Abstract. Connections between vegetation and soil thermal dynamics are critical for estimating the vulnerability of permafrost to thaw with continued climate warming and vegetation changes. The interplay of complex biophysical processes results in a highly heterogeneous soil temperature distribution on small spatial scales. Moreover, the link between topsoil temperature and active layer thickness remains poorly constrained. Sixty-eight temperature loggers were installed at 1–3 cm depth to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover, and active layer thickness. The mean annual topsoil temperature varied between −3.7 and 0.1 ∘C within 0.5 km2. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in snow depth are strongly related with vegetation type and show complex associations with late-summer thaw depth. While cold winter soil temperature is associated with deep active layers in the following summer for lichen and dwarf shrub tundra, we observed the opposite beneath tall shrubs and tussocks. In contrast to winter observations, summer topsoil temperature is similar below all vegetation types with an average summer topsoil temperature difference of less than 1 ∘C. Moreover, there is no significant relationship between summer soil temperature or cumulative positive degree days and active layer thickness. Altogether, our results demonstrate the high spatial variability of topsoil temperature and active layer thickness even within specific vegetation types. Given that vegetation type defines the direction of the relationship between topsoil temperature and active layer thickness in winter and summer, estimates of permafrost vulnerability based on remote sensing or model results will need to incorporate complex local feedback mechanisms of vegetation change and permafrost thaw.

Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

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Abstract. Soil temperatures in permafrost regions are highly heterogeneous on small scales, in part due to variable snow and vegetation cover. Moreover, the temperature distribution that results from the interplay of complex biophysical processes remains poorly constrained. Sixty-eight temperature loggers were installed to record the distribution of topsoil temperatures at the Trail Valley Creek study site in the Northwestern Canadian Arctic. The measurements were distributed across six different vegetation types characteristic for this landscape. Two years of topsoil temperature data were analysed statistically to identify temporal and spatial characteristics and their relationship to vegetation, snow cover and active layer thickness. The mean annual topsoil temperature varied between −3.7 °C and 0.1 °C within a 1.2 km distance, with an approximate average across the landscape of −2.3 °C in 2017 and −1.7 °C in 2018. The observed variation can, to a large degree, be explained by variation in snow cover. Differences in height between vegetation types cause spatially variable snow depth during winter, leading to spatially variable snow melt timing in spring, causing pronounced differences in topsoil mean temperature and temperature variability during those time periods. Summer topsoil temperatures were quite similar below most vegetation types, and not consistently related to active layer thickness at the end of August. The small-scale pattern of vegetation and its influence on snow cover height and snow melt governs the annual topsoil temperature in this permafrost-underlain landscape.

<p>The terrestrial Arctic is subject to extreme climatic changes including increases in temperature and changes in precipitation patterns. At the heart of these developments lie changes in the land surface energy budget (SEB), which couples important earth system processes including the carbon and water cycles. However, despite the importance of the SEB, uncertainties in predictions of high-latitude SEBs persist, specifically for the SEB-components sensible and latent heat fluxes.</p><p>These uncertainties have in part been attributed to insufficient representation of Arctic vegetation in land surface components of Earth system models. However, to date, a quantitative understanding of the relative importance of Arctic vegetation for the SEB compared to other important SEB-drivers is missing.</p><p>Here we harmonize <em>in situ</em> observations from regional and global monitoring networks and provide a quantitative, circumpolar assessment of the magnitude and seasonality of observed SEB-components over treeless land >60°N in the time period 1994-2021. Using a variance partitioning analysis, we identify vegetation type as an important predictor for SEB-components during Arctic summer, in comparison with other SEB-drivers including meteorological conditions, snow cover duration, topography, and permafrost extent. Differences among vegetation types are especially high for mean summer magnitudes of sensible and latent heat fluxes, where they reach up to 8% and 9% of the potential incoming shortwave radiation, respectively. Our comparison with SEB-observations across glacier sites show that importantly, these differences among vegetation types are of similar magnitude as differences between vegetation and glacier surfaces. In our seasonality synthesis we find that net radiation (Rnet), sensible (H) and ground (G) heat fluxes have an unexpected early start of summer-regime (when daily mean values > 0 Wm<sup>-2</sup>), preceding the end of snowmelt by 56, 33, and 39 days, respectively. An elevated variability among vegetation types in the estimated onset (and end) dates of net positive Rnet and H (and G) relative to snowmelt (and onset) date, suggests that vegetation types differentially affect the distribution, trapping and density of snow cover, with important consequences for the cumulative energy fluxes from and to the atmosphere. Finally, we find that long-term, year-round SEB data series of Arctic tundra are still very scarce, especially in the Arctic regions of Eastern Canada and Western Russia.</p><p>In conclusion, we provide quantitative evidence of the importance of vegetation types for predicting Arctic surface energy budgets at circumpolar scale. We highlight that substantial differences among vegetation types are not only found for mean magnitudes but also the seasonality of surface energy fluxes. We contend that the land surface components of Earth system models should account for Arctic vegetation types to improve climate projections in the rapidly changing terrestrial Arctic.</p>

<p>Remotely sensed point clouds provide detailed structural data of landscapes and ecosystem characteristics. Especially in the analysis of forests and topography, this data type has proven its ability to derive relevant quantitative parameters such as biomass or subsidence rates. Arctic and boreal permafrost ecosystems are severely affected by climate change and resulting vegetation shifts, environmental disturbances, and permafrost thaw which lead to rapid changes in these northern environments that can be detected and characterized with point cloud datasets. In recent decades, the amount of point clouds acquired and generated in high-latitude regions by terrestrial (TLS), mobile (MLS), unmanned aerial system (UAS) based (ULS), up to airborne-based (ALS) LiDAR (Light detection and ranging) and Structure from Motion (SfM) has steadily increased. Multi-temporal datasets are available for a wide range of observation targets.</p> <p>The characteristics of the point clouds such as the extent of the area covered as well as the point density and thus the level of detail differ depending on the sensor, method, and the acquisition specifications. To use point cloud data for topographic, morphological, and forestry analysis, segmentation and classification of the point cloud into specific components such as individual trees, stems, foliage, or terrain features is essential. This is a time-consuming manual process and not feasible when addressing large datasets. Several previous analyses showed the potential for machine learning-based semantic segmentation of a single point cloud type, e.g., terrestrial LiDAR (TLS) with identical acquisition mode and sensor. We aim at an automated segmentation of different point cloud types generated by i) TLS, MLS, ULS and ALS as well as ii) SfM using (multi)spectral UAS and airborne image data to enable an analysis of Arctic and boreal permafrost ecosystems. Thereby, we will focus on the following questions:</p> <p>1) How can we reduce the time consuming process of labeling the point clouds?</p> <p>2) Can we train a model for segmentation using all point clouds or does transfer learning lead to better results?</p> <p>3) To what level of detail can we accurately segment and classify the different point cloud types?</p> <p>With this automated segmentation and classification, we aim to open up the possibility of exploiting the information contained in the multitude of point cloud data for a variety of ecological research applications.</p>

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