Tundra Research Articles

Mapping and assessment of ecological vulnerability to wildfires in Europe

BackgroundWildfires play a significant and complex role in ecosystems, influencing various aspects of their functioning and structure. These natural disturbances can positively and negatively impact ecosystems, shaping landscapes, nutrient cycles, biodiversity, and ecological processes. This study focuses on assessing and integrating the different factors that affect the ecological vulnerability to wildfires at the European scale. Our methodology follows three steps. Firstly, ecological values based on biological distinctiveness and conservation status were estimated to understand pre-fire conditions better. Secondly, we obtain vegetation’s coping capacity (or resistance) to the impacts of fire, considering the functional traits of plants and fire characteristics through a fire extreme scenario. Finally, post-fire recovery time was calculated by considering the species-specific recovery time, recovery starting time, growth recovery rate, and the environmental constraints affecting the optimal vegetation response. These three variables were combined using a dynamic model that assumed the change of value due to wildfires integrated throughout the recovery time.ResultsOur results indicate that the tundra biome emerges as the most ecologically vulnerable to fire, primarily due to its high ecological values and long recovery time, which outweigh its moderate coping capacity. Following closely, the temperate conifer forests also exhibit high vulnerability driven by their high recovery time, despite moderate ecological and coping capacity values. The boreal forests rank next, with moderate vulnerability due to their long recovery time and moderate coping capacity. The Mediterranean region, although having moderate ecological values and recovery time, shows a notable vulnerability influenced by lower coping capacity. The temperate broadleaf and mixed forests demonstrate relatively lower vulnerability owing to their balanced ecological values, moderate recovery time, and substantial coping capacity. Lastly, the temperate grasslands, savannas, and shrublands are the least vulnerable, benefiting from lower ecological values and the fastest recovery time, alongside moderate coping capacity, which collectively reduce their overall fire vulnerability.Furthermore, we found that coping capacity is the factor that most influenced ecological vulnerability to wildfires.ConclusionsThe study identifies key zones for European or national policies on fire prevention and post-wildfire regeneration. It offers insights into effective forest management and conservation policies, applicable to current conditions. Additionally, the methods can predict future ecological vulnerability to wildfires based on climatic and socio-economic trends.

Was the steppe bison a grazing beast in Pleistocene landscapes?

The history and palaeoecology of the steppe bison (Bison priscus) remain incompletely understood despite its widespread distribution. Using dental microwear textural analysis (DMTA) and vegetation modelling, we reconstructed the diet and assessed the habitat of steppe bison inhabiting Eurasia and Alaska since the Middle Pleistocene. During the Late Pleistocene, steppe bison occupied a variety of biome types: from the mosaic of temperate summergreen forest and steppe/temperate grassland (Serbia) to the tundra biomes (Siberia and Alaska). Despite the differences in the identified biome types, the diet of steppe bison did not differ significantly among populations in Eurasia. DMTA classified it as a mixed forager in all populations studied. The DMTA of Bb1 bison-a recently identified genetically extinct sister-clade of Bison bonasus-was typical of a highly grazing bovid species and differed from all B. priscus populations. The results of the study temper the common perception that steppe bison were grazers in steppe habitats. The dietary plasticity of the steppe bison was lower when compared with modern European bison and may have played an important role in its extinction, even in the stable tundra biome of eastern Siberia, where it has survived the longest in all of Eurasia.

