Synoptic atmospheric conditions drive microclimatic variability in a high-latitude landscapes

Synoptic atmospheric conditions drive microclimatic variability in a high-latitude landscapes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synoptic atmospheric conditions drive microclimatic variability in a high-latitude landscapes Johanna Lehtinen, Juha Aalto, Pekka Niittynen, Miska Luoto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6784547/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Climate Dynamics → Version 1 posted 5 You are reading this latest preprint version Abstract Microclimatic heterogeneity in high-latitude landscapes plays a key role in shaping ecosystem functioning, biodiversity and resilience to environmental change. Microclimates are shaped by topography, vegetation and synoptic atmospheric conditions. However, the translation of macroscale synoptic conditions into fine-scale temperature variability has rarely been investigated empirically. We examined summer near-surface temperatures in relation to synoptic conditions (classified from calm and clear to windy and cloudy) and analyzed changes in microclimatic drivers and spatial heterogeneity. The study was conducted in a high-latitude landscape, utilizing macroclimate data from weather stations and microclimate data from a dense network of 193 stations distributed across a heterogeneous landscape characterized by a strong environmental gradient. Our results revealed that fine-scale temperature heterogeneity is strongly connected to synoptic conditions. The temperature range across the landscape was highest (10°C T min and 16°C T max ) on calm, clear days, whereas on windy and cloudy days differences were significantly smaller (5°C T min and 7°C T max ). Macroscale variations influenced microscale temperature heterogeneity differently depending on landscape properties: topography primarily affected minimum temperatures, while both topography and vegetation properties contributed to variations in maximum temperatures. Our findings highlight the variation in microclimate temperature heterogeneity across a high-latitude landscape, largely driven by synoptic conditions that regulate air mixing and radiation fluxes. By demonstrating how large-scale atmospheric patterns influence fine-scale thermal variability, our results offer deeper insight into key microclimatic drivers under different weather conditions. This understanding is crucial for predicting how microclimates will respond to climate change in high-latitude ecosystems. Microclimate Air temperature Thermal Heterogeneity Synoptic conditions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Climate change is significantly impacting high latitude environments by increasing temperatures three to four times faster than the global average (Rantanen et al. 2022 ). Along with warming, seasonal changes such as the extended summer growing season and reduced winter snow cover are expected to become more pronounced (Post 2009). Consequently, climate change will alter both biotic and abiotic processes, affecting various aspects such as carbon cycling, biodiversity, species distribution and soil processes (De Frenne et al. 2021 ; Lawrence and Swenson 2011 ; Lenoir et al. 2017 ; Post 2009). Several studies have demonstrated that widely used coarse-resolution macroclimatic data alone is insufficient to accurately describe these ongoing changes as many processes are more closely linked to localised fluctuations in temperature and humidity i.e., microclimatic conditions (Haesen et al. 2021 ; Lembrechts et al. 2019 ). Synoptic atmospheric conditions, related to e.g. cloud cover and wind velocity, that regulate surface radiation budgets and air mixing across landscapes (Geiger 1965 ; Thompson 1998 ), ultimately drive microclimatic conditions. For example, clear-sky and low-wind conditions can result in pronounced diurnal temperature fluctuations, with cold nights due to radiative cooling and hot days from intense solar heating and reduced air mixing (Hansen et al. 1995 ; Davy and Esau 2014b ). In contrast, cloudy and high-wind conditions produce smaller diurnal variations, maintaining more stable temperatures by reducing heat loss and enhancing atmospheric mixing (Campbell and Norman 1998; Dai et al. 1999 ; Pepin and Norris 2005 ; Davy and Esau 2014a ). Synoptic conditions further interact with the local environment to modify air mixing, humidity, and radiation budgets near the ground surface, leading to the development of distinct microclimates and associated microenvironments (Ashcroft and Gollan 2013 ; Barry and Blanken 2016 ; Lenoir et al. 2017 ). These interactions between macro- and microclimates are fundamental drivers of key ecosystem processes, including vegetation growth, soil respiration, and nutrient cycling (Bonan and Shugart 1989 ; Nilsson and Wardle 2005 ; Fernández-Alonso et al. 2018 ). Therefore, understanding how microclimates form under varying synoptic patterns is essential for linking global climate systems to local environmental processes and assessing future ecosystem dynamics. Microclimatic conditions can significantly differ from broader macroclimatic conditions (Dobrowski 2011 ; Graae et al. 2012 ; De Frenne et al. 2019 ) and show large variation in near-surface temperature conditions across space and time (i.e temperature heterogeneity) (Ashcroft et al. 2009 ; Scherrer and Körner 2011 ; Lenoir et al. 2013 ). Vegetation buffering and topographic forcing are two critical microclimatic mechanisms whose interaction with synoptic conditions shapes local temperature variability, environmental conditions and ecosystem functionality (Bennie et al. 2008 ; De Frenne et al. 2021 ). Vegetation can mitigate (i.e. buffer) temperature extremes, stabilizing local conditions and protecting species during heatwave and frost events (Renaud et al. 2009; De Frenne et al. 2019 ), while topographic forcing can increase diurnal microclimatic variability by influencing air mixing and leading to phenomena such as cold-air pooling at night or regulating solar radiation exposure during daytime (Bennie et al. 2008 ; Dobrowski 2011 ). Buffered microclimates, often created by vegetation cover or topographic features, can shelter species from macroclimatic extremes, supporting biodiversity and ecosystem services (Ashcroft et al. 2012 ; De Frenne et al. 2013 ; Zellweger 2019) and the formation of cool microclimates during summer months. These cool microclimates can provide refugia where cold-adapted species persist beyond their macro-climatic range, even under climate warming (Hannah et al. 2014 ). By examining the relative role of the microclimatic drivers under varying synoptic patterns, predictions of biodiversity patterns, carbon cycling, and ecosystem vulnerability can be improved. Previous studies have addressed the role of radiation (e.g., Bennie et al. 2008 ; Fridley 2009 ; Maclean et al. 2017 ) and varying wind conditions (e.g., Lundquist and Cayan 2006) in influencing temperatures at 1–10 m height in mountainous and grassland environments. Additionally, research has highlighted the atmospheric drivers of broader-scale diurnal temperature variation (Dai et al. 1999 ). This study examines microclimate temperature variation and its environmental drivers under different synoptic conditions, with a particular focus on near-surface (15 cm) temperatures. We analyzed variations on minimum and maximum temperatures, as these variations can have the most significant impacts on biodiversity over both short- and long-term timescales (Körner and Hiltbrunner 2018 ; Maxwell et al. 2019 ). By conducting this research in a heterogeneous landscape that includes a diverse range of environmental conditions and very dense network of microclimatic loggers, we aim to improve our understanding of the formation of fine-scale temperature variability in relation to macroclimate in high-latitude ecosystems. Thus, we addressed three research questions: (1) How does temperature variability differ under synoptic conditions characterised by combinations of cloud cover and wind speed? (2) Do the microclimatic drivers differ between synoptic conditions? (3) How does spatial temperature heterogeneity across the landscape vary between the synoptic conditions? 2. Methods 2.1 Study area and design The research area (120 km²) is located in the subarctic landscape of Northern Finland (Fig. 1 ) and encompasses multiple ecologically important ecosystems, including the ecotone between boreal forest and tundra. In this topographically diverse landscape, the elevation ranges from ca. 270 to 810 meters above sea level and is characterized by abundant woodlands, wetlands and water bodies. Vegetation alters from dense forests dominated by coniferous and deciduous species to low-growing dwarf shrubs at higher elevations, with lichen and moss predominating the ground layer. In the region, the growing season typically begins between late May and late June and ends in September (Aalto, Pirinen et al. 2022 ). Due to the area’s northern location, solar position strongly influences growing season conditions, resulting in 24-hour solar exposure during midsummer. The area represents a convergence zone for Arctic and Atlantic air masses, where shifting low- and high-pressure systems drive daily variability in wind patterns, cloud cover, precipitation, and temperature. Furthermore, the region's macroclimatic conditions exhibit continental characteristics, influenced by the rain shadow effect of the Scandes Mountains to the west (Tikkanen 2005). The study focused on the 50-day period from June 19 to August 7, representing the peak growing season in the area across two consecutive years (2023 and 2024). During the study period, the mean macroclimatic temperature was 14.0°C in 2023 and 15.5°C in 2024 (Kittilä Lompolonvuoma, 270 m a.s.l). In comparison, during the climate period 1991–2020, the mean July temperature in the area was 14.3°C, with a corresponding precipitation sum of 75 mm (Jokinen et al. 2021 ). For the study, 193 temperature loggers were installed in the landscape, and their location was chosen using the same protocol as Aalto et al. 2022 . In brief, the first 93 loggers were placed in 2022 using a random stratified sampling approach based on total canopy cover, deciduous canopy cover, vegetation density, distance to forest edge, elevation, topographic radiation and position index. To increase the environmental coverage of our data, an additional 100 loggers were installed in 2023 with their placement mainly focused on higher elevations and forested areas on hill slopes. 2.2 Macroclimate data Macroclimatic data was obtained from four weather stations operated by the Finnish Meteorological Institute, all situated within the study area in varying elevations: Muonio Laukukero (760 m a.s.l), Muonio Sammaltunturi (555 m), Kittilä Kenttärova (347 m) and Kittilä Lompolonvuoma (270 m). Each of the weather stations produces an average wind speed at ten-minute intervals, in addition one of the stations records cloud cover in oktas (0–8; 0 = cloud-free sky; 4 = sky half cloudy; 8 = sky completely cloudy, overcast) in ten-minute intervals. For the year 2024, six days did not have any cloud cover data, therefore, their cloud cover information has been retrieved from the nearest possible weather station Muonio Oustajärvi (located ~ 15 km southeast of the research area). Average wind speed of each day is an average from all four weather stations to best cover the effect of elevation changes in the area. Similarly, the mean daily air temperature was calculated as the average across the four stations. The daily averages were chosen after determining that differences between daytime (6:00 AM to 6:00 PM) and nighttime (6:00 PM to 6:00 AM) wind velocity and cloud cover remain relatively small during the research period. 2.3 Microclimate data Microclimate data collection was done with Tomst TMS-4 loggers (Wild et al. 2019 ), which measure temperature at 15 cm height with a 15-minute interval (Fig. 1 ) and a precision of 0.0625 ℃ and an accuracy of ± 0.5 ℃. Data preprocessing and quality control followed the same protocol as described in Aalto et al. 2022 . In summary, first clearly erroneous data was removed based on visual quality check, including cases when the logger had dislocated (easily visible from the time series of soil temperature and moisture time series of the logger) or its radiation shield was broken or detached (based on field notes). Secondly, all the temperature sensors in the loggers were calibrated based on the mutual difference they exhibited under constant temperature conditions in storage. Lastly, all the unnatural peaks in the data were removed based on careful inspection e.g. the peak was marked as unnatural if the temperature difference between two consecutive measurements was over 20°C. To minimise the error from the heating of the sensor due to solar radiation, we defined the maximum temperature from each sensor based on 95 percentile of the instantaneous measurements (Aalto et al. 2022 ). After quality control, data from 187 loggers were implemented to the study for the year 2023 of which 182 loggers remained usable for the year 2024. The most common reasons for excluding loggers were dislocation and damage to the radiation shield. 2.4 Environmental data Environmental variables were chosen based on their hypothesized and documented effects on microclimate in boreal and tundra biomes, while considering the correlations among them (Aalto et al. 2022 ). The selected variables included elevation, potential incoming solar radiation, topographic position index (TPI), slope, canopy cover, soil type, and the cover of wetlands and waterbodies (Table 1 ). These factors influence microclimatic conditions through multiple mechanisms, including modifying surface energy partitioning, affecting radiation fluxes through shading, promoting cold air pooling, driving soil moisture variability and affecting air flow (Bennie et al. 2008 ; Fridley 2009 ; Suggitt et al. 2011 ; De Frenne et al. 2021 , Aalto et al. 2022 ; Słowińska et al. 2022 ). Topographical metrics were based on digital elevation model (2m) provided by the National Land Survey of Finland ( https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/elevation-model-2-m ). Potential Incoming Solar Radiation tool in the SAGA-GIS software (version 7.6.2; http://www.saga-gis.org/saga_tool_doc/7.6.2/ta_lighting_2.html ) was used to determine the effect of topography on solar radiation assuming clear sky conditions. The radiation was calculated for the 15th of July with a one hour interval. Topographic Position Index tool in SAGA-GIS ( http://www.saga-gis.org/saga_tool_doc/7.6.2/ ta_morphometry_18.html) was used to calculate the difference in elevation between given location and mean elevation with a 100 m radius around the focal raster cell. Inclination of the slopes were derived using the Slope tool in the SAGA-GIS software ( http://www.saga-gis.org/saga_tool_doc/7.0.0/ta_morphometry_0.html ). The SAGA-GIS software was operated in R through the Rsagacmd R package (Pawley 2024 ). The portion of wetlands and waterbodies within a 250 m radius was calculated using the Finnish national Topographic database which offers all water bodies and wetlands in vector format ( https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/topographic-database ). Canopy cover values were based on National Forest Inventory conducted by Natural Resources Institute Finland (described in Mäkisara et al. 2022 ) produced in 2021. Soil type was defined using CORINE land cover classification made by Finnish Environment institute ( https://ckan.ymparisto.fi/dataset/corine-maanpeite-2018 ). Additionally, CORINE data were utilised to classify logger locations into six distinct land cover classes, which were used exclusively for result classification. These classes were open wetland (19 sites, Carex spp., Sphagnum spp.), transitional woodland/shrub (16 sites, canopy cover < 10% ), tundra (26 sites, Empetrum nigrum, Betula nana , bare-ground), deciduous forest (7 sites, Betula pubescens, Populus tremula ), coniferous forest (87, Picea abies, Pinus sylvestris ), mixed forest (31, B. pub, Picea abies, Pop.tremula ). The explanatory variables were resampled to a common 10 m x 10 m grid irrespective of their original resolution. The environmental values for the points were derived from the resampled rasters to ensure they formed a coherent dataset. Table 1 Descriptive statistics of environmental variables between the logger locations (n = 193). Environmental variable Continous Elevation (m.a.s.l) Radiation (kJ/m²) Topographic positon index Slope (mrad) Canopy cover (%) Water cover (%) Wetland cover (%) MEAN 376.1 6637.5 -41.4 703.4 27.0 0.6 20.3 MEDIAN 371.8 6693.3 -43.3 449.9 21.8 0.0 13.3 MIN 268.0 4985.0 -531.7 42.7 0.0 0.0 0.0 MAX 557.9 7527.5 664.7 3262.7 64.0 28.5 97.3 RANGE 289.9 2542.5 1196.3 3220.0 64.0 28.5 97.3 STANDARD DEVITATION 79.2 366.6 185.1 638.7 15.3 2.8 23.2 Soil type Categorical Mineral soil Peatland Bare rock NUMBER OF POINTS 125 63 5 2.5 Statistical analyses and modelling The studied days were classified into four categories, each assigned to represent a distinct synoptic condition: 1) calm (wind speed < 4.5 m/s) and clear (oktas 4.5) (26 days); 3) windy (speed > 4.5) and clear (13 days), and 4.) windy and cloudy (31 days). The spatial temperature range (i.e. spatial temperature heterogeneity) was calculated for both maximum and minimum temperatures, and the average value was determined for each synoptic condition. In addition, the average daily spatial temperature heterogeneity across land cover classes was calculated in different synoptic conditions. To investigate variability in extreme temperatures across land cover types, we also determined the average minimum and maximum temperature for each logger over the study period. Generalized additive model (Hastie and Tibshirani 1986 ; later GAM) with temporal autocorrelation structure was used to investigate the effect that cloud cover and wind velocity has on spatial heterogeneity. The GAM model included cloud cover, wind velocity, their interaction, and mean air temperature. Generalized boosted model (Ridgeway 2007 ; later GBM, interaction depth 3, number of trees 3000) was applied to investigate the relationship between response (T min and T max ) and the predictor variables. Models were trained separately for each day, and the results were subsequently combined and analyzed by the prevailing synoptic condition. Response shape curve types (i.e. describing the main direction of influence) were determined by dividing the predictor data into three equal-sized classes and comparing the mean values of each class to one another. Furthermore, using the best iteration of the model, T min and T max values were predicted across the study area, generating a temperature surface for each day from which the average temperature variation (i.e temperature heterogeneity) by each synoptic condition was calculated. Evaluation of the GBM models’ prediction performance was done by using leave-one-out cross validation, and calculating Pearson’s correlation coefficients and root mean square errors (RMSE) between observed and predicted values. To better categorize and assess the results by the synoptic conditions, the generalized least squares model (Pinheiro et al. 2025 ; later GLS) was fitted in the results to determine whether the temperatures had statistically significant differences between the synoptic conditions. In the GLS model, temporal autocorrelation between consecutive days was explicitly accounted for. The model was applied to the minimum and maximum temperature range, as well as cross-validated correlation coefficients and RMSE values. Additionally, a simple linear model was fitted to examine the effect of cloud cover and wind speed on the calculated cross-validation results. 