• Energy Research

Global change is associated with variable shifts in the annual production of aboveground plant biomass, suggesting localized sensitivities with unclear causal origins. Combining remotely sensed normalized difference vegetation index data since the 1980s with contemporary field data from 84 grasslands on 6 continents, we show a widening divergence in site-level biomass ranging from +51% to -34% globally. Biomass generally increased in warmer, wetter and species-rich sites with longer growing seasons and declined in species-poor arid areas. Phenological changes were widespread, revealing substantive transitions in grassland seasonal cycling. Grazing, nitrogen deposition and plant invasion were prevalent in some regions but did not predict overall trends. Grasslands are undergoing sizable changes in production, with implications for food security, biodiversity and carbon storage especially in arid regions where declines are accelerating.

These five cores were obtained from a terrestrial permafrost area in northeastern Siberia (U1: March 2019; B3, B2, B1, U3: July 2019) during the Chersky 2019 field campaign. This campaign was part of both the CACOON and the PeCHEc project and was based at the Northeast Science Station in Chersky, Sakha, Russia (RU-LAND-2019/CHERSKIY; 02.07.2019 - 19.07.2019). They were taken from the area of the Pleistocene Park experiment (Zimov, 2005; doi:10.1126/science.1113442) and cover areas of a drained thermokarst lake basin (B) as well as the adjacent Yedoma uplands (U). The cores were drilled in areas of different large herbivore impact intensities (3= intensive; 2= extensive; 1= no animal presence). The data are used in a study exermining the impact of large herbivore presence on permafrost stability, vegetation composition and ground carbon storage. The active layer was sampled excavating profiles with a spade using fixed volume cylinders on the profile wall (250 ccm). The frozen ground was sampled using a SIPRE permafrost auger with a Stihl motor. All samples taken from these cores were analyzed between January 2020 and March 2021 at the facilities of AWI. The samples were analyzed for water/ice content, water isotopes, pH, conductivity, DOC, TC, TN, TOC, δ13C, mass specific magnetic susceptibility, grain size composition and radiocarbon age. Water/ice content was derived from weight differences before and after freeze-drying the samples. Water isotope ratios (δ18O, δ2H, d excess), pH, conductivity and DOC were measured using pore water extracted from the sediment samples using Rhizone samplers. Water isotopes were measured at AWI Potsdam Stable Isotope Laboratory using a Finnigan MAT Delta-S mass spectrometer. DOC was measured at AWI Potsdam Hydrochemistry Laboratory using a Shimadzu TOC-V CPH Total Organic Carbon Analyzer. TC and TN were measured at AWI Potsdam CARLA Laboratory using a vario EL III Element Analyzer. TOC was measured at the same laboratory using a varioMAX C Element Analyzer. δ13C was measured at AWI Potsdam Stable Isotope Laboratory using a Delta V Advantage Isotope Ratio MS supplement equipped with a Flash 2000 Organic Elemental Analyzer. Mass specific magnetic susceptibility was measured using a Bartington MS-2 Magnetic Susceptibility System. Grain size composition was determined using a Malvern Mastersizer 3000 equipped with a Malvern Hydro LV wet-sample dispersion unit. Statistics were calculated for this using Gradistat 8.0. Radiocarbon dating was carried out using the Mini Carbon Dating System (MICADAS) at AWI Bremerhaven.

