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24 Research products

  • European Marine Science
  • 2019-2023
  • Research data
  • Other research products
  • GB
  • English
  • Aurora Universities Network

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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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      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 . 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: 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.

    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/ 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/
    Authors: González-Dávila, Melchor; Droste, Elise Sayana; Santana-Casiano, Juana Magdalena; Schuller, Daniel; +3 Authors

    A fraction of oceanographic profiles (from CTD casts), the temperature, salinity, oxygen, and nutrient data have already previously been published (https://doi.org/10.1594/PANGAEA.910673). We have included these to maintain consistency with the rest of the profiles in the dataset (for which these variables have not been published yet). In addition to the fact that exactly these data were used in the calculations for the marine carbonate system in Droste et al. (2022), it is also easier for users to work with this dataset in the future. The DOI of the already published data is included in the comment column of the dataset itself. Details on sample collection, analysis methods, and salinity-normalisation of DIC and TA can be found in Droste et al. (2022). Discrete seawater samples in a Weddell Sea coastal polynya along the Ekström Ice Shelf were collected from two sets of repeat CTD casts, capturing tidal variability in the water column. One set was collected during RV POLARSTERN expedition PS89, between 8 and 11 January 2015. The second set was collected during RV POLARSTERN expedition PS117, between 11 and 12 January 2019. Dissolved inorganic carbon (DIC) and total alkalinity (TA) were measured using coulometric titration and potentiometric titration, respectively, on a VINDTA 3C system. DIC and TA have been normalised to salinity: nDIC and nTA. Nutrients were measured with UV-Vis spectrophotometry and a continuous gas-segmented flow auto-analyser.

    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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      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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    Authors: Vasudeva, Ramakrishnan; Sutter, Andreas; Sales, Kris; Dickinson, Matthew E.; +2 Authors

    Rising and more variable global temperatures pose a challenge for biodiversity, with reproduction and fertility being especially sensitive to heat. Here, we assessed the potential for thermal adaptation in sperm and egg function using Tribolium flour beetles, a warm-temperate-tropical insect model. Following temperature increases through adult development, we found opposing gamete responses, with males producing shorter sperm and females laying larger eggs. Importantly, this gamete phenotypic plasticity was adaptive: thermal translocation experiments showed that both sperm and eggs produced in warmer conditions had superior reproductive performance in warmer environments, and vice versa for cooler production conditions and reproductive environments. In warmer environments, gamete plasticity enabled males to double their reproductive success, and females could increase offspring production by one-third. Our results reveal exciting potential for sensitive but vital traits within reproduction to handle increasing and more variable thermal regimes in the natural environment.

    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
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    ZENODO
    Dataset . 2020
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2020
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2020
      License: CC 0
      Data sources: Datacite
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    Authors: Sonne, Jesper; Vizentin-Bugoni, Jeferson; Maruyama, Pietro K.; Araújo, Andréa C.; +25 Authors

    Data consisting of 24 quantitative interaction networks sampled throughout the Americas, in areas mostly or entirely covered with native vegetation (Table ESM2). The networks comprise 106 hummingbird species, 31% of all described hummingbird species in the world according to the IOC World Bird List v.7.3 (ESM3), and 450 plant species belonging to 57 plant families (ESM4, see ESM5a for additional details on sampling). The abundance of plant species was measured as the number of flowers produced per species in each community throughout the entire sampling period. Flowers were counted in plots or transects estimated regularly throughout the sampling period. The abundance of hummingbirds within sites was measured in the field by counting the number of visual and aural detections of individuals across transects (n=12 networks) or point counts (n=4 networks), or the number of individuals captured by mist-netting (n=8 networks; ESM5a). Because the abundance sampling protocols were not standardized among networks, we treated the data as relative abundance, i.e. for all species we calculate their abundance as the proportion of the total number of individuals within a given community. Still, we note that mist nets may be especially efficient for surveying elusive understory species, such as traplining hummingbirds, whereas transects and point counts may be better at surveying species at higher vegetation strata [34]. We recognize that the caveat inherent in using different sampling schemes across networks may influence the outcome of our analyses. However, as we used relative abundances to model interaction frequencies within networks (not between networks), we believe that the different sampling schemes had a minimal influence on our results. The phenology of each plant and hummingbird species in each network was determined as the presence-absence of, respectively, open flowers and individuals at each sampling period (usually months). Flower morphology was characterized by the effective corolla length, measured as the distance from the nectary to the corolla opening. The effective corolla length reflects the minimum length of mouthparts required for pollinators to access the nectar legitimately. Hummingbird bill morphology was measured as the length of the exposed culmen from captured hummingbird individuals (see ESM5b further details on sampling). Interactions between species are influenced by different ecological mechanisms, such as morphological matching, phenological overlap, and species abundances. How these mechanisms explain interaction frequencies across environmental gradients remains poorly understood. Consequently, we also know little about the mechanisms that drive the geographical patterns in network structure, such as complementary specialization and modularity. Here, we use data on morphologies, phenologies and abundances to explain interaction frequencies between hummingbirds and plants at a large geographic scale. For 24 quantitative networks sampled throughout the Americas, we found that the tendency of species to interact with morphologically matching partners contributed to specialized and modular network structures. Morphological matching best explained interaction frequencies in networks found closer to the equator and in areas with low temperature seasonality. When comparing the three ecological mechanisms within networks, we found that both morphological matching and phenological overlap generally outperformed abundances in the explanation of interaction frequencies. Together, these findings provide insights into the ecological mechanisms that underlie geographical patterns in resource specialization. Notably, our results highlight morphological constraints on interactions as a potential explanation for increasing resource specialization towards lower latitudes.

