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Impact of Anthropogenic Heat Release on Regional Climate in Three Vast Urban Agglomerations in China


doi: 10.1007/s00376-013-3041-z

  • We simulated the impact of anthropogenic heat release (AHR) on the regional climate in three vast city agglomerations in China using the Weather Research and Forecasting model with nested high-resolution modeling. Based on energy consumption and high-quality land use data, we designed two scenarios to represent no-AHR and current-AHR conditions. By comparing the results of the two numerical experiments, changes of surface air temperature and precipitation due to AHR were quantified and analyzed. We concluded that AHR increases the temperature in these urbanized areas by about 0.5C-1C, and this increase is more pronounced in winter than in other seasons. The inclusion of AHR enhances the convergence of water vapor over urbanized areas. Together with the warming of the lower troposphere and the enhancement of ascending motions caused by AHR, the average convective available potential energy in urbanized areas is increased. Rainfall amounts in summer over urbanized areas are likely to increase and regional precipitation patterns to be altered to some extent.
    摘要: We simulated the impact of anthropogenic heat release (AHR) on the regional climate in three vast city agglomerations in China using the Weather Research and Forecasting model with nested high-resolution modeling. Based on energy consumption and high-quality land use data, we designed two scenarios to represent no-AHR and current-AHR conditions. By comparing the results of the two numerical experiments, changes of surface air temperature and precipitation due to AHR were quantified and analyzed. We concluded that AHR increases the temperature in these urbanized areas by about 0.5C-1C, and this increase is more pronounced in winter than in other seasons. The inclusion of AHR enhances the convergence of water vapor over urbanized areas. Together with the warming of the lower troposphere and the enhancement of ascending motions caused by AHR, the average convective available potential energy in urbanized areas is increased. Rainfall amounts in summer over urbanized areas are likely to increase and regional precipitation patterns to be altered to some extent.
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Manuscript received: 01 March 2013
Manuscript revised: 27 April 2013
通讯作者: 陈斌, bchen63@163.com
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Impact of Anthropogenic Heat Release on Regional Climate in Three Vast Urban Agglomerations in China

    Corresponding author: WANG Jun; 
  • 1. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;
  • 2. University of Chinese Academy of Sciences, Beijing 100049
Fund Project:  This study was supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences (Grant No. XDA05090000), the National Key Program for Developing Basic Sciences of China (Grant No. 2009CB421401), the Special Fund for Meteorological Scientific Research in Public Interest (Grant No. GYHY201106028), and the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-EW-202). We also thank JIA Gensuo and HU Yonghong for supplying the highresolution land use data for China.

Abstract: We simulated the impact of anthropogenic heat release (AHR) on the regional climate in three vast city agglomerations in China using the Weather Research and Forecasting model with nested high-resolution modeling. Based on energy consumption and high-quality land use data, we designed two scenarios to represent no-AHR and current-AHR conditions. By comparing the results of the two numerical experiments, changes of surface air temperature and precipitation due to AHR were quantified and analyzed. We concluded that AHR increases the temperature in these urbanized areas by about 0.5C-1C, and this increase is more pronounced in winter than in other seasons. The inclusion of AHR enhances the convergence of water vapor over urbanized areas. Together with the warming of the lower troposphere and the enhancement of ascending motions caused by AHR, the average convective available potential energy in urbanized areas is increased. Rainfall amounts in summer over urbanized areas are likely to increase and regional precipitation patterns to be altered to some extent.

1. Introduction
  • Against the background of global climate change, associated regional warming and unusual rainfall anomalies have aroused the concern of both governments and the public. Heat waves in 2003 in Europe (Robine et al., 2008) and in 2010 in Russia (Barriopedro et al., 2011) support the notion that increasing greenhouse gas concentrations are likely to induce more unfavorable climate events in coming decades. At the regional or local scale, aside from greenhouse gas emissions, other human activities have a great influence on climate change and need to be investigated and quantified. As the population in urban areas continues to increase, concerns about urban climate change and the role of human activities in the urban environment have been raised in recent years. Most previous studies have focused on the effect of land use associated with urbanization on the regional or local climate (e.g., Shepherd and Burian, 2003; Shepherd, 2005; Chen et al., 2007; Lo et al., 2007; Lin et al., 2008, 2011; Shem and Shepherd, 2009; Zhang et al., 2009; Lu et al., 2010; Carter et al., 2011; Ao et al., 2011; Li et al., 2011; Zhang et al., 2011), but only a few have estimated the contribution of other climate forcing factors, such as anthropogenic heat release (AHR), on regional climate change.

