Spatio-temporal variance and meteorological drivers of the urban heat island in a European city

Urban areas are especially vulnerable to high temperatures, which will intensify in the future due to climate change. Therefore, both good knowledge about the local urban climate as well as simple and robust methods for its projection are needed. This study has analysed the spatio-temporal variance of the mean nocturnal urban heat island (UHI) of Hamburg, with observations from 40 stations from different suppliers. The UHI showed a radial gradient with about 2 K in the centre mostly corresponding to the urban densities. Temporarily, it has a strong seasonal cycle with the highest values between April and September and an inter-annual variability of approximately 0.5 K. Further, synoptic meteorological drivers of the UHI were analysed, which generally is most pronounced under calm and cloud-free conditions. Considered were meteorological parameters such as relative humidity, wind speed, cloud cover and objective weather types. For the stations with the highest UHI intensities, up to 68.7 % of the variance could be explained by seasonal empirical models and even up to 76.6 % by monthly models.     

Summer temperature variability across four urban neighborhoods in Knoxville,Tennessee, USA

The urban heat island (UHI) is a well-documented effect of urbanization on local climate, identified by higher temperatures compared to surrounding areas, especially at night and during the warm season. The details of a UHI are city-specific, and microclimates may even exist within a given city. Thus, investigating the spatiotemporal variability of a city’s UHI is an ongoing and critical research need. We deploy ten weather stations across Knoxville, Tennessee, to analyze the city’s UHI and its differential impacts across urban neighborhoods: two each in four neighborhoods, one in more dense tree cover and one in less dense tree cover, and one each in downtown Knoxville and Ijams Nature Center that serve as control locations. Three months of temperature data (beginning 2 July 2014) are analyzed using paired-sample t tests and a three-way analysis of variance. Major findings include the following: (1) Within a given neighborhood, tree cover helps negate daytime heat (resulting in up to 1.19 ^°C lower maximum temperature), but does not have as large of an influence on minimum temperature; (2) largest temperature differences between neighborhoods occur during the day (0.38–1.16 ^°C difference), but larger differences between neighborhoods and the downtown control occur at night (1.04–1.88 ^°C difference); (3) presiding weather (i.e., air mass type) has a significant, consistent impact on the temperature in a given city, and lacks the differential impacts found at a larger-scale in previous studies; (4) distance from city center does not impact temperature as much as land use factors. This is a preliminary step towards informing local planning with a scientific understanding of how mitigation strategies may help minimize the UHI and reduce the effects of extreme weather on public health and well-being.     

Temporal characteristics of the Beijing urban heat island

Summary This paper describes the inter-annual trend, and the seasonal and hourly variation of the near surface urban heat island (UHI) in Beijing. The surface air temperature data (mean, maximum, and minimum) from one urban (downtown Beijing) and one rural (70 km from downtown Beijing) station were used for the period 1977 and 2000. It is found that the temperatures in both urban and rural stations show an increasing tendency. Specifically, minimum temperature shows the greatest tendency at the urban station whereas maximum temperature shows the greatest increase at the rural station. The UHI intensity obtained by calculating the difference in temperatures between the two stations identifies that the intensity is greatest and has the greatest increasing trend for minimum temperature, while the UHI intensity of maximum temperature shows a slow decrease over time. UHI intensity for minimum temperature has a strong positive correlation with the increase in the urban population and the expansion of the yearly construction area. Seasonal analyses showed the UHI intensity is strongest in winter. This seasonal UHI variation tends to be negatively correlated with the seasonal variation of relative humidity and vapor pressure. Hourly variation reveals that the strongest UHI intensity is observed in the late nighttime or evening, while the weakest is observed during the day.     

