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118 results for “ocean observation”
Data for: Saturation of ocean surface wave slopes observed during hurricanes
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Data for: Observed declines in upper ocean phosphate-to-nitrate availability
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Data for: Ocean surface wave slopes and wind-wave alignment observed in Hurricane Idalia
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Decoded telegrams of surface weather observations in the Southern Ocean on board the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains decoded reports of surface weather observations in the Southern Ocean made aboard the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE), in the austral summer of 2016/2017. The observations were made by a meteorologist on board the ship and sent as telegrams encoded with World Meteorological Organisation (WMO) FM 13 code form. Posteriorly, the telegrams were decoded to produce this dataset.</p> <p>The telegrams were recorded on a 3-hourly basis and contain information about present and past weather; latitude, longitude, speed and direction of the ship; visibility; type, height and amount of clouds; speed and direction of wind; air, dew-point and sea-surface temperatures; barometric pressure; period, height and direction of waves; concentration of sea ice; etc. For more information on this type of report, please see the WMO Manual on Codes (WMO-No.306), Volume I.1, Part A. The original encoded files have been published separately (Veledin and Gorodetskaya, 2020; DOI 10.5281/zenodo.3734884).</p> <p>Data files have undergone no processing or quality-checking, therefore, if the variable you need is available in another ACE dataset, it is recommended to use the quality-checked data. Quality-checked meteorological data collected during ACE have been published separately (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_telegram_decoded_YYYY_MM_DD_HHh.txt, data file, ASCII comma-separated</li> <li>README.txt, metadata, text file</li> <li>data_file_header.txt, metadata, text file</li> <li>Telegram_code_format.pdf, metadata, portable document format</li> </ul> <p><strong>Dataset license</strong></p> <p>This set of decoded meteorological telegrams are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
Original telegrams of surface weather observations in the Southern Ocean on board the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains original reports of surface weather observations in the Southern Ocean made aboard the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE), in the austral summer of 2016/2017. The observations were made by a meteorologist on board the ship and encoded with FM 13–XIV Ext. SHIP code form (Report of surface observation from a sea station). The encoded telegrams were sent to the World Meteorological Organisation (WMO) and archived on the ship.</p> <p>The telegrams were usually recorded on a 3-hourly basis (at 0000, 0300, 0600, 0900, 1200, 1500, 1800, 2100 UTC) and on some dates on a 6-hourly basis. They contain information about present and past weather; latitude, longitude, speed and direction of the ship; visibility; type, height and amount of clouds; speed and direction of wind; air, dew-point and sea-surface temperatures; barometric pressure; period, height and direction of waves; concentration of sea ice; information about land ice. Data coverage is from November 2016 until April 2017, with some data missing from February and March.</p> <p>For more information on this type of report, please see the WMO Manual on Codes (WMO-No.306), Volume I.1, Part A:<br> https://library.wmo.int/index.php?lvl=notice_display&id=13617#.XoHf4oW3Ih4</p> <p>Data files have undergone no processing or quality-checking, therefore, if the variable you need is available in another ACE dataset, it is recommended to use the quality-checked data. These telegrams have been decoded and are available in another related dataset.</p> <p><strong>Dataset contents</strong></p> <ul> <li>UBXH3mmdd.HHh, data file, text file</li> <li>README.txt, metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This set of original meteorological telegrams are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</p>
Data sets used in "Is there a tropical response to recent observed Southern Ocean cooling?"
The dataset contains processed data for the GRL publication (to be submitted) "Is there a tropical response to recent observed Southern Ocean cooling?" The dataset includes global maps of linear trends of various fields presented in the figures, as well as pattern correlation coefficients for the three experiment ensembles using CESM1: Large Ensemble, Tropical Pacific Pacemaker Experiment, and Southern Ocean Pacemaker Experiment.
