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Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.
<p>We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>). </p> <p>The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles: </p> <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, <em>submitted</em>.</p> <p>Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p> <p>Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.</p> <p> </p>
Effect of changing ocean circulation on deep ocean temperature in the last millennium: simulation output data
<ul> <li>This dataset contains the output of model simulations used in the paper:<br> Scheen, Jeemijn and Stocker, Thomas F., "Effect of changing ocean circulation on deep ocean temperature in the last millennium", Earth System Dynamics Discussions, https://doi.org/10.5194/esd-11-925-2020, 2020 </li> <li>All figures can be reproduced when combining this dataset with the published analysis code. <br> </li> <li>In addition this dataset contains the data behind Fig. 2 of the paper:<br> Gebbie, G. and Huybers, P. : "The Little Ice Age and 20th-century deep Pacific cooling", Science, 363, 70-74, https://doi.org/10.1126/science.aar8413, 2019<br> </li> <li>Download either the small (unzipped 5 Gb) or large (unzipped 22 Gb) version of the dataset. <strong>Warning: this needs to be loaded into memory when running the notebook.</strong> You only need the small version to run the github notebook and reproduce the figures, but you are free to explore additional variables in the large version.</li> </ul> <p>Overview of doi's:</p> <ul> <li>paper: <a href="https://doi.org/10.5194/esd-11-925-2020">https://doi.org/10.5194/esd-11-925-2020</a></li> <li>code (analysis and figures): <a href="https://doi.org/10.5281/zenodo.4022947">https://doi.org/10.5281/zenodo.4022947</a></li> <li>data (simulation output): <a href="https://doi.org/10.5281/zenodo.4022927">https://doi.org/10.5281/zenodo.4022927</a></li> </ul>
Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019)
<p>Argo-based mixed layer depth profiles derived from the CORA product as described in Whitt Nicholson Carranza. A binned 2-degree climatology was published previously:</p> <p>https://github.com/danielwhitt/globalimpacts_2019_whittetal/blob/master/MonthlyClimatology_ARGO_MLDbmax_TEOS10_Copernicus_PF_2000-2017_all_jun252019_nc.nc</p> <p>with:</p> <p>Whitt, D. B., Nicholson, S. A., & Carranza, M. M. (2019). Global Impacts of Subseasonal (< 60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth. <em>Journal of Geophysical Research: Oceans</em>, <em>124</em>(12), 8798-8831</p> <p>Contact the authors with questions. </p> <p>The chosen mixed layer depth definition is the same as "HMXL", a standard output of the Community Earth System Model (CESM) ocean component.</p>
Daily ocean bottom pressure anomalies 2007-2009 from global numerical models
<p>Data supplement to: Schindelegger, M., Harker, A. A., Ponte, R. M., Dobslaw, H., & Salstein, D. A. (2021). Convergence of daily GRACE solutions and models of submonthly ocean bottom pressure variability. <em>Journal of Geophysical Research: Oceans</em>, 126, e2020JC017031. <a href="https://doi.org/10.1029/2020JC017031">https://doi.org/10.1029/2020JC017031</a></p> <p><strong>Contents:</strong></p> <p>Daily ocean bottom pressure anomalies over 2007-2009 obtained from two global forward simulations:</p> <ol> <li><strong>DEBOT</strong> (<em>David Einspigel Barotropic Ocean Tide Model</em>): 1/3° horizontal grid spacing, single-layer model</li> <li><strong>MITgcm LLC270</strong> (<em>Massachusetts Institute of Technology general circulation model, Lat-Lon-Cap 270</em>): nominal 1/3° horizontal grid spacing, 50 vertical layers</li> </ol> <p>Common specifications:</p> <ul> <li>Yearly files (<em>yyyy</em>) for each model run (<em>DEBOT_OBP_n180_yyyy.nc</em>, <em>LLC270_OBP_n180_yyyy.nc</em>)</li> <li>Temporal mean 2007-2009 reduced</li> <li>Daily fields centered at 12 UTC</li> <li>Units: cm of equivalent water height</li> <li>Data given on regular 1° grid</li> <li>Synthesized from spherical harmonic expansion truncated at degree 180 (<em>n180</em>)</li> <li>Degree 1 terms: included</li> <li>Static atmospheric contribution to bottom pressure: included</li> <li>Atmospheric forcing: ERA-Interim (6-hourly)</li> </ul> <p> </p> <p>Contact: M. Schindelegger (schindelegger@igg.uni-bonn.de)</p>
Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean
<p>This data set includes ocean ammonium concentrations, atmospheric ammonia gas concentrations, and calculated air-sea ammonia fluxes from the Atlantic sector of the Southern Ocean during summer. Associated with the folloiwng paper: </p> <p> https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963</p>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
Supplementary Dataset for "Extracting near-field seismograms from ocean-bottom pressure gauge inside the focal area: application to the 2011 Mw 9.1 Tohoku-Oki earthquake"
<p>Datasets S1 contains the results obtained by the analysis in this study, such as the spatial and temporal configuration of the basis functions. Dataset S2 contains the ocean-bottom pressure gauge data used in this study.</p> <p>The manuscript is available at: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091664</p> <p> </p> <p> </p> <div> </div>
Next generation global ice-ocean-biogeochemistry coupled model with 13C-cycling (GFDL MOM5-BLING13C)
<p> </p> <p>======= DESCRIPTION =======</p> <p>This is the model output supporting our paper <em>A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C</em> (2021 Global Biogeochemical Cycles).</p> <p>This model output simulates the transient response of ocean carbon biogeochemistry to anthropogenic CO<sub>2</sub> and <sup>13</sup>CO<sub>2</sub> atmospheric emissions with a nominal lateral resolution of 1° and 50 vertical levels. The model uses the NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) MOM5 coupled to the NOAA-GFDL Biogeochemistry with Light Iron Nutrients and Gas (BLING) with <sup>13</sup>C-cycling. Atmospheric forcing is prescribed using the repeating annual cycle of the Common Ocean Reference Experiment version 2 normal year forcing dataset (COREv2-NYF). The implementation of <sup>13</sup>C-cycling applies isotopic fractionations during air-sea gas exchange, photosynthetic production of organic matter, and formation of calcium carbonate. The sensitivity of dissolved inorganic <sup>13</sup>C in the ocean to the CO<sub>2</sub> gas exchange rate is explored by repeating the simulation twice, once using the latest OMIP-CMIP6 protocol for the k-U<sub>10 </sub>parameterization (standard) and once using the previous OCMIP2 protocol (fast-gas-exchange).</p> <p> </p> <p>Files information:</p> <ul> <li><strong>ocean_static.nc</strong>: Static fields (longitude, latitude, area).</li> <li><strong>1990-2002.ocean_month.nc</strong>: Monthly output between 1990 and 2002 of ocean physical variables (temperature, salinity, averaged mixed layer depth, maximum mixed layer depth).</li> <li><strong>1990-2002.ocean_bling_trc_month_CMIP6.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OMIP-CMIP6 air-sea gas exchange protocol.</li> <li><strong>1970_1989_d13c_org_mldave_CMIP6.nc</strong>: Monthly output between 1970 and 1989 of d<sup>13</sup>C of organic matter averaged over the mixed layer.</li> <li><strong>1990-2002.ocean_bling_trc_month_OCMIP2.nc</strong>: Monthly output between 1990 and 2002 of biogeochemical variables* for the simulation using the OCMIP2 air-sea gas exchange protocol.</li> </ul> <p>* Biogeochemical variables are dissolved inorganic carbon, dissolved inorganic carbon-13, oxygen, and dissolved inorganic phosphate.</p> <p> </p> <p>======= HOW TO CITE =======</p> <p>This model output can be freely distributed, but please cite it using the following paper:</p> <p>Claret, M., Sonnerup, R. E., & Quay, P. D. (2021). A next generation ocean carbon isotope model for climate studies I: Steady state controls on ocean <sup>13</sup>C. <em>Global Biogeochemical Cycles</em>, 35, e2020GB006757. <a href="https://doi.org/10.1029/2020GB006757">https://doi.org/10.1029/2020GB006757</a></p> <p> </p> <p>======= ACKNOWLEDGEMENTS =======</p> <p>This work was funded by the National Science Foundation (NSF-OCE 1356756 and NSF-OCE 1829796). We would also like to acknowledge high-performance computing support from Cheyenne (<a href="https://doi.org/10.5065/D6RX99HX">doi:10.5065/D6RX99HX</a>) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF.</p> <p> </p> <p>======= QUESTIONS AND REQUESTS? =======</p> <p>Please contact Mariona Claret (mclaret@uw.edu) or Rolf Sonnerup (rolf@uw.edu).</p> <p> </p>
Eddy Kinetic Energy and SST gradients global datasets and trends. Additionally, this dataset includes ocean basins and ocean processes masks.
