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Surface urban heat islands in 57 cities across different climates in northern Fennoscandia
Urban Climate 31 (2020) 100575
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Surface urban heat islands in 57 cities across different climates in northern Fennoscandia
T
Victoria Miles , Igor Esau ⁎
Nansen Environmental and Remote Sensing Center (NERSC)/ Bjerknes Centre for Climate Research, Thormøhlens Gate 47, N-5006 Bergen, Norway
ARTICLE INFO
ABSTRACT
Keywords: Fennoscandia Boreal Urban heat island Population Land surface temperature Microclimate Arctic MODIS
The urban heat island (UHI) is one of the most evident local climate phenomena in urbanized areas. Although much is known about the UHI in low- and mid-latitude cities, knowledge about the UHI in high latitudes is still fragmentary. Understanding this urban climate phenomenon in the high latitudes is essential to support sustainability and resilience of northern settlements that experience accelerated Arctic warming. This study focuses on Fennoscandia, which is the most urbanized northern region. Here, small and medium sized cities are numerous. The urban population is expected to grow as well as the importance of the region for the global resource supply and geopolitical arena. This study includes all 57 cities located above 64° N in this complex region. Their combined urban population is of 1,700,000. The study is based on statistical analysis of Land Surface Temperature (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data in the period 2001–2017. The analysis of the LST data determines a surface UHI or SUHI. We find strong and persistent winter and summer SUHIs in most of the studied cities. The SUHI intensity varies from city to city. It is remarkably heterogeneous due to bioclimatic and socioeconomic differences between the cities. The SUHI is particularly intense in the coastal cities of the Atlantic region. Our analysis shows that the cities with warmer rural background and larger fraction of land covered by more productive vegetation (low albedo) around the cities have lower SUHI intensity. Conversely, the cities with colder background rural climate and less fraction of land with more productive vegetation (high albedo) have higher SUHI. Large thermal inertia of water bodies additionally complicates the determination and interpretation of the SUHI as both the relative strength and the direction of the urban effects (cooling or warming) depend on the fraction of water surfaces in the rural background. In this maritime climate zone, neither population nor the city area size reveal strong correlations with the SUHI intensity. The size of the population is found to be the strongest SUHI predictor in cities with more continental climate. The mean SUHI intensity is found in the range 0–5 °C. The intensity is larger for the largest cities of Murmansk and Oulu (3–5 °C).
1. Introduction The urban environment is home to 80% of the Arctic population. The quality of this environment will define further development and sustainability of the northern cities. While northern cities are the primary source of economic development and social prosperity, they are also the main source of anthropogenic pressure on northern ecosystems and consumer of local natural resources. The urban areas are characterized by higher temperatures – the so-called urban heat island (UHI) (Oke, 1973) – lower windspeeds and longer ⁎
Corresponding author. E-mail address: [email protected] (V. Miles).
https://doi.org/10.1016/j.uclim.2019.100575 Received 16 August 2019; Received in revised form 6 December 2019; Accepted 17 December 2019 2212-0955/ © 2019 Elsevier B.V. All rights reserved.
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growing period. The UHI phenomenon was previously considered not significant in high latitudes, especially in wintertime with its limited incoming solar radiation. However, the UHI effect has been found in all Arctic cities studied in recent decades, e.g., in Fairbanks, Alaska (Magee et al., 1999), Norilsk, central Siberia (Varentsov et al., 2014), 28 cities in northwest Siberia (Esau and Miles, 2016; Miles and Esau, 2017; Konstantinov et al., 2018) and in Apatity, Kola peninsula (Konstantinov et al., 2015: Varentsov et al., 2018). To date, this subject has not been substantially studied in Fennoscandia, although a few studies show the existence of a strong UHI in two sub-boreal cities in southern Finland (Suomi, 2018 and references within). Better knowledge on the UHI in Fennoscandia or other northern regions is important, because this urban climate phenomenon may induce both beneficial and adverse socio-environmental effects. For instance, warmer urban temperatures may extend outdoor activity, reduce damage of city infrastructure from frost (Yang and Bou-Zeid, 2018), and promote development of urban green space construction (Esau et al., 2016). At the same time, UHI can weaken soil-bearing capacity (Shiklomanov et al., 2016; Streletskiy et al., 2019), increase the risk of urban flooding and attract invasive species of plant and animals (Knapp et al., 2010). Waste heat due to anthropogenic heat release and urban metabolism can intensify the UHI (Ichinose et al., 1999) or even be a primary driver for the UHI during winter in polar areas (Konstantinov et al., 2015; Esau and Miles, 2016; Varentsov et al., 2018). However, the modified physical properties of the land surface account mainly for the genesis of the UHI. The surface reflectance and roughness of different land-cover types lead to differences in its thermal properties (Deng et al., 2018; Burakowski et al., 2018) and as a result, a set of different microclimates developed around or within/across the city (Alcoforado and Andrade, 2008). Finally, the land surface and atmosphere together create an interactive system. Energy redistribution through convection between the surface and the atmospheric boundary layer can either increase or reduce the UHI, depending on whether the efficiency of convection over urban land is suppressed or enhanced relative to that over adjacent rural land. This convection effect has dependences on the local background climate. (Zhao et al., 2014). While in general the amplitude of the UHI has been positively correlated with urban density, Imhoff et al. (2010) and Alberti and Marzluff (2004) consider that ecological context has consequences on intensity and sign through its influence on the thermal characteristics of the rural area. Therefore, the UHI intensity is dependent on environmental parameters that affect urban and rural temperature differently (Grimmond, 2007). Studies of the urban microclimate require synchronous observations of air temperature from several urban and rural stations. This condition is seldom met in small and medium cities, as well as urban observational networks are sparse and often non-representative in complex anthropogenic landscapes. The urban heat island phenomenon can also be characterized by surface temperature. Therefore, urban climatologists are increasingly referring to remotely-sensed LST as a convenient, accessible and cost-effective option to analyse and represent urban climate as a spatially continuous phenomenon. Whereas a canopy-layer UHI is based on the air temperature anomaly, a surface UHI or SUHI is based on the LST anomaly. However, it is not strictly proportional to the UHI as the atmospheric stability may strongly modify the near-surface vertical temperature gradients (Sheng et al., 2017; Sun et al., 2015). SUHI appears to be closely linked to land use, whereas for the canopy UHI, advective processes appear to play an important role (Zhou et al., 2019). The different nature of drivers for the UHI and SUHI affects diurnal and seasonal cycles of the urban temperature anomalies (Voogt and Oke, 2003). The LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) make it possible to develop a comparative study of the SUHI (e.g. Peng et al., 2012; Zhao et al., 2014) for a large number of cities over a vast region such as Fennoscandia. This study addresses the degree of urban climate modification in the different bioclimatic environments. We investigate urban LST anomalies in 57 cities with a population of > 4000 persons. We present a comparative analysis of seasonal, diurnal and spatial variability of the SUHI in different bioclimatic zones, based on the 17-year MODIS LST climatology. 2. Study area, data and method 2.1. Study area and bioclimatic zones Fennoscandia is defined as a region consisting of the Scandinavian peninsula, Finland, Karelia and the Kola peninsula. Fennoscandia is an area where climatic variations are large due to its complex geography and varying terrain. The regional variation in climate and vegetation can be expressed in terms of vegetation or bioclimatic zones. Bioclimatic zones are defined by climate and botanic criteria (vegetation types, vegetation physiognomy and floristics) and show a positive correlation with each other. Bioclimatic zones are considered to mostly reflect temperature sums and precipitation, and therefore generally show a latitudinal pattern coinciding with global radiation in general and represent a good integration of climatic and biotic parameters (Karlsen et al., 2009). We used the bioclimatic distribution for Fennoscandia presented by Moen et al. (1999). In this study, the original map was modified and updated according to Karlsen et al. (2009) (see Fig. 1). Two gradients have been recognized in Fennoscandia in a comprehensive expert classification into bioclimatic regions achieved by Moen et al. (1999): a temperature-related gradient, from low to high latitudes and low to high elevation; and a gradient from oceanic to continental climates. In general, the Fennoscandian climate is characterized by a long cold season and snow cover that lasts 6–7 months. Growing season is short, and mean summer temperature ranges between 4 and 14 °C in the region. The studied cities are located in three bioclimatic zones: (1) Atlantic-coastal humid regions, which have small annual temperature amplitudes and receive high precipitation in all seasons (mean summer temperature is 4 °C, winter is −3 °C, the annual precipitation varies from 730 mm (Svolvær, Norway) to 1750 mm (Mosjoen, Norway), (2) Middle boreal (mean summer temperature is 15 °C, winter is −8 °C, precipitation usually not higher than 500 mm (3) Northern Boreal (mean summer temperature is 12 °C, winter is −16 °C, precipitation < 450 mm). The dominant pattern of development in Fennoscandia – except Norway – between 2000 and 2017 is population growth in larger 2
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Fig. 1. Distribution of cities among bioclimatic zones of Fennoscandia. Color of the circle shows the cities zone affiliation and circle size shows the size of population. The population data has been retrieved from national statistics web sites: Norway (https://www.ssb.no); Sweden (https://www. scb.se) Finland (https://www.stat.fi) and Russia (http://www.statdata.ru). Stars indicate the largest cities with the population > 50 thousand.
