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Characterizing the Urban Temperature Trend Using Seasonal Unit Root Analysis: Hong Kong from 1970 to 2015


doi: 10.1007/s00376-016-6113-z

  • This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169°C (10 yr)-1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data —— much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865°C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.
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  • Beaulieu J. J., J. A. Miron, 1993: Seasonal unit roots in aggregate U.S. data. Journal of Econometrics, 55( 1-2), 305- 328.10.1016/0304-4076(93)90019-2b59a900281f093550dfdd4ed97706e0fhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F030440769390018Zhttp://www.sciencedirect.com/science/article/pii/030440769390018ZIn this paper we provide evidence on the presence of seasonal unit roots in aggregate U.S. data. The analysis is conducted using the approach developed by Hyllebcrg, Engle, Granger and Yoo (1990). We first derive the mechanics and asyrnptotics of the HEGY procedure for monthly data and use Monte Carlo methods to compute the finite sample critical values of the associated test statistics. We then apply quarterly and monthly HEGY procedures to aggregate U.S. data. The data reject the presence of unit roots at most seasonal frequencies in a large fraction of the series considered.
    Camilloni I., V. Barros, 1997: On the urban heat island effect dependence on temperature trends. Climatic Change, 37( 4), 665- 681.10.1023/A:1005341523032e02e2f916a33f742613455abfa58583fhttp%3A%2F%2Flink.springer.com%2Farticle%2F10.1023%2FA%253A1005341523032http://link.springer.com/article/10.1023/A%3A1005341523032For U.S., Argentine and Australian cities, yearly mean urban to rural temperature differences (T u-r ) and rural temperatures (T r ) are negatively correlated in almost every case, suggesting that urban heat island intensity depends, among other parameters on the temperature itself. This negative correlation is related to the fact that interannual variability of temperature is generally lower in urban environments than in rural areas. This seems to hold true at low frequencies leading to opposite trends in the two variables. Hence, urban stations are prone to have lower trends in absolute value than rural ones. Therefore, regional data sets including records from urban locations, in addition to urban growth bias may have a second type of urban bias associated with temperature trends. A bulk estimate of this second urban bias trend for the contiguous United States during 1901-1984 indicates that it could be of the same order as the urban growth bias and of opposite sign. If these results could be extended to global data, it could be expected that the spurious influence of urban growth on global temperature trends during warming periods will be offset by the diminishing of the urban heat island intensity.
    Chan H. S., M. H. Kok, and T. C. Lee, 2012: Temperature trends in Hong Kong from a seasonal perspective. Climate Research, 55( 1), 53- 63.10.3354/cr01133902ccfb3-29a6-40ee-b1fe-4d7c006aef6aWOS:000311164800004b6e01fec64f6c781adc6922346d28131http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F272864398_Temperature_trends_in_Hong_Kong_from_a_seasonal_perspectivehttp://www.researchgate.net/publication/272864398_Temperature_trends_in_Hong_Kong_from_a_seasonal_perspectiveABSTRACT: We examined the seasonal trends of mean and extreme temperatures in Hong Kong using data from 1885-2010. The analysis revealed that the daily maximum temperature (T), daily mean temperature (T), and daily minimum temperature (T) of Hong Kong had a significant long-term increasing trend in all 4 seasons and that the warming trend was more prominent in winter and spring. The relatively higher rate of increase in temperatures in winter and spring could be attributed to local urbanization effects and the weakening of the East Asian winter monsoon in the last few decades. For extreme indices, we observed a significant increase in the hot indices (TN90p and TX90p) and a significant decrease in the cold indices (TX10p and TN10p) in all seasons. The seasonal variations in the heating and cooling degree-days (HDD and CDD) also indicated that CDD in spring, summer, and autumn had a significant increasing trend, while HDD in spring, autumn and winter had a decreasing trend. Analysis of the hot and cool periods in Hong Kong showed a significant decreasing (increasing) trend in the number of cool (hot) days. Also, the cool (hot) period has become shorter (longer) over the last century.
    Chattopadhyay S., D. R. Edwards, 2016: Long-term trend analysis of precipitation and air temperature for Kentucky, United States. Climate, 4,10, doi: 10.3390/cli4010010.10.3390/cli4010010357547b65e0bc12cfa23d4f346c2c236http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F292994869_Long-Term_Trend_Analysis_of_Precipitation_and_Air_Temperature_for_Kentucky_United_Stateshttp://www.researchgate.net/publication/292994869_Long-Term_Trend_Analysis_of_Precipitation_and_Air_Temperature_for_Kentucky_United_StatesVariation in quantities such as precipitation and temperature is often assessed by detecting and characterizing trends in available meteorological data. The objective of this study was to determine the long-term trends in annual precipitation and mean annual air temperature for the state of Kentucky. Non-parametric statistical tests were applied to homogenized and (as needed) pre-whitened annual series of precipitation and mean air temperature during 1950-2010. Significant trends in annual precipitation were detected (both positive, averaging 4.1 mm/year) for only two of the 60 precipitation-homogenous weather stations (Calloway and Carlisle counties in rural western Kentucky). Only three of the 42 temperature-homogenous stations demonstrated trends (all positive, averaging 0.01 C/year) in mean annual temperature: Calloway County, Allen County in southern-central Kentucky, and urbanized Jefferson County in northern-central Kentucky. In view of the locations of the stations demonstrating positive trends, similar work in adjacent states will be required to better understand the processes responsible for those trends and to properly place them in their larger context, if any.
    Chen K., L. Huang, L. Zhou, Z. W. Ma, J. Bi, and T. T. Li, 2015: Spatial analysis of the effect of the 2010 heat wave on stroke mortality in Nanjing, China. Scientific Reports, 5,10816, doi: 10.1038/srep10816.10.1038/srep1081626034864b13d25fb4ba29606dcc96b640b4c2b5ehttp%3A%2F%2Fpubmedcentralcanada.ca%2Fpmcc%2Farticles%2FPMC4451699%2Fhttp://pubmedcentralcanada.ca/pmcc/articles/PMC4451699/To examine the spatial variation of stroke mortality risk during heat wave, we collected 418 stroke mortality cases with permanent addresses for a severe heat wave (July 28–August 15, 2010) and 624 cases for the reference period (July 29–August 16, 2009 and July 27–August 14, 2011) in Nanjing, China. Generalized additive models were used to explore the association between location and stroke mortality risk during the heat wave while controlling individual-level risk factors. Heat wave vulnerability was then applied to explain the possible spatial variations of heat-wave-related mortality risk. The overall risk ratio (95% confidence intervals) of stroke mortality due to the heat wave in Nanjing was 1.34 (1.21 to 1.47). Geolocation was found to be significantly associated with the heat-wave-related stroke mortality risk. Using alternative reference periods generated similar results. A district-level risk assessment revealed similar spatial patterns. The highest stroke mortality risk observed in Luhe district was due to the combination of high heat exposure and high vulnerability. Our findings provide evidence that stroke mortality risk is higher in rural areas during heat waves and that these areas require future interventions to reduce vulnerability.
    Chen X. L., H. M. Zhao, P. X. Li, and Z. Y. Yin, 2006: Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ., 104, 133- 146.10.1016/j.rse.2005.11.016fe3b4d0a506618f7b216b47d998636a8http%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical_NSTL_QKJJ028423339.aspxhttp://d.wanfangdata.com.cn/Periodical_NSTL_QKJJ028423339.aspxGlobal warming has obtained more and more attention because the global mean surface temperature has increased since the late 19th century. As more than 50% of the human population lives in cities, urbanization has become an important contributor for global warming. Pearl River Delta (PRD) in Guangdong Province, southern China, is one of the regions experiencing rapid urbanization that has resulted in remarkable Urban Heat Island (UHI) effect, which will be sure to influence the regional climate, environment, and socio-economic development. In this study, Landsat TM and ETM+ images from 1990 to 2000 in the PRD were selected to retrieve the brightness temperatures and land use/cover types. A new index, Normalized Difference Bareness Index (NDBaI), was proposed to extract bare land from the satellite images. Additionally, Shenzhen, which has experienced the fastest urbanization in Guangdong Province, was taken as an example to analyze the temperature distribution and changes within a large city as its size expanded in the past decade. Results show that the UHI effect has become more prominent in areas of rapid urbanization in the PRD region. The spatial distribution of heat islands has been changed from a mixed pattern, where bare land, semi-bare land and land under development were warmer than other surface types, to extensive UHI. Our analysis showed that higher temperature in the UHI was located with a scattered pattern, which was related to certain land-cover types. In order to analyze the relationship between UHI and land-cover changes, this study attempted to employ a quantitative approach in exploring the relationship between temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBaI and temperature are negative when NDVI is limited in range, but positive correlation is shown between NDBI and temperature.
    Conti S., P. Meli, G. Minelli, R. Solimini, V. Toccaceli, M. Vichi, C. Beltrano, and L. Perini, 2005: Epidemiologic study of mortality during the summer 2003 heat wave in Italy. Environ. Res., 98( 3), 390- 399.10.1016/j.envres.2004.10.0091591079549cdc249d70b3d2b1b52d5d093e474b3http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0013935104002154http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM15910795Introduction : It is widely recognized that extreme climatic conditions during summer months may constitute a major public health threat. Owing to what is called the “urban heat island effect,” as well as to the consequences of heat waves on health, individuals living in cities have an elevated risk of death when temperature and humidity are high compared to those living in suburban and rural areas. Studies on heat wave-related mortality have further demonstrated that the greatest increases in mortality occur in the elderly. Following the unusually hot summer of 2003 and the dramatic news from neighboring countries such as France, the Italian Minister of Health requested the Istituto Superiore di Sanità-Bureau of Statistics to undertake an epidemiologic study of mortality in Italy during Summer 2003 to investigate whether there had been an excess of deaths, with a particular focus on the elderly population. Materials and methods : Communal offices, which maintain vital statistics, were asked for the individual records of death of residents registered daily during the period 1 June–31 August 2003 and during the same period of 2002 for each of the 21 capitals of the Italian regions. As it was necessary to obtain mortality data quickly from many municipalities and to make the analysis as soon as possible, the method adopted was comparison of mortality counts during the heat wave with figures observed during the same period of the previous year. Results : Compared with 2002, between 1 June and 31 August 2003, there was an overall increase in mortality of 3134 (from 20,564 to 23,698). The greatest increase was among the elderly; 2876 deaths (92%) occurred among people aged 75 years and older, a more than one-fifth increase (21.3%, from 13.517 to 16.393%). The highest increases were observed in the northwestern cities, which are generally characterized by cold weather, and in individuals 75 years and older: Turin (44.9%), Trento (35.2%), Milan (30.6%), and Genoa (22.2%). Of note are also the increases observed in two southern cities, L’Aquila (24.7%) and Potenza (25.4%), which are located, respectively, at 700 and 80002m above see level. For Bari and Campobasso, both in the South, with a typically hot summer climate, the increase during the last 15 days of August was 186.2 and 450%, respectively. Conclusions : The relationship between mortality and discomfort due to climatic conditions as well as the short lag time give a clear public health message: preventive, social, and health care actions must be administered to the elderly and the frail to avoid excess deaths during heat waves.
