Find research datasets worth reusing

restrictedcc-by-4.0Aug 2024View details →

Dataset for the paper "Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023"

This code and related datasets are used for generating the figures for the paper "Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023". Includes corrected REMA DSM stripes at the central ice shelf region of Pine Island Ice Shelf, the double differential vertical displacement results from 2014 to 2023 which cazlculated from the offset tracking results output from GAMMA software. Other dataset for other analysis are also included in the ZIP file. Each MATLAB codes in MATLAB_function.zip includes the discriptions that guide the user how to used it and how to find the dataset that used for processing. Some sample files are provided in data_and_results.zip that can let user test the code easily.  These data can be accessed after the paper is accepted.

opencc-by-4.0May 2021View details →

Nansen Ice Shelf data (2016-2019)

Data collected at Nansen Ice Shelf between 2016 and 2019 including:1. Ice-penetrating radar data with dGPS surface elevations. These are divided into three sites with site 1 closest to the grounding line. Each file contains: Easting(m), Northing (m), ice thickness (m), ice surface elevation (m asl) and ice draft (m asl).2. Airborne radar transect with columns as follows: Easting(m), Northing (m), ice draft (m asl).3. Ocean glider data from adjacent to, and underneath the Nansen Ice Shelf with columns as follows: Latitude, Longitude, depth below ocean surface (m)Easting and northing data are Antarctic Polar Stereographic coordinates

opencc-by-4.0Nov 2024View details →

Hydrographic gridded data set for the South Brazil Bight and Southern Brazilian Shelf

This dataset includes climatological and seasonal maps, spanning data from 1972 to 2024, across 8 different depth levels: 5, 10, 25, 50, 100, 200, 500, and 1000 dBar, with a spatial resolution of 10 km. The maps were generated using the griddata function with triangulation-based natural neighbor interpolation. The variables included in this dataset are conservative temperature (°C), absolute salinity (g kg⁻¹), neutral density (kg m⁻³), dissolved oxygen (mL L⁻¹), total alkalinity (µmol kg⁻¹), total dissolved inorganic carbon (µmol kg⁻¹), pH (total scale), partial pressure of carbon dioxide (µatm), nitrate (µmol kg⁻¹), and phosphate (µmol kg⁻¹). The name description of each variable is provided in the readme_DatasetATLAS.txt. The dataset can be directly accessed using Ocean Data View (ODV) software.

opencc-by-4.0Nov 2021View details →

Bathymetry beneath the Amery ice shelf, East Antarctica, revealed by airborne gravity

We estimated the seafloor topography beneath the Amery Ice Shelf, East Antarctica, from airborne gravity anomaly through a nonlinear inversion method called simulated annealing. The estimation results provide a view of the seafloor beneath the Amery Ice Shelf, where direct bathymetric observations are rare. The model, 'gravity_estimated_seafloor_topography_beneath_the_Amery_Ice_Shelf.nc', is in NetCDF format which can be read through MATLAB commands "ncdisp" and "ncread". Contents of the model can be found in "contents.txt". The MATLAB program "nc2mat.m" reads the NetCDF ".nc" format model and saves the variables in the model to a MATLAB ".mat" format file.

opencc-by-4.0Jan 2022View details →

Spatially gridded cross-shelf hydrographic sections and monthly climatologies from shipboard survey data collected along the Newport Hydrographic Line, 1997-2021

This data set, described in detail in <a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al. (2022)</a>, contains Newport Hydrographic Line station data; gridded, cross-shelf hydrographic sections; and derived monthly climatologies for temperature, practical salinity, potential density, spiciness, and dissolved oxygen. It consists of CSV (Comma Separated Values) files (newport_hydrographic_line_station_data.zip) that contain CTD observations collected at the seven hydrographic stations located 1, 3, 5, 10, 15, 20 and 25 nautical miles west of Newport, Oregon between March 1997 and July 2021. Additionally, the data set contains three NetCDF files that follow CF (Climate and Forecast) metadata conventions: newport_hydrographic_line_gridded_sections.nc contains observations gridded to a 0.01o x 1 dbar longitude - pressure grid to create cross-shelf hydrographic sections for each of the five variables for each cruise. newport_hydrographic_line_gridded_section_climatologies.nc contains climatological hydrographic sections, calculated using harmonic analysis over the 24-year period March 1997 to February 2021 and reported here for the middle of each month, and newport_hydrographic_line_gridded_section_coefficients.nc contains the associated linear regression model coefficients for all five variables. From the regression coefficients, users can construct seasonal cycles at any location in the gridded section with a temporal resolution that best suits their specific needs. Finally, this data set includes example MATLAB and R scripts that show how to read the data files, plot cross-shelf hydrographic sections, and calculate daily and monthly climatologies using the regression coefficients.

