Open AccessArticle

Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi

Aerzuna Abulimiti

¹,

Yongqiang Liu

¹,

Jianping Tang

Ali Mamtimin

^3,4,5,6,7

Junqiang Yao

^3,4,5,6,

Yong Zeng

^3,8,9

and

Abuduwaili Abulikemu

^1,*

Xinjiang Key Laboratory of Oasis Ecology, College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China

Key Laboratory of Mesoscale Severe Weather/MOE, School of Atmosphere Sciences, Nanjing University, Nanjing 210023, China

Institute of Desert Meteorology, China Meteorological Administration (CMA), Urumqi 830002, China

⁴

National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi 830002, China

⁵

Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi 830002, China

⁶

Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China

⁷

Dabancheng National Special Test Field for Comprehensive Meteorological Observation, Urumqi 830002, China

⁸

Field Scientific Observation Base of Cloud Precipitation Physics in West Tianshan Mountains, Urumqi 830002, China

⁹

Xinjiang Cloud Precipitation Physics and Cloud Water Resources Development Laboratory, Urumqi 830002, China

Author to whom correspondence should be addressed.

Remote Sens. 2024, 16(16), 2939; https://doi.org/10.3390/rs16162939

Submission received: 22 July 2024 / Revised: 5 August 2024 / Accepted: 8 August 2024 / Published: 10 August 2024

(This article belongs to the Special Issue Comprehensive Analysis Based on Observation, Remote Sensing, and Numerical Models to Understand the Meteorological Environment in Arid Areas and Their Surrounding Areas)

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Abstract

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism.

Keywords:

urbanization effect; urban thermal environment; numerical simulation; land surface energy balance; arid region; Urumqi

1. Introduction

The urbanization effect (UE) on the regional thermal environment is one of the prominent topics in the study of urban climates [1,2]. Research on UE in China is primarily concentrated on the developed eastern regions [3], with considerable attention given to the UE on the thermal environments of local climates [4,5]. Both meteorological station data and remote sensing satellite data have been used in the study of UE on regional climates [6,7].

Urbanization changes original natural land cover into impervious artificial surfaces, such as cement and masonry, leading to significant changes in roughness and heat capacity. This further alters the momentum and heat exchange between the urban surface and the atmosphere, thereby affecting the urban thermal environment. Thus, the UE on urban climate can be explained by changes in the surface energy budget due to altered underlying surface properties, subsequently changing the regional climate [8,9].

The urban thermal environment refers to the physical environmental system, composed of various external factors related to heat in urban areas that affect the survival and activities of residents. Given that surface temperature (Ts) and air temperature at 2 m (T2m) are the core elements of the urban thermal environment, surface radiation (or heat) flux is an essential component [10,11]. Therefore, these factors (Ts, T2m, and surface radiation flux) are collectively referred to as the urban thermal environment in this study. The urban thermal environment is a crucial component of the urban ecological environment, and the urban heat island (UHI) effect is a significant indicator of the urban thermal environment. The UHIs are classified into surface UHIs, canopy UHIs, boundary layer UHIs, and subsurface UHIs [12]. Among them, surface UHIs and canopy UHIs are most closely linked to human activities and have received the most attention [13,14,15].

In the early stage of urban climate research, some scholars examined the UE on the urban thermal environment by comparing observational data from urban and rural areas. For example, Karl et al. [16] utilized observational data to investigate the UHI in the United States, and they indicate that the urban temperature increased by 0.06 °C, with a more pronounced effect on nighttime temperatures than daytime temperatures. As remote sensing techniques advanced, researchers shifted towards analyzing urban thermal conditions using remote sensing data. For instance, Shastri et al. [17] utilized MODIS surface temperature data to explore the UHI effect in India, and they revealed that increased evapotranspiration in urban areas altered latent and sensible heat fluxes, resulting in a daytime surface cool island effect during summer. In recent years, there has been a growing emphasis on the study of global warming, prompting more scholars to investigate its mechanisms and impacts [18,19]. Li et al. [20] employed the Weather Research and Forecasting (WRF) model coupled with the Urban Canopy Model (WRF-UCM) to convert farmland or grassland to urban and construction land and found the most significant influence on latent and sensible heat fluxes, followed by net radiation and geothermal flux, while the impact on other fluxes was minimal. Zhao et al. [21] indicated that the rise in surface sensible heat flux in the old city was primarily driven by anthropogenic heat emissions, whereas the increase in surface sensible heat flux in the new city was attributed to changes in land use.

Site observation, remote sensing monitoring, and numerical simulation are the most commonly used methods for studying the UE on climate. However, sometimes, due to the limited number of meteorological stations in cities or rural areas, the observed data may not accurately reflect the spatial pattern of UE, especially when examining the UE on climate for a specific city. Higher spatial and temporal resolution data are necessary to investigate the UE in different locations in a city. Nevertheless, remote sensing observation may not adequately capture the spatial and temporal changes of UE due to its low temporal resolution and susceptibility to cloud interference [22]. In contrast, numerical simulation can be utilized to study the UE on local weather and climate, with high spatial and temporal resolution (up to hundreds or even tens of meters of spatial resolution and a few minutes of time resolution). It can also quantitatively differentiate the UE on various climate factors in the region through different sensitivity experiments [12,22].

