Long term meteorological records (
Changes in the surface energy balance of urban areas caused by, for example, increased thermal admittance of urban materials, limited radiative and advective cooling (due to urban morphology), lowered evapotranspiration cooling (due to sealed surfaces and reduced vegetation coverage), and additional anthropogenic heat release, tend to cause increased temperatures in urban areas compared to surrounding rural environments (e.g. Arnfield, 2003). This well documented urban heat island (UHI) effect is generally most pronounced in larger settlements with dense, tall built structures and sparse vegetation (e.g. Oke, 1982), but observable UHI effects are found in towns (e.g. Magee et al., 1999; Steeneveld et al., 2011) and small villages (Hinkel et al., 2003).
To assess long-term temperature trends, meteorological (met) stations are
classified according to potential urban influence, e.g. “associated with
urban area” or “rural”, where “rural” is considered to have no
significant urban warming bias (e.g. Hansen et al., 2010).
However, metadata from long-term “rural” stations often reveal that these
stations are located in or near villages. The influences on met stations
associated with large cities (e.g. populations
The objective of this study is to assess local and microclimatic variations around two long-term, rural-village, met stations, relative to their long-term records. The analysis uses air temperature sensors installed which are representative of the current and past met stations locations, as well as the general area.
Description of examined met stations and associated village. Source of data are the meta-data archives of DWD (Deutscher Wetterdienst, Klima und Umwelt, Climate Data Centre, Frankfurter Straße 135, 63067 Offenbach, Germany) and SMHI (SMHI, Folkborgsvägen 17, 601 76 Norrköping, Sweden).
Description of temperature sensor network.
A network of air temperature sensors was installed around a long-term
Deutsche Wetterdienst (DWD) station in Geisenheim, Germany and a Swedish
Meteorological and Hydrological Institute (SMHI) station, Haparanda in
Sweden (Table 1). In the Global Historical Climatological Network (GHCN)
database (
Temperature sensors (HOBO Pro v2 U23-001 in radiation shields RS1, Onset
Computer Corporation, Bourne, MA 02532, USA) were installed at multiple
sites in the village and vicinity (Table 2, Fig. 1) in mid-2013. Analysis
of the meta-data files in the DWD and SMHI archives was used to identify
past met-station locations so they, or comparable locations, could be
instrumented. The sensors record a sample every 30 min. Analysis of the
pre-deployment 22 day inter-instrument comparison, over a
The Geisenheim station has been re-located multiple times, but due to incomplete metadata, the exact location is only known for the first and the last two locations. For the remaining three station locations, only minute-precision coordinates and short site descriptions are available. Sensors were deployed in the known sites, as well as in representative area types for the village and surroundings (Fig. 1). Although the current met station is situated to avoid urban influences (Behrendt et al., 2011) the nearby vineyard was used as a reference as unfortunately our sensor deployed at the current met site had to be removed for part of the year. The vineyard temperatures was found not to differ significantly based on analysis of concurrent data. In Haparanda the well documented previous met station locations were all identified for sensor placements (Fig. 1). The current met station is situated to avoid urban influence (Andersén, 2010) so can be used as a reference for analyzing the urban influence.
Location of the met stations and temperature sensors in Geisenheim (left panels) and Haparanda (right panels): previous (triangles) and current (black location known, grey location uncertain or has changed substantially since station was located there), circles mark sensor locations. Photos show locations of sensors used in this study. Satellite images from Google earth.
Boxplots of seasonal
To examine potential urban effects, differences in measured maximum, average
and minimum temperature (
The daily differences of the averages between each site and the met station
site (
The warming influence is most pronounced in the two village centres, where
significantly increased daily average temperatures were found in all seasons
(median yearly
Evidence of a moderating influence from waterbodies were found in both
villages, particularly during summer, when higher minimum and lower maximum
temperatures were measured near the rivers. In Haparanda, the influence in
Temperature differences are larger for
Substantial seasonal differences include more pronounced temperature
residuals in summer than winter. The enhanced summertime solar radiation
generates spatially heterogeneous heating of built and vegetated surfaces
during the daytime. If strong surface heating occurs, the influence of
site-specific nocturnal cooling is more important. The larger summer
temperature differences (median
Although the general patterns are the same in the two villages, several
differences are found in the data. The larger
Sources for inhomogeneity in climate data are many, for example, relocations of measurement sites, changes in surroundings, sheltering, exposure and instrumentation, calculation methods and observation practices (Aguilar et al., 2003). The urban influences documented in this study in Geisenheim and Haparanda are of the same order of magnitude as the temperature trends recorded in the long term records of the stations (Table 3). This suggests that station relocations in villages could potentially cause substantial bias in the data recorded and should be taken into consideration when homogenizing data.
Analysis of previous met station locations in the two examined villages (Fig. 1) show that at no point had the Geisenheim station been located in the village centre. Thus it is less likely to have been biased by urban effects. The station was originally placed in the centrally located park, which was slightly warmer than the current met station location. However, the north-west part of the park has been converted into a parking lot since the station was located there, which limits the possibility of accurately determining the exact bias for this location. Incomplete meta-data for some prior Geisenheim station sites only benefits from the knowledge they were always outside the most densely built area.
In Haparanda, the station was located in the village centre for the first
83 years of operation, then moved to the riverside (where several minor moves
took place), then to two residential locations, before its current location
outside the residential area. Historical maps show that the village centre
is still very similar to 1924, which suggest the temperature differences
from this study can be assumed to be close to those for the siting 90 years
ago, although it is important to recognise that changes in heat sources
(wood burning to central heating) and house insulation will have affected
heat emissions by the local residents. Historically, the Haparanda station
likely does contain an urban warming bias, primarily in data from the first
station location (in the urban centre). The second location (river side)
also shows a bias, likely a combination of warming from the nearby urban
area and the river. The residential areas would have had a smaller but still
significant bias, particularly in
Linear temperature trend in the complete raw data set from DWD Geisenheim (1882–2014) and SMHI Haparanda (1859–2014), and median of the daily temperature difference between the village centre and the site of the current met station for the measurement period.
In this paper it is shown that the urban effects in villages can be sufficient to significantly modify temperatures, thus potentially causing a warming bias in rural-village met stations. The effect is largest in minimum temperatures, and during summer, and influenced by latitude (stronger seasonal differences at higher latitude) and relief (less variability in the data in sloping terrain compared to flat). Urban influences of similar order were found in Geisenheim (Germany) and Haparanda (Sweden) potentially causing substantial biases in the temperature trends from these stations. Thus the classification of stations to indicate rural or village may provide a key flag to the interpretation of data in sets such at the GHCN.
We are grateful to the Hermann Mächel at the DWD (Climate Data Centre, Frankfurter Straße 135, Offenbach, Germany) and Sverker Hellström at the SMHI (Enheten för statistik och information, Folkborgsvägen 17, Norrköping, Sweden) for supplying the data and meta-data used in this study. The help of local authorities in finding suitable locations and getting permissions to install sensors is also greatly appreciated. Edited by: I. Auer Reviewed by: P. Štastný and two anonymous referees