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This data set displays a refined age scale for the U1361A marine sediment core (64.41°S, 143.89°E, 3,454 m water depth), recovered from the continental rise offshore of the Wilkes Subglacial Basin, during the Integrated Ocean Drilling Program (IODP) Expedition (Escutia et al. 2011). This age scale is a refined version of the age scale published in Wilson et al. 2018. Here we use the AICC2012 ice core chronology as a reference curve in order to compare the late Pleistocene sediment core data from U1361A to the TALDICE ice core record. Specifically, we refine the existing U1361A age model through the alignment of barium/aluminium (Ba/Al) ratios from XRF-scanning7 with the EDC δD record on the AICC2012 age scale. We apply a conservative tuning strategy to align the two records, using tie points (derived by visual matching) only at the mid-points of the major glacial terminations I-V. Over the interval of interest for the present study (i.e. ~100-350 ka), the new age model for core U1361 differs by only 0 to 6 ka compared to the previous age model in which the sedimentation rate was assumed to be constant. The Nd and IBRD (ice rafted debris) and 143 Nd/144 Nd records for the U1361A published in Wilson et al. (2018) are drawn on the refined age scale. IBRD was measured by weighing after wet-sievingIBRD (ire rafted debris) and 143Nd/144Nd records of the U1361A sediment core (Wilson et al. 2018) drawn of the refined age scale tuned on the AICC2012 chronology for EDC (Bazin et al. 2013)

This data set displays a refined age scale for the U1361A marine sediment core (64.41°S, 143.89°E, 3,454 m water depth), recovered from the continental rise offshore of the Wilkes Subglacial Basin, during the Integrated Ocean Drilling Program (IODP) Expedition (Escutia et al. 2011). This age scale is a refined version of the age scale published in Wilson et al. 2018. Here we use the AICC2012 ice core chronology as a reference curve in order to compare the late Pleistocene sediment core data from U1361A to the TALDICE ice core record. Specifically, we refine the existing U1361A age model through the alignment of barium/aluminium (Ba/Al) ratios from XRF-scanning7 with the EDC δD record on the AICC2012 age scale. We apply a conservative tuning strategy to align the two records, using tie points (derived by visual matching) only at the mid-points of the major glacial terminations I-V. Over the interval of interest for the present study (i.e. ~100-350 ka), the new age model for core U1361 differs by only 0 to 6 ka compared to the previous age model in which the sedimentation rate was assumed to be constant. The Nd and IBRD (ice rafted debris) and 143 Nd/144 Nd records for the U1361A published in Wilson et al. (2018) are drawn on the refined age scale. 2 s.e. = internal precision (2s level) on sample measurement 2 s.d. = external reproducibility estimated from the within-session standard deviation (2s.d.) on JNdi-1 standards Over the course of the analyses, measurements of rock standard BCR-2 gave 143Nd/144Nd = 0.512640 ± 0.000016 (n=31)IBRD (ire rafted debris) and 143Nd/144Nd records of the U1361A sediment core (Wilson et al. 2018) drawn of the refined age scale tuned on the AICC2012 chronology for EDC (Bazin et al. 2013)

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.