    AHR is an emission related to energy consumption mainly in cities, and is generated from many sources, such as industrial and power plants, heat conduction from commercial and residential buildings, combustion in vehicles, and human metabolism (Sailor and Lu, 2004). The amount of energy consumed has considerably increased with economic growth. This energy is immediately converted to AHR and dissipated into the land-atmosphere system, thus contributing to changes in the climate. In certain circumstances, AHR can exceed the net radiation energy on the land surface. Pioneering efforts have used simple climate models to estimate the impact of AHR in climate simulations. For example, using early general circulation models and adapting the heat release to population density, (Washington, 1972) investigated the atmospheric effects of a large AHR, and indicated that the effect of thermal energy is small compared with the model's natural fluctuations. (Washington and Chervin, 1979) performed numerical experiments to study the climatic impact of thermal energy released along the east coast of the United States. They found that there was a greater temperature increase in January and a smaller but still significant increase in July. Pronounced changes in precipitation and soil moisture were restricted to the specific region of heat release. Unfortunately, these early studies applied only specific or unrealistic AHR values. Recently, some studies have used inventory-based and energy balance closure methods to estimate more realistic AHR values. With data from energy statistics, (Ichinose et al., 1999) suggested that the AHR in central Tokyo exceeded 400 W m-2 during the day; the winter maximum AHR even reached 1590 W m-2. Considering AHR as the residual of the land surface energy balance, (Offerle et al., 2005) suggested that the average AHR in a downtown area reached 32 W m-2 from October to March. Studies have also incorporated AHR as an additional source term in the land surface energy balance within numerical models to investigate AHR impacts (Ichinose et al., 1999; Fan and Sailor, 2005; Ohashi et al., 2007; de Munck et al., 2013). However, most of these studies only selected representative weather cases to detect the AHR effects. (Block et al., 2004) provided the first test of AHR effects on regional climate. However, this only performed numerical simulations for a winter period, and the climatological impacts of AHR in other seasons remained unclear. Some researchers have also explored AHR effects from a global perspective. Using current and future global inventories of AHR, (Flanner, 2009) showed statistically significant surface warming (0.4°C-0.9°C) caused by an AHR scenario in 2100. Most studies have either performed short-term numerical simulations or used global and regional climate models with relatively coarse resolution to examine the climatic effects of AHR. Few studies have conducted multi-year simulations with nested, fine spatial resolution to study the AHR impact on the regional climate in highly urbanized areas.

    China, particularly its three vast urban agglomerations, has experienced dramatic economic growth since the 1980s. This rapid development has been accompanied by increasing energy consumption. (Wu and Yang, 2013) analyzed the impact of urbanization in eastern China on surface warming and suggested that the temporal and spatial differences of the urban warming effect may be related to the variation of regional climate background and the change of AHR. (Feng et al., 2012) numerically simulated the effects of underlying surface changes from urbanization and AHR on the regional climate over all of China, based only on a semi-virtual design. The resolution of the model domain was 30 km, which is inadequate to describe many cities. (Feng et al., 2012) stated that the urban area was somewhat exaggerated compared with reality. Only three values of annual mean AHR were assigned to three urban types (industrial/commercial, high-density residential and low-density residential). The authors focused on the relative impacts of the underlying surface change from urbanization and AHR on the regional climate over a larger scale (all of China). Therefore, a compelling question remains: how much does AHR influence the local or regional climate in highly urbanized, extensive city agglomerations? In this work, we conducted nested, high-resolution modeling to explore the potential AHR impacts on the regional climate in three large Chinese city agglomerations. This paper is organized as follows. The data and methods are presented in section 2, followed by the main results in section 3, and discussion and conclusions in section 4.

2. Data and methods
  • The regional climate model used was the Weather Research and Forecasting (WRF) Model, coupled with the Urban Canopy Model (UCM) (Kusaka et al., 2001; Kusaka and Kimura, 2004). The UCM is a single-layer model used to calculate the energy and momentum between the urban surface and the atmosphere. It includes the influence of street canyons, the shadowing from buildings, and the reflection of radiation. It can estimate both the surface temperature of, and heat fluxes from, three surface types: roof, wall, and road (Kusaka et al., 2001).