3. Results 3.1 Observed temperature variability Microclimatic data revealed substantial variability in near-surface temperatures throughout the 50-day observational period in both study years. Statistically significant differences in both minimum and maximum temperatures (paired t- test p-value < 2.2e-16) were observed between the years, with 2024 exhibiting minimum temperatures that were on average 0.7°C warmer and maximum temperatures 1.5°C on average higher than 2023. Daily minimum temperatures ranged from − 6.1 to 14.5°C (average 7.7°C) in 2023 and − 1.6 to 16.2°C (average 8.4°C) in 2024, while daily maximum temperatures varied from 7.0 to 38.5°C (average 19.6°C) in 2023 and 9.8 to 39.8°C (average 21.5°C) in 2024 (Fig. 2 .). Minimum temperatures primarily reflect the nighttime conditions within the landscape, whereas maximum temperature observations typically occur in the afternoon, with relatively large day-to-day variation. The spatial temperature heterogeneity differed significantly (GLS model p < 0.0001, see appendix 1) between synoptic conditions (Fig. 3 ). Significant differences were observed among all conditions, with the exception of differences between conditions 1 and 3, and between conditions 2 and 4, where no statistically significant differences were detected. For minimum temperatures, spatial heterogeneity varied from 2.0°C to 14.3°C, with the lowest average heterogeneity (5.3°C) observed on windy and cloudy days (condition 4). In contrast, the highest differences (10.1°C) occurred under calm and clear conditions (1). For maximum temperatures, the spatial temperature heterogeneity ranged from 2.1°C to 21.6°C. On average, heterogeneity was lowest (7.2°C) during windy and cloudy conditions (4), and highest (16.0°C) under calm and clear conditions (1). Spatial heterogeneity in minimum temperatures was significantly influenced by cloud cover (GAM model Fig. 3 ., p < 2 × 10⁻¹⁶) and the interaction between cloud cover and wind velocity (p = 0.00178), with an adjusted R² of 0.71. Similarly, for maximum temperatures spatial heterogeneity was significantly affected by cloud cover (p < 2 × 10⁻¹⁶) and mean air temperature (p = 0.00211), with an adjusted R² of 0.83. Variations in temperature heterogeneity were pronounced within different land cover classes under different synoptic conditions (see Online Resource 1 table S1 .). Along with landscape scale temperature heterogeneity, daily variation within land cover classes is greater in clear conditions (1 and 3) and smaller in cloudy conditions (2 and 4). Observed mean minimum and maximum temperatures in different land cover classes showed similar results (Fig. 4 ) highlighting the higher temperature fluctuations in maximum temperatures and more stable minimum temperature conditions. In calm and clear conditions lowest average minimum temperatures (5.4°C) were observed in open wetlands, while the highest minimum temperatures were observed in tundra (8.8°C). In windy and cloudy conditions, the average minimum temperatures were highest in coniferous forest (8.7°C) and lowest in transitional woodlands (8.3°C). Maximum temperatures in calm and clear conditions were highest in open areas such as tundra, open wetlands and transitional woodland (26.2°C) and lowest in coniferous forest (24.5°C). In windy and cloudy conditions maximum temperatures were highest in open wetland (17.5°C) and lowest in tundra and coniferous and mixed forest (16.1°C). 3.2 Environmental drivers of the temperature variation Statistical modeling indicates that minimum temperatures are primarily driven by variables related to landscape topography (Fig. 5 ). Under clear conditions (1 and 3), relative importance of slope (23% in condition 1 and 20% in condition 3) and TPI (16% in condition 1 and 18% in condition 3) lead to cooling of local depressions and flat areas. In cloudy conditions (condition 2. 23% and condition 4. 34%) elevational gradient shapes the minimum temperature variation by lowering temperatures towards higher elevation. In addition to topographical variables, under clear conditions canopy cover (13% in condition 1 and 14% in condition 3) and cover of wetlands (15% in condition 1 and 13% in condition 3) had clear effect on temperatures suggesting lower temperatures to occur in areas with low canopy cover, such as near the edge of the wetlands. In turn, under windy and cloudy conditions, the effects were ambiguous. Maximum temperatures under clear conditions were primarily driven by the cooling effect of canopy cover (22% in condition 1 and 21% in condition 3) and topographic radiation (18% in conditions 1 and 3). In contrast, during cloudy conditions, elevation exerts (17% in condition 2 and 24% in condition 4) a stronger cooling effect for maximum temperatures than potential solar radiation. Canopy cover showed similar responses across synoptic conditions, whereas other variables exhibited varying responses depending on the synoptic condition. For example, in cloudy conditions (2 and 4) slope angle lowers the maximum temperature but in the clear conditions (1 and 3) the effect is the opposite. For both minimum and maximum temperatures, soil type and the proportion of water bodies were the least influential variables, each showing only a minor influence on the observed temperature variation. 3.3 The accuracy of temperature predictions Cross-validated correlations for the minimum temperature models suggest relatively consistent prediction performance in all synoptic conditions with correlation coefficient averaging between 0.5 (in condition 2) and 0.7 (in condition 1) and RMSE averaging between 0.7°C (in condition 4) and 1.5°C (in condition 1) (Fig. 6 ). Differences in correlations between the synoptic conditions were non-significant, whereas RMSE values were found to differ significantly (GLS model, p < 0.0001, see Online Resource 1 Fig. S1 ). Correlation coefficients for maximum temperature predictions showed more variability between the synoptic conditions (GLS, p < 0.0001) than minimum temperature predictions. The average correlation was 0.28 under windy and clear conditions (3) and 0.50 under windy and cloudy conditions (4). Furthermore, the average RMSE values changed significantly (GLS, p < 0.0001) from 3.1°C in calm and clear conditions (1) to 1.3°C under windy and cloudy conditions (4), with overall variability ranging from 0.4°C to 3.8°C. Hence, maximum temperature models demonstrate higher prediction performance under windy and cloudy conditions (4) compared to other conditions. The RMSE of the minimum temperature model exhibited a linear negative relationship with cloud cover and wind speed (R² = 0.62) in a multivariate setting based on a linear regression model, with cloud cover showing a statistically significant effect (p < 2e-16), while the effect of wind speed was not significant (p = 0.117). Minimum temperature models correlation coefficient did not show a linear relationship with cloud cover and wind speed. For the maximum temperature model, RMSE had a negative linear relationship (R² = 0.75), with both cloud cover (p < 2e-16) and wind speed (p = 0.0225) being statistically significant predictors. The correlation coefficient for maximum temperature yielded an R² of 0.40, with cloud cover (p = 8.4e-10) and wind speed (p = 0.04) both demonstrating statistical significance in a multivariate linear regression model. 3.4 Landscape-scale temperature heterogeneity Synoptic atmospheric condition was found to have a significant impact on landscape-scale temperature heterogeneity (Fig. 7 ). Predicted spatial temperature heterogeneity follows a pattern governed by the respective synoptic condition, where only minor temperature differences can be distinguished in cloudy and windy conditions, and relatively larger heterogeneity is forming under clear and calm conditions. For minimum temperatures, temperature heterogeneity (i.e. spatial range in temperatures) was found to change from 10.4°C in calm and clear conditions (1), to 3.3°C in windy and cloudy conditions (4). The synoptic condition with the largest temperature heterogeneity (condition 1, 10.4°C) encompasses both the coldest and warmest average values, whereas the temperature ranges of other synoptic conditions remain within these extremes. The lowest minimum temperatures occurred in the conditions with low cloud cover (1.7°C in conditions 1 and 3.0°C in condition 3), and highest values were more evenly distributed between different conditions. Average minimum temperature over the landscape was warmest in calm and cloudy condition (2, ₸ = 9.5°C), and averaging ca. 7.9°C in other synoptic conditions. The coolest minimum temperatures were consistently predicted in wetlands across all synoptic conditions, while tundra areas also exhibited low temperatures under windy and cloudy conditions. In contrast, the highest minimum temperatures were found in tundra during clear conditions, and in forested areas on north-east slopes during cloudy conditions. For maximum temperatures, the temperature heterogeneity was greatest in calm and clear (condition 1, 13.3°C) and windy and clear conditions (3, 11.0°C). Similarly, in those conditions the average temperature over the landscape (condition 1, ₸ = 24.7°C and condition 3, ₸ = 23.4°C) were highest. Calm and cloudy, as well as windy and cloudy conditions had the lowest heterogeneity (condition 2, 6.3°C and condition 4, 5.0°C). Highest average temperatures were in clear conditions (condition 1, 31.5°C and condition 3, 29.2°C), whereas in windy and cloudy conditions temperatures were notably lower. In contrast to minimum temperatures, the highest maximum temperatures were primarily predicted in wetlands and tundra areas. However, under windy and cloudy conditions, only wetlands exhibited distinctly different temperatures in the landscape. The coolest maximum temperatures were predicted over forested areas throughout the landscape, regardless of synoptic condition. 4. Discussion Our data revealed significant differences in microclimate conditions over the study area and their driving factors, as modified by the different synoptic atmospheric conditions. These findings support the argument that synoptic conditions play a fundamental role in forming both local minimum and maximum temperature heterogeneity within high-latitude landscapes. 4.1 Temperature heterogeneity in relation to synoptic conditions Substantial differences in temperatures occur particularly frequently under low cloud cover conditions, and especially with maximum temperatures. Our results demonstrate that the observed differences transfer to a smaller scale as temperatures can deviate substantially in open areas as well as in different land covers, e.g. in coniferous forest and tundra. Such large temperature variability indicates the diversity of microenvironments within high-latitude landscapes with a wide spectrum of thermal niches, supporting greater ecological diversity and resilience in these landscapes (Kemppinen et al. 2024 ). On a landscape scale, in calm and clear conditions, the high temperature heterogeneity in minimum temperatures arises from consistently lower temperatures in the wetlands, located in the local depressions and hillslopes, and higher temperatures found in the open tundra and forested areas. This variation can be partially connected to cold-air pooling, which occurs in calm cloudless nights (Burns and Chemel 2014 ), when cooler air flows and stratifies in the local depressions and valleys (Greiger 1965; Lundquist 2008). However, in our results the temperature heterogeneity is similar in windy and clear conditions, when cold-air pooling is usually not occurring (Price et al. 2011 ), indicating that it is possible that areas with relatively high soil moisture experience lower temperatures regardless of the existence of cold air pool when cloud cover is minimal (Seneviratne et al. 2006 ; Schwingshackl et al. 2017 ). On calm and clear conditions (i.e. when landscape scale temperature heterogeneity is highest) maximum temperatures are the highest in open areas, and the lowest in areas where vegetation offers shading from radiation and high soil moisture lowers the temperature extremes (Kuuluvainen and Pukkala 1989 ; Zellweger et al. 2019 ; De Frenne et al. 2021 ; Słowińska et al. 2022 ). These areas include forests, and wetlands located on the hillslopes. Furthermore, coniferous and mixed forest have the largest variation within land cover classes, showing the diversity between different parts of the boreal forest formed by varying density, canopy cover and moisture conditions. Conversely, in windy and cloudy conditions, the temperature differences across the landscape and within land cover classes remain notably smaller than in other synoptic conditions. Small landscape-scale temperature heterogeneity indicates that despite the varying vegetation and topographical differences the microclimates exhibit fairly similar temperature conditions. In windy and cloudy conditions, the coolest and warmest areas in the landscape form into similar locations as in calm and clear conditions, indicating that the main landscape scale temperature patterns for both minimum and maximum temperatures stay consistent between synoptic conditions. Based on our research, in windy and cloudy conditions, maximum temperatures deviate least in deciduous forest where low vegetation density enhances air mixing, thus leveling off temperature differences (De Frenne et al. 2021 ). More densely vegetated areas such as coniferous forest have high maximum temperature variation even under overcast conditions possibly due to varying wind conditions affected by canopy gaps and distance from the forest edge (Chen et al. 1995 ; Pohlman et al. 2009 ). Similarly to maximum temperatures, the temperature variation in minimum temperatures is largest in coniferous forests. However, it is important to consider that coniferous forests cover a large proportion of the study area including large variation e.g. in topography and moisture conditions, which may partially explain the magnitude of observed variations in those areas. Our results suggest that near-surface landscape temperature heterogeneity is primarily driven by changes in cloud cover, which can be modified by wind speed, rather than by variations in landscape-scale wind conditions alone. Wind speeds near the ground surface can differ significantly from those at higher altitudes due to increased surface friction (He et al. 2010 ), potentially decoupling surface conditions from broader atmospheric dynamics. Our findings show that maximum temperatures exhibit a greater temperature range across all synoptic conditions compared to minimum temperatures, consistent with Macek et al. ( 2019 ). Furthermore, the observed temperature range for maximum temperatures in clear conditions in our study was found to be of the same magnitude as the range observed by Greiser et al. ( 2018 ) across the variating forest density. Similarly the minimum temperature range across forests in their study is the same magnitude as the minimum temperature range in cloudy conditions observed in our study setting. While maximum temperature averages across the research area clearly differ between synoptic conditions, minimum temperature averages remain more stable. However, the temperature range, i.e temperature heterogeneity, varies notably for both variables. This study highlights that temperature heterogeneity varies by synoptic conditions, with the most pronounced spatial differences occurring under low air mixing and high radiation. 