opencc-by-4.0Jan 2022View details →

The Three Rs: Resolving Respiration Robotically in Shelf Seas

Ocean gliders were deployed to conduct 'virtual mooring’ profiles at a study site in the seasonally stratified central Celtic Sea (station CCS, 49° 24’ N, 8° 36’ W) (see Fig. 1) during spring 2015 (6th April to- 28th April, decimal day 95 to 117) and summer 2015 (15th July and- 2nd August, decimal day 195 to 213). The integrated approach adopted in this study, combining ship based and glider measurements enabled estimates of spatial gradients while also minimising tidal aliasing that would likely be introduced by long spatial transects with the glider. A Slocum (Teledyne Webb Research, Falmouth, USA) Ocean Microstructure Glider (OMG, see Palmer et al., 2015 for full details) was equipped with a MicroRider microstructure package (Rockland Scientific International) to measure turbulentthe microstructure of velocity shear, a Seabird SBE42 CTD sensor to measure temperature, salinity and pressure, and an Aanderaa 4831 oxygen optode to measure O2 (precision 0.2 µmol kg-1). Measurements were taken within 5 m of the bed and 2 m of the surface on most dives, with each yo-yo profile taking approximately 20 minutes. Glider salinity data was corrected for thermal inertia following Palmer et al. (2015). The glider AA4831 optode is known to experience severe lag across strong oxygen gradients, and therefore oxygen data was corrected where possible for optode membrane lag following Bittig et al. (2014). Where optode lag across the oxycline was too great and so not correctable using this method, it was omitted and oxygen data from coinciding CTDs was used. In comparison to other oxygen optodes, the AA4831 has been documented by various scientific studies as being an extremely stable optode with low detectable drift ( <0.5% yr-1) and high precision of <0.2 µmol kg-1 (Kortzinger et al., 2004; Nicholson et al., 2008; Johnson et al., 2010; Champenois & Borges, 2012). Optode drift was calculated in this study by comparing discrete Winkler-analysed samples taken at deployment and recovery of the gliders, identifying a downward drift of 0.001% d-1, in close agreement with quoted manufacturer values. Glider sensors (temperature, salinity and ) were calibrated against nearby ship CTD profiles (CTD calibrated 1 month prior to cruise, SBE 43 precision = 2% of  saturation) and discrete water samples collected within 3 hours and 2 km of glider deployment and recovery times and glider position, respectively, as part of the Shelf Sea Biogeochemistry programme (RRS Discovery, DY029 and DY033). Error estimates for the total change in  (µmol kg-1) were calculated as the sum of the optode precision (0.2 µmol kg-1) and drift over the entire respective deployments (<0.1 µmol kg-1). Currents, tides, salinity and temperature were monitored throughout the glider deployments by a mooring at the CCS study site, which was equipped with an acoustic current profiler (ADCP), salinometer and thermistors that provided near-continuous data (Wihsgott et al., 2019; Ruiz-Castello et al., 2019).

