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Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution. ISSN:1354-1013 ISSN:1365-2486

Forest management practices modify the composition and three-dimensional structure of boreal forests. Such changes affect light regimes, microclimate and habitat availability in forests, thus contributing to the regulation of ecosystem functioning and biodiversity. Compared to traditional even-aged forestry, prevalent in Fenno-scandia since the mid-20th century, uneven-aged forestry has emerged as an alternative approach that may lead to more diverse and resilient forests, as well as a more sustainable provision of ecosystem services. However, the biophysical impacts of uneven-aged strategies have not yet been robustly quantified. Here, we present a detailed assessment of the structural characteristics of even-aged and uneven-aged boreal forest stands in southern Finland, aiming to clarify how these different management approaches regulate three-dimensional forest structure. Using terrestrial laser scanning, we derived and compared several structural traits closely linked to forest functioning. We found that, although different management types result in similar patterns in total plant material, they differ significantly on how plant material is allocated within vertical strata. Moreover, we demonstrated that commonly used structural diversity metrics cannot not capture the morphological intricacies arising from the three-dimensional dispersion of plant material inside the forest stand. Our results highlight the dynamic nature of even-aged forests, where the structural characteristics change with the rotation, whereas uneven-aged forest structures are more stable over time. Precise quantification of forest structure will help to understand which attributes impact the environmental functions and conditions that modify ecosystem services, and how these are altered with forest management.

Accurate estimates of net ecosystem CO2 exchange (NEE) would improve the understanding of natural carbon sources and sinks and their role in the regulation of global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting year-round 6 h NEE over 1996–2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve considerably the accuracy of both ML approaches compared to using only the nearest grid cell and time step. Both ML methods can explain temporal variability of NEE in the observational site of this study with meteorological predictors, but the GB method was more accurate. Only minor signs of overfitting could be detected for the GB algorithm when redundant variables were included. The accuracy of the approaches, measured mainly using cross-validated R2 score between the model result and the observed NEE, was high, reaching a best estimate value of 0.92 for GB and 0.88 for RF. In addition to the standard RF approach, we recommend using GB for modeling the CO2 fluxes of the ecosystems due to its potential for better performance.

Cold subarctic pond ecosystems will be threatened due to the increase in global temperatures. Therefore, it is important to gain more knowledge on how their biota may respond to global warming. The aim of this research was to illustrate the variability in diatom species richness and community composition along environmental gradients in northernmost Finland and Norway and reveal the variables most strongly associated with diatom biodiversity. We also compared diatom biodiversity among different biotope types and placed emphasis on the distribution of rare diatom taxa. A total of 100 subarctic ponds from the Finnish and Norwegian Lapland covering an elevational gradient of 8-887 m above sea level were sampled. A generalised linear model and hierarchical partitioning were used to identify variables associated with diatom species richness. To identify variables associated with diatom community composition, a non-metric multidimensional scaling ordination was performed. Finally, a principal component analysis and permutational multivariate analysis of variance were used for the investigation of environmental and biotic differences among biotope types. Water pH, aluminium concentration, and air temperature best explained the variation in species richness and community composition. Diatom species richness and community composition did not differ among the biotope types, but environmental variables in pine forests differed significantly from other biotope types. Many diatom species occupied the entire elevational gradient, while rare taxa seemed to appear at the ends of the elevational gradient as well around the mid-elevational zone. We found that elevation was not sufficient to explain the variation in diatom species richness and community composition, but diatom biodiversity was shaped by a variety of local-scale environmental variables, some of which are in turn correlated with elevation. Our findings suggest that local abiotic factors and especially water chemistry are important factors in describing the variability in diatom community composition and species richness in subarctic ponds. It seems that the lowest and the highest elevations provide suitable habitats for rare diatom taxa that have unique environmental preferences but some of the rare taxa were also constrained to mid elevations. Our study provides new insights into the role of local abiotic variables in shaping subarctic mountain pond biodiversity. We urge researchers not only to study elevational gradients per se in mountain areas, but also pay special attention to environmental covariates that may play a notable role in maintaining freshwater biodiversity in the subarctic.