Freshwater runoff on the east Greenland shelf

The Greenland Ice Sheet releases fresh water from ice melt, tundra snow melt, and solid ice at an increasing rate during recent decades resulting in an increasing amount of freshwater runoff into the ocean. As a result, freshwater runoff is changing the continental shelf circulation by increasing the amount of fresher water on the shelf that may then enter the deep ocean. Observational studies show that dense water does traverse the east Greenland shelf near the ocean bottom but it is unclear to what extent the transport of near surface fresh water, as a result of runoff, reaches deeper water and enters the Irminger Basin. Using 4 km resolution nested numerical model simulations with and without freshwater runoff, we show freshwater runoff increases salinity variability with increased baroclinicity. While higher salinity variability and baroclinicity suggest a greater potential for water mass exchange across the East Greenland Current, most freshwater runoff along east Greenland remains on the shelf. From freshwater runoff alone, salinity and salt mass decreases by 0.22% on the continental shelf compared to a 0.01% in the rest of the Nordic Seas. There is a 0.05% reduction in salt mass on the Greenland shelf region that makes up 8% of the simulation domain, suggesting little water exits the shelf along the east coast of Greenland. The largest reduction in salt mass occurs around Iceland, where substantial freshwater runoff exists. A calculation of baroclinic conversion rate suggests likely pathways for runoff to exit the continental shelf and enter deep water in the Denmark Strait and over the Greenland/Scottland ridge east of Iceland. Most of the fresh water, however, released along the east coast of Greenland continues towards Cape Farewell, outside our modeling domain, and into the Labrador Sea.

Postglacial vegetation and climate change in the Lake Onega region of eastern Fennoscandia derived from a radiocarbon-dated pollen record

With its numerous environmental archives stored in lake and peat sediments and relatively low human pressure, the Lake Onega region in eastern Fennoscandia is regarded as a particularly promising area for studying past changes in vegetation and climate since the Lateglacial period. The 885-cm-long sediment core RZ19 (62°27′53″N, 34°26′4″E) was collected from Razlomnoe Peat on the northern shore of Lake Onega in 2019, radiocarbon-dated and analysed for pollen and cryptogam spores. The age-depth model suggests continuous sedimentation since ca. 11,800 a BP (all ages given in years (a) or kiloyears (ka) before present (BP) with BP referring to 1950 CE). The results of pollen analysis and pollen-based biome reconstruction show rapid afforestation of the area in the Early Holocene, although the scores of the tundra biome remain relatively high prior to ca. 11,450 a BP, suggesting that the vegetation was likely more open than today. Between 8300 and 8000 a BP, Betula sect. Albae shows a marked increase in pollen percentage, while Pinus sylvestris experiences a marked decrease. These changes coinciding with the 8.2 ka BP cooling event indicate less favourable conditions for Scots pine while being beneficial for fast-growing birch. The transition from the Early to Middle Holocene (i.e. from Greenlandian to Northgrippian) is marked by an increase in pollen productivity, spread of Picea and further afforestation of the area. The decrease in arboreal and Picea pollen percentages and the abrupt increase in landscape openness after ca. 4000 a BP mark the onset of the Late Holocene (i.e. Northgrippian-Meghalayan transition) and likely reflect the combined effect of insolation-induced temperature decrease and associated paludification and forest retreat rather than a decrease in atmospheric precipitation and/or spread of Late Neolithic agriculture.

Net Primary Production of Ecoregions Across North America in Response to Drought and Wildfires From 2015 to 2022

AbstractEcosystem models are valuable tools to make climate‐related assessments of change when ground‐based measurements of water and carbon fluxes are not adequate to realistically capture regional variability. The Carnegie‐Ames‐Stanford Approach (CASA) is one such model based on satellite observations of monthly vegetation cover to estimate net primary production (NPP) of terrestrial ecosystems. CASA model predictions from 2015 to 2022 revealed several notable high and low periods in growing season NPP totals in certain biomes. Both Temperate Broadleaf and Boreal Forest production shifted from relatively high average NPP values in 2015 through 2019 to lower levels in 2020, typically representing a loss of 10%–14% of growing season NPP flux. This rapid decline in growing season NPP from 2019 to 2020–2021 was also estimated for the Temperate Grasslands and Savanna, Temperate Conifer Forest, and Tundra biomes. In contrast to the climate patterns in the temperate biomes that developed into severe widespread drought in 2020 and 2021 due to low precipitation totals and extreme hot temperatures, growing season NPP in the Tundra biome was depressed in these same years by colder temperature induced drought conditions at the high latitudes of North America. Drought severity classes were closely associated with different levels of decline in NPP in most biomes. Trends in NPP in areas of the largest wildfires in North America that burned between 2012 and 2021 were examined to assess recovery of vegetation and the resiliency of ecosystems during extreme drought periods.

