Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Measurements of benzene and toluene in underway surface seawater and ambient air in the Atlantic sector of the Southern Ocean on cruise ANDREXII/JR18005 between February and April 2019.

Benzene and toluene cycling iin the unpolluted marine environment s poorly understood. Due to a paucity of measurements, the role of the ocean in the atmospheric budgets of atmospheric benzene and toluene is unknown. In order to quantify the air-sea fluxes of these gases and obtain insights to their biogeochemical cycling, we measured their seawater concentrations (surface and depth profiles) and air mixing ratios in the Atlantic sector of the Southern Ocean, along a ~11000 km long transect at approximately 60o S in Feb-Apr 2019. The measurements were made using a Proton Transfer Reaction Mass Spectrometer coupled to a Segmented Flow Coil Equilibrator. Concentrations, oceanic saturations and calculated fluxes benzene and toluene are presented here.    The data is further presented and discussed in a manuscript:  Marine biogenic benzene and toluene emissions and their impact on secondary organic aerosol in the polar regions. Charel Wohl, Qinyi Li, Carlos A. Cuevas, Rafael P. Fernandez, Mingxi Yang, Alfonso Saiz-Lopez, Rafel Simó, Submitted to Atmospheric Atmospheric Chemistry and Physics, 2022   Computation of the air-sea gas fluxes is explained in detail in the linked manuscript about benzene and toluene.  Positive values indicate oceanic outgassing, thus sea to air flux.   Definitions of acronyms, site abbreviations, or other project-specific designations: deg = degree  SW = seawater concentration ATM= atmosphere SAT = saturation flux= air-sea flux in (micro)umol_m^(2)_d^(-1) nM = nano Molar seawater concentration defined as nmol dm^(-3) LAT, LONG = Latitude, Longitude. (negative indicates west and south) The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY_HH:MM Empty data cells/points are listed as an impossible number of -999. Interruptions in the measurements are due to calibrations and other instrument maintenance.Interruptions in the calculated flux are due to missing auxiliary data at those sampling points e.g. no wind speed or underway auxiliary data. Fluxes and saturations computed using the interpolated air mixing ratio (see linked manuscript) are indicated with the suffix "_2"   Negative values correspond to readings below the blank and detection limit.  They are effectively zero and are included here as the computed negative concentration to avoid skewing the mean.   Data last modified 06.05.2022. Version 1 uploaded on that date. No further maintenance planned. This is the final data.

opencc-by-4.0May 2022View details →

Lagrangian overturning in the eastern subpolar North Atlantic Ocean - ORCA025-GJM189 Particle Trajectory Dataset

This dataset contains the output of Lagrangian particle tracking experiments using 5-day mean velocity and hydrographic fields from the ORCA025-GJM189 ocean sea-ice model hindcast configured during the Drakkar project in which numerical particles are initialised along the northward inflows across the Overturning in the Subpolar North Atlantic Program (OSNAP) East section. Particles are advected using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool using the regular step-wise stationary advection scheme and an adapted implementation of the vertical turbulent mixing parameterisation created by Paris et al. (2013) for the Connectivity Modelling System Lagrangian particle tracking tool. This vertical turbulent mixing scheme only acts on particles found within the surface mixed layer (as evaluated along particle trajectories) and randomly reshuffles them according to a maximum vertical velocity of 10 cm/s - characteristic of vertical convective plumes. Note, particles cannot be artificially subducted across the base of the mixed layer into the ocean interior using this scheme. Particles are initialised on the first-available day of each month (based on the centre of the model fields 5-day mean windows) between 1976 and 2008 (inclusive) before being advected within the Iceland and Irminger Basins until any one of three termination conditions are met: 1) particles return southward across OSNAP East, 2) particles flow northward across the Greenland-Scotland Ridge, or 3) particles reach the maximum advection time of 7-years. The 7-year maximum advection time ensures >99.1% of all initialised particles meet one of conditions 1) or 2), hence only 0.9% of all particles are terminated between OSNAP East and the Greenland-Scotland Ridge. The number of particles initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 2.5 mSv (mSv == 1E-3 Sv), enabling the calculation of robust Lagrangian statistics. Particle locations (referenced to the original ORCA025 model grid) and properties (potential temperature, salinity, potential density and local mixed layer depth) are stored in the output files on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. In total, four Lagrangian experiments were conducted at the Department of Earth Sciences, University of Oxford. Please see README.md for a full description of all Lagrangian experiments and the accompanying output files. For a complete description of the ORCA025-GJM189 hindcast configuration see: https://github.com/meom-configurations/ORCA025.L75-GJM189. For a complete description of TRACMASS v7.1 see: https://github.com/TRACMASS/tracmass

opencc-by-4.0Oct 2024View details →

Dataset for Clock drift corrections for large aperture ocean bottom seismometer arrays: application to the UPFLOW array in the mid-Atlantic Ocean

