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209 Research products

  • European Marine Science
  • 2014-2023
  • Research data
  • Other research products
  • EU
  • GB
  • English
  • Aurora Universities Network
  • European Marine Science

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Karstensen, Johannes; Krahmann, Gerd;

    Seabird 911plus systems equipped with dual temperature-conductivity-oxygen sensors were employed. All systems had a 24-bottle water sampling rosette with 10 l Niskin bottles. Water sampling, processing, and calibration followed GO-SHIP recommendations (Swift, 2010; McTaggart et al., 2010; Uchida et al., 2010) and included the recommended steps Data Conversion, Sensor Time-Alignment, Creation of Bottle Files, Outlier Removal, Pressure Sensor Filtering, Conductivity Cell Thermal Mass Correction, Ship Roll Correction and Deck Offset Correction by Loop Editing, as well as Derivation of Calculated Properties. After these steps, conductivity and oxygen readings were calibrated against values determined with salinometry and Winkler titration , respectively. Finally, the downcast data was averaged over 1 dbar wide intervals. An independent upcast calibration was used to obtain calibrated CTDO values coincident with the discrete water samples. PO-GLOBAL-SVN = 689 used on 10-Jun-2020 15:15:01Matlab = 9.4.0.813654 (R2018a)Release = 3nc_uncertainty_p = 2.000000nc_uncertainty_t = 0.002000nc_uncertainty_s = 0.003000nc_uncertainty_o = 1.000000

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Droste, Elise Sayana; Bakker, Dorothee C E; Hoppema, Mario; Ossebaar, Sharyn; +4 Authors

    Discrete seawater samples for the determination of dissolved inorganic carbon (DIC) and total alkalinity (TA) were collected from CTD stations during RV POLARSTERN expedition PS117, between 15 December 2018 and 7 February 2019. Seawater samples were collected from stations that used the AWI-operated CTD, as well as the Ultra-Clean-CTD, operated by NIOZ. DIC and TA were measured using coulometric titration and potentiometric titration, respectively, on a VINDTA 3C system. Nutrients were measured with UV-Vis spectrophotometry and a continuous gas-segmented flow auto-analyser. Data for station 41 have previously been published on Pangaea and are excluded from this dataset (https://doi.org/10.1594/PANGAEA.946363). Bottle data (including nutrients) from the AWI CTD and Ultra-Clean-CTD stations have already been published and are stored on Pangaea under https://doi.org/10.1594/PANGAEA.910673 and https://doi.org/10.1594/PANGAEA.940209, respectively. Data quality flags follow the WOCE quality code definitions for water sample measurements. Details on sample collection and analysis methods for DIC and TA can be found in Droste et al. (2022).

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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  • Authors: de Bar, Marijke W; Weiss, Gabriella M; Yildiz, Caglar; Rampen, Sebastiaan W; +13 Authors

    #0 = coel.; *0 = not available.Note 1: If there was no salinity data (WOA13) available for a certain core-top location, and there was also no LDI or C32 1,15-diol fractional abundance value for this core-top, then we have not adopted data from nearby grids.Note 2: If there was no SST data (WOA13) available for a certain core-top location, and there was also no LDI value for this core-top, then we have not adopted data from nearby grids.Note 3: If there was no seasonal SST or nutrient data available for a certain core-top location from WOA13, and this core-top was not included in the final LDI calibration, then we have not adopted data from nearby grids.Note 4: The fractional abundances of a few core-tops from the Iberian margin do not sum up to exactly 1, this is because in the original paper (de Bar et al., 2016) the core-tops were analyzed two to three times, and the fractional abundances were averaged.Note 4: The fractional abundances of a few core-tops from the "Rampen et al., 2012" reference do not sum up to exactly 1, because the original data was not available, and thus the reported data from Rampen et al. (2012) was used which are rounded to two decimal places.Note 5: Long chain diols marked as "n.a." or "coel." (=co-elutes) were treated as zero for calculating the final fractional abundances. In case one of the LDI or Diol Index diols was marked as "n.a." or "coel.", the LDI or Diol Index was not calculated.Note 6: For certain samples fractional abundances are given but not the LDI (mainly in Arctic and Antarctic), as the relative LDI diol abundances were considered too low for index calculation.Note that the number of decimals shown here is different from the original dataset, i.e. the dataset in the supplements of the article.