Dataset abstract TEP are operationally defined as gel particles that are retained on 0.4 ��m polycarbonate filters and stained with the cationic copper phthalocyanine dye Alcian Blue 8GX at pH 2.5. CSP are gel particles retained on 0.4 ��m polycarbonate filters that are stained with a solution of Coomassie Brilliant Blue G (CCB) at pH 7.4. Seawater surface samples (5 m) were collected every 6 hours from the ship���s underway pump. In addition, vertical profiles (6 depths, generally from 5 to 100-150 m) were sampled from 19 CTD casts using a SBE 911 Plus attached to a rosette of 24 12-L PVC Niskin bottles. This dataset presents TEP and CSP from seawater samples collected from the ship���s underway pump and CTDs. Samples were collected around the Southern Ocean on the R/V Akademik Tryoshnikov in the austral summer of 2016/2017, as part of the Antarctic Circumnavigation Expedition (ACE). Dataset contents ace_seawater_csp.csv, data file, comma-separated values ace_seawater_tep.csv, data file, comma-separated values data_file_header_csp.txt, metadata, text data_file_header_tep.txt, metadata, text README.txt, metadata, text Dataset license This TEP and CSP dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/ {"references": ["Passow, U., and Alldredge, A. L.: A dye-binding assay for the spectrophotometric measurement of transparent exopolymer particles (TEP), Limnology and Oceanography, 40, 1326-1335, 1995.", "Cisternas-Novoa, C., Lee, C., and Engel, A.: A semi-quantitative spectrophotometric, dye-binding assay for determination of Coomassie Blue stainable particles, Limnology and Oceanography: Methods, 12, 604-616, 10.4319/lom.2014.12.604, 2014."]} The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. MZ was supported by a FPU predoctoral fellowship (FPU13/04630) from the Spanish Ministry of Education and Culture.

This study presents simulations of Greenland surface melt for the Eemian interglacial period (∼130 000 to 115 000 years ago) derived from regional climate simulations with a coupled surface energy balance model. Surface melt is of high relevance due to its potential effect on ice core observations, e.g., lowering the preserved total air content (TAC) used to infer past surface elevation. An investigation of surface melt is particularly interesting for warm periods with high surface melt, such as the Eemian interglacial period. Furthermore, Eemian ice is the deepest and most compressed ice preserved on Greenland, resulting in our inability to identify melt layers visually. Therefore, simulating Eemian melt rates and associated melt layers is beneficial to improve the reconstruction of past surface elevation. Estimated TAC, based on simulated melt during the Eemian, could explain the lower TAC observations. The simulations show Eemian surface melt at all deep Greenland ice core locations and an average of up to ∼30 melt days per year at Dye-3, corresponding to more than 600 mm water equivalent (w.e.) of annual melt. For higher ice sheet locations, between 60 and 150 mmw.e.yr-1 on average are simulated. At the summit of Greenland, this yields a refreezing ratio of more than 25 % of the annual accumulation. As a consequence, high melt rates during warm periods should be considered when interpreting Greenland TAC fluctuations as surface elevation changes. In addition to estimating the influence of melt on past TAC in ice cores, the simulated surface melt could potentially be used to identify coring locations where Greenland ice is best preserved.

Dataset abstract Cloud Condensation Nuclei (CCN) are a subclass of atmospheric aerosol particles, which can be activated to cloud droplets at a certain supersaturation, with respect to water. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as CCN. It was found that CCN are relevant for the Earth’s radiation budget, by affecting cloud albedo and lifetime. When giving a number concentration of CCN, also the supersaturation at which it was measured has to be given. With additional information on particle number size distribution, the hypothetical diameter of particle activation (critical diameter) was derived. Further, the particle hygroscopicity parameter (kappa) was calculated using the critical diameter. Values of kappa can be a proxy for bulk chemical composition of the sampled CCN population. Our dataset gives CCN number concentrations measured by a CCN counter (type CCN-100 by DMT, Boulder, US) operated at five different levels of supersaturation (0.15%, 0.2%, 0.3%, 0.5%, 1%) during the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project. Temporal coverage is from December 20, 2016 to March 19, 2017. We give 5-minute averaged and quality controlled CCN number concentrations, critical diameter and kappa values. Dataset contents ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_number_concentration_SS030.