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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.

As part of the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP), several climate modeling centers performed a coordinated pre-study experiment with interactive stratospheric aerosol models simulating the volcanic aerosol cloud from an eruption resembling the 1815 Mt. Tambora eruption (VolMIP-Tambora ISA ensemble). The pre-study provided the ancillary ability to assess intermodel diversity in the radiative forcing for a large stratospheric-injecting equatorial eruption when the volcanic aerosol cloud is simulated interactively. An initial analysis of the VolMIP-Tambora ISA ensemble showed large disparities between models in the stratospheric global mean aerosol optical depth (AOD). In this study, we now show that stratospheric global mean AOD differences among the participating models are primarily due to differences in aerosol size, which we track here by effective radius. We identify specific physical and chemical processes that are missing in some models and/or parameterized differently between models, which are together causing the differences in effective radius. In particular, our analysis indicates that interactively tracking hydroxyl radical (OH) chemistry following a large volcanic injection of sulfur dioxide (SO2) is an important factor in allowing for the timescale for sulfate formation to be properly simulated. In addition, depending on the timescale of sulfate formation, there can be a large difference in effective radius and subsequently AOD that results from whether the SO2 is injected in a single model grid cell near the location of the volcanic eruption, or whether it is injected as a longitudinally averaged band around the Earth.

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).

Quantitative constraints on soil organic matter (SOM) dynamics are essential for comprehensive understanding of the terrestrial carbon cycle. Deep soil carbon is of particular interest, as it represents large stocks and its turnover times remain highly uncertain. In this study, SOM dynamics in both the top and deep soil across a climatic (average temperature ~1-9 °C) gradient are determined using time-series (~20 years) 14C data from bulk soil and water-extractable organic carbon (WEOC). Analytical measurements reveal enrichment of bomb-derived radiocarbon in the deep soil layers on the bulk level during the last two decades. The WEOC pool is strongly enriched in bomb-derived carbon, indicating that it is a dynamic pool. Turnover time estimates of both the bulk and WEOC pool show that the latter cycles up to a magnitude faster than the former. The presence of bomb-derived carbon in the deep soil, as well as the rapidly turning WEOC pool across the climatic gradient implies that there likely is a dynamic component of carbon in the deep soil. Precipitation and bedrock type appear to exert a stronger influence on soil C turnover time and stocks as compared to temperature. Data_and_code_Van_der_Voort_et_al_2019_BiogeosciencesThis zip file contains all the code and data accompanying the paper: Dynamics of deep soil carbon - insights from 14C time series across a climatic gradient (Van der Voort et al., Biogeosciences, 2019). The data of each figure can be found in the excel file. The MatLab codes referenced in the paper can also be found in the zip file. They are thoroughly commented so that users can easily re-use it.Time_Series_Data_Repo_Folder_Dryad.zip

{"references": ["Jonkers, L., Cartapanis, O., Langner, M., McKay, N., Mulitza, S., Strack, A., and Kucera, M.: Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis, Earth Syst. Sci. Data, 12, 1053\u20131081, https://doi.org/10.5194/essd-12-1053-2020, 2020"]} Palaeoclimate time series in the PALMOD 130k marine palaeoclimate data synthesis v1.0.1. This table lists the site names and location, parameters including additional information as well as the source of the data and the original publications where the data were presented.

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