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    ZENODO
    Dataset . 2020
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2020
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2020
      License: CC 0
      Data sources: Datacite
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    Authors: Bajo, Petra; Drysdale, Russell N; Woodhead, Jon D; Hellstrom, John C; +10 Authors

    2020-05-18: Correction of depth values to meter (multiplication of prior values by 100), parameter set to "DEPTH, sediment/rock" (corrected revised meters composite depth); update of PIs

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    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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    Authors: Cramwinckel, Margot J; Woelders, Lineke; Huurdeman, Emiel P; Peterse, Francien; +6 Authors
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    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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    Authors: Warren-Thomas, Eleanor; Nelson, Luke; Juthong, Watinee; Bumrungsri, Sara; +7 Authors

    Monocultural rubber plantations have replaced tropical forest, causing biodiversity loss. While protecting intact or semi-intact biodiverse forest is paramount, improving biodiversity value within the 11.4 million hectares of existing rubber plantations could offer important conservation benefits, if yields are also maintained. Some farmers practice agroforestry with high-yielding clonal rubber varieties to increase and diversify incomes. Here, we ask whether such rubber agroforestry improves biodiversity value or affects rubber yields relative to monoculture. We surveyed birds, fruit-feeding butterflies and reptiles in 25 monocultural and 39 agroforest smallholder rubber plots in Thailand, the world’s biggest rubber producer. Management and vegetation structure data were collected from each plot, and landscape composition around plots was quantified. Rubber yield data were collected for a separate set of 34 monocultural and 47 agroforest rubber plots in the same region. Reported rubber yields did not differ between agroforests and monocultures, meaning adoption of agroforestry in this context should not increase land demand for natural rubber. Butterfly richness was greater in agroforests, where richness increased with greater natural forest extent in the landscape. Bird and reptile richness were similar between agroforests and monocultures, but bird richness increased with the height of herbaceous vegetation inside rubber plots. Species composition of butterflies differed between agroforests and monocultures, and in response to natural forest extent, while bird composition was influenced by herbaceous vegetation height within plots, the density of non-rubber trees within plots (representing agroforestry complexity), and natural forest extent in the landscape. Reptile composition was influenced by canopy cover and open habitat extent in the landscape. Conservation priority and forest-dependent birds were not supported within rubber. Synthesis and applications. Rubber agroforestry using clonal varieties provides modest biodiversity benefits relative to monocultures, without compromising yields. Agroforests may also generate ecosystem service and livelihood benefits. Management of monocultural rubber production to increase inter-row vegetation height and complexity may further benefit biodiversity. However, biodiversity losses from encroachment of rubber onto forests will not be offset by rubber agroforestry or rubber plot management. This evidence is important for developing guidelines around biodiversity-friendly rubber and sustainable supply chains, and for farmers interested in diversifying rubber production. The accompanying ReadMe.txt file explains the contents of each .csv file, including definitions of each column. Sampling protocols are outlined in the paper in Journal of Applied Ecology.

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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2019
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2019
      License: CC 0
      Data sources: Datacite
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    Authors: Cunliffe, Andrew; Myers-Smith, Isla; Kerby, Jeffrey; Palmer, William;

    Drone surveys were conducted using two platforms: (i) a lightweight flyingwing Zeta Phantom FX-61 with a PixHawk flight controller equipped with a Sony RX-100ii camera (100 CMOS sensor with 20.2 megapixels) and (ii) a multi-rotor DJI Phantom 4 Pro (100 CMOS sensor with 20 megapixels). Typical flying altitude was between 100 and 120 m above ground level, yielding ground sampling distances of ca. 20 to 40mm, with image overlap of more than nine photographs across most (90%) of the scene. Spatial constraint of the orthomosaic was achieved through geotagging of individual camera positions with the UAV location, combined with n=132 black and white ground control markers deployed across the scene and precisely geolocated to an absolute accuracy of approximately 0.02m using real-time kinematic global navigation satellite system (GNSS) equipment (Leica Geosystems). These photographs were processed using Agisoft PhotoScan (version 1.3.3) (https://www.agisoft.com/), to a spatial reference system of NAD83 UTM 7N (EPSG: 26907). The following quality settings were used: Image quality assessment Minimum quality score >=0.7 Image alignment Accuracy: Highest Generic preselection: Yes Reference preselection: Yes Key point limit: 40,000 Tie point limit: 0 Adaptive camera model fitting: No Tie point filtering Reprojection error threshold: 0.45 Parameter optimisation: Enabled parameters: F, Cz, Cy, B1, B2, K1, K2, K3, P1, P2 Fit rolling shutter: No Dense cloud Quality: High Depth filtering: Mild Orthomosaic Mapping mode: Orthophoto Blending mode: Mosaic Enable colour correction: Yes Enable hole filling: Yes For additional information on the production and use of this dataset, please refer to: Rapid retreat of permafrost coastline observed with aerial drone photogrammetry, Cunliffe, A. M., Tanski, G., Radosavljevic, B., Palmer, W. F., Sachs, T., Lantuit, H., Kerby, J. T., and Myers-Smith, I. H.: Rapid retreat of permafrost coastline observed with aerial drone photogrammetry, The Cryosphere, 13, 1513-1528, https://doi.org/10.5194/tc-13-1513-2019, 2019 and also the supplementary information accompanying the article. This red-green-blue (RGB) orthomosaic is composite created from 8994 photographs collected withunmanned aerial vehicles (UAVs) over the eastern part of Qikiqtaruk, Herschel Island, in the Canadian Yukon (69.5N, 138.8W). The images were collected on the 10th and 11th of August 2017. Further details on the image processing are provided in the Lineage section. This dataset was created by Andrew Cunliffe, with support from Isla Myers-Smith, William Palmer, Jeffrey Kerby and other members of Team Shrub (https://teamshrub.com/), in order to inform ongoing ecological monitoring studies in this area. Part of this orthomosaic was used for a study into permafrost coastline retreat, published in The Cryosphere (Cunliffe, A. M., Tanski, G., Radosavljevic, B., Palmer, W. F., Sachs, T., Lantuit, H., Kerby, J. T., and Myers-Smith, I. H.: Rapid retreat of permafrost coastline observed with aerial drone photogrammetry, The Cryosphere, 13, 1513-1528, https://doi.org/10.5194/tc-13-1513-2019, 2019). There are some small missing areas within the orthomosaic, due to insufficient image overlap of these areas (nodata is indicated by value of 255 in the Red, Green and Blue bands). Some artefacts (distortion) in the orthomosaic are expected around the periphery of the survey area, particularly where (reflective or moving) water is present.

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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
      Data sources: B2FIND
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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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    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: González-Dávila, Melchor; Droste, Elise Sayana; Santana-Casiano, Juana Magdalena; Schuller, Daniel; +3 Authors

    A fraction of oceanographic profiles (from CTD casts), the temperature, salinity, oxygen, and nutrient data have already previously been published (https://doi.org/10.1594/PANGAEA.910673). We have included these to maintain consistency with the rest of the profiles in the dataset (for which these variables have not been published yet). In addition to the fact that exactly these data were used in the calculations for the marine carbonate system in Droste et al. (2022), it is also easier for users to work with this dataset in the future. The DOI of the already published data is included in the comment column of the dataset itself. Details on sample collection, analysis methods, and salinity-normalisation of DIC and TA can be found in Droste et al. (2022). Discrete seawater samples in a Weddell Sea coastal polynya along the Ekström Ice Shelf were collected from two sets of repeat CTD casts, capturing tidal variability in the water column. One set was collected during RV POLARSTERN expedition PS89, between 8 and 11 January 2015. The second set was collected during RV POLARSTERN expedition PS117, between 11 and 12 January 2019. Dissolved inorganic carbon (DIC) and total alkalinity (TA) were measured using coulometric titration and potentiometric titration, respectively, on a VINDTA 3C system. DIC and TA have been normalised to salinity: nDIC and nTA. Nutrients were measured with UV-Vis spectrophotometry and a continuous gas-segmented flow auto-analyser.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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