J. Meteor. Res.  2017, Vol. 31 Issue (6): 1161-1166   PDF    
The Chinese Meteorological Society

Article Information

SHUMILOV, Oleg I., Elena A. KASATKINA, and Alexander G. KANATJEV, 2017.
Urban Heat Island Investigations in Arctic Cities of Northwestern Russia. 2017.
J. Meteor. Res., 31(6): 1161-1166

Article History

Received April 10, 2017
in final form July 20, 2017
Urban Heat Island Investigations in Arctic Cities of Northwestern Russia
Oleg I. SHUMILOV, Elena A. KASATKINA, Alexander G. KANATJEV     
Polar Geophysical Institute, Apatity 184209, Russia
ABSTRACT: Urban microclimate peculiarities in two Arctic cities in northwestern Russia—Kirovsk (67.62°N, 33.67°E) and Apatity (67.57°N, 33.38°E)—were investigated by using mobile temperature records. The experiment was carried out in and around Apatity and Kirovsk in February 2014 and December 2016. The DS18B20 digital thermometer was installed on the roof of a car (height: approximately 1.2 m) to measure and record temperature variations automatically. In addition to the digital thermometer, the car was also equipped with an onboard global positioning system, allowing every temperature measurement to be referenced with an altitude and a latitude/longitude position. The possibility of urban heat island formation in these polar cities, above the Arctic Circle, was studied. Our analysis indicated that on 11 February 2014, the temperature varied in accordance with the background environmental lapse rate (–0.0045°C m–1), and nearly corresponded to it (–0.0165°C m–1) on 12 February 2014. On 6 December 2016, a strong local temperature inversion with a positive value of 0.032°C m–1 was detected, seemingly caused by the formation of a cold air pool in the valley near Kirovsk. It was found that the temperature variations within and outside these cities are strongly influenced by local topographic effects and the physical conditions of the atmospheric boundary layer.
Key words: urban microclimate     topo-climate     urban heat island     mobile temperature measurement    
1 Introduction

Owing to increasing levels of urbanization and industrialization, studying urban microclimates is becoming increasingly important. The urban heat island (UHI) phenomenon, in which urban centers experience higher temperatures than surrounding suburban and rural areas, is considered to be one of the major consequences of anthropogenic influence on weather and climate (Oke, 1987). It is known that in 2008, the world’s urban population exceeded its rural population for the first time in history, and has still continued to grow (Satterthwaite et al., 2010). According to some forecasts, urbanization will result in an estimated 6 billion urban dwellers by 2050 (McCarthy et al., 2010). The need to study this phenomenon relates to the possible negative impact of UHIs on urban climate and human health (Oke, 1987; Rizwan et al., 2008; Peng et al., 2012).

In summer, the negative impact of a UHI, in combination with high temperatures and air pollution, is a serious problem for megacities. Conversely, in winter, the generation of a UHI at high latitudes can have a positive effect, due to lower energy consumption for heating. However, interest in studying the UHI phenomenon does not only stem from the possible economic benefits of reducing the energy consumption associated with heating in winter and cooling in summer. For instance, some studies have reported that a significant part of the observed global warming trend could be accounted for by the UHI contribution (Jones et al., 2008). In particular, recent studies have indicated an urbanization effect of 30% in the annual mean temperature trends for mainland China (Sun et al., 2016; Ren et al., 2017). If this is the case, UHI effects should be taken into account in models toward the goal of limiting the global temperature increase to 1.5 °C above pre-industrial levels, in accordance with the recommendations given by the Paris Agreement (UNFCCC, 2015).

The magnitude of a UHI is defined as the temperature difference between the urban area and its surrounding rural area (Oke, 1987). In some megacities, the maximum UHI intensity can exceed 12°C (Kim and Baik, 2002; Khaikine et al., 2006; Rizwan et al., 2008; Peng et al., 2012). The main reasons for the generation of a UHI are all the factors that affect the flow of incoming solar and longwave radiation re-radiated by the surface and atmosphere (meteorological parameters, seasonal and diurnal cycles, urban density, road surface, municipal forestry, air pollution, and so on), as well as the anthropogenic sources (heating, industry, and transport) (Kim and Baik, 2002; Rizwan et al., 2008; McCarthy et al., 2010). Research on UHIs has typically focused on low- and mid-latitude cities (Kim and Baik, 2002; Khaikine et al., 2006; Rizwan et al., 2008; McCarthy et al., 2010; Peng et al., 2012; Zhang et al., 2013; Zhou et al., 2014), and most studies have been able to confirm the basic regularities of the formation of heat islands in large cities, including the topographic features of the areas studied. Heat islands in polar cities, however, are fundamentally different from those at lower latitudes. At high latitudes, during the polar night, the sun is below the horizon, there is no inflow of solar radiation, and anthropogenic impacts are becoming the main sources of UHI generation.

