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10,391 results for “oceans”
Accessible Oceans: Auditory Display. 2015 Axial Seamount Eruption
<p>The ten tracks make up an auditory display of the 2015 Axial Seamount Eruption. The ten tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/qRKlQUoRe1n8TWDZOhTn">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/axial-eruption/">https://datalab.marine.rutgers.edu/ooi-nuggets/axial-eruption/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Accessible Oceans: Data Sonification Wrapper Earcons
<p>Original, earcon sounds to play before and after a data sonification. These auditory icons ensure there is a clear notification of the start and stop of the sonifications so that the learner knows when to start fully listening and then knows when the sonification is over.</p> <p>A semi-structured interview with two BLV teachers at the Perkins School for the Blind offered several ideas for helpful tactics in how to use sound to explain the principles of graphs. Sounds wrapping data sonifications was one best practice that emerged from the interview. We created original earcons for our project to serve this specific function.</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2023
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2023 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/r7xa-bt92">https://doi.org/10.25921/r7xa-bt92</a>) is a quality-controlled dataset containing 35.6 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2023 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2023 dataset.</p> <p>The original SOCAT version 2023 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2023with_header.tsv, SOCATv2023.nc, SOCATv2023with_header_ESACCI.tsv and SOCATv2023_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p><strong>Previous versions</strong></p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p>v2022: <a href="https://doi.org/10.5281/zenodo.8228585">https://doi.org/10.5281/zenodo.8228585</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
Reanalysed (depth and temperature consistent) surface ocean CO₂ atlas (SOCAT) version 2022
<p><strong>Note: The authors recommend the use of the ESA CCI-SST version of this dataset.</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) version 2022 dataset (Bakker et al., 2016; <a href="https://doi.org/10.25921/1h9f-nb73">https://doi.org/10.25921/1h9f-nb73</a>) is a quality-controlled dataset containing 33.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore, the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models.</p> <p>Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values; Watson et al., 2020; Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system.</p> <p>Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in Goddijn-Murphy et al. (2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes.</p> <p>This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2022 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Holding et al., 2019; Shutler et al., 2016). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The reanalysis dataset was produced for two climate quality and depth consistent temperature datasets: (1) The ESA SST-CCI sea surface temperature product (Merchant et al., 2019) and (2) The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Banzon et al., 2016; Huang et al., 2021; Reynolds et al., 2007).</p> <p>For both datasets, the original daily data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2022 dataset.</p> <p>The original SOCAT version 2022 data are included in full, with four additional columns containing the reanalysed data:</p> <p>* T_reynolds - The temperature (in degrees C) taken from the consistent temperature field for the corresponding time and location.</p> <p>* fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>* pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature.</p> <p>* pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds.</p> <p>The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al., 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file.</p> <p><strong>Comments</strong></p> <p>1. Due to the temporal range of the OISST and CCI-SST datasets the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns.</p> <p>2. This submission contains four files contained within a single zip file: SOCATv2022with_header.tsv, SOCATv2022.nc, SOCATv2022with_header_ESACCI.tsv and SOCATv2022_ESACCI.nc. The first two files correspond to the OISST version, and the second two the ESA SST-CCI version. The .tsv files are the ungridded data, and the .nc files are the gridded data for the corresponding temperature datasets.</p> <p>3. Please contact Daniel J. Ford (d.ford@exeter.ac.uk) if there are any questions on the dataset.</p> <p><strong>How to cite these data</strong></p> <p>Please cite the DOI of this dataset, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Holding et al., 2019; Shutler et al., 2016) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013).</p> <p>Previous versions:</p> <p>v2019: <a href="https://doi.org/10.1594/PANGAEA.905316">https://doi.org/10.1594/PANGAEA.905316</a></p> <p>v2020: <a href="https://doi.org/10.18160/vmt4-4563">https://doi.org/10.18160/vmt4-4563</a></p> <p>v2021: <a href="https://doi.org/10.1594/PANGAEA.939233">https://doi.org/10.1594/PANGAEA.939233</a></p> <p><strong>Acknowledgements</strong></p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p>These data were produced with funding from the Ocean ICU project (<a href="https://ocean-icu.eu/">https://ocean-icu.eu/</a>) and the Convex Seascape Survey (<a href="https://convexseascapesurvey.com/">https://convexseascapesurvey.com/</a>). The UK part of the Horizon Europe OceanICU project is funded by UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10063673].</p>