4.2 Environmental drivers and accuracy of temperature predictions The results reveal the clear connection between microclimatic drivers and prevailing synoptic conditions. Based on our data the minimum temperatures are primarily driven by local topographical conditions (Aalto et al. 2014 ; Pike et al. 2013 ). The prominent positive effect of slope and topographic position index on minimum temperatures under calm and clear conditions can be attributed to cold-air pooling, which forms cooler areas in even level terrain and local topographic depressions, and valleys (Lundquist 2008). In contrast, under mixed conditions, as indicated by high wind speed and cloud cover, elevational gradient through the atmospheric lapse rate is a more dominant factor causing lower temperatures in the higher areas. Furthermore, prior studies have identified a positive relationship between canopy cover and elevated minimum temperatures due to the insulating properties of the canopy (De Frenne et al. 2021 ). Such an effect is evident also in our data, but with a notation that under windy and clear conditions sheltering effect requires denser canopy cover, that could enhance air mixing at the forest edge and in sparsely forested areas. Similarly to minimum temperatures, synoptic conditions modify the drivers of maximum temperatures. In our data, the cooling effect of canopy cover plays a crucial role in modulating maximum temperatures across all synoptic conditions. Canopy cover shields the ground surface from direct shortwave radiation and reduces wind speeds in areas with dense vegetation (De Frenne et al. 2021 ), leading to lowered maximum temperatures. The effect of solar radiation on temperature variation is more profound on clear days (Greiser et al. 2018 ) especially on open areas (Dingman et al. 2013 ; George et al. 2015 ; Maclean et al. 2017 ), whereas logically its importance is lowered on cloudy days. Additionally, wetlands were found to reduce maximum temperatures under clear conditions, due to the high thermal inertia of the moist peat (Brown and Williams 1972 ). However, in cloudy conditions, this effect diminished. Moreover, our data indicate that the influence of elevation on maximum temperatures is dependent on wind speeds, while the effects of topographic radiation and other topographical features are altered by cloud cover. Therefore, for maximum temperatures, the same microclimatic drivers can lead to different spatial variations in near-surface temperatures depending on the synoptic condition. The accuracy of temperature predictions was found to vary significantly with synoptic conditions. For minimum temperature models, we identified a clear relationship between synoptic conditions and prediction performance, with variations in cloud cover accounting for a large share of the day-to-day variation in cross-validation metrics. For maximum temperatures, this relationship was even more pronounced, with cloud cover and wind speed jointly accounting for the majority of the variation in the evaluation metrics. Maximum temperature predictions showed higher accuracy under windy and cloudy conditions when the effects of large environmental gradients e.g. elevation dominate, and temperature gradients are small due to high air mixing and reduced solar heating. These findings highlight that large-scale weather conditions are key drivers of prediction accuracy, which should be taken into account particularly when modeling microclimate at sub-weekly temporal resolution. 4.3 Methodological uncertainties Microclimates result from the interaction of multiple physical processes operating across diverse temporal and spatial scales (Barry and Blanken 2016 ). These complex processes are often analyzed using explanatory variables that represent local environmental conditions, inherently introducing a degree of uncertainty into their statistical modeling related to the selected methodology of environmental data collection. Furthermore, while land cover classifications provide valuable context for understanding spatial temperature heterogeneity, they represent broad categorizations that may not fully capture the heterogeneity of microclimates within each class. For example, differences in vegetation density, soil moisture, or canopy structure within the same land cover type can lead to substantial local temperature variability that is not explicitly accounted for in the analyses (Davis et al. 2019 ; De Frenne et al. 2021 ; Greiser et al. 2024 ). We used the Generalized boosted model (GBM) to examine the variability of both minimum and maximum temperature variations. While the GBM algorithm is well-performing at capturing complex and non-linear relationships, its predictions are sensitive to the quality, quantity and representativeness of input variables (Kim and Park 2022 ). Additionally, the model's performance may be influenced by potential overfitting or underfitting issues during training (Kim and Park 2022 ). These factors could contribute to deviations in the modelled temperatures from actual conditions, particularly in regions with sparse observational data. In the observational data, the measurement of maximum temperatures can be challenging, because even with the use of radiation shields, temperature loggers are susceptible to overheating particularly under direct sunlight, potentially leading to an overestimation of recorded maximum temperatures and its spatial heterogeneity (Maclean et al. 2021 ). However, this is likely less of an issue here due to the northern location of the study area and thus less intensive solar radiation. This limitation in sensor performance highlights the need for cautious interpretation of temperature peaks in open, sun-exposed environments particularly under conditions of reduced air mixing. However, the magnitude of the error can be partly reduced when processing the data, for example by carefully filtering out the unrealistic values (Aalto et al. 2022 ). 5 Conclusion Our results highlight that synoptic conditions play a key role in forming near-surface temperature variability in high-latitude landscapes, greatly influencing both minimum and maximum temperatures. These conditions, categorized based on cloud cover and wind speeds, modulate the magnitude of thermal buffering, the relative importance of its environmental drivers, and the landscape-scale spatial temperature heterogeneity. Here, we showed that large temperature differences are observed at both the landscape scale and within individual land cover classes particularly under conditions of high shortwave radiation associated with low cloud cover and limited air mixing indicated by low wind speeds, whereas temperature differences tend to diminish under cloudy conditions with enhanced atmospheric mixing. By showing how microclimatic conditions are related to prevailing synoptic conditions, we can better understand the intricate links between global climate and weather systems, local environmental processes, and ecosystem functioning. This understanding provides insights for climate adaptation, biodiversity conservation, and sustainable nature management strategies. Declarations Funding This study was funded by the Maj and Tor Nessling Foundation. Juha Aalto and Miska Luoto were partly funded by the Academy of Finland (grant nr. 1342890). Johanna Lehtinen acknowledges travel funding from Nordenskiöld Society. Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Johanna Lehtinen: Writing -review & editing, Writing - original draft, Visualization, Data curation, Methodology, Investigation, Formal analysis, Conceptualization Acknowledgments This study was funded by the Maj and Tor Nessling Foundation. Juha Aalto and Miska Luoto were partly funded by the Academy of Finland (grant nr. 1342890). Johanna Lehtinen acknowledges travel funding from Nordenskiöld Society. Data availability Data sets generated during the current study are available from the corresponding author on reasonable request. Macroclimate temperature and wind speed from Finnish meteorological institute are available at https://www.ilmatieteenlaitos.fi/havaintojen-lataus References Aalto J, le Roux PC, Luoto M (2014) The meso-scale drivers of temperature extremes in high-latitude Fennoscandia. Clim Dyn 42(1–2):237–252. https://doi.org/10.1007/s00382-012-1590-y Aalto J, Pirinen P, Kauppi PE, Rantanen M, Lussana C, Lyytikäinen-Saarenmaa P, Gregow H (2022) High-resolution analysis of observed thermal growing season variability over northern Europe. Clim Dyn 58(5–6):1477–1493. https://doi.org/10.1007/s00382-021-05970-y Aalto J, Tyystjärvi V, Niittynen P, Kemppinen J, Rissanen T, Gregow H, Luoto M (2022) Microclimate temperature variations from boreal forests to the tundra. Agric For Meteorol 323. https://doi.org/10.1016/j.agrformet.2022.109037 Ashcroft MB, Chisholm LA, French KO (2009) Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob Change Biol 15(3):656–667. https://doi.org/10.1111/j.1365-2486.2008.01762.x Ashcroft MB, Gollan JR (2012) Fine-resolution (25 m) topoclimatic grids of near-surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 × 300 km) and diverse region. Int J Climatol 32(14):2134–2148. https://doi.org/10.1002/joc.2428 Ashcroft MB, Gollan JR (2013) Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia. Agric For Meteorol 176:77–89. https://doi.org/10.1016/j.agrformet.2013.03.008 Ashcroft MB, Gollan JR, Warton DI, Ramp D (2012) A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix. Glob Change Biol 18(6):1866–1879. https://doi.org/10.1111/j.1365-2486.2012.02661.x Barry RG, Blanken PD (2016) Microclimate and Local Climate Bennie J, Huntley B, Wiltshire A, Hill MO, Baxter R (2008) Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model 216(1):47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010 Bonan GB, Shugart HH (1989) Environmental Factors and Ecological Processes in Boreal Forests. Annu Rev Ecol Syst 20(1):1–28. https://doi.org/10.1146/annurev.es.20.110189.000245 Brown R, Williams G (1972) The freezing of peatland. Tech Paper Div Building Res Canada(No 381), 24pp Burns P, Chemel C (2014) Evolution of Cold-Air-Pooling Processes in Complex Terrain. Boundary Layer Meteorol 150(3):423–447. https://doi.org/10.1007/s10546-013-9885-z Campbell GS, Norman JM, Gaylon S, Campbell, John M, Norman (1998) (2nd edn.). Springer Chen J, Franklin JF, Spies TA (1995) Growing-season microclimatic gradients from clearcut edges into old-growth Douglas-fir forests. Ecol Appl 5(1):74–86. https://doi.org/10.2307/1942053 Dai A, Trenberth KE, Karl TR (1999) Effects of Clouds, Soil Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range. J Clim 12(8):2451–2473. https://doi.org/10.1175/1520-0442(1999)0122.0.co;2 Daly C, Conklin DR, Unsworth MH (2010) Local atmospheric decoupling in complex topography alters climate change impacts. Int J Climatol 30(12):1857–1864. https://doi.org/10.1002/joc.2007 Davis KT, Dobrowski SZ, Holden ZA, Higuera PE, Abatzoglou JT (2019) Microclimatic buffering in forests of the future: the role of local water balance. Ecography (Copenhagen) 42(1):1–11. https://doi.org/10.1111/ecog.03836 Davy R, Esau I (2014a) Global climate models' bias in surface temperature trends and variability. Environ Res Lett 9(11):114024. https://doi.org/10.1088/1748-9326/9/11/114024 Davy R, Esau I (2014b) Surface air temperature variability in global climate models. Atmospheric Sci Lett 15(1):13–20. https://doi.org/10.1002/asl2.456 De Frenne P, Lenoir J, Luoto M et al (2021) Forest microclimates and climate change: Importance, drivers and future research agenda. Glob Change Biol 27(11):2279–2297. https://doi.org/10.1111/gcb.15569 De Frenne P, Rodríguez-Sánchez F, Coomes DA et al (2013) Microclimate moderates plant responses to macroclimate warming. Proceedings of the National Academy of Sciences - PNAS, 110(46), 18561–18565. https://doi.org/10.1073/pnas.1311190110 De Frenne P, Zellweger F, Rodríguez-Sánchez F et al (2019) Global buffering of temperatures under forest canopies. Nat Ecol Evol 3(5):744–749. https://doi.org/10.1038/s41559-019-0842-1 Dingman JR, Sweet LC, McCullough I, Davis FW, Flint A, Franklin J, Flint LE (2013) Cross-scale modeling of surface temperature and tree seedling establishment in mountain landscapes. Ecol processes 2(1):1–15. https://doi.org/10.1186/2192-1709-2-30 Dobrowski SZ (2011) Climatic basis for microrefugia: the influence of terrain on climate. Glob Change Biol 17(2):1022–1035. https://doi.org/10.1111/j.1365-2486.2010.02263.x Fernández-Alonso MJ, Díaz-Pinés E, Ortiz C, Rubio A (2018) Disentangling the effects of tree species and microclimate on heterotrophic and autotrophic soil respiration in a Mediterranean ecotone forest. For Ecol Manag 430:533–544. https://doi.org/10.1016/j.foreco.2018.08.046 Fridley JD (2009) Downscaling Climate over Complex Terrain: High Finescale (< 1000 m) Spatial Variation of Near-Ground Temperatures in a Montane Forested Landscape (Great Smoky Mountains). J Appl Meteorol climatology 48(5):1033–1049. https://doi.org/10.1175/2008JAMC2084.1 Geiger R (1965) The climate near the ground / Rudolf Geiger. Harvard University Press George AD, Thompson FR, Faaborg J (2015) Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies. Remote Sens Lett 6(12):924–932. https://doi.org/10.1080/2150704X.2015.1088671 Graae BJ, De Frenne P, Kolb A et al (2012) On the use of weather data in ecologicalstudies along altitudinal and latitudinal gradients. Oikos 121(1):3–19. https://doi.org/10.1111/j.1600-0706.2011.19694.x Greiser C, Hederová L, Vico G, Wild J, Macek M, Kopecký M (2024) Higher soil moisture increases microclimate temperature buffering in temperate broadleaf forests. Agric For Meteorol 345:109828. https://doi.org/10.1016/j.agrformet.2023.109828 Greiser C, Meineri E, Luoto M, Ehrlen J, Hylander K (2018) Monthly microclimate models in a managed boreal forest landscape. Agric For Meteorol 250–251. https://doi.org/10.1016/j.agrformet.2017.12.252 Haesen S, Lembrechts JJ, De Frenne P et al (2021) ForestTemp – Sub-canopy microclimate temperatures of European forests. Glob Change Biol 27(23):6307–6319. https://doi.org/10.1111/gcb.15892 Hannah L, Flint L, Syphard AD, Moritz MA, Buckley LB, McCullough IM (2014) Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends Ecol Evol (Amsterdam) 29(7):390–397. https://doi.org/10.1016/j.tree.2014.04.006 Hansen J, Sato M, Ruedy R (1995) Long-term changes of the diurnal temperature cycle: implications about mechanisms of global climate change. Atmos Res 37(1):175–209. https://doi.org/10.1016/0169-8095(94)00077-Q Hastie T, Tibshirani R (1986) Generalized Additive Models. Stat Sci 1(3):297–318. https://doi.org/10.1214/ss/1177013604 He Y, Monahan A, Jones C, Dai A, Biner S, Caya D, Winger K (2010) Probability distributions of land surface wind speeds over North America. J Geophys Res Atmos 115:D04103. https://doi.org/10.1029/2008JD010708 Jokinen P et al (2021) Climatological and oceanographic statistics of Finland 1991–2020. – Finnish Meteorological Inst Kemppinen J, Lembrechts JJ, Van Meerbeek K et al (2024) Microclimate, an important part of ecology and biogeography Kim C, Park T (2022) Predicting Determinants of Lifelong Learning Intention Using Gradient Boosting Machine (GBM) with Grid Search. Sustainability 14(9):5256. https://doi.org/10.3390/su14095256 Kuuluvainen T, Pukkala T (1989) Simulation of within-tree and between-tree shading of direct radiation in a forest canopy: effect of crown shape and sun elevation. Ecol Model 49(1):89–100. https://doi.org/10.1016/0304-3800(89)90045-8 Körner C, Hiltbrunner E (2018) The 90 ways to describe plant temperature. Perspectives in plant ecology. Evol Syst 30:16–21. https://doi.org/10.1016/j.ppees.2017.04.004 Lawrence DM, Swenson SC (2011) Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environ Res Lett 6(4):45504. https://doi.org/10.1088/1748-9326/6/4/045504 Lembrechts JJ, Lenoir J, Roth N et al (2019) Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Glob Ecol Biogeogr 28(11):1578–1596. https://doi.org/10.1111/geb.12974 Lenoir J, Graae BJ, Aarrestad PA et al (2013) Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob Change Biol 19(5):1470–1481. https://doi.org/10.1111/gcb.12129 Lenoir J, Hattab T, Pierre G (2017) Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography (Copenhagen) 40(2):253–266. https://doi.org/10.1111/ecog.02788 Lundquist JD, Cayan DR (2007) Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California. J Geophys Res D Atmos 112(D11). /a https://doi.org/10.1029/2006JD007561 Lundquist JD, Pepin N, Rochford C (2008) Automated algorithm for mapping regions of cold-air pooling in complex terrain. J Geophys Research: Atmos 113(D22). /a https://doi.org/10.1029/2008JD009879 Macek M, Kopecký M, Wild J (2019) Maximum air temperature controlled by landscape topography affects plant species composition in temperate forests. Landscape Ecol 34(11):2541–2556. https://doi.org/10.1007/s10980-019-00903-x Maclean I, Suggitt A, Wilson R, Duffy J, Bennie J (2017) Fine-scale climate change: modelling spatial variation in biologically meaningful rates of warming. Glob Change Biol 23(1):256–268. https://doi.org/10.1111/gcb.13343 Maclean IMD, Duffy JP, Haesen S et al (2021) On the measurement of microclimate. Methods Ecol Evol 12(8):1397–1410. https://doi.org/10.1111/2041-210X.13627 Maxwell SL, Butt N, Maron M, McAlpine CA, Chapman S, Ullmann A, Segan DB, Watson JEM (2019) Conservation implications of ecological responses to extreme weather and climate events. Divers Distrib 25(4):613–625. https://doi.org/10.1111/ddi.12878 Mäkisara K, Katila M, Peräsaari J (2022) The multi-source national forest inventory of Finland—methods and results 2017 and 2019 Nilsson M, Wardle D (2005) Understory Vegetation as a Forest Ecosystem Driver: Evidence from the Northern Swedish Boreal Forest. Frontiers in ecology and the environment, 3 (8), 421–428. https://doi.org/10.1890/1540-9295(2005)003[0421:uvaafe]2.0.co;2 Pawley S (2024) Rsagacmd: Linking R with the Open-Source 'SAGA-GIS' Software. R package version 0.4.3. https://stevenpawley.github.io/Rsagacmd/ Pepin NC, Daly C, Lundquist J (2011) The influence of surface versus free-air decoupling on temperature trend patterns in the western United States. J Phys Res 116(D10). /a Pepin NC, Norris JR (2005) An examination of the differences between surface and free-air temperature trend at high-elevation sites: Relationships with cloud cover, snow cover, and wind. J Geophys Research: Atmos 110(D24). 