EOOffshore: CCMP v0.2.1.NRT Wind Data for the Irish Continental Shelf Region

EOOffshore: Sentinel-1 Wind Data for the Irish Continental Shelf Region

<a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products. The <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a> and services are based on data collected from EO satellites, in particular, the <a href="https://sentinels.copernicus.eu/web/sentinel/home">Sentinel satellite missions</a>. This includes the <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">Sentinel-1 mission</a>, which consists of C-band Synthetic Aperture Radar (SAR) imaging satellites in polar orbit. One of its main objectives is the provision of ocean monitoring services, where its <a href="https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-2">Level-2 Ocean (OCN)</a> products include an Ocean WInd field (OWI) component. This provides gridded estimates of wind speed and direction at 10 m above the surface, with a typical spatial resolution of 1 km. This particular catalog data set (eooffshore_ics_level3_sentinel1_ocn.zarr.tar.gz) contains 2015-2021 OCN wind products for the ICS region, which were retrieved from the <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub (COAH)</a> and the <a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (ASF)</a>. The data set was used in the EOOffshore project outputs presented (<a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a>) at the <a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session. Description and example usage of the Sentinel-1 data set in EOOffshore: <ul> <li><a href="https://eooffshore.github.io/Sentinel-1_ICS_Wind_Data.html">Sentinel-1 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> As requested by the <a href="https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice">Legal Notice on the use of Copernicus Sentinel Data and Service Information</a>, this data set: <ul> <li>Contains modified Copernicus Sentinel data [2015 - 2021]</li> </ul>

EOOffshore: New European Wind Atlas (NEWA) Data for the Irish Continental Shelf Region

<a href="https://eooffshore.github.io/">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products. The <a href="https://www.neweuropeanwindatlas.eu/">New European Wind Atlas (NEWA)</a> provides wind statistics covering onshore Europe, 100km offshore over European seas, and the complete North and Baltic Seas, based on <a href="https://map.neweuropeanwindatlas.eu/about">30 years of mesoscale simulations</a>. These catalog data sets contain 2009-2018 products for the ICS region, provided by the <a href="https://map.neweuropeanwindatlas.eu/">NEWA Map Layers and Datasets</a> website, featuring variables at multiple heights (metres above surface level). They were used in the EOOffshore project outputs presented (<a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a>) at the <a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session. <ul> <li>eooffshore_ics_newa_celticsea.zarr.tar.gz <ul> <li>Data set for a North Celtic Sea area of interest.</li> </ul> </li> <li>eooffshore_ics_newa_irishsea.zarr.tar.gz <ul> <li>Data set for an Irish Sea area of interest.</li> </ul> </li> <li>eooffshore_ics_newa_m3.zarr.tar.gz <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M3 buoy</a> coordinates.</li> </ul> </li> <li>eooffshore_ics_newa_m4.zarr.tar.gz <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M4 buoy</a> coordinates.</li> </ul> </li> </ul> Description and example usage of the NEWA data sets in EOOffshore: <ul> <li><a href="https://eooffshore.github.io/NEWA_ICS_Wind_Data.html">NEWA Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> As requested by the <a href="https://map.neweuropeanwindatlas.eu/about">NEWA Terms of use</a>, the following attribution is declared: <ul> <li>Data [2009 - 2018] obtained from the New European Wind Atlas (NEWA), a free, web-based application developed, owned and operated by the NEWA Consortium. For additional information see <a href="http://www.neweuropeanwindatlas.eu/">www.neweuropeanwindatlas.eu</a>.</li> </ul>

