Urbanization in the Arctic and sub-Arctic is an increasingly important factor for the anthropogenic influence on the local and regional climate and ecosystems. One of the most evident and widely documented climatological effects associated with urbanization is the urban heat island (UHI) effect, for which urban and suburban areas are warmer than rural areas [1
]. Urban areas alter the weather and climate, and feedbacks influence human health and energy consumption; these two aspects alone are enough to motivate interest in UHI studies. UHIs affect the climate of cities [3
], shift plant phenology [4
] and develop habitats for introduced or invasive species of plants and animals [5
]. With rising global warming, the intensity of the UHI is also likely to increase, and its effects will become more significant in the future [6
Literature has revealed that the UHI effect increases with latitude [7
]. There is, however, a significant knowledge gap, as all but a few of the studied cities have been located below 60°N latitude. There are only a few in situ studies of the UHI effect for cities and towns above 60°N [8
]. Although the high-latitude UHI has received relatively little attention in literature, there is a growing demand to identify the relevant physical and ecological processes.
Increasing UHI temperatures induce more significant impact in high latitudes, threatening the infrastructure, building and road stability [10
]. Shiklomanov et al. (2016) [11
] have already found a weakening of the soil bearing capacity and an increasing vulnerability of urban infrastructure to increasing surface temperatures in the cold climate regions. Other authors have associated shifting phenological phases [12
] and the advance of alternative ecosystems [13
] to higher urban temperatures.
The question regarding effective drivers for the UHI in high latitudes remains unresolved. UHIs in low latitudes and mid-latitudes are typically driven by a reduction in evaporative cooling over urban landscapes [3
]. A number of studies suggest that anthropogenic heating [1
] as well as CO2
and pollutant emissions [14
] additionally increase the urban–rural temperature difference. There are also statistical linkages between the UHI intensity and various descriptive indicators of cities, such as the urban area size and its density and population. Imhoff et al. (2010) [16
] suggest that the effect is more or less pronounced depending on the type of landscape. The intensity of the UHI depends on the ecosystem it replaced. In the boreal forest environment, forest cleanings are found to have a local cooling effect [17
], thus damping the UHI intensity. UHIs also depend on the regional climate [3
], forming a rather distinct annual cycle in the northern climate zones [18
]. At the same time, within the same ecological background, larger cities have larger heat islands in terms of both magnitude and area [14
The UHI can be identified by comparing air temperature observations from a meteorological station network or from special observational campaigns [9
]. Urban observational networks are, however, sparse and are often non-representative in complex anthropogenic landscapes. Moreover, not every city has pairs of rural and urban weather stations, and it is difficult to rely on in situ data alone to obtain information about the UHI. Therefore, urban climatologists are increasingly referring to the remotely sensed land surface temperature (LST) as a convenient and accessible means to characterize UHIs (or surface urban heat islands (SUHIs) [20
]). Because no commonly accepted terminology is in use, we refer in this study to UHI when discussing general effects of the urban temperature anomaly, whereas SUHI is used when LST data and features are specifically addressed. This separation was proposed by Vogt (2004) [21
] and was followed by Huang et al. (2017) [22
The use of satellite remote-sensing data, such as the LST data from the moderate-resolution imaging spectroradiometer (MODIS), makes it possible to study the UHI of a large number of cities. The MODIS is particularly useful for LST data because of its global coverage, radiometric resolution and dynamic range for a variety of land-cover (LC) types. It has high calibration accuracy in multiple thermal infrared bands designed for retrievals of LST and atmospheric properties [23
]. Despite some noted difficulties and inaccuracies [24
], the LST data retrieved by the MODIS sensors aboard the Terra (EOS AM) and Aqua (EOS PM) NASA satellites are widely used for UHI studies [16
Here, we use MODIS LST data to test the UHI effect north of 60°N, focused on northern West Siberia (hereafter NWS). There are strong physical reasons to expect that the cold continental climate in the NWS region could exacerbate the UHI trapping the additional heat in a shallow planetary boundary layer of the persistently stably stratified lower atmosphere [28
]. These factors make this region an ideal testbed to study the intensity, magnitude, and spatial and temporal variability of the UHI effect in high latitudes, here for the first time for a large number of cases spanning an extensive area.