List of Ameriflux, AON and FLUXNET sites contained in this dataset and their corresponding siteid's and doi's: CA-SCB (https://doi.org/10.17190/AMF/1498754), FI-Lom (https://doi.org/10.18140/FLX/1440228), GL-NuF (https://doi.org/10.18140/FLX/1440222), GL-ZaF (https://doi.org/10.18140/FLX/1440223), GL-ZaH (https://doi.org/10.18140/FLX/1440224), RU-Che (https://doi.org/10.18140/FLX/1440181), RU-Cok (https://doi.org/10.18140/FLX/1440182), RU-Sam (https://doi.org/10.18140/FLX/1440185), RU-Tks (https://doi.org/10.18140/FLX/1440244), RU-Vrk (https://doi.org/10.18140/FLX/1440245), SE-St1 (https://doi.org/10.18140/FLX/1440187), SJ-Adv (https://doi.org/10.18140/FLX/1440241), SJ-Blv (https://doi.org/10.18140/FLX/1440242), US-A03 (https://doi.org/10.17190/AMF/1498752), US-A10 (https://doi.org/10.17190/AMF/1498753), US-An1 (https://doi.org/10.17190/AMF/1246142), US-An2 (https://doi.org/10.17190/AMF/1246143), US-An3 (https://doi.org/10.17190/AMF/1246144), US-Atq (https://doi.org/10.17190/AMF/1246029), US-Brw (https://doi.org/10.17190/AMF/1246041), US-EML (https://doi.org/10.17190/AMF/1418678), US-HVa (https://doi.org/10.17190/AMF/1246064), US-ICh (https://doi.org/10.17190/AMF/1246133), US-ICs (https://doi.org/10.17190/AMF/1246130), US-ICt (https://doi.org/10.17190/AMF/1246131), US-Ivo (https://doi.org/10.17190/AMF/1246067), US-NGB (https://doi.org/10.17190/AMF/1436326), US-Upa (https://doi.org/10.17190/AMF/1246108), US-xHE (https://doi.org/10.17190/AMF/1617729), US-xTL (https://doi.org/10.17190/AMF/1617739). Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models.This dataset comprises harmonized, standardized and aggregated in-situ observations of surface energy budget components measured at 64 sites on vegetated and glaciated sites north of 60° latitude, in the time period from 1994 till 2021. The surface energy budget components include net radiation, sensible heat flux, latent heat flux, ground heat flux, net shortwave radiation, net longwave radiation, surface temperature and albedo, which were aggregated to daily mean, minimum and maximum values from hourly and half-hourly measurements. Data were retrieved from the monitoring networks FLUXNET, AmeriFlux, AON, GC-Net and PROMICE.

This dataset provides estimates of live, oven-dried aboveground biomass of all plants (tree, shrub, graminoid, forb, bryophyte) and all woody plants (tree, shrub) at 30-meter resolution across the Arctic tundra biome. Estimates of woody plant dominance are also provided as: (woody plant biomass / plant biomass) * 100. Plant biomass and woody plant biomass were estimated for each pixel (grams per square meter [g / m2]) using field harvest data for calibration/validation along with modeled seasonal surface reflectance data derived using Landsat satellite imagery and the Continuous Change Detection and Classification algorithm, and other supplementary predictors related to topography, region (e.g. bioclimate zone, ecosystem type), land cover, and derivative spectral products. Modeling was performed in a two-stage process using random forest models. First, biomass presence/absence was predicted using probability forests. Then, biomass quantity was predicted using regression forests. The model outputs were combined to produce final biomass estimates. Pixel uncertainty was assessed using Monte Carlo iterations. Field and remote sensing data were permuted during each iteration and the median (50th percentile, p500) predictions for each pixel were considered best estimates. In addition, this dataset provides the lower (2.5th percentile, p025) and upper (97.5th percentile, p975) bounds of a 95% uncertainty interval. Estimates of woody plant dominance are not modeled directly, but rather derived from plant biomass and woody plant biomass best estimates. The Pan Arctic domain includes both the Polar Arctic, defined using bioclimate zone data from the Circumpolar Arctic Vegetation Mapping Project (CAVM; Walker et al., 2005), and the Oro Arctic (treeless alpine tundra at high latitudes outside the Polar Arctic), defined using tundra ecoregions from the RESOLVE ecoregions dataset (Dinerstein et al., 2017) and treeline data from CAVM (CAVM Team, 2003). The mapped products focus on Arctic tundra vegetation biomass, but the coarse delineation of this biome meant some forested areas were included within the study domain. Therefore, this dataset also provides a tree mask product that can be used to mask out areas with canopy height ≥ 5 meters. This mask helps reduce, but does not eliminate entirely, areas of dense tree cover within the domain. Users should be cautious of predictions in forested areas as the models used to predict biomass were not well constrained in these areas. This dataset includes 132 files: 128 cloud-optimized GeoTIFFs, 2 tables in comma-separated values (CSV) format, 1 vector polygon in Shapefile format, and one figure in JPEG format. Raster data is provided in the WGS 84 / North Pole LAEA Bering Sea projection (EPSG:3571) at 30 meter (m) resolution. Raster data are tiled with letters representing rows and numbers representing columns, but note that some tiles do not contain unmasked pixels. We included all tiles nonetheless to maintain consistency. Tiling information can be found in the ‘metadata’ directory as a figure (JPEG) or shapefile.

