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26 results for “Ocean salinity”
MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: salinity, temperature and current at CTD and ADCP mooring FOR01 from 2004 ongoing
Moored and bottom-mounted instrumentation (ADCP, CTD, thermistors, wave-tide meters) sampled year-round on the reef of Moorea Island, French Polynesia at LTER 1 fore reef site (FOR01). Sampling began in 2005. All data have been interpolated onto a 20 min grid. ADCP data are organized into 1-meter bins, measured as height from the bottom, to a maximum of 20 bins. All bins may not be filled, and although post-processing attempted to exclude data from bins 'above the surface', users need to exercise caution with the near-surface bins. CTD parameters include Pressure, Temperature, Conductivity, Salinity, and Density. The two moored CTD packages are located approximately 5 and 14 meters above the bottom and there are up to 5 additional temperature thermistors spaced vertically along the mooring line. Note that the upper CTD and temperature thermistor data were terminated in 2020, and the bottom CTD was terminated in February 2022. There are 2 bottom mounted thermistors in the vicinity of the mooring at 10 and 20 meter depth. A bottom-mounted wave-tide meter at 10-meter depth provides a measure of significant wave height and dominant wave period at a 2-hour sampling interval. Tide-related data may be calculated from bottom-mounted pressure sensor data.
MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: salinity, temperature and current at CTD and ADCP mooring FOR04 from 2004 ongoing
Moored and bottom-mounted instrumentation (ADCP, CTD, thermistors, wave-tide meters) sampled year-round on the reef of Moorea Island, French Polynesia at LTER 4 fore reef site (FOR04). Sampling began in 2005. All data have been interpolated onto a 20 min grid. ADCP data are organized into 1-meter bins, measured as height from the bottom, to a maximum of 20 bins. All bins may not be filled, and although post-processing attempted to exclude data from bins 'above the surface', users need to exercise caution with the near-surface bins. CTD parameters include Pressure, Temperature, Conductivity, Salinity, and Density. The two moored CTD packages are located approximately 5 and 14 meters above the bottom, and there are up to 5 additional temperature thermistors spaced vertically along the mooring line. Note that the upper CTD and temperature thermistor data were terminated in January 2020. Bottom CTD was terminated July 2021. There are 2 bottom mounted thermistors in the vicinity of the mooring at 10 and 20 meter depth. A bottom-mounted wave-tide meter provides a measure of significant wave height and dominant wave period at a 2-hour sampling interval. Tide-related data may be calculated from bottom-mounted pressure sensor data.
MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: salinity, temperature and current at CTD and ADCP mooring FOR05 from 2005 ongoing
Moored and bottom-mounted instrumentation (ADCP, CTD, thermistors, wave-tide meters) sampled year-round on the reef of Moorea Island, French Polynesia at LTER 5 fore reef site (FOR05). Sampling began in 2005. All data have been interpolated onto a 20-minute grid. ADCP data are organized into 1-meter bins, measured as height from the bottom, to a maximum of 20 bins. All bins may not be filled, and although post-processing attempted to exclude data from bins 'above the surface', users need to exercise caution with the near-surface bins. CTD parameters include Pressure, Temperature, Conductivity, Salinity, and Density. The two moored CTD packages are located approximately 5 and 14 meters above the bottom, and there are up to 5 additional temperature thermistors spaced vertically along the mooring line. Note that the upper CTD was terminated in July 2020, the bottom CTD was terminated in October 2021, and the temperature thermistor data was terminated in January 2020. There are 2 bottom-mounted thermistors in the vicinity of the mooring at 10 and 20 meter depth. A bottom-mounted wave-tide meter provides a measure of significant wave height and dominant wave period at a 2-hour sampling interval. Tide-related data may be calculated from bottom-mounted pressure sensor data.
Gulf Stream Daily Temperature, Salinity and Mixed Layer Depth fields from Ocean Stratification network (OSnet).