Ground ice content of the Arctic soils largely dictates the effects of climate change-induced permafrost degradation and top ground destabilization. The current circumarctic information on ground ice content is overly coarse for many key applications, including assessments of hazards to Arctic infrastructure, while detailed data are restricted to very few regions. This study aims to address these gaps by presenting spatially comprehensive data on pore and segregated ground ice content across the Northern Hemisphere permafrost region at a 1-km resolution. First, ground ice content datasets (n=437 and 380 1-km grid cells for volumetric and gravimetric ice content, respectively) were compiled from field observations over the permafrost region. Spatial estimates of ground ice content in the near-surface permafrost north of the 30th parallel north were then produced by relating observed ground ice content to physically relevant environmental data layers of climate, soil, topography, and vegetation properties using a statistical modelling framework. The produced data show that ground ice content varies substantially across the permafrost region. The highest ice contents are found on peat-dominated Arctic lowlands and along major river basins. Low ice contents are associated with mountainous areas and many sporadic and isolated permafrost regions. The modelling yields relatively small prediction errors (a mean absolute error of 13.6 % volumetric ice content) over evaluation data and broadly congruent spatial distributions with earlier regional-scale studies. The presented data allow the consideration of ground ice content in various geomorphological, ecological, and environmental impact assessment applications at a scale that is more relevant than previous products. The produced ground ice data are available in the supplement for this study and at Zenodo https://doi.org/10.5281/zenodo.7009875 (Karjalainen et al., 2022).

Aapa mires are EU priority habitats that harbour unique biodiversity values but face increasing global change threats. Here, we investigate the exposure of red-listed aapa mire species inhabiting fen and flark fen habitats to the impacts of land use and climate change. Climate change-based threats were assessed across the aapa mire zone of Finland based on climate velocities (a metric describing the speed and direction of climate movement) measured for mean January temperature (TJan), growing degree days (GDD5) and mean annual water balance (WAB). Land use threats were assessed based on the cover of drainage ditches and three other adverse land use types around the species occurrences. Our results suggest that rapid changes in TJan may alter winter thermal conditions and thereby also species performance, particularly in the northernmost part of the aapa mire zone, where the most valuable concentrations of red-listed species are situated. The land use and GDD5 threats are highest in the southern regions where the red-listed aapa mire species occurrences are sparser but face severe risks to their persistence. In the central part of the aapa mire zone, a number of valuable aapa mires with red-listed species are exposed to both intermediately high TJan and GDD5 velocities and a spatially varying amount of ditching. Three conservation approaches to support the persistence of red-listed aapa mire species: (i) restora-tion, (ii) establishment of new protected areas, and (iii) monitoring of the key habitats, should be flexibly and complementarily applied to the preservation of aapa mires subject to accelerating climate change.

A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait–environment relationships are generalizable across plant communities and spatial scales. We collected data from 6,720 plots and 217 species across four distinct tundra regions from both hemispheres. We combined these data with over 76,000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH and potential solar radiation. Results revealed strong, consistent trait–environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems. acceptedVersion

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (

The anthropogenic climate change threatens northern permafrost environments. This compromises the existence of permafrost landforms, such as palsas and peat plateaus, which have been assessed to be critically endangered habitats. In this study, for the first time we integrated geospatial datasets and statistical methods, to model the distribution of palsas and peat plateaus across the Northern Hemisphere permafrost region. The models were calibrated using data from years 1950–2000. The effects of climate change on the future distribution of palsas were assessed by using moderate and high emission scenarios (Representative Concentration Pathways; RCP4.5 and RCP8.5, respectively) for two periods (2041–2060 and 2061–2080). Hotspots for palsas and peat plateaus occurred in Northern Europe, Western Siberia, and subarctic Canada. Climate change was predicted to cause an almost complete loss (˗98.2 %) of suitable environmental spaces under a high emissions scenario by 2061–2080, while under a moderate emissions scenario 89.3 % were predicted to disappear. The comparison with previously published thermokarst data supported our findings regarding the recent degradation of palsa and peat plateau environments. Our results fill the knowledge gaps in the distribution of the permafrost landforms in less studied areas such as Central and Eastern Siberia. In addition, the projections provide insights into the changing geoecological conditions of the circumpolar region with important implications for greenhouse gas emissions.

