1. Introduction
Temperature varies over small temporal and spatial scales in a landscape with many factors influencing it at any given location (Geiger 1965). Our ability to represent microclimatic variations is, however, limited by the coarse resolution (>10–100 km) provided by global circulation models (GCMs) and regional circulation models (RCMs). While this inability may be acceptable for large-scale climatic simulations, planning strategies that involve ecosystems and their adaptation to a changing climate need to account for much smaller spatiotemporal variability. Determining the relative role of the main parameters influencing small-scale variations of temperature, especially in landscapes that are relatively complex and experience the most variability (e.g., Mahrt 2006; Broxton et al. 2009; Simoni et al. 2011), can be a step toward the goal of accounting for the small-scale spatiotemporal variability.
As several of the factors that drive microclimatic variations in temperature experience seasonal variations, the relative influence of these factors on temperature could in turn differ depending on the time of the year. Among the major factors influencing average near-surface air temperature, direct beam solar radiation, for example, has a very marked seasonal behavior in northern latitudes (Yang et al. 2011; Pike et al. 2012). This seasonal cycle is controlled by different physical parameters such as the course of the sun, duration of sunshine hours, slope aspect, and shading by adjacent hill slopes (Pierce et al. 2005), many of which can be easily accounted for using the current generation of models based on geographical information systems (GIS) (e.g., Fu and Rich 1999a,b, 2002).
Previous work by Vercauteren et al. (2013) developed and used a network of temperature measurements to assess the impact of topography and the nearby sea on the spatiotemporal variations of local temperature. This allowed for explicit investigation of microscale temperature variations in space and time in a forested landscape on the coast of the Baltic Sea. Specifically, the results of that study showed a strong linear influence of insolation on the evolution of mean monthly temperature during the growing season (June–September) in the studied coastal site in northern Sweden. In addition, a time lag of approximately one month was observed between incoming mean solar radiation and subsequent mean air temperature. This lag time decreases exponentially with increasing distance to the sea. The linear relationship between mean monthly temperature and insolation was shown to be robust for a large number of measurement sites over the growing season.
In the present study, we extend the modeling procedure developed by Vercauteren et al. (2013) outside of the growing season to test its applicability during periods of the year when the influence of solar radiation on average monthly temperature could change drastically. Indeed, the amount of incoming solar radiation received at the surface decreases sharply after the growing season and other factors influencing temperature could, thus, become more important. These factors include synoptic meteorology and snow cover, among others (Stahl et al. 2006; Yang et al. 2011). Furthermore, snow cover is also itself influenced by insolation and will in turn affect near surface temperature. We therefore here investigate if the solar radiation influences the spatiotemporal snow cover variation in a clear way, and if the distance to the sea affects snowmelt patterns. We found previously (Vercauteren et al. 2013) that the presence of the sea affects the time lag between mean air temperature and mean solar radiation. In this continuation study, we also assess to what extent the presence of the sea affects the snow cover duration and its dependence on the solar radiation.
2. Material and methods
a. Site description and location
The study area is located in Sweden on the High Coast (Höga Kusten) of the Baltic Sea, in the municipalities of Kramfors and Härnösand and is described in Vercauteren et al. (2013). Sampling in this area is possible at a wide range of elevations, irrespective of the distance to the coast because of the unique geological settings offered by Höga Kusten. Instrumentation consists of a total of 98 Maxim 1922L iButton temperature sensors (63 measuring air temperature and 35 measuring ground temperature) (Hubbart et al. 2005) placed in a 2500 m2 area ranging from latitude 62°4′ to 63°1′N and from longitude 17°14′ to 18°33′E (Fig. 1). The locations of temperature measurements are chosen to represent prevailing differences in slope orientation and elevation. Elevation in the study area ranges from 0 to 470 m above sea level. The area is characterized by a wide coniferous forest cover, ensuring natural shielding for the temperature sensors (Lundquist and Huggett 2008) and land cover homogeneity across the sampling sites. All the sampling points are located under the tree line under forest cover, and the forest density is rather homogeneous across sites, with slightly higher density of forest cover on the north-facing slopes.