Dataset from Clock drift corrections for large aperture ocean bottom seismometer arrays: application to the UPFLOW array in the mid-Atlantic Ocean DOI: 10.1093/gji/ggae354. This dataset includes the clock drift polynoms refered to jthe deployment date (jul day from 2022) and consecutive days up to the recovery date. Two types of formats. <ol> <li>Txt files</li> <li>Python Pickle files with a Dictionary containing the NumPY polynom1D and additional information.</li> </ol>

opencc-by-3.0-usFeb 2019View details →

Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).

This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III. All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude. ww3-2.inp and Bathymetry2.ascii are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. gfs-2.nc is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. ww3-2.nc is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain. ww3-3.inp and Bathymetry3.ascii are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. gfs-3.nc is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. ww3-3.nc is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain. The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).   Other data that complement this dataset: <a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375">Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</a> <a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a>

opencc-by-4.0Dec 2022View details →

SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints

This dataset contains an initial set of curated and processed oceanographic observations collected as part of a joint effort between the EU SO-CHIC project and a UKRI Future Leaders Fellowship. It is partly intended to be used as a set of observational constraints for a Weddell Gyre region state estimate, although it can be used for more general analysis purposes as well. It has been used as part of an unsupervised clustering analysis [see Jones (2022) for software and Jones and Zhou (2022) for labelled dataset, see references].  Overall spatial and temporal coverage <ul> <li>Latitude: 85°S-30°S</li> <li>Longitude: 65°W-80°E</li> <li>Time: 1974-2020</li> </ul> Contents <ul> <li>CPOM_SSH: sea-ice corrected sea surface height </li> <li>CTD: temperature and salinity profiles from ship-based CTD casts </li> <li>FLOATS: temperature and salinity profiles from Argo floats </li> <li>SEALS: temperature and salinity profiles from seal-mounted profilers</li> <li>Stress_and_EKE: sea-ice corrected surface stress and EKE </li> <li>XBT: temperature and salinity profiles from expendable bathythermographs (XBTs)</li> </ul> Profile quality control We only consider profiles with good position and time flags, as well as good temperature, salinity, and pressure measurements with good flags. Duplicated profiles are identified when multiple profiles are found within 24 hours over the same 2 km x 2 km grid cell, and only one profile within the spatio-temporal window is used. We then used the MITprof toolbox (Forget, G., 2017) to pre-process the selected profiles, re-gridding them onto 72 standard pressure levels; the vertical interval varies from 20 dbar at the surface to 100 dbar in the deep ocean.  SSH processing SSH data is sea-ice corrected version provided by the Centre for Polar Observation and Modelling (CPOM) in the UK. It is composed by two satellite missions, Envisat (2004/05-2012/03) and Cryosat-2 (2010/07-2020/04). The data is available in montly along-track format. A gaussian 300km filter, ±3 std outliner removal and 0.5x0.25 deg interpolation is applied to grid the data. Intersatellite offset is removed using the overlapped period between two missions using the mean difference map. SSH is referenced to EIGEN6C4 geoid to obtain the dynamic ocean topography feild for the computation of geostrophic velocity. See the README in the Stress_and_EKE directory for more information.  Sources <ul> <li>Argo floats: <a href="http://argo.ucsd.edu">http://argo.ucsd.edu</a></li> <li>World Ocean Database: <a href="https://www.ncei.noaa.gov/products/world-ocean-database">https://www.ncei.noaa.gov/products/world-ocean-database</a></li> <li>MEOP-CTD Database (seal profilers): <a href="https://www.meop.net/">https://www.meop.net/</a></li> <li>CDRv4 available via NSIDC: <a href="https://nsidc.org/data/G02202">https://nsidc.org/data/G02202</a></li> <li>Polar Pathfinder sea ice drift data via NSIDC: <a href="https://nsidc.org/data/nsidc-0116">https://nsidc.org/data/nsidc-0116</a></li> </ul> Version This is a pre-production version, in that it has not yet been used with a state estimate.

opencc-by-4.0Jul 2020View details →

Developing a deep Learning network to retrieve ocean hydrographic profiles in the North Atlantic from combined satellite and in situ measurements: test datasets.

