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10,391 results for “oceans”
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model
<p>This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.</p> <p> </p> <p>This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.</p>
Supporting Movies from: Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California
<div> <div> <div> <p>This repository includes supplementary movies from the manuscript titled, "Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California," submitted to the Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>Movies S1 and S2. These two movies taken during array deployment 4 on October 20, 2023 show the NW tip of Coal Oil Point at the left of the field of view and Sands Beach northwest of that toward the right. Frames have the same figure layout as Figure 4 of the main text.</p> <p>Movie S3. Same as Movies S1 and S2 but with the NW tip of Coal Oil Point at the right of the field of view and Devereux Beach southeast of that toward the left.</p> </div> </div> </div>
BSIOM Baltic Sea-Ice Ocean Model 1950-2022
<p>Daily temperature, dissolved oxygen and salinity concentration data from the <a name="_Hlk167710471"></a>Baltic Sea Ice Ocean Model (BSIOM) from 1950 to 2022. A detailed description of the equations and modifications made, necessary to adapt the model to the Baltic Sea, can be found in Lehmann et al. (see references below). The model is forced realistically using the ERA5 global re-analysis in the preliminary extension version back to 1950. The resolution of the original output from BSIOM is specified with vertical 60 levels, which enables to resolve the upper 100m by layers of 3 m thickness. The horizontal resolution of the model is 2.5km. The datasets here presented are divided into surface and bottom files. We calculated the sea surface temperature (SST) using the average values from the first three depth layers (upper nine meters), while the sea bottom temperature (SBT) was the average of the last three depth layers following the bathymetry of the Western Baltic Sea (lower nine meters). The values for dissolved oxygen and salinity concentration were calculated in the same manner. The data is spatially constrained to the area between 9° 45’ to 14° 45’ East and 53° 53’ to 56° 30’ North.</p> <p>In the datasets, the following data is available: </p> <ul> <li>Lat: latitude values in degrees (°)</li> <li>Long: longitude values in degrees (°)</li> <li>Depth: depth values (m). The surface file shows a constant value of 1.5, while the bottom file shows the maximum depth (m) for that pixel. To show the depth in reference to the sea-level reference (0 meters) the values should be multiplied by -1.</li> <li>temp: temperature (°C)</li> <li>SO: salinity (g/kg)</li> <li>O2: dissolved oxygen (ml/L-1)</li> <li>t: day of the year (YYYY-MM-DD)</li> <li>LongLat: string with the combination of longitute and latitude values (only for the bottom file)</li> <li>GridID: identification value for each set of coordinates (Long and Lat)</li> </ul>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"
<p>The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:</p> <p> 1. <code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.<br> 2. <code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.</p> <h2>Contents of the Dataset</h2> <p>For each version, the dataset includes spectral inherent and apparent optical properties such as:</p> <p> • Remote Sensing Reflectance (Rrs)<br> • Pure Water Absorption (aw)<br> • Absorption Coefficient of Detritus (ad)<br> • Total Absorption Coefficient without Pure Water (agp)<br> • Absorption Coefficient of Phytoplankton (aph)<br> • Backscattering Coefficient of Total Particulate Matter (bbp)<br> • Scattering Coefficient of Total Particulate Matter (bp)<br> • Scattering Coefficient of Pure Water (bw)</p> <p>Additionally, the dataset includes various environmental and biological parameters:</p> <p> • Chlorophyll a Concentration (Chl)<br> • Inorganic Suspended Matter Concentration (ISM)<br> • Colored Dissolved Organic Matter Absorption at 440 nm (ag440)<br> • Single-Scattering Albedo of Detritus at 550 nm (A_d)<br> • Power Law Exponent of Detritus Attenuation (G_d)<br> • Water Salinity (Sal)<br> • Water Temperature (Temp)<br> • Fraction for Diminished Coccolithophore Absorption (a_frac)<br> • Fraction of Coccolithophore Group (cocco_frac)</p> <h2>Optical Water Types</h2> <p>The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,</p> <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> <p>The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:</p> <p> • Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)</a><br> • OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a></p> <h2>References</h2> <p> 1. OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606<br> 2. Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352<br> 3. Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools<br> 4. Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155</p> <h2>Authors and Contact</h2> <p> • Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers<br> • Creator: Shun Bi, Shun.Bi@hereon.de</p> <h2>Example Python Code to Read Data</h2> <p>Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library:</p> <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>