Aim: Geomorphological processes profoundly affect plant establishment and distributions, but their influence on functional traits is insufficiently understood. Here, we unveil trait–geomorphology relationships in Arctic plant communities. Location: High-Arctic Svalbard, low-Arctic Greenland and sub-Arctic Fennoscandia. Time period: 2011– 2018. Major taxa studied: Vascular plants. Methods: We collected field-quantified data on vegetation, geomorphological processes, microclimate and soil properties from 5,280 plots and 200 species across the three Arctic regions. We combined these data with database trait records to relate local plant community trait composition to dominant geomorphological processes of the Arctic, namely cryoturbation, deflation, fluvial processes and solifluction. We investigated the relationship between plant functional traits and geomorphological processes using hierarchical generalized additive modelling. Results: Our results demonstrate that community-level traits are related to geomorphological processes, with cryoturbation most strongly influencing both structural and leaf economic traits. These results were consistent across regions, suggesting a coherent biome- level trait response to geomorphological processes. Main conclusions: The results indicate that geomorphological processes shape plant community traits in the Arctic. We provide empirical evidence for the existence of generalizable relationships between plant functional traits and geomorphological processes. The results indicate that the relationships are consistent across these three distinct tundra regions and that geomorphological processes should be considered in future investigations of functional traits.

Accurate estimates of the net ecosystem CO2 exchange (NEE) would improve the understanding of the natural carbon sources and sinks and their role in the regulation of the global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting the year-round 6 hourly NEE over 1996–2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of the NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve the accuracy compared to using only the nearest grid cell and time step. Both ML methods can explain the temporal variability of the NEE in the observational site of this study with the meteorological predictors, but the GB method was more accurate. It was more effective in separating the important predictors from non-important ones, showing no signs of overfitting despite many redundant variables. The accuracy of the GB (RF), here measured mainly using cross-validated Pearson correlation coefficient between the model result and the observed NEE, was high (good), reaching a best estimate value of 0.96 (0.94) and the root mean square value of 1.18 µmol m⁻² s⁻¹ (1.35 µmol m⁻² s⁻¹). We recommend using GB instead of RF for modeling the CO2 fluxes of the ecosystems due to its better performance.

Harsh winters are a characteristic element of Arctic ecosystems, yet the importance of winter conditions for Arctic plant communities is still underrepresented in climate change impact studies. Here, we use fine-scale microclimate data with plot-scale records of vascular plants, lichens and bryophytes from three Arctic areas, and show that topographically heterogeneous tundra holds marked spatial variation, especially in winter temperatures. Winter conditions are the strongest environmental variable related to the fine-scale patterns in tundra vegetation, whereas summer temperatures mainly explain the coarse-scale differences among the Arctic areas. Nonetheless, the three plant groups (and also individual species) show often contrasting and complex responses along the local environmental gradients. Our results highlight the importance of local conditions and heterogeneity for tundra plants, and knowing that the Arctic winters are warming faster than summers, a greater focus should be placed on winter conditions in simulations of climate change impacts in tundra ecosystems. Winter conditions are the strongest environmental variable relating to fine-scale patterns in tundra vegetation. Vascular plants, lichens and bryophytes nonetheless show complex responses across environmental gradients, highlighting the importance of local heterogeneity.

In this paper, we present daily gridded evapotranspiration dataset covering Finland over the 40-year period 1981–2020. The dataset representing evapotranspiration from a reference surface is freely available in a 1 km × 1 km grid.