EOOffshore: ASCAT Wind Data for the Irish Continental Shelf Region

<a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products. The <a href="https://marine.copernicus.eu/">Copernicus Marine Service (CMS), or Copernicus Marine Environment Monitoring Service (CMEMS)</a>, is the marine component of the <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a>. It provides free, regular and systematic ocean data products on a global and regional scale. The CMS <a href="https://marine.copernicus.eu/about/producers/wind-tac">Surface Wind Thematic Assembly Center (Wind TAC)</a> is responsible for the collection, processing, qualification and distribution of surface winds data products derived from scatterometer satellite missions, including near-real time (NRT) and delayed mode (REP) processing of global wind observations. These catalog data sets contain CMS wind speed and direction data products generated using the Advanced SCATterometer (ASCAT) instruments deployed on the Metop satellites. <ul> <li>eooffshore_ics_cmems_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT.zarr.tar.gz <ul> <li>2007-2021 data products from the <a href="https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005/INFORMATION">Global Ocean Daily Gridded Reprocessed (REP) Level-3 Sea Surface Winds from Scatterometer</a> data set.</li> </ul> </li> <li>eooffshore_ics_cmems_WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002_MetOp_ASCAT.zarr.tar.gz <ul> <li>2016-2021 data products from the <a href="https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002">Global Ocean Daily Gridded Near Real Time (NRT) Level-3 Sea Surface Winds from Scatterometer</a> data set.</li> </ul> </li> </ul> The products feature 0.125 degree grids, based on 12.5 km scatterometer swath observations, for all combinations of Metop A/B (REP) and Metop A/B/C (NRT) satellites and ASCending, DEScending passes. These ASCAT data sets were used in the EOOffshore project outputs presented (<a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a>) at the <a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session. Description and example usage of the ASCAT data sets in EOOffshore: <ul> <li><a href="https://eooffshore.github.io/ASCAT_ICS_Wind_Data.html">ASCAT Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> As requested by the <a href="https://marine.copernicus.eu/user-corner/service-commitments-and-licence">Copernicus Marine Service Service Commitments and Licence</a>, these Zarr stores were: <ul> <li> Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00182">https://doi.org/10.48670/moi-00182</a>; <a href="https://doi.org/10.48670/moi-00183">https://doi.org/10.48670/moi-00183</a>; </li> </ul>

opencc-by-4.0Jul 2024View details →

Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"

Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)". Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea. Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report. Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.   Manuscript abstract: In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.

opencc-by-4.0Sep 2024View details →

Velocity from the shelf at Station CSI 6 in 2017

<div>Title: Velocity from the shelf at Station CSI 6 in 2017</div> <div> </div> <div>Velocity Data from ADCP deployed at CSI 6 (~ 20 m)</div> <div>Time - days counted from Jan. 1, 2017 (UTC) - Jan. 1 is Day 1.</div> <div>u, v, horizontal velocity in east and north direction in m/s</div> <div>Zmab (bin depth meter above bottom)</div> <div>Invalid data is indicated by a large velocity of -32.768 m/s</div> <div>PI: Chunyan Li at Louisiana State University</div> <div>Project: NSF 1736713 </div> <div>File name: Velocity_from_shelf_CSI6_2017.dat</div> <div> </div> <div>Pubications: </div> <div>Li, C. A. Sheremet, W. Huang, P. Dash, A. Katkar, M. N. Allahdadi, N. Chaichitehrani, C. M. Bachmann, V. H. Rivera-Monroy (2024). Dynamic impact of transiting weather systems on coastal currents in the northern Gulf of Mexico, Deep Sea Research Part II: Topical Studies in Oceanography, Volume 216, 2024, 105395, https://doi.org/10.1016/j.dsr2.2024.105395.</div>

opencc-by-4.0Oct 2024View details →

VMP and SADCP at Dotson Ice Shelf outflow (2022)

Matlab files of vertical microstructure profiler (VMP) 2000 and shipboard acoustic doppler current profiler (SADCP) transects at the Dotson Ice Shelf (DIS) outflow measured during the NBP2202 expedition between 22 Jan 2022 and 06 Feb 2022, used in the manuscript Dotto et al. (in prep).   Please, read the ReadMe for an explanation of the variables and dataset.   These data were collected onboard Nathaniel B Palmer, and are part of the the Thwaites-Amundsen Regional Survey and Network Integrating Atmosphere-Ice-Ocean Processes (TARSAN) project, a component of the International Thwaites Glacier Collaboration (ITGC; https://thwaitesglacier.org/).   Citing paper: Tiago S. Dotto, Peter M. F. Sheehan, Yixi Zheng, Rob A. Hall, Gillian M. Damerell and Karen J. Heywood (in prep.) Heterogeneous Mixing Processes Observed in the Dotson Ice Shelf Outflow, Antarctica

opencc-by-4.0Jan 2023View details →

A Stitch in Time: Combining More than Two Decades of Mooring Data from the Central Oregon Shelf