    We designed multiple nested domains with horizontal resolutions of 30 km (D1; mesh size 100× 109), 10 km (D2; mesh size 145× 262), and 3.3 km (D3, D4, and D5; mesh sizes 136× 124, 142× 124, and 148× 127) (Fig. 1). D3, D4, and D5 represent the three vast city agglomerations (Pearl River Delta, Yangtze River Delta, and Jing-Jin-Ji metropolitan area). National Centers for Environmental Prediction Global Final Analysis 6-h data (Stunder, 1997) were used for the first-guess initial field and lateral boundary conditions. The deep soil temperature and sea surface temperature were updated. The simulation period was from 0000 UTC 01 December 2006 to 0000 UTC 01 January 2010. The first month was considered as the spin-up period.

    Since we assumed that AHR is mainly from human activities in urban areas, the accuracy of the land-use type classification, especially for urban land, was crucial for investigating the AHR impact. The land use dataset used was derived from multi-source land cover products, including the Moderate Resolution Imaging Spectroradiometer land cover datasets and the Chinese land use datasets (Hu and Jia, 2010). The ultimate goal of this dataset is to improve model simulation and surface parameterization. The dataset uses an International Geosphere-Biosphere Programme (IGBP) land cover classification scheme and has been validated by high-resolution imagery (Landsat Thematic Mapper/Enhanced Thematic Mapper+, TM/ETM+) to improve its accuracy with a fractional cover value for each class. Three spatial resolutions (3.3 km, 10 km, and 30 km) were chosen to provide nested land use information for climate simulation across China and its principal megacity regions. Urban land use data from 2009 were used, which revealed that about 13%, 18%, and 12% of the land cover in domains D3, D4, and D5 is urban (not shown).

    Figure 1.  Configuration scheme for nested model domains. Domains D3, D4, and D5 represent the three vast city agglomerations in China, namely the Pearl River Delta, Yangtze River Delta, and the Jing-Jin-Ji metropolitan area, respectively. Contours represent terrain height.

    Based on data from the China Energy Statistics Yearbook (2009), calorific values, and the utilization efficiency of every energy type, we translated the primary energy consumption into annual mean AHR values for each city in the three agglomerations and assigned them to model grids covering these cities. If a grid box was fully covered by a city, then the AHR of the grid was equal to that of this city. If a grid box was covered by several cities, then the AHR of the grid was calculated by the area-weighted average of the inner parts of these cities. Over the grids where the land-use type was urban but the AHR was missing, the average AHR value was adopted. Figure 2 shows the distribution and magnitude of AHR in the three regions. The diurnal AHR cycle has been described in a parameterization of the UCM (Sailor and Lu, 2004, Fig. 3), but the actual data of the seasonal AHR change are lacking. The three agglomerations are in different latitude zones, and the seasonal AHR variances are considerable (Sailor and Lu, 2004). The same amount of AHR may therefore lead to different seasonal impacts. To account for seasonal variability, parameterization of the AHR seasonal cycle determined from the equations of (Flanner, 2009) was incorporated. The annual mean AHR values in the D3, D4, and D5 urban areas were estimated at about 23.6 W m-2, 44.5 W m-2, and 33.1 W m-2, respectively. Using a combination of bottom-up and top-down modeling approaches, (Quah and Roth, 2012) found that the mean hourly AHR in Singapore reached a maximum value of 113 W m-2 in commercial areas, 17 W m-2 in high-density public housing, and 13 W m-2 in low-density residential areas. Our AHR values were slightly higher than their estimates for high-density public housing. This is somewhat reasonable when considering other AHR sources, such as energy consumption from industrial and commercial activities in these urban regions. (Pigeon et al., 2007) found that at the agglomeration scale (Toulouse, France), observed AHR in summer varied between 25 W m-2 in the densest areas to less than 5 W m-2 in suburban areas, which is consistent with our estimates.

    Two sensitivity simulations were performed to identify the AHR effects on the regional climate. The control run (S0) was conducted with urban land use information updated by 2009 land cover data, with other land cover fractions changed proportionally. Based on case S0, a sensitivity run (S1) was added with a certain amount of AHR in urban areas. The difference between these two simulations gave a quantitative estimate of the impact of AHR on climate and environment at regional and local scales.