    Debbage N., J. M. Shepherd, 2015: The urban heat island effect and city contiguity. Computers, Environment and Urban Systems, 54, 181- 194.10.1016/j.compenvurbsys.2015.08.00219e8f7b6-baa5-4d35-83cc-024197943b0be472e7e533835577a6b11f14603b7a52http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0198971515300089refpaperuri:(91fd6856d815b8bfe7247affbed54748)http://www.sciencedirect.com/science/article/pii/S0198971515300089The spatial configuration of cities can affect how urban environments alter local energy balances. Previous studies have reached the paradoxical conclusions that both sprawling and high-density urban development can amplify urban heat island intensities, which has prevented consensus on how best to mitigate the urban heat island effect via urban planning. To investigate this apparent dichotomy, we estimated the urban heat island intensities of the 50 most populous cities in the United States using gridded minimum temperature datasets and quantified each city's urban morphology with spatial metrics. The results indicated that the spatial contiguity of urban development, regardless of its density or degree of sprawl, was a critical factor that influenced the magnitude of the urban heat island effect. A ten percentage point increase in urban spatial contiguity was predicted to enhance the minimum temperature annual average urban heat island intensity by between 0.3 and 0.4C. Therefore, city contiguity should be considered when devising strategies for urban heat island mitigation, with more discontiguous development likely to ameliorate the urban heat island effect. Unraveling how urban morphology influences urban heat island intensity is paramount given the human health consequences associated with the continued growth of urban populations in the future.
    Dickey D. A., W. A. Fuller, 1981: Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057- 1072.10.2307/19125175edc642292b033f4d5fcea23e6064cc9http%3A%2F%2Fwww.jstor.org%2Fstable%2F1912517http://www.jstor.org/stable/1912517No abstract is available for this item.
    Erdem, H. H., Coauthors, 2010: Thermodynamic analysis of an existing coal-fired power plant for district heating/cooling application. Applied Thermal Engineering, 30( 2-3), 181- 187.10.1016/j.applthermaleng.2009.08.003f2297198522005ee26b32ce67078a803http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1359431109002476http://www.sciencedirect.com/science/article/pii/S1359431109002476In a conventional coal-fired power plant, which is only designed for electricity generation, 2/3 of fuel energy is wasted through stack gases and cooling water of condensers. This waste energy could be recovered by trigeneration; modifying the plants in order to meet district heating/cooling demand of their locations. In this paper, thermodynamical analysis of trigeneration conversion of a public coal-fired power plant, which is designed only for electricity generation, has been carried out. Waste heat potentials and other heat extraction capabilities have been evaluated. Best effective steam extraction point for district heating/cooling system; have been identified by conducting energetic and exergetic performance analyses. Analyses results revealed that the low-pressure turbine inlet stage is the most convenient point for steam extraction for the plant analyzed.
    Franses P. H., 1991: Seasonality, non-stationarity and the forecasting of monthly time series. International Journal of Forecasting, 7( 2), 199- 208.10.1016/0169-2070(91)90054-Yc673fb7b73a29a49dd834484d364b42dhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F016920709190054Yhttp://www.sciencedirect.com/science/article/pii/016920709190054YNo abstract is available for this item.
    Franses P. H., B. Hobijn, 1997: Critical values for unit root tests in seasonal time series. Journal of Applied Statistics, 24( 1), 25- 48.10.1080/0266476972386477262c0208ed8521f0622df9086bba5ahttp%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Fabs%2F10.1080%2F02664769723864http://www.tandfonline.com/doi/abs/10.1080/02664769723864textabstractIn this paper, we present tables with critical values for a variety of tests for seasonal and non-seasonal unit roots in seasonal time series. We consider (extensions of) the Hylleberg et al. and Osborn et al. test procedures. These extensions concern time series with increasing seasonal variation and time series with structural breaks in the seasonal means. For each case, we give the appropriate auxiliary test regression, the test statistics, and the corresponding critical values for a selected set of sample sizes. We also illustrate the practical use of the auxiliary regressions for quarterly new car sales in the Netherlands. Supplementary to this paper, we provide Gauss programs with which one can generate critical values for particular seasonal frequencies and sample sizes.
    He Y. T., G. S. Jia, Y. H. Hu, and Z. J. Zhou, 2013: Detecting urban warming signals in climate records. Adv. Atmos. Sci.,30(4), 1143-1153, doi: 10.1007/s00376-012-2135-3.10.1007/s00376-012-2135-3b912ae38a0f9ce19f2928c71da59bd23http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-DQJZ201304015.htmhttp://d.wanfangdata.com.cn/Periodical_dqkxjz-e201304015.aspxDetermining whether air temperatures recorded at meteorological stations have been contaminated by the urbanization process is still a controversial issue at the global scale. With support of historical remote sensing data, this study examined the impacts of urban expansion on the trends of air temperature at 69 meteorological stations in Beijing, Tianjin, and Hebei Province over the last three decades. There were significant positive relations between the two factors at all stations. Stronger warming was detected at the meteorological stations that experienced greater urbanization, i.e., those with a higher urbanization rate. While the total urban area affects the absolute temperature values, the change of the urban area (urbanization rate) likely affects the temperature trend. Increases of approximately 10% in urban area around the meteorological stations likely contributed to the 0.13C rise in air temperature records in addition to regional climate warming. This study also provides a new approach to selecting reference stations based on remotely sensed urban fractions. Generally, the urbanization-induced warming contributed to approximately 44.1% of the overall warming trends in the plain region of study area during the past 30 years, and the regional climate warming was 0.30C (10 yr) -1 in the last three decades.
    Hinkel K. M., F. E. Nelson, A. E. Klene, and J. H. Bell, 2003: The urban heat island in winter at Barrow, Alaska. International Journal of Climatology, 23( 15), 1889- 1905.10.1002/joc.971ef0d219972276d0ba5e2522c64d74885http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjoc.971%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/joc.971/fullThe village of Barrow, Alaska, is the northernmost settlement in the USA and the largest native community in the Arctic. The population has grown from about 300 residents in 1900 to more than 4600 in 2000. In recent decades, a general increase of mean annual and mean winter air temperature has been recorded near the centre of the village, and a concurrent trend of progressively earlier snowmelt in the village has been documented. Satellite observations and data from a nearby climate observatory indicate a corresponding but much weaker snowmelt trend in the surrounding regions of relatively undisturbed tundra. Because the region is underlain by ice-rich permafrost, there is concern that early snowmelt will increase the thickness of the thawed layer in summer and threaten the structural stability of roads, buildings, and pipelines. Here, we demonstrate the existence of a strong urban heat island (UHI) during winter. Data loggers (54) were installed in the 150 km2 study area to monitor hourly air and soil temperature, and daily spatial averages were calculated using the six or seven warmest and coldest sites. During winter (December 2001-March 2002), the urban area averaged 2.2 C warmer than the hinterland. The strength of the UHI increased as the wind velocity decreased, reaching an average value of 3.2 C under calm (<2 m s-1) conditions and maximum single-day magnitude of 6 C. UHI magnitude generally increased with decreasing air temperature in winter, reflecting the input of anthropogenic heat to maintain interior building temperatures. On a daily basis, the UHI reached its peak intensity in the late evening and early morning. There was a strong positive relation between monthly UHI magnitude and natural gas production/use. Integrated over the period September-May, there was a 9% reduction in accumulated freezing degree days in the urban area. The evidence suggests that urbanization has contributed to early snowmelt in the village.
    HKEMSD, 2005a: Hong Kong Energy End-Use Data 1993-2003. Hong Kong Electrical and Mechanical Services Department,Hong Kong. p.3.0a69045c66f4b8e097a155253a4d62e5http%3A%2F%2Fwww.mendeley.com%2Fresearch%2Fhong-kong-energy-enduse-data%2Fhttp://www.mendeley.com/research/hong-kong-energy-enduse-data/
    HKEMSD, 2015b: Hong Kong Energy End-Use Data 2003-2013. Hong Kong Electrical and Mechanical Services Department,Hong Kong. p.60.0a69045c66f4b8e097a155253a4d62e5http%3A%2F%2Fwww.mendeley.com%2Fresearch%2Fhong-kong-energy-enduse-data%2Fhttp://www.mendeley.com/research/hong-kong-energy-enduse-data/
    Hu X. M., M. Xue, P. M. Klein, B. G. Illston, and S. Chen, 2016: Analysis of urban effects in Oklahoma City using a dense surface observing network. J. Appl. Meteor. Climatol., 55, 723- 741.10.1175/JAMC-D-15-0206.137f4daf058e39a07a4b651ea2d56f8d4http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2016JApMC..55..723Hhttp://adsabs.harvard.edu/abs/2016JApMC..55..723HNot Available Not Available
    Hu Y. H., G. S. Jia, 2010: Influence of land use change on urban heat island derived from multi-sensor data. International Journal of Climatology, 30( 9), 1382- 1395.10.1002/joc.1984ea39954f34f1b21f857ed0a82d9f2ab1http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20103269003.htmlhttp://onlinelibrary.wiley.com/doi/10.1002/joc.1984/fullAbstract Top of page Abstract 1.Introduction 2.Methods 3.Results and discussion 5.Conclusions Acknowledgements References Regional climate change was demonstrated to be likely influenced by anthropogenic dominated land surface processes. Urban heat island (UHI) is one of the important outcomes of such land surface processes induced by urbanization, and it is an urban climate phenomenon influenced by land use pattern and it represents the difference in albedo, roughness, and heat flux exchange of land surface. This study tries to examine the influence of land use change on UHI in greater Guangzhou from 1980-2007 by analysing Landsat MSS/TM/ETM+ and MODIS satellite data, meteorological records, and census data. An integrated and modified single-channel method was used to retrieve land surface temperature (LST). Decadal changes in land use fraction and UHI pattern show that cropland decreased in parallel to the increase in built-up area and the correlation coefficient reached 0.97. The UHI effect expanded from urban areas to surrounding suburban areas and countryside with an increase in land surface temperature (mean LST increased by 2.48 from 1990 to 2007) and a decrease in the green vegetation fraction (GVF) (mean GVF decreased by 0.16 from 1990 to 2007). The spatial heterogeneity of UHI expansion can be explained by spatial patterns of economic development, population increase, and abundance of vegetation cover. In addition, remarkable changes in air temperature due to relocation of meteorological stations are significant signals for detecting the influence of urbanization on urban heat island. Copyright 2009 Royal Meteorological Society
    Huang S. P., M. Taniguchi, M. Yamano, and C. H. Wang, 2009: Detecting urbanization effects on surface and subsurface thermal environment case study of Osaka. Science of the Total Environment, 407, 3142- 3152.
    IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,Solomon et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996pp.7d2adf1194b64b296b147d612fb71ad2http%3A%2F%2Fwww.mendeley.com%2Fcatalog%2Fipcc-2007-climate-change-2007-physical-science-basis-contribution-working-group-i-fourth-assessment-1%2Fhttp://www.mendeley.com/catalog/ipcc-2007-climate-change-2007-physical-science-basis-contribution-working-group-i-fourth-assessment-1/
    Kalnay E., M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature, 423( 6939), 528- 531.10.1038/nature0167512774119f045f40323bbd07262e08cc0a4bcbbedhttp%3A%2F%2Fwww.nature.com%2Fnature%2Fjournal%2Fv423%2Fn6939%2Fabs%2Fnature01675.htmlhttp://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM12774119The most important anthropogenic influences on climate are the emission of greenhouse gases and changes in land use, such as urbanization and agriculture. But it has been difficult to separate these two influences because both tend to increase the daily mean surface temperature. The impact of urbanization has been estimated by comparing observations in cities with those in surrounding rural areas, but the results differ significantly depending on whether population data or satellite measurements of night light are used to classify urban and rural areas. Here we use the difference between trends in observed surface temperatures in the continental United States and the corresponding trends in a reconstruction of surface temperatures determined from a reanalysis of global weather over the past 50 years, which is insensitive to surface observations, to estimate the impact of land-use changes on surface warming. Our results suggest that half of the observed decrease in diurnal temperature range is due to urban and other land-use changes. Moreover, our estimate of 0.27 degrees C mean surface warming per century due to land-use changes is at least twice as high as previous estimates based on urbanization alone.
    Kataoka K., F. Matsumoto, T. Ichinose, and M. Taniguchi, 2009: Urban warming trends in several large Asian cities over the last 100 years. Science of the Total Environment, 407( 9), 3112- 3119.10.1016/j.scitotenv.2008.09.0151899043347e8b7e99d85cf492670ca7f7d47501bhttp%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F18990433http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM18990433In this paper, the long-term trends in surface temperature in several large Asian cities (Seoul, Tokyo, Osaka, Taipei, Manila, Bangkok, and Jakarta) have been analyzed for estimating the effects of urban warming. A new index, E-HII, is proposed: it is the value obtained by subtracting the temperature data of the four grids around the city from the observational temperature data in the city. Osa...
    Kim Y. H., J. J. Baik, 2002: Maximum urban heat island intensity in Seoul. J. Appl. Meteor., 41( 6), 651- 659.10.1175/1520-0450(2002)0412.0.CO;2c21da3d3c803d262acde6bc3ee64e34ehttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002JApMe..41..651Khttp://adsabs.harvard.edu/abs/2002JApMe..41..651KThe maximum urban heat island (UHI) intensity in Seoul, Korea, is investigated using data measured at two meteorological observatories (an urban site and a rural site) during the period of 1973-96. The average maximum UHI is weakest in summer and is strong in autumn and winter. Similar to previous studies for other cities, the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is pronounced for clear skies. A multiple linear regression analysis is performed to relate the maximum UHI to meteorological elements. Four predictors considered in this study are the maximum UHI intensity for the previous day, wind speed, cloudiness, and relative humidity. The previous-day maximum UHI intensity is positively correlated with the maximum UHI, and the wind speed, cloudiness, and relative humidity are negatively correlated with the maximum UHI intensity. Among the four predictors, the previous-day maximum UHI intensity is the most important. The relative importance among the predictors varies depending on time of day and season. A three-layer back-propagation neural network model with the four predictors as input units is constructed to predict the maximum UHI intensity in Seoul, and its performance is compared with that of a multiple linear regression model. For all test datasets, the neural network model improves upon the regression model in predicting the maximum UHI intensity. The improvement of the neural network model upon the regression model is 6.3% for the unstratified test data, is higher in the daytime (6.1%) than in the nighttime (3.3%), and ranges from 0.8% in spring to 6.5% in winter.
    Lenten L. J. A., I. A. Moosa, 2003: An empirical investigation into long-term climate change in Australia. Environmental Modelling & Software, 18( 1), 59- 70.10.1016/S1364-8152(02)00036-1e6e2fe0958e4070af114a9b6ba140e75http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1364815202000361http://www.sciencedirect.com/science/article/pii/S1364815202000361In this paper, we undertake an empirical investigation into the possibility of climate change in Australian centres using average monthly air temperatures for six sites around the country over the period 1901:1-1998:12. By estimating a multivariate structural time series model and carrying out the appropriate tests, it is concluded that the temperature series is I(1). A graphical inspection of the extracted trends reveals that temperature has an upward trend in many centres. Given that the data may involve measurement errors, the results should be treated with caution.
    Leung Y. K., M. C. Wu, K. K. Yeung, and W. M. Leung, 2007: Temperature projections in Hong Kong based on IPCC fourth assessment report (Hong Kong Observatory, Trans.). [Available online at: .]http://www.weather.gov.hk/publica/reprint/r764.pdf
    Li J. X., C. H. Song, L. Cao, F. G. Zhu, X. L. Meng, and J. G. Wu, 2011: Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment, 115( 12), 3249- 3263.10.1016/j.rse.2011.07.008605446cd-7b6c-44d8-a3ed-ef1a528d3be2WOS:000298311300024a9665e4ae92f0da447ed5d4397036f9chttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0034425711002525http://www.sciencedirect.com/science/article/pii/S0034425711002525Urbanization is taking place at an unprecedented rate around the world, particularly in China in the past few decades. One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). Understanding the effects of landscape pattern on UHI is crucial for improving the ecology and sustainability of cities. This study investigated how landscape composition and configuration would affect UHI in the Shanghai metropolitan region of China, based on the analysis of land surface temperature (LST) in relation to normalized difference vegetation index (NDVI), vegetation fraction (Fv), and percent impervious surface area (ISA). Two Landsat ETM+ images acquired on March 13 and July 2, 2001 were used to estimate LST, Fv, and percent ISA. Landscape metrics were calculated from a high spatial resolution (2.5 - 2.5 m) land-cover/land-use map. Our results have showed that, although there are significant variations in LST at a given fraction of vegetation or impervious surface on a per-pixel basis, NDVI, Fv, and percent ISA are all good predictors of LST on the regional scale. There is a strong negative linear relationship between LST and positive NDVI over the region. Similar but stronger negative linear relationship exists between LST and Fv. Urban vegetation could mitigate the surface UHI better in summer than in early spring. A strong positive relationship exists between mean LST and percent ISA. The residential land is the biggest contributor to UHI, followed by industrial land. Although industrial land has the highest LST, it has limited contribution to the overall surface UHI due to its small spatial extend in Shanghai. Among the residential land-uses, areas with low- to-middle-rise buildings and low vegetation cover have much high temperatures than areas with high-rise buildings or areas with high vegetation cover. A strong correlation between the mean LST and landscape metrics indicates that urban landscape configuration also influences the surface UHI. These findings are helpful for understanding urban ecology as well as land use planning to minimize the potential environmental impacts of urbanization.
    Li Q., H. Zhang, X. Liu, J. Huang, 2004: Urban heat island effect on annual mean temperature during the last 50 years in China. Theor. Appl. Climatol., 79( 3-4), 165- 174.10.1007/s00704-004-0065-4dbcf8efc87b8eb8d287474d1274158b4http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00704-004-0065-4http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s00704-004-0065-4Based on China’s fifth population survey (2000) data and homogenized annual mean surface air temperature data, the urban heat island (UHI) effect on the warming during the last 50 years in China was a
    Lo C. P., D. A. Quattrochi, 2003: Land-use and land-cover change, urban heat island phenomenon, and health implications: A remote sensing approach. Photogrammetric Engineering & Remote Sensing, 69( 9), 1053- 1063.10.1109/TMI.2003.817660238bcb8b-d521-4397-b70e-f1cbe4d82cad5781f09a3d026ce4a3cfe6a66a440a27http%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Fasprs%2Fpers%2F2003%2F00000069%2F00000009%2Fart00011refpaperuri:(b6c7e524c4392492aaa0ab1d130a9d86)http://www.ingentaconnect.com/content/asprs/pers/2003/00000069/00000009/art00011This article reports on a study of the impact of land-use and land-cover change in the city of Atlanta, Georgia, for the past 30 years on urban heat island development, environmental quality, and health implications. Land-use and land-cover maps of Atlanta Metropolitan Area in Georgia were produced from Landsat MSS and TM images for 1973, 1979, 1983, 1987, 1992, and 1997, spanning a period of 25 years. The authors stress that dramatic changes in land use and land cover have occurred, with loss of forest and cropland to urban use. In particular, low-density urban use, which includes largely residential use, has increased by over 119 percent between 1973 and 1997. The analysis of Landsat images revealed an increase in surface temperature and a decline in Normalized Difference Vegetation Indices (NDVI) from 1973 to 1997. These changes have forced the development of a significant urban heat island effect at both the urban canopy and urban boundary layers as well as an increase in ground level ozone production. The authors discussed the interplay between surface temperatures and NDVI, volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions, the rates of cardiovascular and chronic lower respiratory diseases, and other factors, including demographic and socioeconomic variables. The authors conclude that high-resolution satellite remote sensing provides historical and current data on biophysical and land-cover characteristics of the urban environment.
    Michelozzi, P., Coauthors, 2007: Assessment and prevention of acute health effects of weather conditions in Europe,the PHEWE project: Background, objectives, design. Environmental Health, 6, 1-10, doi: 10.1186/1476-069X-6-12.10.1186/1476-069X-6-12174562360654a62163e6f037a650e657a5c748f9http%3A%2F%2Fwww.biomedcentral.com%2Fpubmed%2F17456236http://www.biomedcentral.com/pubmed/17456236BACKGROUND: The project "Assessment and prevention of acute health effects of weather conditions in Europe" (PHEWE) had the aim of assessing the association between weather conditions and acute health effects, during both warm and cold seasons in 16 European cities with widely differing climatic conditions and to provide information for public health policies. METHODS: The PHEWE project was a three-year pan-European collaboration between epidemiologists, meteorologists and experts in public health. Meteorological, air pollution and mortality data from 16 cities and hospital admission data from 12 cities were available from 1990 to 2000. The short-term effect on mortality/morbidity was evaluated through city-specific and pooled time series analysis. The interaction between weather and air pollutants was evaluated and health impact assessments were performed to quantify the effect on the different populations. A heat/health watch warning system to predict oppressive weather conditions and alert the population was developed in a subgroup of cities and information on existing prevention policies and of adaptive strategies was gathered. RESULTS: Main results were presented in a symposium at the conference of the International Society of Environmental Epidemiology in Paris on September 6th 2006 and will be published as scientific articles. The present article introduces the project and includes a description of the database and the framework of the applied methodology. CONCLUSION: The PHEWE project offers the opportunity to investigate the relationship between temperature and mortality in 16 European cities, representing a wide range of climatic, socio-demographic and cultural characteristics; the use of a standardized methodology allows for direct comparison between cities.