Soil moisture has a fundamental influence on the processes and functions of tundra ecosystems. Yet, the local dynamics of soil moisture are often ignored, due to the lack of fine resolution, spatially extensive data. In this study, we modelled soil moisture with two mechanistic models, SpaFHy (a catchment-scale hydrological model) and JSBACH (a global land surface model), and examined the results in comparison with extensive growing-season field measurements over a mountain tundra area in northwestern Finland. Our results show that soil moisture varies considerably in the study area and this variation creates a mosaic of moisture conditions, ranging from dry ridges (growing season average 12 VWC%, Volumetric Water Content) to water-logged mires (65 VWC%). The models, particularly SpaFHy, simulated temporal soil moisture dynamics reasonably well in parts of the landscape, but both underestimated the range of variation spatially and temporally. Soil properties and topography were important drivers of spatial variation in soil moisture dynamics. By testing the applicability of two mechanistic models to predict fine-scale spatial and temporal variability in soil moisture, this study paves the way towards understanding the functioning of tundra ecosystems under climate change. This article is protected by copyright. All rights reserved.

Climate velocity is an increasingly used metric to detect habitats, locations and regions which are exposed to high rates of climate change and displacement. In general, velocities are measured based on the assumption that future climatically similar locations can occur anywhere in the study landscape. However, this assumption can provide a biased basis for habitats which are constrained to specific environmental conditions. For such habitats, a set of selected suitable locations may provide ecologically more realistic velocity measures. Here, we focus on one environmentally constrained habitat, aapa mires, which are peat-accumulating EU Habitats Directive priority habitats, whose ecological conditions and biodiversity values may be jeopardised by climate change. We assess the climate exposure of aapa mires in Finland by developing velocity metrics separately for the whole ≥10 ha aapa mire complexes (‘aapa mires’) and their wettest flark-dominated parts (‘flark fens’). Velocity metrics were developed for six bioclimatic variables (growing degree days (GDD5), mean January and July temperatures, annual precipitation, and May and July water balance, based on climate data for 1981–2010 and for 2040–2069 as derived from global climate models for two Representative Concentration Pathways (RCP4.5 and RCP8.5). For the six variables, velocities were calculated based on the distance between climatically similar present-day and nearest future mire, divided by the number of years between the two periods, and by excluding the unsuitable matrix. Both aapa mires and flark fens showed high exposure (>5 km/year) to changes in January temperature, and often also considerably high velocities for GDD5 and July temperatures. The flark fens showed significantly higher climate velocities than the aapa mires and had a smaller amount of corresponding habitat in their surroundings. Thus, many of the studied mires, particularly the flark fens, are likely to face increased risks of exposure due to changes in winter and summer temperatures. Moreover, considerable changes in precipitation-related conditions may occur at the southern margin of the aapa mire zone. Our results show that specifically tailored climate velocity metrics can provide a useful quantitative tool to inform conservation and management decisions to support the ecosystem sustainability of this EU Habitats Directive biotope and targeting restoration towards the most vulnerable aapa mires. Highlights • Aapa mires are EU Habitats Directive priority habitats of high conservation value • Velocity assessments enable quantifying the exposure of habitats to climate change • Boreal aapa mires will face notable exposure risks due to warming climate • Wet flark fens with open water pools and their biodiversity are particularly at risk • Climate velocity information supports adaptive biodiversity conservation planning

Forest canopies buffer macroclimatic temperature fluctuations. However, we do not know if and how the capacity of canopies to buffer understorey temperature will change with accelerating climate change. Here we map the difference (offset) between temperatures inside and outside forests in the recent past and project these into the future in boreal, temperate and tropical forests. Using linear mixed-effect models, we combined a global database of 714 paired time series of temperatures (mean, minimum and maximum) measured inside forests vs. in nearby open habitats with maps of macroclimate, topography and forest cover to hindcast past (1970–2000) and to project future (2060–2080) temperature differences between free-air temperatures and sub-canopy microclimates. For all tested future climate scenarios, we project that the difference between maximum temperatures inside and outside forests across the globe will increase (i.e. result in stronger cooling in forests), on average during 2060–2080, by 0.27 ± 0.16 °C (RCP2.6) and 0.60 ± 0.14 °C (RCP8.5) due to macroclimate changes. This suggests that extremely hot temperatures under forest canopies will, on average, warm less than outside forests as macroclimate warms. This knowledge is of utmost importance as it suggests that forest microclimates will warm at a slower rate than non-forested areas, assuming that forest cover is maintained. Species adapted to colder growing conditions may thus find shelter and survive longer than anticipated at a given forest site. This highlights the potential role of forests as a whole as microrefugia for biodiversity under future climate change.