<p>The OSnet Gulf Stream product consists of 4D daily Temperature, Salinity and Mixed Layer Depth, available on a 1/4 degree regular grid and on 51 depth levels from the surface down to 1000m, in the Gulf Stream region from 01-01-1993 to 31-12-2019.<br> We introduce OSnet (Ocean Stratification network) in the Gulf Stream region, a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The interpolation scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000m and the Mixed Layer Depth (MLD) from satellite data covering 1993 to 2019. The inputs are sea surface temperature and sea level anomaly, complemented with mean dynamic topography, bathymetry, longitude, latitude and the day of the year. The in-situ profiles are from the CORA database and include Argo floats and ship-based profiles. The prediction of the MLD is used to adjust a posteriori the vertical gradients of predicted T-S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4 degree daily grid, producing four-dimensional fields of temperature, salinity and mixed layer depth, with their associated confidence interval issued from the bootstrap.</p> <p>The full dataset is downloadable with the <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> tool and the command :<code> zenodo_get 6011144</code></p>
Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model
<p>This data is only used for paper submitted to the JPO entitled 'Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model'.</p>
Dataset for "Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO2: perspectives from model perturbation experiments and a theoretical framework"
<p>Reference: Kewei Lyu, Xuebin Zhang, John A. Church, Quran Wu, Russell Fiedler, and Fabio Boeira Dias (2022), Roles of surface forcing in the Southern Ocean temperature and salinity changes under increasing CO<sub>2</sub>: perspectives from model perturbation experiments and a theoretical framework, <em>Journal of Physical Oceanography</em>, <a href="https://doi.org/10.1175/JPO-D-22-0095.1">https://doi.org/10.1175/JPO-D-22-0095.1</a></p>
Data mining-based machine learning methods for improving hydrological data: a case study of salinity field in the Western Arctic Ocean
<p><span><span>Salinity variations in Arctic Ocean determine the strength of stratification,</span> <span>ocean circulation, and biogeochemical cycles. Therefore, a<span>ccurate </span>salinity product is of great significance for our study of the Arctic Ocean. The mean density structure and wind-driven surface circulation of the Arctic Ocean are largely dominated by the anti-cyclonic Beaufort Gyre in the Canadian Basin, along with the Transpolar Drift<span> (Hall </span><span>et al.,2022)</span>. We focus on the salinity in Western Arctic Ocean. Multiple machine learning methods were used to reconstruct annual salinity product in the Western Arctic Ocean temporal for the period 2003-2022.</span></span></p>
Reconstructing Ocean Subsurface Temperature and Salinity with Satellite Observations
<p> Ocean data are crucial for ocean science and climate change research. While moored buoys and Argo floats can provide data on ocean temperature and salinity from the surface to the deep ocean, their spatial and temporal distribution is sparse and discontinuous. In recent years, satellite-based ocean observations have been widely used. These observations offer high spatial resolution and temporal continuity but are often limited to surface ocean quantities. In this study, we develop a novel algorithm to infer ocean subsurface temperature and salinity using satellite observations of ocean surface properties. Different from current prevalent machine learning methods, the algorithm proposed is efficient and interpretable. The resultant dataset has a global coverage with a high spatial resolution (0.25°x0.25°) and has been validated against in-situ observations with satisfactory accuracy.</p>
Supporting Data: The Effect of Ocean Salinity on Climate and its Implications for Earth's Habitability
<p>Data supporting "The Effect of Ocean Salinity on Climate and its Implications for Earth's Habitability" by SL Olson, MF Jansen, DS Abbot, I Halevy, and C Goldblatt, submitted to GRL on April 18, 2022. The ROCKE-3D rundecks and input files required to reproduce these results are also included. </p>
Datasets used for Publication "Contribution of Non-Water Ices to Salinity and Electrical Conductivity in Ocean Worlds" - Geophysical Research Letters
<p>These files father the output data used in the figures published in the Geophysical Research paper:</p> <p><strong>Contribution of Non-Water Ices to Salinity and Electrical Conductivity in Ocean Worlds</strong></p> <p>One output file may be used for multiple figures, as indicated in the name of the Excel spreadsheet.</p>
