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179 Research products

  • European Marine Science
  • 2013-2022
  • Research data
  • Other research products
  • European Commission
  • 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: 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.

    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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    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/
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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
      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/
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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.

    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/ PANGAEA - Data Publi...arrow_drop_down
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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/ PANGAEA - Data Publi...arrow_drop_down
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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.

    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/ PANGAEA - Data Publi...arrow_drop_down
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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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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: 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

    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/ PANGAEAarrow_drop_down
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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
    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/
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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/ PANGAEAarrow_drop_down
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    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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    Authors: Gómez de la Peña, Laura; Maesano, Francesco; Basili, Roberto; Romano, Fabrizio;

    We present the results of our analysis of the seismogenic and tsunamigenic structure of the Alboran Basin (westernmost Mediterranean). In particular, we upload two different types of files: 1) the 3D model of a fault plane and 2) the results of the numerical tsunami simulations for the 4 main faults in the area, the Alboran Ridge Fault System (ARFS), the Carboneras Fault System (CFS), the Yusuf Fault System (YFS) and the Al-Idrissi Fault System (AIFS). In order to perform a first approach to the tsunamigenic potential of these active structures, different models have been run with different input parameters (see metadata description). The fault plane has been obtained based on the analysis of active seismic data collected in the area, and the tsunami simulations have been obtained using the HySEA code. For details about the method, and the discussion of the different parameters used in the models, please see the related article "A first appraisal of the seismogenic and tsunamigenic potential of the largest fault systems of the westernmost Mediterranean" (Gómez de la Peña et al., Marine Geology, 2022). Tsunami simulations files: grid files netCDF (*.nc) containing the maximum amplitude wave at each point of the Alboran Basin (geographical coordinates). For details and discussion of each scenario, please see the related article (Gómez de la Peña et al., Marine Geology).

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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: Bracher, Astrid; Wiegmann, Sonja;

    We present a data set on remote sensing reflectance (RRS) at 1nm resolution from 350 to 800nm obtained from measurements in the North Sea and Sogne Fjord from 30 April to 7 May 2016. For the measurements we used radiometric hyperspectral (3.3 nm sampling, 10 nm FWHM) underwater profile measurements down to the 0.1 % light level using RAMSES (TriOS GmbH, Germany) sensors which measured depth resolved the upwelling radiance and downwelling irradiance, both corrected by incident sunlight fluctuations with a second RAMSES sensor measuring the above water downwelling irradiance. The later sensor data were also used to finally calculate RRS. We followed the protocol by Mueller et al. (2003) further modified by Matsuoka et al. (2007) and Stramski et al. (2008), as described for our instrument set-up in Taylor et al. (2011). Our method is further described and assessed for its uncertainty in Tilstone et al. (2020). The same campaign was sampled for optical constituents hyperspectral absorption data in Bracher et al. (2021a-d) and for phytoplankton pigments in Bracher and Wiegmann (2019).

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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: Bracher, Astrid; Cheah, Wee;

    We present a data set on remote sensing reflectance (RRS) at 1nm resolution from 350 to 800nm obtained from measurements in the coastal and open ocean areas of the South China Sea and Sulu Sea from 18 to 27 November 2011. For the measurements we used radiometric hyperspectral (3.3 nm sampling, 10 nm FWHM) underwater profile measurements down to the 0.1 % light level using RAMSES (TriOS GmbH, Germany) sensors which measured depth resolved the upwelling radiance and downwelling irradiance, both corrected by incident sunlight fluctuations with a second RAMSES sensor measuring the above water downwelling irradiance. The later sensor data were also used to finally calculate RRS. We followed the protocol by Mueller et al. (2003) further modified by Matsuoka et al. (2007) and Stramski et al. (2008), as described for our instrument set-up in Taylor et al. (2011). Our method is further described and assessed for its uncertainty in Tilstone et al. (2020). The campaign is described in detail in Cheah et al. (2013) and was also optical constituents hyperspectral absorption data (Bracher et al. 2021a, b) and phytoplankton pigments (Bracher 2014) were measured. We are indebted to Maria Altenburg-Soppa, Sonja Wiegmann and Joseph Palermo for their assistance in the sampling on RV Sonne and acknowledge the help of the chief scientist Birgit Quack, the crew and captain of the RV Sonne during SHIVA-Sonne in performing our measurements.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
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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_M4U = 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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      Dataset . 2022
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    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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    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
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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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      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/ PANGAEA - Data Publi...arrow_drop_down
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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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