To date, only a few studies on the formation of UHIs in winter behind the Arctic circle have been conducted (Magee et al., 1999; Hinkel et al., 2003; Demin et al., 2015; Konstantinov et al., 2015). Magee et al. (1999) and Hinkel et al. (2003) reported a maximum UHI magnitude of 6 °C in Alaskan village, Barrow, with a population of 6000 residents, and a maximum UHI intensity of 2 °C in Fairbanks, Alaska (with approximately 35000 residents), respectively. Konstantinov et al. (2015) used satellite data to report a maximum surface-temperature-based UHI intensity of 3.2 °C in Apatity, Murmansk Oblast (approximately 57000 residents); while using weather station data, Demin et al. (2015) reported the absence of any sign of a UHI in Murmansk (approximately 302000 residents)—the world’s largest city north of the Arctic circle. It should be noted that satellite temperature measurements do not always provide sufficient accuracy; for example, the measurement error in the case of Apatity’s UHI was calculated to be as large as 1.68 °C (Konstantinov et al., 2015). Besides, satellite UHI magnitudes show systematic overestimation compared with ground-based measurements (Rizwan et al., 2008; Konstantinov et al., 2015). Moreover, assessing the UHI intensity using the temperature difference between two weather stations only (urban and rural) can also lead to distorted results, particularly in complex hilly terrain because of the orographic effect (Zhou et al., 2014).

In the present study, we used mobile temperature surveys to determine the factors controlling the urban microclimates of two polar cities: Kirovsk (with approximately 27000 residents) and Apatity. The method of taking mobile temperature measurements, or thermal mapping, has been used for more than 40 yr in road climatology, and has also been used in several studies where the influence of local topography on temperature variations in and around cities was studied (Chapman et al., 2001).

2 Site description and methodology

The climate of the Kola Peninsula differs from other Arctic regions. It is formed under the influence of both the warm air masses from the North Atlantic Ocean and the cold air masses from Arctic regions. The proximity to the Gulf Stream provides an ice-free coast and a relatively mild and stable climate. In the central regions of the Kola Peninsula, the climate is more continental. The geographical location of Apatity (67.57°N, 33.38°E) and Kirovsk (67.62°N, 33.67°E) determines their climatic features. Apatity is located behind the Arctic circle in the center of the Kola Peninsula between Lake Imandra and the Khibiny Mountains. The climate is normally cool in summer and relatively warm in winter. The mean monthly temperature in January and February ranges from –13 to –15 °C. Kirovsk is located 20 km northeast of Apatity on the coast of Lake Boljshoy Vudyavr. It is surrounded by the Khibiny Mountains to the west, north, and east. The mean monthly temperature in January and February ranges from –8 to –14 °C. The polar night in Kirovsk and Apatity, when the sun is below the horizon, lasts from 15 to 28 December.

The experiment was carried out in and around Apatity and Kirovsk in February 2014 and December 2016. A DS18B20 digital thermometer (Manufacturer: Maxim Integrated, www.maximintegrated.com) was installed on the roof of a car (height: approximately 1.2 m) to measure and record temperature variations automatically. The instrument has an operating temperature range of –55 to 125 °C, an accuracy of ± 0.5 °C, data acquisition time of 750 ms, and a precision of 0.0625 °C at 12-bit resolution. In addition to the digital thermometer, a car was also equipped with an onboard global positioning system, allowing every temperature measurement to be referenced with an altitude and a latitude/longitude position. Mobile temperature measurements enable us to investigate in detail the influence of local topography on temperature variations in and around cities.

To determine the similarities and differences between the temperature and altitude, we calculated the Pearson’s correlation coefficient. The regression analysis was carried out with the help of the MATLAB software package (Software Company: MathWorks, www.mathworks.com). The statistical significance of the correlation coefficients and the confidence intervals for the regression slopes were calculated with the Student’s t-test.