settlements and a decline in surrounding smaller settlements. The cities with the highest population are Murmansk, Severomorsk and Apatity, Russia; Oulu and Rovaniemi, Finland; Lulea, Sweden; and Tromsø and Bodo, Norway. Further information on the cities' geographical coordinates, population, area, elevation etc. is given in Table 1 in the supplementary material. 2.2. MODIS LST climatology We explored the intensity of urban LST anomalies and their dependence on local climate and different urban characteristics. In this study, geographical affiliation to the bioclimate zone was considered as a common feature for the group of cities. We estimate the Table 1 Pearson's correlation coefficient (r) between diurnal and seasonal SUHI, background rural and urban LST and urban population. Atlantic Trsd Tusd Trsn Tusn Log (P) Trwd Tuwd Trwn Tuwn Log (P) ⁎⁎ ⁎
∆Tsd 0.0 0.599⁎ 0.4 ∆Twd −0.5 0.2 0.3
Middle boreal ∆Tsn −0.5 0.2 −0.1 ∆Twn −0.722⁎⁎ −0.2 0.2
∆Tsd 0.2 0.780⁎⁎ 0.642⁎⁎ ∆Twd −0.3 0.2 0.3
Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed). 3
Northern boreal ∆Tsn 0.2 0.803⁎⁎ 0.625⁎⁎ ∆Twn −0.579⁎⁎ 0.0 0.1
∆Tsd 0.829⁎⁎ 0.0 0.4 ∆Twd 0.451⁎ 0.838⁎⁎ 0.578⁎⁎
∆Tsn −0.2 0.594⁎⁎ 0.738⁎⁎ ∆Twn 0.547⁎⁎ 0.883⁎⁎ 0.484⁎
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urban thermal intensity as the difference between the maximum LST of the urban area and the mean LST around the city (see 2.3). The background rural LST, urban LST and population were considered to explain the SUHI for each group of cities (see 2.4). The LST and emissivity product, MOD11A2 collection 6, of Terra-MODIS was used in the study. The MOD11A2 product provides the LST in 8-day composites with relevant metadata, including quality assessment (QA) information. More technical details on a theoretical basis and an overview of the LST retrieval research can be found in specialized publications such as Dash et al. (2002), Tomlinson et al. (2011), Hachem et al. (2012), Clinton and Gong (2013), and Hu and Brunsell (2015). LST composites were downloaded from http://reverb.echo.nasa.gov/. The data were re-projected from the sinusoidal projection to the Universal Transverse Mercator (UTM) zone 33_N projection system with the WGS84 datum, reformatted from HDF-EOS to GeoTIFF format. More details about data and processing steps described in Miles and Esau (2017). The long-term MODIS LST averages are not particularly sensitive to the periods with significant cloud cover. Chen et al. (2017) showed that the MODIS LST bias does not increase even with a seasonal cloud cover of 60 to 80%. In this study, we use seasonally averaged data for winter (December, January, February) and summer (June, July, August) daytime and nighttime periods for 17 years between 2001 and 2017. For Terra-MODIS, the satellite overpass times are approximately 10:30 and 22:30 local time. Thus, this study focused on 4 time periods: summer day (sd), summer night (sn), winter day (wd) and winter night (wn). 2.3. Calculating urban LST anomalies There are a number of UHI and SUHI indicators reported in the literature (Schwarz et al., 2011). Following Voogt and Oke (2003) we associate urban LST anomalies with a SUHI. Clinton and Gong (2013) and other studies have shown high utility of the MODIS LST products to the SUHI studies. For all cities, we computed the annual mean LST per pixel by aggregating the available 8-day mean composite separately for winter and summer. Both the seasonal and annual average daytime and night-time values were computed. The mean values were calculated based on the 17-year time series. As a result, for each urban territory we produced annual and temporally averaged summer and winter LST maps. We use a 10-km buffer around the city edge in this study. To find the most informative buffers, we tested several different buffer sizes. Due to complicated plan-shapes of many cities (see table 3 in the supplementary), buffers smaller than 10 km interfere with the urbanized areas – an issue that severely affected the SUHI database quality in Chakraborty and Lee (2019). This buffer size study also shows that around some cities the suburban area could be highly modified and homogeneous and therefore cannot be considered as the truly natural rural. This issue is quite typical for the cities in the middle boreal zone. Urban pixels were allocated by the city polygon; the surrounding, non-urban land in a 10-km buffer were considered as rural. The resulting SUHI intensity, ∆T, represents the difference between the maximum LST of the city cluster (Tu) and the mean LST of land outside the city (Tr): (1)
T = Tu –Tr
The SUHI calculations are repeated for each season over the study period 2001–2017. As described by Oke (1987) the urban area appears as a ‘plateau’ of warm air with increasing temperature towards the city center. The uniformity of the ‘plateau’ is interrupted by the influence of distinct intra-urban land-uses such as parks, lakes and open areas (cool), and commercial, industrial or dense building areas (warm). The urban core shows a final ‘peak’ to the heat island where the urban maximum temperature is found. The difference between this value and the background rural temperature defines the SUHI intensity (ΔTu-r). Similar definition of the SUHI intensity is found in Schwarz et al. (2011). Zonal statistics were used to extract thematic information for different vector data: bioclimatic zone, rural area, and urban area. The processing and analysis have been done in the ArcGIS software. The set of extracted variables is presented in Table 2 (supplementary material). 2.4. Statistics Relationships between the variables were investigated using correlation analysis and ordinary least squares (OLS) multiple regression. We use summer day (ΔTsd), summer night (ΔTsn), summer mean (ΔTs); winter day (ΔTwd), winter night (ΔTwn), winter mean (ΔTw) SUHI as the dependent variable, and a number of independent or predictor variables: background rural LST (Tr) for summer day (Trsd) and night (Trsn) and winter day (Trwd) and night (Trwn), urban LST (Tu) for summer day (Tusd) and night (Tusn) and winter day (Tuwd) and night (Tuwn)and city population (P and log(P)). Statistical significance for correlation analysis was based on a non-directional (“two-tailed”) null hypothesis except for the population (P and log(P)) for which any significant correlation is assumed a priori to be positive. Statistical calculations were made using the IBM Statistical Package for the Social Sciences (SPSS). 3. Results Analysis of the 17-year LST climatology reveals the difference between city (Tu) and background rural (Tr) LST. For the majority of studied cities, the urban areas are warmer than rural (Fig. 2a, b). The spatial and temporal variability of LST both inside and outside of the urban area has an effect on the diurnal and seasonal variations of SUHI. The SUHI responses in each zone were different and clearly show the effect of ecological context on seasonal and diurnal SUHI amplitudes (Fig. 2). It is clearly seen from the results 4
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Fig. 2. Average SUHI (∆T) in 57 cities in Fennoscandia. Left panels (a, c) show summer (∆Ts) and right panels (b, d) show winter (∆Tw).