Desert heat island study in winter by mobile transect and remote sensing techniques

A familiar problem in urban environments is the urban heat island (UHI), which potentially increases air conditioning demands, raise pollution levels, and could modify precipitation patterns. The magnitude and pattern of UHI effects have been major concerns of a lot of urban environment studies. Typically, research on UHI magnitudes in arid regions (such as Phoenix, AZ, USA) focuses on summer. UHI magnitudes in Phoenix (more than three million population) attain values in excess of 5°C. This study investigated the early winter period—a time when summer potential evapotranspiration >250 mm has diminished to <90 mm. An analysis of the winter magnitude of the heat island in Phoenix has been studied very little, and therefore with the aid of automobile transects, fixed stations, and remote sensing techniques, we investigated a portion of the large Phoenix metropolitan area known as the East Valley. The eastern fringes of the metropolitan area abut against breaks in sloping terrain. The highest UHI intensity observed was >8.0°C, comparable to summertime UHI conditions. Through analysis of the Oke (1998) weather factor Φ_W, it was determined thermally induced nighttime cool drainage winds could account for inflating the UHI magnitude in winter.     

Synoptic drivers of 400 years of summer temperature and precipitation variability on Mt. Olympus,Greece

The Mediterranean region has been identified as a global warming hotspot, where future climate impacts are expected to have significant consequences on societal and ecosystem well-being. To put ongoing trends of summer climate into the context of past natural variability, we reconstructed climate from maximum latewood density (MXD) measurements of Pinus heldreichii (1521–2010) and latewood width (LWW) of Pinus nigra (1617–2010) on Mt. Olympus, Greece. Previous research in the northeastern Mediterranean has primarily focused on inter-annual variability, omitting any low-frequency trends. The present study utilizes methods capable of retaining climatically driven long-term behavior of tree growth. The LWW chronology corresponds closely to early summer moisture variability (May–July, r = 0.65, p < 0.001, 1950–2010), whereas the MXD-chronology relates mainly to late summer warmth (July–September, r = 0.64, p < 0.001; 1899–2010). The chronologies show opposing patterns of decadal variability over the twentieth century (r = ?0.68, p < 0.001) and confirm the importance of the summer North Atlantic Oscillation (sNAO) for summer climate in the northeastern Mediterranean, with positive sNAO phases inducing cold anomalies and enhanced cloudiness and precipitation. The combined reconstructions document the late twentieth—early twenty-first century warming and drying trend, but indicate generally drier early summer and cooler late summer conditions in the period ~1700–1900 CE. Our findings suggest a potential decoupling between twentieth century atmospheric circulation patterns and pre-industrial climate variability. Furthermore, the range of natural climate variability stretches beyond summer moisture availability observed in recent decades and thus lends credibility to the significant drying trends projected for this region in current Earth System Model simulations.     

Simulation of urban climate with high-resolution WRF model: A case study in Nanjing, China

In this study, urban climate in Nanjing of eastern China is simulated using 1-km resolution Weather Research and Forecasting (WRF) model coupled with a single-layer Urban Canopy Model. Based on the 10-summer simulation results from 2000 to 2009 we find that the WRF model is capable of capturing the high-resolution features of urban climate over Nanjing area. Although WRF underestimates the total precipitation amount, the model performs well in simulating the surface air temperature, relative humidity, and precipitation frequency and inter-annual variability. We find that extremely hot events occur most frequently in urban area, with daily maximum (minimum) temperature exceeding 36°C (28°C) in around 40% (32%) of days. Urban Heat Island (UHI) effect at surface is more evident during nighttime than daytime, with 20% of cases the UHI intensity above 2.5°C at night. However, The UHI affects the vertical structure of Planet Boundary Layer (PBL) more deeply during daytime than nighttime. Net gain for latent heat and net radiation is larger over urban than rural surface during daytime. Correspondingly, net loss of sensible heat and ground heat are larger over urban surface resulting from warmer urban skin. Because of different diurnal characteristics of urban-rural differences in the latent heat, ground heat and other energy fluxes, the near surface UHI intensity exhibits a very complex diurnal feature. UHI effect is stronger in days with less cloud or lower wind speed. Model results reveal a larger precipitation frequency over urban area, mainly contributed by the light rain events (< 10 mm d^?1). Consistent with satellite dataset, around 10?C20% more precipitation occurs in urban than rural area at afternoon induced by more unstable urban PBL, which induces a strong vertical atmospheric mixing and upward moisture transport. A significant enhancement of precipitation is found in the downwind region of urban in our simulations in the afternoon.     