We provide here the datasets used for the test and assessment of a deep learning algorithm which is presently candidate for the development of a daily 3D ocean product covering the North Atlantic at 1/10° resolution, over the 2010-2018 period, as part of the European Space Agency World Ocean Circulation project (ESA-WOC). The method is based on a stacked Long Short-Term Memory neural network, coupled to a Monte-Carlo dropout approach, and allows to project satellite-derived sea surface temperature, sea surface salinity and absolute dynamic topography data at depth after training with sparse co-located in situ vertical hydrographic profiles (Buongiorno Nardelli, 2020, doi:<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.3390%2Frs12193151?_sg%5B0%5D=0xE-347r7Hvb80klJcEo811AhUiXq-twG_E6l4yB-BfIKkVtW-lVLGcO02mTFkUczvozYYI0WCPyUBFR3kzWNGGZKg.ftvLheFrzHIJriO4qW2bdxalvR_TWt3MpwUfvto3EemhRgvDRGwJ9Mdy4Xr0IcGCfICivf4j-VqTgKxVvXRogA">10.3390/rs12193151</a>).  The test dataset presented here includes different sets of co-located temperature and salinity vertical profiles:  <ul> <li>in situ observations extracted from the quality controlled Argo and CTD profiles produced by Copernicus Marine Environment Monitoring Service CORA 5.2 (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) and interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals);</li> <li>climatological profiles extracted from World Ocean Atlas 2013 optimally interpolated monthly fields (Locarnini et al., 2013; Zweng et al., 2013), interpolated through a spline on a regularly spaced vertical grid (with 10 m intervals), upsized to a 1/10° horizontal grid through a cubic spline and linearly interpolated in time between the central day of each month;</li> <li>synthetic profiles obtained through three different techniques: multivariate EOF reconstruction, a 2 layer feed-forward network (with 1000 units in each hidden layer) and a stacked LSTM network (with 2 LSTM layers and 35 hidden units)</li> </ul> References: Buongiorno Nardelli, B.: A Deep Learning network to retrieve ocean hydrographic profiles from combined satellite and in situ measurements, 2020, submitted. Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D. R., Hamilton, M. and Seidov, D.: World Ocean Atlas 2013. Vol. 1: Temperature., S. Levitus, Ed.; A. Mishonov, Tech. Ed.; NOAA Atlas NESDIS, 73(September), 40, doi:10.1182/blood-2011-06-357442, 2013. Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019. Zweng, M. M., Reagan, J. R., Antonov, J. I., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov, D. and Bidlle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity, NOAA Atlas NESDIS, 119(1), 227–237, doi:10.1182/blood-2011-06-357442, 2013.

opencc-by-4.0Dec 2020View details →

Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean

This data set includes ocean ammonium concentrations, atmospheric ammonia gas concentrations, and calculated air-sea ammonia fluxes from the Atlantic sector of the Southern Ocean during summer. Associated with the folloiwng paper:   https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963

opencc-by-4.0Mar 2022View details →

Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)

Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North  Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.

opencc-by-4.0May 2022View details →

Mapping the Atlantic Ocean i.e. the Gulf of Maine to identify suitable cultivation sites for kelp species

Input source: <ul> <li>Temperature data</li> <li>Depth data</li> <li>Wave data</li> <li>Nutrients data</li> <li>Current data</li> <li>Marine use data</li> </ul> All from other available sources outside the project   DATA SET GENERATED: <ul> <li>Environmental data</li> <li>Training/validation data</li> <li>The socioeconomic datasets</li> </ul> <ul> <li>Map of suitable sites</li> <li>Model using GIS</li> </ul>