Urumqi is the capital of the Xinjiang Uyghur Autonomous Region in China, located deep inland of the Eurasian continent (Figure 1). It is the city farthest from the ocean in the world and has a continental arid climate in the temperate zone [23]. Positioned in the core area of the “Belt and Road” initiative, Urumqi is a significant economic center in Northwestern China and exerts a strong influence on the Central Asian region. In addition, Urumqi is considered a typical arid city in northwest China. Urumqi has experienced rapid urbanization and economic development, and the urban population has been increasing, the urban area has been expanding, and the urban climate effect has become increasingly significant [24]. However, the ecological environment in Urumqi is extremely fragile, and intense human activities pose a severe threat to the natural ecological environment system of the oasis [25]. Many researchers suggest that the significant climate changes in Xinjiang may be linked to human activities like urbanization [26,27].

However, the existing research on urban climate effects mostly focuses on the more developed large cities in eastern China [3,28], with very few studies on cities in the arid northwestern regions of China. In addition, Fan et al. [29] conducted a statistical review study on the UE in oasis cities in the northwest arid region of China, and they found that there is an overall lack of research papers related to UHIs in arid areas, with low citation frequencies and a scarcity of high-resolution numerical simulation studies. In addition, research concerning the UE in Urumqi mainly focuses on surface temperature and largely relies on remote sensing data; as a result, existing studies have neglected the nighttime UHI effect, and they have not adequately quantified the differences in UHIs across various orientations within the city [30,31]. Furthermore, the mechanism of the UE in the northwest arid region of China and the UE on surface energy balance remains unexplored. Additionally, compared to humid regions, arid regions will face more severe climate change risks [32]. Because the UEs are largely influenced by the background climate [33,34,35], research on urban climate in arid regions has become increasingly important.

In view of the above research status of the UE on local climate in arid regions, the aim of this study is to quantitatively investigate the UE on the regional thermal environment and its mechanisms in Urumqi for the first time, based on high-resolution WRF model data covering 10 consecutive years (from 2012 to 2021). This study can be considered as the first attempt to address the gap in numerical simulation of the UE on urban climates in arid regions of northwest China and enhance the understanding of the contribution of human activities to regional climates in Urumqi. This study could offer valuable insights into the impact of human activities on regional climate change and attribution, thereby enhancing research on climate change in arid regions.

The rest of this paper is organized as follows: An introduction of the data and methods used in this study is presented in Section 2. The Section 3 provides a detailed description of the results, and the discussions and conclusions are presented in Section 4 and Section 5.

2. Data and Methods

2.1. Data

Three basic meteorological elements, i.e., daily mean air temperature (Tmean), daily maximum air temperature (Tmax), and daily minimum air temperature (Tmin), from ground-based automatic weather stations (AWSs) and conventional national observation stations in Urumqi were used in this study. Given the limited number of AWSs in Urumqi, along with the fact that some stations were established relatively late or had gaps in their data, 20 stations (including 17 AWSs and 3 conventional national observation stations) were selected to verify the simulation results based on data integrity and quality, as shown in Figure 2. All of these observational data were provided by the National Meteorological Center of the China Meteorological Administration (CMA).

Furthermore, the fifth generation of the global atmospheric reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF) (i.e., ERA5 data) is used in this study. The ERA5 data provide large-scale atmospheric circulation fields and initial soil parameters (such as soil water, soil moisture, and soil temperature) for the numerical simulation. In addition, the CLCD (China Land Cover Dataset, https://zenodo.org/records/8176941, accessed on 13 January 2023) data (with 30 m spatial resolution) is used to update the land use categories in the numerical model to improve the simulation accuracy.

2.2. Methods

Numerical simulations were conducted using the Weather Research and Forecasting (WRF) model (version 4.2.2) in this study. It is a widely used mesoscale numerical weather prediction model developed for atmospheric science research [36,37,38,39,40,41,42,43]. In this study, the single-layer Urban Canopy Model (UCM) is coupled with the WRF model [44]; hence, the model is abbreviated as the WRF-UCM model. The introduction of the urban canopy scheme facilitates the consideration of radiation shading, radiation trapping, building drag, and the influence of the urban canopy on turbulence [45].

The numerical simulation was performed with two-way nested two-level domains covering a consecutive 10-year period from 2012 to 2021 in this study. The first 10 days of each simulation experiment serve as the startup time for the model. The model output interval is 1 h, with a 1 km horizontal resolution, allowing for the analysis of diurnal variation patterns of all meteorological elements. To analyze the diurnal variation characteristics of basic meteorological elements, the temporal resolution of the simulation output was set to 1 h. Given the relatively small urban area of Urumqi and the focus of this study on different directions of the urban centroid of the built-up area of Urumqi, a spatial resolution that is too low would be inappropriate. Thus, a spatial resolution of 1 km is considered suitable. As shown in Figure 3a, the outer domain (d01) has a horizontal resolution of 3 km, with 200 × 200 grid points in the x and y directions, covering most of northern Xinjiang. The inner domain (d02) has a horizontal resolution of 1 km, with 220 × 220 grid points, covering the entire city of Urumqi. There are 35 vertical levels, with the highest level reaching 50 hPa. The microphysics parameterization adopted the Thompson graupel scheme [46], the Rapid Radiative Transfer Model (RRTM) longwave radiation scheme [47], the RRTMG shortwave radiation scheme [48], the Yonsei University (YSU) boundary layer parameterization scheme [49], the Noah Land Surface Model (Noah LSM) scheme [50], and the modified MM5 MO parameterization scheme [51]. Due to the sufficient horizontal resolutions (3 km and 1 km) for considering the convections in the simulation, the cumulus convection parameterization scheme was turned off in both domains.

