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234 results for “Global Ocean”
Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment
<p>Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO<sub>2</sub> sink - an ocean model subsampling experiment, Philosophical Transactions A</p> <p>Surface ocean partial pressure of CO<sub>2 </sub>(pCO<sub>2</sub>) and air-sea CO<sub>2</sub> flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019).</p> <p>Also, all FESOM-REcoM output fields that were used in the reconstructions are provided.</p> <p>We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).</p> <p> </p>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
Global continental and ocean basin reconstructions since 200 Ma
<div>Description of Resources - Seton et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Seton, M., Müller, R. D., Zahirovic, S., Gaina, C., Torsvik, T., Shephard, G., Talsma, A., Gurnis, M., Turner, M., Maus, S., Chandler, M. (2012). Global continental and ocean basin reconstructions since 200 Ma. Earth-Science Reviews, 113(3), 212-270. doi:<a href="https://doi.org/10.1016/j.earscirev.2012.03.002" target="_blank" rel="noopener">10.1016/j.earscirev.2012.03.002</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div> </div> <div>The files associated with this data collection allow for the visualisation and/or manipulation of the global plate motion model presented by Seton et al. (2012), they include:</div> <div>• <strong>Rotations </strong>- Global rotation model that contains the reconstruction poles that describe the motions of the continents and oceans.</div> <div>* Seton_etal_ESR2012_2012.1.rot (410 KB)</div> <div> </div> <div>• <strong>Plate IDs </strong>- A list of all the plate IDs used in the rotation and geometry files and their corresponding plate names.</div> <div>* Seton_etal_ESR2012_PlateIDs.pdf (102 KB) </div> <div> </div> <div>• <strong>Coastlines </strong>- Geometries of the present-day coastlines.</div> <div>* Seton_etal_ESR2012_Coastline_2012.1.gpml (17.5 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.shp (3.3 MB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.txt (2.7 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.kml (4.4 MB)</div> <div> </div> <div>• <strong>Continent-ocean boundaries </strong>(COBs) - Locations of the boundaries between oceanic and continental crust for plates involved in this study.</div> <div>* Seton_etal_ESR2012_COB_2012.1.gpml (410 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.shp (152 KB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_COB_2012.1.txt (86 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.kml (176 KB)</div> <div> </div> <div>• <strong>Plate polygons and boundary geometries</strong> - Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals (0-200 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation file. </div> <div>* Seton_etal_ESR2012_PP_2012.1.gpml (29.5 MB)</div> <div> </div> <div>Note:</div> <div>Paleo age grids used by Seton et al. (2012) are released in Müller et al. (2013) [Müller, R. D., Dutkiewicz, A., Seton, M., & Gaina, C. (2013). Seawater chemistry driven by supercontinent assembly, breakup, and dispersal. Geology, 41(8), 907-910. doi: <a href="https://doi.org/10.1130/G34405.1" target="_blank" rel="noopener">10.1130/g34405.1</a>]</div> <div> </div> <div>The paleo age grids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/</a></div>
Ocean Basin Evolution and Global-Scale Plate Reorganization Events Since Pangea Breakup
<p><strong>Abstract </strong></p> <p>We present a revised global plate motion model with continuously closing plate boundaries ranging from the Triassic at 230 Ma to the present day, assess differences between alternative absolute plate motion models, and review global tectonic events. Relatively high mean absolute plate motion rates around 9–10 cm yr-1 between 140 and 120 Ma may be related to transient plate motion accelerations driven by the successive emplacement of a sequence of large igneous provinces during that time. A ~100 Ma event is most clearly expressed in the Indian Ocean and may reflect the initiation of Andean-style subduction along southern continental Eurasia, while an ~80 Ma acceleration of mean rates from 6 to 8 cm yr-1 reflects the initial northward acceleration of India and simultaneous speedups of plates in the Pacific. An event at ~50 Ma expressed in relative, and some absolute plate motion changes around the globe and in a reduction of global mean velocities from about 6 to 4–5 cm yr-1, indicates that an increase in collisional forces (such as the India-Eurasia collision) and ridge subduction events in the Pacific (such as the Izanagi-Pacific Ridge) play a significant role in modulating plate velocities.</p> <p><strong>Muller et al. (2016) AREPS model file versions</strong></p> <p>This model has been maintained for some time after initial publication. There are six versions of the model that we provide, including:</p> <ul> <li>v1.10 – Some minor fixes were made to plate topologies, and so conforms to the originally-published model.</li> <li>v1.11 – A back-arc basin north of Arabia was introduced in the Cretaceous (see note below), and hence slightly diverges from the original model in plate topologies, velocities, and seafloor age-grids for this region.</li> <li>v1.14 – The latest version of the model that has duplicated topology segments cleaned from the evolving polygons, which helps with quantifying plate boundary lengths in the resolved topology output.</li> <li>v1.15 – The correction to the pre-83 Ma Pacific rotations according to Torsvik et al. (2019) has been applied.</li> <li>v1.16 – Some fixes to topologies</li> <li>v1.17 – Major update to the seafloor age-grids and topologies. Age-grids are consistent with v1.15 and 1.16 as well. We strongly recommend you use this version of the model.