opencc-by-4.0Jun 2022View details →

River flows and nutrient discharges to the Atlantic ocean basin

This dataset includes i) River flows and ii) nutrient discharges to the Atlantic ocean basin: River flow data are a subset of the WaterGAP 2.2d model (Monthly data on a 0.5° x 0.5° grid between 90°N and 60°S. 1901–2016) (Müller Schmied et al. 2020) The original watergap2.2d data is provided in netcdf format. Our postprocessing includes the selection of the coastal cells and the extraction of the monthly values in these cells. The resulting dataset is provided as a shapefile (Global_YearMonthly_River_flow_watermap22_coast.shp), including Date, latitude and longitude of the coastal cell’s centroids and the discharge values (m3s-1). Watergap2.2d outputs offers a very good spatio-temporal extent and resolution, and according to the Müller Schmied et al. 2020, the validation results for streamflow (or discharges values) are reasonably satisfactory, although there is some spatial variability in the performance results. Moreover, the recently published “Global Freshwater Fluxes into the World's Oceans (GRDC, 2021)” product and paper, uses the yearly outcomes of this model, which has also supported our selection. River nutrient discharges are a subset of observations from the “Global River Water Quality Archive”. (<a href="https://essd.copernicus.org/preprints/essd-2021-51/">Virro et al, 2021</a>), that among the publicly available and downloadable datasets, gathers the highest number of observations as includes data from different international and national databases. The GRQA data is provided as csv files (one file for each nutrient). These files have been processed to subset only observations at stations near the coastline. To do this a intersection between station locations and a buffer of 0.2 degrees around the coastline has been made. For each nutrient, a shapefile with the subsetted observations is provided. All shapefiles provided are accompanied by a “.qmd” file that includes metadata information in QGIS 3 format.

opencc-by-4.0Mar 2022View details →

Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication

The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the Journal of Geophysical Research: Atmospheres. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets: <ul> <li> Radiosonde data from 2-3 February 2020 (Stephan et al., 2021) </li> <li> Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020, </li> <li> GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020. </li> <li> Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017). </li> <li> W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below. </li> </ul> The present dataset is structured as follows: <ul> <li> diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle  (filenames with extended dates 20200202 and 20200203) </li> <li> diurnal_cycle: files containing the diurnal cycle of each variable used in the publication </li> <li> binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper. </li> <li> satellite_data: a folder containing all satellite data used in the publication </li> </ul> Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls: <ul> <li> cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022) </li> <li> ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>) </li> <li> hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components.  </li> </ul>   References; Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>. Schmit, T.  et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a> Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a> Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a> Chin, T. M. et al.,  (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a>

opencc-by-4.0Sep 2024View details →

Supporting Data: Phylogeny of Arbacia Gray, 1835 (Echinoidea) reveals diversification patterns in the Atlantic and Pacific Oceans.

This dataset contains: <ol> <li>Appendix S1, metadata asociated with the specimens (collection localities, specimen numbers)</li> <li>The aligned sequence files for each marker: COI_fasta, 16S_fasta, CR_fasta</li> <li>The concatenated sequence file COI + 16S + CRA + 28S Arbacia_supermatrix_fasta and the partition file partitions_concat</li> <li>The Bayesian trees for COI, 16S, CRA, and the supermatrix: BI_tree_16S, BI_tree_COI, BI_tree_CR, BI_tree_Arbacia_supermatrix</li> <li>The ML tree of the supermatrix: ML_tree_Arbacia_supermatrix</li> <li>Appendix S2, which includes various information on the primers used, PCR cycles, etc.</li> <li>Appendix S3, which includes the index calculations for each marker.</li> </ol>

opencc-by-4.0Aug 2022View details →

Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean

These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean in support of open-code, transparency, and repeatability. Abstract: Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.

opencc-by-4.0Apr 2023View details →

Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign

We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.

opencc-by-4.0Jan 2020View details →

They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1990-2002 (2 of 2)

Supporting data for Kelly et al.: They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean (Earth's Future, submitted) Trajectories saved by year of release in the Bering Strait. All months from that year are included in the same file, with the first 1000 trajectories corresponding to January release, second 1000 from February release, and so on.  Due to the size of files, this is split into two uploads. Part 1 covers 1970-1989 releases, 1990 onward is saved in Part 2.

opencc-by-4.0Jan 2020View details →

They Came From The Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean - Lagrangian Data 1970-1989 (1 of 2)

opencc-by-4.0Jun 2018View details →

Fig. 6 in Reproductive biology of the eyespot skate Atlantoraja cyclophora (Elasmobranchii: Arhynchobatidae) an endemic species of the Southwestern Atlantic Ocean (34ºS - 42ºS)