    Nichol J. E., 1996: Analysis of the urban thermal environment with Landsat data. Environment and Planning B: Planning & Design, 23, 733- 747.10.1068/b230733229c22bda40d4525131f17ba76426299http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F23540998_Analysis_of_the_urban_thermal_environment_with_LANDSAT_datahttp://www.researchgate.net/publication/23540998_Analysis_of_the_urban_thermal_environment_with_LANDSAT_dataResearch was carried out with two dates of thermal satellite imagery and field climatic data in six high-rise housing estates in Singapore to investigate the relationship between satellite-derived values and the urban microclimate. GIS techniques were used to register the image data for two detailed study areas to digital plans of street and building outlines, including street canyons at different orientations to solar azimuth, and detailed comparisons between image data and urban morphology were undertaken. A method is described for enhancing the image spatial resolution to an appropriate level for evaluating the thermal character of individual streets, buildings and building complexes, and roadside trees. Field data obtained in two street canyons at the same time of year as the image data suggest a significant relationship between surface and air temperature and suggest that the satellite view of the high-rise urban environment in the study area is representative.
    Nichol J. E., 2005: Remote sensing of urban heat islands by day and night. Photogrammetric Engineering & Remote Sensing, 71( 5), 613- 621.10.14358/PERS.71.5.6137b4c51a2c02f425c81bbaa3f19978d57http%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Fasprs%2Fpers%2F2005%2F00000071%2F00000005%2Fart00004http://www.ingentaconnect.com/content/asprs/pers/2005/00000071/00000005/art00004A night-time thermal image from the ASTER satellite sensor, of the western New territories of Hong Kong is compared with a daytime Landsat Enhanced Thematic Mapper Plus (ETM+) thermal image obtained nineteen days earlier. Densely built high rise areas which appear cool on daytime images are conversely, relatively warm on nighttime images, though the temperature differences are not well developed at night. Lower temperature gradients between different land cover types observed on the night time image result in meso-scale, rather than micro-scale climatic patterns being dominant, suggestive of processes operating in the Urban Boundary Layer (UBL), as opposed to the Urban Canopy Layer (UCL) which is dominant in the daytime. Thus, at night, proximity to extensive cool surfaces such as forested mountain slopes appears to be influential in maintaining cooler building temperatures. The relevance of satellite-derived surface temperatures for studies of urban microclimate is supported by field data of surface and air temperatures collected in the study area. Comparison of the ASTER Kinetic Temperature standard product with a thermal image processed using locally derived emissivity and atmospheric data indicated higher accuracy for the latter.
    Nijman T. E., F. C. Palm, 1990: Predictive accuracy gain from disaggregate sampling in ARIMA models. Journal of Business & Economic Statistics, 8( 4), 405- 415.10.1080/07350015.1990.105098110d38c5c0ce31141e59c892cf83d1bcdchttp%3A%2F%2Fwww.jstor.org%2Fstable%2F1391515http://www.jstor.org/stable/1391515We compare the forecast accuracy of autoregressive integrated moving average (ARIMA) models based on data observed with high and low frequency, respectively. We discuss how, for instance, a quarterly model can be used to predict one quarter ahead even if only annual data are available, and we compare the variance of the prediction error in this case with the variance if quarterly observations were indeed available. Results on the expected information gain are presented for a number of ARIMA models including models that describe the seasonally adjusted gross national product (GNP) series in the Netherlands. Disaggregation from annual to quarterly GNP data has reduced the variance of short-run forecast errors considerably, but further disaggregation from quarterly to monthly data is found to hardly improve the accuracy of monthly forecasts.
    Ning L., R. S. Bradley, 2014: Winter precipitation variability and corresponding teleconnections over the northeastern United States. J. Geophys. Res. Atmos., 119, 7931- 7945.10.1002/2014JD021591a3c56e233206a9bf3cb44a63eaa07599http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JD021591%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1002/2014JD021591/abstractThe variability of winter precipitation over the northeastern United States and the corresponding teleconnections with five dominant large-scale modes of climate variability (Atlantic Multidecadal Oscillation, AMO; North Atlantic Oscillation, NAO; Pacific-North American pattern, PNA; Pacific Decadal Oscillation, PDO; and El Nino-outhern Oscillation, ENSO) were systemically analyzed in this study. Three leading patterns of winter precipitation were first generated by empirical orthogonal function (EOF) analysis. The correlation analysis shows that the first pattern is significantly correlated with PNA and PDO, the second pattern is significantly correlated with NAO and AMO, and the third pattern is significantly correlated with ENSO, PNA, and PDO. To verify the physical sense of the EOF patterns and their correlations, composite analysis was applied to the precipitation anomalies, which reproduced the three EOF spatial patterns. Multiple linear regression models generated using indices of all five modes of climate variability show higher explained variances. Composite analyses of geopotential height, sea level pressure, relative humidity, and moisture flux field were performed to find the physical mechanisms behind the teleconnections. When the findings are applied to the extreme drought of the 1960s, it is found that besides a continuous negative NAO pattern, a negative PNA pattern and La Nina conditions also contributed to the drought of winter season by influencing moisture flux and the position of storm tracks. Another case, the 2009/2010 winter with positive precipitation anomalies over the coastal region, is found to be resulted from circulation patterns dominated by major El Nino condition with high-PNA and PDO indices
    Ning L., E. E. Riddle, and R. S. Bradley, 2015: Projected changes in climate extremes over the northeastern United States. J.Climate, 28, 3289- 3310.10.1175/JCLI-D-14-00150.163350cf06dc7e8162dc5a9d6d5045ec9http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015JCli...28.3289Nhttp://adsabs.harvard.edu/abs/2015JCli...28.3289N"Projections of historical and future changes in climate extremes are examined by applying the "bias-correction-spatial disaggregation" (BCSD) statistical downscaling method to five general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). For this analysis, 11 extreme temperature and precipitation indices that are relevant across multiple disciplines (e.g., agriculture and conservation) are chosen. Over the historical period, the simulated means, variances, and cumulative distribution functions (CDFs) of each of the 11 indices are first compared with observations, and the performance of the downscaling method is quantitatively evaluated. For the future period, the ensemble average of the five GCM simulations points to more warm extremes, fewer cold extremes, and more precipitation extremes with greater intensities under all three scenarios. The changes are larger under higher emissions scenarios. The inter-GCM uncertainties and changes in probability distributions are also assessed. Changes in the probability distributions indicate an in- crease in both the number and interannual variability of future climate extreme events. The potential deficiencies of the method in projecting future extremes are also discussed."
    Oke T. R.1973: City size and the urban heat island. Atmos. Environ., 7( 8), 769- 779.10.1016/0004-6981(73)90140-643f379a441439d3b212074ebc88549f5http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F0004698173901406http://www.sciencedirect.com/science/article/pii/0004698173901406The analysis shows the heat island intensity under cloudless skies to be related to the inverse of the regional windspeed, and the logarithm of the population. A simple model is derived which incorporates these controls. In agreement with an extension of Summers' model the heat island appears to be approximately proportional to the fourth root of the population. With calm and clear conditions the relation is shown to hold remarkably well for North American settlements, and in a slightly modified form, for European towns and cities.
    Ren G. Y., Y. H. Ding, Z. C. Zhao, J. Y. Zheng, T. W. Wu, G. L. Tang, and Y. Xu, 2012: Recent progress in studies of climate change in China. Adv. Atmos. Sci.,29(5), 958-977, doi: 10.1007/s00376-012-1200-2.10.1007/s00376-012-1200-229169122696fca272eaf5f6c48eb86abhttp%3A%2F%2Fwww.cqvip.com%2FQK%2F84334X%2F201205%2F42901342.htmlhttp://d.wanfangdata.com.cn/Periodical/dqkxjz-e201205004An overview of basic research on climate change in recent years in China is presented. In the past 100 years in China, average annual mean surface air temperature (SAT) has increased at a rate ranging from 0.03°C (10 yr) 611 to 0.12°C (10 yr) 611 . This warming is more evident in northern China and is more significant in winter and spring. In the past 50 years in China, at least 27% of the average annual warming has been caused by urbanization. Overall, no significant trends have been detected in annual and/or summer precipitation in China on a whole for the past 100 years or 50 years. Both increases and decreases in frequencies of major extreme climate events have been observed for the past 50 years. The frequencies of extreme temperature events have generally displayed a consistent pattern of change across the country, while the frequencies of extreme precipitation events have shown only regionally and seasonally significant trends. The frequency of tropical cyclone landfall decreased slightly, but the frequency of sand/dust storms decreased significantly. Proxy records indicate that the annual mean SAT in the past a few decades is the highest in the past 400–500 years in China, but it may not have exceeded the highest level of the Medieval Warm Period (1000–1300 AD). Proxy records also indicate that droughts and floods in eastern China have been characterized by continuously abnormal rainfall periods, with the frequencies of extreme droughts and floods in the 20th century most likely being near the average levels of the past 2000 years. The attribution studies suggest that increasing greenhouse gas (GHG) concentrations in the atmosphere are likely to be a main factor for the observed surface warming nationwide. The Yangtze River and Huaihe River basins underwent a cooling trend in summer over the past 50 years, which might have been caused by increased aerosol concentrations and cloud cover. However, natural climate variability might have been a main driver for the mean and extreme precipitation variations observed over the past century. Climate models generally perform well in simulating the variations of annual mean SAT in China. They have also been used to project future changes in SAT under varied GHG emission scenarios. Large uncertainties have remained in these model-based projections, however, especially for the projected trends of regional precipitation and extreme climate events.