24112.24111-n/a Pike G, Pepin N, Schaefer M (2013) High latitude local scale temperature complexity: The example of Kevo Valley, Finnish Lapland. Int J Climatol 33(8):2050–2020 Pinheiro J, Bates D, Core Team R (2025) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–168. https://CRAN.R-project.org/package=nlme Pohlman C, Turton S, Goosem M (2009) Temporal variation in microclimatic edge effects near powerlines, highways and streams in Australian tropical rainforest. Agric For Meteorol 149(1):84–95. https://doi.org/10.1016/j.agrformet.2008.07.003 Post E, Forchhammer M, Bret-Harte M, Callaghan T, Christensen TR, Elberling B, Fox A, Gilg O, Hik D, Høye T, Ims RA, Jeppesen E, Klein DR, Madsen J, McGuire AD, Rysgaard S, Schindler DE, Stirling I, Tamstorf MP et al (2009) Ecological Dynamics Across the Arctic Associated with Recent Climate Change. Science (American Association for the Advancement of Science) Price JD, Vosper S, Brown A et al (2011) COLPEX: Field and Numerical Studies over a Region of Small Hills. Bull Am Meteorol Soc 92(12):1636–1650. https://doi.org/10.1175/2011BAMS3032.1 Rantanen M, Karpechko AY, Lipponen A, Nordling K, Hyvärinen O, Ruosteenoja K, Vihma T, Laaksonen A (2022) The Arctic has warmed nearly four times faster than the globe since 1979. Commun earth Environ 3(1):1–10. https://doi.org/10.1038/s43247-022-00498-3 Renaud V, Rebetez M (2009) Comparison between open-site and below-canopy climatic conditions in Switzerland during the exceptionally hot summer of 2003. Agric For Meteorol 149(5):873–880. https://doi.org/10.1016/j.agrformet.2008.11.006 Ridgeway G (2007) Generalized Boosted Models: A guide to the gbm package. Update, 1(1), 2007 Scherrer D, Körner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J Biogeogr 38(2):406–416. https://doi.org/10.1111/j.1365-2699.2010.02407.x Schwingshackl C, Hirschi M, Seneviratne SI (2017) Quantifying Spatiotemporal Variations of Soil Moisture Control on Surface Energy Balance and Near-Surface Air Temperature. J Clim 30(18):7105–7124. https://doi.org/10.1175/jcli-d-16-0727.1 Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land–atmosphere coupling and climate change in Europe. Nature 443(7108):205–209. https://doi.org/10.1038/nature05095 Słowińska S, Słowiński M, Marcisz K, Lamentowicz M (2022) Long-term microclimate study of a peatland in Central Europe to understand microrefugia. Int J Biometeorol 66(4):817–832. https://doi.org/10.1007/s00484-022-02240-2 Suggitt AJ, Gillingham PK, Hill JK, Huntley B, Kunin WE, Roy DB, Thomas CD (2011) Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120(1):1–8. https://doi.org/10.1111/j.1600-0706.2010.18270.x Thompson RD, Russell D (1998) Thompson. Routledge Wild J, Kopecký M, Macek M, Šanda M, Jankovec J, Haase T (2019) Climate at ecologically relevant scales: A new temperature and soil moisture logger for long-term microclimate measurement. Agric For Meteorol 268:40–47. https://doi.org/10.1016/j.agrformet.2018.12.018 Zellweger F, Coomes D, Lenoir J et al (2019) Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Glob Ecol Biogeogr 28(12):1774–1786. https://doi.org/10.1111/geb.12991 Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Climate Dynamics → Version 1 posted Editorial decision: Major Revision 15 Sep, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers invited by journal 22 Jul, 2025 Editor assigned by journal 11 Jun, 2025 First submitted to journal 31 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6784547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489061600,"identity":"2361f686-4775-4bb8-aa4e-15ab64bde8b5","order_by":0,"name":"Johanna Lehtinen","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0001-2350-141X","institution":"University of Helsinki: Helsingin Yliopisto","correspondingAuthor":true,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Lehtinen","suffix":""},{"id":489061601,"identity":"213b827f-3934-4159-876e-2105fae3e0e2","order_by":1,"name":"Juha Aalto","email":"","orcid":"","institution":"Finnish Meteorological Institute: Ilmatieteen Laitos","correspondingAuthor":false,"prefix":"","firstName":"Juha","middleName":"","lastName":"Aalto","suffix":""},{"id":489061602,"identity":"f7219631-330d-4450-babf-05663f626962","order_by":2,"name":"Pekka Niittynen","email":"","orcid":"","institution":"University of Jyväskylä: Jyvaskylan Yliopisto","correspondingAuthor":false,"prefix":"","firstName":"Pekka","middleName":"","lastName":"Niittynen","suffix":""},{"id":489061603,"identity":"d906d540-1239-4acc-bdb6-cb7848eb4164","order_by":3,"name":"Miska Luoto","email":"","orcid":"","institution":"University of Helsinki: Helsingin Yliopisto","correspondingAuthor":false,"prefix":"","firstName":"Miska","middleName":"","lastName":"Luoto","suffix":""}],"badges":[],"createdAt":"2025-05-30 12:05:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6784547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6784547/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00382-026-08170-8","type":"published","date":"2026-04-20T15:58:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87525183,"identity":"a7f67b54-76b6-40a4-9f8e-6dcc2774834f","added_by":"auto","created_at":"2025-07-24 19:22:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":738204,"visible":true,"origin":"","legend":"\u003cp\u003eResearch area in Pallas-Yllästunturi National Park, in northern Finland. Points represent the loggers’ locations measuring temperatures at 15 cm above the ground surface, and their colour depict the potential incoming solar radiation in July.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/e205f43d770eb8dadad55fe3.png"},{"id":87525544,"identity":"4d6cffd4-766f-44af-ac75-0f0785f5d6f7","added_by":"auto","created_at":"2025-07-24 19:30:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":243008,"visible":true,"origin":"","legend":"\u003cp\u003eObserved temperature variation over the 50-day observational period for both research years. Colors represent different synoptic conditions. Synoptic conditions were classified as: 1= calm and clear, 2= calm and cloudy, 3= windy and clear, and 4= windy and cloudy.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/6dcb2cc5ed775de2d098159d.png"},{"id":87525994,"identity":"4302d527-9f58-428e-b21d-838d184bd073","added_by":"auto","created_at":"2025-07-24 19:38:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229853,"visible":true,"origin":"","legend":"\u003cp\u003eObserved average spatial temperature heterogeneity in the study area for a. minimum and b. maximum temperatures on different days based on cloud cover and wind speed. Each point represents a day, with color indicating the temperature range (lowest to highest) for the selected variable across the landscape and shape distinguishing the year of observation. Average temperature heterogeneity (ΔT\u003csub\u003eAVG\u003c/sub\u003e ) in each synoptic condition is marked in the bottom right corner. And GAM modelled temperature heterogeneity for c. minimum and d. maximum temperatures in relation to cloud cover and wind velocity.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/9a4decbe9454023270d282a7.png"},{"id":87525992,"identity":"ea4cea32-e67e-49e9-8e99-0128248e0633","added_by":"auto","created_at":"2025-07-24 19:38:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":290590,"visible":true,"origin":"","legend":"\u003cp\u003eObserved variability in extreme temperatures across land cover types. For a. minimum temperatures and b. maximum temperatures, observed mean temperature variation (whiskers) and average temperature (circle) in different land cover classes, in different synoptic conditions (color).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/382fd5e811cd68df6521b14f.png"},{"id":87525185,"identity":"5cdcd6b4-7502-49c3-9d2d-59c07147fa6e","added_by":"auto","created_at":"2025-07-24 19:22:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188537,"visible":true,"origin":"","legend":"\u003cp\u003eRelative importance of the environmental variables in different synoptic conditions for a. Minimum and b. Maximum temperature based on GBM model. Colors represent different response shape curve types, expressed as a percentage of all response curves for that group. Soil is a factor variable, hence it is specified by its own color.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/6326c84bf40d476919c14d59.png"},{"id":87525186,"identity":"bc9f1e3e-8208-4dc8-b799-a7feefb2cbf6","added_by":"auto","created_at":"2025-07-24 19:22:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187402,"visible":true,"origin":"","legend":"\u003cp\u003eAccuracy of temperature predictions analyzed by cross-validated correlation coefficient (represented by size) and RMSE (represented by color) for a. minimum and b. maximum temperature models. The grey dot in the diagram represents the mean correlation coefficient for each synoptic condition, while the grey rectangle indicates the mean RMSE value.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/1ddbc189a99dc8fb1042e5e3.png"},{"id":87525993,"identity":"c5d13037-6f9c-4b79-a778-45f9d91ae110","added_by":"auto","created_at":"2025-07-24 19:38:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":206442,"visible":true,"origin":"","legend":"\u003cp\u003eAverage minimum (a-d) and maximum (e-h) temperatures, temperature range (ΔT), standard deviation (SD) and mean temperature (₸) over the research area in different synoptic conditions. Segments represent the temperature range inside each map. Contours depict elevation at 50 meter intervals.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/3566111e453d039dd671e7ff.png"},{"id":107928738,"identity":"850726ab-bf46-41fc-ae5f-338169c335a4","added_by":"auto","created_at":"2026-04-27 16:12:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2444090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/7c084799-0563-432b-bdad-bc91818011db.pdf"},{"id":87525569,"identity":"5b6bf4db-604b-4631-8302-f02c9fabf609","added_by":"auto","created_at":"2025-07-24 19:30:38","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":545184,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6784547/v1/b5d98c822fc1ff1dc3f5687f.docx"}],"financialInterests":"","formattedTitle":"Synoptic atmospheric conditions drive microclimatic variability in a high-latitude landscapes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change is significantly impacting high latitude environments by increasing temperatures three to four times faster than the global average (Rantanen et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Along with warming, seasonal changes such as the extended summer growing season and reduced winter snow cover are expected to become more pronounced (Post 2009). Consequently, climate change will alter both biotic and abiotic processes, affecting various aspects such as carbon cycling, biodiversity, species distribution and soil processes (De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lawrence and Swenson \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lenoir et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Post 2009). Several studies have demonstrated that widely used coarse-resolution macroclimatic data alone is insufficient to accurately describe these ongoing changes as many processes are more closely linked to localised fluctuations in temperature and humidity i.e., microclimatic conditions (Haesen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lembrechts et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSynoptic atmospheric conditions, related to e.g. cloud cover and wind velocity, that regulate surface radiation budgets and air mixing across landscapes (Geiger \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Thompson \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), ultimately drive microclimatic conditions. For example, clear-sky and low-wind conditions can result in pronounced diurnal temperature fluctuations, with cold nights due to radiative cooling and hot days from intense solar heating and reduced air mixing (Hansen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Davy and Esau \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e). In contrast, cloudy and high-wind conditions produce smaller diurnal variations, maintaining more stable temperatures by reducing heat loss and enhancing atmospheric mixing (Campbell and Norman 1998; Dai et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Pepin and Norris \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Davy and Esau \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e). Synoptic conditions further interact with the local environment to modify air mixing, humidity, and radiation budgets near the ground surface, leading to the development of distinct microclimates and associated microenvironments (Ashcroft and Gollan \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Barry and Blanken \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lenoir et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These interactions between macro- and microclimates are fundamental drivers of key ecosystem processes, including vegetation growth, soil respiration, and nutrient cycling (Bonan and Shugart \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Nilsson and Wardle \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fern\u0026aacute;ndez-Alonso et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, understanding how microclimates form under varying synoptic patterns is essential for linking global climate systems to local environmental processes and assessing future ecosystem dynamics.\u003c/p\u003e\u003cp\u003eMicroclimatic conditions can significantly differ from broader macroclimatic conditions (Dobrowski \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Graae et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; De Frenne et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and show large variation in near-surface temperature conditions across space and time (i.e temperature heterogeneity) (Ashcroft et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Scherrer and K\u0026ouml;rner \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lenoir et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Vegetation buffering and topographic forcing are two critical microclimatic mechanisms whose interaction with synoptic conditions shapes local temperature variability, environmental conditions and ecosystem functionality (Bennie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Vegetation can mitigate (i.e. buffer) temperature extremes, stabilizing local conditions and protecting species during heatwave and frost events (Renaud et al. 2009; De Frenne et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while topographic forcing can increase diurnal microclimatic variability by influencing air mixing and leading to phenomena such as cold-air pooling at night or regulating solar radiation exposure during daytime (Bennie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dobrowski \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Buffered microclimates, often created by vegetation cover or topographic features, can shelter species from macroclimatic extremes, supporting biodiversity and ecosystem services (Ashcroft et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; De Frenne et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zellweger 2019) and the formation of cool microclimates during summer months. These cool microclimates can provide refugia where cold-adapted species persist beyond their macro-climatic range, even under climate warming (Hannah et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By examining the relative role of the microclimatic drivers under varying synoptic patterns, predictions of biodiversity patterns, carbon cycling, and ecosystem vulnerability can be improved.\u003c/p\u003e\u003cp\u003ePrevious studies have addressed the role of radiation (e.g., Bennie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fridley \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Maclean et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and varying wind conditions (e.g., Lundquist and Cayan 2006) in influencing temperatures at 1\u0026ndash;10 m height in mountainous and grassland environments. Additionally, research has highlighted the atmospheric drivers of broader-scale diurnal temperature variation (Dai et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This study examines microclimate temperature variation and its environmental drivers under different synoptic conditions, with a particular focus on near-surface (15 cm) temperatures. We analyzed variations on minimum and maximum temperatures, as these variations can have the most significant impacts on biodiversity over both short- and long-term timescales (K\u0026ouml;rner and Hiltbrunner \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maxwell et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By conducting this research in a heterogeneous landscape that includes a diverse range of environmental conditions and very dense network of microclimatic loggers, we aim to improve our understanding of the formation of fine-scale temperature variability in relation to macroclimate in high-latitude ecosystems. Thus, we addressed three research questions: (1) How does temperature variability differ under synoptic conditions characterised by combinations of cloud cover and wind speed? (2) Do the microclimatic drivers differ between synoptic conditions? (3) How does spatial temperature heterogeneity across the landscape vary between the synoptic conditions?