In recent years, the pan-Arctic region has experienced increasingly extreme fire seasons. Fires in the northern high latitudes are driven by current and future climate change, lightning, fuel conditions, and human activity. In this context, conceptualizing and parameterizing current and future Arctic fire regimes will be important for fire and land management as well as understanding current and predicting future fire emissions. The objectives of this review were driven by policy questions identified by the Arctic Monitoring and Assessment Programme (AMAP) Working Group and posed to its Expert Group on Short-Lived Climate Forcers. This review synthesizes current understanding of the changing Arctic and boreal fire regimes, particularly as fire activity and its response to future climate change in the pan-Arctic have consequences for Arctic Council states aiming to mitigate and adapt to climate change in the north. The conclusions from our synthesis are the following. (1) Current and future Arctic fires, and the adjacent boreal region, are driven by natural (i.e. lightning) and human-caused ignition sources, including fires caused by timber and energy extraction, prescribed burning for landscape management, and tourism activities. Little is published in the scientific literature about cultural burning by Indigenous populations across the pan-Arctic, and questions remain on the source of ignitions above 70∘ N in Arctic Russia. (2) Climate change is expected to make Arctic fires more likely by increasing the likelihood of extreme fire weather, increased lightning activity, and drier vegetative and ground fuel conditions. (3) To some extent, shifting agricultural land use and forest transitions from forest–steppe to steppe, tundra to taiga, and coniferous to deciduous in a warmer climate may increase and decrease open biomass burning, depending on land use in addition to climate-driven biome shifts. However, at the country and landscape scales, these relationships are not well established. (4) Current black carbon and PM2.5 emissions from wildfires above 50 and 65∘ N are larger than emissions from the anthropogenic sectors of residential combustion, transportation, and flaring. Wildfire emissions have increased from 2010 to 2020, particularly above 60∘ N, with 56 % of black carbon emissions above 65∘ N in 2020 attributed to open biomass burning – indicating how extreme the 2020 wildfire season was and how severe future Arctic wildfire seasons can potentially be. (5) What works in the boreal zones to prevent and fight wildfires may not work in the Arctic. Fire management will need to adapt to a changing climate, economic development, the Indigenous and local communities, and fragile northern ecosystems, including permafrost and peatlands. (6) Factors contributing to the uncertainty of predicting and quantifying future Arctic fire regimes include underestimation of Arctic fires by satellite systems, lack of agreement between Earth observations and official statistics, and still needed refinements of location, conditions, and previous fire return intervals on peat and permafrost landscapes. This review highlights that much research is needed in order to understand the local and regional impacts of the changing Arctic fire regime on emissions and the global climate, ecosystems, and pan-Arctic communities.

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately. ispartof: GLOBAL CHANGE BIOLOGY vol:27 issue:23 pages:6307-6319 ispartof: location:England status: published