Preferential Hydrologic States and Tipping Characteristics of Global Surface Soil Moisture

AbstractA dynamic transition in soil hydrologic states through meteorological variability and terrestrial feedback governs soil‐vegetation‐climate (SVC) interactions, constrained by critical soil moisture (SM) thresholds. However, observational and scaling constraints limit critical SM threshold estimation at the remote‐sensing (RS) footprint scale. Using global surface SM (θRS) from NASA’s Soil Moisture Active Passive (SMAP) satellite, we characterize the seasonal preferential hydrologic states of θRS and derive three tipping characteristics to estimate the intensity (Mean Tipping Depth, ), frequency (Tipping Count, η), and duration (Mean Tipped Time, ) of the excursion of θRS from wet‐ to dry‐average conditions. The preferential state provides the seasonally dominant hydrological states of θRS, while tipping characteristics capture the ecosystem linkages of the dynamic transition in θRS hydrologic states. Globally, θRS predominantly exhibits a (unimodal) dry‐preferential state, especially over arid/semi‐arid drylands and a unimodal wet‐preferential θRS state in high‐latitude boreal forests and tundra biomes. Prevalence of (bimodal) bistable θRS state overlaps with regions of strong positive SM‐precipitation coupling and monsoonal climate in semi‐arid/subhumid climates. Seasonal preferential hydrologic states co‐vary with the regional variability in plant water stress threshold and land‐atmospheric coupling strength. Tipping characteristics of θRS show sensitivity to intra‐biome variability in SVC coexistence patterns and display high skill in partitioning global ecoregions. While and η are climate‐controlled, is moderated by soil and vegetation through their influence over θRS drydown during water‐limited evapotranspiration. Preferential states and tipping characteristics find applications in quantifying SVC coexistence patterns, climate model diagnosis, and assessing ecosystem sensitivity to climate change.

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m3. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m3 and a total area of 0.025 km2. ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m3 and 1,160,000 ± 85,000 m3, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Analysis of taiga and tundra lake browning trends from 2002 to 2021 using MODIS data

Lakes in taiga and tundra regions may be silently undergoing changes due to global warming. One of those changes is browning in lake color. The browning interacts with the carbon cycle, ecosystem dynamics, and water quality in freshwater systems. However, spatiotemporal variabilities of browning in these regions have not been well documented. Using MODIS remote sensing reflectance at near ultraviolet wavelengths from 2002 to 2021 on the Google Earth Engine platform, we quantified long-term browning trends across 7616 lakes (larger than 10 km2) in taiga and tundra biomes. These lakes showed an overall decreased trend in browning (Theil-Sen Slope = 0.00015), with ∼36% of these lakes showing browning trends, and ∼1% of these lakes showing statistically significant (p-value <0.05) browning trends. The browning trends more likely occurred in small lakes in high latitude, low ground ice content regions, where air temperature increased and precipitation decreased. While temperature is projected to increase in response to climate change, our results provide one means to understand how biogeochemical cycles and ecological dynamics respond to climate change.

Soil microenvironmental variation drives below‐ground trait variation and interacts with macroclimate to structure above‐ground trait variation of arctic shrubs

Abstract Intraspecific trait variation can influence plant performance in different environments and may thereby determine the ability of individual plants to respond to climate change. However, our understanding of its patterns and environmental drivers across different spatial scales is incomplete, especially in understudied regions like the Arctic. To fill this knowledge gap, we examined above‐ground and below‐ground traits from three shrub taxa expanding across the tundra biome and evaluated their relationships with multiple microenvironmental and macroclimatic factors. The traits reflected plant size and structure (plant height, leaf area and root to shoot ratio), leaf economics (specific leaf area, nitrogen content), and root economics and collaboration with mycorrhizal fungi (specific root length, root tissue density, nitrogen content, and ectomycorrhizal colonisation intensity). We also measured leaf and root δ15N and leaf δ13C to characterise nitrogen source and acquisition pathways and plant water stress. Traits were measured in replicated plots (N = 135) varying in soil microclimate, thaw depth and organic layer thickness established across five sites spanning a macroclimate gradient in northern Alaska. This hierarchical design allowed us to disentangle the independent and combined effects of fine‐scale and broad‐scale factors on intraspecific trait variation. We found substantial intraspecific variation at fine spatial scales for most traits and less variation along the macroclimate gradient and between shrub taxa. Consistent with these patterns, microenvironmental factors, mainly soil moisture and thaw depth, interacted with macroclimate, mainly climatic water deficit, to structure size‐structural and leaf trait variation. In contrast, most root traits responded additively to thaw depth and macroclimate. Synthesis. Our results demonstrate that above‐ground and below‐ground tundra shrub traits respond differently to microenvironmental and macroclimatic variation. These differing responses contribute to substantial trait variation at fine spatial scales and may decouple above‐ground and below‐ground trait responses to climate change.