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_number_concentration_SS050.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_number_concentration_SS100.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_critical_diameter_SS015.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_critical_diameter_SS020.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_critical_diameter_SS030.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_critical_diameter_SS050.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_critical_diameter_SS100.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS015.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS020.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS030.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS050.csv, data file, comma-separated values ACESPACE_cloud_condensation_nuclei_hygroscopicity_parameter_SS100.csv, data file, comma-separated values data_file_header_number_concentration.txt, metadata, text data_file_header_critical_diameter.txt, metadata, text data_file_header_hygroscopicity_parameter.txt, metadata, text change_log.txt, metadata, text README.txt, metadata, text The files listed above contain Cloud Condensation Nuclei (CCN) number concentration (N_CCN), critical diameter (D_crit) and particle hygroscopicity parameter (KAPPA) values for the Antarctic Circumnavigation Expedition from in-situ measurements. Each file contains only N_CCN, D_crit or KAPPA values for one of the five measured levels of supersaturation (SS), e.g., N_CCN at SS=0.15% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS015.csv or N_CCN at SS=0.2% in ACESPACE_cloud_condensation_nuclei_number_concentration_SS020.csv etc. In addition, for each N_CCN value the respective temperature of the CCNCs measurement column (T_col) is given. Values are from 1 Hz measurements and averaged to represent 5-minute intervals. For every given value of CCN number concentration, the respective supersaturation level is given, although files only contain values for one level only. Additionally, longitude and latitude for the ship’s position at the start time of the averaging period are given. For latitude and longitude nan values are given, in cases where positioning data was not available for the given time period. There are no nan values for CCN number concentration included, in a way that only quality assured data is given. Change log v1.1 - data files updated change dataset title to specify ACE cruise change time resolution to 5 minutes addition of critical diameter data addition of hygroscopicity parameter data create separate data_file_headers add change log v1.0 - initial release of dataset {"references": ["Gysel, M. & Stratmann, F. (2014). WP3\u2010 NA3: In\u2010situ chemical, physical and optical properties of aerosols Deliverable D3.11: Standardized protocol for CCN measurements. http://www.actris.net/Portals/97/Publications/quality%20standards/aerosol%20insitu/WP3_D3.13_M24_CCNC_SOP_v130514.pdf", "David W H Walton, & Jenny Thomas. (2018, November 22). Cruise Report - Antarctic Circumnavigation Expedition (ACE) 20th December 2016 - 19th March 2017 (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1443511", "Herenz, P., Wex, H., Henning, S., Kristensen, T. B., Rubach, F., Roth, A., Borrmann, S., Bozem, H., Schulz, H., and Stratmann, F.: Measurements of aerosol and CCN properties in the Mackenzie River delta (Canadian Arctic) during spring\u2013summer transition in May 2014, Atmos. Chem. Phys., 18, 4477\u20134496, https://doi.org/10.5194/acp-18-4477-2018, 2018.", "Khlystov, A., C. Stanier, and S. N. Pandis, 2004: An algorithm for combining electrical mobility and aerodynamic size distributions data when measuring ambient aerosol special issue of aerosol science and technology on findings from the fine particulate matter supersites program. Aerosol Science and Technology, 38, 229\u2013238, doi:10.1080/02786820390229543.", "Kristensen, T., T. M\u00fcller, K. Kandler, N. Benker, M. Hartmann, J. Prospero, A. Wiedensohler, and F. Stratmann, 2015: Properties of cloud condensa- tion nuclei (ccn) in the trade wind marine boundary layer of the eastern caribbean sea. Atmospheric Chemistry & Physics Discussions, 