Two sets of numerical simulation experiments are designed in the study. The first one is called the Urban (control) experiment, which uses the land use categories derived from the CLCD dataset in both domains (Figure 4), reflecting real urban built-up land use conditions. The second one is called the NoUrban (sensitivity) experiment, which uses the land use categories in which the original built-up area in Urumqi was replaced by grassland (Figure 5), with other settings consistent with the Urban experiment. The UE on a certain meteorological element indicates the difference of this certain meteorological element between the Urban experiment and the NoUrban experiment in this paper.

In order to study the spatial distribution characteristics of the UE on various meteorological elements in Urumqi, the urban centroid of the built-up area of Urumqi (indicated as a black dot in the central area in Figure 4b) was calculated, and eight box areas (labelled with different abbreviated letters) were divided in the study area in different directions to this urban centroid of Urumqi. Each area is a rectangle of 10 km × 10 km, with the exception of the south-western area, which has almost no built-up area.

The surface energy budget (SEB) will be calculated and analyzed to investigate the mechanisms of the UE on the thermal environments in Urumqi in this study. As shown in Equation (1) [52], the

E_{b}

represents the SEB,

R_{n}

S H

L H

, and

G_{0}

indicate net radiation, sensible heat flux, latent heat flux, and ground (or soil) heat flux, respectively. Since the

S H

and the

L H

obtained by the WRF-NJU simulation results represent heat loss terms (i.e., deductions) for the SEB, these two fluxes are reversed (the positive and negative signs of their values) in this study. The

R_{n}

is calculated according to Equation (2) [52], where

α

is the albedo,

ε

is the emissivity of the surface,

R_{s w d}

is the downward solar radiation,

R_{l w d}

is the downward longwave radiation,

σ

is the Stefan-Bolzmann constant, and

T_{0}

is the surface temperature.

E_{b} = R_{n} - S H - L H + G_{0}

(1)

R_{n} = (1 - α) \cdot R_{s w d} + ε \cdot R_{l w d} - ε \cdot σ \cdot T_{0}^{4}

(2)

3. Results

3.1. Verification of Simulation Results

In order to establish the credibility of numerical simulation, the simulation results are verified from the perspective of three basic meteorological elements (i.e., Tmax, Tmean, Tmin), which indicate the basic thermal environment. Figure 6 displays scatter plots and regression fitting lines, comparing the monthly average values (from 2012 to 2021) of these three basic meteorological elements based on the observations from the 20 stations (Figure 2) and the WRF numerical simulation results from the same locations of these 20 stations in Urumqi in four seasons. The monthly averaged observation data were used to verify the results of the model simulation. Urumqi, being a mid-latitude inland city, experiences significant variations in monthly averaged temperatures (indicated by small circles) during the spring and autumn months, while the differences between summer and winter months are smaller. This leads to the scatter plots of temperatures in summer and winter clustering together by season, whereas those in autumn and winter cluster together by month. Statistics of Pearson’s correlation coefficient (r) and root mean square error (RMSE) are shown in Table 1 and Table 2, respectively. It can be seen from these two Tables that the values of r of all three elements from numerical simulations and observations in all seasons are relatively high. The values of r of all three elements reached above 0.977, 0.763, 0.987, and 0.697 in spring, summer, autumn, and winter, respectively. The values of RMSE of all three elements in all seasons are between 0.721 and 1.424. In general, it can be seen that the simulation results demonstrated relatively high accuracies in autumn and spring, with a slightly lower accuracy in winter compared to other seasons, but still within a reasonable error range. Therefore, it can be deduced that the WRF model simulation results reproduced these three basic meteorological elements in the study area well. The UE on regional thermal environments and its mechanisms will be further investigated in the following section. It is noteworthy that the UE on a certain meteorological element in this paper indicates the difference of this certain meteorological element between the Urban experiment and the NoUrban experiment.

To quantitatively investigate the UE of Urumqi on the regional thermal environment and its mechanisms, the following part of this study focuses on the differences in surface temperature (Ts), air temperature at 2 m (T2m), and components of the surface energy budgets between the Urban and NoUrban experiments.

3.2. The UE on Ts

3.2.1. Seasonal Variation Characteristics of the UE on Ts

Figure 7 shows the UE average value (calculated in the consecutive 10-year period from 2012 to 2021; hereafter, the “average value” without any other specific statements indicates the same calculation of average value) of Ts in Urumqi in four seasons. The warming UE on Ts is obvious, and it varies across seasons and in different directions of the urban centroid of the built-up area of Urumqi. It is noticeable that urbanization in Urumqi causes the most significant increase in Ts in spring (Figure 7a,e), with an average of 3.14 °C, and the northern part of the Urumqi shows the largest increase (reaching 3.43 °C).