Soil freeze–thaw cycles affect spring phenology by changing phenological sensitivity in the Northern Hemisphere

The use of frozen soil-vegetation feedback for predictive models is undergoing enormous changes under rapid climate warming. However, the influence of soil freeze–thaw (SFT) cycles on vegetation phenology and the underlying mechanisms remain poorly understood. By synthesizing a variety of satellite-derived data from 2002 to 2021 in the Northern Hemisphere (NH), we demonstrated a widespread positive correlation between soil thawing and the start of the growing season (SOS). Our results also showed that the SFT cycles had a significant impact on vegetation phenology mainly by altering the phenological sensitivities to daytime and nighttime temperatures, solar radiation and precipitation. Moreover, the effects of SFT cycles on the sensitivity of the SOS were more pronounced than those on the sensitivity of the end of the growing season (EOS) and the length of growing season (LOS). Furthermore, due to the degradation of frozen soil, the changes in phenological sensitivity in the grassland and tundra biomes were significantly larger than those in the forest. These findings highlighted the importance of incorporating the SFT as an intermediate process into process-based phenological models.

Pollen analysis in reconstruction of paleo vegetation on the southwestern part of the Yamal Peninsula

The aim of this study is to explore the pollen palynomorphs from the bottom sediments of an unnamed tundra lake whilst attempt to reconstruct the past vegetation and paleoecological conditions for the southwestern part of the Yamal Peninsula. 34 pollen (11 boreal, 18 arboreal) and 5 spore palynomorphs were identified. Pollen spectra characterized by a predominance of arboreal pollen (Betula sect. Nanae, Betula sect. Albae, Alnus). It was found in the range of ~6000-5100 cal. yr BP during the Holocene optimum birch forest and shrubby vegetation with bogs has been developed in the lake’s catchment area. The tundra biome was dominant, however, around ~5800 cal. yr BP, the indicator of the taiga biome increases to maximal values. Since the beginning of the Subatlantic period (~2200 cal. yr BP), local vegetation began to evolve into the modern southern tundra with shrubs (dwarf birch, alder, willow), and sedge-sphagnum bogs in lowlands.

Maps of active layer thickness in northern Alaska by upscaling P-band polarimetric synthetic aperture radar retrievals

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5–9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11–12 cm and bias of 2.7–6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow

Abstract. Improved modelling of snow emissivity is needed to improve the assimilation of surface-sensitive atmospheric sounding observations from satellites in polar regions for numerical weather prediction (NWP). This paper evaluates emissivity simulated with the Snow Microwave Radiative Transfer (SMRT) model using observations of Arctic tundra snow at frequencies between 89 and 243 GHz. Measurements of snow correlation length, density and layer thickness were used as input to SMRT, and an optimisation routine was used to assess the impact of each parameter on simulations of emissivity when compared to a set of Lambertian emissivity spectra, retrieved from observations of tundra snow from three flights of the Facility for Airborne Atmospheric Measurements (FAAM) aircraft. Probability distributions returned by the optimisation routine demonstrate parameter uncertainties and the sensitivity of simulations to the different snow parameters. Results showed that SMRT was capable of reproducing a range of observed emissivities between 89 and 243 GHz. Varying correlation length alone allowed SMRT to capture much of the variability in the emissivity spectra; however, MAE (MAPE) decreased from 0.018 (3.0 %) to 0.0078 (1.2 %) overall when the thickness of the snow layers was also varied. When all three parameters were varied, simulations were similarly sensitive to both correlation length and density, although the influence of density was most evident when comparing spectra from snowpacks with and without surface snow. Simulations were most sensitive to surface snow and wind slab parameters, while sensitivity to depth hoar depended on the thickness and scattering strength of the layers above, demonstrating the importance of representing all three parameters for multi-layer snowpacks when modelling emissivity spectra. This work demonstrates the ability of SMRT to simulate snow emissivity at these frequencies and is a key step in the progress towards modelling emissivity for data assimilation in NWP.