Assessing the ocean carbon sink: Assimilation of temperature and salinity into a global ocean biogeochemistry model
<p>Data underlying figures in manuscript draft.</p>
Ocean Temperature, Salinity, Ideal Age for Experiment 34Ma and 40Ma
<p><span>This dataset includes ocean temperature, salinity, and ideal age for experiments 34Ma and 40Ma. Both experiments start from the same initial conditions and run for 3000 model years. The dataset represents the mean values for the last 200 model years.</span></p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
Reconstructed upper-ocean temperature, salinity, and sea ice at the Last Glacial Maximum
<p>These data accompany the published article</p> <p>Amrhein, D. E., Wunsch, C., Marchal, O., & Forget, G. (2018). A global glacial ocean state estimate constrained by upper-ocean temperature proxies. <em>Journal of Climate</em>, <em>31</em>(19), 8059-8079. <a href="https://doi.org/10.1175/JCLI-D-17-0769.1">https://doi.org/10.1175/JCLI-D-17-0769.1</a></p> <p>Seasonal reconstructions of upper-ocean temperatures, salinity, and sea ice concentrations from Amrhein et al. (2018). Two sets of fields are available: GLACIAL, from the last glacial maximum ocean state estimation, and MODERN, from a similar model configuration but run to quasi-equilibrium under modern forcing conditions. GLACIAL minus MODERN anomaly values are likely to be more robust than standalone values from GLACIAL because of ocean drifts over millennial time scales present in both GLACIAL and MODERN. See publication for further information. See the MITgcm documentation for additional details on model variables.</p> <p>Files in Amrhein_etal_2018_LGM_MODERN_area_heff_seasonal.mat are:</p> <p>lat: Vector of latitudes<br> lon: Vector of longitudes<br> lgm_areai_seas: Monthly maps of fractional sea ice area estimated at the LGM<br> lgm_heffi_seas: Monthly maps of sea ice thickness estimated at the LGM<br> mod_areai_seas: Monthly maps of fractional sea ice area simulated for modern climate<br> mod_heffi_seas: Monthly maps of sea ice thickness simulated for modern climate</p> <p>Files in Amrhein_etal_2018_LGM_MODERN_T_SST_seasonal.mat are:</p> <p>lat: Vector of latitudes<br> lon: Vector of longitudes<br> lgm_Si_seas: Monthly maps of upper-ocean salinity estimated at the LGM<br> lgm_Ti_seas: Monthly maps of upper-ocean temperature estimated at the LGM<br> mod_Si_seas: Monthly maps of upper-ocean salinity simulated for modern climate<br> mod_Ti_seas: Monthly maps of upper-ocean temperature simulated for modern climate</p> <p>A simple plot of, e.g., estimated LGM January upper-ocean temperatures can be made in MATLAB by putting the file in your working directory and running</p> <pre><code>load Amrhein_etal_2018_LGM_MODERN_T_SST_seasonal.mat pcolor(lon,lat,lgm_Si_seas(:,:,1)),shading flat</code></pre> <p>These data are a subset of those generated in the full state estimate. If additional variables are needed, please contact damrhein@ucar.edu and I can update this repository.</p>
Mineralogy and organic content are major predictors of shell loss in bivalves under reduced salinity, ocean freshening conditions
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Ocean current, temperature, salinity and turbulence collected from the R/V Walton Smith, R/V Pelican, and R/V Pt. Sur during two summer seasons of intensive field campaigns in the Coastal Waters of Gulf of Mexico and the Gulf of Mexico from 20210620 to 20220707
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Monthly mean temperature and salinity outputs from the Southern Ocean high-resolution (SOhi) simulation
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ECCO Ocean Temperature and Salinity - Monthly Mean llc90 Grid (Version 4 Release 4)
This dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean potential temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean Three-Dimensional Salinity Fluxes - Monthly Mean llc90 Grid (Version 4 Release 4)
This dataset provides monthly-averaged ocean three-dimensional salinity fluxes on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height and model sea level anomaly (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
ECCO Ocean Temperature and Salinity - Monthly Mean 0.5 Degree (Version 4 Release 4)
This dataset contains monthly-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.
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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.