    Siu L. W., M. A. Hart, 2013: Quantifying urban heat island intensity in Hong Kong SAR, China. Environmental Monitoring and Assessment, 185( 5), 4383- 4398.10.1007/s10661-012-2876-6230077989465dc34-3c12-457d-b671-bc82843f2fb0WOS:000316968500063ba0c6729135f87c1201bb7a517361fdehttp%3A%2F%2Feuropepmc.org%2Farticles%2FPMC3613573%2Fhttp://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM23007798This paper addresses the methodological concerns in quantifying urban heat island (UHI) intensity in Hong Kong SAR, China. Although the urban heat island in Hong Kong has been widely investigated, there is no consensus on the most appropriate fixed point meteorological sites to be used to calculate heat island intensity. This study utilized the Local Climate Zones landscape classification system to classify 17 weather stations from the Hong Kong Observatory's extensive fixed point meteorological observation network. According to the classification results, the meteorological site located at the Hong Kong Observatory Headquarters is the representative urban weather station in Hong Kong, whereas sites located at Tsak Yue Wu and Ta Kwu Ling are appropriate rural or nonurbanized counterparts. These choices were validated and supported quantitatively through comparison of long-term annual and diurnal UHI intensities with rural stations used in previous studies. Results indicate that the rural stations used in previous studies are not representative, and thus, the past UHI intensities calculated for Hong Kong may have been underestimated.
    Sonali P., D. N. Kumar, 2013: Review of trend detection methods and their application to detect temperature changes in India. J. Hydrol., 476, 212- 227.10.1016/j.jhydrol.2012.10.0342fd821a0a689afcbd590a8f2e1389626http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0022169412009341http://www.sciencedirect.com/science/article/pii/S0022169412009341Present study performs the spatial and temporal trend analysis of annual, monthly and seasonal maximum and minimum temperatures ( t max , t min ) in India. Recent trends in annual, monthly, winter, pre-monsoon, monsoon and post-monsoon extreme temperatures ( t max , t min ) have been analyzed for three time slots viz. 1901–2003, 1948–2003 and 1970–2003. For this purpose, time series of extreme temperatures of India as a whole and seven homogeneous regions, viz. Western Himalaya (WH), Northwest (NW), Northeast (NE), North Central (NC), East coast (EC), West coast (WC) and Interior Peninsula (IP) are considered. Rigorous trend detection analysis has been exercised using variety of non-parametric methods which consider the effect of serial correlation during analysis. During the last three decades minimum temperature trend is present in All India as well as in all temperature homogeneous regions of India either at annual or at any seasonal level (winter, pre-monsoon, monsoon, post-monsoon). Results agree with the earlier observation that the trend in minimum temperature is significant in the last three decades over India ( Kothawale et al., 2010 ). Sequential MK test reveals that most of the trend both in maximum and minimum temperature began after 1970 either in annual or seasonal levels.
    To W. M., 2014: Association between energy use and poor visibility in Hong Kong SAR, China. Energy, 68, 12- 20.10.1016/j.energy.2014.02.06227702ed977339446dce284bbb3fea55chttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0360544214001960http://www.sciencedirect.com/science/article/pii/S0360544214001960A city's reliance on energy increases when it is developed. Moreover, the combustion of fossil fuels inevitably generates air pollutants including carbon dioxide, nitrogen oxides, sulfur dioxide, particulate matter, and others. Combining with other anthropogenic air pollutants, visibility in many Asian cities including Hong Kong have deteriorated rapidly in the past decades. This paper explores the relationships between energy use, meteorological factors, and change in visibility in Hong Kong using long-term time-series data. The total use of primary energy increased from 146,700 TJ in 1971 to 1,270,865 TJ in 2011 while the number of hours of reduced visibility increased from 184 h to 1398 h during the same period of time. Bivariate correlations show that poor visibility was significantly associated with energy use and annual mean air temperature. Multiple regression analysis indicates that the burning of aviation gasoline significantly, adversely affect visibility. Results illustrate that the number of clear days in Hong Kong will decrease, in particular due to the increase in air traffic.
    To W. M., 2015: Greenhouse gases emissions from the logistics sector: the case of Hong Kong, China. Journal of Cleaner Production, 103, 658- 664.10.1016/j.jclepro.2014.10.062425c5791979d41dbc7536f248f1fd7d6http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0959652614011056http://www.sciencedirect.com/science/article/pii/S0959652614011056In this study, greenhouse gases (GHG) emissions from the logistics sector were investigated using Hong Kong as an example. The data including cargo freight between Hong Kong and other places by different transport modes (i.e. aircraft, container ships, trucks, and trains) for the period of 2007-2012 were collected. Combining transport data with the GHG emitted from each mode of transport in terms of tonne CO 2 -eq per kilotonne-kilometer, GHG emissions for each transport mode and the total amount of GHG emissions were determined. In 2012, the total cargo freight between Hong Kong and other places via air freight was 4024 kilotonnes and produced 22,623 kilotonnes of CO 2 -eq. The total cargo freight via sea freight was 269,283 kilotonnes and produced 12,784 kilotonnes of CO 2 -eq. The total cargo freight via land freight was 26,215 kilotonnes and produced 463 kilotonnes of CO 2 -eq. The total amount of GHG emissions was 35,834 kilotonnes of CO 2 -eq. The environmental effectiveness of Hong Kong's logistics sector was obtained by normalizing the total amount of GHG emissions with respect to the value added. The calculated value was 534 tonnes of CO 2 -eq per million HKD value added. The results of scenario analysis showed that the amount of GHG emissions could be reduced at about 100 kilotonnes of CO 2 -eq. per 100 kilotonnes of cargo by switching a portion of air cargo movements to and from mainland China to land freight or sea freight.
    To W. M., T. M. Lai, W. C. Lo, H. K. Lam, and W. L. Chung, 2012: The growth pattern and fuel life cycle analysis of the electricity consumption of Hong Kong. Environmental Pollution, 165, 1- 10.10.1016/j.envpol.2012.02.00722390975cd417ae14a6fc5790184dea0143e47c9http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0269749112000802http://www.sciencedirect.com/science/article/pii/S0269749112000802As the consumption of electricity increases, air pollutants from power generation increase. In metropolitans such as Hong Kong and other Asian cities, the surge of electricity consumption has been phenomenal over the past decades. This paper presents a historical review about electricity consumption, population, and change in economic structure in Hong Kong. It is hypothesized that the growth of electricity consumption and change in gross domestic product can be modeled by 4-parameter logistic functions. The accuracy of the functions was assessed by Pearson's correlation coefficient, mean absolute percent error, and root mean squared percent error. The paper also applies the life cycle approach to determine carbon dioxide, methane, nitrous oxide, sulfur dioxide, and nitrogen oxide emissions for the electricity consumption of Hong Kong. Monte Carlo simulations were applied to determine the confidence intervals of pollutant emissions. The implications of importing more nuclear power are discussed.
    UN, 2014: World Urbanization Prospects The 2014 Revision. The United Nations' Population Division,New York, 7-16 pp.10.2307/280804189ea2fe6a60e8c31c5f9ce604577ffe1http%3A%2F%2Fagris.fao.org%2Fagris-search%2Fsearch.do%3FrecordID%3DXF2015034444http://agris.fao.org/agris-search/search.do?recordID=XF2015034444CiteSeerX - Scientific documents that cite the following paper: World urbanization prospects. The 1996 revision. Economic and Social Affairs
    Wang W. C., Z. M. Zeng, and T. R. Karl, 1990: Urban heat islands in China. Geophys. Res. Lett., 17( 13), 2377- 2380.10.1029/GL017i013p023771721b8d20c7008a74291749ba61a00bfhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2FGL017i013p02377%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/GL017i013p02377/fullABSTRACT We used 1954&ndash;1983 surface temperature from 42 Chinese urban (average population 1.7*106) and rural (average population 1.5*105) station pairs to study the urban heat island effects. Despite the fact that the rural stations are not true rural stations, the magnitude of the heat islands was calculated to average 0.23 C over the thirty-year period with a minimum value during the 1964&ndash;1973 decade and maximum during the most recent decade. The urban heat islands were found to have seasonal dependence which varied considerably across the country. The urban heat islands also had a strong regional dependence with the Northern Plains dominating the magnitude of the heat islands. The changes in heat island intensity over three decades studied suggest a general increase in heat island intensity of about 0.1C, but this has not been constant in time. These results suggest that caution must be exercised when attributing causes to observed trends when stations are located in the vicinity of metropolitan areas.
    Ye X. F., R. Wolff, W. W. Yu, P. Vaneckova, X. C. Pan, and S. L. Tong, 2012: Ambient temperature and morbidity: A review of epidemiological evidence. Environmental Health Perspectives, 120( 1), 19- 28.10.1289/ehp.10031982182485551544c5c5594c8bafedb2dcdd461d535http%3A%2F%2Feuropepmc.org%2Farticles%2Fpmc3261930%2Fhttp://europepmc.org/articles/pmc3261930/In this paper, we review the epidemiological evidence on the relationship between ambient temperature and morbidity. We assessed the methodological issues in previous studies and proposed future research directions.We searched the PubMed database for epidemiological studies on ambient temperature and morbidity of noncommunicable diseases published in refereed English journals before 30 June 2010. Forty relevant studies were identified. Of these, 24 examined the relationship between ambient temperature and morbidity, 15 investigated the short-term effects of heat wave on morbidity, and 1 assessed both temperature and heat wave effects.Descriptive and time-series studies were the two main research designs used to investigate the temperature-orbidity relationship. Measurements of temperature exposure and health outcomes used in these studies differed widely. The majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities. However, there were some inconsistencies in the direction and magnitude of nonlinear lag effects. The lag effect of hot temperature on morbidity was shorter (several days) compared with that of cold temperature (up to a few weeks). The temperature-orbidity relationship may be confounded or modified by sociodemographic factors and air pollution.There is a significant short-term effect of ambient temperature on total and cause-specific morbidities. However, further research is needed to determine an appropriate temperature measure, consider a diverse range of morbidities, and to use consistent methodology to make different studies more comparable.
    Zhou D. C., S. Q. Zhao, L. X. Zhang, G. Sun, and Y. Q. Liu, 2015: The footprint of urban heat island effect in China. Scientific Reports, 5,11 160, doi: 10.1038/srep11160.10.1038/srep1116044619189ed99fcb284cb0d0c515350ea323679ahttp%3A%2F%2Fpubmedcentralcanada.ca%2Fpmcc%2Farticles%2FPMC4461918%2Fhttp://pubmedcentralcanada.ca/pmcc/articles/PMC4461918/Urban heat island (UHI) is one major anthropogenic modification to the Earth system that transcends its physical boundary. Using MODIS data from 2003 to 2012, we showed that the UHI effect decayed exponentially toward rural areas for majority of the 32 Chinese cities. We found an obvious urban/rural temperature "cliff", and estimated that the footprint of UHI effect (FP, including urban area) was 2.3 and 3.9 times of urban size for the day and night, respectively, with large spatiotemporal heterogeneities. We further revealed that ignoring the FP may underestimate the UHI intensity in most cases and even alter the direction of UHI estimates for few cities. Our results provide new insights to the characteristics of UHI effect and emphasize the necessity of considering city- and time-specific FP when assessing the urbanization effects on local climate.
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Manuscript received: 20 April 2016
Manuscript revised: 30 July 2016
Manuscript accepted: 01 August 2016
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Characterizing the Urban Temperature Trend Using Seasonal Unit Root Analysis: Hong Kong from 1970 to 2015

