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The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. This data set uses the data set of Kruse (2023) and applies a threshold of 0.68 km m-2 to differentiate forested areas according to the 2018 field inventories (Shevtsova et al., 2021). In this data set the total forest cover was summed up and the percent of total available areas is presented for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 1800, 1860, 1900, 1990, 2000 and in 5-year steps until 3000 CE and presents the mean over the three repeats of the sum of AGB of the whole study region: extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax). Format: csv, with headers 1-year, Year in CE, 2-average percent forests cover for the study region, 3-upper and 4-lower, is the minimum and maximum value of the three simulations, 5-RCP, is the RCP scenario, 6-Cooling, contains in case of the cooling scenario the string “Cooling”. {"references": ["Stefan Kruse, Mareike Wieczorek, Florian Jeltsch and Ulrike Herzschuh (2016) Treeline dynamics in Siberia under changing climates as inferred from an individual-based model for Larix. Ecological Modelling, 338, 101\u2013121. http://dx.doi.org/10.1016/j.ecolmodel.2016.08.003 Additional data and results are available at https://doi.pangaea.de/10.1594/PANGAEA.863584", "Stefan Kruse (2023). StefanKruse/LAVESI: LAVESI-WIND with landscape (v2.0). Zenodo. https://doi.org/10.5281/zenodo.7505539", "Shevtsova, Iuliia, Herzschuh, Ulrike, Heim, Birgit, & Kruse, Stefan. (2023). Simulated above ground biomass of forests (larch) aggregated over the vicinity of the Ilirney lake system region, Chukotka, Russia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7505616", "Shevtsova I, Herzschuh U, Heim B, Schulte L, St\u00fcnzi S, Pestryakova LA, Zakharov ES, Kruse S: Recent above-ground biomass changes in central Chukotka (Russian Far East) using field sampling and Landsat satellite data. Biogeosciences, 18, 3343\u20133366, https://doi.org/10.5194/bg-18-3343-2021, 2021."]} This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).

Forest density estimates visually determined by describing the amount of present trees satellite imagery from Esri basemap (Esri) at an area of ~30x30 m qualitatively for 6515 stratified sampled locations at an equal number of locations based on elevation, aspect and slope angle. The density was categorized ranging from 1: single trees to 4: dense tree stands present, and contains a 0: no trees present. Format: ESRI shapefile, points; projection UTM58N; extent: 642085.1, 654775.1, 7462263, 7492833 m (xmin, xmax, ymin, ymax) This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852). {"references": ["Esri: \"High-resolution satellite and aerial imagery, typically within 3-5 years\" [basemap]. Scale Not Given. \"World Imagery\". https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9, Accession: 15 December 2020."]}

The elevation was accessed for the area of interest in 90 m spatial resolution from the TanDEM-X 90 m digital elevation model (DEM) product (Krieger et al, 2013). Prior to spatial topographical parameters extraction, the DEM was resampled from the 90-m cell spacing to a 30-m resolution. The result was classified into 589 different possible combinations of elevation, slope angle, aspect. For the classification we used the possible combinations of elevation, slope, and aspect which were grouped into the following categories: Elevation: 0-400 m 400-450m 450-500m 500-600m 600-650m 650-700m 700-1000m 1000-1500m Slope: 0-2° 2-4° 4-6° 6-8° 8-10° 10-12° 12-16° 16-18° 18-20° 20-25° 25-50° Aspect: 0-45° 45-90° 90-135° 135-180° 180-225° 225-270° 270-315° 315-360° Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 642010.1, 654910.1, 7462218, 7492908 m (xmin, xmax, ymin, ymax) {"references": ["Krieger G, Zink M, Bachmann M, Br\u00e4utigam B, Schulze D, Martone M, Rizzoli P, Steinbrecher U, Antony JW, De Zan F, Hajnsek I, Papathanassiou K, Kugler F, Rodriguez Cassola M, Younis M, Baumgartner S, L\u00f3pez-Dekker P, Prats P, Moreira A: TanDEM-X: a radar interferometer with two formation-flying satellites. Acta Astronautica, 89, 83\u201398, https://doi.org/10.1016/j.actaastro.2013.03.008, 2013."]} This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).