Data presented in figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)"
<p>This dataset includes the data shown in the figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)". Read me files containing information on the reported data can be found in the different folders. </p>
Idealized wave data in support of Directional Breaking Kinematics Observations from 3D Stereo Reconstruction of Ocean Waves
<p>You will find the WaveWatchIII data output from idealized solutions of Romero 2019, ST4 and ST6</p> <p>Data are in Netcdf format and include metadata.</p> <p>Each file corresponds to a duration-limited solution with constant wind speed of 13 m/s</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>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Data used in "The daily-resolved Southern Ocean mixed layer: regional contrasts assessed using glider observations"
<p>These are the data used in the analysis and creation of figures in du Plessis et al. 2022: <em>The daily-resolved Southern Ocean mixed layer: regional contrasts assessed using glider observations</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Water mass transformation in the Southern Ocean is vital for driving the large-scale overturning circulation, which transports heat from the surface to the ocean interior. Using profiling gliders, this study investigates the role of summertime buoyancy forcing and wind-driven processes on the intraseasonal (1-10 days) mixed layer thermohaline variability in three Southern Ocean regions southwest of Africa important for water mass transformation - the Subantarctic Zone (SAZ), Polar Frontal Zone (PFZ) and Marginal Ice Zone (MIZ). At intraseasonal time scales, heat flux was shown as the main driver of buoyancy gain in all regions. In the SAZ and MIZ, shallow mixed layers and strong stratification enhanced mixed layer buoyancy gain by trapping incoming heat, while buoyancy loss resulted primarily from the entrainment of cold, salty water from below. In the PFZ, rapid mixing linked to Southern Ocean storms set persistently deep mixed layers and suppressed mixed layer intraseasonal (1-10 days) thermohaline variability. In the polar regions, lateral stirring of meltwater from seasonal sea-ice melt dominated daily mixed layer salinity variability. We propose that these meltwater fronts are advected to the PFZ during late summer, indicating the potential for seasonal sea-ice freshwater to impact a region where the upwelling limb of overturning circulation reaches the surface. This study reveals a regional dependence of how the mixed layer thermohaline properties respond to small spatio-temporal processes, emphasizing the importance of surface forcing occurring between 1-10 days on the mixed layer water mass transformation in the Southern Ocean.</p> <p><strong>Related code:</strong> The accompanying code can be found at: <a href="http://doi.org/10.5281/zenodo.5076119">http://doi.org/10.5281/zenodo.5076119. </a>Navigate to github.com/marcelduplessis/duplessis-2021-SO-thermohaline for the latest version</p>
Ocean surface wind estimation from waves based on small GPS buoy observations in a bay and the open ocean
<p>This is dataset of ocean surface wave and wind used in the paper "Ocean surface wind estimation from waves based on small GPS buoy observations in a bay and the open ocean" by Shimura et al. (2022, JGR-Oceans, <a href="https://doi.org/10.1029/2022JC018786">https://doi.org/10.1029/2022JC018786</a> ).</p> <p>"data_bayObservation.nc" contains the observed wind, estimated wind, and observed wave spectral data during the bay observations.</p> <p>"data_openOceanObservation.nc" contains the reanalysis wind, estimated wind, and observed wave spectral data during the open ocean observations.</p> <p> </p>
Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality
<p>Codes for the analysis and figure generation for drifter data</p>
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>
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>
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
Biophysical larval dispersal models of observed bonefish (Albula vulpes) spawning events in Abaco, The Bahamas: An assessment of population connectivity and ocean dynamics
<p>Biophysical models are a powerful tool for assessing population connectivity of marine organisms that broadcast spawn. <em>Albula</em> <em>vulpes</em> is a species of bonefish that is an economically and culturally important sportfish found throughout the Caribbean and that exhibits genetic connectivity among geographically distant populations. We created ontogenetically relevant biophysical models for bonefish larval dispersal based upon multiple observed spawning events in Abaco, The Bahamas in 2013, 2018, and 2019. Biological parameterizations were informed through active acoustic telemetry, CTD casts, captive larval rearing, and field collections of related albulids and anguillids. Ocean conditions were derived from the Regional Navy Coastal Ocean Model American Seas dataset. Each spawning event was simulated 100 times using the program Ichthyop. Ten thousand particles were released at observed and putative spawning locations and were allowed to disperse for the full 71-day pelagic larval duration for <em>A</em>. <em>vulpes</em>. Settlement densities in defined settlement zones were assessed along with interactions with oceanographic features. The prevailing Northern dispersal paradigm exhibited strong connectivity with Grand Bahama, the Berry Islands, Andros, and self-recruitment to lower and upper Abaco. Ephemeral gyres and flow direction within Northwest and Northeast Providence Channels were shown to have important roles in larval retention to the Bahamian Archipelago. Larval development environments for larvae settling upon different islands showed few differences and dispersal was closely associated with the thermocline. Settlement patterns informed the suggestion for expansion of conservation parks in Grand Bahama, Abaco, and Andros, and the creation of a park in Eleuthera and the Berry Islands to protect fisheries. Further observation of spawning events and the creation of biophysical models will help to maximize protection for bonefish spawning locations and nursery habitat, and may help to predict year-class strength for bonefish stocks throughout the Greater Caribbean.</p>