<p>This dataset includes the post-processed data used for the paper titled "Mesoscale kinetic energy response to changing oceans". The original data was obtained from AVISO+ SSH altimetry and NOAA optimal interpolated sea surface temperature (OISST):</p> <p>AVISO+ SSH: https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html</p> <p>NOAA-OISST: https://www.ncdc.noaa.gov/oisst</p> <p>From satellite observations of sea surface height (SSH) and sea surface temperature (SST) over the satellite record (1993 - 2019), EKE and SST gradients are derived. </p> <p>Then the fields are then temporally smoothed using a running average of 12 months. Trends and the significance of each field are finally computed with linear regression and a modified Mann–Kendall test (https://github.com/josuemtzmo/xarrayMannKendall).</p> <p>Geographical regions consist of the following ocean basins: the Southern Ocean, the Indian Ocean, the Pacific Ocean, and the Atlantic ocean. These ocean basins were expert-defined to capture ocean processes at all scales (ocean_basins_and_dynamical_masks.nc).</p> <p>Dynamical regions (Fig. 5d): the Antarctic Circumpolar Current (ACC), the boundary currents and their extensions, the tropics, the subtropical ocean gyres, and the remaining regions (ocean_basins_and_dynamical_masks.nc).</p> <p>Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/EKE_SST_trends</p>
Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p><p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet.</strong></p><p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p><p><strong>What is in the files?</strong></p><p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p><p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p><p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br><br>(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p><p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p><p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Future Ocean Warming May Threaten Key Photosynthetic Microbes
<h2>Description</h2> <p>The datasets supporting the conclusions of this article, including field measurements of <em>Prochlorococcus</em> division rates, are available in this repository. </p> <p>The R code performs the following tasks:</p> <ul> <li>Loads data from various sources, including lab experiments, dilution experiments, and in-situ measurements.</li> <li>Calculates thermal norm predictions using different models (Eppley, Hinshelwood, Eppley-Norberg) to predict division rates based on temperature.</li> <li>Generates figures to visualize the results, including latitudinal and temperature effects on division rates, model predictions compared with observed data, and changes in primary production under different emission scenarios.</li> <li>Fits the Hinshelwood model to culture data and extracts best-fit parameters.</li> <li>Performs bootstrapping to estimate uncertainty in the Hinshelwood model parameters.</li> <li>Calculates confidence intervals for the bootstrapped parameters.</li> </ul> <h2>R Scripts</h2> <ul> <li><strong>Ribalet_main.R:</strong> This script contains the main analysis code, including data loading, model fitting, figure generation, and bootstrapping.</li> <li><strong>Ribalet_fitting.R:</strong> This script defines functions for fitting different growth models to the data and estimating model parameters.</li> </ul> <h2>Requirements</h2> <ul> <li>R version 4.4.2 (2024-10-31)<br>Platform: aarch64-apple-darwin20<br>Running under: macOS Sequoia 15.1.1</li> <li>Matrix products: default<br>BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib <br>LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0</li> <li>attached base packages:<br>[1] parallel stats graphics grDevices utils datasets <br>[7] methods base </li> <li>other attached packages:<br> [1] DEoptim_2.2-8 arrow_15.0.1 ggpubr_0.6.0 lubridate_1.9.3<br> [5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 <br> [9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 <br>[13] tidyverse_2.0.0</li> </ul> <h2>Installation</h2> <p>Install the required R packages:</p> <div> <div>Code snippet</div> <div> <div> <pre><code>install.packages(c("tidyverse", "ggpubr", "arrow", "DEoptim")) </code></pre> </div> </div> </div> <h2>Usage</h2> <p>The scripts will generate figures and output files in the same directory.</p> <h2>Input Data</h2> <p>The code requires the following input data files:</p> <ul> <li>culture.csv</li> <li>dilution.csv</li> <li>abundance.csv</li> <li>mpm.csv</li> <li>model_results.parquet</li> <li>bootstrap_projections.csv</li> <li>modeled-thermal-traits.tsv</li> <li>sst.parquet</li> <li>culture_syn.csv</li> </ul> <p>Please ensure that these files are present in the same directory as the R script files.</p> <h2>Output Data</h2> <p>The code generates the following output files:</p> <ul> <li>Figures: Figure1.png, Figure2.png, Figure3.png, FigureS1.png, FigureS2.png, FigureS3.png, FigureS4.png, FigureS5.png, FigureS6.png, FigureS9.png, FigureS11.png, FigureS12.png, FigureS13.png, FigureS14.png, FigureS15.png</li> <li>CSV files: bootstrap_parameters.csv, cultures_thermal_reactions.csv</li> </ul> <h2>License</h2> <p>This code is licensed under the MIT License.</p>