15.", "Modini, R. L., A. A. Frossard, L. Ahlm, L. M. Russell, C. E. Corrigan, G. C. Roberts, L. N. Hawkins, J. C. Schroder, A. K. Bertram, R. Zhao, A. K. Y. Lee, J. P. D. Abbatt, J. Lin, A. Nenes, Z. Wang, A. Wonasch\u00fctz, A. Sorooshian, K. J. Noone, H. Jonsson, J. H. Seinfeld, D. Toom-Sauntry, A. M. Macdonald, and W. R. Leaitch, 2015: Primary marine aerosol-cloud interactions off the coast of california. Journal of Geophysical Research: Atmospheres, 120, 4282\u20134303, doi:10.1002/2014JD022963. URL https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014JD022963", "P\u00e9rez, F., & Granger, B. E. (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering, 9(3), 21-29.", "Petters, M. and S. Kreidenweis, 2007: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity. Atmospheric Chemistry and Physics, 7, 1961\u20131971.", "Schmale, J., Henning, S., Henzing, B. et al. Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition. Sci Data 4, 170003 (2017). https://doi.org/10.1038/sdata.2017.3", "Schmale, Julia, Henning, Silvia, Tummon, Fiona, Hartmann, Markus, Baccarini, Andrea, Welti, Andr\u00e9, \u2026 Gysel-Beer, Martin. (2019a). Sub-micron aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition. (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2636700", "Schmale, Julia, Henning, Silvia, Tummon, Fiona, Hartmann, Markus, Baccarini, Andrea, Welti, Andr\u00e9, \u2026 Gysel-Beer, Martin. (2019b). Coarse mode aerosol particle size distribution collected in the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition. (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2636709", "Schmale, J., Baccarini, A., Thurnherr, I., Henning, S., Efraim, A., Regayre, L., Bolas, C., Hartmann, M., Welti, A., Lehtipalo, K., Aemisegger, F., Tatzelt, C., Landwehr, S., Modini, R. L., Tummon, F., Johnson, J. S., Harris, N., Schnaiter, M., Toffoli, A., Derkani, M., Bukowiecki, N., Stratmann, F., Dommen, J., Baltensperger, U., Wernli, H., Rosenfeld, D., Gysel-Beer, M., & Carslaw, K. S. (2019c). Overview of the Antarctic Circumnavigation Expedition: Study of Preindustrial-like Aerosols and Their Climate Effects (ACE-SPACE), Bulletin of the American Meteorological Society, 100(11), 2260-2283. https://doi.org/10.1175/BAMS-D-18-0187.1"]} The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. We acknowledge funding from DFG within SPP 1158 (Grant no. STR 453/12-1). For more information about this dataset, please contact Christian Tatzelt (tatzelt@tropos.de) or Silvia Henning (henning@tropos.de).

Dataset abstract Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods. Dataset contents ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values data_file_header_frozen_fraction.txt, metadata, text format data_file_header_number_concentration.txt, metadata, text format README.txt, metadata, text format change_log.txt, metadata, text format Change log v1.1 - data files updated change dataset title to reflect low-volume sampling method addition of INP number concentration data from different temperatures addition of fraction of frozen droplets data addition of field blank filter data create separate data_file_header files add change log v1.0 - initial release of dataset {"references": ["Hartmann, M., Blunier, T., Br\u00fcgger, S.\u00a0O., Schmale, J., Schwikowski, M., Vogel, A., Wex, H., and Stratmann, F.: Variation of ice nucleating particles in the European Arctic over the last centuries, Geophys. Res. Lett., 46, https://doi.org/10.1029/2019GL082311, 2019.", "Polen, M., Brubaker, T., Somers, J., and Sullivan, R. C.: Cleaning up our water: reducing interferences from nonhomogeneous freezing of \"pure\" water in droplet freezing assays of ice-nucleating particles, Atmos. Meas. Tech., 11, 5315-5334, https://doi.org/10.5194/amt-11-5315-2018, 2018.", "Fernando P\u00e9rez, Brian E. Granger, IPython: A System for Interactive Scientific Computing, Computing in Science and Engineering, vol. 9, no. 3, pp. 21-29, May/June 2007, doi:10.1109/MCSE.2007.53. URL: https://ipython.org", "Python Software Foundation. Python Language Reference, version 2.7.14. Available at http://www.python.org"]} The Antarctic Circumnavigation Expedition (ACE) was a scientific expedition carried out under the auspices of the Swiss Polar Institute, supported by funding from the ACE Foundation and Ferring Pharmaceuticals. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority programme "Antarctic Research with comparative investigations in the Arctic sea ice areas" SPP 1158 (grant STR 453/12-1).