that the spatial distribution in LST is associated with the bioclimatic location (Fig. 1). The cities in the Atlantic zone showed the highest difference between urban and rural LST and cities in the Middle boreal zone showed the lowest SUHI. The diurnal SUHI amplitude (DSA) (Fig. 3a, b) and the seasonal SUHI amplitude (SSA) (Fig. 3c), defined as the SUHI differences between the day and night, and between summer and winter (Zhou et al., 2014) were calculated to quantify the diurnal and seasonal SUHI amplitudes for each city. Finally, we estimated the seasonal mean SUHI in all three zones during the day and night (Fig. 3d). The diurnal and seasonal SUHI in the different zones are substantially different. On average we found that the winter SUHI was stronger than the summer SUHI; however, for most of the cities in the middle zone, the summer SUHI was stronger (Fig. 3). The SUHI intensity is larger during daytime than during nighttime and the differences between cites location (zone) tend to be amplified during the daytime as well. For the coastal cities in the Atlantic zone, the winter day SUHI is the most significant and for the middle zone the summer day SUHI was the strongest (Fig. 3). The weak correlation between summer day and nighttime SUHI (r = 0.4, P < .01) (Fig. 4a) suggests that the factors driving the SUHI during the day are not the same as those during the night. And conversely, the high significant correlation during winter (r = 0.9, P < .001) (Fig. 4b) suggests similar drivers of diurnal winter SUHI. Summer and winter SUHI are positively correlated with the logarithm of the population (log (P)) (Fig. 5a) for the entire set of studied urban territories without its allocation to bioclimatic zones, (correlation coefficient in summer r = 0.55, P < .01 and in winter r = 0.48, P < .01). In each bioclimatic zone, we grouped the cities based on size and analyzed diurnal and seasonal SUHI (Fig. 5b-d). The number of cities in each group varies in every zone; the middle group counts for the highest number of cities in every zone. The SUHI was invariant to background LST (summer r = 0.14 and winter r = 0.28). Very weak correlations with background LST confirm that the nature of SUHI has local or zonal dependencies (Zhao et al., 2014). Further we explore the variability of urban and rural LST variations for groups of cities in every zone. Table 1 presents the results of correlation analysis. 3.1. Atlantic region All cities in the Atlantic zone show persistent urban temperature LST anomalies during winter and summer. On average, the SUHI is slightly higher for the winter season and especially winter day (Fig. 3). The mean summer SUHI (∆Ts) is 1.7 °C and winter SUHI 5
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Fig. 3. Diurnal and seasonal SUHI amplitude. (a) summer diurnal SUHI amplitude (DSA); (b) winter DSA: blue circles indicate that the nocturnal SUHI are larger than diurnal and yellow circles indicate that the diurnal SUHI are larger than nocturnal. (c) seasonal SUHI amplitude (SSA): blue circles indicate that the winter SUHI are larger than summer and yellow indicate the opposite that the summer SUHI are larger than winter. (d) averaged SUHI: sd-summer day; sn-summer night; wd-winter day; wn-winter night for cities grouped by bioclimatic location. A- Atlantic; MB-middle boreal; NB-northern boreal. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4. Relationship between daytime and nighttime SUHI. (a) summer and (b) winter.
(∆Tw) is 2.0 °C. The SUHI in the Atlantic zone shows the highest diurnal and seasonal variability compared to the two other zones (Fig. 3). Summer day SUHI shows high dependence on urban LST (r = 0.6, P < .05, Fig. 6a) and insensitivity to the rural LST. The highest negative correlation was found for the winter night SUHI and background rural temperature (r = −0.7, P < .01, Fig. 6b). That means that cities in colder local climate have higher SUHI. The total population of the studied cities in the Atlantic zone is ~250,000. The largest city is Tromso, with population ~75,000. The correlation analysis indicates no connection between log (P) and ∆T and shows that in the Atlantic zone, the population of a city has no significant effect on urban LST. Despite no statistically significant correlation being found, Fig. 5b shows generally increasing summer day SUHI with increasing city population.
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Fig. 5. Relationship between SUHI intensity and urban population. (a) for the entire set of cities correlation between ∆T and logarithm of population (log P) in summer and winter. The least-squares best-fit line is indicated. The SUHI intensity in cites with different amount of population in every bioclimatic zone. (b) Atlantic, (c) Middle boreal and (d) Northern boreal.
Fig. 6. Correlation between (a) summer day SUHI and urban LST (b) winter night SUHI and rural LST in Atlantic zone.
3.2. Northern boreal Most of the cities show higher urban LST relative to background rural LST. However, for some cities, no temperature difference or slightly negative difference was detected. ∆T ≤ 0 was observed in four cities (Alakyrtti (Fig. 7), Murashi, Polyarnie Zory and Revda) in winter and in two cities (Alta and Snezhnogorsk (Fig. 8)) in summer. The range of summer LST for urban areas is 8.1–12.0 °C and rural areas is 7.1–11.0 °C. The winter urban LST is −7.6 to −16.8 °C and rural is −11.0 to −17.0) °C. The average ∆Ts is 1.20 °C and ∆Tw is 1.44 °C. In the Northern boreal zone, the SUHI show the lowest diurnal and seasonal variability (Fig. 3). ∆Tw shows no correspondence with background (rural) LST or urban LST. Despite no general significant correlation between ∆Tw and Trw and Tuw, we observed that cities in warmer environments (Alta, Narvik, Polyarny and Vadso) have higher ∆T. ∆Ts shows positive significant dependence on city LST, meaning that in summer, warmer cities have a higher contrast with rural LST. Conversely, cities with winter ∆T close to zero have the lowest background LST and the same for the summer negligible ∆T observed in two cities with the lowest summer LST. In total, ~630,000 inhabitants live in 25 cities located in the Northern boreal bioclimatic zone. ∆Tw and ∆Ts are correlated with log (P) (Table 1). Summer day and night ∆T have high correlation with urban LST (Tusd) (r = 0.82, P < .01) and Tusn (r = 0.6, P < .01). In summer ∆T has a high significant correlation with population (r = 0.74, P < .01) during the night. And in winter, the ∆T shows stronger dependence on population during the day (r = 0.57, P < .01) (Fig. 9). The highest contrast between urban and rural LST was detected in Murmansk (Northern boreal zone). Both winter and summer SUHI are strong. The SUHI intensity reaches 5.4 °C in winter and 3.6 °C in summer. Murmansk is the largest city in the Arctic. The city is located on the eastern shore of the Kola Bay and has very strong differences in elevation. The highest point in the city is 306 m and the lowest point is at sea level. The city is also relatively green, with mean NDVI of 0.7. Forests occupy 43% of the area of the city.
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Fig. 7. Example of negative SUHI in winter. Alakyrtti is a small town with population 3.500. The hilltop and lakes in rural areas have warmer surface during the winter then urban surfaces. a) ArcGIS «Imagery» base map; b) MODIS LST map.
Fig. 8. Example of negative SUHI in summer. Snezhnogorsk has a population of 12,600. The city rural area is not developed and represents the natural land cover types, mostly rocks covered by tundra vegetation mixed with numerous different sizes of lakes and small patches of forest. The higher elevation and water bodies have a cooling effect on urban surfaces. The city elevation varied between 90–160 m. a) ArcGIS «Imagery» base map; b) MODIS LST map.
Fig. 9. Relationship between surface urban heat island intensity (∆T) and logarithm of urban population log (P) in a) summer night and b) winter day SUHI. The least-squares best-fit line is indicated.