  • 1. Macao Polytechnic Institute, Rua de Luis Gonzaga Gomes, Macao SAR, China

Abstract: This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169°C (10 yr)-1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data —— much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865°C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.

1. Introduction
  • Urbanization is a global phenomenon that has transformed landscapes. According to a study by the United Nations' Population Division (UN, 2014), the world's urban population is projected to increase from 3.9 billion in 2014 to 5.0 billion in 2030, and then to 6.3 billion in 2050. By 2025, 7 out of 37 megacities with a population exceeding 10 million are expected to be located in mainland China. By 2050, China will have the largest urban population, totaling about 1 billion. Urbanization transforms lands for residential, commercial, industrial, and transportation purposes. When more people live in a confined space such as a city, they will consume more energy, resulting in large amounts of emissions and waste heat. Moreover, the modified land surface of a city affects the storage and radiative and turbulent transfers of the city's heat. Hence, urban warming trends and the relative warmth of a city compared with its surrounding rural areas——known as the Urban Heat Island (UHI) effect——have become key environmental issues (IPCC, 2007; Ning et al., 2015). Epidemiological studies (Conti et al., 2005;Michelozzi et al., 2007; Ye et al., 2012; Chen et al., 2015) show that people in urban areas have an elevated risk of mortality due to the effect of higher air temperature and heat waves, as compared to those in suburban and rural areas.

    Over the past three decades, researchers have studied urban warming trends using annual, quarterly, and monthly mean air temperature data (Kalnay and Cai, 2003; Huang et al., 2009; Kataoka et al., 2009; Ren et al., 2012; Sonali and Kumar, 2013; Chattopadhyay and Edwards, 2016). (Ren et al., 2012) indicated that annual mean air temperature increased at a rate ranging from 0.03°C (10 yr)-1 to 0.12°C (10 yr)-1 in China in the past 100 years, and the warming was more significant in winter and spring and was more evident in northern China. (Sonali and Kumar, 2013) studied temperature trends across all regions in India. They also reported that significant trends could be found using winter maximum temperature data in India. (Kalnay and Cai, 2003) studied the impact of urbanization and other land-use changes on monthly mean surface temperature. By comparing trends in observed surface temperatures at U.S. surface stations with that derived from the reconstruction of surface temperatures based on global climate model from 1950-99, they determined that the mean surface warming was about 0.27°C (100 yr)-1 in the U.S. due to urbanization as well as other land-use changes. (Chattopadhyay and Edwards, 2016) studied long-term temperature trends using annual mean data at 42 different locations in Kentucky. They reported that only three of the 42 locations had a linear temperature trend of about 0.01°C yr-1. (Huang et al., 2009) studied long-term annual surface air temperature and reported that the mean warming rate in Osaka was about 2.0°C (100 yr)-1 during the period 1883-2006. A slightly higher value of temperature increase was reported by (Kataoka et al., 2009). (Chan et al., 2012) studied temperature trends in Hong Kong from a seasonal perspective. They calculated the daily maximum temperature, daily mean temperature, and daily minimum temperature for every three months, i.e., each season, from 1885 to 2010, and reported that the warming trend was more prominent in winter and spring in Hong Kong. Nevertheless, analyzing urban warming trends using annual and quarterly temperature data has relatively weak statistical power because the availability of data points is limited (Wang et al., 1990; Li et al., 2004; Huang et al., 2009; Chan et al., 2012). (Lenten and Moosa, 2003) used a general multivariate structural time series model and found that the monthly air temperature series in Australian cities were I(1), i.e., with a trend. They also reported that temperature had an upward trend in most cities between January 1901 and December 1998.

    To determine the UHI intensity, researchers have either calculated the temperature difference between an urban site and a rural site (Wang et al., 1990; Camilloni and Barros, 1997; Li et al., 2004; Debbage and Shepherd, 2015) or used remote sensing techniques (Nichol, 1996, 2005; Lo and Quattrochi, 2003; Zhou et al., 2015). (Wang et al., 1990) investigated the UHI effect in 42 urban areas in China between 1954 and 1983. (Wang et al., 1990) found that the presence of a UHI led to urban areas being on average 0.23°C warmer than rural areas across all seasons and regions in China. On the other hand, (Li et al., 2004) reported that the average UHI effect for the whole of China during the 50 years between 1951 and 2001 was less than 0.06°C——a figure that was insignificant when compared to the background trend of increasing temperature. Nichol (1996, 2005) used Landsat data to study the UHI effect in Singapore and Hong Kong. (Chen et al., 2006) investigated the relationship between UHIs and land use in the Pearl River Delta using Landsat TM and ETM+ (Enhanced TM Plus) thermal IR images obtained between 1990 and 2000 at an interval of two or threeyears. They found that the UHI was proportionally related to urban size and population density. (Li et al., 2011) studied the UHI in Shanghai using Landsat ETM+ images and found that the manmade impervious surface area significantly affects the UHI. (Lo and Quattrochi, 2003) used different Landsat images from 1973 to 1997 to study changes in land use and land cover and surface temperature in the Atlanta metropolitan area in Georgia. They found that an increase in surface temperature was associated with the decline in vegetation or natural biomass. (Zhou et al., 2015) used MODIS data to study the footprint of the UHI effect in China. They indicated that ignoring the footprint may underestimate the UHI intensity in many cities in China.