    Figure 2.  Spatial distributions of AHR in the three domains, D3, D4, and D5. Units: W m-2.

    Figure 3.  Diurnal cycle of the AHR fractions applied in the WRF-UCM model.

3. Results
  • Figure 4 shows the difference of the annual mean temperature between the sensitivity and control runs in the three urban agglomerations. As shown in Table 1, after including AHR, the regional average temperatures over the three domains increased by about 0.12°C, 0.34°C, and 0.21°C. There was more obvious warming over urbanized areas, with increases of 0.40°C, 0.91°C, and 0.78°C. Through numerical simulation, (Ichinose et al., 1999) suggested that reducing energy consumption could decrease the surface air temperature by at most -0.5°C. (Ohashi et al., 2007) suggested that a temperature increase of 1°C-2°C on weekdays was caused by air conditioning in Tokyo office areas. (de Munck et al., 2013) attributed a temperature increase of 0.5°C to current waste heat release and demonstrated the significance of air conditioning use in enhancing air temperatures in the streets in Paris. These results are comparable with our results. (Block et al., 2004) suggested a permanent warming in the Ruhr region of Germany from AHR, ranging from 0.15 K with a heat flux increase of 2 W m-2 to 0.5 K with a 20 W m-2 increase. As shown in Fig. 5, the regional average temperature changes in winter caused by AHR were 0.17°C, 0.48°C, and 0.37°C, also comparable with the results of (Block et al., 2004). Considering that the practices and amounts of energy consumption vary between Europe and China, the results of our simulations are reasonable. The simulation results of (Feng et al., 2012) showed that AHR increased the regional average temperature in the three urban agglomerations by 0.40°C, 0.89°C, and 0.36°C. Given the fine treatment of AHR and the nested high-resolution modeling here, we suggest that our quantitative estimation of temperature change from AHR is more reliable than that in (Feng et al., 2012). It is implied that a simulation with coarse resolution tends to exaggerate the AHR effects on temperature.

    Through the results of numerical simulation using two distinct urban land use scenarios, (Zhang et al., 2010) indicated that the mean surface air temperature in urban areas in the Yangtze River Delta increased by 1.9°C ± 0.55°C in summer and 0.45°C ± 0.43°C in winter. Comparing with the annual mean temperature rise caused by urban land use change, we suggest that AHR plays an equally important role in urban warming.

    Figure 4.  Spatial pattern of change in annual surface air temperature (S1 minus S0) caused by AHR in domains D3, D4, and D5. Units: °C.

    Figure 5.  Seasonal variation of annual mean surface air temperature change (S1 minus S0) from AHR.

    Figure 6.  Changes in (a-c) annual and (d-f) summer precipitation pattern (S1 minus S0) attributed to AHR in D3, D4, and D5. Units: mm d-1.

    Figure 7.  Percentage change of monthly precipitation (S1 minus S0) from effects of AHR in (a) three regional areas and (b) three urban areas. Percentage changes in monthly precipitation (S1 minus S0) in (c) three regional areas and (d) three urban areas for 2007, 2008, and 2009.

    Unlike the impact of urban land use change, the temperature rise caused by AHR in winter is much more significant than in summer (Fig. 5). One explanation for this is that solar radiation is much weaker in winter than in summer. Therefore, the equivalent AHR has more contribution to the near-surface energy budget in winter (Fan and Sailor, 2005). Another reason is that the parameterization of the seasonal AHR variation tends to assign larger values in winter.

  • Changes of annual and summer mean precipitation caused by AHR in the three urban agglomerations are shown in Fig. 6. The patterns of change for annual rainfall are similar to those in summer, because the three urban agglomerations are in a typical summer monsoon area. Overall, AHR increased precipitation over and near the urban areas of D3 and D4, but had no significant impact on precipitation over D5 (the Jing-Jin-Ji region).

    Figure 7 shows the percentage change in monthly precipitation from AHR across entire regions and urban areas. As shown in Fig. 7a, the impact of AHR on precipitation is mainly in summer. The rainfall changes are significant in summer because the amount of precipitation in East Asia is controlled by the Asian monsoon, which brings in a lot of water vapor from nearby oceans.