Files from barotropic and baroclinic idealized model runs of the Southern Indian Ocean
<p>These data files correspond to two idealized model runs of the Southern Indian Ocean using the Regional Ocean Modelling System (ROMS) as a framework. Both simulations are forced with monthly mean QuikSCAT winds and are run at a 1/3 degree resolution. </p> <p>The barotropic model is single layer with realistic ETOPO2 bathymetry, a two arc minute ocean-floor elevation data-set smoothed to a resolution of 55.2 km. The file corresponding to this simulation is named: roms_avg_barotropic.</p> <p>The baroclinic model is a 1 and a half layer model where the value of the pycnocline depth and the reduced gravity parameter is set at the initialization stage. Two simulations are presented, the first where 'relaistic' initialization parameters of H=800m and g'= 0.0134 m/s(^2), and the second where the density gradient between the active and passive layers is reduced to a g' of 0.0076 m/s(^2). The two data sets corresponding to these simulations are titled: roms_avg_800_0134 and roms_avg_800_0076</p> <p>Below find a list of variable names and descriptions:</p> <p>zeta=anomaly in thickness of active layer<br> ubar= mean zonal velocity of active layer<br> vbar= mean meridional velocity of active layerh=depth of bathymetry in barotropic model; pycnocline depth in baroclinic model<br> coast=coastline<br> lon_rho=longitude corresponding the density coordinates<br> lat_rho=latitude corresponding the density coordinates<br> lon_u=longitude corresponding the zonal velocities<br> lat_u=latitude corresponding the zonal velocities<br> lon_v=longitude corresponding the density velocities<br> lat_v=longitude corresponding the meridional velocities<br> time=days since model simualtion started</p>
Homisland-IO: a homogeneous land cover over the small islands of the southwest Indian Ocean
<p>This dataset is a landcover product, called Homisland-IO<strong>,</strong> based on the analysis of high spatial resolution images acquired by the SPOT 5 satellite between December 2012 and July 2014 and produced at the SEAS-OI Station. We used an object-based image analysis method to identify the 11 major classes of land cover / land use of these tropical islands. This methodology together with a good knowledge of the field has enabled us to achieve an overall accuracy of 86%, making it an operational product. Homisland-IO is<strong> </strong>freely accessible through a web portal and thus available for future uses.</p>
Interannual iceberg meltwater fluxes over the Southern Ocean
<p><strong>Monthly Iceberg Meltwater Fluxes over the Southern Ocean (1972-2017) :</strong></p> <p>Here is an update of the iceberg meltwater climatology initially provided by Merino et al (2016). This flux is still derived from NEMO's Lagrangian iceberg module developed by Marsh et al. (2015) and updated by Merino et al. (2016). It is run within a global ORCA025 ocean simulation running from 1958 to 2017, forced by the Drakkar Forcing Set (DFS-5.2; Dussin et al 2016). The 1958-1972 period is used to spin up the model, and the meltwater fluxes are provided over 1972-2017. The iceberg calving fluxes are constant, but their meltwater fluxes varies seasonally and interannually. Ice-shelf meltwater fluxes are reconstructed from glaciological observations (Merino et al. 2018), and here vary linearly from 1990 to 2010 (constant before and after). </p> <p><strong>Known caveats:</strong></p> <ul> <li>The ocean grid is the old "ORCA025" grid, which does not extend southward of 70°S, i.e. iceberg do not follow the southernmost ice shelf edges (e.g. Ronne ice shelf).</li> </ul> <p><strong>References:</strong></p> <ul> <li>Dussin, Raphael, Bernard Barnier, Laurent Brodeau, and Jean Marc Molines (2016). Drakkar Forcing Set DFS5.</li> <li>Marsh, R., Ivchenko, V. O., Skliris, N., Alderson, S., Bigg, G. R., Madec, G., and others (2015). NEMO-ICB (v1. 0): interactive icebergs in the NEMO ocean model globally configured at eddy-permitting resolution. <em>Geoscientific Model Development</em>, <em>8</em>(5), 1547-1562.</li> <li>Merino, N., Le Sommer, J., Durand, G., Jourdain, N. C., Madec, G., Mathiot, P. and Tournadre, J. (2016). Antarctic icebergs melt over the Southern Ocean: Climatology and impact on sea ice. <em>Ocean Modelling</em>, <em>104</em>, 99-110.</li> <li>Merino, N., Jourdain, N. C., Le Sommer, J., Goosse, H., Mathiot, P. and Durand, G. (2018). Impact of increasing antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. <em>Ocean Modelling</em>, <em>121</em>, 76-89.</li> </ul>
Water Body Checklists 2019: Southern Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Southern Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists 2019: Indian Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Indian Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists 2019: Arctic Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Arctic Ocean region using effechecka and modified polygons from the International Hydrographic Organization.