Furthermore, it is obvious that the maximum increases in Ts due to urbanization in spring, summer, and autumn are all located in the northern and northwestern parts of the built-up area of Urumqi. In winter (Figure 7d,h), the average UE on Ts is 1.96 °C, with the maximum increase not occurring in the northern part but in the southern and central parts of the built-up area of Urumqi. The southern part experiences the greatest UE in winter, with a temperature increase of 2.26 °C. Overall, urbanization significantly warmed the Ts of the built-up area in Urumqi. The greatest warming due to urbanization occurs in spring, followed by autumn, with the smallest increase occurring in winter. This may be attributed to the rapid temperature rise in urban areas (in Urban experiment) in spring, while the non-urban areas (NoUrban experiment) remain frozen, resulting in the maximum difference in Ts between urban and non-urban areas in spring. The regions where urbanization has the strongest warming effect on Ts in spring, summer, and autumn are mainly in the northern part of the city, while the area with the greatest warming in winter is in the southern part of the built-up area in Urumqi. The differences in Ts between urban and non-urban areas are mainly determined by differences in surface energy budgets (SEBs), and urbanization may mainly affect Ts by influencing components of the SEB, such as net shortwave radiation and net longwave radiation.

3.2.2. Diurnal Variation Characteristics of the UE on Ts

Some previous studies [53] have shown that the UE on Ts has distinct diurnal variation characteristics, and some insights into the causes of the UE on Ts can be obtained by examining their diurnal variation characteristics. Therefore, this study also focuses on the diurnal variation characteristics of the UE on Ts (Figure 8). There was a positive UE of Urumqi on Ts in both spring (Figure 8a) and autumn (Figure 8c), indicating that urbanization has a warming effect both during daytime and nighttime in these seasons, with nighttime warming being more pronounced. The peak of nighttime urbanization-induced warming in spring occurred in the first half of the night (at around 9 p.m.), with an average increase of 4.3 °C, and the northern part of the built-up area experienced a temperature increase of 4.8 °C. The maximum UE of Urumqi appeared in the northwestern part of the built-up area at around 6 p.m. in autumn, with an increase of 5.2 °C; whereas the lowest warming in autumn occurred at around 10 a.m. in the northern part of the built-up area, with only a slight increase of 0.3 °C. This suggests that there is considerable diurnal variability in UE in Urumqi.

During summer (Figure 8b) and winter (Figure 8d), there was a weak daytime cooling effect, similar to the urban cool island (UCI) phenomenon. This kind of cooling effect in summer lasts for approximately 3 h (from 9 a.m. to 1 p.m.), with the northern part of the built-up area experiencing cooling of −0.4 °C. The cooling effect in winter is relatively short-lived, occurring between 10 a.m. and 11 a.m. In all seasons, the UE on Ts displayed a pattern of stronger nighttime warming and weaker daytime warming, with the warming of Ts reaching a peak around 6 a.m. and rapidly decreasing from then on, followed by another peak at around 6 p.m.

The diurnal variation characteristics of the UE on average Ts (Figure 9) are investigated in the following section to explain the reasons for the temperature increase similar to the surface UHI phenomenon found in this study, as well as the UCI effect caused by urbanization during the day in summer and winter. As depicted in Figure 9, the Ts in the built-up area (Urban experiment) during the night was always higher than that in the non-urban area (NoUrban experiment), with both urban and rural surfaces gradually cooling down. Some other studies [54] have indicated that the rise in Ts due to urbanization is greatly influenced by the humidity conditions in the suburbs. In arid regions, the suburbs dry out faster in the early morning compared to the urban area, resulting in a cooling effect akin to the UCI effect during the day. Furthermore, the drier rural surface can sustain a higher nocturnal cooling rate, thus intensifying nocturnal surface warming. These are the distinctive characteristics found in Urumqi as an arid city.

3.3. The UE on T2m

3.3.1. Seasonal Variation Characteristics of the UE on T2m

The T2m in cities has a closer connection to local human thermal comfort and the urban atmospheric environment compared to the Ts. Therefore, the UE on T2m is also investigated in the following. Figure 10 illustrates the UE of Urumqi on T2m, representing the change in T2m induced by urbanization. Figure 10 indicate that urbanization has a positive effect on T2m in all seasons, indicating that urbanization leads to an increase in T2m in urban areas. However, the magnitude of this warming effect varies seasonally and spatially. In spring (Figure 10a,e), the UE on T2m reached 1.10 °C, with the northern regions of the built-up area of Urumqi experiencing the greatest UE of 1.21 °C. Figure 10d,h depicts the UE of Urumqi on T2m in winter, showing a relatively weaker overall effect in winter, with an average warming of only 0.66 °C. The southern part of the built-up area of Urumqi experienced the most significant UE of 0.85 °C, while the least affected area was in the northeast of the built-up area of Urumqi, with an UE of 0.52 °C.

The spring exhibited the most significant warming UE of Urumqi on T2m, followed by autumn, with winter showing the least UE. Areas experiencing the most intense UE on T2m during spring, summer, and autumn were mainly located in the northern part of the built-up area of Urumqi, while the southern part experienced the least notable UE. The spatial distribution patterns of T2m caused by urbanization in different seasons closely resemble those of Ts influenced by the urbanization of Urumqi. The primary process responsible for heating the atmosphere is the absorption of solar radiation by the surface, followed by the transfer of heat from the surface to the atmosphere. The main contributors to the rise of T2m are the release of sensible heat flux and the emitted longwave radiation from the surface. The intensities of longwave radiation and sensible heat flux are primarily influenced by Ts. As a result, the seasonal variations and spatial distribution of the UE on T2m are consistent with those of Ts. However, the UE on T2m is weaker than that on Ts mainly due to the dissipation of energy resulting from the absorption of longwave radiation by greenhouse gases in the atmosphere during the surface-to-atmosphere heat transfer process.