Coastal systems can act as filters for anthropogenic nutrient input into marine environments. Here, we assess the processes controlling the removal of phosphorus (P) and nitrogen (N) for four sites in the eutrophic Stockholm archipelago. Bottom water concentrations of oxygen (O2) and P are inversely correlated. This is attributed to the seasonal release of P from iron-oxide-bound (Fe-oxide-bound) P in surface sediments and from degrading organic matter. The abundant presence of sulfide in the pore water and its high upward flux towards the sediment surface (∼4 to 8 mmol m−2 d−1), linked to prior deposition of organic-rich sediments in a low-O2 setting (“legacy of hypoxia”), hinder the formation of a larger Fe-oxide-bound P pool in winter. This is most pronounced at sites where water column mixing is naturally relatively low and where low bottom water O2 concentrations prevail in summer. Burial rates of P are high at all sites (0.03–0.3 mol m−2 yr−1), a combined result of high sedimentation rates (0.5 to 3.5 cm yr−1) and high sedimentary P at depth (∼30 to 50 µmol g−1). Sedimentary P is dominated by Fe-bound P and organic P at the sediment surface and by organic P, authigenic Ca-P and detrital P at depth. Apart from one site in the inner archipelago, where a vivianite-type Fe(II)-P mineral is likely present at depth, there is little evidence for sink switching of organic or Fe-oxide-bound P to authigenic P minerals. Denitrification is the major benthic nitrate-reducing process at all sites (0.09 to 1.7 mmol m−2 d−1) with rates decreasing seaward from the inner to outer archipelago. Our results explain how sediments in this eutrophic coastal system can remove P through burial at a relatively high rate, regardless of whether the bottom waters are oxic or (frequently) hypoxic. Our results suggest that benthic N processes undergo annual cycles of removal and recycling in response to hypoxic conditions. Further nutrient load reductions are expected to contribute to the recovery of the eutrophic Stockholm archipelago from hypoxia. Based on the dominant pathways of P and N removal identified in this study, it is expected that the sediments will continue to remove part of the P and N loads.

Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition, 2016-2017. Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations measured on seawater samples from the Southern Ocean. Samples were collected with a trace metal clean rosette system to a maximum depth of 1000 m during Legs 1 and 2 of the Antarctic Circumnavigation Expedition (ACE), 2016-2017. Samples were filtered through Akropak Supor filters (0.2 um) in a class 100 clean container, acidified to pH ≤ 2 and stored until analysis (>6 months). Samples from Leg 1 (TMR Casts 3-7) were collected during a transect from Cape Town, South Africa to Hobart, Australia. Samples from Leg 2 (TMR casts 8-20) were collected during a transect from Hobart, Australia to Punta Arenas, Chile. Data cover environments near subantarctic and Antarctic islands (TMR 3, 4, 13-15), in the Mertz Glacier Polynya (TMR 11-12) and near the Antarctic Peninsula (TMR 18), as well as meridional transects to and from the Antarctic continent (TMR 7-12, TMR 18-20). These data were collected as part of the Antarctic Circumnavigation Experiment, which was organized by the Swiss Polar Institute with funding from Ferring Pharmaceuticals. This dataset is accompanied by the following open access publication: Janssen, D.J., Sieber, M., Ellwood, M.J., Conway, T.M., Barrett, P.M., Chen, X., de Souza, G.F., Hassler, C.S., Jaccard, S.L. (2020). Trace metal and nutrient dynamics across broad biogeochemical gradients in the Indian and Pacific sectors of the Southern Ocean. Marine Chemistry. https://doi.org/10.1016/j.marchem.2020.103773 {"references": ["Janssen, D.J., Sieber, M., Ellwood, M.J., Conway, T.M., Barrett, P.M., Chen, X., de Souza, G.F., Hassler, C.S., Jaccard, S.L. (2020). Trace metal and nutrient dynamics across broad biogeochemical gradients in the Indian and Pacific sectors of the Southern Ocean. Marine Chemistry. https://doi.org/10.1016/j.marchem.2020.103773"]}