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Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011). Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).

Permafrost is warming globally which leads to widespread permafrost thaw. Particularly ice-rich permafrost is vulnerable to rapid thaw and erosion, impacting whole landscapes and ecosystems. Abrupt permafrost disturbances, such as retrogressive thaw slumps (RTS), expand by several meters each year and lead to an increased soil organic carbon release. We applied the disturbance detection algorithm LandTrendr for automated large-scale RTS mapping and high temporal thaw dynamic assessment to Northeast Siberia (8.1 × 10^6km^2). We adapted and parametrised the temporal segmentation algorithm for abrupt disturbance detection to incorporate Landsat+Sentinel-2 mosaics, conducted spectral filtering, spatial masking and filtering, and a binary machine-learning object classification of the disturbance output to separate between RTS and false positives (F1 score: 0.61). Ground truth data for calibration and validation of the workflow was collected from 9 known RTS cluster sites using very high-resolution RapidEye and PlanetScope imagery. The data set presents the results of the first automated detection and assessment of RTS and their temporal dynamics at large-scale for 2001–2019. We identified 50,895 RTS and a steady increase in RTS-affected area from 2001 to 2019 across Northeast Siberia, with a more abrupt increase from 2016 onward. Overall the RTS-affected area increased by 331% compared to 2000 (2000: 20,158 ha, 2001-2019: 66,699 ha). Contrary to this, focus sites show spatio-temporal variability in their annual RTS dynamics, with alternating periods of increased and decreased RTS development, indicating a close relationship to thaw drivers. The detected increase in RTS dynamics suggests advancing permafrost thaw and underlines the importance of assessing abrupt permafrost disturbances with high spatial and temporal resolution at large-scales. This consistenly obtained disturbance product will help to parametrise regional and global climate change models.

Coccolithophorid algae are microscopic but prolific calcifiers in modern and ancient oceans. When the pH of seawater is modified, as may occur in the future due to ocean acidification, different species and strains of coccolithophorids have exhibited diverse calcification responses in laboratory culture. Since their biomineralization is a completely intracellular process, it is unclear why their response should be affected by extracellular seawater pH. Variations in the B/Cain coccoliths are potential indicators of pH shifts in the intracellular coccolith vesicle where calcification occurs, because B/Ca in abiogenic calcites increases at higher pH due to the greater abundance of borate ions, the only B species incorporated into calcite. We used a SIMS ion probe to measure B/Ca of coccoliths from three different strains of Emiliania huxleyi and one strain of Coccolithus braarudiibraarudiicultured under different seawater pH conditions to ascertain if the B/Ca can be used to elucidate how coccolithophorids respond to changing ocean pH.These data are interpreted with the aid of a conceptual model of cellular boron acquisition by coccolithophorids. Based on uptake in other plants, we infer that boron uptake by coccolithophorid cells is dominated by passive uptake of boric acid across the lipid bilayer. Subsequently, in the alkaline coccolith vesicle (C.V.), boron speciates according to the C.V. pH, and borate is incorporated into the coccolith. At increasing seawater pH, the relative abundance of the neutral boric acid in seawater decreases, lowering the potential B flux into the cell. Homeostasis or constant pH of the coccolith vesicle results in a decrease of the B/Cain the coccolith with increasing seawater pH. In contrast, if coccolith vesicle pH increases with increasing seawater pH, then the B/Ca will increase as the fraction of borate in the coccolith vesicle increases. The coccolith B/Ca is also expected to depend inversely on the dissolved inorganic carbon (DIC) concentration in the coccolith vesicle. The B/Ca in cultured coccoliths is much lower than that of foraminifera or corals and limits precision in the analysis. Modest variations in DIC or pH of the coccolith vesicle can account for the observed trends in B/Ca in cultured coccoliths. The model shows that paired measurements of B/Ca and B isotopic composition of the calcite could distinguish between regulation of pH or DIC in the coccolith vesicle. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2021) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2022-10-21.