GO-SHIP Easy Ocean: Formatted and gridded ship-based hydrographic section data
<p><a href="https://www.go-ship.org">GO-SHIP</a> (The Global Ocean Ship-based Hydrographic Investigations Program) has developed the protocols and methods to generate a data product that concatenates all occupations of individual sections into a time-series; the GO-SHIP Easy Ocean. Here we provide access to the analysis-ready gridded GO-SHIP Easy Ocean product that enhances the accessibility of this unique data set that spans four decades, comprised of more than 40 cross-ocean transects, many with multiple repeats.</p> <p>This product, of uniformly calibrated CTD (temperature, salinity and oxygen) data, provides easy access to and use of the high-quality hydrographic temperature and salinity data that span more than 40 years. The GO-SHIP Easy Ocean product will underpin the quality control of autonomous platforms, provide a ready assessment of ocean-only and coupled climate model simulations, and be used in specific research projects. The GO-SHIP Easy Oceanis a companion to the GLODAP inorganic and carbon product. The section data are available from Zenodo in two standard arrangements: Uninterpolated (reported) and interpolated (gridded). For both arrangements, five quantities are recorded; in situ temperature in ITS-90 scale, in situ salinity in PSS-78 scale, the dissolved oxygen concentration in μmol/kg, Conservative Temperature in °C, and Absolute Salinity in g/kg. The data are available in various formats.</p> <p>Cite <a href="https://doi.org/10.1038/s41597-022-01212-w">Katsumata et al (2022)</a> when using this product and include the following acknowledgment statement in any publication or derived product:</p> <p><em>Data were collected and made publicly available by the International Global Ship-based Hydrographic Investigations Program GO-SHIP (https://www.go-ship.org/) and the national programs that contribute to it.</em></p>
Ocean Drilling Program Site 959 Datasets
<p><strong>-Version V4 </strong>now includes the raw data associated with the paper "Evidence for limited atmospheric pCO2 rise at the onset of the Miocene Climatic Optimum", by Wubben et al.: item 'Miocene CO2; raw data' (md5:972e685a012a1f1915b4cfb6a3c5292b). An early version of the manuscript is published in the PhD thesis of Evi Wubben, entitled "Long-term and orbital-scale climate and carbon cycle change across the Miocene Climatic Optimum", ISBN 978-90-6266-692-8, openly available at the Utrecht University Repository. doi: 10.33540/2518. </p> <p> </p> <p><strong>-Version 1.1.0</strong></p> <p>Datasets updated with new Eocene Site 959 data added as supplement to:</p> <ul> <li>"Global warming and equatorial Atlantic paleoceanographic changes during early Eocene carbon cycle perturbation V" by Kegel et al.</li> </ul> <p> </p> <p><strong>-Version 1.0.0: </strong></p> <p>Data supplement to:</p> <ul> <li> <p>"Polar amplification of orbital-scale climate variability in the early Eocene greenhouse world" by Fokkema et al. (2024). </p> </li> <li> <p>"Tropical Warming and Intensification of the West African Monsoon during the Miocene Climatic Optimum" by Wubben et al. (2024).</p> </li> <li>"Early to Middle Miocene Orbitally-Paced Climate Dynamics in the Eastern Equatorial Atlantic" by Spiering et al. (2024). </li> </ul> <p>Updated age model and datasets of bulk magnetic susceptibility, bulk carbonate oxygen and carbon isotopes, bulk organic carbon isotopes, ICP-OES, palynology and GDGTs from Ocean Drilling Program (ODP) Leg 159 Site 959.</p> <p>This upload contains datasets by Kegel et al. (2024); Fokkema et al. (2024); Wubben et al. (2024); Spiering et al. (2024); Cramwinckel et al. (2018); Frieling et al. (2018; 2019) and Van der Weijst et al. (2022).</p>
Still images from the KuramBio expedition 2012 (Stations 3-6, 8-11) obtained with the Ocean Floor Observation System
