Implications of temporal change in urban heat island intensity observed at Beijing and Wuhan stations

Ren, Guoyu; Chu, Ziying; Chen, Z. H.; Ren, Yue

doi:10.1029/2006gl027927

Cited by 188 publications

(148 citation statements)

References 9 publications

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“…Beijing is characterized by a warm temperature zone and has a typical continental monsoon climate with four distinct seasons. Rapid urbanization and city expansion started in the late 1980s, resulting in significant UHI effects [38,39]. Previous studies have found that the mean daily temperature in urban areas is 4.6 higher than the mean daily temperature in the suburbs [40].…”

Section: Study Areamentioning

confidence: 99%

Assessing the stability of annual temperatures for different urban functional zones

Sun

Chen

et al. 2013

Building and Environment

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a b s t r a c tThe urban functional zone (UFZ) is the basic unit of urban planning, which is defined as an area of similar social and economic functions. Despite the importance of UFZs, the stability of their annual temperature between winter and summer has seldom been investigated. With an understanding of the thermal impacts that planning decisions can have, it is essential to know how UFZs can be designed to regulate temperatures in the urban environment. 690 UFZs were identified using ALOS images in 2009 in Beijing. Land surface temperature (LST) was extracted from daytime Landsat TM (2002) and ASTER (2009) images. The regional LST variation of 31 district-sized sub-regions was correlated to the types of UFZs in the region and structural features of the region such as area, size, diversity, complexity and connectivity. Results showed that: (1) UFZ types, in order from highest to lowest LST variation, were commercial, campus, high density residential, water, recreational, low density residential, road, preservation, and agricultural zones; (2) the regional LST variation was positively correlated with the area of campus, commercial, high density residential, water, and road zones, but negatively correlated with the area of agricultural and low density residential zones; (3) increased connectivity and complexity decreased regional LST variations. The results indicated that the stability of annual temperatures was determined not only by the UFZ type and size but also by the connectivity and complexity. These results are clearly useful and essential pieces of information that can be applied in urban planning to improve climate adaptability.Crown

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Section: Study Areamentioning

confidence: 99%

Assessing the stability of annual temperatures for different urban functional zones

Sun

Chen

et al. 2013

Building and Environment

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“…The key of this method depends on whether or not those sites or stations can be objectively classified. In general, such a classification utilizes either population data [6][7][8][9][10][11][12][13][14][15][16][17] or satellite data (such as nighttime light imagery and land cover dataset) [18][19][20][21][22][23] as well as the geographic location of the stations. In addition, Empirical Orthogonal Function (EOF) [24,25], Principal Component Analysis (PCA) [26,27] and station metadata [28] can also be applied to define reference or rural stations.…”

Section: Citationmentioning

confidence: 99%

“…Some previous studies indicated that urbanization has little impact on regional warming [7,13,26,27]. However, recent investigations have suggested that the urbanization process can not only increase the local daily temperature, but also play an essential role in regional climate change [6,9,12,[14][15][16][17][20][21][22][23][24][28][29][30]35,36]. Using the OMR method, Zhou et al [29] and Zhang et al [30] found that the urbanization exerts a significant influence on temperature trends in eastern China.…”

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confidence: 99%

“…However, they found that the urban heat island effect is more remarkable in winter than summer, unlike that in the YRD region [20]. Ding and Dai [40] and Ren et al [14,15] argued that urbanization is one of major sources for uncertainties in climate change analysis. Yang et al [22] showed that the contribution of urbanization to the surface warming appears to be comparable to that of GHG in the YRD region.…”

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Urbanization and heterogeneous surface warming in eastern China

Yang

2013

Chin. Sci. Bull.

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With the homogeneity-adjusted surface air temperature (SAT) data at 312 stations in eastern China for 1979-2008 and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, the spatial heterogeneities of the SAT trends on different scales are detected with a spatial filtering (i.e. moving spatial anomaly) method, and the impact of urbanization in eastern China on surface warming is analyzed. Results show that the urbanization can induce a remarkable summer warming in Yangtze River Delta (YRD) city cluster region and a winter warming in Beijing-Tianjin-Hebei (BTH) city cluster region. The YRD warming in summer primarily results from the significant increasing of maximum temperature, with an estimated urban warming rate at 0.132-0.250°C per decade, accounting for 36%-68% of the total regional warming. The BTH warming in winter is primarily due to the remarkable increasing of minimum temperature, with an estimated urban warming rate at 0.102-0.214°C per decade, accounting for 12%-24% of the total regional warming. The temporal-spatial differences of urban warming effect may be attributed to the variation of regional climatic background and the change of anthropogenic heat release.heterogeneous surface warming, urbanization, surface air temperature, maximum temperature, minimum temperature, eastern China Citation:Wu K, Yang X Q. Urbanization and heterogeneous surface warming in eastern China. Chin Sci Bull, 2013Bull, , 58: 13631373, doi: 10.1007 Surface warming over recent five decades is attributed to natural climate change and anthropogenic forcing. The anthropogenic forcing mainly includes the emissions of greenhouse gases (GHG) and aerosols, as well as the land use/ cover change (LUCC) [1]. Relative to the enhanced greenhouse effect, however, the climatic effect of LUCC has not been drawn sufficient attention. The urbanization is one of the extreme processes in LUCC [2]. It can alter surface vegetation distribution, induce regional climate change and increase uncertainty in future climate projection [3]. Moreover, large-scale urbanization can affect regional surface energy and water balance [4], and thus may intensify the frequency of extreme weather/climate events. Since both GHG and urbanization tend to increase the surface air temperature (SAT), it is quite difficult to estimate the relative contribution of either effect to the surface warming [5]. A variety of methods have been adopted to detect the climatic effect of urbanization. The most conventional and direct method is the so-called urban-minus-rural (UMR) method in which the SAT at urban and rural sites is contrasted. The key of this method depends on whether or not those sites or stations can be objectively classified. In general, such a classification utilizes either population data [6][7][8][9][10][11][12][13][14][15][16][17] or satellite data (such as nighttime light imagery and land cover dataset) [18][19][20][21][22][23] as well as the geographic location of the stations. In addition, Empirical O...

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“…In addition to the small population of the residential areas near stations and the similar physiographic characteristics to the target station, the reference stations are also required to have the continuous observation records with as possible as less the missing observational values. The two weather stations were ever used as reference stations in previous studies of urbanization effect on the SAT trends of Beijing station Ren et al 2007). …”

Section: Methodsmentioning

confidence: 99%

Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality

Zhang

Ren

et al. 2013

Theor Appl Climatol

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Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are −0.004°C/10 year and −0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change.

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Implications of temporal change in urban heat island intensity observed at Beijing and Wuhan stations

Cited by 188 publications

References 9 publications

Assessing the stability of annual temperatures for different urban functional zones

Assessing the stability of annual temperatures for different urban functional zones

Urbanization and heterogeneous surface warming in eastern China

Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality

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