\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area and design\u003c/h2\u003e\u003cp\u003eThe research area (120 km\u0026sup2;) is located in the subarctic landscape of Northern Finland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and encompasses multiple ecologically important ecosystems, including the ecotone between boreal forest and tundra. In this topographically diverse landscape, the elevation ranges from ca. 270 to 810 meters above sea level and is characterized by abundant woodlands, wetlands and water bodies. Vegetation alters from dense forests dominated by coniferous and deciduous species to low-growing dwarf shrubs at higher elevations, with lichen and moss predominating the ground layer. In the region, the growing season typically begins between late May and late June and ends in September (Aalto, Pirinen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to the area\u0026rsquo;s northern location, solar position strongly influences growing season conditions, resulting in 24-hour solar exposure during midsummer. The area represents a convergence zone for Arctic and Atlantic air masses, where shifting low- and high-pressure systems drive daily variability in wind patterns, cloud cover, precipitation, and temperature. Furthermore, the region's macroclimatic conditions exhibit continental characteristics, influenced by the rain shadow effect of the Scandes Mountains to the west (Tikkanen 2005).\u003c/p\u003e\u003cp\u003eThe study focused on the 50-day period from June 19 to August 7, representing the peak growing season in the area across two consecutive years (2023 and 2024). During the study period, the mean macroclimatic temperature was 14.0\u0026deg;C in 2023 and 15.5\u0026deg;C in 2024 (Kittil\u0026auml; Lompolonvuoma, 270 m a.s.l). In comparison, during the climate period 1991\u0026ndash;2020, the mean July temperature in the area was 14.3\u0026deg;C, with a corresponding precipitation sum of 75 mm (Jokinen et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the study, 193 temperature loggers were installed in the landscape, and their location was chosen using the same protocol as Aalto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e. In brief, the first 93 loggers were placed in 2022 using a random stratified sampling approach based on total canopy cover, deciduous canopy cover, vegetation density, distance to forest edge, elevation, topographic radiation and position index. To increase the environmental coverage of our data, an additional 100 loggers were installed in 2023 with their placement mainly focused on higher elevations and forested areas on hill slopes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Macroclimate data\u003c/h2\u003e\u003cp\u003eMacroclimatic data was obtained from four weather stations operated by the Finnish Meteorological Institute, all situated within the study area in varying elevations: Muonio Laukukero (760 m a.s.l), Muonio Sammaltunturi (555 m), Kittil\u0026auml; Kentt\u0026auml;rova (347 m) and Kittil\u0026auml; Lompolonvuoma (270 m). Each of the weather stations produces an average wind speed at ten-minute intervals, in addition one of the stations records cloud cover in oktas (0\u0026ndash;8; 0\u0026thinsp;=\u0026thinsp;cloud-free sky; 4\u0026thinsp;=\u0026thinsp;sky half cloudy; 8\u0026thinsp;=\u0026thinsp;sky completely cloudy, overcast) in ten-minute intervals. For the year 2024, six days did not have any cloud cover data, therefore, their cloud cover information has been retrieved from the nearest possible weather station Muonio Oustaj\u0026auml;rvi (located\u0026thinsp;~\u0026thinsp;15 km southeast of the research area). Average wind speed of each day is an average from all four weather stations to best cover the effect of elevation changes in the area. Similarly, the mean daily air temperature was calculated as the average across the four stations. The daily averages were chosen after determining that differences between daytime (6:00 AM to 6:00 PM) and nighttime (6:00 PM to 6:00 AM) wind velocity and cloud cover remain relatively small during the research period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Microclimate data\u003c/h2\u003e\u003cp\u003eMicroclimate data collection was done with Tomst TMS-4 loggers (Wild et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which measure temperature at 15 cm height with a 15-minute interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and a precision of 0.0625 ℃ and an accuracy of \u0026plusmn;\u0026thinsp;0.5 ℃. Data preprocessing and quality control followed the same protocol as described in Aalto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e. In summary, first clearly erroneous data was removed based on visual quality check, including cases when the logger had dislocated (easily visible from the time series of soil temperature and moisture time series of the logger) or its radiation shield was broken or detached (based on field notes). Secondly, all the temperature sensors in the loggers were calibrated based on the mutual difference they exhibited under constant temperature conditions in storage. Lastly, all the unnatural peaks in the data were removed based on careful inspection e.g. the peak was marked as unnatural if the temperature difference between two consecutive measurements was over 20\u0026deg;C. To minimise the error from the heating of the sensor due to solar radiation, we defined the maximum temperature from each sensor based on 95 percentile of the instantaneous measurements (Aalto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After quality control, data from 187 loggers were implemented to the study for the year 2023 of which 182 loggers remained usable for the year 2024. The most common reasons for excluding loggers were dislocation and damage to the radiation shield.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Environmental data\u003c/h2\u003e\u003cp\u003eEnvironmental variables were chosen based on their hypothesized and documented effects on microclimate in boreal and tundra biomes, while considering the correlations among them (Aalto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The selected variables included elevation, potential incoming solar radiation, topographic position index (TPI), slope, canopy cover, soil type, and the cover of wetlands and waterbodies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These factors influence microclimatic conditions through multiple mechanisms, including modifying surface energy partitioning, affecting radiation fluxes through shading, promoting cold air pooling, driving soil moisture variability and affecting air flow (Bennie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fridley \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Suggitt et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Aalto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Słowińska et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTopographical metrics were based on digital elevation model (2m) provided by the National Land Survey of Finland (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/elevation-model-2-m\u003c/span\u003e\u003cspan address=\"https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/elevation-model-2-m\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Potential Incoming Solar Radiation tool in the SAGA-GIS software (version 7.6.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.saga-gis.org/saga_tool_doc/7.6.2/ta_lighting_2.html\u003c/span\u003e\u003cspan address=\"http://www.saga-gis.org/saga_tool_doc/7.6.2/ta_lighting_2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to determine the effect of topography on solar radiation assuming clear sky conditions. The radiation was calculated for the 15th of July with a one hour interval. Topographic Position Index tool in SAGA-GIS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.saga-gis.org/saga_tool_doc/7.6.2/\u003c/span\u003e\u003cspan address=\"http://www.saga-gis.org/saga_tool_doc/7.6.2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ta_morphometry_18.html) was used to calculate the difference in elevation between given location and mean elevation with a 100 m radius around the focal raster cell. Inclination of the slopes were derived using the Slope tool in the SAGA-GIS software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.saga-gis.org/saga_tool_doc/7.0.0/ta_morphometry_0.html\u003c/span\u003e\u003cspan address=\"http://www.saga-gis.org/saga_tool_doc/7.0.0/ta_morphometry_0.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The SAGA-GIS software was operated in R through the \u003cem\u003eRsagacmd\u003c/em\u003e R package (Pawley \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The portion of wetlands and waterbodies within a 250 m radius was calculated using the Finnish national Topographic database which offers all water bodies and wetlands in vector format (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/topographic-database\u003c/span\u003e\u003cspan address=\"https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/topographic-database\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCanopy cover values were based on National Forest Inventory conducted by Natural Resources Institute Finland (described in M\u0026auml;kisara et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) produced in 2021. Soil type was defined using CORINE land cover classification made by Finnish Environment institute (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ckan.ymparisto.fi/dataset/corine-maanpeite-2018\u003c/span\u003e\u003cspan address=\"https://ckan.ymparisto.fi/dataset/corine-maanpeite-2018\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Additionally, CORINE data were utilised to classify logger locations into six distinct land cover classes, which were used exclusively for result classification. These classes were open wetland (19 sites, \u003cem\u003eCarex\u003c/em\u003e spp., \u003cem\u003eSphagnum\u003c/em\u003e spp.), transitional woodland/shrub (16 sites, \u003cem\u003ecanopy cover\u0026thinsp;\u0026lt;\u0026thinsp;10%\u003c/em\u003e), tundra (26 sites, \u003cem\u003eEmpetrum nigrum, Betula nana\u003c/em\u003e, bare-ground), deciduous forest (7 sites, \u003cem\u003eBetula pubescens, Populus tremula\u003c/em\u003e), coniferous forest (87, \u003cem\u003ePicea abies, Pinus sylvestris\u003c/em\u003e), mixed forest (31, \u003cem\u003eB. pub, Picea abies, Pop.tremula\u003c/em\u003e). The explanatory variables were resampled to a common 10 m x 10 m grid irrespective of their original resolution. The environmental values for the points were derived from the resampled rasters to ensure they formed a coherent dataset.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of environmental variables between the logger locations (n\u0026thinsp;=\u0026thinsp;193).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEnvironmental variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElevation (m.a.s.l)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRadiation (kJ/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTopographic positon index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSlope (mrad)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCanopy cover (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWater cover (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWetland cover (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMEAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e376.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6637.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-41.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e703.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMEDIAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e371.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6693.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-43.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e449.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e268.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4985.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-531.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e557.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7527.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e664.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3262.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRANGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e289.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2542.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1196.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3220.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTANDARD DEVITATION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e366.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e638.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategorical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMineral soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeatland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBare rock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNUMBER OF POINTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analyses and modelling\u003c/h2\u003e\u003cp\u003eThe studied days were classified into four categories, each assigned to represent a distinct synoptic condition: 1) calm (wind speed\u0026thinsp;\u0026lt;\u0026thinsp;4.5 m/s) and clear (oktas\u0026thinsp;\u0026lt;\u0026thinsp;4.5) (30 days); 2) calm and cloudy (oktas\u0026thinsp;\u0026gt;\u0026thinsp;4.5) (26 days); 3) windy (speed\u0026thinsp;\u0026gt;\u0026thinsp;4.5) and clear (13 days), and 4.) windy and cloudy (31 days). The spatial temperature range (i.e. spatial temperature heterogeneity) was calculated for both maximum and minimum temperatures, and the average value was determined for each synoptic condition. In addition, the average daily spatial temperature heterogeneity across land cover classes was calculated in different synoptic conditions. To investigate variability in extreme temperatures across land cover types, we also determined the average minimum and maximum temperature for each logger over the study period.\u003c/p\u003e\u003cp\u003eGeneralized additive model (Hastie and Tibshirani \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; later GAM) with temporal autocorrelation structure was used to investigate the effect that cloud cover and wind velocity has on spatial heterogeneity. The GAM model included cloud cover, wind velocity, their interaction, and mean air temperature.\u003c/p\u003e\u003cp\u003eGeneralized boosted model (Ridgeway \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; later GBM, interaction depth 3, number of trees 3000) was applied to investigate the relationship between response (T\u003csub\u003emin\u003c/sub\u003e and T\u003csub\u003emax\u003c/sub\u003e ) and the predictor variables. Models were trained separately for each day, and the results were subsequently combined and analyzed by the prevailing synoptic condition. Response shape curve types (i.e. describing the main direction of influence) were determined by dividing the predictor data into three equal-sized classes and comparing the mean values of each class to one another. Furthermore, using the best iteration of the model, T\u003csub\u003emin\u003c/sub\u003e and T\u003csub\u003emax\u003c/sub\u003e values were predicted across the study area, generating a temperature surface for each day from which the average temperature variation (i.e temperature heterogeneity) by each synoptic condition was calculated.\u003c/p\u003e\u003cp\u003eEvaluation of the GBM models\u0026rsquo; prediction performance was done by using leave-one-out cross validation, and calculating Pearson\u0026rsquo;s correlation coefficients and root mean square errors (RMSE) between observed and predicted values. To better categorize and assess the results by the synoptic conditions, the generalized least squares model (Pinheiro et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; later GLS) was fitted in the results to determine whether the temperatures had statistically significant differences between the synoptic conditions. In the GLS model, temporal autocorrelation between consecutive days was explicitly accounted for. The model was applied to the minimum and maximum temperature range, as well as cross-validated correlation coefficients and RMSE values. Additionally, a simple linear model was fitted to examine the effect of cloud cover and wind speed on the calculated cross-validation results.