</li> </ul> <p>Note about the evolution of the western Tethys in this model: The Western Tethys, north of Arabia, is punctuated by ophiolite formation and obduction in Cretaceous times. The first end-member involves applying the central and eastern Tethys analogues of back-arc opening and closure following ophiolite obduction, much like is usually implied in the Kohistan-Ladakh and Greater India collision zone. This scenario makes the Western Tethys north of Arabia consistent with the model of the eastern Tethys. However, a second end-member interpretation for the formation of many of the ophiolites in the region is that they develop when a mid-oceanic ridge inverts to become a subduction zone. Both options are plausible, but we implemented a change in this plate model after it was published to reflect the first end-member scenario in order to link the region to the eastern Tethys in a plausible way. This scenario is based on back-arc opening from ~125 Ma (Jolivet et al., 2016), with subduction of back-arc initiating in Albian times from ~110 Ma (Ghazi et at., 2003; Aygul et al., 2015). Obduction and Arabia collision with an arc occurs at 85 Ma (Jolivet et al., 2016; Jagoutz et al., 2016). The scenario is also consistent with the recent work of Morris et al. (2016) on the Oman Ophiolite.</p> <p> </p> <p>The agegrids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/</a></p>
The FORCIS database: A global census of planktonic Foraminifera from ocean waters
<p>The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.</p> <p>The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively). </p> <p>Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:</p> <ul> <li> <p>Block 1: metadata (from column 1 to 71)</p> </li> <li> <p>Block 2: original counts (from column 72 to 274)</p> </li> <li> <p>Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”</p> </li> <li> <p>Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”</p> </li> </ul> <p>Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:</p> <table> <tbody> <tr> <td> <p><strong>count_type</strong></p> </td> <td> <p><strong>unit</strong></p> </td> </tr> <tr> <td> <p>Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> <tr> <td> <p>Bin_Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Bin_Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Bin_Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Bin_Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> </tbody> </table> <p> </p> <p>For more details about the FORCIS database column description, please check the data descriptor paper <strong>Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).</strong></p> <p>The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.</p>
ECCO Iter22 Global Ocean State Estimate - 1 January 2004 to 30 April 2005
<p>Time series of global ocean temperature, salinity, and sound speed derived from the “Estimating the Circulation and Climate of the Ocean" (ECCO) program Iter22 state estimates. The sound speed fields were computed for simulation of acoustic propagation over basin scales or longer in a realistic oceanic environment. These estimates were computed in 2010 by the JPL-MIT-SIO ECCO program. <br>Original link: http://ecco2.jpl.nasa.gov/data9/cube/iter22/lat_lon/quart_80S_80N/THETA/ , now defunct.</p> <p>The solution is mesoscale permitting. The solution was obtained on a cube sphere grid between 80S and 80N with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005, NASA supercomputer improves prospects for ocean <br>climate research, Eos Trans., AGU 86, 89, 95–96.). State estimates were averaged over a 3-day interval. File 003 is averaged over 2004/1/1 -- 2004/1/3. Three-day-mean temperature and salinity profiles from the iter22 solution were provided on 1/4 degree <br>grid for the period 1 January 2004 to 30 April 2005. There are 162 snapshots at 3-day intervals. </p> <p>Depths were decimated to the standard 33 depths of the World Ocean Atlas to 5500 m. YearDay 1 is 1 January 1992. The number of the filename indicates the yearday in 2004. In situ temperature was computed from model potential temperature. Sound speed was computed using the Del Grosso sound speed equation. Original model profiles descended only to the model sea floor. Temperature, salinity and sound speed were filled in on a uniform grid using nearest neighbor to 5500 m depth. Values on a regular grid make life easier. Product documented in Dushaw and Menemenlis, 2014, Antipodal acoustic thermometry: 1960, 2004, Deep Sea Research Part I: Oceanographic Research Papers, 86, 1–20, https://doi.org/10.1016/j.dsr.824 2013.12.008.</p> <p>Each snapshot is stored as a netcdf 4 file. Latitude, Longitude, Depth, and YearDay variables given separately in sspgrid.nc . <br>N.B.: Values in the files are stored as 32-bit or 16-bit integers to save disk space:</p> <p>Sound Speed: saved as "round( (c-1000)*1000 )", so to get actual c: c=1000. + double(c)/1000. <br>Sound speed is stored to 3 decimal places as a 32-bit integer.</p> <p>Temperature: saved as "round( (T-10)*1000 )", so to get actual T: T=10. + double(T)/1000. <br>Temperature is stored to 3 decimal places as a 16-bit integer. Note that abyssal temperature can sometimes be negative.</p> <p>Salinity: saved as "round( (S-10)*1000 )", so to get actual S: S=10. + double(S)/1000. <br>Salinity is stored to 3 decimal places as a 16-bit integer.</p> <p>Data directory also has two matlab routines: get_section.m and dist.m. get_section.m shows how to load the files, compute the physical variable from the stored value, and compute a section of ssp, T, or S. dist.m is a utility for computing geodesics; it relies on R. Pawlowitz's m_map package which can be downloaded freely from his University of Vancouver web page.</p> <p>$ md5sum *tgz <br>53ca3621f422b89c599c92fbab71d2fc S_ecco_iter22.tgz (2.99 GB)<br>a9e9109b6dae7355bf2fa0926d436d9a ssp_ecco_iter22.tgz (6.09 GB)<br>0cb8d1c2cdee4c7377353dff722ece34 T_ecco_iter22.tgz (4.33 GB)</p>