Observed ocean surface albedo from a fixed offshore platform
<p>The ocean surface albedo (OSA) is an important parameter in numerical ocean models and air‒sea heat flux calculations. Radiation observations were conducted to study the OSA in the northern South China Sea using a fixed offshore tower-based platform. The platform was located at 21.44° N, 111.39° E, with an average water depth of approximately 16 m. It consisted of a main tower and two auxiliary towers. The auxiliary towers were approximately 6 m from the main tower and their lower edges were approximately 6 m from the mean sea surface.</p> <p>A four-component net radiometer (CNR4) was installed on the lower edge of the southeast auxiliary tower through a 2-m long boom to simultaneously measure the upward and downward broadband solar radiation having spectral bands between 305 and 2800 nm. A bottom-mounted Nortek acoustic Doppler current profiler was used to measure surface waves. Other ancillary meteorological instruments were used to measure meteorological parameters, including wind speed and water vapor pressure.</p> <p>The observation period lasted from September 2017 to December 2018. The measured broadband radiation was collected in 30-min segments. To obtain an accurate and reliable radiation dataset, three specific types of data were removed from the dataset: (1) local afternoon data; (2) data with solar altitude angles < 10°; (3) data with downward solar radiation of < 30 W m<sup>−2</sup>. After this quality control, 2433 segments of data can be used to study the OSA.</p>
Lagrangian trajectories tracking the origin and fate of a hydrothermal plume observed in the southeastern Pacific sector of the Southern Ocean
<p>A dataset of Lagrangian trajectories which were used to estimate the origin and fate of a hydrothermal plume in the southeastern Pacific sector of the Southern Ocean observed during the DY111 CUSTARD cruise. All variables have long names and units. These files have been used for the analysis in Birchill and Baker et al. ‘Pathways and timescales of Southern Ocean hydrothermal iron and manganese transport’ with further information about the methodology available in the paper. There are five forward-tracking experiments from suspected vent sites (CUSTARD_NEPR.nc, CUSTARD_SEPR.nc, CUSTARD_PAR.nc, CUSTARD_WCR.nc and CUSTARD_ECR.nc) included in the paper and one forward-tracking experiment that was not included in the paper (CUSTARD_EM.nc) as no trajectories crossed our sampling site. There was one backward-tracking experiment undertaken from the Ocean Observatories Initiative (OOI) station at which the hydrothermal signal was observed (CUSTARD_OOI_backward.nc).</p>
Western Indian Ocean coral diversity observations from 1998–2022
<p><span>Coral reefs are threatened by climate change, thus effective policies and management require spatial prioritization for conservation investments. Our aim was to develop a spatially explicit ecological model to predict current (2020) and future (2050) numbers of coral taxa at moderate scales (i.e., ~6 km<sup>2</sup>). Our machine learning predictive models of coral community attributes in 7039 mapped reef cells in the western Indian Ocean were based on 35 spatially complete influential environmental proxies trained with ~1000 field surveys. Four models explored: influences of climate change, water quality, direct human-resource extraction, and variable selection processes on numbers of coral taxa. Two predictive models examined the predictions of all variables and compared them to a variable-restricted climate change (8 commonly used variables) and human influence model (9 variables). The most frequently selected temperature variables in all models were the median, skewness, excess heat, rate of temperature rise, and kurtosis. However, non-temperature variables of observer, depth, wave energy, dissolved oxygen, salinity, chlorophyll-a, calcite concentrations, sunlight, and net primary productivity were frequently as important or stronger. Human influences of national jurisdiction, distance to people, sediments, and nutrients were selected but less influential when compared to the climate or the full variable models. Comparing models indicated the importance of variable pre-selection processes and variable interactions in predicting climate change and human influences on coral diversity. Comparing climate scenarios in the moderate RCP2.6 and extreme RCP8.5 emission scenarios indicated fewer losses in coral taxa (RCP2.6 = 5.2%, RCP8.5 = 8.1% respectively) relative to cover (RCP2.6 = 14%, RCP8.5 = 34%) over the 30 years. Excess heat and rate of temperature rise variables used in the Intergovernmental Panel on Climate Change (IPCC) forecasts predict more negative effects on corals than our four models but shown here to have low to modest effects.</span></p>
ScienceDex guides
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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.