Location and digital elevation model (DEM; 50-m resolution) of the forested study site.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
Air temperature was collected at about 1 m above the ground. In addition, ground temperature was collected by placing sensors under the moss cover (applicable for half of the sites). Temperature was recorded every half hour between the end of May 2011 and the beginning of October 2011 and every hour between October 2011 and the beginning of June 2012.
b. Temperature analysis
During the growing season between June and September, the slope A of the linear model in Eq. (1), which determines the delayed temporal response of average temperature 
In the present analysis, we test if the linear Eq. (1) and the delayed temporal response represented by the lag time N (number of days) still hold outside the growing season period for which they were developed. We separate the analysis into different 4-month periods to assess the variability of the results during the course of a year.
c. Snow cover analysis
Temperature sensors of the type employed in the present study can be used to monitor the spatial and temporal pattern of snow cover in an inexpensive way (Lundquist and Lott 2008; Lyon et al. 2008). Our sampling design (which included temperature sensors placed under a moss cover) thus provided a record of the presence or absence of snow cover because snow acts as a strong insulating layer dampening near-surface soil temperature oscillations. Isolating the periods with negligible temperature oscillations was shown by Lundquist and Lott (2008) to give a reliable estimate of snow cover periods.
In the present analysis, we use a similar threshold method such that periods with oscillations of temperature that are smaller than the threshold value are defined as snow-covered periods. To define the threshold value, we first compute the standard deviation of temperature for a snow-free period of two months, for each measurement location. We then compute the standard deviation of temperature for successive periods of three days, in a moving window approach. The first period of three consecutive days for which the standard deviation of temperature is smaller than one-tenth of the previously computed snow-free standard deviation is defined as the start of the snow cover season, and the last such period of three days is defined as the end of the snow cover period.
We also use remotely sensed observations of snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to have an independent spatial assessment of the snow cover for comparison with our temperature-derived estimates.
3. Results
a. Monthly temperature
Figure 2 shows the relationship between the solar radiation and the mean monthly air temperature and its evolution throughout the year. The air temperature measurements were filtered to exclude locations that have the temperature sensor covered by snow for a certain period of time (i.e., locations that show very low diurnal oscillations for an extended period of time). This removed two locations from the mean monthly temperature calculation.
Comparative evolution of the monthly averaged (b) air temperature and (a) insolation during a yearly cycle. The lines in (a) and (b) are averaged for all measurement points located close to the sea (black) and far from the sea (gray). (c),(d) The black points show an example for one sampling location whereas the gray points show all the different sites. The black line is a linear fit through the values for the example location.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
To see the influence of the presence of the Baltic Sea on the evolution of the monthly temperature, locations closer than 30 km from the shore are shown separately from those further than 30 km from the shore (Figs. 2a,b). The presence of a river valley affects the calculation of the distance to the coast in a way that can be visualized in Vercauteren et al. (2013, their Fig. 6). We use a 1-km resolution for the coastline, and that includes the initially penetrating, wider part of the estuary. Figure 2b shows a sharper initial temperature decrease at the inland sites after the maximum has occurred in June 2011, but thereafter the evolution of the temperature follows a similar decay at all sites. Temperature evolution is nearly indistinguishable after the minimum temperature has occurred in February 2011.
We further investigate the time lag N (number of days) between solar radiation and temperature in Eq. (1) and its dependence on the distance to the coast. The optimal (best fit) time lag was computed in the manner described in Vercauteren et al. (2013). Considering periods of 4 months that start from each month of the year, we first compute the number of days N of lag between solar radiation and temperature that maximizes the correlation between averaged radiation and lagged averaged temperature for each location. We thus obtain values of N for each point in the landscape for each period of 4 months. We then try to fit of an exponential decay (relative to the distance to the sea) on these values of N for each successive period of 4 months, in the same manner that is described in Vercauteren et al. (2013). The previously analyzed period from June to September shows an r2 of 80% for the exponential best fit, whereas the r2 for the exponential best fit for the period July–October drops to 30%, and this coefficient continues to drop for the later periods (analysis not shown).