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3.3. Middle boreal The middle boreal bioclimatic zone has the most continental climate in Fennoscandia. It leads to remarkable localization of the climate anomalies. Significant SUHIs are found in every city in this zone. The range of summer LST for urban areas is 10.1–15.3 °C and rural areas is 9.8–11.6 °C. The winter urban LST range is −11.2 to −15.6 °C and rural is -–11.9 to −16 °C. The average ∆Ts = 1.6 °C and ∆Tw = 1.0 °C. When compared to other bioclimatic zones, one finds that ∆Tw is the smallest anomaly, but the summer day SUHI is the largest one (Fig. 2). The total urban population in this zone is ~808,000. the city size is found to be the dominant predictor to the summer SUHI (Fig. 5b). Both ∆Tsd and ∆Tsn have positive correlations with the urban population (r = 0.64 and r = 0.62, P < .01). In wintertime, however, the effect of population is negligible (Table 1). Cities with higher diurnal variability have stronger SUHI. ∆Tsd has a high correlation with Tusd (r = 0.78, P < .01), ∆Tsn has a high correlation with Tusn (r = 0.8, P < .01). 4. Discussion The analysis shows strong and persistent winter and summer LST anomalies associated with urban areas in northern Fennoscandia. The SUHI in the region has remarkable spatial heterogeneity due to climatic and socioeconomic differences between cities. The urban climate anomalies exhibit strong variations within diurnal and seasonal cycles as well as on longer inter-annual time scales. Large seasonal changes of SUHI with evident spatial distributions were observed for cities in three zones. Atlantic cities, with their mild humid climate, exhibit large seasonal SUHI amplitude. The largest seasonal SUHI amplitude was observed for the small-size cities. The nighttime SUHI show no seasonal variation, but daytime SUHI shows a strong seasonal difference: low in summer and high in winter. The similar seasonally reverse “flip-flop” daytime SUHI was already discovered for coastal cities in lower latitudes (Hua et al., 2008), and the same, unexpected pattern was associated with vegetation in the city buffer (Shastri et al., 2017). During winter, the SUHI in the Atlantic zone has negative correlations with background rural temperature, meaning that the cities in warmer, and more vegetated areas have smaller differences between urban and rural LST, and oppositely the cities with low vegetation located in colder areas have larger LST difference between urban and rural areas. The significant role of vegetation coverage to mediates the UHI in autumn and winter for the coastal cities was also recognized by Wu et al. (2019). The ‘rougher’ vegetation land cover triggers turbulence, and turbulence removes heat from the surface to the atmosphere (Arnfield, 2003). But where there is a smoother surface, there is less convection and the heat will be trapped in the surface (Zhao et al., 2014). This convection effect depends on background ecology (amount and type of vegetation) and varies with local climate, leading to warming in warm humid climates and cooling in drier colder climates (Manoli et al., 2019). In the Middle boreal zone, the daytime SUHI is more intense in summer mainly because the drier urban surfaces produce more significant heating, while the vegetation in suburbs produces stronger evaporative cooling effects. A similar effect has been mentioned by Suomi (2018) for Lahti in southern Finland. The diurnal summer LST difference is higher for the urban than for the rural areas, such that the city gets warmer faster and cools faster than the rural area. Most of the incident solar radiation on urban surfaces is stored during the day and released at night. The city warms up during the day and oppositely during the night the buffer keeps warm (Shastri et al., 2017). This suggests that the factors driving UHI during the day are different than those that driving during the night. This can be explained by water's cooling and warming effect. The role of warmer water surfaces in winter becomes important in the northernmost settlements where it reduces the apparent SUHI intensities. The warming effect during winter is explained mainly by the fact that the ice season usually extends from mid-winter until mid-spring. During the first half of the winter, lakes are still warmer than the land surface would be if lakes are replaced with land (Samuelsson et al., 2010). Another contributing factor to the winter warming could be heat transfer through the ice. Using satellite remote sensing data, Jeffries et al. (1999) estimated the heat flux through lake ice in Alaska to be about 10 W m−2 higher than the heat flux from the soil. Compared to other environmental factors, water bodies have a more distinct seasonal character both in the relative strength and the direction of the effect either warming or cooling (Cosgrove and Berkelhammer, 2018). During the winter, the heating impact of water bodies weakens for the cities around Bothnian Bay (Middle boreal), which has an ice cover for 100–140 days a year, whereas it strengthens for the cities in the Norwegian (Atlantic) and Barents Sea (Northern boreal) regions. In the coldest Northern boreal zone, the anthropogenic heat release is supposed to be the main heat driver in the urban area in winter. Short days, weak solar radiation and snow cover favor inversions in winter by trapping the heat (Miles and Esau, 2017; Varentsov et al., 2018). The magnitude of local LST differences is driven by various environmental or anthropogenic factors. The UHI dependence on the urban size and population has been recognized. In this study, the mean SUHI intensity for the largest cities is higher than neighboring smaller cities in terms of the population. This is a sign that cities with higher population have a higher anthropogenic heat flux (Zhou et al., 2017). We found a significant correlation between SUHI and population in Middle and Northern boreal zones, whereas the correlation for the Atlantic zone was insignificant. 5. Conclusions This study documents the existence and persistence of the surface urban heat island (SUHI) in all 57 cities in Fennoscandia in three bioclimatic locations. The SUHI intensity here depends on the cities' location, topography and land cover. The strength of the 9
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SUHI is to some degree a surprising finding as these cities are of small and medium size and located in high latitudes where enhanced urban albedo and reduced evapotranspiration are not expected to drive urban surface climate anomalies. Moreover, the identified SUHIs are much larger than it could be expected for such small and low-density cities located in lower latitudes. It emphasizes the leading role of the high intensity urban metabolism and anthropogenic heat fluxes in the Fennoscandian cities. Unfortunately, interrelations between the apparent SUHI and diverse socio-economic and socio-environmental factors are too complicated to be addressed in this study – this is a topic for future research. Because SUHI characterizes the urban–rural land surface temperature difference, it depends on the local surface heat balance as well as on the mean intensity of the vertical turbulent mixing in the lowermost atmosphere (Davy and Esau, 2016). We found that a significant fraction of open water (sea or lake) or wet soil (swaps, bogs, mires) in the rural areas modifies SUHI intensity and its diurnal/seasonal variations in the region. The differences in the dominant land cover types control the SUHI across three considered bioclimatic zones. The water fraction, which controls background LST, and rural land cover types dominate the SUHI intensity in the Atlantic bioclimatic zone, whereas the anthropogenic factors dominate the SUHI in the more continental climate of middle and northern boreal zones. The SUHI in the northern boreal zone is the best predicted by the urban population size. We conclude that the urban surface temperature anomalies are an essential and influential feature of the local climate in Fennoscandian cities. It is known that urban planners currently include only limited information on the urban climate into decision making and policy processes. As this study reveals, one should account for the background bioclimatic conditions when urban climate change potential is considered. Declaration of Competing Interest None. Acknowledgement This study was supported by the Belmont Forum and Norwegian Research Council project Anthropogenic Heat Islands in the Arctic: Windows to the Future of the Regional Climates, Ecosystems, and Societies (no. 247468). We thank the reviewers for their thorough reviews and highly appreciate the comments and suggestions, which significantly contributed to improving the quality of the publication. The authors thank Martin Miles for technical discussions and editing. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.uclim.2019.100575. References Alberti, M., Marzluff, J., 2004. Resilience in urban ecosystems: linking urban patterns to human and ecological functions. 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PLoS One 14 (6), e0217850. https://doi.org/10.1371/journal.pone. 0217850. Yang, J., Bou-Zeid, E., 2018. Should cities embrace their heat Islands as shields from extreme cold? J. Appl. Meteorol. Climatol. 57, 1309–1320. https://doi.org/10. 1175/JAMC-D-17-0265.1. Zhao, L., Lee, X., Smith, R.B., Oleson, K., 2014. Strong contributions of local background climate to urban heat islands. Nature 511, 214–219. https://doi.org/10.1038/ nature13462. Zhou, D., Zhao, S., Liu, S., Zhang, L., Zhu, C., 2014. Surface urban heat island in China's 32 major cities: spatial patterns and drivers. Remote Sens. Environ. 152, 51–61. https://doi.org/10.1016/j.rse.2014.05.017. Zhou, B., Rybski, D., Kropp, J.P., 2017. The role of city size and urban form in the surface urban heat island. Sci. Rep. 7, 4791. https://doi.org/10.1038/s41598-01704242-2. Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Frolking, S., Yao, R., Qiao, Z., Sobrino, J.A., 2019. Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sens. 11, 48. https://doi.org/10.3390/rs11010048. Victoria Miles is a researcher at the Nansen Environmental and Remote Sensing Center (NERSC) Bergen, Norway with a specialization in boreal ecology and remote sensing. Her current research efforts are in the arctic and boreal ecosystems. She is examining the recent dynamic of urban climate with connection to natural environmental and climatic factors. Igor Esau is a senior researcher at the Nansen Environmental and Remote Sensing Center (NERSC) Bergen, Norway. His primary scientific interest is to investigate links between small scale and global scale processes. Specifically,he focuses on understanding the role played by the stably stratified boundary layers in regulating the air quality, micro-climate and connections between the large-scale circulation and micro-physics of the Earth's climate system.