3 Results and discussion

It is well known that polar UHIs are prominent on clear and windless nights in winter and their intensities are negatively correlated with wind speed and cloud cover (Magee et al., 1999; Hinkel et al., 2003; Rizwan et al., 2008). These conditions are also favorable to fully evaluate the influence of the local topography (Chapman et al., 2001). Therefore, this paper presents results related to the winter season—specifically, on 11 February 2014, 12 February 2014, and 6 December 2016. According to the weather station data, the temperature in February 2014 did not exceed –2 °C, with the wind speed ranging from 6 m s–1 (11 February) to 2 m s–1 (12 February). Figure 1 shows data from the aerological sounding at Kandalaksha (67.2°N, 32.4°E) for the period. As can be seen, during the first period (11–12 February 2014), temperature varied with altitude in accordance with the background environmental lapse rate γ (γ = –dT/dh, where Tis the atmospheric temperature and h is the height above sea level), up to a maximum of –0.98 °C (100 m)–1, but more typically –0.65 °C (100 m)–1 (Sheridan et al., 2010). A strong inversion with a positive vertical gradient up to 2.2 °C (100 m)–1 occurred on 6 December 2016 (Fig. 1c).

Figure 1 Vertical profiles of temperature variations according to the data from aerological soundings carried out at Kandalaksha (67.2°N, 32.4°E) at 1200 UTC (available online at http://weather.uwyo.edu) (a) 11 February 2014, (b) 12 February 2014, and (c) 6 December 2016.

The study routes and corresponding mobile temperature records are shown in Figs. 2, 3, respectively.

Figure 2 The study routes on (a) 11 February 2014 (the road from the city of Apatity to Khibiny airport, (b) 12 February 2014 (the territory of Apatity), and (c) 6 December 2016 (the road from Apatity to Kirovsk and the territory of Kirovsk). Numbers indicate the temperature (color scale bar; °C) and altitude (m).

Mobile temperature measurements were carried out in Apatity and along a road to Khibiny airport (distance: approximately 14 km) during daytime on 11 February 2014 (Figs. 2a, 3a).

Figure 3 Recordings of air temperature (line with diamonds) and altitude (line) at (a) 1130 UTC 11 February 2014, (b) 1200 UTC 12 February 2014, and (c) 1200 UTC 6 December 2016.

As can be seen from Figs. 2a, 3a, the air temperature decreased with altitude along the road and, in general, corresponded to the environmental lapse rate. No significant temperature rise in the city, compared with the surrounding area, was observed. Moreover, even a slight drop in the urban temperature compared with the suburbs recorded in the eastern part of Apatity was clearly consistent with the observed altitude changes (Fig. 2a). The relationship in this case can hardly be considered as strictly linear; however, the Pearson’s correlation coefficient was significant (r = –0.53 and p = 0.05). It should be noted that the measurements on 11 February 2014 were carried out in cloudy conditions with an average wind speed of 6 m s–1. It is known that the maximum UHI is usually observed in association with wind speeds of less than 4 m s–1; however, according to some estimates, a polar UHI of higher than 1 °C can still be observed in association with wind speeds of around 8 m s–1 (Hinkel et al., 2003). Figures 2b and 3b show that the study road and corresponding mobile temperature records carried out during daytime on 12 February 2014. That the measurements were carried out inside the city limits with the same urban density. It can be seen that a slight decrease in air temperature occurred above the higher eastern part of the city, while the temperature increased with decreasing altitude towards the west (Figs. 2b, 3b). The maximum temperature difference was no more than 0.7–0.8 °C with a height difference up to 40 m inside the city (Fig. 2b). A linear fit was calculated for all cases. Corresponding scatter plots of the mobile air temperature versus a location’s altitude are shown in Fig. 4. The slopes of these plots (with the 95% confidence level) are –0.0045 °C m–1 (–0.0088, –0.0002) and –0.0165 °C m–1 (–0.022, –0.011) for 11 and 12 February 2014, respectively (Figs. 4a, b). These slopes indicate a significant dependency of temperature on altitude and nearly correspond to the standard atmospheric lapse rate (thin lines) (Figs. 4a, b). The lapse rate calculated for 12 February 2014 was slightly higher than the background value, which seemed to be related to the cloud cover. For example, it was found to increase during cloudy, calm days (Sheridan et al., 2010).