South East Australian Coastal Ocean Forecast System (SEA-COFS)
<p>A suite of high resolution hydrodynamic ocean models for south eastern Australia that form the South East Australian Coastal Ocean Forecast System (SEA-COFS).<br> <br> The modelling suite includes output from several different configurations of the Regional Ocean Modeling System hydrodynamic simulation of the East Australian Current (EAC) System. These various model configurations include free running hindcast models, data assimilating state estimates, ocean forecasts and various nested high resolution runs. There is also a biogeochemical configuration of the fennel model on the EAC parent grid.<br> <br> At the time of upload there were four model grids (check later versions for new and revised / extended grids).</p> <p>The SEA-COFS domain covers the southeastern Australia oceanic region from 25.1-41.5 S and 147.1-162.2 E.</p> <p><br> <strong>SEACOFS_EAC_Grid.nc</strong> Is the parent grid of the EAC Domain 2.5-6km resolution</p> <ul> <li>Kerry, C. G. and M. Roughan, (2020). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5e683944e1369"> 10.26190/5e683944e1369</a>. </li> <li>Kerry, C. G. and M. Roughan,<strong> </strong>Powell, Brian, Oke, Peter (2020). A high-resolution reanalysis of the East Australian Current System assimilating an unprecedented observational data set using 4D-Var data assimilation over a two-year period (2012-2013). Version 2017. UNSW.dataset. <a href="https://doi.org/10.26190/5ebe1f389dd87"> 10.26190/5ebe1f389dd87</a>. <br> Kerry, C. , Powell, B. Roughan, M. and Oke, P. (2016) <a href="http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf">Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation</a>. Geosci. Model Dev, 9, 3779-3801, 10.5194/gmd-9-3779-2016<br> </li> </ul> <p><strong>SEACOFS_CoffsHarbour_Grid.nc</strong> Coffs Harbour Region 0.75-1km resolution</p> <ul> <li>Kerry, C., Roughan, M., & Powell, B. (2020). <a href="https://doi.org/10.1016/j.jmarsys.2019.103286">Predicting the submesoscale circulation inshore of the East Australian Current</a>. Journal of Marine Systems (Vol. 204, p. 103286)</li> </ul> <p><strong>SEACOFS_HSM_Grid.nc</strong> - Hawkesbury Shelf Model (HSM) 750m resolution </p> <ul> <li>Ribbat N., M. Roughan, B. Powell, C. Kerry, S. Rao, (2020). A high-resolution (750m) free-running hydrodynamic simulation of the Hawkesbury Shelf region off Southeastern Australia (2012-2013) using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5ec35ca34752e">DOI: 10.26190/5ec35ca34752e</a>. <a href="https://researchdata.ands.org.au/high-resolution-750m-ocean-modeling/1460879">Data access and more information.</a></li> </ul> <p><strong>SEACOFS_Narooma_Grid.nc </strong> Narooma Model 0.75-1km resolution<br> <br> <strong>EAC_BGC Fennel Model </strong></p> <ul> <li>Rocha, C., Edwards, C. A., Roughan, M., Cetina-Heredia, P., & Kerry, C. (2019) <a href="https://doi.org/10.5194/gmd-12-441-2019">A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system</a> Geosci. Model Dev., 12, 441-456</li> </ul> <p> </p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></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.
MCR LTER: Coral Reef: Water Column: Offshore Ocean Acidification: Water Profiles, CTD, and Chemistry from 2005 to 2012
This data package contains water chemistry measurements taken 2 to 4 times per year at a station 5 km offshore of the north shore of Moorea, French Polynesia. Measurements include standard CTD parameters, phosphate, silicate, total alkalinity (TA) and total dissolved inorganic carbon (DIC). Sampling began in August, 2005. All water samples were collected with Niskin Bottles. This data includes excerpts from CTD data were collected with a SBE19-Plus Seacat Profiler. CTD and bottle samples were taken on separate casts at each station. (For full CTD data refer to knb-lter-mcr.10.) All other parameters were calculated from temperature, pressure, nutrients, TA and DIC with CO2Sys programs available at: http://cdiac.ornl.gov/oceans/co2rprt.html (Lewis E. and D. Wallace Program Developed for CO2 System Calculations) using constants K1, K2 from Mehrbach et al, 1973 refit by Dickson and Millero, 1987, Dickson KHSO4, and the Seawater pH scale (mol/kg-SW). If users wish to use different constants and scales, they will need to recalculate using the emperically collected data (TA and DIC).