    The objectives of the present study are not only to determine the urban temperature trend in Hong Kong using annual mean air temperature data, but also to demonstrate that urban temperature should be rigorously modeled based on seasonal unit root tests for unadjusted monthly mean air temperatures. The UHI intensity was estimated using the temperature difference between an urban site and a rural site in Hong Kong. The study also explores whether increases in population, gross domestic product (GDP), urban land use, and energy use were significantly associated with the change in urban temperature in Hong Kong. Following this introduction, section 2 describes the area studied and the data used; section 3 presents the results and analysis; and section 4 concludes the study.

2. Methods
  • Hong Kong has a land area of 1104 km2 and is an international finance center, trade and logistics center, and a popular tourist destination (To, 2015). Hong Kong has experienced substantial demographic, economic, and environmental changes over the past four decades. Hong Kong's population increased from 4.0 million in 1970 to 7.3 million in 2015, while its GDP increased from HKD 195.2 trillion to HKD 2246.4 trillion during the same period of time. The number of visitors increased from 1.3 million in 1970 to 59.3 million in 2015. Electricity consumption and associated greenhouse gas emissions have increased almost tenfold (To et al., 2012)——the same as the increase in the total use of primary energy, including oil products, coal, and natural gas (To, 2014)——over the past four decades. Unfortunately, the number of hours of reduced visibility increased from 184 hours in 1971 to 1398 hours in 2011 (To, 2014).

  • The data for this study, including annual mean air temperature, monthly mean air temperature, population, and GDP, between 1970 and 2015, were extracted from databases provided by the Hong Kong Observatory and Hong Kong Census and Statistics Department. Annual and monthly mean air temperatures were recorded at the weather station in the headquarters of Hong Kong Observatory in Tsim Sha Tsui —— an urban site (Chan et al., 2012). Similar to the studies by (Ren et al., 2012), (Huang et al., 2009), and (Kalnay and Cai, 2003), mean air temperature was used to determine the urban temperature trend in Hong Kong over the past four decades. The size of developed areas between 1970 and 2015 was obtained from publications of the Hong Kong Buildings and Lands Department and the Hong Kong Planning Department. Energy end-use data were obtained from the reports published by the Hong Kong Electrical and Mechanical Services Department (HKEMSD, 2005a, b).

    Figure 1.  Annual mean air temperature in Hong Kong from 1970 to 2013.

  • Annual mean air temperature data between 1970 and 2013 were analyzed using the ordinary least-squares (OLS) approach with linear trend, like most other UHI studies (Li et al., 2004; Huang et al., 2009; Chan et al., 2012). The major problem with using annual mean air temperature data rather than monthly data is the reduction in the number of observations, thus reducing the statistical power of the estimation and the reliability of the estimates. Hence, we applied seasonal unit root tests to the monthly mean air temperature data between January 1970 and December 2013, as described by (Franses, 1991), (Beaulieu and Miron, 1993), and (Franses and Hobijn, 1997). The seasonal unit root test produces a much more accurate estimate of the temperature trend because the monthly data are fully utilized, with in which the loss of information is small (Nijman and Palm, 1990).

    In monthly time series, the test of the presence of multiple unit roots cannot be accomplished by the standard augmented Dickey-Fuller Test (Dickey and Fuller, 1981). As different unit roots might exist in a monthly seasonal process due to the stochastic nature of climatic conditions, we adopted the method developed by (Franses, 1991) and used the critical values suggested by (Franses and Hobijn, 1997). Specifically, the test for the presence of seasonal unit roots was based on the following auxiliary regression (Franses, 1991): \begin{eqnarray} \emptyset(B)y_{8,t}&=&\mu_t+\pi_1y_{1,t-1}+\pi_2y_{2,t-1}+\pi_3y_{3,t-1}+\pi_4y_{3,t-2}+\nonumber\\ &&\pi_5y_{4,t-1}+\pi_6y_{4,t-2}+\pi_7y_{5,t-1}+\pi_8y_{5,t-2}+\pi_9y_{6,t-1}+\nonumber\\ &&\pi_{10}y_{6,t-2}+\pi_{11}y_{7,t-1}+\pi_{12}y_{7,t-2}+\varepsilon_t , (1)\end{eqnarray} where B is the backward shift operator, in which Bkyt=yt-k and \(\emptyset(B)\) is an autoregressive polynomial, μt is the constant term, πi is the coefficient of yi, and εt is the error term. The autoregressive model was used because monthly mean air temperature depends on the current weather condition as well as the past states of the climate system. The y values are given in Eq. (2): \begin{eqnarray} y_{1,t}&=&(1+B)(1+B^2)(1+B^4+B^8)y_t ;\nonumber\\ y_{2,t}&=&-(1-B)(1+B^2)(1+B^4+B^8)y_t ;\nonumber\\ y_{3,t}&=&-(1-B^2)(1+B^4+B^8)y_t ;\nonumber\\ y_{4,t}&=&-(1-B^4)(1-\sqrt{3}B+B^2)(1+B^2+B^4)y_t ;\nonumber \end{eqnarray} \begin{eqnarray} y_{5,t}&=&-(1-B^4)(1+\sqrt{3}B+B^2)(1+B^2+B^4)y_t ;\nonumber\\ y_{6,t}&=&-(1-B^4)(1-B+B^2)(1-B^2+B^4)y_t ;\nonumber\\ y_{7,t}&=&-(1-B^4)(1+B+B^2)(1-B^2+B^4)y_t ;\nonumber\\ y_{8,t}&=&(1-B^{12})y_t .(2) \end{eqnarray}

    Tests were conducted with the auxiliary equation for π2 that is equal to zero and a set of joint tests of the hypotheses for pairs of consecutive πi that is equal to zero for i from 3 to 12. We also tested for two scenarios——one with a trend and another without a trend——using the set of critical values established by (Franses and Hobijn, 1997). The model obtained from the seasonal unit root analysis was used to predict monthly mean air temperatures between January 2014 and December 2015. The accuracy of this model was assessed using the mean absolute error, the RMSE, and the root-mean-square percentage error.

    To evaluate the UHI intensity, we compared annual and monthly air temperatures at the urban site and at a rural site——Tu Kwu Ling, Hong Kong. Correlation analyses were performed between annual mean air temperature data, population, GDP, urban land use, and energy use.

3. Results and analysis
  • Figure 1 shows Hong Kong's annual mean air temperature for the period 1970 to 2013. The correlation between "year" and annual mean air temperature was 0.600 and significant at the 0.001 level. A linear trend was identified by using the OLS approach. The analyzed result shows that annual mean air temperature increased by 0.174°C (10 yr)-1 during this period of time. The coefficient of determination, R2, indicated that the temperature model identified using the OLS approach explained 36% of the variance in the measured data, similar to other studies (Wang et al., 1990; Li et al., 2004; Huang et al., 2009; Kataoka et al., 2009).

    Figure 2.  Monthly mean air temperature in Hong Kong for January-Decembr (from 1970 to 2013): (a) January (filled diamonds), February (plus signs) and March (multiplication signs); (b) April (filled circles), May (multiplication signs) and June (filled triangles); (c) July (filled triangles), August (multiplication signs) and September (filled circles); (d) October (multiplication signs), November (plus signs) and December (filled diamonds).

    Figure 2 shows that, although the variations in the monthly data were quite significant (ranging from 16°C in January to 29°C in July), there were upward trends in all monthly time series during the period of 1970-2013. Correlation analyses showed that only three sets of monthly data (August, October and November) were significantly associated with "year" at the 0.05 level (for August, with a Pearson's correlation coefficient of 0.36) and at the 0.01 level (for October and November, with a Pearson's correlation coefficient of 0.41), respectively.

    To rigorously examine the long-term stochastic trend with the seasonal phenomenon, we applied seasonal unit root tests and included all monthly data from January 1970 to December 2013 in the analysis. We performed seasonal unit root tests using the approach described by (Beaulieu and Miron, 1993), (Franses, 1991) and (Franses and Hobijn, 1997).

    When the auxiliary equation contained a trend (or a trend was absent), seasonal components and a constant, the test statistics showed that there was a unit root at zero frequency based on the t-statistics. Besides, π2 was significantly different from zero at the 0.01 level. The joint F-tests of the pairs, from π3-π4 to π11-π12, rejected the presence of a unit root at the 0.01 level. As suggested by (Franses and Hobijn, 1997), we conducted joint tests for π1…π12 and π2…π12, and found that the results were significant. Thus, the results confirmed that a non-seasonal unit root existed.

    Table 1 presents the estimation results of two models: one without a trend and another with a trend. All coefficients were significant at the 0.01 level. From the Akaike information criterion, Bayesian information criterion, and Hannan-Quinn information criterion, the model with a trend showed a better ability in minimizing the information loss. This provided evidence of the presence of a stochastic linear trend in the series. The coefficient of determination, R2, which indicates how well the model fits the measured values, was 0.959. In other words, the temperature model obtained from the seasonal unit root analysis explained 95.9% of the variance in the measured monthly data——much higher than that of the model obtained using the OLS approach with annual mean air temperature data.

    Figure 3.  The measured mean air temperature versus the predicted mean air temperature in Hong Kong (1970-2013), based on (a) the seasonal unit root model, (b) the OLS model using monthly data, and (c) the OLS model using annual data.

    Table 1 shows that the coefficient of trend was significant for the model with a trend. The results showed that mean air temperature changed with respect to time by accounting for the fluctuation of temperature due to the seasonal phenomenon. In other words, time is an important determinant of mean air temperature. The coefficient of trend was obtained based on monthly data. Thus, mean air temperature increased by 0.0014045°C (month)-1 [or 0.169°C (10 yr)-1]. This trend was not noticeable in normal statistical analysis due to the variance of monthly temperature data. However, after accounting for the variances due to the seasonal components, our econometric testing results revealed and confirmed such an impact of time on temperature. In summary, the change in mean air temperature was 0.169°C (10 yr)-1 [or 0.174°C (10 yr)-1 using annual data] in Hong Kong, which was higher than the increase in global mean air temperature [0.13°C (10 yr)-1 using the data from 1956 to 2005, or 0.07°C (10 yr)-1 between 1906 and 2005 due to global warming] (IPCC, 2007; Ye et al., 2012). The seasonal unit root model of mean air temperature in Hong Kong is given in Eq. (3): \begin{eqnarray} T_t&=&15.7126+0.5941\pi_2+2.9245\pi_3+6.4662\pi_4+\nonumber\\ &&9.8353\pi_5+11.8725\pi_6+12.6688\pi_7+12.4265\pi_8+\nonumber\\ &&11.5751\pi_9+9.2692\pi_{10}+5.5041\pi_{11}+1.7686\pi_{12}+\nonumber\\ &&0.0014t , (3)\end{eqnarray} where Tt is mean air temperature in month t, πn=1 when n=t+12k, in which t is the number of the month from January 1970, k=0,1,2,…; while πn=0 for all others. The coefficients of π values were obtained from Table 1.