Abundance and biovolume data of the community of larger phytoplankton from the mesocosm experiment conducted in the Canary Islands in autumn 2019. Depth-integrated (0-2.5m) water samples were taken in 2-days intervals over the course of 33 days and autotrophic taxa assessed to the lowest taxonomic level possible using Utermöhl microscopy. Only taxa larger than approx. >5 µm could be considered with this method. Biovolume was calculated based on geometrical measurements (dominant taxa) or the literature (rare taxa). Carbon biomass estimates were purposefully not provided, as the standard literature conversion factors from biovolume to carbon biomass did not apply to many of our samples, likely due to low carbon density within cells. Predominantly mixotrophic or heterotrophic taxa are not provided in this dataset. The upwelling treatment started on day 6. Methodological details in Goldenberg et al. (doi:10.3389/fmars.2022.1015188).

Two-dimensional maps of the snow surface, i.e., digital elevation models (DEMs), were generated from daily sets of photos during the summer season of 2019 at the EastGRIP deep drilling site in the accumulation zone of the Greenland Ice Sheet. From mid-May to the beginning of August 2019, about 170 photos were taken every day along a 40 m long transect using a Sony α 7R camera and a fixed lens of 35 mm focal length. The camera was mounted at a height of ~2 m. The entire covered area is 400 m^2 (= 40 x 10 m). Following a Structure-from-Motion photogrammetry approach described in Zuhr et al. (2021) and using the software Agisoft Metashape, DEMs were generated for each day with suitable weather conditions. The DEMs have a resolution of 1 x 1 cm. Missing areas in the DEMs are either caused by a snow sampling scheme carried out in the same area or by insufficient coverage in the point cloud. The largest gap between consecutive DEMs is three days and happened once. Gaps of two days occurred five times and a one-day gap seven times.

Snow height information derived from digital elevation models (DEMs) from the summer season of 2019 at the EastGRIP deep drilling site (75° 38'N, 36° 00'W, ~2,700 m altitude) in the accumulation zone of the Greenland Ice Sheet. The DEMs cover an area of 400 m^2 (x = 40 m, y = 10 m). A representative area of 20 cm width from y = 1.9 to y = 2.1 m along the 40 m transect was chosen in order to study the snow height evolution throughout the 2019 season (mid-May to the beginning of August). The largest gap between consecutive DEMs is three days and happened once. Gaps of two days occurred five times and a one-day gap seven times. A 20-point moving average is used to provide snow height information for stable water isotope data sampled in the same transect.

Pigment concentration and pigment-based phytoplankton community composition data from the mesocosm experiment conducted in the Canary Islands in autumn 2019. Depth-integrated (0-2.5m) water samples were taken in 2-days intervals over the course of 33 days. One set of filters (one filter per sampling day and mesocosm) was analysed fluorometrically for Chl a. Another set of filters was analysed for a range of photosynthetic pigments using reverse-phase high-performance liquid chromatography (HPLC). Based on pigment concentrations, phytoplankton community composition was approximated using the CHEMTAX software with the original pigment ratios from Mackey et al (1996, doi:10.3354/meps144265). The input included Chl a, b, c2, and c3, peridinin, 19'-butanoyloxyfucoxanthin, fucoxanthin, neoxanthin, prasinoxanthin, violaxanthin, 19'-hexanoyloxyfucoxanthin, alloxanthin, and zeaxanthin. Divinyl Chl a was instead fully associated with Prochlorophyceae. The presence of the main phytoplankton groups is expressed in Chl a equivalents and their contribution to the phytoplankton community as percentage to total Chl a. The upwelling treatment started on day 6. Methodological details in Goldenberg et al. (doi:10.3389/fmars.2022.1015188).