MCR LTER: Data from Duvall, Rosman and Hench, in review. Representation of coral reef roughness using obstacle and surface-based approaches, submitted to JGR: Oceans
This archive contains natural coral reef topography data from the northern coast of Mo’orea, French Polynesia. These data were used to compute reef roughness density using obstacle- and surface-based estimates and models, and to compare the two approaches for representing reef topography. Primary support for this product came from the National Science Foundation Physical Oceanography program (OCE-1435530 and OCE-1435133), and as well as Duke University and the University of North Carolina at Chapel Hill. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
SBC LTER: OCEAN: Particulate Organic Matter Content and Composition of Stream, Estuarine, and Marine Sediments
An unprecedented five-year drought in California, coupled with conditions of anomalously low ocean productivity and the prospect of one of the strongest El Niño periods on record with above average rainfall were the impetus for this RAPID award, which seeks to test specific hypotheses pertaining to the origin, distribution, processing, and bioavailability of terrestrial organic matter in coastal marine sediments and their potential for serving as a reservoir of nitrogen storage to fuel nearshore primary production during periods when nitrate concentrations are low. The goals of the research were to: (1) measure bulk properties and biomarker tracers of particulate organic matter (POM) in stream water and in coastal marine sediments at SBC LTER and other reef sites differing in exposure to terrestrial runoff prior to and following large storm events, (2) determine the bioavailability of dissolved organic matter (DOM) released from POM in marine sediments following large runoff events, and (3) measure changes in concentrations of dissolved inorganic and organic nitrogen in pore water of marine sediments near to and distant from stream mouths in the Santa Barbara Channel. Samples were analyzed for organic matter content using a loss-on-ignition combustion method, and samples were also analyzed for organic carbon and nitrogen content and isotopes using a stable isotope mass spectrometer interfaced with an elemental analyzer. Subsamples were shipped to the laboratory of Marc Lucotte at the University of Québec, Montréal for analysis of lignin content using the cupric oxidation method.
SBC LTER: Land Ocean Reef: Foodweb Stable Isotopes: Isotope data
Potentially important food sources to primary consumers on shallow subtidal reefs include phytoplankton-dominated seston, kelp-derived detritus, and for locations adjacent to sources of freshwater runoff, terrestrially-derived POM. The SBCLTER is using stable carbon and nitrogen isotope ratio analysis of monthly water, kelp tissue and samples consumers of varying trophic status to evaluate the relative contribution of these sources to reef food webs. SBCLTER research has focused on beginning to characterize variability in the isotope values of potential food sources (phytoplankton, kelp, and terrestrial POM). To investigate sources of variability in values of marine POM, including contributions from phytoplankton, we are collecting monthly water samples and filtering them for POM from on and offshore of the reef, at locations less likely to be influenced by inputs from kelp or terrestrial runoff and more likely to reflect a primarily phytoplankton source at the three core SBCLTER research sites (Arroyo Quemado, Naples, Carpinteria). To identify the range of variability of isotopic ratio in kelp tissue, we concurrently collect kelp tissue samples from the three core research sites. To identify food sources used by reef consumers under different conditions of runoff, ocean climate and kelp production we have begun sampling a variety of reef consumers chosen to represent different trophic levels. Tissue samples were collected in April 2002 from the same species of consumers at four reef sites (Carpinteria, Naples, Mohawk, Arroyo Quemada), which vary in their proximity to sources of runoff and in their standing stock of giant kelp. We have also collected sediment samples at fixed distances from the source of runoff at four research sites (Naples, Goleta Bay, Mohawk, Carpinteria). This information will be used to evaluate whether these isotopic values differ enough from one another to permit the use of mixing models to estimate the contribution of each source to the reef
Data to accompany "Exploring the Complexity of Ocean Acidification: An Ecosystem Comparison of Coastal pH Variability"
