Long-term atmospheric CO2 concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site-years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Abstract. Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

Abstract 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.

Abstract. Snowpack microstructure controls the transfer of heat to, as well as the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow micropenetrometer profiles allowed for snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n=1050) compared to traditional snowpit observations (3 cm vertical resolution; n=115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE=5.8 ∘C). Two different approaches were taken to reduce this bias: alternative parameterisations of snow thermal conductivity and the application of a correction factor. All the evaluated parameterisations of snow thermal conductivity improved simulations of wintertime soil temperatures, with that of Sturm et al. (1997) having the greatest impact (RMSE=2.5 ∘C). The required correction factor is strongly related to snow depth (R2=0.77,RMSE=0.066) and thus differs between the two snow seasons, limiting the applicability of such an approach. Improving simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures are an important control on subnivean soil respiration and hence impact Arctic winter carbon fluxes and budgets.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Abstract 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.

This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice‐rich permafrost‐underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert‐driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1‐year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web‐based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area‐based assessment. Using the expert‐driven quantification as reference, we validate the other methods, including in‐situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert‐driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in‐situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark‐based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long‐term monitoring of permafrost subsidence.

Thaw slumps in ice‐rich permafrost can retreat tens of metres per summer, driven by the melt of subaerially exposed ground ice. However, some slumps retain an ice‐veneering debris cover as they retreat. A quantitative understanding of the thermal regime and geomorphic evolution of debris‐covered slumps in a warming climate is largely lacking. To characterize the thermal regime, we instrumented four debris‐covered slumps in the Canadian Low Arctic and developed a numerical conduction‐based model. The observed surface temperatures 20°C and steep thermal gradients indicate that debris insulates the ice by shifting the energy balance towards radiative and turbulent losses. After the model was calibrated and validated with field observations, it predicted sub‐debris ice melt to decrease four‐fold from 1.9 to 0.5 m as the thickness of the fine‐grained debris quadruples from 0.1 to 0.4 m. With warming temperatures, melt is predicted to increase most rapidly, in relative terms, for thick (~0.5‐1.0 m) debris covers. The morphology and evolution of the debris‐covered slumps were characterized using field and remote sensing observations, which revealed differences in association with morphology and debris composition. Two low‐angle slumps retreated continually despite their persistent fine‐grained debris covers. The observed elevation losses decreased from ~1.0 m/yr where debris thickness ~.2 m to 0.1 m/yr where thickness ~1.0 m. Conversely, a steep slump with a coarse‐grained debris veneer underwent short‐lived bursts of retreat, hinting at a complex interplay of positive and negative feedback processes. The insulative protection and behaviour of debris vary significantly with factors such as thickness, grain size and climate: debris thus exerts a fundamental, spatially variable influence on slump trajectories in a warming climate.

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

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.

Knowledge of soil moisture conditions is important for modeling soil temperatures, as soil moisture influences the thermal dynamics in multiple ways. However, in permafrost regions, soil moisture is highly heterogeneous and difficult to model. Satellite soil moisture data may fill this gap, but the degree to which they can improve permafrost modeling is unknown. To explore their added value for modeling soil temperatures, we assimilate fine‐scale satellite surface soil moisture into the CryoGrid‐3 permafrost model, which accounts for the soil moisture's influence on the soil thermal properties and the surface energy balance. At our study site in the Canadian Arctic, the assimilation improves the estimates of deeper (>10 cm) soil temperatures during summer but not consistently those of the near‐surface temperatures. The improvements in the deeper temperatures are strongly contingent on soil type: They are largest for porous organic soils (30%), smaller for thin organic soil covers (20%), and they essentially vanish for mineral soils (only synthetic data available). That the improvements are greatest over organic soils reflects the strong coupling between soil moisture and deeper temperatures. The coupling arises largely from the diminishing soil thermal conductivity with increasing desiccation thanks to which the deeper soil is kept cool. It is this association of dry organic soils being cool at depth that lets the assimilation revise the simulated soil temperatures toward the actually measured ones. In the future, the increasing availability of satellite soil moisture data holds promise for the operational monitoring of soil temperatures, hydrology, and biogeochemistry.

Abstract. This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).