<p>Repository with still images obtained from the videos of the KuramBio 2012 expedition in 5 second intervals. Still images belong to deep-sea stations where the Ocean Floor Observation System (OFOS) was deployed and enough video survey was obtained (i.e., Stations 3-6, 8-11). The Station 7 survey length was enough but has no HD video. Thus, images were not retrieved since the quality does not allow accurate lebensspuren or benthic fauna identification. </p><p>The OFOS was lowered into the water at the CTD position. The first 300 meters lowering was conducted with 0.5 m/sec, and then the speed was increased to 0.8 m/sec while the ship was kept in position. At 500 meters above ground the speed was reduced to 0.5 m/sec, and further reduced to 0.3 m/sec at 200 meters above ground. As soon as visual contact with the bottom was established, the winch was stopped. The ship started moving with 0.5 knots in the appropriate direction, which was chosen depending on current and wind situation at the according station. The OFOS was kept in an appropriate distance to the seafloor, enabling the scientists to watch the macrofaunal organisms. The approximate size of the observed animals could be calculated with the help of two laser pointers having a distance of 10 cm between each other (see Fig. 1-3). Generally, the survey lasted slightly over one hour, then the ship was stopped and heaving of the OFOS started. This was first conducted at 0.5 m/sec and accelerated to 1.0 m/sec. For more information see the cruise report (RV Sonne cruise SO223; doi:10.1016/j.dsr2.2014.11.001).</p>
Carbon outwelling and uptake along a tidal glacier-lagoon-ocean continuum
<p>Data_Jokulsarlon2022: Excel file containing raw data collected at Jökulsárlón Glacial Lagoon in September 2022. </p><p>The data set includes parameters measured in the surface water of our spatial survey and timeseries. This includes temperature and salinity, oxygen concentration, nutrients (total dissolved nitrogen, phosphate, silica), dissolved organic carbon, photosynthetic pigments (chlorophyll a and fucoxanthin) and carbonate species (total alkalinity and dissolved inorganic carbon), as well as atmospheric data (temperature and wind speed).</p>
MPAS-Ocean Shallow Water Meshes
<p>The MPAS_Ocean_Shallow_Water_Meshes directory contains planar hexagonal mesh files in NetCDF format necessary for running a verification suite of shallow water test cases for the barotropic solver of ocean models using a mimetic finite volume spatial discretization based on the TRiSK scheme. It also contains mesh plots showing the cell centers, edge centers, vertices, and orientation of the normal vectors at the edges; plots of high resolution meshes superimposed on low resolution ones; plots of state variables interpolated from edges and vertices to cell centers along with the interpolation error; plots of state variables interpolated from a high resolution mesh to a low resolution one; and plots of various spatial operators of the TRiSK scheme applied to the state variables along with their error and convergence plots. The associated code can be cloned from the Github repository <a href="https://github.com/siddharthabishnu/Rotating_Shallow_Water_Verification_Suite.git">Rotating_Shallow_Water_Verification_Suite</a>. Please download the MPAS_Ocean_Shallow_Water_Meshes.zip file, unzip it, and place the resulting directory within the meshes directory of Rotating_Shallow_Water_Verification_Suite.</p>
Antarctic Ice-Ocean Coupled Projections
<p>UKESM1-Ice Antarctic Ocean-Ice sheet coupled projections associated with the SSP1-1.9 and SSP5-8.5 climate change scenarios for the 2015-2100 period. Each scenario is composed by four ensemble members. The <strong>README.txt</strong> file includes a general description of the dataset. Details about the projections and the model implementation can be found in <a href="https://tc.copernicus.org/articles/16/4053/2022/">https://tc.copernicus.org/articles/16/4053/2022/</a>. </p>
FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas
<p>This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.</p><p>The data were created using python scripts provided at <a href="https://doi.org/10.5281/zenodo.10295920">https://doi.org/10.5281/zenodo.10295920</a>. </p><p>Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:</p><ul><li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li><li>simA, ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a></li><li>simA, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a></li><li>simA, ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a></li><li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li><li>simB: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC</a></li><li>simC, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC</a></li></ul><p> </p>
[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu
<p>## "Spatial scales of kinetic energy in the Arctic Ocean"</p> <p>Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9).</p> <p>## Description</p> <p>This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9.</p> <p>1) Jupyter notebook scripts for visualization<br>the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly.</p> <p>2) Matlab scripts for plots<br>All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.