3.3.2. Diurnal Variation Characteristics of the UE on T2m

Figure 11 depicts the UE of Urumqi on T2m in four seasons, and it is evident that urbanization has a stronger warming effect on nighttime T2m than during the daytime. In summer (Figure 11b) and winter (Figure 11d), there was even a cooling effect resembling the UCI phenomenon during the daytime. The peak of summer warming occurs at around 4 a.m., with the northern part of the built-up area of Urumqi experiencing a temperature increase of up to 2.6 °C. In winter (Figure 11d), the peak warming induced by urbanization appeared at around 5 p.m., with the southern part of the built-up area of Urumqi experiencing a temperature increase of 1.6 °C.

The warming UE of Urumqi on T2m is weaker compared to that on the Ts, but the timing of peak warming for both is essentially the same, and the spatial distribution patterns are also similar. The maximum warming in spring, summer, and autumn occurred in the northern part of the built-up area of Urumqi, while the peak warming occurred in the southern part in winter. During winter and summer, the UE on both T2m and Ts exhibited cooling effects resembling the UCI effect. In summer, the canopy UCI effect induced by urbanization lasts for an additional 4 h compared to the surface UCI effect, while the canopy UCI effect is shorter than the surface UCI effect in winter.

As illustrated in Figure 12, urbanization leads to cooling phenomena similar to the UCI effect in both summer and winter. The reasons for this can be explained that, just before sunrise, the rural areas are notably cooler than urban areas, the non-urban areas (NoUrban experiment) are exposed to the sun after sunrise, resulting in a rapid increase in Ts shortly after sunrise. Meanwhile, during this time, the T2m in urban areas experiences a slower rise due to most street canyons in urban areas being in shadow, along with the high thermal conductivity of urban materials, resulting in a delayed response of T2m to Ts. By noon, the temperature difference between urban and suburban areas is minimal as the nocturnal canopy UHI effect dissipates quickly, resulting in a slight negative difference between urban and suburban areas, indicative of characteristics of the UCI effect. After reaching peak values of Ts in the afternoon, both urban and suburban areas begin to cool down, with the suburban surface cooling at a faster rate than the urban area. This explains why the period from early afternoon to evening is when the UE has the fastest warming rate on T2m.

3.4. Mechanisms of the UE on the Thermal Environments

The distinct characteristics of surface energy budget (SEB) of urbans are the reasons for many observed urban climatic effects. Quantitative analysis of the UE on the SEB can explain the mechanisms of the UE on the regional thermal environments [12]. It is found that the greatest UE of Urumqi on Ts and T2m occurred in spring, therefore, the mechanisms of this phenomenon are investigated from the perspective of UE on the components of the SEB in this section. Figure 13 shows the UE of Urumqi on the components of the SEB, including net longwave radiation (LWn), net shortwave radiation (SWn), sensible heat flux (SH), latent heat flux (LH), ground heat flux (GH), and sum of the surface energy budget (SEB) in four seasons. It is fount that, the greatest UE on SWn, LWn, SH, and GH occurred in spring. The UE on the SWn and GH in spring reached 33.10 W m⁻² and 10.97 W m⁻², respectively, which is conducive to increasing the Ts. Consequently, the greatest UE of Urumqi on Ts occurred in in spring. The UE on the LWn and SH in spring reached −22.07 W m⁻² and −32.74 W m⁻², respectively, which tends to increase the T2m. Therefore, the greatest UE of Urumqi on the T2m occurred in spring. The occurrence of the greatest UE on the SWn, LWn, SH, and GH in spring could explain why the season with the greatest increase in Ts and T2m caused by urbanization is also in spring.

As mentioned above, the areas with the greatest UE on Ts and T2m are located in the northern part of the built-up area of Urumqi in spring, summer, and autumn, while those on Ts and T2m in winter occurred in the southern part of the built-up area of Urumqi. The mechanisms of this phenomenon are also investigated in the following section. It can be found from the UE on SEB in the northern and southern parts of the built-up area of Urumqi in four seasons, as well as from their difference (Figure 14, Figure 15 and Figure 16), that the UE of Urumqi on SWn and GH showed greater values in the northern part than in the southern part in spring, summer, and autumn, resulting in a larger increase in Ts in the northern part compared to the southern part of the built-up area of Urumqi in these seasons.

Similarly, the UE on the LWn and SH showed more significant values in the northern part than in the southern part, favoring a greater increase in T2m in the north compared to the south of the built-up area of Urumqi. Therefore, the increase in Ts and T2m due to UE is greater in the northern part of the city than in the south of the built-up area of Urumqi in spring, summer, and autumn. The UE on the SWn and GH has greater values in the southern part of Urumqi during winter compared to the north, which favors an increase in Ts in the southern part of the built-up area of Urumqi. Additionally, the UE on the LWn and SH is also greater in the south during winter compared to the other three seasons, which further heats the near-surface air in the south, leading to an increase in the T2m in the southern part of the built-up area of Urumqi. Therefore, the areas with the highest UE on these factors are located in the southern part of the built-up area of Urumqi during winter.

