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955 results for “Ocean data”
Subset of global model sea level data for "Challenges, Advances and Opportunities in Regional Sea Level Projections: the Role of Ocean-shelf Dynamics"
<p>Monthly sea surface height above the geoid data in NW European seas from six global simulations using the NEMO ocean model (https://www.nemo-ocean.eu/) for 1990 to 2009</p> <p><strong>ORCA0083_DFS_NWS_ssh_1990_2009, ORCA025_DFS_NWS_ssh_1990_2009, ORCA1_DFS_NWS_ssh_1990_2009,</strong> are the N006 simulation set created by Andrew Coward and the NOC Marine Systems Modelling team as used by:</p> <p>Baker et al 2022 Biological Carbon Pump Sequestration Efficiency in the North Atlantic: A Leaky or a Long-Term Sink? Global Biogeochemical Cycles <a href="https://doi.org/10.1029/2021GB007286">https://doi.org/10.1029/2021GB007286</a>,</p> <p>Wilson, C. <em>et al.</em> 2021 Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. <em>Commun. Earth Environ.</em> <strong>2</strong>, 164. <a href="https://doi.org/10.1038/s43247-021-00237-0">https://doi.org/10.1038/s43247-021-00237-0</a> (2021).</p> <p>These simulations are forced by the Drakkar Forcing Set 5.2 (DFS) and initialised at 1958, with a nominal 1/12, 1/4 and 1 degree resolution. See references for further model details.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009, ORCA025_JRA_tides_NWS_ssh_1990_2009, ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009, </strong>are new simulations produced by Chris Wilson, James Harle and the Shelf Enabled NEMO team. All are forced by the JRA reanalysis, initialised in 1976.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009</strong> is a reference run based on GO9, an evolution of the Joint Marine Modelling Programme configuration described by Storkey et al 2018 UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geoscientific Model Development https://gmd.copernicus.org/articles/11/3187/2018/</p> <p><strong>ORCA025_JRA_tides_NWS_ssh_1990_2009</strong> adds explicit tides to this.</p> <p><strong>ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009</strong> adds tides, Generic Length Scale Mixing and Multi-envelope vertical coordinates</p> <p>Details of these simulations can be found here:</p> <p>https://github.com/NOC-MSM/SE-NEMO </p>
Data and scripts (2) for Storkey et al, "Resolution dependence of interlinked Southern Ocean biases in global coupled HadGEM3 models", GMD (2024)
<p>================================================================<br> Data and scripts for producing plots from Storkey et al (2024):<br> "Resolution dependence of interlinked Southern Ocean biases in<br> global coupled HadGEM3 models"<br> ================================================================</p> <p>The plots in the paper consist of 10-year mean fields from the third <br>decade of the spin up and timeseries of scalar quantities for the first<br>150 years of the spin up. The data to produce these plots are stored<br>in the MEANS_YEARS_21-30 and TIMESERIES_DATA directories respectively.</p> <p>Note that due to the size limit on records on Zenodo, the 10-year mean <br>output from the N216-ORCA12 integration has been stored as a separate<br>record.</p> <p>Scripts to produce the plots are in SCRIPT, with section definitions<br>in SECTIONS. Bespoke plotting scripts are included in SCRIPT. They use<br>python 3 including the Matplotlib, Iris and Cartopy packages. The <br>plotting of the timeseries data used the Marine_Val VALSO-VALTRANS <br>package which is available here:</p> <p> https://github.com/JMMP-Group/MARINE_VAL/tree/main/VALSO-VALTRANS </p> <p>Much of the processing of the model output data was performed with the<br>CDFTools package, which is available here:</p> <p> https://github.com/meom-group/CDFTOOLS</p> <p>and the NCO package:</p> <p> https://web.mit.edu/course/13/13.715/nco-2.8.1/doc/</p>
Ocean variability drives severe increases in heavy rainfall in the Yellow River Basin-Data availability part
Open the record for dataset details and reuse information.