The highly biologically productive northern California Current, which includes the Oregon continental shelf, is an archetypal eastern boundary region with summertime upwelling driven by prevailing equatorward winds and wintertime downwelling driven by prevailing poleward winds. Between 1960 and 1990, monitoring programs and process studies conducted off the central Oregon coast advanced the understanding of many oceanographic processes, including coastal trapped waves, seasonal upwelling and downwelling in eastern boundary upwelling systems, and seasonal variability of coastal currents. Starting in 1997, the U.S. Global Ocean Ecosystems Dynamics – Long Term Observational Program (GLOBEC-LTOP) continued those monitoring and process study efforts by conducting routine CTD (Conductivity, Temperature, and Depth) and biological sampling survey cruises along the Newport Hydrographic Line (NHL; 44.652°N, 124.1 – 124.65°W), located west of Newport, Oregon. Additionally, GLOBEC-LTOP maintained a mooring slightly south of the NHL, nominally at 44.64°N, 124.30°W, on the 81-meter isobath. This location is referred to as NH-10, as it is located 10 nautical miles or 18.5 km west of Newport. A mooring was first deployed at NH-10 in August 1997. This subsurface mooring collected water column velocity data using an upward-looking acoustic Doppler current profiler. A second mooring with a surface expression was deployed at NH-10 starting in April 1999. This mooring included velocity, temperature and conductivity measurements throughout the water column as well as meteorological measurements. GLOBEC-LTOP and the Oregon State University (OSU) National Oceanographic Partnership Program (NOPP) provided funding for the NH-10 moorings from August 1997 to December 2004. Since June 2006, the NH-10 site has been occupied by a series of moorings operated and maintained by OSU with funding from the Oregon Coastal Ocean Observing System (OrCOOS), the Northwest Association of Networked Ocean Observing Systems (NANOOS), the Center for Coastal Margin Observation & Prediction (CMOP), and most recently the Ocean Observatories Initiative (OOI). While the objectives of these programs differed, each program contributed to long-term observing efforts with moorings routinely measuring meteorological and physical oceanographic variables. This article provides a brief description of each of the six programs, their associated moorings at NH-10, and our efforts to combine over twenty years of temperature, practical salinity, and velocity data into one coherent, hourly averaged, quality-controlled data set. Additionally, the data set includes best-fit seasonal cycles calculated at a daily temporal resolution for each variable using harmonic analysis with a three-harmonic fit to the observations.

opencc-by-4.0Jun 2023View details →

High-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category: Dataset and Code

Dataset of identified high-wind events on the Southern New England continental shelf (2015-2022), their impact on shelf stratification, and corresponding high-wind event category, as well as the associated code to reproduce the figures of accompanying publication. The data have been recorded by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Array. Accompanying publication: Taenzer, L.L., Gawarkiewicz, G., and Plueddemann, A. (2023). Categorization of High-Wind Events and Their Contribution to the Seasonal Breakdown of Stratification on the Southern New England Shelf. Journal of Geophysical Research: Oceans, 128, e2022JC019625. https://doi.org/10.1029/2022JC019625Contact: Lukas Taenzer (lukas.taenzer@whoi.edu)Structure of provided code:<ul><li>PART A: Local high-wind ocean impact analysis</li><li>PART B: Analysis of seasonal high-wind impacts on stratification</li><li>PART C: High-wind event categorization and the impact of different categories</li></ul>Code has been written in MATLAB R2023a.Output:<ul><li>Processed data of all locally detected high-wind events incl. scalar forcing and shelf impact estimates as well as their corresponding high-wind event category:<ul><li>'OOIcp_HighWindEvents_ScalarMetrics.nc' (see userflag 'save_peak_ooi')</li><li>See README_HighWindEvents_ScalarMetrics for further details and license.</li></ul></li><li>Figures 2, 3, 4, 5, 6, 7, 8, and 9 of accompanying publication<ul><li>saved as .png file (always)</li><li>saves as .eps file (see userflag 'save_fig_eps')</li></ul></li></ul>Input for Analysis:<ul><li>Gridded Hydrography and Bulk Air-Sea interactions time series observed by the Ocean Observatories Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) (Taenzer et al., 2023). The required fields to reproduce the results of the accompanying publication are provided:<ul><li>Input/OOIcp_Met_Combined.nc</li><li>Input/OOIcp_CTD_ISSM_stat.nc</li><li>Input/OOIcp_CTD_PMUI_prof.nc</li></ul></li><li>High-wind event categorization based on their spatio-temporal sea level pressure and temporal surface wind stress signatures around/at the OOI Coastal Pioneer Array location:<ul><li>Input/storm_type_2015-2021_v5.mat</li></ul></li></ul>Additional input for reproducing figures:<ul><li>Manually determined cyclone tracks for cyclones that occur during the fall destratification seasons 2015-2021:<ul><li>Input/stormtracks_cyclones_20152021_save.mat</li></ul></li><li>ERA5 sea level pressure data (Hersbach et al., 2018) on a 6-hour temporal and a 1°x1° spatial resolution for the time period 2015-01-01 to 2022-06-30 and across the Eastern US, Canada, and the Northwest Atlantic with the OOI Coastal Pioneer Array in the center<ul><li>Input/ERA5_6h_2015-2022_region_1x1.mat</li></ul></li></ul>