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Surface urban heat islands in 57 cities across different climates in northern Fennoscandia
T
Victoria Miles , Igor Esau ⁎
Nansen Environmental and Remote Sensing Center (NERSC)/ Bjerknes Centre for Climate Research, Thormøhlens Gate 47, N-5006 Bergen, Norway
ARTICLE INFO
ABSTRACT
Keywords: Fennoscandia Boreal Urban heat island Population Land surface temperature Microclimate Arctic MODIS
The urban heat island (UHI) is one of the most evident local climate phenomena in urbanized areas. Although much is known about the UHI in low- and mid-latitude cities, knowledge about the UHI in high latitudes is still fragmentary. Understanding this urban climate phenomenon in the high latitudes is essential to support sustainability and resilience of northern settlements that experience accelerated Arctic warming. This study focuses on Fennoscandia, which is the most urbanized northern region. Here, small and medium sized cities are numerous. The urban population is expected to grow as well as the importance of the region for the global resource supply and geopolitical arena. This study includes all 57 cities located above 64° N in this complex region. Their combined urban population is of 1,700,000. The study is based on statistical analysis of Land Surface Temperature (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data in the period 2001–2017. The analysis of the LST data determines a surface UHI or SUHI. We find strong and persistent winter and summer SUHIs in most of the studied cities. The SUHI intensity varies from city to city. It is remarkably heterogeneous due to bioclimatic and socioeconomic differences between the cities. The SUHI is particularly intense in the coastal cities of the Atlantic region. Our analysis shows that the cities with warmer rural background and larger fraction of land covered by more productive vegetation (low albedo) around the cities have lower SUHI intensity. Conversely, the cities with colder background rural climate and less fraction of land with more productive vegetation (high albedo) have higher SUHI. Large thermal inertia of water bodies additionally complicates the determination and interpretation of the SUHI as both the relative strength and the direction of the urban effects (cooling or warming) depend on the fraction of water surfaces in the rural background. In this maritime climate zone, neither population nor the city area size reveal strong correlations with the SUHI intensity. The size of the population is found to be the strongest SUHI predictor in cities with more continental climate. The mean SUHI intensity is found in the range 0–5 °C. The intensity is larger for the largest cities of Murmansk and Oulu (3–5 °C).
1. Introduction The urban environment is home to 80% of the Arctic population. The quality of this environment will define further development and sustainability of the northern cities. While northern cities are the primary source of economic development and social prosperity, they are also the main source of anthropogenic pressure on northern ecosystems and consumer of local natural resources. The urban areas are characterized by higher temperatures – the so-called urban heat island (UHI) (Oke, 1973) – lower windspeeds and longer ⁎
Corresponding author. E-mail address: [email protected] (V. Miles).
https://doi.org/10.1016/j.uclim.2019.100575 Received 16 August 2019; Received in revised form 6 December 2019; Accepted 17 December 2019 2212-0955/ © 2019 Elsevier B.V. All rights reserved.
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growing period. The UHI phenomenon was previously considered not significant in high latitudes, especially in wintertime with its limited incoming solar radiation. However, the UHI effect has been found in all Arctic cities studied in recent decades, e.g., in Fairbanks, Alaska (Magee et al., 1999), Norilsk, central Siberia (Varentsov et al., 2014), 28 cities in northwest Siberia (Esau and Miles, 2016; Miles and Esau, 2017; Konstantinov et al., 2018) and in Apatity, Kola peninsula (Konstantinov et al., 2015: Varentsov et al., 2018). To date, this subject has not been substantially studied in Fennoscandia, although a few studies show the existence of a strong UHI in two sub-boreal cities in southern Finland (Suomi, 2018 and references within). Better knowledge on the UHI in Fennoscandia or other northern regions is important, because this urban climate phenomenon may induce both beneficial and adverse socio-environmental effects. For instance, warmer urban temperatures may extend outdoor activity, reduce damage of city infrastructure from frost (Yang and Bou-Zeid, 2018), and promote development of urban green space construction (Esau et al., 2016). At the same time, UHI can weaken soil-bearing capacity (Shiklomanov et al., 2016; Streletskiy et al., 2019), increase the risk of urban flooding and attract invasive species of plant and animals (Knapp et al., 2010). Waste heat due to anthropogenic heat release and urban metabolism can intensify the UHI (Ichinose et al., 1999) or even be a primary driver for the UHI during winter in polar areas (Konstantinov et al., 2015; Esau and Miles, 2016; Varentsov et al., 2018). However, the modified physical properties of the land surface account mainly for the genesis of the UHI. The surface reflectance and roughness of different land-cover types lead to differences in its thermal properties (Deng et al., 2018; Burakowski et al., 2018) and as a result, a set of different microclimates developed around or within/across the city (Alcoforado and Andrade, 2008). Finally, the land surface and atmosphere together create an interactive system. Energy redistribution through convection between the surface and the atmospheric boundary layer can either increase or reduce the UHI, depending on whether the efficiency of convection over urban land is suppressed or enhanced relative to that over adjacent rural land. This convection effect has dependences on the local background climate. (Zhao et al., 2014). While in general the amplitude of the UHI has been positively correlated with urban density, Imhoff et al. (2010) and Alberti and Marzluff (2004) consider that ecological context has consequences on intensity and sign through its influence on the thermal characteristics of the rural area. Therefore, the UHI intensity is dependent on environmental parameters that affect urban and rural temperature differently (Grimmond, 2007). Studies of the urban microclimate require synchronous observations of air temperature from several urban and rural stations. This condition is seldom met in small and medium cities, as well as urban observational networks are sparse and often non-representative in complex anthropogenic landscapes. The urban heat island phenomenon can also be characterized by surface temperature. Therefore, urban climatologists are increasingly referring to remotely-sensed LST as a convenient, accessible and cost-effective option to analyse and represent urban climate as a spatially continuous phenomenon. Whereas a canopy-layer UHI is based on the air temperature anomaly, a surface UHI or SUHI is based on the LST anomaly. However, it is not strictly proportional to the UHI as the atmospheric stability may strongly modify the near-surface vertical temperature gradients (Sheng et al., 2017; Sun et al., 2015). SUHI appears to be closely linked to land use, whereas for the canopy UHI, advective processes appear to play an important role (Zhou et al., 2019). The different nature of drivers for the UHI and SUHI affects diurnal and seasonal cycles of the urban temperature anomalies (Voogt and Oke, 2003). The LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) make it possible to develop a comparative study of the SUHI (e.g. Peng et al., 2012; Zhao et al., 2014) for a large number of cities over a vast region such as Fennoscandia. This study addresses the degree of urban climate modification in the different bioclimatic environments. We investigate urban LST anomalies in 57 cities with a population of > 4000 persons. We present a comparative analysis of seasonal, diurnal and spatial variability of the SUHI in different bioclimatic zones, based on the 17-year MODIS LST climatology. 2. Study area, data and method 2.1. Study area and bioclimatic zones Fennoscandia is defined as a region consisting of the Scandinavian peninsula, Finland, Karelia and the Kola peninsula. Fennoscandia is an area where climatic variations are large due to its complex geography and varying terrain. The regional variation in climate and vegetation can be expressed in terms of vegetation or bioclimatic zones. Bioclimatic zones are defined by climate and botanic criteria (vegetation types, vegetation physiognomy and floristics) and show a positive correlation with each other. Bioclimatic zones are considered to mostly reflect temperature sums and precipitation, and therefore generally show a latitudinal pattern coinciding with global radiation in general and represent a good integration of climatic and biotic parameters (Karlsen et al., 2009). We used the bioclimatic distribution for Fennoscandia presented by Moen et al. (1999). In this study, the original map was modified and updated according to Karlsen et al. (2009) (see Fig. 1). Two gradients have been recognized in Fennoscandia in a comprehensive expert classification into bioclimatic regions achieved by Moen et al. (1999): a temperature-related gradient, from low to high latitudes and low to high elevation; and a gradient from oceanic to continental climates. In general, the Fennoscandian climate is characterized by a long cold season and snow cover that lasts 6–7 months. Growing season is short, and mean summer temperature ranges between 4 and 14 °C in the region. The studied cities are located in three bioclimatic zones: (1) Atlantic-coastal humid regions, which have small annual temperature amplitudes and receive high precipitation in all seasons (mean summer temperature is 4 °C, winter is −3 °C, the annual precipitation varies from 730 mm (Svolvær, Norway) to 1750 mm (Mosjoen, Norway), (2) Middle boreal (mean summer temperature is 15 °C, winter is −8 °C, precipitation usually not higher than 500 mm (3) Northern Boreal (mean summer temperature is 12 °C, winter is −16 °C, precipitation < 450 mm). The dominant pattern of development in Fennoscandia – except Norway – between 2000 and 2017 is population growth in larger 2
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Fig. 1. Distribution of cities among bioclimatic zones of Fennoscandia. Color of the circle shows the cities zone affiliation and circle size shows the size of population. The population data has been retrieved from national statistics web sites: Norway (https://www.ssb.no); Sweden (https://www. scb.se) Finland (https://www.stat.fi) and Russia (http://www.statdata.ru). Stars indicate the largest cities with the population > 50 thousand.