Most of the UHI intensity studies are limited to the 60°N latitude. In this study, we use remote sensing data from the MODIS platform to assess the UHIs and to define the most influential factor on UHI formation and intensity for the cities in NWS, which lies between 60°N and 70°N latitude.
We moved from an analysis of fragmented cases to an investigation of 28 NWS cities. A strong UHI is found in both summer and winter seasons. The mean UHI intensity is greater in the winter season. However, the expected dependence between the mean background temperature and winter UHI was not found. The anthropogenic heating does not overcompensate for the temperature drop, suggesting that the heat generation and distribution standards are sufficiently robust to account for the regional climate conditions. At the same time, the large UHI intensity and footprint area, as well as its strong dependence on the population, clearly identify that the standards do not account for the UHI. Urban temperature corrections for the heat distributing units could decrease the observed overheating with a considerable economic effect when accounting for more than 250 heating days per annum in the NWS.
The cold continental climates are characterized by prolonged periods of stable atmospheric stratification, when damped vertical turbulent mixing effectively traps the surface temperature anomalies in the lowermost air layers [26
]. This trapping favors better agreement between the LST and the surface air temperature, as we have seen in the MODIS data discussion (Section 2
). In such circumstances, the dominant urban development paradigm directed towards compact urban planning with a predominance of medium- and high-rise apartment blocks works for UHI intensification with all the noted detrimental consequences to the urban infrastructure. Norilsk is a clear example of the challenge [11
]. This study shows that the UHI intensity is high and seasonally persistent in the NWS cities. Moreover, the urban green space intended to alleviate the UHI will likely result in the opposite effect. The boreal vegetation, particularly the dark coniferous trees and shrubs, has a warming rather than a cooling effect [17
]. At the same time, the UHI may help to improve the city comfort and create an even deeper sense of place for the urban dwellers, supporting more diverse urban vegetation (see the review on the matter in [38
The leading UHI driver switches from anthropogenic to direct solar heating during the long summer days in northern latitudes. The short summer nights do not have analogs at other latitudes, and therefore the night-time UHI variability cannot be studied in other regions. The strong dependence of the summer UHI on the background temperature confirms the strong climatic basis for summer UHI formation. Zhou et al. (2013) [18
] discovered increasing UHI intensities with an increasing boundary temperature for some cities in Europe. Synergistic interactions between UHIs and the warming climate are also evident (i.e., the magnitude of the urban–rural temperature difference is also increased when the background temperature increases) [40
For three cities located in the tundra zone in the north of our study region (above 64°N latitude) for the summer season, an opposite UHI was found. Perhaps surprisingly, it is reminiscent of an oasis effect
] that typically exists in arid areas. Here, the towns have been built more recently and generally implement better construction standards. This reduces the UHI, making the higher albedo factor dominant. The background tundra vegetation (mostly lichens and dwarf shrubs) warms at greater rates as a result of a lower albedo than the urban area built on a sandy base and that has a very low amount of vegetation (high albedo).
There is no apparent relationship between UHI intensity and vegetation greenness in or around the cities. The general vegetation trend for NWS is an increase (“greening”) in tundra and a decrease (“browning”) in the boreal forest zone [13
]. However, at the same time, it has been observed that there is an accelerated increase of the NDVI in the urban areas located in the taiga “browning” zone, and in contrast, a decreased NDVI in the tundra “greening” zone [37
]. We did not establish a direct connection with vegetation greenness; however, there is some correlation between the UHI intensity and vegetation change in and around the city. The forest has warming effects in northern latitudes [17
]; thus we can assume that an increase/decrease in the city vegetation can also have an effect on the UHI intensity and can cause an UHI increase in the southern part of the area and an UHI decrease in the northern part.