Urban heat island in a coastal urban area in northern Spain

This work examines the characteristics of the urban heat island (UHI) in a medium-sized city in northern Spain (Bilbao) using 5-year climate data (2005–2009) and the results of three specific measurement campaigns (2009–2010). Urban climate variables are not only compared with those in rural sites but also local climatic differences occurring inside the city are analysed. The findings presented in this paper show the influence of complex topography and sea/land breeze in the urban climate. Spatial characteristics and temporal evolution of UHI is presented. Hourly maximum temperature anomaly (ΔT _{u–r, max}) occurs just after sunrise and an urban cold island (UCI) is developed after midday. Along the year, mean UHI intensity is highest in autumn and the UCI effect increases in spring and summer in relation with sea breeze cooling potential. Diurnal and seasonal variation of air flow patterns appear to influence significantly on UHI intensity.     

The surface urban heat island in the city of Brno (Czech Republic) derived from land surface temperatures and selected reasons for its spatial variability

Thermal infrared images from Landsat satellites are used to derive land surface temperatures (LST) and to calculate the intensity of the surface urban heat island (UHI) during the summer season in and around the city of Brno (Czech Republic). Overall relief, land use structure, and the distribution of built-up areas determine LST and UHI spatial variability in the study area. Land-cover classes, amount and vigor of vegetation, and density of built-up areas are used as explanatory variables. The highest LST values typically occur in industrial and commercial areas, which contribute significantly to surface UHI intensity. The intensity of surface UHI, defined as the difference between mean LST for urban and rural areas, reached 4.2 and 6.7 °C in the two images analyzed. Analysis of two surface characteristics in terms of the amount of vegetation cover, represented by normalized difference vegetation index, demonstrates the predominance of LST variability (56–67 % of explained variance) over the degree of urbanization as represented by density of buildings (37–40 % of LST variance).     

阳泉市城市热岛效应特征分析

利用阳泉市3个国家级气象站资料分析了阳泉市城市热岛效应的年际变化、季节变化、月变化和日变化特征,结果表明：阳泉市存在弱的城市热岛效应,1972年-2011年平均热岛强度0．554℃。阳泉市热岛强度冬、秋季强,春、夏季弱;12月最强,5月最弱;阳泉市热岛强度整体呈显著上升趋势,热岛强度的增加主要是由于夏季热岛强度的增强。热岛强度日变化表现为12时最小,从傍晚开始随降温逐渐增大,到早晨气温降到最低时最大,日出之后迅速减小;2008年-2011年最强热岛强度出现在2010年1月14日08时达7．9℃。阳泉市主要城市发展因子与霾日数、气温呈显著正相关,在目前的经济发展水平条件下,城市化发展可能使阳泉城市温度增高,城市绿地面积的增加可能对热岛效应有缓解。     

Temporal variability of the Buenos Aires, Argentina, urban heat island

This paper describes the statistical characteristics and temporal variability of the urban heat island (UHI) intensity in Buenos Aires using 32-year surface meteorological data with 1-h time intervals. Seasonal analyses show that the UHI intensity is strongest during summer months and an “inverse” effect is found frequently during the afternoon hours of the same season. During winter, the UHI effect is in the minimal. The interannual trend and the seasonal variation of the UHI for the main synoptic hours for a longer record of 48?years are studied and associated to changes in meteorological factors as low-level circulation and cloud amount. Despite the population growth, it was found a negative trend in the nocturnal UHI intensity that could be explained by a decline of near clear-sky conditions, a negative trend in the calm frequencies and an increase in wind speed. Urban to rural temperature differences and rural temperatures are negatively correlated for diurnal and nocturnal hours both for annual and seasonal scales. This result is due to the lower interannual variability of urban temperatures in comparison to rural ones.     

Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island

The urban thermal environment varies not only from its rural surroundings but also within the urban area due to intra-urban differences in land-use and surface characteristics. Understanding the causes of this intra-urban variability is a first step in improving urban planning and development. Toward this end, a method for quantifying causes of spatial variability in the urban heat island has been developed. This paper presents the method as applied to a specific test case of Portland, Oregon. Vehicle temperature traverses were used to determine spatial differences in summertime ~2 m air temperature across the metropolitan area in the afternoon. A tree-structured regression model was used to quantify the land-use and surface characteristics that have the greatest influence on daytime UHI intensity. The most important urban characteristic separating warmer from cooler regions of the Portland metropolitan area was canopy cover. Roadway area density was also an important determinant of local UHI magnitudes. Specifically, the air above major arterial roads was found to be warmer on weekdays than weekends, possibly due to increased anthropogenic activity from the vehicle sector on weekdays. In general, warmer regions of the city were associated with industrial and commercial land-use. The downtown core, whilst warmer than the rural surroundings, was not the warmest part of the Portland metropolitan area. This is thought to be due in large part to local shading effects in the urban canyons.     

北京中央商务区(CBD)城市热岛效应的研究

利用2012~2013年北京中央商务区（Central Business District,CBD）加密观测资料,分析CBD区域城市热岛（Urban Heat Island,UHI）强度日变化和空间变化特征及其影响因子。研究发现,CBD区域气温高于周边自动站气温,平均偏高0.64℃;CBD区域城市热岛强度呈现夜间强、白天弱的现象,中午甚至存在“城市冷岛”现象。季节平均UHI日变化表现为:在夜间,秋季最强,冬季次之,春季和夏季较弱;在白天,夏季最强,冬季次之,春季和秋季较弱。相对于晴朗无风天气,雾、雨、大风等天气对城市热岛有抑制作用,并结合小波分析结果发现,秋季城市热岛强度强于冬季是由于冬季雾、雨、大风等天气过程发生比例较高的缘故。CBD区域城市热岛空间变化特征研究发现,花园、学校等绿地有助于缓解城市热岛效应。雾日、雨日和大风日的CBD区域城市热岛强度空间变化标准差比晴朗无风日小。     

Large-Eddy Simulation of Mesoscale Circulations Forced by Inhomogeneous Urban Heat Island

The large-eddy simulation mode of the Weather Research and Forecasting model is employed to simulate the planetary boundary-layer characteristics and mesoscale circulations forced by an ideal urban heat island (UHI). In our simulations, the horizontal heterogeneity of the UHI intensity distribution in urban areas is considered and idealized as a cosine function. Results indicate that the UHI heating rate and the UHI intensity heterogeneity affect directly the spatial distribution of the wind field; a stronger UHI intensity produces a maximum horizontal wind speed closer to the urban centre. The strong advection of warm air from the urban area to the rural area in the upper part of the planetary boundary-layer causes a more stable atmospheric stratification over both the urban and rural areas. The mesoscale sensible heat flux caused by the UHI circulation increases with UHI intensity but vanishes when the background wind speed is sufficiently high $(>$ 3.0  $\mathrm{{m\,s}}^{-1})$ .     

Characterization and estimation of urban heat island at Toronto: impact of the choice of rural sites