    (Feng et al., 2012) suggested that AHR reduces summer rainfall in the Yangtze River Delta by 4.6% and increases summer rainfall in southern China (including the Pearl River Delta) by 3.1%. However, because of the coarse model resolution in that study, changes of summer precipitation in urbanized areas could not be quantified. Moreover, the cumulus parameterization scheme in the WRF model must be activated to simulate the precipitation process at a coarse scale, which may cause further uncertainties in the estimate of AHR effects on precipitation. Last but not least, the treatment of AHR in (Feng et al., 2012) was very simple; only three specific values of annual mean AHR were assigned to three urban types. This simplification may cause the model AHR values to be much higher or lower than reality in certain regions. Comparatively, the precipitation change due to AHR in the present study should be more reliable than that of (Feng et al., 2012). (Block et al., 2004) indicated no significant variations in the spatial-average precipitation from AHR over an entire model domain in winter, which is consistent with the present results.

    Figure 7b shows clearly increased warm-season precipitation in the urban areas of D3 and D4. To demonstrate that this rainfall change was not from model internal noise, we also show the monthly precipitation changes in the urban areas of the three regions for 2007, 2008, and 2009 (Fig. 7d). This figure shows that the warm-season precipitation amounts in the urban areas of regions D3 and D4 in 2007, 2008, and 2009 all significantly increased, due to the effects of AHR.

    To study the mechanism through which AHR affects precipitation, we analyzed the change of convective available potential energy (CAPE) in summer. CAPE is accumulated buoyant energy from the free convection level to the equilibrium level. Generally speaking, a higher amount of CAPE favors the initiation and development of convection. Figure 8 shows that CAPE over the urbanized areas of the three model domains increases because of AHR. Further, we infer that AHR tends to increase the atmospheric instability and enhance precipitation in summer over urbanized areas. Additionally, the air temperature below 1 km is increased by AHR, which leads to the formation of an urban heat island and augments atmospheric instability (not shown).

    Regarding precipitation reductions in some areas, we analyzed changes in water vapor and vertical velocity caused by the effects of AHR. We established southwest-northeast cross sections through the points with maximum precipitation change. As shown in Fig. 9, the water vapor mixing ratio in the lower troposphere increased, which may be caused by enhanced convergence around urban areas. More water vapor promotes the development of convective motion and precipitation. Ascending motion was enhanced in the urban areas of regions D3 and D4, which favored cloud formation and increased precipitation. Meanwhile, sinking motions were strengthened in non-urban areas, which may be the principal cause of precipitation reductions there. In addition, as shown in Fig. 10, the water vapor increased and the convergence enhanced in the urban areas in D3 and D4, which can trigger more convective activities. However, the change in vertical motion seemed irregular in region D5, and the horizontal motion in the lower atmosphere changed more significantly. This may be related to strong mountain and valley winds in this region.

    The differences in rainfall change due to AHR between regions D3/D4 and D5 are noteworthy. Although D5 exhibited considerable moistening in the AHR experiment, the impact of AHR on precipitation remained unclear (Fig. 7). This may be related to many other factors. First, the AHR magnitude was smaller than in the other two regions. As shown in Fig. 2, because of a less-dense distribution of cities, the AHR was not sufficiently concentrated in D5. Through sensitivity experiments, (Lin et al., 2011) suggested that AHR has an important impact on precipitation formation over northern Taiwan. However, they estimated an AHR of about 200 W m-2, which is much higher than our values in region D5. Second, as shown in Fig. 1, the topography in the northwest part of D5 is much more complex than D3 or D4. The roughness of the land surface generates enhanced dynamic turbulences, which favor AHR diffusion. In addition, D5 is within the East Asian monsoon marginal belt and the climate is drier than D3 or D4. The inclusion of AHR leads to water vapor convergence in D5, but the temperature increase in the lower troposphere may reduce the relative humidity and counteract the effect of increased water vapor. Finally, large-scale weather processes may dominate local convection to a greater extent in region D5 than in D3/D4. Consequently, AHR has little influence on the rainfall formation in D5.