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Observed temperature variability\u003c/h2\u003e\u003cp\u003eMicroclimatic data revealed substantial variability in near-surface temperatures throughout the 50-day observational period in both study years. Statistically significant differences in both minimum and maximum temperatures (paired \u003cem\u003et-\u003c/em\u003etest p-value\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) were observed between the years, with 2024 exhibiting minimum temperatures that were on average 0.7\u0026deg;C warmer and maximum temperatures 1.5\u0026deg;C on average higher than 2023. Daily minimum temperatures ranged from \u0026minus;\u0026thinsp;6.1 to 14.5\u0026deg;C (average 7.7\u0026deg;C) in 2023 and \u0026minus;\u0026thinsp;1.6 to 16.2\u0026deg;C (average 8.4\u0026deg;C) in 2024, while daily maximum temperatures varied from 7.0 to 38.5\u0026deg;C (average 19.6\u0026deg;C) in 2023 and 9.8 to 39.8\u0026deg;C (average 21.5\u0026deg;C) in 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.). Minimum temperatures primarily reflect the nighttime conditions within the landscape, whereas maximum temperature observations typically occur in the afternoon, with relatively large day-to-day variation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial temperature heterogeneity differed significantly (GLS model p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, see appendix 1) between synoptic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant differences were observed among all conditions, with the exception of differences between conditions 1 and 3, and between conditions 2 and 4, where no statistically significant differences were detected. For minimum temperatures, spatial heterogeneity varied from 2.0\u0026deg;C to 14.3\u0026deg;C, with the lowest average heterogeneity (5.3\u0026deg;C) observed on windy and cloudy days (condition 4). In contrast, the highest differences (10.1\u0026deg;C) occurred under calm and clear conditions (1). For maximum temperatures, the spatial temperature heterogeneity ranged from 2.1\u0026deg;C to 21.6\u0026deg;C. On average, heterogeneity was lowest (7.2\u0026deg;C) during windy and cloudy conditions (4), and highest (16.0\u0026deg;C) under calm and clear conditions (1).\u003c/p\u003e\u003cp\u003eSpatial heterogeneity in minimum temperatures was significantly influenced by cloud cover (GAM model Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e., p\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10⁻\u0026sup1;⁶) and the interaction between cloud cover and wind velocity (p\u0026thinsp;=\u0026thinsp;0.00178), with an adjusted R\u0026sup2; of 0.71. Similarly, for maximum temperatures spatial heterogeneity was significantly affected by cloud cover (p\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10⁻\u0026sup1;⁶) and mean air temperature (p\u0026thinsp;=\u0026thinsp;0.00211), with an adjusted R\u0026sup2; of 0.83.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eVariations in temperature heterogeneity were pronounced within different land cover classes under different synoptic conditions (see Online Resource 1 table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.). Along with landscape scale temperature heterogeneity, daily variation within land cover classes is greater in clear conditions (1 and 3) and smaller in cloudy conditions (2 and 4). Observed mean minimum and maximum temperatures in different land cover classes showed similar results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) highlighting the higher temperature fluctuations in maximum temperatures and more stable minimum temperature conditions. In calm and clear conditions lowest average minimum temperatures (5.4\u0026deg;C) were observed in open wetlands, while the highest minimum temperatures were observed in tundra (8.8\u0026deg;C). In windy and cloudy conditions, the average minimum temperatures were highest in coniferous forest (8.7\u0026deg;C) and lowest in transitional woodlands (8.3\u0026deg;C). Maximum temperatures in calm and clear conditions were highest in open areas such as tundra, open wetlands and transitional woodland (26.2\u0026deg;C) and lowest in coniferous forest (24.5\u0026deg;C). In windy and cloudy conditions maximum temperatures were highest in open wetland (17.5\u0026deg;C) and lowest in tundra and coniferous and mixed forest (16.1\u0026deg;C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Environmental drivers of the temperature variation\u003c/h2\u003e\u003cp\u003eStatistical modeling indicates that minimum temperatures are primarily driven by variables related to landscape topography (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Under clear conditions (1 and 3), relative importance of slope (23% in condition 1 and 20% in condition 3) and TPI (16% in condition 1 and 18% in condition 3) lead to cooling of local depressions and flat areas. In cloudy conditions (condition 2. 23% and condition 4. 34%) elevational gradient shapes the minimum temperature variation by lowering temperatures towards higher elevation. In addition to topographical variables, under clear conditions canopy cover (13% in condition 1 and 14% in condition 3) and cover of wetlands (15% in condition 1 and 13% in condition 3) had clear effect on temperatures suggesting lower temperatures to occur in areas with low canopy cover, such as near the edge of the wetlands. In turn, under windy and cloudy conditions, the effects were ambiguous.\u003c/p\u003e\u003cp\u003eMaximum temperatures under clear conditions were primarily driven by the cooling effect of canopy cover (22% in condition 1 and 21% in condition 3) and topographic radiation (18% in conditions 1 and 3). In contrast, during cloudy conditions, elevation exerts (17% in condition 2 and 24% in condition 4) a stronger cooling effect for maximum temperatures than potential solar radiation. Canopy cover showed similar responses across synoptic conditions, whereas other variables exhibited varying responses depending on the synoptic condition. For example, in cloudy conditions (2 and 4) slope angle lowers the maximum temperature but in the clear conditions (1 and 3) the effect is the opposite. For both minimum and maximum temperatures, soil type and the proportion of water bodies were the least influential variables, each showing only a minor influence on the observed temperature variation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The accuracy of temperature predictions\u003c/h2\u003e\u003cp\u003eCross-validated correlations for the minimum temperature models suggest relatively consistent prediction performance in all synoptic conditions with correlation coefficient averaging between 0.5 (in condition 2) and 0.7 (in condition 1) and RMSE averaging between 0.7\u0026deg;C (in condition 4) and 1.5\u0026deg;C (in condition 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Differences in correlations between the synoptic conditions were non-significant, whereas RMSE values were found to differ significantly (GLS model, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, see Online Resource 1 Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCorrelation coefficients for maximum temperature predictions showed more variability between the synoptic conditions (GLS, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) than minimum temperature predictions. The average correlation was 0.28 under windy and clear conditions (3) and 0.50 under windy and cloudy conditions (4). Furthermore, the average RMSE values changed significantly (GLS, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) from 3.1\u0026deg;C in calm and clear conditions (1) to 1.3\u0026deg;C under windy and cloudy conditions (4), with overall variability ranging from 0.4\u0026deg;C to 3.8\u0026deg;C. Hence, maximum temperature models demonstrate higher prediction performance under windy and cloudy conditions (4) compared to other conditions.\u003c/p\u003e\u003cp\u003eThe RMSE of the minimum temperature model exhibited a linear negative relationship with cloud cover and wind speed (R\u0026sup2; = 0.62) in a multivariate setting based on a linear regression model, with cloud cover showing a statistically significant effect (p\u0026thinsp;\u0026lt;\u0026thinsp;2e-16), while the effect of wind speed was not significant (p\u0026thinsp;=\u0026thinsp;0.117). Minimum temperature models correlation coefficient did not show a linear relationship with cloud cover and wind speed. For the maximum temperature model, RMSE had a negative linear relationship (R\u0026sup2; = 0.75), with both cloud cover (p\u0026thinsp;\u0026lt;\u0026thinsp;2e-16) and wind speed (p\u0026thinsp;=\u0026thinsp;0.0225) being statistically significant predictors. The correlation coefficient for maximum temperature yielded an R\u0026sup2; of 0.40, with cloud cover (p\u0026thinsp;=\u0026thinsp;8.4e-10) and wind speed (p\u0026thinsp;=\u0026thinsp;0.04) both demonstrating statistical significance in a multivariate linear regression model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Landscape-scale temperature heterogeneity\u003c/h2\u003e\u003cp\u003eSynoptic atmospheric condition was found to have a significant impact on landscape-scale temperature heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Predicted spatial temperature heterogeneity follows a pattern governed by the respective synoptic condition, where only minor temperature differences can be distinguished in cloudy and windy conditions, and relatively larger heterogeneity is forming under clear and calm conditions. For minimum temperatures, temperature heterogeneity (i.e. spatial range in temperatures) was found to change from 10.4\u0026deg;C in calm and clear conditions (1), to 3.3\u0026deg;C in windy and cloudy conditions (4). The synoptic condition with the largest temperature heterogeneity (condition 1, 10.4\u0026deg;C) encompasses both the coldest and warmest average values, whereas the temperature ranges of other synoptic conditions remain within these extremes. The lowest minimum temperatures occurred in the conditions with low cloud cover (1.7\u0026deg;C in conditions 1 and 3.0\u0026deg;C in condition 3), and highest values were more evenly distributed between different conditions. Average minimum temperature over the landscape was warmest in calm and cloudy condition (2, ₸ = 9.5\u0026deg;C), and averaging ca. 7.9\u0026deg;C in other synoptic conditions. The coolest minimum temperatures were consistently predicted in wetlands across all synoptic conditions, while tundra areas also exhibited low temperatures under windy and cloudy conditions. In contrast, the highest minimum temperatures were found in tundra during clear conditions, and in forested areas on north-east slopes during cloudy conditions.\u003c/p\u003e\u003cp\u003eFor maximum temperatures, the temperature heterogeneity was greatest in calm and clear (condition 1, 13.3\u0026deg;C) and windy and clear conditions (3, 11.0\u0026deg;C). Similarly, in those conditions the average temperature over the landscape (condition 1, ₸ = 24.7\u0026deg;C and condition 3, ₸ = 23.4\u0026deg;C) were highest. Calm and cloudy, as well as windy and cloudy conditions had the lowest heterogeneity (condition 2, 6.3\u0026deg;C and condition 4, 5.0\u0026deg;C). Highest average temperatures were in clear conditions (condition 1, 31.5\u0026deg;C and condition 3, 29.2\u0026deg;C), whereas in windy and cloudy conditions temperatures were notably lower. In contrast to minimum temperatures, the highest maximum temperatures were primarily predicted in wetlands and tundra areas. However, under windy and cloudy conditions, only wetlands exhibited distinctly different temperatures in the landscape. The coolest maximum temperatures were predicted over forested areas throughout the landscape, regardless of synoptic condition.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur data revealed significant differences in microclimate conditions over the study area and their driving factors, as modified by the different synoptic atmospheric conditions. These findings support the argument that synoptic conditions play a fundamental role in forming both local minimum and maximum temperature heterogeneity within high-latitude landscapes.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Temperature heterogeneity in relation to synoptic conditions\u003c/h2\u003e\u003cp\u003eSubstantial differences in temperatures occur particularly frequently under low cloud cover conditions, and especially with maximum temperatures. Our results demonstrate that the observed differences transfer to a smaller scale as temperatures can deviate substantially in open areas as well as in different land covers, e.g. in coniferous forest and tundra. Such large temperature variability indicates the diversity of microenvironments within high-latitude landscapes with a wide spectrum of thermal niches, supporting greater ecological diversity and resilience in these landscapes (Kemppinen et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn a landscape scale, in calm and clear conditions, the high temperature heterogeneity in minimum temperatures arises from consistently lower temperatures in the wetlands, located in the local depressions and hillslopes, and higher temperatures found in the open tundra and forested areas. This variation can be partially connected to cold-air pooling, which occurs in calm cloudless nights (Burns and Chemel \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), when cooler air flows and stratifies in the local depressions and valleys (Greiger 1965; Lundquist 2008). However, in our results the temperature heterogeneity is similar in windy and clear conditions, when cold-air pooling is usually not occurring (Price et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), indicating that it is possible that areas with relatively high soil moisture experience lower temperatures regardless of the existence of cold air pool when cloud cover is minimal (Seneviratne et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schwingshackl et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn calm and clear conditions (i.e. when landscape scale temperature heterogeneity is highest) maximum temperatures are the highest in open areas, and the lowest in areas where vegetation offers shading from radiation and high soil moisture lowers the temperature extremes (Kuuluvainen and Pukkala \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Zellweger et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Słowińska et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These areas include forests, and wetlands located on the hillslopes. Furthermore, coniferous and mixed forest have the largest variation within land cover classes, showing the diversity between different parts of the boreal forest formed by varying density, canopy cover and moisture conditions.\u003c/p\u003e\u003cp\u003eConversely, in windy and cloudy conditions, the temperature differences across the landscape and within land cover classes remain notably smaller than in other synoptic conditions. Small landscape-scale temperature heterogeneity indicates that despite the varying vegetation and topographical differences the microclimates exhibit fairly similar temperature conditions. In windy and cloudy conditions, the coolest and warmest areas in the landscape form into similar locations as in calm and clear conditions, indicating that the main landscape scale temperature patterns for both minimum and maximum temperatures stay consistent between synoptic conditions.\u003c/p\u003e\u003cp\u003eBased on our research, in windy and cloudy conditions, maximum temperatures deviate least in deciduous forest where low vegetation density enhances air mixing, thus leveling off temperature differences (De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More densely vegetated areas such as coniferous forest have high maximum temperature variation even under overcast conditions possibly due to varying wind conditions affected by canopy gaps and distance from the forest edge (Chen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Pohlman et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Similarly to maximum temperatures, the temperature variation in minimum temperatures is largest in coniferous forests. However, it is important to consider that coniferous forests cover a large proportion of the study area including large variation e.g. in topography and moisture conditions, which may partially explain the magnitude of observed variations in those areas.\u003c/p\u003e\u003cp\u003eOur results suggest that near-surface landscape temperature heterogeneity is primarily driven by changes in cloud cover, which can be modified by wind speed, rather than by variations in landscape-scale wind conditions alone. Wind speeds near the ground surface can differ significantly from those at higher altitudes due to increased surface friction (He et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), potentially decoupling surface conditions from broader atmospheric dynamics.\u003c/p\u003e\u003cp\u003eOur findings show that maximum temperatures exhibit a greater temperature range across all synoptic conditions compared to minimum temperatures, consistent with Macek et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, the observed temperature range for maximum temperatures in clear conditions in our study was found to be of the same magnitude as the range observed by Greiser et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) across the variating forest density. Similarly the minimum temperature range across forests in their study is the same magnitude as the minimum temperature range in cloudy conditions observed in our study setting.