The goal of this project was to create a science lesson at the middle school level with data illustrating the variablilty of pH and temperature in nature. The lesson allows students to interpret pH data and gain knowledge of abiotic and biotic processes that contribute to pH differences between tropical, temperate and polar marine ecosystems. Students use what they have learned to interpret data from a 'mystery' site and develop a hypothesis as to which ecosystem the unknown data was collected from. The full cirriculum is described in Kapsenberg, L, AL Kelley, LA Francis, and SB Raskin (2015) Exploring the complexity of ocean acidification: an ecosystem comparison of coastal pH variability. Science Scope 39(3): 51-60. doi: 10.2505/4/ss15_039_03_51 This dataset contains an Excel workbook with five worksheets: A "Readme" tab with citations for additional reading and data attribution.Three time-series of pH and temperature from coastal locations: a temperate kelp forest in the Santa Barbara Channel (Spring season, 2 months, 20 min interval). a coral reef near the island of Moorea, Tahiti (Summer season, 3 weeks, 30 min interval), and the polar ocean of Cape Evans, McMurdo Bay, Antarctica (Spring/Summer, 6 months, bi-hourly interval). A fourth worksheet contains data for a "mystery site" for student examination. Data for the study were contributed by the Santa Barbara Coastal LTER, Moorea Coral Reef LTER and the G. Hofmann lab (University of California, Santa Barbara). Additional pH data are available from both LTER sites. Antarctic data in this dataset are also available from NSF's Biological and Chemical Oceanographic Data Management Office (see Cape Evans Mooring, 2012).
Simulated Seafloor Pressures for "The Ocean's Impact on Slow Slip Events"
<p>These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.These files in this archive contain the simulated seafloor pressures used in the study of Gomberg et al. (2020). These seafloor pressures were derived from a Regional Ocean Modeling System (ROMS) on the seafloor of the Hikurangi subduction zone off New Zealand [<em>Hadfield et al.</em>, 2007] at offshore sites deployed during the 10-month 2014-2015 Hikurangi Ocean Bottom Investigation of Tremor and Slow Slip (HOBITSS) experiment [<em>Wallace et al.</em>, 2016]. Details of each file are described in the Readme.pdf file.</p>
Arctic Ocean state estimates for 2013 using the GECCO model
<p>The dataset contains the 2013 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2012 using the GECCO model
<p>The dataset contains the 2012 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
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>
Review of the evidence for Oceans and Human Health relationships in Europe: A systematic map.
<p>This database details the results of a systematic mapping exercise linking marine exposures to measured human health outcomes for the Seas, Oceans and Public Health in Europe Project.</p>
Raw meteorological dataset from the Southern Ocean collected on board the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Vaisala MAWS240 meteorological station was installed on the R/V Akademik Tryoshnikov during a circumnavigation of Antarctica in the austral summer season of 2016/2017. This dataset contains the raw text files of meteorology data collected in the Southern Ocean and Atlantic Ocean as part of the Antarctic Circumnavigation Expedition (ACE). Data coverage is from 17th November 2016 until 11th April 2016.</p> <p>Data files have undergone no processing or quality-checking and are as-recorded, directly from the instrumentation.</p> <p>Wind speed and direction parameters were recorded with a resolution of three seconds. Air temperature, relative humidity, dew point, solar radiation, ultraviolet radiation, cloud level and sky cover were recorded with a resolution of 30 seconds.</p> <p>Time of the measurement should be used with the TIMEDIFF to convert it to UTC. Latitude and longitude recorded are not corrected. Underway seawater measurements were recorded as null values.</p> <p><strong>Dataset contents</strong></p> <ul> <li>MAWS__SMSAWS__YYYYMMDD.txt, data file, ASCII tab-separated</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_raw_meteorology_data_change_log.txt, metadata, text format</li> </ul> <p>Data files contain data for one day and are named by that date.</p> <p>Null values are recorded as ///, // or /</p> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data files with coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added this change_log file.</p> <p><strong>v1.0</strong> - Initial release of raw meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This raw meteorological 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>
mom6 cobalt model result for oceanic carbon response to El Niños