4. Discussion and conclusion
  • China has experienced dramatic economic development over the past three decades. Increasing AHR from all types of energy use may contribute to changes in the regional climate and environment. In this study, we used the WRF model coupled with the UCM to quantify regional climate change produced by AHR in the three major urban agglomerations in China. We designed two scenarios to represent no AHR and current AHR conditions, and conducted nested high-resolution regional climate simulations over a multi-year period. The simulation results appeared reliable and could therefore be used to investigate AHR effects on temperature, precipitation, and atmospheric circulation. The simulation results indicated that the regional average temperatures in the three agglomerations, D3, D4, and D5, increased by 0.12°C, 0.34°C, and 0.21°C, respectively. Using a homogenized daily surface air temperature dataset (Li and Yan, 2009), we estimated that the regional average temperatures in D3, D4, and D5 increased by 0.85°C, 1.24°C, and 1.77°C, respectively, during 1960-2008. Compared with the simulated temperature change due to AHR, we suggest that AHR might contribute about 10%-30%, with an assumption that the AHR in the 1960s was small. In the urbanized areas of these three domains, the temperature changes reached about 0.40°C, 0.91°C, and 0.78°C. AHR produced a temperature increase that was more pronounced in winter than in summer. However, there are uncertainties in the estimation of temperature changes due to AHR in different regions. In particular, uncertainties may arise from the AHR data limitation and the model's parameterizations. The present sensitivity experiments using WRF-UCM nested modeling only provided some preliminary estimates of the AHR effects on regional climate.

    Based on multi-year simulations, we also found that AHR can modify both the amount and distribution of precipitation. Summer rainfall is disturbed by the AHR effects. Analyzing a related physical variable (CAPE), we discovered that convective activities are promoted by the AHR effects, which may increase summer precipitation in urban areas. AHR had no significant impact on precipitation change in region D5. We inferred that competing factors, such as the relative lack and spatial dispersion of the AHR, plus a complex orography in this region, may have produced this result. Furthermore, we suggest that the AHR effects on precipitation may vary with regions and atmospheric conditions. Therefore, the positive effect of AHR on summer rainfall in urban areas necessitates further study.

    Figure 8.  Change in convective available potential energy (CAPE) in summer (S1 minus S0) from AHR in D3, D4, and D5. Units: J kg-1.

    This study is the first attempt to estimate the impact of AHR on urban regional climate through nested high-resolution simulations. With energy consumption and urban areas increasing steadily, AHR should be parameterized in regional climate models. In fact, many researchers have pointed out the importance of including AHR in regional or even global-scale climate modeling. For example, (Block et al., 2004) suggested that regional climate models should consider AHR to improve their performance, given the steady increase of worldwide energy consumption and urban areas. (Flanner, 2009) also indicated that the inclusion of AHR will become important as climate models incorporate increasingly sophisticated representations of urban systems and are applied to understand linkages between pollutants, climate, and human health. Since AHR is concentrated in urban areas, it may be particularly important in the modeling urban climate change. Considering the positive feedback between warmer climate and AHR (Crutzen, 2004), we further suggest that parameterization of interactions between climate and AHR is an urgent requirement in regional models, particularly those with finer spatial scales.

    Figure 9.  Cross section of changes in water vapor mixing ratio and horizontal/vertical velocity in summer, along southwest-northeast lines in D3, D4, and D5. Geographic locations of the central grids through which the planes pass are (23.30°N, 114.00°E), (31.15°N, 119.76°E), and (38.89°N, 116.66°E). Units for water vapor mixing ratio: g kg-1. Units for horizontal and vertical velocities: m s-1 and dm s-1, respectively.

    Figure 10.  Changes in the water vapor mixing ratio and the circulation of the lower troposphere in summer due to AHR. Units for water vapor mixing ratio: g kg-1. Units for wind velocities: m s-1.

    There are many factors beyond AHR that may contribute to changes in the urban regional climate and environment, such as urban land use and aerosol emissions (Rosenfeld, 2000; Rosenfeld et al., 2007). (Jin et al., 2005) suggested that urban cloud properties demonstrate an opposite phase to the seasonality of aerosols, but no clear relationship exists between monthly mean aerosols and the amount of urban rainfall. (Jin and Shepherd, 2008) indicated that aerosols are not the only physical process affecting urban clouds and dynamic processes related to factors like urban land cover may play at least an equally critical role in cloud formation. Due to the complex, nonlinear relationship between atmospheric dynamics and microphysics, there is still a need to investigate the effects of urban aerosols on regional climate. Also, AHR may significantly modify the evolution of urban aerosols (Crutzen, 2004). To fully understand the impacts of urbanization on regional climate, future works should investigate the climate impact of these factors and the relationships among them.

Reference

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