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  • Energy Research
  • 7. Clean energy
  • PANGAEA

  • 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: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

    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
    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/
    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/
    B2FIND
    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/
    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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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
      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/
      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/
      B2FIND
      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/
      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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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: Schneemann, Jörge; VoĂŸ, Stephan; Rott, Andreas; KĂ¼hn, Martin;

    Scanning Doppler wind lidar scans and atmospheric measurements at the offshore wind farm "Global Tech I" in the German North Sea. Wakes of neighbouring wind farm clusters are present in the data.

    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
    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
    Other ORP type . 2019
    License: CC BY
    Data sources: PANGAEA
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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
      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
      Other ORP type . 2019
      License: CC BY
      Data sources: PANGAEA
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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: Mulvaney, Kathleen M; Selin, Noelle E; Giang, Amanda; Muntean, Marilena; +5 Authors

    National commitments under the Paris Agreement on climate change interact with other global environmental objectives, such as those of the Minamata Convention on Mercury. We assess how mercury emissions and deposition reductions from national climate policy in China under the Paris Agreement could contribute to the country's commitments under the Minamata Convention. We examine emissions under climate policy scenarios developed using a computable general equilibrium model of China's economy, end-of-pipe control scenarios that meet China's commitments under the Minamata Convention, and these policies in combination, and evaluate deposition using a global atmospheric transport model. We find climate policy in China can provide mercury benefits when implemented with Minamata policy, achieving in the year 2030 approximately 5\% additional reduction in mercury emissions and deposition in China when climate policy achieves a 5% reduction per year in carbon intensity (CO2 emissions 9.7 Gt in 2030). This corresponds to 63 Mg additional mercury emissions reductions in 2030 when implemented with Minamata Convention policy, compared to Minamata policy implemented alone. Climate policy provides emissions reductions in sectors not considered under the Minamata Convention, such as residential combustion. This changes the combination of sectors that contribute to emissions reductions.This data submission includes scripts to project China's 2012 mercury emissions from the Emissions Database for Global Atmospheric Research (EDGAR) and prepare them for input to the global chemical transport model, GEOS-Chem. It also includes scripts to plot projected emissions and plot deposition results (with required raw results from GEOS-Chem) for the figures included in the Environmental Science and Technology article.

    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
    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/
    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/
    B2FIND
    Dataset . 2020
    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/
    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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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
      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/
      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/
      B2FIND
      Dataset . 2020
      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/
      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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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: Nicolaus, Marcel; Hoppmann, Mario; Tao, Ran; Katlein, Christian;

    Solar radiation over and under sea ice was measured by radiation station 2020R21, an autonomous platform, installed on a drifting melt pond in the Arctic Ocean during MOSAiC (Leg 5) 2019/20. The resulting time series describes radiation measurements as a function of place and time between 27 August 2020 and 14 November 2020 in sample intervals of 1 hour. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 1 m above the sea ice surface. The third sensor was mounted 0.5 m underneath the sea ice measuring the downward transmitted irradiance. Along with the radiation measurements, this autonomous platform consisted of a lightchain, which measured counts of red, green and blue light at 49 positions at hourly intervals. In addition, the evolution of snow height is measured at hourly intervals. All times are given in UTC. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

    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
    Dataset . 2023
    Data sources: PANGAEA
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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
      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/
      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
      Dataset . 2023
      Data sources: PANGAEA
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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: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

    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/
    B2FIND
    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/
    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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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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      B2FIND
      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/
      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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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: Jiang, Hou; Lu, Ning;

    Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2017 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

    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/ B2FINDarrow_drop_down
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    B2FIND
    Dataset . 2019
    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/
    PANGAEA
    Dataset . 2019
    License: CC BY NC SA
    Data sources: PANGAEA
    PANGAEA - Data Publisher for Earth and Environmental Science
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Datacite
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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/ B2FINDarrow_drop_down
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      B2FIND
      Dataset . 2019
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2019
      License: CC BY NC SA
      Data sources: PANGAEA
      PANGAEA - Data Publisher for Earth and Environmental Science
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Datacite
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Nicolaus, Marcel; Hoppmann, Mario; Tao, Ran; Katlein, Christian;

    Solar radiation over and under sea ice was measured by radiation station 2020R22, an autonomous platform, installed on drifting sea ice in the Arctic Ocean during MOSAiC (Leg 5) 2019/20. The resulting time series describes radiation measurements as a function of place and time between 21 August 2020 and 12 September 2020 in sample intervals of 10 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 1 m above the sea ice surface. The third sensor was mounted 0.5 m underneath the sea ice measuring the downward transmitted irradiance. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Nicolaus, Marcel; Gerland, Sebastian;

    Solar radiation over and under sea ice was measured by an autonomous platform, installed on drifting sea ice in the Arctic Ocean during the SV TARA drift campaign in 2007 (station name 2007R24). The resulting time series describes radiation measurements as a function of place and time between 28 April 2007 and 05 September 2007 in sample intervals of 30 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm) and photosynthetically active radiation (PAR, 400 to 700 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 2 m above the sea ice surface. The third sensor was mounted 1.4 m underneath the sea ice measuring the downward transmitted irradiance. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Buzhdygan, Oksana Y; Meyer, Sebastian Tobias; Weisser, Wolfgang W; Eisenhauer, Nico; +22 Authors

    This data set contains measures of energy-use efficiency, energy flow, and energy storage in units of dry biomass that quantify the multitrophic ecosystem functioning realized in grassland ecosystems of differing plant diversity. Given are both the measures integrated over whole ecosystems (total network measures) as well as the energy dynamics associated with individual ecosystem compartments including the entire biological community and detrital compartments across the above- and belowground parts of the ecosystem.Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment, see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Study plots are grouped in four blocks in parallel to the river in order to account for any effect of a gradient in abiotic soil properties. Each block contains an equal number of plots of each plant species richness and plant functional group richness level. Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5.5 x 6 m and plots were weeded three times per year.Trophic-network models were constructed for 80 of the experimental plots, and represent the ecosystem energy budget in the currency of dry-mass (g m-2 for standing stocks and g m-2 d-1 for flows). All trophic networks have the same topology, but they differ in the estimated size of the standing stock biomass of individual compartments (g m-2) and flows among the compartments (g m-2 d-1). Each trophic network contains twelve ecosystem compartments representing distinct trophic groups of the above- and belowground parts of the ecosystem (i.e., plants, soil microbial community, and above- and belowground herbivores, carnivores, omnivores, decomposers, all represented by invertebrate macro- and mesofauna) and detrital pools (i.e., surface litter and soil organic matter). Vertebrates were not considered in our study due to limitations of data availability and because the impact of resident vertebrates in our experimental system is expected to be minimal. Larger grazing vertebrates were excluded by a fence around the field site, though there was some occasional grazing by voles.Compartments are connected by 41 flows. Flows (fluxes) constitute 30 internal flows within the system, namely feeding (herbivory, predation, decomposition), excretion, mortality, and mechanical transformation of surface litter due to bioturbation plus eleven 11 external flows, i.e. one input (flows entering the system, namely carbon uptake by plants) and ten output flows (flows leaving the system, namely respiration losses). The ecosystem inflow (a flow entering the system) and outflows (flows leaving the system) represent carbon uptake and respiration losses, respectively. In the case of consumer groups, the food consumed (compartment-wide input flow) is further split into excretion (not assimilated organic material that is returned to detrital pools in the form of fecesfaeces) and assimilated organic material, which is further split into respiration (energy lost out of the system to the environment) and biomass production, which is further consumed by higher trophic levels due to predation or returned to detrital pools in the form of mortality (natural mortality or prey residues). In case of detrital pools (i.e. surface litter and soil organic matter), the input flows are in the form of excretion and mortality from the biota compartments, and output flows are in the form of feeding by decomposers and soil microorganisms (i.e. decomposition). Surface litter and soil organic matter are connected by flows in the form of burrowing (mechanical transportation) of organic material from the surface to the soil by soil fauna. Organism immigration and emigration are not considered in our study due to limited data availability.Flows were quantified using resource processing rates (i.e. the feeding rates at which material is taken from a source) multiplied with the standing biomass of the respective source compartment. To approximate resource processing rates, different approaches were used: (i) experimental measurements (namely the aboveground decomposition, fauna burial activity (bioturbation), microbial respiration, and aboveground herbivory and predation rates); (ii) allometric equations scaled by individual body mass, environmental temperature and phylogenetic group (for the above- and belowground fauna respiration rates and plant respiration); (iii) assimilation rates scaled by diet type (for quantification of belowground fauna excretion and natural mortality); (iv) literature-based rates scaled by biomass of trophic groups (for microbial mortality); and (v) mass-balance assumptions (carbon uptake, plant and aboveground fauna mortality, belowground decomposition, belowground herbivory, and belowground predation). Mass-balance assumption means that the flows are calculated assuming that resource inputs into the compartment (i.e. feeding) balance the rate at which material is lost (i.e. the sum of through excretion, respiration, predation, and natural death). We used constrained nonlinear multivariable optimization to perturb the initial flow rates estimated from the various sources. We assigned confidence ratings for each flow rate, reflecting the quality of empirical data it is based on. We then used the 'fmincon' function from Matlab's optimization toolbox, which utilizes the standard Moore-Penrose pseudoinverse approach to achieve a balanced steady state ecological network model that best reflects the collected field data. Measured data used to parameterize the trophic network models were collected mostly in the year 2010.Network-wide measures that quantify proxies for different aspects of multitrophic ecosystem functioning were calculated for each experimental plot using the 'enaR' package in R. In particular, total energy flow was measured as the sum of all flows through each ecosystem compartment. Flow uniformity was calculated as the ratio of the mean of summed flows through each individual ecosystem compartment divided by the standard deviation of these means. Total-network standing biomass was determined as the sum of standing biomass across all ecosystem compartments. Community maintenance costs were calculated as the ratio of community-wide respiration related to community-wide biomass. Supplement to: Buzhdygan, Oksana Y; Meyer, Sebastian Tobias; Weisser, Wolfgang W; Eisenhauer, Nico; Ebeling, Anne; Borrett, Stuart R; Buchmann, Nina; Cortois, Roeland; De Deyn, Gerlinde B; de Kroon, Hans; Gleixner, Gerd; Hertzog, Lionel R; Hines, Jes; Lange, Markus; Mommer, Liesje; Ravenek, Janneke; Scherber, Christoph; Scherer-Lorenzen, Michael; Scheu, Stefan; Schmid, Bernhard; Steinauer, Katja; Strecker, Tanja; Tietjen, Britta; Vogel, Anja; Weigelt, Alexandra; Petermann, Jana S (2020): Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nature Ecology & Evolution Most of the data used to parameterize these trophic networks were collected in 2010.A diagram depicting the conceptual trophic-network model developed to describe multitrophic ecosystem functioning can be found in the paper (REF to the NEE paper). This paper also shows the relationship between the individual flows and compartment sizes as well as the network-wide measures with plant species richness. Further sensitivity analyses for the influence of including the highest diversity level are also provided in the paper.

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    PANGAEA
    Dataset . 2020
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2020
      Data sources: PANGAEA
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Schneemann, Jörge; VoĂŸ, Stephan; Rott, Andreas; KĂ¼hn, Martin;

    Scanning Doppler wind lidar scans and atmospheric measurements at the offshore wind farm "Global Tech I" in the German North Sea. Wakes of neighbouring wind farm clusters are present in the data.

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    PANGAEA
    Other ORP type . 2019
    License: CC BY
    Data sources: PANGAEA
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      PANGAEA
      Other ORP type . 2019
      License: CC BY
      Data sources: PANGAEA
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    Authors: Mulvaney, Kathleen M; Selin, Noelle E; Giang, Amanda; Muntean, Marilena; +5 Authors

    National commitments under the Paris Agreement on climate change interact with other global environmental objectives, such as those of the Minamata Convention on Mercury. We assess how mercury emissions and deposition reductions from national climate policy in China under the Paris Agreement could contribute to the country's commitments under the Minamata Convention. We examine emissions under climate policy scenarios developed using a computable general equilibrium model of China's economy, end-of-pipe control scenarios that meet China's commitments under the Minamata Convention, and these policies in combination, and evaluate deposition using a global atmospheric transport model. We find climate policy in China can provide mercury benefits when implemented with Minamata policy, achieving in the year 2030 approximately 5\% additional reduction in mercury emissions and deposition in China when climate policy achieves a 5% reduction per year in carbon intensity (CO2 emissions 9.7 Gt in 2030). This corresponds to 63 Mg additional mercury emissions reductions in 2030 when implemented with Minamata Convention policy, compared to Minamata policy implemented alone. Climate policy provides emissions reductions in sectors not considered under the Minamata Convention, such as residential combustion. This changes the combination of sectors that contribute to emissions reductions.This data submission includes scripts to project China's 2012 mercury emissions from the Emissions Database for Global Atmospheric Research (EDGAR) and prepare them for input to the global chemical transport model, GEOS-Chem. It also includes scripts to plot projected emissions and plot deposition results (with required raw results from GEOS-Chem) for the figures included in the Environmental Science and Technology article.

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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      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/
      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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    Authors: Nicolaus, Marcel; Hoppmann, Mario; Tao, Ran; Katlein, Christian;

    Solar radiation over and under sea ice was measured by radiation station 2020R21, an autonomous platform, installed on a drifting melt pond in the Arctic Ocean during MOSAiC (Leg 5) 2019/20. The resulting time series describes radiation measurements as a function of place and time between 27 August 2020 and 14 November 2020 in sample intervals of 1 hour. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 1 m above the sea ice surface. The third sensor was mounted 0.5 m underneath the sea ice measuring the downward transmitted irradiance. Along with the radiation measurements, this autonomous platform consisted of a lightchain, which measured counts of red, green and blue light at 49 positions at hourly intervals. In addition, the evolution of snow height is measured at hourly intervals. All times are given in UTC. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

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    PANGAEA
    Dataset . 2023
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2023
      Data sources: PANGAEA
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Jiang, Hou; Lu, Ning;

    Surface solar radiation drives the water cycle and energy exchange on the earth's surface, and its diffuse component can promote carbon uptake in ecosystems by increasing the plant productivity. The accurate knowledge of their spatial distribution is of great importance to many studies and applications, such as the estimation of agricultural yield, carbon dynamics of terrestrial systems, site selection of solar power plants, as well as trends of regional climate changes. Therefore, we produce the hourly surface radiation datasets based on the hourly Multi-functional Transport Satellite (MTSAT) satellite imagery and the ground observations from the China Meteorology Administration (CMA) through deep learning techniques. The deep network is trained using training samples in 2008, and then utilized to generate the hourly radiation for other years. This dataset provides the gridded surface global and diffuse solar radiation in 2017 within 71.025°E - 141.025°E and 14.975°N - 59.975°N with an increment of 0.05°. Both the direct predicted hourly values and the integrated daily and monthly total values are available. The dataset should be useful for the analysis of the regional differences and temporal cycles of solar radiation in fine scales, and the impact of diffuse radiation on plant growth etc.

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    B2FIND
    Dataset . 2019
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2019
    License: CC BY NC SA
    Data sources: PANGAEA
    PANGAEA - Data Publisher for Earth and Environmental Science
    Dataset . 2019
    License: CC BY NC SA
    Data sources: Datacite
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      B2FIND
      Dataset . 2019
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2019
      License: CC BY NC SA
      Data sources: PANGAEA
      PANGAEA - Data Publisher for Earth and Environmental Science
      Dataset . 2019
      License: CC BY NC SA
      Data sources: Datacite
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    Authors: Lonardi, Michael; Pilz, Christian; Siebert, Holger; Ehrlich, André; +1 Authors

    The tethered balloon system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was operated during leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). The balloon was operated from the Balloon Town site in the central observatory, close to RV Polarstern (Shupe et al., 2022, Elementa). Balloon payload included an extended meteorological package, an ultrasonic anemometer package, a broadband radiation package, the video ice particle sampler, and the cubic aerosol measurement platform. An overview showing the value of the combined observation is displayed by Lonardi et al. (in review). The data processing is described in Pilz et al. (in preparation). The present dataset covers the solar irradiances measured by the broadband radiation package on 18 flights between 29 June and 27 July 2020. Profiles of downward and upward raw solar radiation, latitude, longitude, roll, pitch, yaw, and radiometer icing flag. Geopotential height is derived from the pressure.

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Nicolaus, Marcel; Hoppmann, Mario; Tao, Ran; Katlein, Christian;

    Solar radiation over and under sea ice was measured by radiation station 2020R22, an autonomous platform, installed on drifting sea ice in the Arctic Ocean during MOSAiC (Leg 5) 2019/20. The resulting time series describes radiation measurements as a function of place and time between 21 August 2020 and 12 September 2020 in sample intervals of 10 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 1 m above the sea ice surface. The third sensor was mounted 0.5 m underneath the sea ice measuring the downward transmitted irradiance. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Nicolaus, Marcel; Gerland, Sebastian;

    Solar radiation over and under sea ice was measured by an autonomous platform, installed on drifting sea ice in the Arctic Ocean during the SV TARA drift campaign in 2007 (station name 2007R24). The resulting time series describes radiation measurements as a function of place and time between 28 April 2007 and 05 September 2007 in sample intervals of 30 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm) and photosynthetically active radiation (PAR, 400 to 700 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 2 m above the sea ice surface. The third sensor was mounted 1.4 m underneath the sea ice measuring the downward transmitted irradiance. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2022
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: PANGAEA
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    Authors: Buzhdygan, Oksana Y; Meyer, Sebastian Tobias; Weisser, Wolfgang W; Eisenhauer, Nico; +22 Authors

    This data set contains measures of energy-use efficiency, energy flow, and energy storage in units of dry biomass that quantify the multitrophic ecosystem functioning realized in grassland ecosystems of differing plant diversity. Given are both the measures integrated over whole ecosystems (total network measures) as well as the energy dynamics associated with individual ecosystem compartments including the entire biological community and detrital compartments across the above- and belowground parts of the ecosystem.Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment, see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Study plots are grouped in four blocks in parallel to the river in order to account for any effect of a gradient in abiotic soil properties. Each block contains an equal number of plots of each plant species richness and plant functional group richness level. Plots were maintained in general by bi-annual weeding and mowing. Since 2010, plot size was reduced to 5.5 x 6 m and plots were weeded three times per year.Trophic-network models were constructed for 80 of the experimental plots, and represent the ecosystem energy budget in the currency of dry-mass (g m-2 for standing stocks and g m-2 d-1 for flows). All trophic networks have the same topology, but they differ in the estimated size of the standing stock biomass of individual compartments (g m-2) and flows among the compartments (g m-2 d-1). Each trophic network contains twelve ecosystem compartments representing distinct trophic groups of the above- and belowground parts of the ecosystem (i.e., plants, soil microbial community, and above- and belowground herbivores, carnivores, omnivores, decomposers, all represented by invertebrate macro- and mesofauna) and detrital pools (i.e., surface litter and soil organic matter). Vertebrates were not considered in our study due to limitations of data availability and because the impact of resident vertebrates in our experimental system is expected to be minimal. Larger grazing vertebrates were excluded by a fence around the field site, though there was some occasional grazing by voles.Compartments are connected by 41 flows. Flows (fluxes) constitute 30 internal flows within the system, namely feeding (herbivory, predation, decomposition), excretion, mortality, and mechanical transformation of surface litter due to bioturbation plus eleven 11 external flows, i.e. one input (flows entering the system, namely carbon uptake by plants) and ten output flows (flows leaving the system, namely respiration losses). The ecosystem inflow (a flow entering the system) and outflows (flows leaving the system) represent carbon uptake and respiration losses, respectively. In the case of consumer groups, the food consumed (compartment-wide input flow) is further split into excretion (not assimilated organic material that is returned to detrital pools in the form of fecesfaeces) and assimilated organic material, which is further split into respiration (energy lost out of the system to the environment) and biomass production, which is further consumed by higher trophic levels due to predation or returned to detrital pools in the form of mortality (natural mortality or prey residues). In case of detrital pools (i.e. surface litter and soil organic matter), the input flows are in the form of excretion and mortality from the biota compartments, and output flows are in the form of feeding by decomposers and soil microorganisms (i.e. decomposition). Surface litter and soil organic matter are connected by flows in the form of burrowing (mechanical transportation) of organic material from the surface to the soil by soil fauna. Organism immigration and emigration are not considered in our study due to limited data availability.Flows were quantified using resource processing rates (i.e. the feeding rates at which material is taken from a source) multiplied with the standing biomass of the respective source compartment. To approximate resource processing rates, different approaches were used: (i) experimental measurements (namely the aboveground decomposition, fauna burial activity (bioturbation), microbial respiration, and aboveground herbivory and predation rates); (ii) allometric equations scaled by individual body mass, environmental temperature and phylogenetic group (for the above- and belowground fauna respiration rates and plant respiration); (iii) assimilation rates scaled by diet type (for quantification of belowground fauna excretion and natural mortality); (iv) literature-based rates scaled by biomass of trophic groups (for microbial mortality); and (v) mass-balance assumptions (carbon uptake, plant and aboveground fauna mortality, belowground decomposition, belowground herbivory, and belowground predation). Mass-balance assumption means that the flows are calculated assuming that resource inputs into the compartment (i.e. feeding) balance the rate at which material is lost (i.e. the sum of through excretion, respiration, predation, and natural death). We used constrained nonlinear multivariable optimization to perturb the initial flow rates estimated from the various sources. We assigned confidence ratings for each flow rate, reflecting the quality of empirical data it is based on. We then used the 'fmincon' function from Matlab's optimization toolbox, which utilizes the standard Moore-Penrose pseudoinverse approach to achieve a balanced steady state ecological network model that best reflects the collected field data. Measured data used to parameterize the trophic network models were collected mostly in the year 2010.Network-wide measures that quantify proxies for different aspects of multitrophic ecosystem functioning were calculated for each experimental plot using the 'enaR' package in R. In particular, total energy flow was measured as the sum of all flows through each ecosystem compartment. Flow uniformity was calculated as the ratio of the mean of summed flows through each individual ecosystem compartment divided by the standard deviation of these means. Total-network standing biomass was determined as the sum of standing biomass across all ecosystem compartments. Community maintenance costs were calculated as the ratio of community-wide respiration related to community-wide biomass. Supplement to: Buzhdygan, Oksana Y; Meyer, Sebastian Tobias; Weisser, Wolfgang W; Eisenhauer, Nico; Ebeling, Anne; Borrett, Stuart R; Buchmann, Nina; Cortois, Roeland; De Deyn, Gerlinde B; de Kroon, Hans; Gleixner, Gerd; Hertzog, Lionel R; Hines, Jes; Lange, Markus; Mommer, Liesje; Ravenek, Janneke; Scherber, Christoph; Scherer-Lorenzen, Michael; Scheu, Stefan; Schmid, Bernhard; Steinauer, Katja; Strecker, Tanja; Tietjen, Britta; Vogel, Anja; Weigelt, Alexandra; Petermann, Jana S (2020): Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nature Ecology & Evolution Most of the data used to parameterize these trophic networks were collected in 2010.A diagram depicting the conceptual trophic-network model developed to describe multitrophic ecosystem functioning can be found in the paper (REF to the NEE paper). This paper also shows the relationship between the individual flows and compartment sizes as well as the network-wide measures with plant species richness. Further sensitivity analyses for the influence of including the highest diversity level are also provided in the paper.

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    PANGAEA
    Dataset . 2020
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2020
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