We did not find a significant correlation between the UHI and the land surface diversity (SHEI). This result, however, should not be misinterpreted. On the one hand, the largest surface difference was that between the urban and natural LC, although within each of these classes, the surface had relatively similar thermal properties. On the other hand, it is known that the impact of the surface heterogeneity scales is strongly non-linear and non-monotonic [43
]. In most cases, it could be further moderated by the differences in the evapotranspiration [45
]. Thus, it cannot be expected that the essentially linear statistical analysis, non-discriminative to the spatial scales, would reveal any UHI–SHEI dependences. The primary variables show that the summer mean LST has a significant negative correlation with the SHEI. This is an indicator that higher diversity or heterogeneity of LC around the city means that more heat is absorbed and possibly causes a cooling effect. Changes in vegetation and landscape diversity are plausible controlling factors of UHI intensity in high latitudes. More detailed studies of these effects are needed.
Earlier UHI studies (e.g., [1
]) report a rather strong dependence between ΔT
and the size of the urban population. The implication was that smaller cities should not exhibit considerable temperature anomalies compared to the regional climate. Later, using other UHI results, the dependences were found to be weaker and were even re-considered to be insignificant [48
]. The dependence between ΔT
and log P
is the strongest statistical regression factor in NWS. However, its slope is more in line with results of recent studies [47
] than with the earlier mega-city analysis.
There are some uncertainties and errors that could arise in our inferential study. Complex topography and LC are two influential factors on the LST [48
]. The relationship between the LST and the influential factors varies with the seasons during the year, having either a warming or cooling effect on the Tr
value. In NWS, the background surface heterogeneity around the urban areas is small-scale (much less than for the city areas), as has been objectively shown using Moran’s I index [13
]. In this region, the population is highly concentrated in a few compact and spatially well-separated urban centers. There are virtually no agricultural fields or smaller settlements between these centers. We studied 28 cities with strongly modified land-use/land-cover embedded at larger spatial scales into a relatively homogeneous natural environment.
The NWS landscape is nearly flat; therefore, topography would not be considered an influential factor in our case. However, despite the elimination of water and urban pixels because of their significant influence on the rural LST [35
], we could still have an effect of these two LC classes on the results. Our study identifies the LC using 300 m ESA CCI data. According to the LC CCI accuracy assessment report, the LC data have uncertainties and limitations, mostly related to classification accuracy, and with an overall estimated accuracy of 71%. This indicates that, with such classification accuracy, the error in the eliminating of water and urban pixels is low.
The method used here and its implementation is to a large degree similar to those suggested in previous studies [18
]. Using buffer zones to compute the urban–rural temperature difference has been widely practiced [48
] and found to be consistent and robust for such an analysis. In our study, both the UHI and the rural background buffer could be unambiguously delimited in the NWS area. The study [3
] of statistical connections between the UHI and the background climate in 65 U.S. cities used a method of pairing the pixels in the city centers with the rural buffer pixels. The author argued that this simpler delimitation is sufficient for region-wide studies of the UHI dependence for a selection of cities. The noted [50
] study could be used as a strong background to support these simplest of choices. The latter authors used the objective Petit’s and Rodionov’s tests (individual point Student’s t
-tests) to determine the extent of the UHI in Bucharest. They reported that both methods are rather insensitive (i.e., statistically insignificant) to the urban–rural transition and the selection of the buffer zones. Thus, the conclusion, at least for the well-separated cities, was that the UHI intensity is rather insensitive to the chosen urban boundary delimitation method. In fact, we are working to extend our analysis of Siberian cities in order to include an analysis of the stability classes and typical synoptic situations in a way that is compatible with [51
] but that uses the theoretical background presented in [52