\u003c/p\u003e\u003cp\u003eWhile maximum temperature averages across the research area clearly differ between synoptic conditions, minimum temperature averages remain more stable. However, the temperature range, i.e temperature heterogeneity, varies notably for both variables. This study highlights that temperature heterogeneity varies by synoptic conditions, with the most pronounced spatial differences occurring under low air mixing and high radiation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Environmental drivers and accuracy of temperature predictions\u003c/h2\u003e\u003cp\u003eThe results reveal the clear connection between microclimatic drivers and prevailing synoptic conditions. Based on our data the minimum temperatures are primarily driven by local topographical conditions (Aalto et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pike et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The prominent positive effect of slope and topographic position index on minimum temperatures under calm and clear conditions can be attributed to cold-air pooling, which forms cooler areas in even level terrain and local topographic depressions, and valleys (Lundquist 2008). In contrast, under mixed conditions, as indicated by high wind speed and cloud cover, elevational gradient through the atmospheric lapse rate is a more dominant factor causing lower temperatures in the higher areas. Furthermore, prior studies have identified a positive relationship between canopy cover and elevated minimum temperatures due to the insulating properties of the canopy (De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such an effect is evident also in our data, but with a notation that under windy and clear conditions sheltering effect requires denser canopy cover, that could enhance air mixing at the forest edge and in sparsely forested areas.\u003c/p\u003e\u003cp\u003eSimilarly to minimum temperatures, synoptic conditions modify the drivers of maximum temperatures. In our data, the cooling effect of canopy cover plays a crucial role in modulating maximum temperatures across all synoptic conditions. Canopy cover shields the ground surface from direct shortwave radiation and reduces wind speeds in areas with dense vegetation (De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), leading to lowered maximum temperatures. The effect of solar radiation on temperature variation is more profound on clear days (Greiser et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) especially on open areas (Dingman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; George et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Maclean et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas logically its importance is lowered on cloudy days. Additionally, wetlands were found to reduce maximum temperatures under clear conditions, due to the high thermal inertia of the moist peat (Brown and Williams \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). However, in cloudy conditions, this effect diminished. Moreover, our data indicate that the influence of elevation on maximum temperatures is dependent on wind speeds, while the effects of topographic radiation and other topographical features are altered by cloud cover. Therefore, for maximum temperatures, the same microclimatic drivers can lead to different spatial variations in near-surface temperatures depending on the synoptic condition.\u003c/p\u003e\u003cp\u003eThe accuracy of temperature predictions was found to vary significantly with synoptic conditions. For minimum temperature models, we identified a clear relationship between synoptic conditions and prediction performance, with variations in cloud cover accounting for a large share of the day-to-day variation in cross-validation metrics. For maximum temperatures, this relationship was even more pronounced, with cloud cover and wind speed jointly accounting for the majority of the variation in the evaluation metrics. Maximum temperature predictions showed higher accuracy under windy and cloudy conditions when the effects of large environmental gradients e.g. elevation dominate, and temperature gradients are small due to high air mixing and reduced solar heating. These findings highlight that large-scale weather conditions are key drivers of prediction accuracy, which should be taken into account particularly when modeling microclimate at sub-weekly temporal resolution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Methodological uncertainties\u003c/h2\u003e\u003cp\u003eMicroclimates result from the interaction of multiple physical processes operating across diverse temporal and spatial scales (Barry and Blanken \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These complex processes are often analyzed using explanatory variables that represent local environmental conditions, inherently introducing a degree of uncertainty into their statistical modeling related to the selected methodology of environmental data collection. Furthermore, while land cover classifications provide valuable context for understanding spatial temperature heterogeneity, they represent broad categorizations that may not fully capture the heterogeneity of microclimates within each class. For example, differences in vegetation density, soil moisture, or canopy structure within the same land cover type can lead to substantial local temperature variability that is not explicitly accounted for in the analyses (Davis et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Frenne et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Greiser et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We used the Generalized boosted model (GBM) to examine the variability of both minimum and maximum temperature variations. While the GBM algorithm is well-performing at capturing complex and non-linear relationships, its predictions are sensitive to the quality, quantity and representativeness of input variables (Kim and Park \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, the model's performance may be influenced by potential overfitting or underfitting issues during training (Kim and Park \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These factors could contribute to deviations in the modelled temperatures from actual conditions, particularly in regions with sparse observational data.\u003c/p\u003e\u003cp\u003eIn the observational data, the measurement of maximum temperatures can be challenging, because even with the use of radiation shields, temperature loggers are susceptible to overheating particularly under direct sunlight, potentially leading to an overestimation of recorded maximum temperatures and its spatial heterogeneity (Maclean et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, this is likely less of an issue here due to the northern location of the study area and thus less intensive solar radiation. This limitation in sensor performance highlights the need for cautious interpretation of temperature peaks in open, sun-exposed environments particularly under conditions of reduced air mixing. However, the magnitude of the error can be partly reduced when processing the data, for example by carefully filtering out the unrealistic values (Aalto et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur results highlight that synoptic conditions play a key role in forming near-surface temperature variability in high-latitude landscapes, greatly influencing both minimum and maximum temperatures. These conditions, categorized based on cloud cover and wind speeds, modulate the magnitude of thermal buffering, the relative importance of its environmental drivers, and the landscape-scale spatial temperature heterogeneity. Here, we showed that large temperature differences are observed at both the landscape scale and within individual land cover classes particularly under conditions of high shortwave radiation associated with low cloud cover and limited air mixing indicated by low wind speeds, whereas temperature differences tend to diminish under cloudy conditions with enhanced atmospheric mixing. By showing how microclimatic conditions are related to prevailing synoptic conditions, we can better understand the intricate links between global climate and weather systems, local environmental processes, and ecosystem functioning. This understanding provides insights for climate adaptation, biodiversity conservation, and sustainable nature management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the Maj and Tor Nessling Foundation. Juha Aalto and Miska Luoto were partly funded by the Academy of Finland (grant nr. 1342890). Johanna Lehtinen acknowledges travel funding from Nordenski\u0026ouml;ld Society.\u003c/p\u003e\u003cp\u003eConflict of interest\u003c/p\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eJohanna Lehtinen: Writing -review \u0026amp; editing, Writing - original draft, Visualization, Data curation, Methodology, Investigation, Formal analysis, Conceptualization\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis study was funded by the Maj and Tor Nessling Foundation. Juha Aalto and Miska Luoto were partly funded by the Academy of Finland (grant nr. 1342890). Johanna Lehtinen acknowledges travel funding from Nordenski\u0026ouml;ld Society.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request. Macroclimate temperature and wind speed from Finnish meteorological institute are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ilmatieteenlaitos.fi/havaintojen-lataus\u003c/span\u003e\u003cspan address=\"https://www.ilmatieteenlaitos.fi/havaintojen-lataus\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAalto J, le Roux PC, Luoto M (2014) The meso-scale drivers of temperature extremes in high-latitude Fennoscandia. Clim Dyn 42(1\u0026ndash;2):237\u0026ndash;252. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-012-1590-y\u003c/span\u003e\u003cspan address=\"10.1007/s00382-012-1590-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAalto J, Pirinen P, Kauppi PE, Rantanen M, Lussana C, Lyytik\u0026auml;inen-Saarenmaa P, Gregow H (2022) High-resolution analysis of observed thermal growing season variability over northern Europe. Clim Dyn 58(5\u0026ndash;6):1477\u0026ndash;1493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-021-05970-y\u003c/span\u003e\u003cspan address=\"10.1007/s00382-021-05970-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAalto J, Tyystj\u0026auml;rvi V, Niittynen P, Kemppinen J, Rissanen T, Gregow H, Luoto M (2022) Microclimate temperature variations from boreal forests to the tundra. Agric For Meteorol 323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2022.109037\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2022.109037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshcroft MB, Chisholm LA, French KO (2009) Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation. Glob Change Biol 15(3):656\u0026ndash;667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2008.01762.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2008.01762.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshcroft MB, Gollan JR (2012) Fine-resolution (25 m) topoclimatic grids of near-surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 \u0026times; 300 km) and diverse region. Int J Climatol 32(14):2134\u0026ndash;2148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.2428\u003c/span\u003e\u003cspan address=\"10.1002/joc.2428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshcroft MB, Gollan JR (2013) Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia. Agric For Meteorol 176:77\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2013.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2013.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshcroft MB, Gollan JR, Warton DI, Ramp D (2012) A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix. Glob Change Biol 18(6):1866\u0026ndash;1879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2012.02661.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2012.02661.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarry RG, Blanken PD (2016) Microclimate and Local Climate\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBennie J, Huntley B, Wiltshire A, Hill MO, Baxter R (2008) Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model 216(1):47\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolmodel.2008.04.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolmodel.2008.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBonan GB, Shugart HH (1989) Environmental Factors and Ecological Processes in Boreal Forests. Annu Rev Ecol Syst 20(1):1\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.es.20.110189.000245\u003c/span\u003e\u003cspan address=\"10.1146/annurev.es.20.110189.000245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown R, Williams G (1972) The freezing of peatland. Tech Paper Div Building Res Canada(No 381), 24pp\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurns P, Chemel C (2014) Evolution of Cold-Air-Pooling Processes in Complex Terrain. Boundary Layer Meteorol 150(3):423\u0026ndash;447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10546-013-9885-z\u003c/span\u003e\u003cspan address=\"10.1007/s10546-013-9885-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell GS, Norman JM, Gaylon S, Campbell, John M, Norman (1998) (2nd edn.). Springer\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Franklin JF, Spies TA (1995) Growing-season microclimatic gradients from clearcut edges into old-growth Douglas-fir forests. Ecol Appl 5(1):74\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1942053\u003c/span\u003e\u003cspan address=\"10.2307/1942053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai A, Trenberth KE, Karl TR (1999) Effects of Clouds, Soil Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range. J Clim 12(8):2451\u0026ndash;2473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0442(1999)012\u0026lt;2451:eocsmp\u0026gt;2.0.co;2\u003c/span\u003e\u003cspan address=\"10.1175/1520-0442(1999)012%3C2451:eocsmp%3E2.0.co;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaly C, Conklin DR, Unsworth MH (2010) Local atmospheric decoupling in complex topography alters climate change impacts. Int J Climatol 30(12):1857\u0026ndash;1864. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.2007\u003c/span\u003e\u003cspan address=\"10.1002/joc.2007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavis KT, Dobrowski SZ, Holden ZA, Higuera PE, Abatzoglou JT (2019) Microclimatic buffering in forests of the future: the role of local water balance. Ecography (Copenhagen) 42(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ecog.03836\u003c/span\u003e\u003cspan address=\"10.1111/ecog.03836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavy R, Esau I (2014a) Global climate models' bias in surface temperature trends and variability. Environ Res Lett 9(11):114024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/9/11/114024\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/9/11/114024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavy R, Esau I (2014b) Surface air temperature variability in global climate models. Atmospheric Sci Lett 15(1):13\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/asl2.456\u003c/span\u003e\u003cspan address=\"10.1002/asl2.456\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Frenne P, Lenoir J, Luoto M et al (2021) Forest microclimates and climate change: Importance, drivers and future research agenda. Glob Change Biol 27(11):2279\u0026ndash;2297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.15569\u003c/span\u003e\u003cspan address=\"10.1111/gcb.15569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Frenne P, Rodr\u0026iacute;guez-S\u0026aacute;nchez F, Coomes DA et al (2013) Microclimate moderates plant responses to macroclimate warming. Proceedings of the National Academy of Sciences - PNAS, 110(46), 18561\u0026ndash;18565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1311190110\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1311190110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Frenne P, Zellweger F, Rodr\u0026iacute;guez-S\u0026aacute;nchez F et al (2019) Global buffering of temperatures under forest canopies. Nat Ecol Evol 3(5):744\u0026ndash;749. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41559-019-0842-1\u003c/span\u003e\u003cspan address=\"10.1038/s41559-019-0842-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDingman JR, Sweet LC, McCullough I, Davis FW, Flint A, Franklin J, Flint LE (2013) Cross-scale modeling of surface temperature and tree seedling establishment in mountain landscapes. Ecol processes 2(1):1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/2192-1709-2-30\u003c/span\u003e\u003cspan address=\"10.1186/2192-1709-2-30\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDobrowski SZ (2011) Climatic basis for microrefugia: the influence of terrain on climate. Glob Change Biol 17(2):1022\u0026ndash;1035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2010.02263.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2010.02263.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Alonso MJ, D\u0026iacute;az-Pin\u0026eacute;s E, Ortiz C, Rubio A (2018) Disentangling the effects of tree species and microclimate on heterotrophic and autotrophic soil respiration in a Mediterranean ecotone forest. For Ecol Manag 430:533\u0026ndash;544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2018.08.046\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2018.08.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFridley JD (2009) Downscaling Climate over Complex Terrain: High Finescale (\u0026lt;\u0026thinsp;1000 m) Spatial Variation of Near-Ground Temperatures in a Montane Forested Landscape (Great Smoky Mountains). J Appl Meteorol climatology 48(5):1033\u0026ndash;1049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2008JAMC2084.1\u003c/span\u003e\u003cspan address=\"10.1175/2008JAMC2084.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeiger R (1965) The climate near the ground / Rudolf Geiger. Harvard University Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorge AD, Thompson FR, Faaborg J (2015) Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies. Remote Sens Lett 6(12):924\u0026ndash;932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/2150704X.2015.1088671\u003c/span\u003e\u003cspan address=\"10.1080/2150704X.2015.1088671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGraae BJ, De Frenne P, Kolb A et al (2012) On the use of weather data in ecologicalstudies along altitudinal and latitudinal gradients. Oikos 121(1):3\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0706.2011.19694.