Fig. 6. Seasonal variation in gonadosomatic (GSI) and hepatosomatic (HSI) indexes for a.-b. males and c.-d. females of Atlantoraja cyclophora. The number of samples analyzed is between parentheses. The boxes represent the interquartile range between Q1 and Q3 with the 50% of data, the central line represents the median value and whiskers extend to the maximum and minimum values

opencc-by-4.0Mar 2020View details →

Particle tracking dataset for: Exceptional 20th century ocean circulation in the Northeast Atlantic

Particle tracking data for: "Exceptional 20th century ocean circulation in the Northeast Atlantic" Peter T. Spooner, David J. R. Thornalley, Delia W. Oppo, Alan Fox, Svetlana Radionovskaya, Neil L. Rose, Robbie Mallett, Emma Cooper, J. Murray Roberts VIKING20 (is a 1/20th degree ocean model, forced by a hindcast simulation of the atmosphere: CORE2 (Griffies et al., 2009). The reverse tracks of 113200 particles per year for 50 years, (1959-2009) were simulated with the ARIANE software (Döös, 1995) modified to include independent vertical motion of particles. Particles were seeded at the seabed in 10 km x 10 km boxes centered on MC16-A/17-5P and RAPID-21-3K (representing the settling location). The reverse tracks 'rose' (sinking) at 100 m/day (Takahashi & Be, 1984) and were then allowed to drift freely within the upper 100 m of the water column for six months (i.e. spanning the reasonable lifespan for many species of planktic foraminifera). Track data for the full 50 years are stored in a single netcdf file (output of ncdump -h <filename> given below). The 3D particle positions are in variables traj_lon, traj_lat and traj_depth with the Viking20 model along-track temperature, salinity and density in traj_temp, temp_sal and traj_dens, respectively. The main complication is the obscure storage of time (see also ARIANE software documentation). Variable init_t gives particle start time, counting in 5-day periods from 12:00 pm on 29 December 1957. Viking20 uses a fixed 365 day year so the year can be found for track 'traj' according to:            year    =     1958 + ( (init_t(traj)-1) \ 73 )     where '\' represents integer division, discarding the remainder. All particle tracks 'begin' (actually the end of the track in time as these are tracked backwards) at the start of July (12:00 pm July 1 in model). Particles are ordered by release time, so trajectories 1-113200 are 1959; 113201-226400 are 1960; etc. Positions are stored every 5 days, counting backwards. Further details are available from the authors.   References Döös, K. (1995). Interocean exchange of water masses. Journal of Geophysical Research, 100(C7), 13499. <a href="https://doi.org/10.1029/95JC00337">https://doi.org/10.1029/95JC00337</a> Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., et al. (2009). Coordinated Ocean-ice Reference Experiments (COREs). Ocean Modelling, 26(1–2), 1–46. <a href="https://doi.org/10.1016/J.OCEMOD.2008.08.007">https://doi.org/10.1016/J.OCEMOD.2008.08.007</a> Takahashi, K., & Be, A. W. H. (1984). Planktonic foraminifera: factors controlling sinking speeds. Deep Sea Research Part A. Oceanographic Research Papers, 31(12), 1477–1500. <a href="https://doi.org/10.1016/0198-0149(84)90083-9">https://doi.org/10.1016/0198-0149(84)90083-9</a>   $ ncdump -h ariane_trajectories_qualitative.nc netcdf ariane_trajectories_qualitative { dimensions: ntraj = 5660000 ; nb_output = UNLIMITED ; // (74 currently) variables: double init_x(ntraj) ; init_x:title = "What is init_x ?" ; init_x:longname = "Initial position in i" ; init_x:units = "No dimension" ; init_x:missing_value = 1.e+20 ; double init_y(ntraj) ; init_y:title = "What is init_y ?" ; init_y:longname = "Initial position in j" ; init_y:units = "No dimension" ; init_y:missing_value = 1.e+20 ; double init_z(ntraj) ; init_z:title = "What is init_z ?" ; init_z:longname = "Initial position in k" ; init_z:units = "No dimension" ; init_z:missing_value = 1.e+20 ; double init_t(ntraj) ; init_t:title = "What is init_t ?" ; init_t:longname = "Initial position in l (time)" ; init_t:units = "See global attributes..." ; init_t:missing_value = 1.e+20 ; double init_age(ntraj) ; init_age:title = "What is init_age ?" ; init_age:longname = "Initial age (time)" ; init_age:units = "seconds" ; init_age:missing_value = 1.e+20 ; double init_transp(ntraj) ; init_transp:title = "What is init_transp ?" ; init_transp:longname = "Initial transport" ; init_transp:units = "m3/s" ; init_transp:missing_value = 1.e+20 ; double l_matureage(ntraj) ; l_matureage:title = "What is l_matureage ?" ; l_matureage:longname = "Larval age of maturity" ; l_matureage:units = "days" ; l_matureage:missing_value = 1.e+20 ; double l_descendage(ntraj) ; l_descendage:title = "What is l_descendage ?" ; l_descendage:longname = "Larval age of competency" ; l_descendage:units = "days" ; l_descendage:missing_value = 1.e+20 ; double l_maxspeedup(ntraj) ; l_maxspeedup:title = "What is l_maxspeedup ?" ; l_maxspeedup:longname = "Max upward larval swim speed" ; l_maxspeedup:units = "mm s-1" ; l_maxspeedup:missing_value = 1.e+20 ; double l_maxspeeddown(ntraj) ; l_maxspeeddown:title = "What is l_maxspeeddown ?" ; l_maxspeeddown:longname = "Max downward larval swim speed" ; l_maxspeeddown:units = "mm s-1" ; l_maxspeeddown:missing_value = 1.e+20 ; int l_targetdepth(ntraj) ; l_targetdepth:title = "What is l_targetdepth ?" ; l_targetdepth:longname = "Target shallow depth" ; l_targetdepth:units = "No dimension" ; l_targetdepth:missing_value = -1. ; double final_x(ntraj) ; final_x:title = "What is final_x ?" ; final_x:longname = "Final position in x (or i)" ; final_x:units = "No dimension" ; final_x:missing_value = 1.e+20 ; double final_y(ntraj) ; final_y:title = "What is final_y ?" ; final_y:longname = "Final position in y (or j)" ; final_y:units = "No dimension" ; final_y:missing_value = 1.e+20 ; double final_z(ntraj) ; final_z:title = "What is final_z ?" ; final_z:longname = "Final position in z (or k)" ; final_z:units = "No dimension" ; final_z:missing_value = 1.e+20 ; double final_t(ntraj) ; final_t:title = "What is final_t ?" ; final_t:longname = "Final position in t (time)" ; final_t:units = "See global attributes..." ; final_t:missing_value = 1.e+20 ; double final_age(ntraj) ; final_age:title = "What is fial_age ?" ; final_age:longname = "Final Age." ; final_age:units = "seconds" ; final_age:missing_value = 1.e+20 ; double final_transp(ntraj) ; final_transp:title = "What is final_transp ?" ; final_transp:longname = "Final transport" ; final_transp:units = "m3/s" ; final_transp:missing_value = 1.e+20 ; float traj_lon(nb_output, ntraj) ; traj_lon:title = "What is traj_lon ?" ; traj_lon:longname = "Trajectory: x positions" ; traj_lon:units = "No dimension" ; traj_lon:missing_value = 1.e+20 ; float traj_lat(nb_output, ntraj) ; traj_lat:title = "What is traj_lat ?" ; traj_lat:longname = "Trajectory: y positions" ; traj_lat:units = "No dimension" ; traj_lat:missing_value = 1.e+20 ; float traj_depth(nb_output, ntraj) ; traj_depth:title = "What is