The total snow and ice thickness (distance from the snow surface to the ice-ocean interface) was measured by the electromagnetic induction (EM) method. On MOSAiC transects, we used a broad-band EM instrument sensor (GEM-2 by Geophex Ltd) towed on a small sled (Hunkeler et al, 2015; Hunkeler et al, 2016). The instrument includes a real-time data processing unit including a GPS receiver which communicates with a pocket PC that is operates the sensor and records the EM and GPS data streams. The GEM-2 is a broadband sensor that can transmit multiple configurable frequencies in the kHz range simultaneously. The sensor setup during MOSAiC used 5 frequencies with an approximately logarithmic spacing throughout the frequency range of the sensor (1.525 kHz, 5.325 kHz, 18.325 kHz, 63.025 kHz, and 93.075 kHz). The transect measurements are based on an empirical approach based on a sensor calibration, where the GEM-2 was placed at known heights above the sea ice surface using a wooden ladder on top of level ice with a known thickness determined by 5 drill holes. An exponential function was then fitted to the frequency components as function of distance of the sensor to the ice/ocean interface and then applied to the transect data. The closest-in-time calibration result was used when a GEM-2 survey could not be accompanied with a calibration. The total thickness retrieval with the GEM-2 calibration and survey data was done on-board shortly after each profile. The dataset is therefore labeled as GEM-2 quickview data but has been subject to manual quality control. Using a direct relationship between total thickness and frequency component implies the assumption that the sea ice conductivity is negligible and the ice/water interface constant within the GEM-2 footprint. While this is a reasonable assumption for level ice, the peak thicknesses of ridges are known to be underestimated by as much as 50 % (Pfaffing et al, 2007) and will be subject of further processing. To estimate the snow depth and then subtract its thickness from the total thickness we rely on direct measurements of snow depth with Magnaprobe. The co-inciding snow depth measurements on MOSAiC transect can be found here: https://doi.pangaea.de/10.1594/PANGAEA.937781 Not every GEM-2 transect has complimentary snow depth measurements. An overview of all transect measurements at MOSAiC is given in the attached table. For more details we refer to the MOSAiC transect paper by Itkin et al, 2022: Sea ice and snow mass balance from transects in the MOSAiC Central Observatory, in review at Elementa – Science of Anthropocene.

The data file contains information on each sample (Site, core, depth, age) and the measurements (replicate number, laboratory) in addition to the isotope data. For clumped isotopes (D47), mean values and standard errors are given (on the I-CDES scale, see Bernasconi et al., G3, 2021) as well as temperatures calculated using the foraminifera-based calibration of Meinicke et al. (GCA, 2020), updated to the I-CDES scale by Meinicke et al. (Paleoceanography and Paleoclimatology, 2021). Furthermore, genus-specific d18O and d13C values are reported for Cibicidoides and Nuttalides where available, as well as the calculated isotopic composition of seawater based on the d18O values from Cibicidoides spp., the D47 temperatures, and the calibration of Marchitto et al. (GCA, 2014). d18O of Cibicidoides and resulting seawater d18O are also reported after correction for a hypothetical pH effect using a linear trend through reconstructed deep ocean pH based on d11B and the theoretical pH effect of 1.42 ‰ per pH unit from Zeebe (Paleo3, 2001). This dataset contains clumped isotope (D47), d18O and d13C data from benthic foraminifera from four IODP sites from the Newfoundland margin. The D47 data were used to reconstruct deep ocean temperature across the Cenozoic era. The reported data were generated at ETH Zürich and the University of Bergen between 2015 and 2020. Data for this study were mostly obtained from core catcher samples, with an average time resolution of 1.2 million years. For each sample, 13-45 replicate measurements were performed on different species of benthic foraminifera. Data in this dataset are sample-averaged isotope and temperature data. In addition, replicate-level raw data including standard data for correction are stored at Earthchem (doi:10.26022/IEDA/112213) to allow for reprocessing of the data.