The functional composition of plant communities is a critical modulator of climate change impacts on ecosystems, but it is not a simple function of regional climate. In the Arctic tundra, where climate change is proceeding the most rapidly, communities have not shifted their trait composition as predicted by spatial temperature–trait relationships. Important causal pathways are thus missing from models of trait composition change. Here, we study causes of plant community functional variation in an oroarctic tundra landscape in Kilpisjärvi, Finland. We consider the community-weighted means of plant vegetative height, as well as two traits related to the leaf economic spectrum. Specifically, we model their responses to locally measured summer air temperature, snow conditions, and soil resource levels. For each of the traits, we also quantify the importance of intraspecific trait variation (ITV) for between-community functional differences and trait–environment matching. Our study shows that in a tundra landscape (1) snow is the most influential abiotic variable affecting functional composition, (2) vegetation height is under weak local environmental control, whereas leaf economics is under strong local environmental control, (3) the relative magnitude of ITV differs between traits, and (4) ITV is not very consequential for community-level trait–environment relationships. Our analyses highlight the importance of winter conditions for community functional composition in seasonal areas. We show that winter climate change can either amplify or counter the effects summer warming, depending on the trait. Electronic supplementary material The online version of this article (10.1007/s00442-019-04508-8) contains supplementary material, which is available to authorized users.

The thermal state of permafrost affects Earth surface systems and human activity in the Arctic and has implications for global climate. Improved understanding of the local-scale variability in the global ground thermal regime is required to account for its sensitivity to changing climatic and geoecological conditions. Here, we statistically related observations of mean annual ground temperature (MAGT) and active-layer thickness (ALT) to high-resolution (∼1 km2) geospatial data of climatic and local environmental conditions across the Northern Hemisphere. The aim was to characterize the relative importance of key environmental factors and the magnitude and shape of their effects on MAGT and ALT. The multivariate models fitted well to both response variables with average R2 values being ∼0.94 and 0.78. Corresponding predictive performances in terms of root-mean-square error were ∼1.31 ∘C and 87 cm. Freezing (FDD) and thawing (TDD) degree days were key factors for MAGT inside and outside the permafrost domain with average effect sizes of 6.7 and 13.6 ∘C, respectively. Soil properties had marginal effects on MAGT (effect size =0.4–0.7 ∘C). For ALT, rainfall (effect size =181 cm) and solar radiation (161 cm) were most influential. Analysis of variable importance further underlined the dominance of climate for MAGT and highlighted the role of solar radiation for ALT. Most response shapes for MAGT ≤0 ∘C and ALT were non-linear and indicated thresholds for covariation. Most importantly, permafrost temperatures had a more complex relationship with air temperatures than non-frozen ground. Moreover, the observed warming effect of rainfall on MAGT≤0∘C reverted after reaching an optimum at ∼250 mm, and that of snowfall started to level off at ∼300–400 mm. It is suggested that the factors of large global variation (i.e. climate) suppressed the effects of local-scale factors (i.e. soil properties and vegetation) owing to the extensive study area and limited representation of soil organic matter. Our new insights into the factors affecting the ground thermal regime at a 1 km scale should improve future hemispheric-scale studies.

In recent years, the Pan-Arctic region has experienced increasingly extreme fire seasons. Fires in the northern high latitudes are driven by current and future climate change, lightning, fuel conditions, and human activity. In this context, conceptualizing and parameterizing current and future Arctic fire regimes will be important for fire and land management as well as understanding current and predicting future fire emissions. The objectives of this review were driven by policy questions identified by the Arctic Monitoring and Assessment Programme (AMAP) Working Group and posed to its Expert Group on Short-Lived Climate Forcers. This review synthesises current understanding of the changing Arctic and boreal fire regimes, particularly as fire activity and its response to future climate change in the Pan-Arctic has consequences for Arctic Council states aiming to mitigate and adapt to climate change in the north. The conclusions from our synthesis are the following: (1) Current and future Arctic fires, and the adjacent boreal region, are driven by natural (i.e., lightning) and human-caused ignition sources, including fires caused by timber and energy extraction, prescribed burning for landscape management, and tourism activities. Little is published in the scientific literature about cultural burning by Indigenous populations across the Pan-Arctic and questions remain on the source of ignitions above 70° N in Arctic Russia. (2) Climate change is expected to make Arctic fires more likely by increasing the likelihood of extreme fire weather, increased lightning activity, and drier vegetative and ground fuel conditions. (3) To some extent, shifting agricultural land use, forest-steppe to steppe, tundra-to-taiga, and coniferous-to-deciduous forest transitions in a warmer climate may increase and decrease open biomass burning. However, at the country- and landscape-scales, these relationships are not well established. (4) Current black carbon and PM2.5 emissions from wildfires above 50° N and 65° N are larger than emissions from the anthropogenic sectors of residential combustion, transportation, and flaring, respectively. Wildfire emissions have increased from 2010 to 2020, particularly above 60° N, with 56 % of black carbon emissions above 65° N in 2020 attributed to open biomass burning – indicating how extreme the 2020 wildfire season was and future Arctic wildfire seasons potential. (5) What works in the boreal zones to prevent and fight wildfires may not work in the Arctic. Fire management will need to adapt to a changing climate, economic development, the Indigenous and local communities, and fragile northern ecosystems, including permafrost and peatlands. (6) Factors contributing to the uncertainty of predicting and quantifying future Arctic fire regimes include underestimation of Arctic fires by satellite systems, lack of agreement between Earth observations and official statistics, and still needed refinements of location, conditions, and previous fire return intervals on peat and permafrost landscapes. This review highlights that much research is needed in order to understand the local and regional impacts of the changing Arctic fire regime on emissions and the global climate, ecosystems and Pan-Arctic communities.