Simulations with the spatially explicit and individual-based Siberian forest model LAVESI (Kruse et al., 2016, 2018, 2019) were set-up for transect in four focus regions covering the East Siberian treeline and tundra area (details in Kruse & Herzschuh, submitted). The model was updated to include climate forcing data for 300-800 km long and 20 m wide transects necessary for simulating the forest development between the northern taiga forests and the coast of the Arctic Ocean. Forced with climate forecasts driven by relative concentration pathway (RCP) scenarios 2.6, 4.5 and 8.5 and one with half the warming of RCP 2.6 named 2.6*. These were extended until 3000 AD either following the cooling of the scenarios after peak-warming, or with an arbitrary cooling back to levels of the 20th century. During the simulations, three key variables were extracted in 10-year steps for 2000-3000 AD: single-tree line, treeline, and, forest line, which are defined as the northernmost position of stands with >1 stem (tree > 1.3 m tall) per ha, the northernmost position of a forest cover not falling below 1 stem per ha, and, the northernmost position of a forest cover not falling below 100 stems ha per ha (see for a graphical representation Fig. 2 in Kruse et al., 2019). The determined treeline at year 2000 was used as baseline expansion and subtracted from each following years’ values. Furthermore, the tundra area was estimated for each of the four regions as the area between the treeline and the Arctic Ocean, based on interpolating the treeline position at the four transects over the complete modern treeline (Walker et al., 2005). Content of Table 1 "Kruse_and_Herzschuh_2022_Forest_expansion_in_Siberia_2010_to_3000_CE.csv": Column 1: Scenario: RCP scenario used Column 2: Region: One of the four regions, from east-to-west Taimyr Peninsula, Buor Khaya Peninsula, Kolyma River Basin, Chukotka Column 3: Year: Year in CE of the simulation in 10 year steps Column 4: Forest line in m Column 5: Treeline in m Column 6: Single-tree line in m Content of Table 2 "Kruse_and_Herzschuh_2022_Tundra_area_in_Siberia_2010_to_3000_CE.csv": Column 1: Scenario: RCP scenario used Column 2: Year: Year in CE of the simulation in 10 year steps Column 3: Tundra area at region Taimyr Peninsula in km² Column 4: Tundra area at region Buor Khaya Peninsula in km² Column 5: Tundra area at region Kolyma River Basin in km² Column 6: Tundra area at region Chukotka in km² The zip-file "Kruse_and_Herzschuh_2022_Forest_expansion_maps_in_Siberia_2010_to_3000_CE.zip" contains shape files with the tundra area in 10 year steps starting in 2000 until 3000 CE projection: Albers azimuthal equidistant projection centered at Longitude of 100 °E (PROJ4 string: "+proj=aea +lat_1=50 +lat_2=70 +lat_0=56 +lon_0=100 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") This study was supported by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852). {"references": ["Kruse, S., Gerdes, A., Kath, N. J., Epp, L. S., Stoof-Leichsenring, K. R., Pestryakova, L. A., & Herzschuh, U. (2019). Dispersal distances and migration rates at the arctic treeline in Siberia \u2013 a genetic and simulation-based study. Biogeosciences, 16(6), 1211\u20131224. doi:10.5194/bg-16-1211-2019", "Kruse, S., Gerdes, A., Kath, N. J., & Herzschuh, U. (2018). Implementing spatially explicit wind-driven seed and pollen dispersal in the individual-based larch simulation model: LAVESI-WIND 1.0. Geoscientific Model Development, 11(11), 4451\u20134467. doi:10.5194/gmd-11-4451-2018", "Kruse, S., Wieczorek, M., Jeltsch, F., & Herzschuh, U. (2016). Treeline dynamics in Siberia under changing climates as inferred from an individual-based model for Larix. Ecological Modelling, 338, 101\u2013121. doi:10.1016/j.ecolmodel.2016.08.003", "Walker, D. A., Raynolds, M. K., Dani\u00ebls, F. J. A. A., Einarsson, E., Elvebakk, A., Gould, W. A., \u2026 Team, the other members of the C. (2005). The circumpolar Arctic vegetation map. Journal of Vegetation Science, 16(3), 267\u2013282. doi:10.1658/1100-9233(2005)016[0267:TCAVM]2.0.CO;2"]}

