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) 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/ 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/ 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/ 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/ 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/ 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/ : 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/ 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/ 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/ 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) 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/ 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/ 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/ 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.
HKEMSD, 2015b: Hong Kong Energy End-Use Data 2003-2013. Hong Kong Electrical and Mechanical Services Department,Hong Kong.
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/ 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/ 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,
Kalnay E., M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature, 423( 6939), 528- 531.10.1038/ 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/ 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; 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) 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: .]
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/ 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/ 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/ 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/ 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/ 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/ 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/ 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/ 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/"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) 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/ 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/ 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/ 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/ 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/ 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/ 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/ - 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/ 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/ 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/ 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.