<p>GEOS_Chem atmospheric transport model, monthly, 2.5 degree resolution in tropical Pacific Ocean (120-300E, 30N-30S)<br> 1. GEOS_Chem_tropical_pacific_monthly_clim_1992_2017.nc<br> 2. GEOS_Chem_tropical_pacific_monthly_iav_1992_2017.nc<br> 1 and 2 are GEOS_Chem atmospheric transport model results</p> <p>MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: if the file name has a label "_detrend_deseason", this file is detrended and deseasonized using full value in 1982-2018 with CDO<br> "cdo -ymonsub -detrend $fin -ymonmean -detrend $fin $fout"<br> Note3: ocean/sea surface is the first layer which is 1 meter deep<br> Note4: MLD_001_temp/salt/dic/alk/kd is the vertical mean value in the mixing layer depth (criterial of 0.01 kg/m3)</p> <p>MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018.nc<br> (delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018.nc<br> (air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.01 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity gradient (dalk/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_1982_2018_detrend_deseason.nc<br> (first compute vertical mean dissolved inorganic carbon (DIC) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean DIC (ddic/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_Kd_interface_1982_2018_detrend_deseason.nc<br> (first compute vertical mean Diapycnal diffusivity at interfaces layers (kd_interface) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_salt_1982_2018_detrend_deseason.nc<br> (first compute vertical mean salinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_temp_1982_2018_detrend_deseason.nc<br> (first compute vertical mean temperature in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_u_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity u in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_v_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity v in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_003_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.03 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized sea surface pCO2 (ocean pCO2))</p> <p>MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018.nc<br> (sea surface pCO2 (ocean pCO2))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_chl_1982_2018.nc<br> (sea surface chlorophyll)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_dic_1982_2018.nc<br> (sea surface dissolved inorganic carbon (DIC))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_no3_1982_2018.nc<br> (sea surface nitrate, NO3)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_po4_1982_2018.nc<br> (sea surface phosphate, PO4)<br> MOM6_COBALT_tropical_pacific_monthly_SSS_1982_2018.nc<br> (sea surface salinity)<br> MOM6_COBALT_tropical_pacific_monthly_SST_1982_2018.nc<br> (sea surface temperature)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018.nc<br> (wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018.nc<br> (wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tc_depth_1982_2018.nc<br> (thermocline depth defined as depth where the temperature equals 20oC)</p> <p>JRA_rain_tropical_pacific_monthly_prrn_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized JRA rainfall)<br> </p> <p>Budget terms based MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: this is the vertical mean result in the mixing layer depth (criterial of 0.01 kg/m3) after detrend and deseasonalize</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_pco2w_budget_1982_2018_detrend_deseason.nc<br> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> pco2_hadv_hdif: horizontal transport term, H_circ;<br> dpco2_hadvx: zonal advection term;<br> dpco2_hadvy: meridional advection term;<br> dpco2_hdif: horizontal diffusivity term;<br> dpco2_vadv_vdif: vertical transport term;<br> dpco2_dic_vdif_vdif: vertical transport term induced by DIC;<br> dpco2_alk_vdif: vertical transport term induced by Alk;<br> dpco2_bio: biological term<br> dpco2_rain: surface freshwater term<br> dpco2_sst: thermal term<br> dpco2_dt: pco2 response term<br> dpco2_flux: CO2 flux response term<br> </p>
Data set: Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?
<p>Relevant autonomous float data for <a href="https://doi.org/10.5194/bg-17-4119-2020">Gordon et al. (2020)</a>. Following a similar structure to the Argo network's "synthetic" profile files, one file per float is produced with all relevant variables (temperature, salinity, chlorophyll, backscatter, dissolved oxygen) on a common depth and time grid. The Electro-Magnetic Autonomous Profiling Explorer (EM-APEX) floats were deployed in the northern Gulf of Mexico in May 2017 - see <a href="https://doi.org/10.1109/CWTM43797.2019.8955168">Shay et al. (2019)</a> for more information. </p> <p>The data published here contains a timestamp for each data point. Another version of this data which contains some additional variables is hosted on <a href="https://data.gulfresearchinitiative.org/data/R5.x275.281:0001">GRIIDC</a>, but does not contain a timestamp for each data point, but rather for each profile. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.