Data from: Direct observational evidence of strong CO2 uptake in the Southern Ocean
<p>These are the eddy covariance air-sea CO2 flux dataset, the subsampled CO2 flux products, and the neural network-based interpolation of the SOCCOM-weighted and SOCAT plus SOCCOM datasets. These datasets are used in the article 'Direct observational evidence of strong CO2 uptake in the Southern Ocean'.</p>
Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model
<p>This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.</p>
Data from: Connections between the Southern Ocean and the Eastern tropical Pacific in unforced and forced climate model simulations
<p>The sea surface temperature (SST) over the eastern tropical Pacific significantly influences global-mean climate feedback and may be driven in part by the SST over the Southern Ocean. Previous studies demonstrated a teleconnection from the Southern Ocean to the eastern tropical Pacific by perturbing the Southern Ocean climate. We investigate if this teleconnection holds in a fully coupled, freely running climate system using CMIP6 models. We assess the relationship between the Southern Ocean (SO) and the eastern tropical Pacific (SEP) by calculating correlations between SO and SEP SST timeseries within each model and regressions between mean SO and SEP SSTs across models. We show robust, positive SO-SEP relationships in an unforced climate using pre-industrial SSTs, in a forced climate using SST anomalies between pre-industrial and quadrupled CO<sub>2</sub> simulations, and in the SST pattern of the forced response relative to the global-mean SST anomaly. The strength of SO-SEP correlations is positively related to the stratocumulus cloud feedback off the west coast of South America, and negatively related to ocean heat uptake in the same region. As both shortwave cloud feedback and ocean heat uptake are underestimated in climate models, understanding their effects on SO-SEP teleconnections and their interactions is crucial for determining the strength of SO-SEP teleconnection in the real world and its trustworthiness in climate model projection.</p>
Data for ''A note on systematic biases in the ocean due to the air-sea flux calculation in coupled models''
<p>Data used to in a JAMES publication.</p> <p> </p> <p>Plotting routines can be found at: https://github.com/RafaelAbel/Coarse_Graining</p> <p>Manuscript DOI: tba</p>
Numerical model code, input files and output data for publication "Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current"
<p>Contains numerical model data (code, input files, selected output, matlab diagnostic routines) to supplement publication ``Rapid mixing and exchange of deep-ocean waters in an abyssal boundary current'', by Naveiro Garabato and co-authors. All numerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations, and reproduction of diagnostics shown in plots in the above-referenced paper.</p>
Data supporting "Non-hydrostatic, non-linear processes in the surf zone", by Martins et al., submitted to JGR-Oceans
<pre>This file describes the structure of the sub-surface pressure and surface elevation data used in the paper "Non-hydrostatic, non-linear processes in the surf zone", submitted by Martins et al. to Journal of Geophysical Research: Oceans. The data set is composed of a single .mat file, which contains all raw timeseries for the 52 bursts used in the paper. Metadata and description of the data structure and variables are provided in the structure directly. This data set is distributed under the Creative Commons Attribution 4.0 International license. </pre> <p>The collection of this data set was funded by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/N019237/1, Waves in Shallow Water, awarded to Chris Blenkinsopp (University of Bath).</p>
Data for "Trend and variability in global upper-ocean stratification since the 1960s"
<p>Abstract of associated paper: Many studies on future climate projection point out that, with progressing of global warming, upper-ocean stratification will strengthen over this century and consequently global-averaged ocean primary production will decrease. Observed long-term changes in the stratification to date, however, still show large uncertainties of the change itself and its driver. Focusing on the vertical difference in the emergence of the global warming signals, we used only observational profiles to describe the spatiotemporal characteristic of long-term trend and variability in the upper-ocean stratification. Rapid strengthening of the stratification (defined as the density difference between the surface and 200 m depth) since the 1960s was detected over most of the global ocean. Although the global average increase over 58 years (1960–2017) corresponds to 3.3–6.1% of the mean stratification, these strengthening trends considerably change depending on the regions. In addition to the well-documented explanation of strengthening stratification, namely that the surface intensification of global warming signal, we found that changes in subsurface temperature and salinity stratification associated with changes in atmospheric/ocean circulations and the global water cycle significantly contribute to the long-term change in the stratification and setting its regional difference. In mid- and high-latitude ocean of the northern hemisphere, the long-term trend in density stratification has noteworthy seasonality, which shows faster increase in boreal summer than that in winter. From the detrended time series, interannual variabilities correlated with a particular climate mode are detected in several ocean regions, suggesting that these variabilities are mainly driven by associated sea surface temperature variation.</p>