We therefore considered a constant delay N of 30 days for all the analyzed 4-month periods at the exception of the period from June to September for which we consider the fitted exponential decay of N. The slope A and intercept B of the linear model [Eq. (1)], computed at each location for each period of 4 consecutive months, vary during the course of the year (Fig. 3). In a similar analysis as in Vercauteren et al. (2013), we determine the standard deviation of A and B across the different sites to assess if the model is robust across sites or not. The standard deviation of A is very small during the first time period (June–September; standard deviation of 0.13 shown by the width of the error bar in Fig. 3 and corresponding coefficient of variation of 0.06) and increases remarkably in the periods following (July–October; standard deviation of 0.22 with a coefficient of variation of 0.08 and increasing after). The slope A, which determines the delayed temporal response of the average temperature T to a change in average incoming radiation R, is thus very robust across sites for the growing season from June to September, but much more variable during the winter months as shown by the increasing standard deviation in the top panel of Fig. 3 for the later periods. This is to be expected since the influence of insolation on the average temperature will decrease when the amount of incoming radiation decreases after the summer, leaving other physical factors to influence average temperature. Basically, this increased variability reflects extrapolation of the model outside the conditions it was derived to represent since it is a radiation-based temperature model. As such the largest standard deviations are for the periods experiencing lower amounts of incoming radiation (October–January). The exception is clearly the period January–April, which experiences a higher amount of incoming radiation and still has a high standard deviation for A. This can be attributed to the fact that this is the snowmelt period as will be discussed in the next section of this paper.
Variation of the (top) slope and (bottom) intercept obtained through the regression Eq. (1) for different periods of 4 months. The date on the x axis is the starting month for a 4-month period. The standard deviations of A in the top panel and B in the bottom panel across all sites are shown by the width of the error bars. The numbers next to the error bars in the top panel indicate the r2 coefficient of the regression [Eq. (1)], averaged across sites.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
The standard deviation of B is much more constant over the year. In Vercauteren et al. (2013), we pointed out that B was much more variable across sites and that this was due to other factors influencing temperature at a specific location, such as soil moisture, canopy properties, or local airflows. The fact that the standard deviation of B is stable during the course of the year can be seen as an indication that the factors influencing the temperature at a specific location are not so variable in time; that is, a colder than average site will always be colder than average, in a similar manner throughout the year.
The regression in Eq. (1) leads to high r2 values (Fig. 3). It should be noted that regression computations are based on a moving window average, where we consider N-day averages of radiation and temperature for each successive starting day. This technique thus artificially smooths the variations of temperature and inflates the corresponding r2 values.
b. Snow cover
An example of the temperature data collected under the moss cover and in the air above at the same site is shown in Fig. 4 for two sites located on the same hill: one on the north-facing slope and the other on the south-facing slope. There is a distinct period showing no (or relatively low) diurnal oscillations of temperature, indicating snow cover at the site, consistent with previous work using temperature signatures to infer snow coverage (e.g., Lundquist and Lott 2008; Lyon et al. 2008). In this example, showing two sites located very close to each other, the clear influence of the solar radiation on the duration of the snow cover period can be seen. The north-facing slope, which is receiving less solar radiation, has a much longer snow cover period.
Ground (black line) and near surface temperature (gray line) at two neighboring locations (located on the same hill), a (bottom) north-facing slope and a (top) south-facing slope. The asterisks show the estimated start and end of the snow cover period.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
We do not observe a clear correlation of snow cover duration with insolation as computed by the ArcGIS solar radiation model (the values of which are shown in the top panel of Fig. 2). Snow cover duration, however, is on average 20 days shorter for south-facing slopes (which receive higher insolation) than for north-facing slopes (with lower insolation). Specifically, the snow cover begins to develop roughly 7 days later and melts 13 days earlier on south relative to north-facing slopes. The differences can be seen with the lines in Fig. 5, showing the conditional averages for snow cover duration, and first day and last day of snow cover depending on the slope orientation.
(a) Duration, (b) first day, and (c) last day of snow cover season in relation to the distance to the sea. The sampling is independent of elevation. Filled circles represent north-facing slopes and empty circles represent south-facing slopes. The lines show the conditional average of duration, first day, and last day of snow cover for the north-facing (black line) and south-facing (dotted line) slopes.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
With regard to snow cover duration in relation to the distance to the coast, there is a marked decrease in duration over about 20 km from the coast relative to more inland locations (Fig. 5a). This decrease is mostly a result of faster snowmelt along the coast (Fig. 5c). The onset of snow cover (Fig. 5b) shows a less marked relation with the distance to the coast. There is a clear linear relationship between the snow cover duration and elevation (Fig. 6). A further look shows that this relationship is stronger for the north-facing slopes than the south-facing slopes with less scatter for the north-facing slopes. The difference observed in the relationships between snow cover and elevation for the north- or south-facing slopes is most marked for the melting snow pattern seen in Fig. 6c. A possible explanation for this would be the different effect of penetrating radiation through the canopy on north- and south-facing slopes.