Samples to estimate aboveground tree biomass for four boreal forest species (Larix gmelinii, Picea obovata, Pinus sylvestris, Pinus sibirica) were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). From each of the visited site, three living trees (a small, a medium-sized and the talles tree) per each site were cut down after estimating the quantity of the different types to be sampled, namely branches, needles, cones, making up the tree. Further, to estimate the stem weight, tree discs were taken. The discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). If the tree was small with <1.3 m, its stem is included as woody biomass in the branch sample. To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, if present. All harvested samples were weighed fresh in the field and subsampled. The dry weight of all subsamples was recorded after oven drying (60 ��C, 48 h for needle and branch samples, up to one week for tree stem discs). A detailed protocol for total tree and shrub AGB estimation can be found in Shevtsova, et al. (2020). Data format The data consists of one table for each of the four species. The columns (N=13) contain the follwoing information: 1. TreeDataBaseID -> unique Tree Data Base identifier of the individual 2. Site -> Sampling site name 3. SampleID -> Field name given to the individual 4. Species -> Species name 5. Height_cm -> Height of the tree individual in cm 6. Vitality -> Estimate of the vitality state in 6 levels, ++ very good, + good, 0 mediocre, - bad, -- very bad, dead 7. NeedleWeight_g -> Dry weight of needles in g 8. StemWeight_g -> Dry weight of the stem in g 9. BiomassBranchStatus -> 1 if branches are present and included in the biomass estimate or not 10. TotalWeightNonStem_g -> Dry weight of all parts but the stem, which are needles, branches and cones in g 11. DiameterBasal_cm -> Stem diameter at tree stem base (0 cm above ground) in cm 12. DiameterBreast_cm -> Stem diameter at breast height (130 cm above ground) in cm 13. CrownDiameter_cm -> Mean crown diameter in cm Additional information This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817 Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019 Aboveground estimation protocol and further data in: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Ludmila A; Zakharov, Evgenii S (2020): Total above-ground biomass of 39 vegetation sites of central Chukotka from 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923719 All data were collected during the expedition Chukotka and Yakutia 2018 expedition, that has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme 'Kohlenstoff im Permafrost KoPf' (grant no. 03F0764A) and by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).

Tree cores and discs were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). The samples were dried, sanded, digitized and further processed by identifying the ring layers end exporting the tree ring width for each year. The site chronologies were established by cross-dating all samples to each other, which helped coping with small ring sizes but especially with missing rings, and frost rings. We processed samples of four species, Larix gmelinii (LAGM), Picea obovata (PIOB), Pinus sylvestris (PISY) and Pinus sibirica (PISI). These were recorded at a variety of locations: LAGM from Lake Khamra sites EN18079, -80, -81, -83 (N59.974919° E112.958985°, N59.977106° E112.961379°, N59.970583° E112.987096°, N59.974714° E113.002874°) PIOB from Lake Khamra sites EN18079, -81, -83 (59.974919° E112.958985°, 59.970583° E112.987096°, 59.974714° E113.002874°) PISI from Lake Khamra site EN18080 (N59.977106° E112.961379°) PISY from different sites between EN18061 (N62.076376° E129.618586°) and EN18077 (N61.892568° E114.288623°) Data format The data consists of one file in dendrochronological TUCSON format without header for each of the four tree species. Additional information This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817 and an extension to: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Luidmila A; Zakharov, Evgenii S (2020): Individual tree and tall shrub partial above-ground biomass of central Chukotka in 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923784 Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019 All data were collected during the expedition Chukotka and Yakutia 2018 expedition, that has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme 'Kohlenstoff im Permafrost KoPf' (grant no. 03F0764A) and by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).