4. Discussion

(1) Some previous studies (e.g., Peng et al. [54] and Imhoff et al. [55]) based on satellite observations indicate that the surface UHI is largest in summer, and the spatial differences at night are smaller than those during the day, in different seasons over the same city. However, in this study, the simulated surface warming caused by the UE of Urumqi is greatest in spring, and the diurnal variation characteristics of surface warming show a stronger effect at night and a weaker effect during the day. A slight UCI effect even appears during the day in summer and winter, which may be related to the special background climate of arid regions. Peng et al. [54] also found that in cities surrounded by deserts, such as Jeddah in Saudi Arabia and Mosul in Iraq, the surface UHI during the day also shows negative values, which may be caused by the evaporative cooling of vegetation in urban areas of arid regions during summer. Peng et al. [54] also believe that the distribution of the UHI effect at night is mainly positively correlated with differences in albedo and illumination between urban and rural areas, while the distribution of the UHI effect in the daytime is mainly controlled by the differences in vegetation cover between urban and rural areas. In this study, the spatial differences in nocturnal warming are significantly greater than those during the day in all seasons, which may also be due to smaller differences in vegetation between urban and rural areas in arid regions and larger differences in albedo and illumination, resulting in greater spatial differences in Ts at night than during the day.

(2) Table 3 shows the UE of Urumqi and that of Chengdu (a city belonging to a humid area in Sichuan province in China) on Ts and their diurnal temperature range (DTR). A comparison between the UE of these two cities reveals that the seasonal differences of rising Ts due to urbanization are greater in Chengdu, while they are smaller in Urumqi. This difference may be because the seasonal changes in the rise of Ts due to urbanization depend on the differences in surface characteristics, especially soil moisture [53]. In Chengdu, as the relatively humid region, the increase in soil moisture in winter may lead to higher soil heat conduction, reducing nighttime surface cooling, decreasing the surface UHI effect, and resulting in larger temperature differences on the surface caused by the urban underlying surface in winter and summer. Additionally, it was found that the reduction in DTR in Urumqi due to urbanization in summer is significantly greater than that in Chengdu. This may be due to the lower soil moisture in Urumqi in summer, leading to an enhanced nocturnal UHI effect and an increase in minimum temperature at 2 m. Moreover, there is a weak UCI effect during the day in Urumqi in summer, causing a decrease in maximum temperature at 2 m. Therefore, the decrease in DTR in Urumqi in summer due to urbanization is significantly greater than that in Chengdu.

(3) The urbanization process has a direct effect on the surface-atmosphere energy exchange in urban areas. Accurately representing the complex underlying surface characteristics of urban areas is crucial for numerical simulation studies on urban climate effects. Currently, research on improving parameterization schemes for numerical simulations mainly focuses on the description of urban non-uniformity characteristics in eastern cities in China. Urumqi, as the largest city in Xinjiang, is surrounded by mountains on three sides, with a complex underlying surface. Although this study simulated continuously for 10 years to minimize the impact of some uncertainties due to short-term variations in atmosphere (e.g., mesoscale weather systems) on the simulation results, the simulation accuracy of the WRF-UCM model in winter was weaker compared to other seasons. Therefore, future research should focus on enhancing the model’s capabilities to conduct more extensive studies.

(4) Rasul et al. [57] investigated the diurnal and seasonal variation of surface UCI and UHI in the semi-arid city of Erbil, Iraq. They found that the intensity of the surface UHI in spring and summer is higher than in autumn and winter, with the greatest magnitude of surface UCI occurring in the morning. However, our present study found stronger surface UHI in spring and autumn than in summer and winter in Urumqi, with the greatest magnitude of surface UCI occurring around 11 LST. Therefore, it is evident that there are some differences in the UE across various arid (or semi-arid) regions, which merits further investigation. In addition, a previous study [58] on Phoenix, an arid city in the United States, found that human heat emissions, primarily from air conditioning at night, are more likely to raise air temperature than during the day, exacerbating the UHI effect at night. However, our current study focused solely on the UE on the thermal environment, so anthropogenic heat sensitivity tests will be conducted in future research to explore the effect of urban anthropogenic heat emissions on the UHI effect in Urumqi.

(5) Oke [12] stated that frontal precipitation associated with large-scale (synoptic-scale) cyclones (with horizontal scales of 1500–5000 km) is common in midlatitudes, and the large-scale processes driving these systems may obscure any urbanization effect. Valley winds, temperature inversions, and air pollution resulting from the unique geographical location and topography of Urumqi may also influence the spatial and temporal distribution of the urban thermal environment. Therefore, it is essential to design more sensitivity tests in future research on the UE in Urumqi. For example, conducting additional simulation tests of circulation, topography, and different weather systems and quantitatively studying the impact of atmospheric circulation and topography on the urban climate in this region is necessary.

5. Conclusions

The warming UE of Urumqi on Ts and T2m exhibits stronger influence at night and weaker influence during the day throughout all seasons. Moreover, the UE on both of these two meteorological elements is greatest in spring and least in winter. The area with the highest UE for Urumqi is located in the southern part of the city in winter, while it is located in the northern part in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on the T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in the northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. In arid regions, the dry suburbs experience a faster morning warming rate than the city, which can lead to a cooling effect similar to an UCI effect during the day. Additionally, the drier rural surfaces maintain a higher nocturnal cooling rate, while the opposite is true in cities, resulting in higher nocturnal Ts in urban areas compared to the suburbs. Due to the high thermal conductivity of urban materials, the air temperature responds slowly to changes in Ts. Therefore, the duration of temperature reduction caused by urbanization on T2m is longer than that on Ts.