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0706.2011.19694.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreiser C, Hederov\u0026aacute; L, Vico G, Wild J, Macek M, Kopeck\u0026yacute; M (2024) Higher soil moisture increases microclimate temperature buffering in temperate broadleaf forests. Agric For Meteorol 345:109828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2023.109828\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2023.109828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreiser C, Meineri E, Luoto M, Ehrlen J, Hylander K (2018) Monthly microclimate models in a managed boreal forest landscape. Agric For Meteorol 250\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2017.12.252\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2017.12.252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaesen S, Lembrechts JJ, De Frenne P et al (2021) ForestTemp \u0026ndash; Sub-canopy microclimate temperatures of European forests. Glob Change Biol 27(23):6307\u0026ndash;6319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.15892\u003c/span\u003e\u003cspan address=\"10.1111/gcb.15892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHannah L, Flint L, Syphard AD, Moritz MA, Buckley LB, McCullough IM (2014) Fine-grain modeling of species\u0026rsquo; response to climate change: holdouts, stepping-stones, and microrefugia. Trends Ecol Evol (Amsterdam) 29(7):390\u0026ndash;397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2014.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2014.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHansen J, Sato M, Ruedy R (1995) Long-term changes of the diurnal temperature cycle: implications about mechanisms of global climate change. Atmos Res 37(1):175\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0169-8095(94)00077-Q\u003c/span\u003e\u003cspan address=\"10.1016/0169-8095(94)00077-Q\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHastie T, Tibshirani R (1986) Generalized Additive Models. Stat Sci 1(3):297\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/ss/1177013604\u003c/span\u003e\u003cspan address=\"10.1214/ss/1177013604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe Y, Monahan A, Jones C, Dai A, Biner S, Caya D, Winger K (2010) Probability distributions of land surface wind speeds over North America. J Geophys Res Atmos 115:D04103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2008JD010708\u003c/span\u003e\u003cspan address=\"10.1029/2008JD010708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJokinen P et al (2021) Climatological and oceanographic statistics of Finland 1991\u0026ndash;2020. \u0026ndash; Finnish Meteorological Inst\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKemppinen J, Lembrechts JJ, Van Meerbeek K et al (2024) Microclimate, an important part of ecology and biogeography\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim C, Park T (2022) Predicting Determinants of Lifelong Learning Intention Using Gradient Boosting Machine (GBM) with Grid Search. Sustainability 14(9):5256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su14095256\u003c/span\u003e\u003cspan address=\"10.3390/su14095256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuuluvainen T, Pukkala T (1989) Simulation of within-tree and between-tree shading of direct radiation in a forest canopy: effect of crown shape and sun elevation. Ecol Model 49(1):89\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0304-3800(89)90045-8\u003c/span\u003e\u003cspan address=\"10.1016/0304-3800(89)90045-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;rner C, Hiltbrunner E (2018) The 90 ways to describe plant temperature. Perspectives in plant ecology. Evol Syst 30:16\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ppees.2017.04.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ppees.2017.04.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLawrence DM, Swenson SC (2011) Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environ Res Lett 6(4):45504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/6/4/045504\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/6/4/045504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLembrechts JJ, Lenoir J, Roth N et al (2019) Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Glob Ecol Biogeogr 28(11):1578\u0026ndash;1596. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/geb.12974\u003c/span\u003e\u003cspan address=\"10.1111/geb.12974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenoir J, Graae BJ, Aarrestad PA et al (2013) Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob Change Biol 19(5):1470\u0026ndash;1481. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.12129\u003c/span\u003e\u003cspan address=\"10.1111/gcb.12129\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenoir J, Hattab T, Pierre G (2017) Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography (Copenhagen) 40(2):253\u0026ndash;266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ecog.02788\u003c/span\u003e\u003cspan address=\"10.1111/ecog.02788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLundquist JD, Cayan DR (2007) Surface temperature patterns in complex terrain: Daily variations and long-term change in the central Sierra Nevada, California. J Geophys Res D Atmos 112(D11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/a\u003c/span\u003e\u003cspan address=\"http:///a\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2006JD007561\u003c/span\u003e\u003cspan address=\"10.1029/2006JD007561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLundquist JD, Pepin N, Rochford C (2008) Automated algorithm for mapping regions of cold-air pooling in complex terrain. J Geophys Research: Atmos 113(D22). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/a\u003c/span\u003e\u003cspan address=\"http:///a\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2008JD009879\u003c/span\u003e\u003cspan address=\"10.1029/2008JD009879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacek M, Kopeck\u0026yacute; M, Wild J (2019) Maximum air temperature controlled by landscape topography affects plant species composition in temperate forests. Landscape Ecol 34(11):2541\u0026ndash;2556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-019-00903-x\u003c/span\u003e\u003cspan address=\"10.1007/s10980-019-00903-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaclean I, Suggitt A, Wilson R, Duffy J, Bennie J (2017) Fine-scale climate change: modelling spatial variation in biologically meaningful rates of warming. Glob Change Biol 23(1):256\u0026ndash;268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.13343\u003c/span\u003e\u003cspan address=\"10.1111/gcb.13343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaclean IMD, Duffy JP, Haesen S et al (2021) On the measurement of microclimate. Methods Ecol Evol 12(8):1397\u0026ndash;1410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13627\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13627\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaxwell SL, Butt N, Maron M, McAlpine CA, Chapman S, Ullmann A, Segan DB, Watson JEM (2019) Conservation implications of ecological responses to extreme weather and climate events. Divers Distrib 25(4):613\u0026ndash;625. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ddi.12878\u003c/span\u003e\u003cspan address=\"10.1111/ddi.12878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026auml;kisara K, Katila M, Per\u0026auml;saari J (2022) The multi-source national forest inventory of Finland\u0026mdash;methods and results 2017 and 2019\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNilsson M, Wardle D (2005) Understory Vegetation as a Forest Ecosystem Driver: Evidence from the Northern Swedish Boreal Forest. Frontiers in ecology and the environment, \u003cem\u003e3\u003c/em\u003e(8), 421\u0026ndash;428. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/1540-9295(2005)003[0421:uvaafe]2.0.co;2\u003c/span\u003e\u003cspan address=\"10.1890/1540-9295(2005)003[0421:uvaafe]2.0.co;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePawley S (2024) Rsagacmd: Linking R with the Open-Source 'SAGA-GIS' Software. R package version 0.4.3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stevenpawley.github.io/Rsagacmd/\u003c/span\u003e\u003cspan address=\"https://stevenpawley.github.io/Rsagacmd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePepin NC, Daly C, Lundquist J (2011) The influence of surface versus free-air decoupling on temperature trend patterns in the western United States. J Phys Res 116(D10). /a\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePepin NC, Norris JR (2005) An examination of the differences between surface and free-air temperature trend at high-elevation sites: Relationships with cloud cover, snow cover, and wind. J Geophys Research: Atmos 110(D24). 24112.24111-n/a\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePike G, Pepin N, Schaefer M (2013) High latitude local scale temperature complexity: The example of Kevo Valley, Finnish Lapland. Int J Climatol 33(8):2050\u0026ndash;2020\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinheiro J, Bates D, Core Team R (2025) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1\u0026ndash;168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=nlme\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=nlme\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePohlman C, Turton S, Goosem M (2009) Temporal variation in microclimatic edge effects near powerlines, highways and streams in Australian tropical rainforest. Agric For Meteorol 149(1):84\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2008.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2008.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePost E, Forchhammer M, Bret-Harte M, Callaghan T, Christensen TR, Elberling B, Fox A, Gilg O, Hik D, H\u0026oslash;ye T, Ims RA, Jeppesen E, Klein DR, Madsen J, McGuire AD, Rysgaard S, Schindler DE, Stirling I, Tamstorf MP et al (2009) Ecological Dynamics Across the Arctic Associated with Recent Climate Change. Science (American Association for the Advancement of Science)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrice JD, Vosper S, Brown A et al (2011) COLPEX: Field and Numerical Studies over a Region of Small Hills. Bull Am Meteorol Soc 92(12):1636\u0026ndash;1650. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2011BAMS3032.1\u003c/span\u003e\u003cspan address=\"10.1175/2011BAMS3032.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRantanen M, Karpechko AY, Lipponen A, Nordling K, Hyv\u0026auml;rinen O, Ruosteenoja K, Vihma T, Laaksonen A (2022) The Arctic has warmed nearly four times faster than the globe since 1979. Commun earth Environ 3(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43247-022-00498-3\u003c/span\u003e\u003cspan address=\"10.1038/s43247-022-00498-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRenaud V, Rebetez M (2009) Comparison between open-site and below-canopy climatic conditions in Switzerland during the exceptionally hot summer of 2003. Agric For Meteorol 149(5):873\u0026ndash;880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2008.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2008.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRidgeway G (2007) Generalized Boosted Models: A guide to the gbm package. Update, 1(1), 2007\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScherrer D, K\u0026ouml;rner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J Biogeogr 38(2):406\u0026ndash;416. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2699.2010.02407.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2699.2010.02407.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwingshackl C, Hirschi M, Seneviratne SI (2017) Quantifying Spatiotemporal Variations of Soil Moisture Control on Surface Energy Balance and Near-Surface Air Temperature. J Clim 30(18):7105\u0026ndash;7124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/jcli-d-16-0727.1\u003c/span\u003e\u003cspan address=\"10.1175/jcli-d-16-0727.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeneviratne SI, L\u0026uuml;thi D, Litschi M, Sch\u0026auml;r C (2006) Land\u0026ndash;atmosphere coupling and climate change in Europe. Nature 443(7108):205\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature05095\u003c/span\u003e\u003cspan address=\"10.1038/nature05095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSłowińska S, Słowiński M, Marcisz K, Lamentowicz M (2022) Long-term microclimate study of a peatland in Central Europe to understand microrefugia. Int J Biometeorol 66(4):817\u0026ndash;832. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00484-022-02240-2\u003c/span\u003e\u003cspan address=\"10.1007/s00484-022-02240-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuggitt AJ, Gillingham PK, Hill JK, Huntley B, Kunin WE, Roy DB, Thomas CD (2011) Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0706.2010.18270.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0706.2010.18270.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThompson RD, Russell D (1998) Thompson. Routledge\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWild J, Kopeck\u0026yacute; M, Macek M, Šanda M, Jankovec J, Haase T (2019) Climate at ecologically relevant scales: A new temperature and soil moisture logger for long-term microclimate measurement. Agric For Meteorol 268:40\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2018.12.018\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2018.12.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZellweger F, Coomes D, Lenoir J et al (2019) Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Glob Ecol Biogeogr 28(12):1774\u0026ndash;1786. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/geb.12991\u003c/span\u003e\u003cspan address=\"10.1111/geb.12991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Microclimate, Air temperature, Thermal Heterogeneity, Synoptic conditions","lastPublishedDoi":"10.21203/rs.3.rs-6784547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6784547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroclimatic heterogeneity in high-latitude landscapes plays a key role in shaping ecosystem functioning, biodiversity and resilience to environmental change. Microclimates are shaped by topography, vegetation and synoptic atmospheric conditions. However, the translation of macroscale synoptic conditions into fine-scale temperature variability has rarely been investigated empirically.\u003c/p\u003e\u003cp\u003eWe examined summer near-surface temperatures in relation to synoptic conditions (classified from calm and clear to windy and cloudy) and analyzed changes in microclimatic drivers and spatial heterogeneity. The study was conducted in a high-latitude landscape, utilizing macroclimate data from weather stations and microclimate data from a dense network of 193 stations distributed across a heterogeneous landscape characterized by a strong environmental gradient.\u003c/p\u003e\u003cp\u003eOur results revealed that fine-scale temperature heterogeneity is strongly connected to synoptic conditions. The temperature range across the landscape was highest (10\u0026deg;C T\u003csub\u003emin\u003c/sub\u003e and 16\u0026deg;C T\u003csub\u003emax\u003c/sub\u003e) on calm, clear days, whereas on windy and cloudy days differences were significantly smaller (5\u0026deg;C T\u003csub\u003emin\u003c/sub\u003e and 7\u0026deg;C T\u003csub\u003emax\u003c/sub\u003e). Macroscale variations influenced microscale temperature heterogeneity differently depending on landscape properties: topography primarily affected minimum temperatures, while both topography and vegetation properties contributed to variations in maximum temperatures.\u003c/p\u003e\u003cp\u003eOur findings highlight the variation in microclimate temperature heterogeneity across a high-latitude landscape, largely driven by synoptic conditions that regulate air mixing and radiation fluxes. By demonstrating how large-scale atmospheric patterns influence fine-scale thermal variability, our results offer deeper insight into key microclimatic drivers under different weather conditions. This understanding is crucial for predicting how microclimates will respond to climate change in high-latitude ecosystems.\u003c/p\u003e","manuscriptTitle":"Synoptic atmospheric conditions drive microclimatic variability in a high-latitude landscapes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 19:22:33","doi":"10.21203/rs.3.rs-6784547/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Major Revision","date":"2025-09-15T16:01:29+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-07-22T16:33:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T09:09:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T13:45:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2025-05-31T04:35:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"aaaf4a3d-e898-427e-a12b-2652c9543fcb","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:09:49+00:00","versionOfRecord":{"articleIdentity":"rs-6784547","link":"https://doi.org/10.1007/s00382-026-08170-8","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2026-04-20 15:58:54","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-07-24 19:22:33","video":"","vorDoi":"10.1007/s00382-026-08170-8","vorDoiUrl":"https://doi.org/10.1007/s00382-026-08170-8","workflowStages":[]},"version":"v1","identity":"rs-6784547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6784547","identity":"rs-6784547","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}