settlements and a decline in surrounding smaller settlements. The cities with the highest population are Murmansk, Severomorsk and Apatity, Russia; Oulu and Rovaniemi, Finland; Lulea, Sweden; and Tromsø and Bodo, Norway. Further information on the cities' geographical coordinates, population, area, elevation etc. is given in Table 1 in the supplementary material. 2.2. MODIS LST climatology We explored the intensity of urban LST anomalies and their dependence on local climate and different urban characteristics. In this study, geographical affiliation to the bioclimate zone was considered as a common feature for the group of cities. We estimate the Table 1 Pearson's correlation coefficient (r) between diurnal and seasonal SUHI, background rural and urban LST and urban population. Atlantic Trsd Tusd Trsn Tusn Log (P) Trwd Tuwd Trwn Tuwn Log (P) ⁎⁎ ⁎
∆Tsd 0.0 0.599⁎ 0.4 ∆Twd −0.5 0.2 0.3
Middle boreal ∆Tsn −0.5 0.2 −0.1 ∆Twn −0.722⁎⁎ −0.2 0.2
∆Tsd 0.2 0.780⁎⁎ 0.642⁎⁎ ∆Twd −0.3 0.2 0.3
Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed). 3
Northern boreal ∆Tsn 0.2 0.803⁎⁎ 0.625⁎⁎ ∆Twn −0.579⁎⁎ 0.0 0.1
∆Tsd 0.829⁎⁎ 0.0 0.4 ∆Twd 0.451⁎ 0.838⁎⁎ 0.578⁎⁎
∆Tsn −0.2 0.594⁎⁎ 0.738⁎⁎ ∆Twn 0.547⁎⁎ 0.883⁎⁎ 0.484⁎
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urban thermal intensity as the difference between the maximum LST of the urban area and the mean LST around the city (see 2.3). The background rural LST, urban LST and population were considered to explain the SUHI for each group of cities (see 2.4). The LST and emissivity product, MOD11A2 collection 6, of Terra-MODIS was used in the study. The MOD11A2 product provides the LST in 8-day composites with relevant metadata, including quality assessment (QA) information. More technical details on a theoretical basis and an overview of the LST retrieval research can be found in specialized publications such as Dash et al. (2002), Tomlinson et al. (2011), Hachem et al. (2012), Clinton and Gong (2013), and Hu and Brunsell (2015). LST composites were downloaded from http://reverb.echo.nasa.gov/. The data were re-projected from the sinusoidal projection to the Universal Transverse Mercator (UTM) zone 33_N projection system with the WGS84 datum, reformatted from HDF-EOS to GeoTIFF format. More details about data and processing steps described in Miles and Esau (2017). The long-term MODIS LST averages are not particularly sensitive to the periods with significant cloud cover. Chen et al. (2017) showed that the MODIS LST bias does not increase even with a seasonal cloud cover of 60 to 80%. In this study, we use seasonally averaged data for winter (December, January, February) and summer (June, July, August) daytime and nighttime periods for 17 years between 2001 and 2017. For Terra-MODIS, the satellite overpass times are approximately 10:30 and 22:30 local time. Thus, this study focused on 4 time periods: summer day (sd), summer night (sn), winter day (wd) and winter night (wn). 2.3. Calculating urban LST anomalies There are a number of UHI and SUHI indicators reported in the literature (Schwarz et al., 2011). Following Voogt and Oke (2003) we associate urban LST anomalies with a SUHI. Clinton and Gong (2013) and other studies have shown high utility of the MODIS LST products to the SUHI studies. For all cities, we computed the annual mean LST per pixel by aggregating the available 8-day mean composite separately for winter and summer. Both the seasonal and annual average daytime and night-time values were computed. The mean values were calculated based on the 17-year time series. As a result, for each urban territory we produced annual and temporally averaged summer and winter LST maps. We use a 10-km buffer around the city edge in this study. To find the most informative buffers, we tested several different buffer sizes. Due to complicated plan-shapes of many cities (see table 3 in the supplementary), buffers smaller than 10 km interfere with the urbanized areas – an issue that severely affected the SUHI database quality in Chakraborty and Lee (2019). This buffer size study also shows that around some cities the suburban area could be highly modified and homogeneous and therefore cannot be considered as the truly natural rural. This issue is quite typical for the cities in the middle boreal zone. Urban pixels were allocated by the city polygon; the surrounding, non-urban land in a 10-km buffer were considered as rural. The resulting SUHI intensity, ∆T, represents the difference between the maximum LST of the city cluster (Tu) and the mean LST of land outside the city (Tr): (1)
T = Tu –Tr
The SUHI calculations are repeated for each season over the study period 2001–2017. As described by Oke (1987) the urban area appears as a ‘plateau’ of warm air with increasing temperature towards the city center. The uniformity of the ‘plateau’ is interrupted by the influence of distinct intra-urban land-uses such as parks, lakes and open areas (cool), and commercial, industrial or dense building areas (warm). The urban core shows a final ‘peak’ to the heat island where the urban maximum temperature is found. The difference between this value and the background rural temperature defines the SUHI intensity (ΔTu-r). Similar definition of the SUHI intensity is found in Schwarz et al. (2011). Zonal statistics were used to extract thematic information for different vector data: bioclimatic zone, rural area, and urban area. The processing and analysis have been done in the ArcGIS software. The set of extracted variables is presented in Table 2 (supplementary material). 2.4. Statistics Relationships between the variables were investigated using correlation analysis and ordinary least squares (OLS) multiple regression. We use summer day (ΔTsd), summer night (ΔTsn), summer mean (ΔTs); winter day (ΔTwd), winter night (ΔTwn), winter mean (ΔTw) SUHI as the dependent variable, and a number of independent or predictor variables: background rural LST (Tr) for summer day (Trsd) and night (Trsn) and winter day (Trwd) and night (Trwn), urban LST (Tu) for summer day (Tusd) and night (Tusn) and winter day (Tuwd) and night (Tuwn)and city population (P and log(P)). Statistical significance for correlation analysis was based on a non-directional (“two-tailed”) null hypothesis except for the population (P and log(P)) for which any significant correlation is assumed a priori to be positive. Statistical calculations were made using the IBM Statistical Package for the Social Sciences (SPSS). 3. Results Analysis of the 17-year LST climatology reveals the difference between city (Tu) and background rural (Tr) LST. For the majority of studied cities, the urban areas are warmer than rural (Fig. 2a, b). The spatial and temporal variability of LST both inside and outside of the urban area has an effect on the diurnal and seasonal variations of SUHI. The SUHI responses in each zone were different and clearly show the effect of ecological context on seasonal and diurnal SUHI amplitudes (Fig. 2). It is clearly seen from the results 4
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Fig. 2. Average SUHI (∆T) in 57 cities in Fennoscandia. Left panels (a, c) show summer (∆Ts) and right panels (b, d) show winter (∆Tw).