(a) Duration, (b) first day, and (c) last day of snow cover in relation to elevation above sea level. Filled circles represent north-facing slopes and empty circles represent south-facing slopes.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
Indeed, direct radiation plays a major role in snowmelt (e.g., Dahlke and Lyon 2013) and is more likely to penetrate through the variable canopy cover on the south-facing slopes, potentially masking the elevation effect. On the north slopes, however, the presence of shading and denser vegetation leads to the first-order control of elevation on melting of snow. This direct effect of incoming radiation on snowmelt would not be explicitly visible with the ArcGIS modeled insolation since we do not incorporate shading from the vegetation cover because of a lack of vegetation cover data. Further evidence is found in the computed correlation coefficients between the number of days of snow cover and the modeled incoming radiation. The numbers of days of snow cover for the north-facing slopes show less correlation with the accumulated incoming radiation between December and March (i.e., the snow season) relative to south-facing slopes (r2 coefficients of 2% and 32%, respectively).
Spatial estimation of fractional snow cover obtained from MODIS (Fig. 7) supports preferential spring melting close to and along both the coastline and the river valleys (dark zones in the lower, springtime panels of Fig. 7). This preferential melting may be as a result of heat transfer influence of the nearby water and/or lower elevations close to the coast and the river. There is no apparent preferential deposition of snow close to the water bodies (upper, autumn panels of Fig. 7).
MODIS estimated fractional snow cover over the area at 500-m resolution.
Citation: Journal of Applied Meteorology and Climatology 53, 2; 10.1175/JAMC-D-13-0217.1
4. Discussion
Climate data are available at many different spatiotemporal scales. Adjusting for scale mismatches requires a good understanding of local geographical influences on climatic variables. Our analysis expands on previously documented influence of solar radiation on temperature in complex terrain (Huang et al. 2008) and was developed for use in a coastal landscape to test the extent of the influence of solar radiation on temperature during a Nordic annual cycle. Previously, Vercauteren et al. (2013) showed that during the growing season, from June to September, the spatiotemporal response of temperature to solar radiation exhibits a lag of approximately one month. Further, the presence of the Baltic Sea has a regulating effect on mean monthly temperature, not only above the sea itself, but also in its proximity. We confirm those results in this study with data for the whole year, for monthly temperature above zero (lower right panel in Fig. 2). The strength of the modeling approach [Eq. (1)] presented by Vercauteren et al. (2013) and further tested here is that it provides a modeling framework that can explicitly take this regulating effect on the time of response of average temperature to average insolation into account. The present study leverages that strength by exploring it beyond just the growing season and into the winter where snow cover strongly influences landscape-scale responses in high latitudes (e.g., Dahlke and Lyon 2013).
In this high-latitude context, however, the yearly cycle of solar radiation is very sharp and the monthly insolation quickly drops from its maximum in June (approximately 170 kWh m−2; 1 kWh = 3.6 × 106 J) to monthly insolation under 50 kWh m−2 already in September. Our analysis shows that the derived linear relationship from Vercauteren et al. (2013) does not hold for the winter months, with r2 coefficients of the regression in Eq. (1) dropping from the high values in the successive 4-month periods starting between June and October (between 85% and 98%; see the top panel of Fig. 3) to much lower values for the successive 4-month periods starting between November and January (between 45% and 77%). The latter lower performance is accompanied by a lack of spatial robustness in the slope of the linear relationship during the same periods (shown by the high standard deviations for the slope in the top panel of Fig. 3).
In addition, we showed that the regulating effect of the Baltic Sea on the time of response of average temperature to average insolation is only apparent in the growing season from June to September. For the analyzed 4-month periods starting from July until September, we found that the time of response of average temperature to average insolation was similar throughout the landscape and around 30 days. For the later winter periods, the model simply did not perform well (Fig. 3). The present site, with insolation values close to zero part of the year, represents different conditions than those in similar studies by Huang et al. (2008) with minimum monthly insolation during the year of about 50 kWh m−2. For the very low insolation values typically arising in the winter in northern latitudes (<20 kWh m−2; see Fig. 2), the linear relationship between temperature and insolation is not robust, implying that other factors will then also influence temperature, including synoptic meteorology, snow cover, and snowmelt.