Urbanization causes an increase in Ts by affecting net shortwave radiation and ground heat flux and leads to an increase in near-surface air temperature by influencing sensible heat flux and net longwave radiation. The season with the greatest impact of urbanization on net shortwave radiation, net longwave radiation, sensible heat flux, and ground heat flux is spring. This explains why urbanization causes the greatest increase in Ts and T2m in spring. Urbanization has a greater influence on net shortwave radiation and ground heat flux in the northern part of the city compared to the southern part during spring, summer, and autumn, resulting in a larger increase in Ts in the northern part of Urumqi in these seasons. Similarly, urbanization affects net longwave radiation and sensible heat flux more significantly in the north, favoring a greater increase in T2m in the north compared to southern Urumqi. Therefore, during spring, summer, and autumn, the increase in Ts and T2m due to urbanization is greater in the northern part of the city than in the south.

Author Contributions

Data curation: A.A. (Aerzuna Abulimiti) and J.T.; formal analysis: A.A. (Aerzuna Abulimiti) and Y.L.; investigation: A.A. (Aerzuna Abulimiti); methodology: A.A. (Aerzuna Abulimiti), J.T. and Y.L.; project administration: A.A. (Abuduwaili Abulikemu); resources: J.T., A.M., J.Y. and Y.Z.; software: A.A. (Aerzuna Abulimiti) and A.A. (Abuduwaili Abulikemu); supervision: A.A. (Abuduwaili Abulikemu) and Y.L.; validation: A.A. (Aerzuna Abulimiti), J.T. and A.A. (Abuduwaili Abulikemu); writing—original draft: A.A. (Aerzuna Abulimiti); writing—review and editing: A.A. (Aerzuna Abulimiti) and A.A. (Abuduwaili Abulikemu). All authors have read and agreed to the published version of the manuscript.

Funding

This work was sponsored by the National Natural Science Foundation of China (No. 42265003), Natural Science Foundation of Xinjiang Uygur Autonomous Region (No. 2022D01C359), Scientific and Technological Innovation Team (Tianshan Innovation Team) project (Grant No. 2022TSYCTD0007), The Sub-project of the Third Xinjiang Scientific Expedition (No. 2022xjkk030502), National Key Research and Development Program of China (No. 2019YFC151050102, No. 2018YFC1507103).

Data Availability Statement

The ERA5 data can be downloaded from https://rda.ucar.edu/datasets/ds083.2, accessed on 7 May 2023.

Acknowledgments

We thank the four anonymous reviewers and all editors for their valuable comments, suggestions and efforts during the handling of our manuscript. We also thank the High-Performance Computing Center of Nanjing University for performing the numerical calculations in this paper on its IBM Blade cluster system.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographical location and terrain altitude (shading, units: m) of the study area; the sky-blue thick solid line represents the boundary of the built-up area of Urumqi, and the black fine solid lines represent the administrative boundaries of Urumqi and its districts. The small red box in the small globe in the upper left corner shows the location of the study area from a broader perspective.

Figure 2. Terrain altitude (shading, DEM, units: m) and locations of the 20 meteorological stations (indicated by red dots) selected in this study to verify the simulation results. The blue line represents the outline of the built-up area of Urumqi, and the thin black lines indicate administrative boundaries of the districts in Urumqi.

Figure 3. (a) The geographic locations and terrain elevation (shading, DEM, units: m) of the WRF model domains, where d01 represents the outer domain, d02 represents the inner domain, and the thin black lines represent administrative boundaries of Xinjiang. (b) The location of the d01 domain on a map of China; the shading represents the terrain elevation (units: m).

Figure 4. (a) Land use categories (shading) derived from CLCD dataset in the d02 domain of the Urban (control) experiment of numerical simulation. The land use category of “urban and build-up” is highlighted with red underline in the color bar information. (b) The same as (a) but for the enlarged area (main study area) centered around the built-up area of Urumqi. The small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi. The black boxes indicate the proximity areas in different directions of the urban centroid of the built-up area of Urumqi, and the abbreviated letters NW, N, NE, W, C, E, S, and SE represent the corresponding northwestern, northern, northeastern, western, central, eastern, southern, and southeastern areas of Urumqi.

Figure 5. (a) Land use categories (shading) derived from CLCD dataset in the d02 domain of the NoUrban (sensitivity) experiment of numerical simulation. (b) The same as (a) but for the enlarged area, which shows the same area of Figure 4b. The black boxes and corresponding abbreviated letters (NW, N, NE, W, C, E, S, SE) represent the same locations of Urumqi, which are shown in Figure 4b. All of the original built-up areas were replaced by grasslands in the NoUrban (sensitivity) experiment of the numerical simulation. The small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi.

Figure 6. Scatter plots and linear regression fitting lines (small blue circles and red lines), with regression equations, Pearson’s correlation coefficient (r), and root mean square error (RMSE) shown in the top of each panel, showing the correspondence of the WRF numerical simulation results (monthly average value) with corresponding observational data from 2012 to 2021. (a–c) indicate the Tmax, Tmean, and Tmin at 2 m in spring, respectively; (d–f) represent the Tmax, Tmean, and Tmin at 2 m in summer, respectively; (g–i) present the Tmax, Tmean, and Tmin at 2 m in autumn, respectively; (j–l) show the Tmax, Tmean, and Tmin at 2 m in winter, respectively.