AbstractArctic plants are adapted to climatic variability, but their long‐term responses to warming remain unclear. Responses may occur by range shifts, phenological adjustments in growth and reproduction, or both. Here, we compare distribution and phenology of 83 arctic and boreal mountain species, sampled identically in the early 20th (1917–1919) and 21st centuries (2017–2018) from a region of northern Sweden that has warmed significantly. We test two compensatory hypotheses to high‐latitude warming—upward shifts in distribution, and earlier or extended growth and reproduction. For distribution, we show dramatic upward migration by 69% of species, averaging 6.1 m per decade, especially boreal woodland taxa whose upward expansion has reduced arctic montane habitat by 30%. Twenty percent of summit species showed distributional shifts but downward, especially moisture‐associated snowbed flora. For phenology, we detected wide inter‐annual variability in the onset of leafing and flowering in both eras. However, there was no detectable change in growing‐season length, relating to two mechanisms. First, plot‐level snow melt data starting in 1917 demonstrated that melt date, rather than vernal temperatures, better predicts plant emergence, with snow melt influenced by warmer years having greater snowfall—warmer springs did not always result in earlier emergence because snowbeds can persist longer. Second, the onset of reproductive senescence between eras was similar, even when plant emergence was earlier by a month, possibly due to intensified summer heat stress or hard‐wired ‘canalization’ where senescence occurs regardless of summer temperature. Migrations in this system have possibly buffered arctic species against displacement by boreal expansion and warming, but ongoing temperature increases, woody plant invasion, and a potential lack of flexibility in timing of senescence may foreshadow challenges.

Abstract The on‐going climate warming is promoting shrub abundance in high latitudes, but the effect of this phenomenon on ecosystem functioning is expected to depend on whether deciduous or evergreen species increase in response to warming. To explore effects of long‐term warming on shrubs and further on ecosystem functioning, we analysed vegetation and ecosystem CO2 exchange after 20 years of warming in the forest–tundra ecotone in subarctic Sweden. A previous study conducted 9 years earlier had found increased evergreen Empetrum nigrum ssp. hermaphroditum in the forest and increased deciduous Betula nana in the tundra. Following current understanding, we expected continued increase in shrub abundance that would be stronger in tundra than in forest. We expected warming to increase ecosystem respiration (Re) and gross primary productivity (GPP), with a greater increase in Re in tundra due to increased deciduous shrub abundance, leading to a less negative net ecosystem exchange and reduced ecosystem C sink strength. As predicted, vascular plant abundances were higher in the warmed plots with a stronger response in tundra than in forest. However, whereas B. nana had increased in abundance since the last survey, E. hermaphroditum abundance had declined due to several moth and rodent outbreaks during the past decade. In contrast to predictions, Re was significantly lower in the warmed plots irrespective of habitat, and GPP increased marginally only in the forest. The lower Re and a higher GPP under warming in the forest together led to increased net C sink. Re was negatively associated with the total vascular plant abundance. Our results highlight the importance of disturbance regimes for vegetation responses to warming. Climate warming may promote species with both a high capacity to grow under warmer conditions and a resilience towards herbivore outbreaks. Negative correlation between Re and total vascular plant abundance further indicate that the indirect impacts of increased plants on soil microclimate may become increasingly important for ecosystem CO2 exchange in the long run, which adds to the different mechanisms that link warming and CO2 fluxes in northern ecosystems. Read the free Plain Language Summary for this article on the Journal blog.

Abstract In the face of climate change, it is important to estimate changes in key ecosystem properties such as plant biomass and gross primary productivity (GPP). Ground truth estimates and especially experiments are performed at small spatial scales (0.01–1 m2) and scaled up using coarse scale satellite remote sensing products. This will lead to a scaling bias for non-linearly related properties in heterogeneous environments when the relationships are not developed at the same spatial scale as the remote sensing products. We show that unmanned aerial vehicles (UAVs) can reliably measure normalized difference vegetation index (NDVI) at centimeter resolution even in highly heterogeneous Arctic tundra terrain. This reveals that this scaling bias increases most at very fine resolution, but UAVs can overcome this by generating remote sensing products at the same scales as ecological changes occur. Using ground truth data generated at 0.0625 m2 and 1 m2 with Landsat 30 m scale satellite imagery the resulting underestimation is large (8.9%–17.0% for biomass and 5.0%–9.7% for GPP600) and of a magnitude comparable to the expected effects of decades of climate change. Methods to correct this upscaling bias exist but rely on sub-pixel information. Our data shows that this scale-dependency will vary strongly between areas and across seasons, making it hard to derive generalized functions compensating for it. This is particularly relevant to Arctic greening with a predominantly heterogeneous land cover, strong seasonality and much experimental research at sub-meter scale, but also applies to other heterogeneous landscapes. These results demonstrate the value of UAVs for satellite validation. UAVs can bridge between plot scale used in ecological field investigations and coarse scale in satellite monitoring relevant for Earth System Models. Since future climate changes are expected to alter landscape heterogeneity, seasonally updated UAV imagery will be an essential tool to correctly predict landscape-scale changes in ecosystem properties.