In this study, the urban heat island of Toronto was characterized and estimated in order to examine the impact of the selection of rural sites on the estimation of urban heat island (UHI) intensity (?T _u-r). Three rural stations, King Smoke Tree (KST), Albion Hill, and Millgrove, were used for the analysis of UHI intensity for two urban stations, Toronto downtown (Toronto) and Toronto Pearson (Pearson) using data from 1970 to 2000. The UHI intensity was characterized as winter dominating and summer dominating, depending on the choice of the rural station. The analyses of annual and seasonal trends of ?T _u-r suggested that urban heat island clearly appears in winter at both Toronto and Pearson. However, due to the mitigating effect on temperature from Lake Ontario, the estimated trend of UHI intensity was found to be less at Toronto compared to that at Pearson which has no direct lake effect. In terms of the impacts of the rural stations, for both KST and Millgrove, the trends in UHI intensity were found to be statistically significant and also were in good agreement with the estimates of UHI intensities reported for other large cities in the USA. Depending on the choice of the rural station, the estimated trend for the UHI intensity at Toronto ranges from 0.01°C/decade to 0.02°C/decade, and that at Pearson ranges from 0.03°C/decade to 0.035°C/decade during 1970–2000. From the analysis of the seasonal distribution of ?T _u-r, the UHI intensity was found to be higher at Toronto in winter than that at Pearson for all three rural stations. This was likely accounted for by the lower amount of anthropogenic heat flux at Pearson. Considering the results from the statistical analysis with respect to the geographic and surface features for each rural station, KST was suggested to be a better choice to estimate UHI intensity at Toronto compared to the other rural stations. The analysis from the current study suggests that the selection of a unique urban–rural pair to estimate UHI intensity for a city like Toronto is a critical task, as it will be for any city, and it is imperative to consider some key features such as the physiography, surface characteristics of the urban and rural stations, the climatology such as the trends in annual and seasonal variation of UHI with respect to the physical characteristics of the stations, and also more importantly the objectives of a particular study in the context of UHI effect.     

基于MODIS的安徽省代表城市热岛效应时空特征

利用2001—2010年覆盖安徽省的MODIS数据,选取在气候、地理、城市化等方面具有代表性的合肥、芜湖、阜阳作为研究对象,并结合GIS技术,分析地表温度的日变化及季节变化特征,得到安徽省代表城市热岛效应的时空分布。结果表明:安徽省省会合肥的热岛效应最为显著,安徽省南部代表城市芜湖的热岛效应强于北部代表城市阜阳, 同时具有显著的日变化和季节变化特征。近10年来,安徽代表城市热岛面积和热岛强度均呈增加趋势,但合肥热岛强度大于3 ℃的极端热岛效应有一定缓解。白天大片水体对缓解城市的热岛效应作用明显,而夜晚则不明显,甚至成为地表温度的高值中心。夏季地表温度与归一化植被指数的负相关最显著,即提高城市植被覆盖度对降低地表温度和缓解城市热岛效应有重要影响。     

土壤湿度影响中国夏季气候的数值试验

利用"全球土壤湿度计划第2阶段"提供的土壤湿度资料强迫区域气候模式RegCM3,通过数值试验讨论了土壤湿度对东亚夏季气候模拟效果的影响。结果表明,合理考虑土壤湿度的作用,能够提高区域气候模式对中国夏季降水和2 m气温的空间分布型及逐日变化的模拟效果;模拟结果与观测的相关分析显示,降水和2 m气温的年际变化都得到了有效改进,这种改进在气温上尤为明显。不过上述改进具有区域依赖性。数值试验结果表明,气温对土壤湿度的敏感性强于降水,这也从一个侧面说明提高降水模拟效果的难度。总体而言,合理的土壤湿度能够提高区域气候模式对中国夏季气候的模拟能力。因此,合理描述土壤湿度的变化,是提高中国夏季气候预报技巧的潜在途径之一。     

A NUMERICAL STUDY ON THE INFLUENCES OF URBAN PLANNING AND CONSTRUCTION ON THE SUMMER URBAN HEAT ISLAND IN THE METROPOLIS OF SHENZHEN

By means of the regional boundary layer model (RBLM), a study on the influences of the urban planning and construction on the summer urban heat island (UHI) in the metropolis of Shenzhen is performed. In the study, the current summer UHI distribution, the influences of the increasing high-density construction and the energy consumption on the summer air temperature distribution, and the influences of the urban ventilation corridor on the summer air temperature distribution are numerically analyzed. Some conclusions are drawn in the light of the study: (1) The summer UHI is more obvious in day time than that in night time in the summer of Shenzhen, and the maximum values of UHI intensity in the day time appear in the areas with high-density construction, which are located in Nanshan, Futian and Luohu and western Bao′an districts. (2) The increase of construction density and energy consumption in the urban area will lead to the increase of temperature near the ground, and the increase of temperature at nighttime is more obvious than that at daytime. (3) The ventilation corridor can effectively reduce the UHI intensity and can be taken as a method to eliminate the negative climatic effect caused by the increase of high-density construction and energy consumption in the future.     