Further explanation of the seasonal evolution of temperature response can be given when looking at the surface energy balance that connects the different processes that affect air temperature near the ground (Brutsaert 1982). As the sun’s intensity increases during the day or the season, incoming radiation eventually exceeds the net loss of heat (through emitted longwave radiation) from the surface. The ground in turn warms and part of this heat is released to the air through conduction whereas some heat is conducted in the ground. The incoming radiation then decreases, but as long as it exceeds the longwave radiative loss, surplus energy is gained and temperature warms in the ground and in the air. This creates the time lag that we observed in this study. Because water can store more energy than soils, the duration of the heat conduction process is altered and the effect of the Baltic Sea can be seen in a different time lag inland and close to the sea. This effect is discussed in detail by Vercauteren et al. (2013). After the growing season, the sharp decrease of insolation leads to a situation where the insolation no longer exceeds the energy loss and the heat that was stored in the ground or in the water is sent back to the atmosphere. This complete change in the surface energy balance leads to the change of temperature/insolation relationship that was observed in this paper.
One process that could also affect the response of temperature during the winter months at such latitudes is cold air ponding (e.g., Lundquist et al. 2008; Jarvis and Stuart 2001; Stahl et al. 2006) within basin bottoms. Indeed, cold air ponds are trapped in valley bottoms under warmer air and could have different response to insolation than the surrounding landscape, specifically in terms of the response of temperature with elevation. Our dataset unfortunately did not include elevation transects on each slopes so that we could not investigate this phenomenon in detail.
Our dataset, however, enabled us to investigate the snow cover and its relation to incoming radiation. Rather than just showing a threshold of incoming radiation above which the proposed linear relationship between temperature and insolation is valid, our analysis further showed that the performance of the linear relationship was dependent on the snowmelt period (Fig. 3). Indeed, for a similar amount of insolation received in the fall (September–December) and in the late winter (January–April), the proposed linear relationship gave less satisfactory results for the late winter months than for the autumn months. In terms of surface energy balance, the main difference between those two periods is that the late winter is a period of snowmelt. During this period, a large part of the incoming radiation will be consumed to melt snow. This energy is thus no longer available to be transferred back to the atmosphere in the form of heat. For the same reason, the influence of solar radiation was highly visible in the snow cover data. We found that north-facing slopes are covered by snow on average 20 days longer than south-facing slopes, which receive higher loads of solar radiation. Among these 20 days, the timing of snowmelt, which requires an energy input, is the major difference, happening on average 13 days later for north-facing slopes compared to south-facing slopes.
Because of the different energy budget of a water body or a snow-covered landscape, the presence of the Baltic Sea also has a clear effect on the melting pattern. Indeed, we observe a clearly faster melt near the coast, where the temperature is regulated by the presence of a large water body. The increase of melting time with increasing distance to the sea is seen in the first 20 km. This was the distance over which we also observed an effect of the sea on the time of reaction of temperature to insolation during the growing season at this site.
5. Conclusions
Advanced models are readily available as GIS tools (such as the solar radiation tool of ArcGIS) that can estimate solar radiation. These insolation estimates can be used to predict the temporal evolution of temperature in a landscape. However, care is needed, as the linearity between insolation and temperature is potentially valid only for solar radiation levels above a certain threshold, and is affected by snowmelt, which absorbs incoming radiation. This threshold is not attained under winter conditions in northern latitudes, and solar radiation will then not be the single dominant controlling factor of mean temperature at these latitudes.
We show that at the present investigation site, average solar radiation has a linear influence on average temperature for monthly insolation values above 20 kWh m−2. We also show that for similar insolation, the average temperature is influenced linearly by insolation during the fall but not during the snowmelt period, where the slope of the linear model is no longer robust across sites.
Acknowledgments
This work has been funded by the strategic research project EkoKlim (a multiscale cross-disciplinary approach to the study of climate change effects on ecosystem services and biodiversity) at Stockholm University. The authors thank Kristoffer Hylander for his participation in framing the project and thank Johan Dahlberg, Norris Lam, Johannes Forsberg, and Liselott Wilin for their help in the field.
REFERENCES
Blandford, T., K. Humes, B. J. Harshburger, B. C. Moore, V. P. Walden, and H. Ye, 2008: Seasonal and synoptic variations in near-surface air temperature lapse rates in a mountainous basin. J. Appl. Meteor. Climatol., 47, 249–261.
Broxton, P. D., P. A. Troch, and S. W. Lyon, 2009: On the role of aspect to quantify water transit times in small mountainous catchments. Water Resour. Res., 45, W08427, doi:10.1029/2008WR007438.