traj_depth ?" ; traj_depth:longname = "Trajectory: z positions" ; traj_depth:units = "No dimension" ; traj_depth:missing_value = 1.e+20 ; float traj_time(nb_output, ntraj) ; traj_time:title = "What is traj_time ?" ; traj_time:longname = "Trajectory: time positions" ; traj_time:units = "See global attributes" ; traj_time:missing_value = 1.e+20 ; float traj_iU(nb_output, ntraj) ; traj_iU:title = "ind i on grid U" ; traj_iU:longname = "Trajectory: i on grid U" ; traj_iU:units = "No dimension" ; traj_iU:missing_value = 1.e+20 ; float traj_jV(nb_output, ntraj) ; traj_jV:title = "ind j on grid V" ; traj_jV:longname = "Trajectory: j on grid V" ; traj_jV:units = "No dimension" ; traj_jV:missing_value = 1.e+20 ; float traj_kW(nb_output, ntraj) ; traj_kW:title = "ind k on grid W" ; traj_kW:longname = "Trajectory: k on grid W" ; traj_kW:units = "No dimension" ; traj_kW:missing_value = 1.e+20 ; float traj_temp(nb_output, ntraj) ; traj_temp:title = "What is traj_temp ?" ; traj_temp:longname = "Trajectory: temperatures" ; traj_temp:units = "degres" ; traj_temp:missing_value = 1.e+20 ; float traj_salt(nb_output, ntraj) ; traj_salt:title = "What is traj_salt ?" ; traj_salt:longname = "Trajectory: salinities" ; traj_salt:units = "psu" ; traj_salt:missing_value = 1.e+20 ; float traj_dens(nb_output, ntraj) ; traj_dens:title = "What is traj_dens ?" ; traj_dens:longname = "Trajectory: densities" ; traj_dens:units = "..." ; traj_dens:missing_value = 1.e+20 ;   // global attributes: :key_roms = ".FALSE." ; :key_symphonie = ".FALSE." ; :key_B2C_grid = ".FALSE." ; :key_sequential = ".TRUE." ; :key_alltracers = ".TRUE." ; :key_ascii_outputs = ".FALSE." ; :key_iU_jV_kW = ".TRUE." ; :key_read_age = ".FALSE." ; :mode = "qualitative" ; :forback = "backward" ; :bin = "nobin" ; :init_final = "NONE" ; :nmax = 10000000 ; :tunit = 86400. ; :ntfic = 5 ; :tcyc = 1639872000. ; :key_approximatesigma = ".FALSE." ; :key_computesigma = ".TRUE." ; :zsigma = 1000. ; :memory_log = ".TRUE." ; :output_netcdf_large_file = ".FALSE." ; :key_interp_temporal = ".TRUE." ; :maxcycles = 50 ; :delta_t = 86400. ; :frequency = 5 ; :nb_output = 73 ; :mask = ".TRUE." ; :key_region = ".FALSE." ; :key_larvae = ".TRUE." ; :imt = 1784 ; :jmt = 1719 ; :kmt = 46 ; :lmt = 3796 ; :key_computew = ".TRUE." ; :w_surf_option = "" ; :key_partialsteps = ".TRUE." ; :key_jfold = ".FALSE." ; :pivot = "T" ; :key_periodic = ".FALSE." ; :dir_mesh = "./GRID" ; :fn_mesh = "1_mesh_mask.nc" ; :nc_var_xx_tt = "glamt" ; :nc_var_xx_uu = "glamu" ; :nc_var_zz_ww = "gdepw_0" ; :nc_var_e2u = "e2u" ; :nc_var_e1v = "e1v" ; :nc_var_e1t = "e1t" ; :nc_var_e2t = "e2t" ; :nc_var_e3t = "e3t" ; :nc_var_tmask = "tmask" ; :nc_mask_val = 0. ; :c_dir_zo = "./DATA" ; :c_prefix_zo = "V20_nest_5d_" ; :ind0_zo = 1958 ; :indn_zo = 2009 ; :maxsize_zo = 4 ; :c_suffix_zo = "_U.nc" ; :nc_var_zo = "vozocrtx" ; :nc_var_eivu = "NONE" ; :nc_att_mask_zo = "missing_value" ; :c_dir_me = "./DATA" ; :c_prefix_me = "V20_nest_5d_" ; :ind0_me = 1958 ; :indn_me = 2009 ; :maxsize_me = 4 ; :c_suffix_me = "_V.nc" ; :nc_var_me = "vomecrty" ; :nc_var_eivv = "NONE" ; :nc_att_mask_me = "missing_value" ; :c_dir_te = "./DATA" ; :c_prefix_te = "V20_nest_5d_" ; :ind0_te = 1958 ; :indn_te = 2009 ; :maxsize_te = 4 ; :c_suffix_te = "_T.nc" ; :nc_var_te = "votemper" ; :nc_att_mask_te = "missing_value" ; :c_dir_sa = "./DATA" ; :c_prefix_sa = "V20_nest_5d_" ; :ind0_sa = 1958 ; :indn_sa = 2009 ; :maxsize_sa = 4 ; :c_suffix_sa = "_T.nc" ; :nc_var_sa = "vosaline" ; :nc_att_mask_sa = "missing_value" ; }