On 6 December 2016, during a period close to the polar night, mobile temperature measurements were carried out in Kirovsk and along the road to Apatity (Figs. 2c, 3c). During this period, a strong temperature inversion with a magnitude reaching 2.23 °C (100 m)–1 and a wind speed of 1 m s–1 was observed (Fig. 1c). The same inversions were also observed at Murmansk (68.9°N, 33.1°E) and Sodankylä (67.2°N, 26.4°E) (omitted). According to the weather station data, the temperature dropped to –20 °C at Apatity, while the air temperature amplitude at Kirovsk was only –10 °C. The height difference between these two stations is approximately 200 m. In this case, the temperature dependence on altitude was exactly the opposite: the temperature increased with altitude (r = 0.61 and p = 0.001) and the lapse rate had a positive value of 0.032 °C m–1 (Fig. 4c). According to the mobile temperature records, the maximum temperature in the center of Kirovsk reached ​–13.5 °C at an altitude of 363 m, which was nearly 11.3 °C above the minimum temperature (–24.8 °C) in the suburban area (altitude: 193 m) and 6.5 °C above the temperature at Apatity (Figs. 2c, 3c).

Figure 4 Variations in air temperature with elevation obtained from mobile measurements (diamonds) at (a) 1130 UTC 11 February 2014, (b) 1200 UTC 12 February 2014, and (c) 1200 UTC 6 December 2016. The line of best fit (solid) is included in each plot. Thin lines identify the standard atmospheric lapse rates (–0.0065 °C m–1) in (a) and (b), and (c) the regression line (y = 0.023x – 17.46) calculated by using the data from the aerological sounding from the surface to 500 m on 6 December 2016, when a temperature inversion occurred (see Fig. 1c).

As can be seen from Fig. 4c, the calculated lapse rate for 6 December 2016 had a positive value (0.032 °C m–1) and exceeded the background one (0.023 °C m–1). This noticeable increase was probably due to the local microclimate features occurring in a complex hilly terrain where small differences in topography can produce large variations in air temperature (Chapman et al., 2001; Sheridan et al., 2010). In hilly terrain, a drainage of cold air from the hillsides can lead to the stagnation of air in valleys and the formation of cold air pools (Chapman et al., 2001; Clements et al., 2003; Sheridan et al., 2010). Consequently, a local temperature inversion with a maximum lapse rate up to 8 –12 °C (100 m)–1 can be formed (Chapman et al., 2001; Clements et al., 2003; Sheridan et al., 2010), which is consistent with our results. According to our results, such a cold air pool with a temperature difference of 11.3 °C between the valley bottom and city center was formed in a valley close to Kirovsk (approximately 6 km away). Clements et al. (2003) showed that the air over the pool sidewalls gradually became warmer (1–5 °C) than the air over the pool center at the same altitude during the night. Therefore, modeling the spatial temperature patterns in cold air pools is a very complicated process (Chapman et al., 2001; Clements et al., 2003). The results obtained in the present study indicate that the variation of air temperature in the two polar cities Kirovsk and Apatity and in the surrounding area is influenced regionally by meteorological parameters and locally by topographic factors. The influence of the anthropogenic component seems to be negligible. Therefore, a more comprehensive set of mobile temperature measurements is needed to study and simulate the influence of topographical factors on the urban microclimate in polar cities in winter.

4 Summary and conclusions

Based on data gathered from the polar cities of Apatity and Kirovsk in northwestern Russia, the peculiarities of the local microclimate and possible generation of a UHI were investigated. Temperature measurements during the winter period were carried out using an instrument mounted on the roof of a car. The atmospheric boundary layer conditions, in combination with the local topography, were found to be the major factors causing temperature variations within both the urban and suburban sites. During the first period (11–12 February 2014), the temperature varied with altitude almost in accordance with the environmental lapse rate. During a clear, calm night, the lowest temperature could be found in the valleys, owing to the pooling of cold air. Accordingly, a local temperature inversion was observed. On 6 December 2016, such a cold pool was identified near the town of Kirovsk, with a temperature difference as large as 11.3 °C and height difference of 173 m between the urban and suburban sites. The results of this study show that additional mobile temperature measurements are needed to more comprehensively investigate the influence of local topography on temperature variations in and around polar cities.

