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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/ Copernicus Publicati...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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    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).

    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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      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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  • 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.

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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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  • 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: 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
    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
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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: 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: Simmons, Benno I.; Sutherland, William J.; Dicks, Lynn V.; Albrecht, Jörg; +4 Authors

    1.There is growing interest in understanding the functional outcomes of species interactions in ecological networks. For many mutualistic networks, including pollination and seed dispersal networks, interactions are generally sampled by recording animal foraging visits to plants. However, these visits may not reflect actual pollination or seed dispersal events, despite these typically being the ecological processes of interest. 2.Frugivorous animals can act as seed dispersers, by swallowing entire fruits and dispersing their seeds, or as pulp peckers or seed predators, by pecking fruits to consume pieces of pulp or seeds. These processes have opposing consequences for plant reproductive success. Therefore, equating visitation with seed dispersal could lead to biased inferences about the ecology, evolution and conservation of seed dispersal mutualisms. 3.Here we use natural history information on the functional outcomes of pairwise bird‐plant interactions to examine changes in the structure of seven European plant‐frugivore visitation networks after non‐mutualistic interactions (pulp‐pecking and seed predation) have been removed. Following existing knowledge of the contrasting structures of mutualistic and antagonistic networks, we hypothesised a number of changes following interaction removal, such as increased nestedness and lower specialisation. 4.Non‐mutualistic interactions with pulp peckers and seed predators occurred in all seven networks, accounting for 21–48% of all interactions and 6–24% of total interaction frequency. When non‐mutualistic interactions were removed, there were significant increases in network‐level metrics such as connectance and nestedness, while robustness decreased. These changes were generally small, homogenous and driven by decreases in network size. Conversely, changes in species‐level metrics were more variable and sometimes large, with significant decreases in plant degree, interaction frequency, specialisation and resilience to animal extinctions, and significant increases in frugivore species strength. 5.Visitation data can overestimate the actual frequency of seed dispersal services in plant‐frugivore networks. We show here that incorporating natural history information on the functions of species interactions can bring us closer to understanding the processes and functions operating in ecological communities. Our categorical approach lays the foundation for future work quantifying functional interaction outcomes along a mutualism–antagonism continuum, as documented in other frugivore faunas. Classification of European bird-plant frugivore interactionsThe file contains classifications of European bird-plant frugivore interactions as seed dispersal, pulp pecking or seed predation based on literature sources or (when no sources were available) inferred based on interactions with similar plant species or congeneric species.interaction_classification.csv

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    Dataset . 2018
    Data sources: B2FIND
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    DRYAD; ZENODO; NARCIS
    Dataset . 2019
    License: CC 0
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      Dataset . 2018
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      DRYAD; ZENODO; NARCIS
      Dataset . 2019
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    Authors: Rhodes, Rachael H; Brook, Edward J;

    Description and NotesDescription: Methane concentration from the Greenland NEEM-2011-S1 Ice Core from 71 to 408m depth (~270-1961 CE). Methane concentrations analysed online by laser spectrometer (SARA, Spectroscopy by Amplified Resonant Absorption, developed at Laboratoire Interdisciplinaire de Physique, Grenoble, France) on gas extracted from an ice core processed using a continuous melter system (Desert Research Institute). Methane data have a 5 second integration time (raw data acquisition rate 0.6 Hz). Analytical precision, from Allan Variance test, is 0.9 ppb (2 sigma). Long-term reproducibility is 2.6% (2 sigma). Gaps in the record are due to problems during online analysis. Online analysis conducted August-September 2011.Note: Lat-Long provided is for main NEEM borehole. The NEEM-2011-S1 core was drilled 200 m distance away in 2011 to 410 m depth.Methane concentrations are reported on NOAA2004 scale (instrument calibrated on dry synthetic air standards).A correction factor of 1.079 has been applied to all data to correct for methane dissolution in melted ice core sample prior to gas extraction. Correction factor calculated using empirical data (concentrations not aligned/tied to existing discrete methane measurements).Additional methods description provided in:* Stowasser, C., Buizert, C., Gkinis, V., Chappellaz, J., Schupbach, S., Bigler, M., Fain, X., Sperlich, P., Baumgartner, M., Schilt, A., Blunier, T., 2012. Continuous measurements of methane mixing ratios from ice cores. Atmos. Meas. Tech. 5, 999-1013.* Morville, J., Kassi, S., Chenevier, M., Romanini, D., 2005. Fast, low-noise, mode bymode, cavity-enhanced absorption spectroscopy by diode-laser self-locking. Appl. Phys. B Lasers Opt. 80, 1027-01038.* NEEM (North Greenland Eemian Ice Drilling) project information http://neem.dk/ NEEM-2011-S1 CH4 outliers.Data points removed from dataset according to specified cut-off value.Please refer to Rhodes et al. (2013) for full discussion of origins outlying data points. Briefly, these high frequency features are not artifacts of the continuous method and have been replicated by traditional discrete analyses. Comparison to chemistry measurements suggests they are related to biological in situ production of methane.

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    PANGAEA
    Dataset . 2019
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2019
      Data sources: B2FIND
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    Authors: Rhodes, Rachael H; Brook, Edward J;

    Description and NotesDescription: Methane concentration from the Greenland NEEM-2011-S1 Ice Core from 71 to 408m depth (~270-1961 CE). Methane concentrations analysed online by laser spectrometer (SARA, Spectroscopy by Amplified Resonant Absorption, developed at Laboratoire Interdisciplinaire de Physique, Grenoble, France) on gas extracted from an ice core processed using a continuous melter system (Desert Research Institute). Methane data have a 5 second integration time (raw data acquisition rate 0.6 Hz). Analytical precision, from Allan Variance test, is 0.9 ppb (2 sigma). Long-term reproducibility is 2.6% (2 sigma). Gaps in the record are due to problems during online analysis. Online analysis conducted August-September 2011.Note: Lat-Long provided is for main NEEM borehole. The NEEM-2011-S1 core was drilled 200 m distance away in 2011 to 410 m depth.Methane concentrations are reported on NOAA2004 scale (instrument calibrated on dry synthetic air standards).A correction factor of 1.079 has been applied to all data to correct for methane dissolution in melted ice core sample prior to gas extraction. Correction factor calculated using empirical data (concentrations not aligned/tied to existing discrete methane measurements).Additional methods description provided in:* Stowasser, C., Buizert, C., Gkinis, V., Chappellaz, J., Schupbach, S., Bigler, M., Fain, X., Sperlich, P., Baumgartner, M., Schilt, A., Blunier, T., 2012. Continuous measurements of methane mixing ratios from ice cores. Atmos. Meas. Tech. 5, 999-1013.* Morville, J., Kassi, S., Chenevier, M., Romanini, D., 2005. Fast, low-noise, mode bymode, cavity-enhanced absorption spectroscopy by diode-laser self-locking. Appl. Phys. B Lasers Opt. 80, 1027-01038.* NEEM (North Greenland Eemian Ice Drilling) project information http://neem.dk/ NEEM-2011-S1 CH4 no outliers.Data minus data points exceeding cut-off value. Cut-off value is 2*median absolute deviation (MAD)> 15 yr running median. Different MAD values used for 250-1000 AD and 1100-1835 AD sections of record.

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    PANGAEA
    Dataset . 2019
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2019
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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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    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: 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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