Periglacial environments are characterized by highly dynamic landscapes. Freezing and thawing lead to ground movement, associated with cryoturbation and solifluction. These processes are sensitive to climate change and variably distributed depending on multiple environmental factors. In this study, we used multi-geometry Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to investigate the spatial distribution of the mean annual ground velocity in a mountainous landscape in Northern Norway. Statistical modeling was employed to examine how periglacial ground velocity is related to environmental variables characterizing the diverse climatic, geomorphic, hydrological and biological conditions within a 148 km(2) study area. Two-dimensional (2D) InSAR results document mean annual ground velocity up to 15 mm/yr. Vertical and horizontal velocity components in the East-West plane show variable spatial distribution, which can be explained by the characteristics of cryoturbation and solifluction operating differently over flat and sloping terrain. Statistical modeling shows that slope angle and mean annual air temperature variables are the most important environmental factors explaining the distribution of the horizontal and vertical components, respectively. Vegetation and snow cover also have a local influence, interpreted as indicators of the ground material and moisture conditions. The results show contrasted model performance depending on the velocity component used as a response variable. In general, our study highlights the potential of integrating radar remote sensing and statistical modeling to investigate mountainous regions and better understand the relations between environmental factors, periglacial processes and ground dynamics.

Coupling of soil-air temperature is fundamentally relevant to various biophysical and biogeochemical functions near the land surface. However, coupling at an interannual scale is less addressed than at finer timescales, such as diurnal and seasonal scales. Here, we show that interannual variability of soil-air temperature coupling decreased significantly during 1984-2013 across 196 stations in northern Eurasia. Coupling at a depth of 0.2 m remained significant in only 53% of stations during 1999-2013, while it was significant in 80% of stations during 1984-1998. Decreased coupling is mainly attributable to air temperature change in the snow-free season and snow cover retreat. Moreover, change in the extreme snow cover frequency also contributes to the decline, while other climate extremes are less influential. The findings can help us to understand long-term land-climate thermal interactions and to evaluate soil temperature profiles simulated by land surface models. Plain Language Summary The near-surface air temperature closely relates to soil temperature, and the strength of this relationship may be altered by the presence of other environmental elements such as snow cover, soil moisture, and vegetation. In this study, by using paired observations at 196 stations collected from 1984 to 2013 over northern Eurasia, we show that coupling of soil-air temperature was not constant and decreased over time. In the European part of the study region, the coupling consistently decreased; however, there was no clear pattern over the permafrost area. We found that this decline may relate to the increase in air temperature during the snow-free season and snow cover retreat over the period. The decreased coupling was also affected by extreme snow cover frequency. Moreover, we found that the decrease in coupling may accelerate if warming continues in the future. Under a changing climate, the compound impacts of multiple environmental changes on the interannual variability of coupling remain uncertain. The results can advance our knowledge of long-term soil-air temperature coupling and help in evaluating soil temperature profiles simulated by land surface models.