Water Body Checklists: Southern Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Southern Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists: Indian Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Indian Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists: Arctic Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Arctic Ocean region using effechecka and modified polygons from the International Hydrographic Organization.
University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2
<p>Dual-frequency snow depth estimates for the Arctic Ocean in Oct-Apr 2018-2023 derived from gridded 25-km resolution CryoSat-2 and SARAL AltiKa radar freeboards and ICESat-2 laser freeboards. Waveform modelling approach applied to radar altimeters, ICESat-2 laser altimetry freeboards from ATL20 r004. See acompanying publication in The Cryosphere for further details.</p>
Supporting Data: Phylogeny of Arbacia Gray, 1835 (Echinoidea) reveals diversification patterns in the Atlantic and Pacific Oceans.
<p>This dataset contains:</p> <ol> <li>Appendix S1, metadata asociated with the specimens (collection localities, specimen numbers)</li> <li>The aligned sequence files for each marker: COI_fasta, 16S_fasta, CR_fasta</li> <li>The concatenated sequence file COI + 16S + CRA + 28S Arbacia_supermatrix_fasta and the partition file partitions_concat</li> <li>The Bayesian trees for COI, 16S, CRA, and the supermatrix: BI_tree_16S, BI_tree_COI, BI_tree_CR, BI_tree_Arbacia_supermatrix</li> <li>The ML tree of the supermatrix: ML_tree_Arbacia_supermatrix</li> <li>Appendix S2, which includes various information on the primers used, PCR cycles, etc.</li> <li>Appendix S3, which includes the index calculations for each marker.</li> </ol>
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
360-info/tracker-ocean-temperatures: Monthly global ocean surface temperatures: v2024-10-22
<p>Tracks the monthly average sea surface temperatures using the <a href="https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html">OISST v2</a> dataset, created by NASA's <a href="https://psl.noaa.gov">Physical Sciences Laboratory</a>.</p><p>OISST updates both daily and monthly (we use the monthly updates here). The dataset <a href="https://www.ncei.noaa.gov/products/optimum-interpolation-sst">blends sea surface temperature observations</a> from satellites, ships, buoys and Argo floats.</p>
NEMO4.2 eORCA1 configuration files for stable millennial ocean simulations
<p>This data repository contains the configuration files specific to the three model experiments described in the study entitled 'Effects of improved tidal mixing in NEMO one-degree global ocean model'. These experiments, called OLD, NEW and TRA, employ NEMO version 4.2.0 and the eORCA1 global mesh. They have been run for 1000 years under CORE version 2 normal year atmospheric forcing (https://data1.gfdl.noaa.gov/nomads/forms/core/COREv2/CNYF_v2.html).</p> <p>The files provided are: routines modified (compared to released NEMO 4.2.0 code), namelists, initial conditions, other input fields, and restarts for year 1001 of experiment TRA. </p> <p>Namelist filenames include either '_ref' or '_cfg'; the latter overwrite the former before the model reads the full namelists.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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