that the spatial distribution in LST is associated with the bioclimatic location (Fig. 1). The cities in the Atlantic zone showed the highest difference between urban and rural LST and cities in the Middle boreal zone showed the lowest SUHI. The diurnal SUHI amplitude (DSA) (Fig. 3a, b) and the seasonal SUHI amplitude (SSA) (Fig. 3c), defined as the SUHI differences between the day and night, and between summer and winter (Zhou et al., 2014) were calculated to quantify the diurnal and seasonal SUHI amplitudes for each city. Finally, we estimated the seasonal mean SUHI in all three zones during the day and night (Fig. 3d). The diurnal and seasonal SUHI in the different zones are substantially different. On average we found that the winter SUHI was stronger than the summer SUHI; however, for most of the cities in the middle zone, the summer SUHI was stronger (Fig. 3). The SUHI intensity is larger during daytime than during nighttime and the differences between cites location (zone) tend to be amplified during the daytime as well. For the coastal cities in the Atlantic zone, the winter day SUHI is the most significant and for the middle zone the summer day SUHI was the strongest (Fig. 3). The weak correlation between summer day and nighttime SUHI (r = 0.4, P < .01) (Fig. 4a) suggests that the factors driving the SUHI during the day are not the same as those during the night. And conversely, the high significant correlation during winter (r = 0.9, P < .001) (Fig. 4b) suggests similar drivers of diurnal winter SUHI. Summer and winter SUHI are positively correlated with the logarithm of the population (log (P)) (Fig. 5a) for the entire set of studied urban territories without its allocation to bioclimatic zones, (correlation coefficient in summer r = 0.55, P < .01 and in winter r = 0.48, P < .01). In each bioclimatic zone, we grouped the cities based on size and analyzed diurnal and seasonal SUHI (Fig. 5b-d). The number of cities in each group varies in every zone; the middle group counts for the highest number of cities in every zone. The SUHI was invariant to background LST (summer r = 0.14 and winter r = 0.28). Very weak correlations with background LST confirm that the nature of SUHI has local or zonal dependencies (Zhao et al., 2014). Further we explore the variability of urban and rural LST variations for groups of cities in every zone. Table 1 presents the results of correlation analysis. 3.1. Atlantic region All cities in the Atlantic zone show persistent urban temperature LST anomalies during winter and summer. On average, the SUHI is slightly higher for the winter season and especially winter day (Fig. 3). The mean summer SUHI (∆Ts) is 1.7 °C and winter SUHI 5
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Fig. 3. Diurnal and seasonal SUHI amplitude. (a) summer diurnal SUHI amplitude (DSA); (b) winter DSA: blue circles indicate that the nocturnal SUHI are larger than diurnal and yellow circles indicate that the diurnal SUHI are larger than nocturnal. (c) seasonal SUHI amplitude (SSA): blue circles indicate that the winter SUHI are larger than summer and yellow indicate the opposite that the summer SUHI are larger than winter. (d) averaged SUHI: sd-summer day; sn-summer night; wd-winter day; wn-winter night for cities grouped by bioclimatic location. A- Atlantic; MB-middle boreal; NB-northern boreal. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4. Relationship between daytime and nighttime SUHI. (a) summer and (b) winter.
(∆Tw) is 2.0 °C. The SUHI in the Atlantic zone shows the highest diurnal and seasonal variability compared to the two other zones (Fig. 3). Summer day SUHI shows high dependence on urban LST (r = 0.6, P < .05, Fig. 6a) and insensitivity to the rural LST. The highest negative correlation was found for the winter night SUHI and background rural temperature (r = −0.7, P < .01, Fig. 6b). That means that cities in colder local climate have higher SUHI. The total population of the studied cities in the Atlantic zone is ~250,000. The largest city is Tromso, with population ~75,000. The correlation analysis indicates no connection between log (P) and ∆T and shows that in the Atlantic zone, the population of a city has no significant effect on urban LST. Despite no statistically significant correlation being found, Fig. 5b shows generally increasing summer day SUHI with increasing city population.
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Fig. 5. Relationship between SUHI intensity and urban population. (a) for the entire set of cities correlation between ∆T and logarithm of population (log P) in summer and winter. The least-squares best-fit line is indicated. The SUHI intensity in cites with different amount of population in every bioclimatic zone. (b) Atlantic, (c) Middle boreal and (d) Northern boreal.
Fig. 6. Correlation between (a) summer day SUHI and urban LST (b) winter night SUHI and rural LST in Atlantic zone.
3.2. Northern boreal Most of the cities show higher urban LST relative to background rural LST. However, for some cities, no temperature difference or slightly negative difference was detected. ∆T ≤ 0 was observed in four cities (Alakyrtti (Fig. 7), Murashi, Polyarnie Zory and Revda) in winter and in two cities (Alta and Snezhnogorsk (Fig. 8)) in summer. The range of summer LST for urban areas is 8.1–12.0 °C and rural areas is 7.1–11.0 °C. The winter urban LST is −7.6 to −16.8 °C and rural is −11.0 to −17.0) °C. The average ∆Ts is 1.20 °C and ∆Tw is 1.44 °C. In the Northern boreal zone, the SUHI show the lowest diurnal and seasonal variability (Fig. 3). ∆Tw shows no correspondence with background (rural) LST or urban LST. Despite no general significant correlation between ∆Tw and Trw and Tuw, we observed that cities in warmer environments (Alta, Narvik, Polyarny and Vadso) have higher ∆T. ∆Ts shows positive significant dependence on city LST, meaning that in summer, warmer cities have a higher contrast with rural LST. Conversely, cities with winter ∆T close to zero have the lowest background LST and the same for the summer negligible ∆T observed in two cities with the lowest summer LST. In total, ~630,000 inhabitants live in 25 cities located in the Northern boreal bioclimatic zone. ∆Tw and ∆Ts are correlated with log (P) (Table 1). Summer day and night ∆T have high correlation with urban LST (Tusd) (r = 0.82, P < .01) and Tusn (r = 0.6, P < .01). In summer ∆T has a high significant correlation with population (r = 0.74, P < .01) during the night. And in winter, the ∆T shows stronger dependence on population during the day (r = 0.57, P < .01) (Fig. 9). The highest contrast between urban and rural LST was detected in Murmansk (Northern boreal zone). Both winter and summer SUHI are strong. The SUHI intensity reaches 5.4 °C in winter and 3.6 °C in summer. Murmansk is the largest city in the Arctic. The city is located on the eastern shore of the Kola Bay and has very strong differences in elevation. The highest point in the city is 306 m and the lowest point is at sea level. The city is also relatively green, with mean NDVI of 0.7. Forests occupy 43% of the area of the city.
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Fig. 7. Example of negative SUHI in winter. Alakyrtti is a small town with population 3.500. The hilltop and lakes in rural areas have warmer surface during the winter then urban surfaces. a) ArcGIS «Imagery» base map; b) MODIS LST map.
Fig. 8. Example of negative SUHI in summer. Snezhnogorsk has a population of 12,600. The city rural area is not developed and represents the natural land cover types, mostly rocks covered by tundra vegetation mixed with numerous different sizes of lakes and small patches of forest. The higher elevation and water bodies have a cooling effect on urban surfaces. The city elevation varied between 90–160 m. a) ArcGIS «Imagery» base map; b) MODIS LST map.
Fig. 9. Relationship between surface urban heat island intensity (∆T) and logarithm of urban population log (P) in a) summer night and b) winter day SUHI. The least-squares best-fit line is indicated.