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    Authors: Christine McKenna;

    This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. Geophysical Research Letters, 45, 945-954. https://doi.org/10.1002/2017GL076433 Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'. All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are: ua: zonal winds zg: geopotential height ts: surface temperature hfls, hfss, rlds, rlus: surface heatfluxes Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February) The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Romero-Alvarez, Johana; Lupaşcu, Aurelia; Lowe, Douglas; Badia, Alba; +4 Authors

    Tropospheric ozone (O3) concentrations depend on a combination of hemispheric, regional, and local-scale processes. Estimates of how much O3 is produced locally vs. transported from further afield are essential in air quality management and regulatory policies. Here, a tagged-ozone mechanism within the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is used to quantify the contributions to surface O3 in the UK from anthropogenic nitrogen oxide (NOx) emissions from inside and outside the UK during May–August 2015. The contribution of the different source regions to three regulatory O3 metrics is also examined. It is shown that model simulations predict the concentration and spatial distribution of surface O3 with a domain-wide mean bias of −3.7 ppbv. Anthropogenic NOx emissions from the UK and Europe account for 13 % and 16 %, respectively, of the monthly mean surface O3 in the UK, as the majority (71 %) of O3 originates from the hemispheric background. Hemispheric O3 contributes the most to concentrations in the north and the west of the UK with peaks in May, whereas European and UK contributions are most significant in the east, south-east, and London, i.e. the UK's most populated areas, intensifying towards June and July. Moreover, O3 from European sources is generally transported to the UK rather than produced in situ. It is demonstrated that more stringent emission controls over continental Europe, particularly in western Europe, would be necessary to improve the health-related metric MDA8 O3 above 50 and 60 ppbv. Emission controls over larger areas, such as the Northern Hemisphere, are instead required to lessen the impacts on ecosystems as quantified by the AOT40 metric.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Chemistr...arrow_drop_down
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    Authors: Lehmann, Moritz K; Gurlin, Daniela; Pahlevan, Nima; Alikas, Krista; +73 Authors

    Projects include:* Estonian Ministry of Education and Research* Estonian Research Council* European Commission, Award: FP7, H2020, FP7-ENV-2007-1-226224* Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340* Federal Ministry of Education and Research Germany, Award: 03G0218A* Helmholtz Infrastructure Initiative, Award: FRAM* NASA ROSES, Award: 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389* New Zealand Ministry for Business, Innovation & Employment, Award: UOWX1503, UOWX1802, KENTR1601* USGS Landsat Science Team Award, Award: 140G0118C0011* Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329 The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement, chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 53 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Guerreiro, Catarina V; Baumann, Karl-Heinz; Brummer, Geert-Jan A; Valente, André; +4 Authors

    Data refer to export fluxes of carbonate produced by calcifying phytoplankton (coccolithophores), and coccolith-CaCO₃ percent contribution to total carbonate flux across the tropical North Atlantic, from upwelling affected NW Africa, via three ocean sites along 12°N to the Caribbean. Sampling was undertaken by means of a spatial array of four time-series sediment traps (i.e., CB at 21°N 20°W; M1U at 12°N 23°W; M2U at 14°N 37°W; M4U at 12°N 49°W; Guerreiro et al., 2021) collecting particle fluxes in two-week intervals, from October 2012 to February 2014, allowing to track temporal changes along the southern margin of the North Atlantic central gyre. Auxiliary PIC (Particulate Inorganic Carbon) data from NASA's Ocean Biology Processing Group (https://oceancolor.gsfc.nasa.gov) are also provided for the sediment sampling period at all four trap sites. Particle flux data (mg/m²/d) of CaCO₃, organic matter, particulate organic carbon (POC), biogenic silica (bSiO₂) and unspecified residual fraction are provided for sediment trap site CB.

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    Authors: Guerreiro, Catarina V; Baumann, Karl-Heinz; Brummer, Geert-Jan A; Valente, André; +4 Authors

    Data Pangaea Guerreiro et al. 2021_M1U = Coccolith-CaCO₃ fluxes (mg/m²/d), Coccolith-CaCO₃ %, and Coccolith-% contribution to CaCO₃ flux of Calcidiscus leptoporus, Emilinia huxleyi, Florisphaera profunda, Gladiolithus flabellatus, Gephyrocapsa ericsonii, Gephyrocapsa muellerae, Gephyrocapsa oceanica, Helicosphaera spp., Umbellosphaera spp. Rhabdosphaera spp., Umbilicosphaera spp., Reticulofenestra sessilis, other taxa, total coccolith-CaCO₃. Auxiliary PIC (Particulate Inorganic Carbon) data from NASA's Ocean Biology Processing Group (https://oceancolor.gsfc.nasa.gov) are also provided.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Cao, Mengli; Hefter, Jens; Tiedemann, Ralf; Lembke-Jene, Lester; +2 Authors

    We present TEX86 (Tetraether index of tetraethers consisting of 86 carbon atoms) data from sediment cores SO202/1_18-3 and SO202/1_18-6 in the Bering Sea shelf. TEX86 is a sea surface temperature (SST) proxy based on the distribution of archaeal isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs), and reconstructs SST development during the latest two glacial-interglacial cycles. The branched isoprenoid tetraether (BIT) index (Hopmans et al., 2004) is an indicator of the relative contribution of soil-derived GDGT to marine GDGT. Crucially, a global core top calibration of marine sediments reveals a linear correlation between sedimentary TEX86 and in situ annual mean SST where terrestrial inputs are low (Kim et al., 2008). If BIT values are above the critical value of 0.3 (Weijers et al., 2006), where terrigenous GDGTs potentially bias TEX86-derived SST reconstruction. The regional calibration of SST and TEX86 is based on Seki et al. (2014). TEX86= log (GDGT-2/(GDGT-1+GDGT-2+GDGT-3)) SST/℃ = 27.2 × TEX86+ 21.8