Global Carbon Budget 2022, 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 (data-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 data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 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 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because 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):</p> <p><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</p> <p><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <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 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) 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 2022 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><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 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://globalcarbonbudget.org/</p> <p> </p>
Datasets for: A global review of pyrosomes: Shedding light on the ocean’s elusive gelatinous ‘fire-bodies’
These are the datasets used to create all figures included in: "Lilly, L.E., Suthers, I.M., Everett, J.D., Richardson, A.J. (2023). A Global Review of Pyrosomes: Shedding light on the ocean’s elusive gelatinous ‘fire-bodies’. Limnology & Oceanography Letters." The review presents a comprehensive global description of the body of current knowledge on pyrosomes, a zooplanktonic tunicate taxon closely related to salps, doliolids, and appendicularians. For review analyses, we used pyrosome observations and associated information from literature-published studies and four databases: NOAA COPEPOD Urochordates database (NOAA, 2022; https://www.st.nmfs.noaa.gov/copepod/atlas/html/taxatlas_4350000.html), BCO-DMO Jellyfish Database Initiative (JeDI; Condon et al., 2014; https://www.bco-dmo.org/dataset/526852), Global Biodiversity Information Facility (GBIF; https://doi.org/10.15468/dl.a8phvp), and Ocean Biodiversity Information System (OBIS; https://obis.org/taxon/137216). We matched pyrosome observations to corresponding satellite-measured sea surface temperature (NOAA Optimum Interpolation Sea Surface Temperature, V2, high-resolution, https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html) and chlorophyll-a (MODIS-AQUA, 4 km^2 resolution, Melin, 2013; http://data.europa.eu/89h/10161412-a76c-42b0-b4e1-5fcccdc412b2). The files included in this metadata record have been subsetted from all original file sources. Our subsetted files are designed to run with the associated MATLAB scripts to recreate all manuscript files. We include seven MATLAB scripts: 1) A four-part script to clean up all pyrosome observations, divide to species level, and remove duplicate records from multiple databases and within each database, and 2) Three standalone scripts to plot Figs. 1, 2, and 3.
Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).
<p>Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".</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>
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>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <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 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 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 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). 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: global, 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</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. 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 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) 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 2024 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).</p> <p><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>
Monthly global ocean carbonyl sulfide and carbon disulfide flux data (2000–2019)
<p>This data product reports simulated monthly global ocean–atmosphere fluxes of carbonyl sulfide (OCS) and carbon disulfide (CS2) at 0.5° × 0.5° resolution (equivalent to 55 km × 55 km at the equator) between January 2000 and December 2019.</p> <p>Data are contained in two NetCDF files:</p> <ul> <li>ocs-flux-monthly-2000-to-2019.nc: Monthly global ocean OCS fluxes, 2000–2019</li> <li>cs2-flux-monthly-2000-to-2019.nc: Monthly global ocean CS2 fluxes, 2000–2019</li> </ul> <p>Data characteristics</p> <ul> <li>Version: 1.0.1 (2025-04-07)</li> <li>Spatial coverage: global</li> <li>Spatial resolution: 0.5° longitude × 0.5° latitude</li> <li>Temporal coverage: 2000-01-15 thru 2019-12-15 (nominal timestamps fall on the 15th day of each month)</li> <li>Temporal resolution: monthly</li> </ul> <p>Related manuscript</p> <p>Sun, W., Merder, J., Zhao, G., Lennartz, S. T., & Michalak, A. M. (2025). Tropical sources dominate the ocean carbonyl sulfide budget. Under consideration in <em>Global Biogeochemical Cycles.</em></p>
Variability in the global ocean carbon sink from 1959-2020 by correcting models with observations (LDEO-HPD)
<p><strong>* The latest versions of this dataset are maintained and available here: <a href="https://zenodo.org/record/7901433">https://zenodo.org/record/7901433</a> *</strong></p> <p>The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO2 and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO2 data to global coverage by using GOBMs as priors, applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO2 product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO2 growth and volcanic eruptions.</p> <p>This data is the final reconstruction of air-sea CO2 fluxes for 1959-2020 using the mean pCO2 from the corrected models. Both annual flux time series and spatially explicit fluxes are included. RIVERINE CARBON EFFLUX ADJUSTMENTS ARE NOT INCLUDED WITHIN THESE FILES. File metadata provides units.</p>
A data set of monthly global ocean vertical velocity from 1950-2014
<p>This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling.</p> <p>Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns.</p> <p>It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.</p> <p> </p> <p> </p>
A new merged dataset of global ocean chlorophyll-a concentration for better trend detection
<p>Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. </p>
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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.