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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).

This dataset contains the dissolved organic matter (DOM) quantification and optical characterisation results from a KOSMOS mesocosm experiment carried out in the framework of the Ocean Artificial Upwelling project. The experiment was carried out in the autumn of 2018 in the oligotrophic waters of Gran Canaria. During the 39 days of experiment nutrient-rich deep water was added to the mesocosms in two modes (singular vs recurring additions), with four levels of intensity. Dissolved organic carbon, nitrogen and phosphorus were quantified with a Shimadzu TOC-5000 and a QuAAtro AutoAnalyzer. The absorption and fluorescence proprieties of DOM were determined making use of an Ocean Optics USB2000+UV-VIS-ES Spectrometer and a Jobin Yvon Horiba Fluoromax-4 spectrofluorometer, respectively. The aim of this dataset was to study the effect of artificial upwelling on the dissolved organic matter pool and its potential implications for carbon sequestration.

Short sediment cores were taken at six stations in Wismar Bay, southern Baltic Sea (Germany) in May 2019 using a Rumohr-Lot device. Our aim in this study was to investigate the role of diagenetic element fluxes and different fresh water sources, including submarine groundwater discharge, on the water column in the bay. Porewaters were extracted from the sediment cores by applying the rhizon technique at a resolution between 2 and 5 cm. The porewaters were analyzed for major and trace metals and selected nutrients using a ICP-OES (iCAP, 7400, Duo Thermo Fischer Scientific), total sulphide by a Specord 40 spectrophotometer (Analytik Jena), dissolved inorganic carbon (DIC) and δ13CDIC using an isotope gas mass spectrometre (MAT 253) coupled to a Gasbench II, and δ18OH2O, and δ2HH2O using a CRDS system (laser cavity-ring-down-spectroscopy, Picarro L2140- I). Sediment cores were further sliced at 2 to 4 cm resolution and each freeze-dried solid subsample was analyzed for contents of total carbon, nitrogen, and sulphur using an Elemental Analyzer (Euro Vector EuroEA 3, 052), inorganic carbon using an Elemental Analyzer multi EA (Analytik Jena), total mercury by a DMA-80 analyzer, and HCl-extractable Pb, Mn and Fe using an ICP-OES (iCAP, 7400, Duo Thermo Fischer Scientific).

Suspended particulate matter and elemental stoichiometry data from the mesocosms 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. There was no evidence for significant amounts of particulate inorganic carbon, and therefore, POC and TPC measurements were averaged and interpreted as POC. This “POC TPC average” should be used for further analysis. The upwelling treatment started on day 6. Methodological details in Goldenberg et al. (doi: 10.3389/fmars.2022.1015188). The value marked with ? is an outlier likely caused by a measurement error. Exclude from analysis or use with care.

Distance: Distance of boat position from starting point (m). This is not a cumulative distance.Water_depth: Water depth (m) from echo sounderRho_App_P1P2: Apparent resistivity (ohm-m) measured between potential electrodes P1 and P2. The remaining apparent resistivity columns have the same structure.C1_position: Position (m) of current electrode C1 relative to the boat position. For example, -36.6 m indicates that C1 is located 36.6 m behind the boat at the profile start.C2_position: Position (m) of current electrode C2 relative to the boat position. For example, -41.6 m indicates that C2 is located 41.6 m behind the boat at the profile start.P1_position: Position (m) of potential electrode P1 relative to the boat position. For example, -31.6 m indicates that P1 is located 31.6 m behind the boat at the profile start.P2_position: Position (m) of potential electrode P2 relative to the boat position. For example, -46.6 m indicates that P2 is located 46.6 m behind the boat at the profile start.The remaining electrode position columns follow the same structure.Latitude: latitude of boat position measured with GPS (WGS 1984)Longitude: longitude of boat position measured with GPS (WGS 1984) On 26 July 2018, we collected apparent resistivity data (ohm-m) in a sub-aquatic permafrost environment north of the Alaskan coastline at Drew Point in the United States. The data was collected with an IRIS Syscal Pro Deep Marine resistivity system that was equipped with a GPS and an echo sounder to record water depths. The geoelectric cable had an electrode separation of 5 m and the electrodes were arranged in a reciprocal Wenner Schlumberger array. The offset between the first electrode and the boat was 6.6 m. The main goal of the survey was to map the depth to the top of ice-bearing subsea permafrost. The survey started approximately 850 m offshore and ended close to the coastline.