    Figure 4.  The predicted monthly mean air temperature using a deterministic trend with seasonal components versus the measured monthly air temperature, in Hong Kong (January 1970 to December 2013).

    Figure 3 shows the measured mean temperature versus predicted temperature using the seasonal unit root model based on monthly data, using the OLS model based on monthly data, and using the OLS model based on annual data. This figure demonstrates that the overall accuracy of the former model was better than that of the latter models. The R2 values showed that the model obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly mean air temperature data, while the model obtained using OLS was only able to explain 0.29% of the variance of the measured monthly mean air temperature data. The model obtained using OLS based on annual data was able to explain 36% of the variance of the measured annual mean air temperature data.

    Figure 4 shows the measured and predicted monthly air temperatures from 1970 to 2013. It demonstrates the superior performance of using the seasonal unit root model to regenerate the measured monthly mean air temperatures from January 1970 to December 2013.

  • Equation (3) was used to predict the monthly mean air temperature from January 2014 to December 2015. Figure 5 shows the predicted mean air temperatures and the mean air temperatures recorded at the Hong Kong Observatory in Tsim Sha Tsui. The correlation between the predicted and the measured values was 0.989, while the mean absolute error, the RMSE, and the root-mean-square percentage error were 0.659°C, 0.861°C and 4.2%, respectively. Hence, the results confirmed that the seasonal unit root model of monthly mean air temperature has a high predictive power. When the OLS model based on monthly data was tested, the correlation between the predicted and the measured values was 0.247, while the mean absolute error, the RMSE, and the root-mean-square percentage error were 4.5°C, 5.04°C and 25.1%, respectively.

  • Generally, the UHI is characterized by comparing air temperatures recorded at an urban site and a rural site(Wang et al., 1990; Camilloni and Barros, 1997; Li et al., 2004; Chan et al., 2012; Debbage and Shepherd, 2015; Hu et al., 2016). The differences between these two time series are attributed to the effect of the UHI. Unfortunately, nearly all rural stations were established in Hong Kong after the mid-1980s, and some rural areas have been converted to developed areas with suburban or urban land-use (Leung et al., 2007; Chan et al., 2012; Siu and Hart, 2013). The measured air temperatures at Hong Kong's island stations, such as Cheung Chau and Waglan Island, have been found to have been substantially modulated by temporal variations in temperature of the near-shore waters (Leung et al., 2007). Hence, annual mean air temperature and monthly mean air temperature data recorded at Ta Kwu Ling were taken as those for the rural station (Leung et al., 2007). Figure 6 shows the difference between the annual mean air temperature at the urban station, i.e., Hong Kong Observatory at Tsim Sha Tsui, and that at the rural station, i.e., Ta Kwu Ling, between 1989 and 2014. It can be seen that the annual mean air temperature at the urban site was 0.808°C (standard deviation: 0.230°C) higher than that of the rural site.

    Figure 5.  The measured mean air temperature versus the predicted mean air temperature in Hong Kong (January 2014 to December 2015).

    Figure 6.  Annual mean air temperature at an urban site (diamonds) and a rural site (squares) in Hong Kong (1970-2015). Note that air temperature was recorded at the same location of Ta Kwu Ling (rural area) after 1988, and that the annual and monthly mean air temperatures at Ta Kwu Ling for the year 2015 had not been released by the Hong Kong Observatory at the time of writing.

    Monthly mean air temperature data at Ta Kwu Ling between January 1999 and December 2014 were obtained from the database of the Hong Kong Observatory. It was found that the distribution of differences between the monthly air temperature at the urban site and that at the rural site ranged between 0°C and 2.9°C, with a mode of 0.50°C-0.75°C. Results showed that the difference between the monthly mean air temperature at the urban site and that at the rural site was greater during the winter months, with an average of 1.38°C for November, 1.88°C for December, and 1.51°C for January. This phenomenon has also been observed in mainland China (Wang et al., 1990), Seoul (Kim and Baik, 2002), Alaska (Hinkel et al., 2003), and Hong Kong using quarterly data (Chan et al., 2012). Monthly mean air temperature was found to be on average 0.865°C (standard deviation: 0.538°C) warmer at the urban site than at the rural site during the period of January 1999 to December 2014.

  • Figure 7a shows that Hong Kong's population increased from 4.0 million in 1970 to 7.3 million in 2015.Figure 7b shows that Hong Kong's GDP increased from HKD 195.2 trillion to HKD 2246.4 trillion during the same period of time. Figure 7c shows the urbanization of Hong Kong between 1970 and 2015. Data were compiled from reports of the Hong Kong Buildings and Lands Department (on or before 1989) and the Hong Kong Planning Department (from 1990 onwards). The use of energy will definitely lead to an increase in air pollutants (To et al., 2012; To, 2014, 2015), and has an effect on ambient temperature——particularly in a built environment such as Hong Kong. However, the impact of energy use on air temperature is a complex phenomenon. Not all the consumption of primary energy will eventually transform in to thermal energy in air. For example, Hong Kong's power plants have an overall efficiency of about 34%. In other words, about 1/3 of fuel energy in the power plant is converted to electrical energy and 2/3 of fuel energy is wasted through mechanical loss, transmission loss, waste heat discharged to the atmosphere through stack gases, and waste heat discharged to the air and sea through the cooling water of condensers (Erdem et al., 2010). Within the urbanized area of Hong Kong, the use of electricity, gases (including liquefied petroleum gas and town gas), motor gasoline by private vehicles, and diesel oil by buses, trucks and industrial boilers, will cause an increase in the amount of waste heat discharged from chillers, boilers, flue gases, etc. Hence, the use of these forms of energy was included in this study. Data were extracted from Hong Kong's energy end-use reports published by the Hong Kong Electrical and Mechanical Services Department (HKEMSD, 2005a, b). Figure 7d shows the use of energy in terajoule (TJ) for the period 1984-2013.

    Figure 7.  Hong Kong's (a) population (1970-2015), (b) GDP (1970-2015), (c) urbanized area (1970-2015) and (d) energy use (1984-2013). The data in (c) exclude temporary structures/livestock farms, reservoirs, cemeteries, crematoria, mines, quarries etc. The data in (d) were only available after 1984.

    Figure 8.  Scatter plots of annual mean air temperature versus Hong Kong's (a) population, (b) GDP, (c) urbanized area, and (d) total energy use.

    Figure 8 presents scatter plots of annual mean air temperature versus Hong Kong's population, annual mean air temperature versus Hong Kong's GDP, annual mean air temperature versus Hong Kong's urbanized area, and annual mean air temperature versus the total energy use in Hong Kong. Trend and correlation analyses show that there was a moderate and significant relationship between annual mean air temperature and Hong Kong's GDP (R2=0.3746; p<0.001), and moderate (slightly stronger) and significant relationships between annual mean air temperature and Hong Kong's population (R2=0.4244; p<0.001), between annual mean air temperature and Hong Kong's urbanized area (R2=0.4319; p<0.001), and between annual mean air temperature and Hong Kong's total energy use (R2=0.4344; p<0.001).The findings are not surprising, because it is known that urban warming is associated with population and GDP (Oke, 1973; Hu and Jia, 2010). Besides, the correlation between annual mean air temperature and urbanized area was consistent with the findings reported by (He et al., 2013), who indicated that the total urban area of a city was associated with the annual mean air temperature recorded in the city's meteorological stations.

    To identify the relationships between population, GDP, urban land use, energy use, and annual mean air temperature, multiple-linear regression was applied to these five time series (Ning and Bradley, 2014). Population, GDP, urban land use, and energy use were chosen as the independent variables, while annual mean air temperature was selected as the dependent variable. A stepwise procedure was used to identify significant predictors of annual mean air temperature. Results showed that energy use and GDP were able to explain 48.1% of the variance in annual mean air temperature.

4. Conclusion
  • Urban warming is an important environmental issue and its trend should be closely monitored. The present study is one of the first to use annual as well as monthly mean air temperature data and an econometric approach to characterize the long-term urban temperature trend in a built environment such as Hong Kong. Unlike many other studies, which have primarily used annual mean air temperature data (Li et al., 2004; Huang et al., 2009; Chan et al., 2012), our analysis made use of monthly data, resulting in much higher statistical power to identify the stochastic trend of mean air temperature. The seasonal unit root analysis produced a much more accurate estimate of the urban temperature trend because the monthly data were fully utilized and the loss of information was small (Nijman and Palm, 1990). The results of the seasonal unit root analysis showed that monthly mean air temperature has a stochastic trend and seasonal components. It was found that the mean air temperature in Hong Kong increased by 0.169°C (10 yr)-1 over the past four decades using monthly temperature data [or 0.174°C (10 yr)-1 using annual temperature data], and the trend is likely to persist. By comparing mean air temperatures at an urban site and that at a rural site, it was found that mean air temperature was on average 0.865°C higher at the urban site than at the rural site. The results of correlation analysis illustrated that the increase in annual mean air temperature was significantly associated with the increase in population, GDP, urban land use, and energy use. The results of multiple-linear regression showed energy use and GDP to be the most significant predictors of annual mean air temperature.

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