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    PANGAEA
    Dataset . 2022
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jongejans, Loeka Laura; Liebner, Susanne; Knoblauch, Christian; Mangelsdorf, Kai; +1 Authors

    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.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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209 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Karstensen, Johannes; Krahmann, Gerd;

    Seabird 911plus systems equipped with dual temperature-conductivity-oxygen sensors were employed. All systems had a 24-bottle water sampling rosette with 10 l Niskin bottles. Water sampling, processing, and calibration followed GO-SHIP recommendations (Swift, 2010; McTaggart et al., 2010; Uchida et al., 2010) and included the recommended steps Data Conversion, Sensor Time-Alignment, Creation of Bottle Files, Outlier Removal, Pressure Sensor Filtering, Conductivity Cell Thermal Mass Correction, Ship Roll Correction and Deck Offset Correction by Loop Editing, as well as Derivation of Calculated Properties. After these steps, conductivity and oxygen readings were calibrated against values determined with salinometry and Winkler titration , respectively. Finally, the downcast data was averaged over 1 dbar wide intervals. An independent upcast calibration was used to obtain calibrated CTDO values coincident with the discrete water samples. PO-GLOBAL-SVN = 689 used on 10-Jun-2020 15:15:01Matlab = 9.4.0.813654 (R2018a)Release = 3nc_uncertainty_p = 2.000000nc_uncertainty_t = 0.002000nc_uncertainty_s = 0.003000nc_uncertainty_o = 1.000000

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Droste, Elise Sayana; Bakker, Dorothee C E; Hoppema, Mario; Ossebaar, Sharyn; +4 Authors

    Discrete seawater samples for the determination of dissolved inorganic carbon (DIC) and total alkalinity (TA) were collected from CTD stations during RV POLARSTERN expedition PS117, between 15 December 2018 and 7 February 2019. Seawater samples were collected from stations that used the AWI-operated CTD, as well as the Ultra-Clean-CTD, operated by NIOZ. DIC and TA were measured using coulometric titration and potentiometric titration, respectively, on a VINDTA 3C system. Nutrients were measured with UV-Vis spectrophotometry and a continuous gas-segmented flow auto-analyser. Data for station 41 have previously been published on Pangaea and are excluded from this dataset (https://doi.org/10.1594/PANGAEA.946363). Bottle data (including nutrients) from the AWI CTD and Ultra-Clean-CTD stations have already been published and are stored on Pangaea under https://doi.org/10.1594/PANGAEA.910673 and https://doi.org/10.1594/PANGAEA.940209, respectively. Data quality flags follow the WOCE quality code definitions for water sample measurements. Details on sample collection and analysis methods for DIC and TA can be found in Droste et al. (2022).

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
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  • Authors: de Bar, Marijke W; Weiss, Gabriella M; Yildiz, Caglar; Rampen, Sebastiaan W; +13 Authors

    #0 = coel.; *0 = not available.Note 1: If there was no salinity data (WOA13) available for a certain core-top location, and there was also no LDI or C32 1,15-diol fractional abundance value for this core-top, then we have not adopted data from nearby grids.Note 2: If there was no SST data (WOA13) available for a certain core-top location, and there was also no LDI value for this core-top, then we have not adopted data from nearby grids.Note 3: If there was no seasonal SST or nutrient data available for a certain core-top location from WOA13, and this core-top was not included in the final LDI calibration, then we have not adopted data from nearby grids.Note 4: The fractional abundances of a few core-tops from the Iberian margin do not sum up to exactly 1, this is because in the original paper (de Bar et al., 2016) the core-tops were analyzed two to three times, and the fractional abundances were averaged.Note 4: The fractional abundances of a few core-tops from the "Rampen et al., 2012" reference do not sum up to exactly 1, because the original data was not available, and thus the reported data from Rampen et al. (2012) was used which are rounded to two decimal places.Note 5: Long chain diols marked as "n.a." or "coel." (=co-elutes) were treated as zero for calculating the final fractional abundances. In case one of the LDI or Diol Index diols was marked as "n.a." or "coel.", the LDI or Diol Index was not calculated.Note 6: For certain samples fractional abundances are given but not the LDI (mainly in Arctic and Antarctic), as the relative LDI diol abundances were considered too low for index calculation.Note that the number of decimals shown here is different from the original dataset, i.e. the dataset in the supplements of the article.

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    Authors: Christine McKenna;

    This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. Geophysical Research Letters, 45, 945-954. https://doi.org/10.1002/2017GL076433 Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'. All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are: ua: zonal winds zg: geopotential height ts: surface temperature hfls, hfss, rlds, rlus: surface heatfluxes Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February) The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/