Chapman, L., J. E. Thornes, and A. V. Bradley, 2001: Modelling of road surface temperature from a geographical parameter database. Part 1: Statistical. Meteor. Appl., 8, 409–419. DOI:10.1017/S1350482701004030
Clements, C. B., C. D. Whiteman, and J. D. Horel, 2003: Cold-air-pool structure and evolution in a mountain basin: Peter Sinks, Utah. J. Appl. Meteor., 42, 752–768. DOI:10.1175/1520-0450(2003)042<0752:CSAEIA>2.0.CO;2
Demin, V. I., A. P. Antsyferova, and O. I. Mokrotovarova, 2015: Changes of the air temperature in Murmansk since the 19th century. Herald of the Kola Science Centre of the Russian Academy of Sciences, 24, 183–184.
Hinkel, K. M., F. E. Nelson, A. E. Klene, et al., 2003: The urban heat island in winter at Barrow, Alaska. Int. J. Climatol., 23, 1889–1905. DOI:10.1002/(ISSN)1097-0088
Jones, P. D., D. H. Lister, and Q. Li, 2008: Urbanization effects in large-scale temperature records, with an emphasis on China. J. Geophys. Res., 113, D16122. DOI:10.1029/2008JD009916
Khaikine, M. N., I. N. Kuznetsova, E. N. Kadygrov, et al., 2006: Investigation of temporal-spatial parameters of an urban heat island on the basis of passive microwave remote sensing. Theor. Appl. Climatol., 84, 161–169. DOI:10.1007/s00704-005-0154-z
Kim, Y. H., and J. J. Baik, 2002: Maximum urban heat island intensity in Seoul. J. Appl. Meteor., 41, 651–659. DOI:10.1175/1520-0450(2002)041<0651:MUHIII>2.0.CO;2
Konstantinov, P. I., M. Y. Grishchenko, and M. I. Varentsov, 2015: Mapping urban heat islands of Arctic cities using combined data on field measurements and satellite images based on the example of the city of Apatity (Murmansk Oblast). Izvestiya Atmos. Oceanic Phys., 51, 992–998. DOI:10.1134/S000143381509011X
Magee, N., J. Curtis, and G. Wendler, 1999: The urban heat island effect at Fairbanks, Alaska. Theor. Appl. Climatol., 64, 39–47. DOI:10.1007/s007040050109
McCarthy, M. P., M. J. Best, and R. A. Betts, 2010: Climate change in cities due to global warming and urban effects. Geophys. Res. Lett., 37, L09705. DOI:10.1029/2010GL042845
Oke, T. R., 1987: Boundary Layer Climates. 2nd Ed., Routledge, New York, 435 pp.
Peng, S. S., S. L. Piao, P. Ciais, et al., 2012: Surface urban heat island across 419 global big cities. Environ. Sci. Technol., 46, 696–703. DOI:10.1021/es2030438
Ren, G. Y., Y. H. Ding, and G. L. Tang, 2017: An overview of mainland China temperature change research. J. Meteor. Res., 31, 3–16. DOI:10.1007/s13351-017-6195-2
Rizwan, A. M., L. Y. C. Dennis, and C. Liu, 2008: A review on the generation, determination, and mitigation of Urban Heat Island. J. Environ. Sci., 20, 120–128. DOI:10.1016/S1001-0742(08)60019-4
Satterthwaite, D., G. McGranahan, and C. Tacoli, 2010: Urbanization and its implications for food and farming. Philos. Trans. Roy. Soc. B Biol. Sci., 365, 2809–2820. DOI:10.1098/rstb.2010.0136
Sheridan, P., S. Smith, A. Brown, et al., 2010: A simple height-based correction for temperature downscaling in complex terrain. Meteor. Appl., 17, 329–339.
Sun, Y., X. B. Zhang, G. Y. Ren, et al., 2016: Contribution of urbanization to warming in China. Nat. Climate Change, 6, 706–709. DOI:10.1038/nclimate2956
United Nations Framework Convention on Climate Change (UNFCCC), 2015: Paris Agreement. [Available online at http://unfccc.int/documentation/documents/ advanced_search/items/6911.php?priref=600 008831] [Accessed on May 10, 2017].
Zhang, H., Z.-F. Qi, X.-Y. Ye, et al., 2013: Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Appl. Geogr., 44, 121–133. DOI:10.1016/j.apgeog.2013.07.021
Zhou, D. C., S. Q. Zhao, S. G. Liu, et al., 2014: Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sens. Environ., 152, 51–61. DOI:10.1016/j.rse.2014.05.017