Figure 7. (a–d) Spatial distribution of the UE on the average surface temperature (Ts, unit: °C) in Urumqi in spring, summer, autumn, and winter, respectively. The black boxes indicate the proximity areas in different directions of the urban centroid of the built-up area of Urumqi, which are also shown in Figure 4b and Figure 5b; The blue line represents the outline of the built-up area of Urumqi, and the thin black lines indicate administrative boundaries of districts in Urumqi. The small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi. (e–h) The average values of Ts over the eight proximity areas (only calculated values for built-up area) in different directions of the urban centroid of the built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The abbreviated letters NW, N, NE, W, C, E, S, and SE in horizontal axis represent the corresponding eight areas shown in Figure 4b and Figure 5b, and ALL represents the average value of all built-up areas of Urumqi (i.e., the averaged value of all eight areas).

Figure 8. (a–d) Diurnal variation characteristics of UE on the average surface temperature (Ts, unit: °C) in different areas in different directions of the urban centroid of the built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The NW, N, NE, W, C, E, S, and SE represent the corresponding values of Ts calculated in eight areas (only calculated values for built-up area) shown in Figure 4b and Figure 5b, and ALL represents the average value of all built-up areas of Urumqi (i.e., the averaged value of all eight areas).

Figure 9. (a–d) Diurnal variation characteristics of average surface temperature (Ts, unit: °C) over the built-up area of Urumqi in Urban experiment, NoUrban experiment, and the UE on Ts in spring, summer, autumn, and winter, respectively.

Figure 10. (a–d) Spatial distribution of the UE on the average air temperature at 2 m (T2m, unit: °C) in Urumqi in spring, summer, autumn, and winter, respectively. The black boxes indicate the proximity areas in different directions of the urban centroid of the built-up area of Urumqi, which are also shown in Figure 4b and Figure 5b, and the small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi. (e–h) The average values over of T2m in the eight proximity areas (only calculated values on built-up area) in different directions of the urban centroid of built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The abbreviated letters NW, N, NE, W, C, E, S, SE in horizontal axis represent the corresponding eight areas shown in Figure 4b and Figure 5b, and ALL represents the average value of all built-up area of Urumqi (i.e., the averaged value of all eight areas).

Figure 11. (a–d) Diurnal variation characteristics of the UE on average air temperature at 2 m (T2m, unit: °C) in different areas in different directions of the urban centroid of built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The NW, N, NE, W, C, E, S, SE represent the corresponding values of T2m calculated in eight areas (only calculated values on built-up area) shown in Figure 4b and Figure 5b, and ALL represents the average value of all built-up area of Urumqi (i.e., the averaged value of all eight areas).

Figure 12. (a–d) Diurnal variation characteristics of average air temperature at 2 m (T2m, unit: °C) over the built-up area of Urumqi in the Urban experiment, NoUrban experiment, and the UE of Urumqi on T2m in spring, summer, autumn, and winter, respectively.

Figure 13. The UE on surface energy budget (SEB, unit: W m⁻²) in all built-up areas of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.

Figure 14. The UE on surface energy budget (SEB, unit: W m⁻²) in the northern part of the built-up area of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.

Figure 15. The UE on surface energy budget (SEB, unit: W m⁻²) in the southern part of the built-up area of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.

Figure 16. Difference in the UE on surface energy budget (SEB, unit: W m⁻²) between the northern and southern parts of the built-up area of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.

Table 1. Pearson’s correlation coefficient (r) statistics obtained by linear regression calculation between simulation and observation results (shown in Figure 6) of three basic meteorological elements (i.e., Tmean, Tmax, Tmin).

Seasons	Spring	Summer	Autumn	Winter
Tmax	0.991	0.874	0.996	0.756
Tmean	0.987	0.824	0.993	0.706
Tmin	0.977	0.763	0.987	0.697

Table 2. Root Mean Square Error (RMSE) statistics obtained by linear regression calculation between simulation and observation results (shown in Figure 6) of three basic meteorological elements (i.e., Tmean, Tmax, Tmin).

Seasons	Spring	Summer	Autumn	Winter
Tmax	0.824	0.894	0.721	1.145
Tmean	1.018	1.030	0.914	1.353
Tmin	1.209	1.234	1.147	1.424

Table 3. Comparison of the UE on surface temperature (Ts) and diurnal temperature range (DTR) (Urumqi vs. Chengdu).

	Urumqi (Present Study)	Chengdu (Yao et al. [56])
The UE on Ts (summer/winter)	2.09 °C/1.96 °C	2.8 °C/0.6 °C
The UE on DTR (summer/winter)	−1.55 °C/−0.44 °C	0.85 °C/0.6 °C

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MDPI and ACS Style

Abulimiti, A.; Liu, Y.; Tang, J.; Mamtimin, A.; Yao, J.; Zeng, Y.; Abulikemu, A. Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi. Remote Sens. 2024, 16, 2939. https://doi.org/10.3390/rs16162939

AMA Style

Abulimiti A, Liu Y, Tang J, Mamtimin A, Yao J, Zeng Y, Abulikemu A. Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi. Remote Sensing. 2024; 16(16):2939. https://doi.org/10.3390/rs16162939

Chicago/Turabian Style

Abulimiti, Aerzuna, Yongqiang Liu, Jianping Tang, Ali Mamtimin, Junqiang Yao, Yong Zeng, and Abuduwaili Abulikemu. 2024. "Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi" Remote Sensing 16, no. 16: 2939. https://doi.org/10.3390/rs16162939

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