• Energy Research

ABSTRACTThe below‐ground growing season often extends beyond the above‐ground growing season in tundra ecosystems and as the climate warms, shifts in growing seasons are expected. However, we do not yet know to what extent, when and where asynchrony in above‐ and below‐ground phenology occurs and whether variation is driven by local vegetation communities or spatial variation in microclimate. Here, we combined above‐ and below‐ground plant phenology metrics to compare the relative timings and magnitudes of leaf and fine‐root growth and senescence across microclimates and plant communities at five sites across the Arctic and alpine tundra biome. We observed asynchronous growth between above‐ and below‐ground plant tissue, with the below‐ground season extending up to 74% (~56 days) beyond the onset of above‐ground leaf senescence. Plant community type, rather than microclimate, was a key factor controlling the timing, productivity, and growth rates of fine roots, with graminoid roots exhibiting a distinct ‘pulse’ of growth later into the growing season than shrub roots. Our findings indicate the potential of vegetation change to influence below‐ground carbon storage as the climate warms and roots remain active in unfrozen soils for longer. Taken together, our findings of increased root growth in soils that remain thawed later into the growing season, in combination with ongoing tundra vegetation change including increased shrub and graminoid abundance, indicate increased below‐ground productivity and altered carbon cycling in the tundra biome.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic Plant Aboveground Biomass Synthesis Dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass grams per meter squared (g/m^2) on 2327 sample plots in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic Plant Aboveground Biomass Synthesis Dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass grams per meter squared (g/m^2) on 2327 sample plots in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Over the last century in the circumpolar north, notable terrestrial ecosystem changes include shrub expansion and an intensifying wildfire regime. Shrub invasion into tundra may be further accelerated by wildfire disturbance, which creates opportunities for establishment where recruitment is otherwise rare. The Seward Peninsula currently experiences more frequent and larger fires than other tundra regions in Alaska. There are areas of overlapping burn scars dating back to the 1950s. Using a chronosequence approach, we examined vegetation and ecosystem dynamics in tussock tundra. Increasing burn severity and fire frequency corresponded with an increase in grass cover and a decrease in shrub basal area. We used multivariate ordination analysis to create a single integrator variable of fire effect that accounted for time after fire, burn severity, and number of times burned. This fire effect was significantly associated with decreases in soil organic layer thickness and overall plant biomass. Unlike previous studies in Arctic Alaska tundra, we found that increases in fire frequency and severity did not increase shrub cover and biomass. Instead, intensifying fire disturbance, and particularly repeat fires, led to grass dominance. Our findings support the hypothesis that intensifying tundra fire regimes initiate alternative post-fire trajectories that are not shrub dominated and that are structurally and functionally quite different from sedge or shrub-dominated tundra.

Computer code as part of the publication in review: "Climate warming and cooling feedbacks from North American boreal forest fires" Max J. van Gerrevink1, Sander Veraverbeke1,2, Sol Cooperdock3, Stefano Potter3, Qirui Zhong1,4 Michael Moubarak5, Scott J. Goetz6, Michelle C. Mack7, James T. Randerson8, Nick Schutgens1, Merritt R. Turetsky9, Guido R. van der Werf10, and Brendan M. Rogers3 1Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands 2School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom 3Woodwell Climate Research Center, Falmouth, MA, USA 4College of Urban and Environmental Sciences, Peking University, Beijing, China 5Hamilton College, Hamilton, NY, USA 6School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA 7Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA 8Department of Earth System Science, University of California, Irvine, CA, USA 9Renewable and Sustainable Energy Institute, Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA 10Meteorology & Air Quality Group, Wageningen University and Research, Wageningen, The Netherlands Correspondence to: Max J. van Gerrevink (m.j.van.gerrevink@vu.nl) Files contain the computer code used to compute the climate radiative forcing from fire. The computer code is spilt into 7 different scripts: Well-mixed greenhouse gasses, precursors, and aerosol radiative forcing : Radiative_forcing_GHG_precursors_aerosols_boxmodel.py Mapping and uncertainty of Well-mixed greenhouse gasses, precursors, and aerosol radiative forcing : Radiative_forcing_GHG_precursors_aerosols_Mapping_and_uncertainty.py Permafrost greenhouse gas emissions radiative forcing : Radiative_Forcing_Permafrost_GHG.py Changes in surface albedo radiative forcing : Radiative_Forcing_Albedo_change.py Uncertainty in surface albedo radiative forcing : Radiative_Forcing_Albedo_change_uncertainty.py Vegetation recovery radiative forcing : Radiative_Forcing_vegetation_recovery.py Uncertainty in vegetation recovery radiative forcing : Radiative_Forcing_vegetation_recovery_uncertainty.py * The sensitivity analysis for Permafrost greenhouse gas emissions is included in the Radiative_Forcing_Permafrost_GHG.py script. Additionally, input files for atmospheric concentrations and impulse response function data are included as CSV files. 