These are ice-penetrating radar data connecting the newly chosen Beyond EPICA Little Dome C core site to the EPICA Dome C core site, collected in late 2019. These data are presented in a paper in The Cryosphere (https://doi.org/10.5194/tc-2020-345), where full processing and collection methods are described. Data collection and processing Data were collected using a new very high frequency (VHF) radar, built by the Remote Sensing Center at the University of Alabama (Yan et al., 2020). The system transmitted 8 us chirps, with peak transmit power of 125--250 W per channel, at 200 MHz center frequency and 60 MHz bandwidth. There were 5--8 operational channels at various points. The antennas were pulled behind a tracked vehicle, with controlling electronics in the rear of the vehicle. Data were collected at travel speeds of 2--3.5 m/s. Data processing consisted of coherent integration (i.e. unfocused SAR), pulse compression, motion compensation (by tracking internal horizons), coherent channel combination, and de-speckling using a median filter. Two-way travel time was converted to depth assuming a correction of 10 m of firn-air and a constant radar wave speed of 168.5 m/us (e.g., Winter et al., 2017). After other processing was complete, different radargrams were spliced together to create a continuous profile extending from EPICA Dome C to the Beyond EPICA Little Dome C core site, and then the data were interpolated to have constant, 10-m horizontal spacing. The re-interpolated data were used for horizon tracing, which was done semi-automatically to follow amplitude peaks between user-defined clicks. For the bed reflection, we always picked the first notable return in the region of the bed. File description The file format is hdf5, which can be read with many programming languages. There are three groups in the file: processed_data, picks, and geographic_information. The processed_data gives the return power matrix (dB), and the depth (m) and two-way travel time (us) for the fast-time dimension. The picks give the depths (m) of different reflecting horizons traced in the corresponding paper. Ages and age uncertainties (kyr), interpolated from the AICC2012 timescale, are included as attributes on each pick. Bed and basal unit picks are included (ageless). The geographic_information gives latitude and longitude (decimal degrees), and the distance along-profile (km). References Bazin, L., Landais, A., Lemieux-Dudon, B., Toyé Mahamadou Kele, H., Veres, D., Parrenin, F., Martinerie, P., Ritz, C., Capron, E., Lipenkov, V., Loutre, M. F., Raynaud, D., Vinther, B., Svensson, A., Rasmussen, S. O., Severi, M., Blunier, T., Leuenberger, M., Fischer, H., Masson-Delmotte, V., Chappellaz, J., and Wolff, E.: An optimized multi-proxy, multi-site Antarctic ice and gas orbital chronology (AICC2012): 120-800 ka, 9, 1715–1731, https://doi.org/10.5194/cp-9-1715-2013, 2013. Winter, A., Steinhage, D., Arnold, E. J., Blankenship, D. D., Cavitte, M. G. P., Corr, H. F. J., Paden, J. D., Urbini, S., Young, D. A., and Eisen, O.: Comparison of measurements from different radio-echo sounding systems and synchronization with the ice core at Dome C, Antarctica, 11, 653–668, https://doi.org/10.5194/tc-11-653-2017, 2017. Yan, J.-B., Li, L., Nunn, J. A., Dahl-Jensen, D., O’Neill, C., Taylor, R. A., Simpson, C. D., Wattal, S., Steinhage, D., Gogineni, P., Miller, H., and Eisen, O.: Multiangle, Frequency, and Polarization Radar Measurement of Ice Sheets, 13, 2070–2080, https://doi.org/10.1109/JSTARS.2020.2991682, 2020. These data were generated in the frame of Beyond EPICA. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 815384 (Oldest Ice Core). It is supported by national partners and funding agencies in Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland, The Netherlands and the United Kingdom. Logistic support is mainly provided by PNRA and IPEV through the Concordia Station system. The radar shipment and personnel transportation to Antarctica were provided by U.S. NSF under grant 1921418, which also partly supported the development of the VHF radar. Radar development was further supported by internal funding from the University of Alabama. DL and DDJ were partially supported by the Villum Foundation (grant number 16572). Any opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency or other national funding bodies.

Concentrations of dissolved micronutrient trace metals (Fe, Zn, Ni, Cu, Cd, Pb, Mn) in seawater, sea ice, and melt ponds collected on the US GEOTRACES Arctic cruise (HLY1502, GN01) from August to October 2015.

This dataset from a speleothem record from the north-eastern Yucatán peninsula (Mexico) provides a high reslution stable isotope record for the early Holocene between 11,040 and 9,520 a BP on up to sub-decadal scale. Stable isotope samples were micromilled at a resolution of 0.25mm, and measured using an IRMS equipped with a Gasbench. The chronology is based on 17 U-Th ages (Warken et al., 2021) calculated with the half lives of Cheng et al., 2013. The age-depth model was constructed using the algorithm COPRA (Breitenbach et al., 2012).

Hydrological changes during the penultimate deglaciation and Last interglacial as simulated in a transient experiment performed with LOVECLIM and focusing on North Africa A transient experiment of the penultimate deglaciation and Last interglacial (140-120 ka) is performed with LOVECLIM following the PMIP4 protocol (Menviel et al., 2019) https://gmd.copernicus.org/articles/12/3649/2019/