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3.3. Middle boreal The middle boreal bioclimatic zone has the most continental climate in Fennoscandia. It leads to remarkable localization of the climate anomalies. Significant SUHIs are found in every city in this zone. The range of summer LST for urban areas is 10.1–15.3 °C and rural areas is 9.8–11.6 °C. The winter urban LST range is −11.2 to −15.6 °C and rural is -–11.9 to −16 °C. The average ∆Ts = 1.6 °C and ∆Tw = 1.0 °C. When compared to other bioclimatic zones, one finds that ∆Tw is the smallest anomaly, but the summer day SUHI is the largest one (Fig. 2). The total urban population in this zone is ~808,000. the city size is found to be the dominant predictor to the summer SUHI (Fig. 5b). Both ∆Tsd and ∆Tsn have positive correlations with the urban population (r = 0.64 and r = 0.62, P < .01). In wintertime, however, the effect of population is negligible (Table 1). Cities with higher diurnal variability have stronger SUHI. ∆Tsd has a high correlation with Tusd (r = 0.78, P < .01), ∆Tsn has a high correlation with Tusn (r = 0.8, P < .01). 4. Discussion The analysis shows strong and persistent winter and summer LST anomalies associated with urban areas in northern Fennoscandia. The SUHI in the region has remarkable spatial heterogeneity due to climatic and socioeconomic differences between cities. The urban climate anomalies exhibit strong variations within diurnal and seasonal cycles as well as on longer inter-annual time scales. Large seasonal changes of SUHI with evident spatial distributions were observed for cities in three zones. Atlantic cities, with their mild humid climate, exhibit large seasonal SUHI amplitude. The largest seasonal SUHI amplitude was observed for the small-size cities. The nighttime SUHI show no seasonal variation, but daytime SUHI shows a strong seasonal difference: low in summer and high in winter. The similar seasonally reverse “flip-flop” daytime SUHI was already discovered for coastal cities in lower latitudes (Hua et al., 2008), and the same, unexpected pattern was associated with vegetation in the city buffer (Shastri et al., 2017). During winter, the SUHI in the Atlantic zone has negative correlations with background rural temperature, meaning that the cities in warmer, and more vegetated areas have smaller differences between urban and rural LST, and oppositely the cities with low vegetation located in colder areas have larger LST difference between urban and rural areas. The significant role of vegetation coverage to mediates the UHI in autumn and winter for the coastal cities was also recognized by Wu et al. (2019). The ‘rougher’ vegetation land cover triggers turbulence, and turbulence removes heat from the surface to the atmosphere (Arnfield, 2003). But where there is a smoother surface, there is less convection and the heat will be trapped in the surface (Zhao et al., 2014). This convection effect depends on background ecology (amount and type of vegetation) and varies with local climate, leading to warming in warm humid climates and cooling in drier colder climates (Manoli et al., 2019). In the Middle boreal zone, the daytime SUHI is more intense in summer mainly because the drier urban surfaces produce more significant heating, while the vegetation in suburbs produces stronger evaporative cooling effects. A similar effect has been mentioned by Suomi (2018) for Lahti in southern Finland. The diurnal summer LST difference is higher for the urban than for the rural areas, such that the city gets warmer faster and cools faster than the rural area. Most of the incident solar radiation on urban surfaces is stored during the day and released at night. The city warms up during the day and oppositely during the night the buffer keeps warm (Shastri et al., 2017). This suggests that the factors driving UHI during the day are different than those that driving during the night. This can be explained by water's cooling and warming effect. The role of warmer water surfaces in winter becomes important in the northernmost settlements where it reduces the apparent SUHI intensities. The warming effect during winter is explained mainly by the fact that the ice season usually extends from mid-winter until mid-spring. During the first half of the winter, lakes are still warmer than the land surface would be if lakes are replaced with land (Samuelsson et al., 2010). Another contributing factor to the winter warming could be heat transfer through the ice. Using satellite remote sensing data, Jeffries et al. (1999) estimated the heat flux through lake ice in Alaska to be about 10 W m−2 higher than the heat flux from the soil. Compared to other environmental factors, water bodies have a more distinct seasonal character both in the relative strength and the direction of the effect either warming or cooling (Cosgrove and Berkelhammer, 2018). During the winter, the heating impact of water bodies weakens for the cities around Bothnian Bay (Middle boreal), which has an ice cover for 100–140 days a year, whereas it strengthens for the cities in the Norwegian (Atlantic) and Barents Sea (Northern boreal) regions. In the coldest Northern boreal zone, the anthropogenic heat release is supposed to be the main heat driver in the urban area in winter. Short days, weak solar radiation and snow cover favor inversions in winter by trapping the heat (Miles and Esau, 2017; Varentsov et al., 2018). The magnitude of local LST differences is driven by various environmental or anthropogenic factors. The UHI dependence on the urban size and population has been recognized. In this study, the mean SUHI intensity for the largest cities is higher than neighboring smaller cities in terms of the population. This is a sign that cities with higher population have a higher anthropogenic heat flux (Zhou et al., 2017). We found a significant correlation between SUHI and population in Middle and Northern boreal zones, whereas the correlation for the Atlantic zone was insignificant. 5. Conclusions This study documents the existence and persistence of the surface urban heat island (SUHI) in all 57 cities in Fennoscandia in three bioclimatic locations. The SUHI intensity here depends on the cities' location, topography and land cover. The strength of the 9
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SUHI is to some degree a surprising finding as these cities are of small and medium size and located in high latitudes where enhanced urban albedo and reduced evapotranspiration are not expected to drive urban surface climate anomalies. Moreover, the identified SUHIs are much larger than it could be expected for such small and low-density cities located in lower latitudes. It emphasizes the leading role of the high intensity urban metabolism and anthropogenic heat fluxes in the Fennoscandian cities. Unfortunately, interrelations between the apparent SUHI and diverse socio-economic and socio-environmental factors are too complicated to be addressed in this study – this is a topic for future research. Because SUHI characterizes the urban–rural land surface temperature difference, it depends on the local surface heat balance as well as on the mean intensity of the vertical turbulent mixing in the lowermost atmosphere (Davy and Esau, 2016). We found that a significant fraction of open water (sea or lake) or wet soil (swaps, bogs, mires) in the rural areas modifies SUHI intensity and its diurnal/seasonal variations in the region. The differences in the dominant land cover types control the SUHI across three considered bioclimatic zones. The water fraction, which controls background LST, and rural land cover types dominate the SUHI intensity in the Atlantic bioclimatic zone, whereas the anthropogenic factors dominate the SUHI in the more continental climate of middle and northern boreal zones. The SUHI in the northern boreal zone is the best predicted by the urban population size. We conclude that the urban surface temperature anomalies are an essential and influential feature of the local climate in Fennoscandian cities. It is known that urban planners currently include only limited information on the urban climate into decision making and policy processes. As this study reveals, one should account for the background bioclimatic conditions when urban climate change potential is considered. Declaration of Competing Interest None. Acknowledgement This study was supported by the Belmont Forum and Norwegian Research Council project Anthropogenic Heat Islands in the Arctic: Windows to the Future of the Regional Climates, Ecosystems, and Societies (no. 247468). We thank the reviewers for their thorough reviews and highly appreciate the comments and suggestions, which significantly contributed to improving the quality of the publication. The authors thank Martin Miles for technical discussions and editing. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.uclim.2019.100575. References Alberti, M., Marzluff, J., 2004. Resilience in urban ecosystems: linking urban patterns to human and ecological functions. 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Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sens. 11, 48. https://doi.org/10.3390/rs11010048. Victoria Miles is a researcher at the Nansen Environmental and Remote Sensing Center (NERSC) Bergen, Norway with a specialization in boreal ecology and remote sensing. Her current research efforts are in the arctic and boreal ecosystems. She is examining the recent dynamic of urban climate with connection to natural environmental and climatic factors. Igor Esau is a senior researcher at the Nansen Environmental and Remote Sensing Center (NERSC) Bergen, Norway. His primary scientific interest is to investigate links between small scale and global scale processes. Specifically,he focuses on understanding the role played by the stably stratified boundary layers in regulating the air quality, micro-climate and connections between the large-scale circulation and micro-physics of the Earth's climate system.
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