Computer code as part of the publication in review: "Climate impacts from North American boreal forest fires" Max J. van Gerrevink1, Sander Veraverbeke1,2, Sol Cooperdock3, Stefano Potter3, Qirui Zhong1,4 Michael Moubarak5, Scott J. Goetz6, Michelle C. Mack7, James T. Randerson8, Nick Schutgens1, Merritt R. Turetsky9, Guido R. van der Werf10, and Brendan M. Rogers3 1Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands 2School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom 3Woodwell Climate Research Center, Falmouth, MA, USA 4College of Urban and Environmental Sciences, Peking University, Beijing, China 5Hamilton College, Hamilton, NY, USA 6School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA 7Center for Ecosystem Science and Society and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA 8Department of Earth System Science, University of California, Irvine, CA, USA 9Renewable and Sustainable Energy Institute, Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA 10Meteorology & Air Quality Group, Wageningen University and Research, Wageningen, The Netherlands Correspondence to: Max J. van Gerrevink (m.j.van.gerrevink@vu.nl) Files contain the computer code used to compute the climate radiative forcing from fire. The computer code is spilt into 7 different scripts: Well-mixed greenhouse gasses, precursors, and aerosol radiative forcing : Radiative_forcing_GHG_precursors_aerosols_boxmodel.py Mapping and uncertainty of Well-mixed greenhouse gasses, precursors, and aerosol radiative forcing : Radiative_forcing_GHG_precursors_aerosols_Mapping_and_uncertainty.py Permafrost greenhouse gas emissions radiative forcing : Radiative_Forcing_Permafrost_GHG.py Changes in surface albedo radiative forcing : Radiative_Forcing_Albedo_change.py Uncertainty in surface albedo radiative forcing : Radiative_Forcing_Albedo_change_uncertainty.py Vegetation recovery radiative forcing : Radiative_Forcing_vegetation_recovery.py Uncertainty in vegetation recovery radiative forcing : Radiative_Forcing_vegetation_recovery_uncertainty.py * The sensitivity analysis for Permafrost greenhouse gas emissions is included in the Radiative_Forcing_Permafrost_GHG.py script. Additionally, input files for atmospheric concentrations and impulse response function data are included as CSV files. 

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.

AbstractQuantifying global patterns of terrestrial nitrogen (N) cycling is central to predicting future patterns of primary productivity, carbon sequestration, nutrient fluxes to aquatic systems and climate forcing. With limited direct measures of soil N cycling at the global scale, syntheses of the 15N:14N ratio of soil organic matter across climate gradients provide key insights into understanding global patterns of N cycling. In synthesizing data from over 6000 soil samples, we show strong global relationships among soil N isotopes, mean annual temperature (MAT), mean annual precipitation (MAP) and the concentrations of organic carbon and clay in soil. In both hot ecosystems and dry ecosystems, soil organic matter was more enriched in 15N than in corresponding cold ecosystems or wet ecosystems. Below a MAT of 9.8°C, soil δ15N was invariant with MAT. At the global scale, soil organic C concentrations also declined with increasing MAT and decreasing MAP. After standardizing for variation among mineral soils in soil C and clay concentrations, soil δ15N showed no consistent trends across global climate and latitudinal gradients. Our analyses could place new constraints on interpretations of patterns of ecosystem N cycling and global budgets of gaseous N loss.