Summer litter decomposition is moderated by scale‐dependent microenvironmental variation in tundra ecosystems

Tundra soils are one of the world's largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. The landscape‐scale controls of litter decomposition are poorly understood in tundra ecosystems, which hinders our understanding of the global carbon cycle. We examined the extent to which the thermal sum of surface air temperature, soil moisture and permafrost thaw depth influenced litter mass loss and decomposition rates (k), and at which spatial thresholds an environmental variable becomes a reliable predictor of decomposition, using the Tea Bag Index protocol across a heterogeneous tundra landscape on Qikiqtaruk–Herschel Island, Yukon, Canada. We found greater green tea litter mass loss and faster decomposition rates (k) in wetter areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness and higher surface thermal sums. We also found higher decomposition rates (k) on north‐facing relative to south‐facing aspects at microsites that were wetter rather than warmer. Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at local scales (&lt; 50 m2) better than thermal sum. Surprisingly, there was no strong control of elevation or slope on litter decomposition. Our results reveal that there is considerable scale dependency in the environmental controls of tundra litter decomposition, with moisture playing a greater role than the thermal sum at &lt; 50 m2 scales. Our findings highlight the importance and complexity of microenvironmental controls on litter decomposition in estimates of carbon cycling in a rapidly warming tundra biome.

Plant traits poorly predict winner and loser shrub species in a warming tundra biome

Climate change is leading to species redistributions. In the tundra biome, shrubs are generally expanding, but not all tundra shrub species will benefit from warming. Winner and loser species, and the characteristics that may determine success or failure, have not yet been fully identified. Here, we investigate whether past abundance changes, current range sizes and projected range shifts derived from species distribution models are related to plant trait values and intraspecific trait variation. We combined 17,921 trait records with observed past and modelled future distributions from 62 tundra shrub species across three continents. We found that species with greater variation in seed mass and specific leaf area had larger projected range shifts, and projected winner species had greater seed mass values. However, trait values and variation were not consistently related to current and projected ranges, nor to past abundance change. Overall, our findings indicate that abundance change and range shifts will not lead to directional modifications in shrub trait composition, since winner and loser species share relatively similar trait spaces.

Towards long-term records of rain-on-snow events across the Arctic from satellite data

Abstract. Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snowpack properties are changing, and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku-band radar (scatterometer) has been applied so far across the entire Arctic. Data availability at this frequency is limited, however. The utility of other frequencies from passive and active systems needs to be explored to develop a concept for long-term monitoring. The latter are of specific interest as they can be potentially provided at higher spatial resolution. Radar records have been shown to capture the associated snow structure change based on time-series analyses. This approach is also applicable when data gaps exist and has capabilities to evaluate the impact severity of events. Active as well as passive microwave sensors can also detect wet snow at the timing of an ROS event if an acquisition is available. The wet snow retrieval methodology is, however, rather mature compared to the identification of snow structure change since ambiguous scattering behaviour needs consideration. C-band radar is of special interest due to good data availability including a range of nominal spatial resolutions (10 m–12.5 km). Scatterometer and SAR (synthetic aperture radar) data have therefore been investigated. The temperature dependence of C-band backscatter at VV (V – vertical) polarization observable down to −40 ∘C is identified as a major issue for ROS retrieval but can be addressed by a combination with a passive microwave wet snow indicator (demonstrated for Metop ASCAT – Advanced Scatterometer – and SMOS – Soil Moisture and Ocean Salinity). Results were compared to in situ observations (snowpit records, caribou migration data) and Ku-band products. Ice crusts were found in the snowpack after detected events (overall accuracy 82 %). The more crusts (events) there are, the higher the winter season backscatter increase at C-band will be. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to ice crust formation. SAR specifically from Sentinel-1 is promising regarding ice layer identification at better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record, but the consideration of performance differences due to spatial and temporal cover, as well as microwave frequency, is crucial. Retrieval is most robust in the tundra biome, where results are comparable between sensors. Records can be used to identify extremes and to apply the results for impact studies at regional scale.