Monitoring the thermal state of permafrost (TSP) is important in many environmental science and engineering applications. However, such data are generally unavailable, mainly due to the lack of ground observations and the uncertainty of traditional physical models. This study produces novel permafrost datasets for the Northern Hemisphere (NH), including predictions of the mean annual ground temperature (MAGT) at the depth of zero annual amplitude (DZAA) (approximately 3 to 25 m) and active layer thickness (ALT) with 1 km resolution for the period of 2000–2016, as well as estimates of the probability of permafrost occurrence and permafrost zonation based on hydrothermal conditions. These datasets integrate unprecedentedly large amounts of field data (1002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modeling with an ensemble strategy. Thus, the resulting data are more accurate than those of previous circumpolar maps (bias = 0.02±0.16 ∘C and RMSE = 1.32±0.13 ∘C for MAGT; bias = 2.71±16.46 cm and RMSE = 86.93±19.61 cm for ALT). The datasets suggest that the areal extent of permafrost (MAGT ≤0 ∘C) in the NH, excluding glaciers and lakes, is approximately 14.77 (13.60–18.97) × 106 km2 and that the areal extent of permafrost regions (permafrost probability >0) is approximately 19.82×106 km2. The areal fractions of humid, semiarid/subhumid, and arid permafrost regions are 51.56 %, 45.07 %, and 3.37 %, respectively. The areal fractions of cold (≤-3.0 ∘C), cool (−3.0 ∘C to −1.5 ∘C), and warm (>-1.5 ∘C) permafrost regions are 37.80 %, 14.30 %, and 47.90 %, respectively. These new datasets based on the most comprehensive field data to date contribute to an updated understanding of the thermal state and zonation of permafrost in the NH. The datasets are potentially useful for various fields, such as climatology, hydrology, ecology, agriculture, public health, and engineering planning. All of the datasets are published through the National Tibetan Plateau Data Center (TPDC), and the link is https://doi.org/10.11888/Geocry.tpdc.271190 (Ran et al., 2021a).

The climate in the Boreal area is warming at a pace that is exceeding the global average. Both temperature and precipitation is projected to increase due to climate change. The gross primary production in the forested area is also projected to increase, as well as the soil respiration. The burned area is sensitive to the meteorological forcing and the risk of ignition depends on the amount and properties of the litter. Overall climate change has a potential to increase the fire risk in the Boreal forests.The effects of projected climate change on forest fires in Fennoscandia, and in parts of Russia adjacent to Finland, were simulated with the JSBACH-SPITFIRE. JSBACH is the land model in the Earth system models of the Max-Planck Institute for Meteorology. SPITFIRE is a mechanistic fire model, driven by meteorology, vegetation cover, fuel load and fuel properties. The model simulates fire risk, number of fires and burned area fraction. SPITFIRE uses ignition rates and distinguishes between ignition events caused by lightning and humans. Ignition events result in fire only when enough fuel is present, and the fuel is sufficiently dry. The JSBACH-SPITFIRE model was driven by downscaled and bias corrected meteorological data from the EURO-CORDEX initiative, for the period from 1951 to 2100. The model domain was the land area within 55-71°N and 5-38°E. A subset of the EUR-44 domain was regridded to 0.5° resolution for our model domain. The global driving models used for producing the EURO-CORDEX data used here were CanESM2, CNRM-CM5, MIROC5. We selected driver models that represent mid-range regarding the projected change in temperature and precipitation for Finland under RCP4.5 and RCP8.5. We used daily bias corrected data of precipitation and temperature from 1951 to 2100 for both RCP4.5 and RCP8.5 climate change projections. In addition, daily data of relative humidity, wind speed, longwave and shortwave radiation were used for the historical (1951-2005) and scenario period (2006-2100).Preliminary results indicate that the increase in temperature, which affects the drying rate of the fuel, is the major factor for driving the changes in forest fires in the simulations.