Brutsaert, W., 1982: Evaporation into the Atmosphere: Theory, History, and Applications. D. Reidel, 229 pp.
Dahlke, H. E., and S. W. Lyon, 2013: Early melt season snowpack isotopic evolution in the Tarfala valley, northern Sweden. Ann. Glaciol., 54, 149–156, doi:10.3189/2013AoG62A232.
Dubayah, R., and P. M. Rich, 1995: Topographic solar radiation models for GIS. Int. J. Geogr. Info. Syst.,9, 405–419, doi:10.1080/02693799508902046.
Fu, P., and P. M. Rich, 1999a: Design and implementation of the Solar Analyst: An ArcView extension for modeling solar radiation at landscape scales. Proc. 19th ESRI User Conf., San Diego, CA, ESRI. [Available online at http://proceedings.esri.com/librsary/userconf/proc99/proceed/papers/pap867/p867.htm.]
Fu, P., and P. M. Rich, 1999b: TopoView, version 1.0 manual. Helios Environmental Modeling Institute, 48 pp.
Fu, P., and P. M. Rich, 2002: A geometric solar radiation model with applications in agriculture and forestry. Comput. Electron. Agric., 37, 25–35.
Geiger, R., 1965: The Climate near the Ground. 4th ed. Harvard University Press, 642 pp.
Huang, S., P. M. Rich, R. L. Crabtree, C. S. Potter, and P. Fu, 2008: Modeling monthly near-surface air temperature from solar radiation and lapse rate: Application over complex terrain in Yellowstone National Park. Phys. Geogr., 29, 158–178, doi:10.2747/0272-3646.29.2.158.
Hubbart, J., T. Link, C. Campbell, and D. Cobos, 2005: Evaluation of a low-cost temperature measurement system for environmental applications. Hydrol. Processes, 19, 1517–1523, doi:10.1002/hyp.5861.
Jarvis, C. H., and N. Stuart, 2001: A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part I: The selection of “guiding” topographic and land cover variables. J. Appl. Meteor., 40, 1060–1074.
Lundquist, J. D., and B. Huggett, 2008: Evergreen trees as inexpensive radiation shields for temperature sensors. Water Resour. Res., 44, W00D04, doi:10.1029/2008WR006979.
Lundquist, J. D., and F. Lott, 2008: Using inexpensive temperature sensors to monitor the duration and heterogeneity of snow-covered areas. Water Resour. Res., 44, W00D16, doi:10.1029/2008WR007035.
Lundquist, J. D., N. Pepin, and C. Rochford, 2008: Automated algorithm for mapping regions of cold-air pooling in complex terrain. J. Geophys. Res., 113, D22107, doi:10.1029/2008JD009879.
Lyon, S. W., P. A. Troch, P. D. Broxton, N. P. Molotch, and P. D. Brooks, 2008: Monitoring the timing of snowmelt and the initiation of streamflow using a distributed network of temperature/light sensors. Ecohydrology, 1, 215–224, doi:10.1002/eco.18.
Mahrt, L., 2006: Variation of surface air temperature in complex terrain. J. Appl. Meteor. Climatol., 45, 1481–1493.
Pierce, K. B., Jr., T. Lookingbill, and D. Urban, 2005: A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis. Landscape Ecol., 20, 137–147, doi:10.1007/s10980-004-1296-6.
Pike, G., N. C. Pepin, and M. Schaefer, 2012: High latitude local scale temperature complexity: The example of Kevo Valley, Finnish Lapland. Int. J. Climatol., 33, 2050–2067.
Simoni, S., and Coauthors, 2011: Hydrologic response of an alpine watershed: Application of a meteorological wireless sensor network to understand streamflow generation. Water Resour. Res., 47, W10524, doi:10.1029/2011WR010730.
Stahl, K., R. D. Moore, J. A. Floyer, M. G. Asplin, and I. G. McKendry, 2006: Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agric. For. Meteor., 139, 224–236, doi:10.1016/j.agrformet.2006.07.004.
Vercauteren, N., G. Destouni, C. J. Dahlberg, and K. Hylander, 2013: Fine-resolved, near-coastal spatiotemporal variation of temperature in response to insolation. J. Appl. Meteor. Climatol., 52, 1208–1220.
Yang, Z., E. Hanna, and T. V. Callaghan, 2011: Modelling surface-air temperature variation over complex terrain around Abisko, Swedish Lapland: Uncertainties of measurements and models at different scales. Geogr. Ann., 93, 89–112.