Carbon uptake in Eurasian boreal forests dominates the high-latitude net ecosystem carbon budget.

Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003-2015) vegetation gross primary productivity (GPP), ecosystem respiration (Reco ), net ecosystem CO2 exchange (NEE; Reco - GPP), and terrestrial methane (CH4 ) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km2 flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of -850 Tg CO2 -C year-1 . Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH4 emissions from tundra and boreal wetlands (not accounting for aquatic CH4 ) were estimated at 35 TgCH4 -C year-1 . Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change.

Radiation-constrained boundaries cause nonuniform responses of the carbon uptake phenology to climatic warming in the Northern Hemisphere.

Climatic warming has lengthened the photosynthetically active season in recent decades, thus affecting the functioning and biogeochemistry of ecosystems, the global carbon cycle and climate. Temperature response of carbon uptake phenology varies spatially and temporally, even within species, and daily total intensity of radiation may play a role. We empirically modelled the thresholds of temperature and radiation under which daily carbon uptake is constrained in the temperate and cold regions of the Northern Hemisphere, which include temperate forests, boreal forests, alpine and tundra biomes. The two-dimensionality of the temperature-radiation constraint was reduced to one single variable, θ, which represents the angle in a polar coordinate system for the temperature-radiation observations during the start and end of the growing season. We found that radiation will constrain the trend towards longer growing seasons with future warming but differently during the start and end of season and depending on the biome type and region. We revealed that radiation is a major factor limiting photosynthetic activity that constrains the phenology response to temperature during the end-of-season. In contrast, the start of the carbon uptake is overall highly sensitive to temperature but not constrained by radiation at the hemispheric scale. This study thus revealed that while at the end-of-season the phenology response to warming is constrained at the hemispheric scale, at the start-of-season the advance of spring onset may continue, even if it is at a slower pace.

Experimental assessment of tundra fire impact on element export and storage in permafrost peatlands

Extensive studies have been performed on wildfire impact on terrestrial and aquatic ecosystems in the taiga biome, however consequences of wildfires in the tundra biome remain poorly understood. In such a biome, permafrost peatlands occupy a sizable territory in the Northern Hemisphere and present an extensive and highly vulnerable storage of organic carbon. Here we used an experimental approach to model the impact of ash produced from burning of main tundra organic constituents (i.e., moss, lichen and peat) on surrounding aquatic ecosystems. We studied the chemical composition of aqueous leachates produced during short-term (1 week) interaction of ash with distilled water and organic-rich lake water at 5 gsolid L−1 and 20 °C. The addition of ash enriched the fluid phase in major cations (i.e., Na, Ca, Mg), macro- (i.e., P, K, Si) and micronutrients (i.e., Mn, Fe, Co, Ni, Zn, Mo). This enrichment occurred over <2 days of experiment. Among 3 studied substrates, moss ash released the largest amount of macro- and micro-components into the aqueous solution. To place the obtained results in the environmental context of a peatbog watershed, we assume a fire return interval of 56 years and that the entire 0–10 cm of upper peat is subjected to fire impact. These mass balance calculations demonstrated that maximal possible delivery of elements from ash after soil burning to the hydrological network is negligibly small (<1–2 %) compared to the annual riverine export flux and element storage in thermokarst lakes. As such, even a 5–10 fold increase in tundra wildfire frequency may not sizably modify nutrient and metal fluxes and pools in the surrounding aquatic ecosystems. This result requires revisiting the current paradigm on the importance of wildfire impact on permafrost peatlands and calls a need for experimental work on other ecosystem compartments (litter, shrubs, frozen peat) which are subjected to fire events.

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6% and remaining surface components with MAEs between 3.0 and 11.2%. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2% per decade for the entire study area and + 4.2% per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome.