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  • European Marine Science
  • 2014-2023
  • Research data
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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: Wekerle, Claudia;

    This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes: a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (Wekerle2024_FESOM2_ltm_REF.nc) b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (Wekerle2024_FESOM2_annual_avg_REF.nc) c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (Wekerle2024_FESOM2_annual_avg_CLIM.nc) d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc) e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (Wekerle2024_FESOM2_daily_EXP_basal_drag.nc) Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      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/
    Authors: Athanase, Marylou; Sanchez Benitez, Antonio; Goessling, Helge; Jung, Thomas;

    Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment). The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model. Parameters naming convention: - Sea Surface Temperature ("tos"). - Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs"). - Low Clouds Cover ("lcc"). - Mixed Layer Depth ("mlotst"). - Surface Air Temperature ("tas"). - 10 m winds ("u10","v10"). All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). This work was supported by funding from the Federal Ministry of Education and Research (BMBF) and the Helmholtz Research Field Earth & Environment for the Innovation Pool Project SCENIC. Initial storyline simulations were also supported through funding from the Helmholtz-Climate-Initiative for the HI-CAM project. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID ba1264.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
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      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/
    Authors: Zhen, Yicun; Resseguier, Valentin; chapron, bertrand;

    This is the document for the code mentioned in "On a generalization of Optical Flow algorithms for geophysical applications: a differential geometry perspective". (To save time, steps 1,2,3,4 can be skipped since the data is already saved in the corresponding filefolder) 1,GENERATE ENSEMBLE MEMBERS : run getens_doublevortex_256.py 2,GENERATE THE TRUTH : run gettruth_doublevortex_256.py 3,BEFORE MORPHING : run exp/get_dataens.py and exp/get_dataobs.py 4,MORPH THE ENSEMBLE : run exp/get_morphed.py 5,BEFORE DA : run exp/get_ens_LR.py and exp/get_truth_LR.py 6,DA : run exp/EnKF_direct.py and exp/EnKF_morphed.py 7,PLOT DA RESULTS : run exp/plt_comparemean.py and exp/plt_compare_member.py 8,MORPH WITH PLAIN T#(10000 steps) : run exp/get_morphed_noDiffGeo.py 9,MORPH WITH PLAIN T#(6000 steps) : run exp/get_morphed_noDiffGeo2.py 10,PLOT MORPHED ENSEMBLE MEMBERS : run exp/ensemble_morphed/figs/plt_main.py 11,PLOT MORPHED ENSEMBLE MEMBER WITH PLAIN T# : run exp/ensemble_morphed_noDiffGEo/figs/plt_main.py and exp/ensemble_morphed_noDiffGEo2/figs/plt_main.py

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
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      Data sources: ZENODO
      ZENODO
      Dataset . 2023
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  • Authors: Pérez-Gussinyé, Marta; Xin, Yanfang; Cunha, Tiago; Gudipati, Raghuram; +3 Authors

    Movie S1. Evolution of deformation for the three models shown in Figures 2 and 3. Full extension rate is 10 mm/yr. Sedimentation rate is given in Figures 2 and 3 and Methods (see also Table 1). Red shading shows brittle strain rate, blue shading shows ductile strain rate. The sediments are color coded with age since the start of rifting. The color circles are trackers. The color conventions for the phases are the same as in Figures 1 and 2. Movie S2. Top: Thermal evolution of the 10 mm/yr full rifting velocity model without sedimentation along with markers (model shown in Figures 2a-e and 3a-c). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. Bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel. Movie S3. Top: Thermal evolution of the10 mm/yr full rifting velocity model with average sedimentation along with markers (model shown in Figures 2f-j and 3d-g). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. Bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel. Movie S4. Top: Thermal evolution of the 10 mm/yr full rifting velocity model with largest sedimentation along with markers (model shown in Figures 2k-o and 3h-k). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel. Movie S5. Evolution of temperature field for the 10 mm/yr full rifting velocity without sedimentation, (model shown in Figures 2a-e and 3a-c). b) and c) show the geotherms of trackers shown in a) computed from the dynamic model (solid colored lines), and from the ´1-D temperature´ solution (dashed colored lines). The black curve shows the initial geotherm used for the dynamic temperature and for the ´1-D temperature´ calculations. Grey shading indicates strain rate. Note that the horizontal scale is changing to show the rifted margins as the model evolves. Movie S6. Evolution of temperature field for the 100 mm/yr full rifting model without sedimentation, (model shown in Figures 2a-e and 3a-c). b) and c) show the geotherms of trackers shown in a) computed from the dynamic model (solid colored lines), and from the ´1-D temperature´ solution (dashed colored lines). The black curve shows the initial geotherm used for the dynamic temperature and for the ´1-D temperature´ calculations. Grey shading indicates strain rate. Note that the horizontal scale is changing to show the rifted margins as the model evolves.

    ZENODOarrow_drop_down
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    Audiovisual . 2023
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    ZENODO
    Audiovisual . 2023
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      Audiovisual . 2023
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      Audiovisual . 2023
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    Authors: Martínez Moreno, Josué;

    This dataset includes the NEMO 4.0.2 configuration used and analysed in the paper titled "Eddy-driven heterogeneity in sea ice during the ice-growth season". The output data is approximately 4TB for the 3 idealised configuration used in the manuscript, thus we opted to distribute the configuration. Note: The initial conditions for each simulation are compressed into the file `init_cond.zip` The configuration for one of the simulations is compressed in the file `config.zip` In order to reproduce all the runs, it's only required to change the initial conditions in the file namelist_cfg and namelist_ice_cfg. Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/Ice_formation

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    ZENODO
    Model . 2023
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    Data sources: ZENODO
    ZENODO
    Model . 2023
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      ZENODO
      Model . 2023
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      Data sources: ZENODO
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      Model . 2023
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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: Kahnert, Michael;

    The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.

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      ZENODO
      Dataset . 2023
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    Authors: Banerjee, Tridib; Danilov, Sergey; Scholz, Patrick; Klingbeil, Knut; +1 Authors

    FESOM2.5 code with preliminary implementation of the Split-Explicit external mode solver. This project is a contribution to the projects M5 (Reducing spurious mixing and energetic inconsistencies in realistic ocean modelling applications) and S2 (Improved parameterisations and numerics in climate models) of the Collaborative Research Centre TRR 181 "Energy Transfer in Atmosphere and Ocean" funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer 274762653.

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    ZENODO
    Model . 2023
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    Model . 2023
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      ZENODO
      Model . 2023
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    Authors: Düsedau, Luisa; Frediksen, Stein; Brand, Markus; Fischer, Philipp; +4 Authors

    Macroalgal surveys were performed at Hansneset, Blomstrand in Kongsfjorden, Svalbard, from the infralittoral fringe down to 15 m depth in June – August 2021. This dataset is part of a time series currently spanning over 25 years and complements the studies conducted in 1996/98 (Hop et al., 2012) and 2012-14 (Bartsch et al., 2016). The aim was to document changes in Arctic kelp forest dynamics in an Arctic fjord system influenced by glacial melt by repeatedly sampling the same site in a standardized manner. As ocean temperatures in the Arctic have risen substantially and underwater light climate continuously deteriorated over this time period, alterations were observed in the seaweed community, especially kelps as major coastal foundation species. This Zenodo upload contains all datasets supporting the findings of the manuscript Düsedau et al. (2023) "Arctic kelp forest decline - a consequence of melting glaciers?".

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    ZENODO
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    ZENODO
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    Authors: Galgani, Luisa; Certomà, Chiara; Corsi, Alessio; Fornaro, Federico;

    The file contains two sheets: one is the sample log for the sampling performed on the 14th and 15th of July, in Anzio (RM), Italy (harbour and coastal samples), as well as a first description of the samples collected. The two tows with the neuston net allowed an approximate calculation of the average plastic concentration (particles > 300 µm) per cubic meter of seawater along the coast of Anzio. This is the updated version as there was a mistake in the number of plastics found (68 instead of 72 as wrongly reported previously). 

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    ZENODO
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    ZENODO
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      ZENODO
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    Authors: Schulzki, Tobias G.; Harlaß, Jan; Getzlaff, Klaus;

    Visualisation of daily sea surface temperature projected on surface elevation combined with sea ice cover with an overlay of total cloud cover and windspeed from FOCI simulation for one year displaying the Southern Ocean/Atlantic region. This simulation was generated with the open-source software Paraview (www.paraview.org).

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    OceanRep
    Film . 2023
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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: Wekerle, Claudia;

    This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes: a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (Wekerle2024_FESOM2_ltm_REF.nc) b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (Wekerle2024_FESOM2_annual_avg_REF.nc) c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (Wekerle2024_FESOM2_annual_avg_CLIM.nc) d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc) e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (Wekerle2024_FESOM2_daily_EXP_basal_drag.nc) Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
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    Authors: Athanase, Marylou; Sanchez Benitez, Antonio; Goessling, Helge; Jung, Thomas;

    Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment). The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model. Parameters naming convention: - Sea Surface Temperature ("tos"). - Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs"). - Low Clouds Cover ("lcc"). - Mixed Layer Depth ("mlotst"). - Surface Air Temperature ("tas"). - 10 m winds ("u10","v10"). All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). This work was supported by funding from the Federal Ministry of Education and Research (BMBF) and the Helmholtz Research Field Earth & Environment for the Innovation Pool Project SCENIC. Initial storyline simulations were also supported through funding from the Helmholtz-Climate-Initiative for the HI-CAM project. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID ba1264.

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    ZENODO
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    ZENODO
    Dataset . 2023
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      ZENODO
      Dataset . 2023
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    Authors: Zhen, Yicun; Resseguier, Valentin; chapron, bertrand;

    This is the document for the code mentioned in "On a generalization of Optical Flow algorithms for geophysical applications: a differential geometry perspective". (To save time, steps 1,2,3,4 can be skipped since the data is already saved in the corresponding filefolder) 1,GENERATE ENSEMBLE MEMBERS : run getens_doublevortex_256.py 2,GENERATE THE TRUTH : run gettruth_doublevortex_256.py 3,BEFORE MORPHING : run exp/get_dataens.py and exp/get_dataobs.py 4,MORPH THE ENSEMBLE : run exp/get_morphed.py 5,BEFORE DA : run exp/get_ens_LR.py and exp/get_truth_LR.py 6,DA : run exp/EnKF_direct.py and exp/EnKF_morphed.py 7,PLOT DA RESULTS : run exp/plt_comparemean.py and exp/plt_compare_member.py 8,MORPH WITH PLAIN T#(10000 steps) : run exp/get_morphed_noDiffGeo.py 9,MORPH WITH PLAIN T#(6000 steps) : run exp/get_morphed_noDiffGeo2.py 10,PLOT MORPHED ENSEMBLE MEMBERS : run exp/ensemble_morphed/figs/plt_main.py 11,PLOT MORPHED ENSEMBLE MEMBER WITH PLAIN T# : run exp/ensemble_morphed_noDiffGEo/figs/plt_main.py and exp/ensemble_morphed_noDiffGEo2/figs/plt_main.py

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    ZENODO
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  • Authors: Pérez-Gussinyé, Marta; Xin, Yanfang; Cunha, Tiago; Gudipati, Raghuram; +3 Authors

    Movie S1. Evolution of deformation for the three models shown in Figures 2 and 3. Full extension rate is 10 mm/yr. Sedimentation rate is given in Figures 2 and 3 and Methods (see also Table 1). Red shading shows brittle strain rate, blue shading shows ductile strain rate. The sediments are color coded with age since the start of rifting. The color circles are trackers. The color conventions for the phases are the same as in Figures 1 and 2. Movie S2. Top: Thermal evolution of the 10 mm/yr full rifting velocity model without sedimentation along with markers (model shown in Figures 2a-e and 3a-c). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. Bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel. Movie S3. Top: Thermal evolution of the10 mm/yr full rifting velocity model with average sedimentation along with markers (model shown in Figures 2f-j and 3d-g). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. Bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel. Movie S4. Top: Thermal evolution of the 10 mm/yr full rifting velocity model with largest sedimentation along with markers (model shown in Figures 2k-o and 3h-k). Grey shading indicates strain rate. Note the development of small scale convection cells. Middle: Evolution of the basement heat flow of markers shown in top panel. bottom: Evolution of the basement temperature at markers shown in top panel. Note that the horizontal and vertical scale change in the postrift to better show the small scale convection in the top panel. Movie S5. Evolution of temperature field for the 10 mm/yr full rifting velocity without sedimentation, (model shown in Figures 2a-e and 3a-c). b) and c) show the geotherms of trackers shown in a) computed from the dynamic model (solid colored lines), and from the ´1-D temperature´ solution (dashed colored lines). The black curve shows the initial geotherm used for the dynamic temperature and for the ´1-D temperature´ calculations. Grey shading indicates strain rate. Note that the horizontal scale is changing to show the rifted margins as the model evolves. Movie S6. Evolution of temperature field for the 100 mm/yr full rifting model without sedimentation, (model shown in Figures 2a-e and 3a-c). b) and c) show the geotherms of trackers shown in a) computed from the dynamic model (solid colored lines), and from the ´1-D temperature´ solution (dashed colored lines). The black curve shows the initial geotherm used for the dynamic temperature and for the ´1-D temperature´ calculations. Grey shading indicates strain rate. Note that the horizontal scale is changing to show the rifted margins as the model evolves.

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    Authors: Martínez Moreno, Josué;

    This dataset includes the NEMO 4.0.2 configuration used and analysed in the paper titled "Eddy-driven heterogeneity in sea ice during the ice-growth season". The output data is approximately 4TB for the 3 idealised configuration used in the manuscript, thus we opted to distribute the configuration. Note: The initial conditions for each simulation are compressed into the file `init_cond.zip` The configuration for one of the simulations is compressed in the file `config.zip` In order to reproduce all the runs, it's only required to change the initial conditions in the file namelist_cfg and namelist_ice_cfg. Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/Ice_formation

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Model . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Model . 2023
    License: CC BY
    Data sources: Datacite
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