Supplementary Data for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting
<p>This data repository is provided as a supplement to the paper *Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* written by Håvard Heitlo Holm, Martin Lilleeng Sætra and Peter Jan van Leeuwen. It contains the complete datasets (initial conditions and results of the ensemble simulations) obtained from the experiments presented therein.</p> <p>This data set is generated by, and can be further post-processed and visualized by, the code published as *metno/gpu-ocean: Supplementary Software for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* by Håvard Heitlo Holm, Martin Lilleeng Sætra and André Rigland Brodtkorb (DOI 10.5281/zenodo.3458291). </p> <p> </p>
Raw multibeam bathymetry data collected around Southern Thule, part of the South Sandwich Island chain in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around Southern Thule, part of the South Sandwich Island chain in the Southern Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>lineYYYYDDmonHHMMSS.ssv, data file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Raw multibeam bathymetry data collected around Bouvetoya in the South Atlantic Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around Bouvetoya in the South Atlantic Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
The netCDF output data of Parallel Princeton Ocean Model based on OpenACC
<p>This dataset represents the output results from the simulated seamount case, where the outputs vary depending on whether parallel (p) or serial (s) execution is used, as well as the different simulation durations and resolutions applied.</p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
NeurOST-SSH Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using only L3 SSH (not including SST).</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
Supporting data: Human-induced weakening of subsurface ocean temperature seasonality
<p>CESM1 ocean-only experiments for analysis:</p> <p>Liu et al., 2024: <span>Human-induced weakening of subsurface ocean temperature seasonality, in preparation. </span></p>
Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean
<p>Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean:<br>Rasters, R models and scripts</p>
Observed data, predictions and uncertainty associated with the updated distribution of clay minerals in the World Ocean
<p>Sparase observed data for various clay mineral species are .csv file format.</p> <p>Predictions and uncertainty for four seafloor clay mineral species (relative percentages, Kaolinite, Illite, Smectite, Chlorite) are generated via geospatial machine learning (GML). Methodology is outlined in "The updated distribution of clay mineral in the World Ocean"<span>. These files are in net-CDF file format. Files are cell-centered. Further, latitudes and longitudes of grid cells are denoted in the variables of the .nc files.</span></p>
MASCS 1.0: Synchronous atmospheric and oceanic data from a cross-shaped moored array in the northern South China Sea during 2014–2015
<p>This work presents a cross-shaped moored array dataset (MASCS 1.0) comprising five buoys and four moorings with synchronous atmospheric and oceanic data in the northern South China Sea during 2014–2015. The atmospheric data are observed by two meteorological instruments at the buoys. The oceanic data consist of sea surface waves measured using a wave recorder, temperature, and salinity from the surface to a depth of 400 m, and at 10 and 50 m above the ocean bottom using conductivity, temperature, and depth recorders. It also includes currents from the surface to a depth of 850 m measured using acoustic Doppler current profilers and measured at 10, 50, and 100 m above the floor using current meters. Additional measurements were taken for sea surface radiation, air visibility, chlorophyll, turbidity, and chromophoric dissolved organic matter at buoy 3, located at the center of the moored array. The data reveals air–sea interactions and oceanic processes in the upper and bottom ocean, especially the transition of the air–sea interface and ocean conditions from summer to winter monsoon and the effects of six tropical cyclones on the moored array. Multiscale processes were also recorded, such as air–sea fluxes, tides, internal waves, and low-frequency flows. The data are valuable and have many potential applications, including analyzing the phenomena and mechanisms of air–sea interactions and ocean dynamics and validating and improving numerical model simulations, data reanalysis, and assimilations.</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.