opencc-by-4.0Aug 2023View details →

Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.

Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.

openCC (other)Sep 2021View details →

Chlorophyll and phytoplankton composition climatological data on the Northwest Atlantic Shelf from 1978 to 2014: post-processed model data

This dataset includes 8-day composite of surface chlorophyll and bimonthly phytoplankton size composition climatological results on the Northwest Atlantic Shelf from the Gulf of Maine to the Mid-Atlantic Bight based on the physical-biological coupled model results from 1978 to 2014. Two size classes, small phytoplankton (SP) and large phytoplankton (LP), are provided. For more details please see: Zhengchen Zang, Rubao Ji, Zhixuan Feng, Changsheng Chen, Siqi Li, and Cabell S Davis (2021) Spatially varying phytoplankton seasonality on the Northwest Atlantic Shelf: a model-based assessment of patterns, drivers, and implications. ICES Journal of Marine Science, Volume 78, Issue 5, 1920-1934, https://doi.org/10.1093/icesjms/fsab102.

openCC0May 2023View details →

Abundance and parasitoid infection dynamics of Guinardia delicatula on the Northeast U.S. Shelf from 2006 to 2022 determined by Imaging FlowCytobot.

These data include abundances of the diatom, Guinardia delicatula (= Rhizosolenia delicatula), on the Northeast U.S. Shelf from 2006 to 2022 as part of Long-Term Ecological Research (NES-LTER). Abundances are determined from Imaging FlowCytobot (IFCB) deployed in-situ at ~4m depth at the nearshore Martha’s Vineyard Coastal Observatory (MVCO) from 2006 to 2022 and in underway mode (sampling near-surface seawater) on 24 NOAA EcoMon survey cruises from 2013 to 2022. Abundances based on both human and machine learning image classification are provided. Total G. delicatula abundances are divided into two categories based on whether G. delicatula exhibited current or recent infection by the protistan parasitoid, Cryothecomonas aestivalis. Four data tables are provided with abundance values separated by sampling scheme (time series or survey cruise) and image classification approach (human or machine learning).

openCC (other)Oct 2019View details →

Stable Isotope Data for Small Pelagic Fishes across the Northeast U.S. Continental Shelf from 2013-2015

These data represent the carbon and nitrogen stable isotope signatures of small pelagic fishes across the Northeast U.S. Continental Shelf as reported by Suca, J.J., et al. (2018) Feeding dynamics of Northwest Atlantic small pelagic fishes. Progress in Oceanography, 165, 52-62, https://doi.org/10.1016/j.pocean.2018.04.014. The five species of fish in this dataset represent a subset of the species collected in bottom trawls conducted by the NOAA NEFSC Ecosystems Survey Branch from Cape Hatteras to the Gulf of Maine for years 2013-2015. Sampling occurred in the Spring and Fall seasons. Sections of dorsal musculature were analyzed for carbon and nitrogen isotopes using mass spectrometry. Carbon-to-nitrogen isotopic ratios were reported along with the isotopic signatures for carbon and nitrogen respectively. Additionally, a lipid-corrected carbon signature was calculated for the fish muscle tissue. The dataset was supplemented with geospatial and temporal information from NOAA Fisheries trawl databases.