基于MODIS资料分析近15年广州城市热岛特征及演变规律

利用MODIS地表温度数据,计算城市热岛强度指数,分析近15年广州市城市热岛的时空分布特征及演变规律,并结合气象观测数据、社会统计数据定性分析其主要影响因素。结果表明:广州市城市热岛的空间分布受地形地貌影响明显,负热岛区主要分布于森林密集的北部山区,无热岛区主要分布于中部低山丘陵区域,热岛区主要分布于高度城市化的中南部平原区。关于城市热岛的日变化规律,白天热岛区、负热岛区面积均小于夜间,但白天热岛区强度、负热岛区强度大于夜间。关于城市热岛的季节变化规律,冬季热岛区面积最大,热岛强度最小,夏季热岛区面积最小,热岛强度最大;冬季负热岛区面积最小,负热岛强度最小,夏季负热岛区面积最大,负热岛强度最大。对于城市热岛的年际变化规律,近15年来广州市的热岛区、负热岛区占全市总面积的百分比呈上升趋势,无热岛区所占百分比呈下降趋势,人为热排放在城市中心区域的持续增长,加上区内建筑物密度大、植被覆盖度低,导致了热岛区的增加,而北部山区至中部丘陵山区的植被的持续好转,加上地理特征限制了该区域的城市化发展,导致了负热岛区的增加。      

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding,Uncertainties, and Future Research Directions

Urban environments lie at the confluence of social,cultural,and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures.The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island(UHI)effect,referring to the higher temperature in cities compared to their natural surroundings.Besides the UHI effect and heat waves,urbanization also impacts atmospheric moisture,wind,boundary layer structure,cloud formation,dispersion of air pollutants,precipitation,and storms.In this review article,we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies.We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.     

Relationship Between Fine-Particle Pollution and the Urban Heat Island in Beijing,China: Observational Evidence

Urbanization has led to a significant urban heat island (UHI) effect in Beijing in recent years. At the same time, air pollution caused by a large number of fine particles significantly influences the atmospheric environment, urban climate, and human health. The distribution of fine particulate matter (PM_2.5) concentration and its relationship with the UHI effect in the Beijing area are analyzed based on station-observed hourly data from 2012 to 2016. We conclude that, (1) in the last five years, the surface concentrations of PM_2.5 averaged for urban and rural sites in and around Beijing are 63.2 and 40.7 µg m^?3, respectively, with significant differences between urban and rural sites (ΔPM_2.5) at the seasonal, monthly and daily scales observed; (2) there is a large correlation between ΔPM_2.5 and the UHI intensity defined as the differences in the mean (ΔT_ave), minimum (ΔT_min), and maximum (ΔT_max) temperatures between urban and rural sites. The correlation between ΔPM_2.5 and ΔT_min (ΔT_max) is the highest (lowest); (3) a Granger causality analysis further shows that ΔPM_2.5 and ΔT_min are most correlated for a lag of 1–2 days, while the correlation between ΔPM_2.5 and ΔT_ave is lower; there is no causal relationship between ΔPM_2.5 and ΔT_max; (4) a case analysis shows that downwards shortwave radiation at the surface decreases with an increase in PM_2.5 concentration, leading to a weaker UHI intensity during the daytime. During the night, the outgoing longwave radiation from the surface decreases due to the presence of daytime pollutants, the net effect of which is a slower cooling rate during the night in cities than in the suburbs, leading to a larger ΔT_min.