This dataset describes two 17 m long sediment cores taken from beneath two thermokarst lakes in the Yukechi Alas, Central Yakutia, Russia. The first core was taken from below an Alas thermokarst lake (YU-L7; 61.76397°N, 130.46442°E) and the second core below and Yedoma lake (YU-L15; 61.76086°N, 130.47466°E). The dataset presents biogeochemical and biomarker parameters of sediment cores YU-L7 and YU-L15. Biogeochemical analyses include total carbon (TC) content, total organic carbon (TOC) content, total nitrogen (TN) content. Biomarker parameters include the n-alkane concentration, average chain length (ACL), carbon preference index (CPI), brGDGT concentration, archaeol concentration and the isoGDGT-0 concentration. The n-alkanes were measured in the aliphatic fraction by gas chromatography-mass spectromety using a Trace GC Ultra coupled to a DSQ MS. The branched and isoprenoid glycerol dialkyl glycerol tetraethers, as well as the dialkyl glycerol diether lipid (archaeol) were measured in the NSO fraction using a Shimadzu LC-10AD high-performance liquid chromatograph coupled to a Finnigan TSQ 7000 mass spectrometer via an atmospheric pressure chemical ionization interface. The pH soil is the sediment pH which was assessed by adding 6.12 mL of 0.01 M CaCl~2~ to ~2.5 g dried sediment and measuring with a Multilab 540 (WTW) at 20°C.

Video images were obtained with a TV-guided multiple corer (MUC) from a transect down the Laptev Sea continental slope, covering water depths between 60 and 3000 m. Video images allowed observations at the seafloor prior and during the retrieval of sediment cores. Here we use this image material to illustrate benthic communities at the different stations.

In this study, we measured a range of biogeochemical parameters down the Laptev Sea continental slope, from around 50 to 3400 m water depth. These included chlorophyll pigments, extracellular enzymatic activity, diffusive oxygen uptake and prokaryotic cell abundance. Our aim was to compare the measurements between two years about two decades apart (1993 and 2012). Benthic oxygen consumption rates were determined from ex situ microsensor measurements of diffusive oxygen uptake in retrieved sediment cores, consistent with Boetius & Damm 1998 (doi:10.1016/S0967-0637(97)00052-6). Directly after recovery, cores were stored at in situ temperature and the overlying water gently stirred by rotating small magnets to avoid the development of a stagnate water body above the sediment. High-resolution microprofiles across the sediment water interface were measured using Clark-type O2 microelectrodes equipped with a guard cathode (Revsbach 1989 doi:10.4319/lo.1989.34.2.0474) mounted to motorized micromanipulator (Glud et al. 2009 doi:10.4319/lo.2009.54.1.0001). Sensors were calibrated at in situ temperature against (1) air-saturated bottom water taken from the overlying water from the MUC cores (100% saturation), and (2) dithionate-spiked bottom water (anoxic). Microprofiles across the sediment-water interface were measured with a vertical resolution of 100 μm on a total length of max. 5 cm. The diffusive oxygen uptake (DOU, mmol/m²/d¹) was calculated from the O2 gradient just below the sediment surface and Fick's first law of diffusion (Rasmussen and Jorgensen 1992 doi:10.3354/MEPS081289). DOU = por * Ds * δC/δz where Ds (cm⁻² s⁻¹) = molecular diffusion coefficient in sediment, calculated as Ds = D0 * por/m , where por is the porosity, D0 = diffusion coefficient in water and m = 3, C (μM) = solute concentration, z (cm) = depth in the sediment. Carbon flux (remineralization rates, mg C/m²/d) were calculated from oxygen uptake at the seafloor assuming a molar ratio of C = 0.77 O2 as in Boetius & Damm 1998 (doi:10.1016/S0967-0637(97)00052-6).