The warming in East Asia is 3.69°C. 3) Under RCP8.5 for 2080–2099, urban heat island effect contributes slightly less to the warming, in 11 regions for DJF, except for East Africa. It decreases by 0.14°C in East Asia. For the same period, the model under RCP8.5 projects the warming to be decreasing in 6 regions for JJA and –0.10°C in East Asia [Oleson, 2012 ].

Table 3. Regional averages of 11 areas for the present (1986–2005) heat island (urban minus rural temperature), climate change (land air temperature) and heat island change (2080–2099 minus 1986–2005) in RCP8.5 simulation (unit: °C) [ Oleson, 2012 ]
Region Present heat island Climate change Heat island change
DJF JJA DJF JJA DJF JJA
NW America 1.80 1.58 3.80 4.49 –0.16 0.00
NE America 2.04 2.00 4.43 4.66 –0.17 0.11
Central America 1.01 1.06 3.35 3.62 –0.13 –0.11
South America 1.13 1.60 3.65 3.64 –0.08 –0.01
EU 1.35 1.60 2.68 5.06 –0.16 0.16
West Africa 1.86 1.02 3.75 3.52 –0.11 –0.06
Middle East 1.04 1.32 3.80 4.87 –0.10 0.01
East Africa 0.94 1.18 3.35 3.39 0.01 0.00
Central Asia 2.26 1.14 4.19 3.71 –0.02 –0.06
East Asia 1.93 1.29 3.69 3.69 –0.14 –0.10
AU-NZ 1.07 1.44 3.52 3.20 –0.09 –0.13

Note: AU-NZ refers to Australia and New Zealand

3. Evidence of regional climate models to simulate urban heat island effect

Kusaka et al. [2012] tested urban heat island effect by a regional climate model WRF. WRF has a horizontal resolution of 4 km and a vertical resolution of 35 layers. A 1-layer urban cover model (UCM) over the Tokyo Metropolitan Area and a simple slab urban model (SLAB) are nested to WRF, respectively. UCM included urban features and distribution of streets. The multi-layer thermodynamic equation was used to calculate temperature and heat flux on roofs, walls and roads. SLAB calculated temperature by surface thermodynamic equation. The model simulated climate change in August of 2004–2007. The results simulated by WRF nested to UCM or SLAB were compared with the observations, respectively. Based on calculating the bias and root mean square error (RMSE) between simulations and observations, it is found that WRF with UCM are better than WRF with SLAB (Table 4 ). It means that regional climate model (such as WRF) nested to a complex city model (such as UCM) is better than nested to a simple one (such as SLAB). This result evinces from the other side that an earth system model (CCSM4) nested to an urban model is very important as Oleson [2012] provided.

Table 4. Surface temperature bias and RMSE over the entire analysis domain at indicated times for simulations by WRF nested to UCM or SLAB and observations (unit: °C) [Kusaka et al., 2012]
City model Bias RMSE Bias at 05:00 RMSE at 05:00 Bias at 15:00 RMSE at 15:00
SLAB –0.6 (–0.3) 2.5 (2.8) –2.0 (–2.6) 3.1 (3.3) 0.6 (1.8) 2.4 (2.8)
UCM –0.4 (0.7) 2.1 (1.8) –0.1 (1.0) 2.0 (1.8) –0.8 (0.4) 2.4 (2.0)

Note: 05:00 and 15:00 are Japanese standard time; urban values are in the parentheses

Finally, it is to be emphasized that only a few studies are concentrated on the impacts of urban heat island effect on climate change by using earth system models and regional climate models nested to the complicated urban cover models. Therefore, the studies are tentative. How to design and nest city models is worth studying. Some research results need further analysis, examination, and confirmation.

Acknowledgements

This research was supported by the 973 Project (No. 2010CB950500) and National Natural Science Foundation (No. 41175066).

References

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