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1,779 results for “Southern Ocean”
BGQNAPv1.0: A summer macronutrients binned data set for the Northern Antarctic Peninsula, Southern Ocean
<p>We compiled a time series spanning the period from 1996 to 2019 of the seawater hydrographic variables conservative temperature (<sup>o</sup>C), absolute salinity (g kg<sup>­–1</sup>) and dissolved oxygen (μmol kg<sup>­–1</sup>), and the macronutrients DIN (nitrate + nitrite + ammonium), phosphate, and silicic acid (μmol kg<sup>­–1</sup>). The study area covered the northern Antarctic Peninsula regions including the Gerlache Strait and the western, central, and eastern basins of Bransfield Strait. Most data (~90%) were obtained from the Brazilian High Latitude Oceanography Group (GOAL; http://goal.furg.br/) from austral summer field campaigns (January-March). In some years (1996, 2005, 2006, 2010, 2011) we used hydrographic and macronutrient data from GLODAP 2020 (Olsen et al., 2020) along the NAP and exceptionally for 1996 we used data available from December 1995 to February 1996 (the FRUELA cruises, García et al., 2022; Álvarez et al., 2002). Details on the sampling and analysis of macronutrient data obtained from GLODAP dataset can be accessed on the OCADS platform (<a href="https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system-portal/">https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system-portal/</a>).</p> <p>About 97% of the DIN data were composed of nitrate, followed by ammonium (2%) and nitrite (1%). Therefore, in some cases (11% of all data), we considered DIN as the nitrate concentration, when no nitrite and/or ammonium data were available. Discrete seawater samples were collected at irregular depth intervals from surface (5 m) to deep waters (at approximately 15 m from the bottom). We averaged the parameters for each region at regular depth intervals from the surface to the bottom (i.e., 0, 25, 50, 75, 100, 250, 500, 750, 1000, 1250, 1500, 1750, 2000, 2500 m) to obtain an averaged summer profile for each year.</p> <p>All sampling and analyses information of the hydrographic and macronutrients are detailed in Kerr et al. (2018), Mata et al. (2018), Dotto et al. (2021) and Costa et al. (2020), and references therein.</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>
Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean
<p>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: </p> <p> https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963</p>
ACCESS-AM2 Southern Ocean cloud and radiation data and code for SHAP analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) and SHAP analysis code and data used for the study described in Fiddes et al. (2024) '<em>A machine learning approach for evaluating Southern Ocean cloud-radiative biases over the Southern Ocean in a global atmosphere model</em>' accepted in Geoscientific Model Development</p> <p>Included files: </p> <p>- code.zip, inc: </p> <p> - pre-process_modis.ipynb: process the modis data, described in Fiddes et al. 2022 (https://doi.org/10.5194/acp-22-14603-2022)<br> - pre-process.ipynb: organises model and modis data for analysis. Produces the files: COSP_vars_MODIS_2015-2019.nc, COSP_vars_cg207_2015-2019.nc and COSP_vars_bx400_2015-2019.nc<br> - run_XGBoost+SHAP_control.ipynb: run the XGBoost model and SHAP analysis for the control run (bx400). Produces the files: SHAP_values_SWCRE_2015-2019_bx4002.nc, XGBoost_predicted_SWCRE_2015-2019_bx4002.nc, SHAP_interactions_bx400.nc<br> - run_XGBoost+SHAP_ice.ipynb: run the XGBoost model and SHAP analysis for the ice experiment run (cg207). Produces the files: SHAP_values_SWCRE_2015-2019_cg2072.nc, XGBoost_predicted_SWCRE_2015-2019_cg2072.nc<br> - analysis+plots_ML.ipynb: plots and stats presented in paper </p> <p>- COSP_vars_MODIS_2015-2019.nc</p> <p>- COSP_vars_cg207_2015-2019.nc</p> <p>- COSP_vars_bx400_2015-2019.nc</p> <p>- SHAP_values_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_values_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_interaction_bx400.nc</p> <p>The cloud types used in this work can be found at https://doi.org/10.5281/zenodo.6004061 </p>
OSSE dataset for assessing the sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain
<p>The data stored in this repository are part of the manuscript entitled "The sensitivity of pCO<sub>2</sub> reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach" submitted in consideration for publication for in European Geosciences Union: Biogeosciences.</p> <p> </p> <p>The netcdf file includes oceanographic (physical + biogeochemical) data, namely the partial pressure of carbon dioxide (pCO<sub>2</sub>) data at the surface ocean from the high-resolution (±10km) forced NEMO-PISCES coupled ocean model (BIOPERIANT12) and from the semi-idealized observing system simulation experiments (OSSEs) we performed.</p>
Antarctic Circumnavigation Expedition sample log: samples collected in the Southern Ocean during the austral summer of 2016/17.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE) spent 90 days circumnavigating Antarctica on the R/V Akademik Tryoshnikov during the austral summer of 2016/17. This dataset provides a record of the samples that were collected during the expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_sample_log.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This sample log is made available under a 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>
Ensemble statistics for modelled Eddy Kinetic Energy in the Southern Ocean
<p>This dataset contains surface eddy kinetic energy over the Southern Ocean region, sourced from a 50-member ensemble of 0.25° ocean model simulations. It is used in the paper "Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics" published in Journal of Geophysical Research - Oceans.</p> <p>This dataset has been computed from the OceaniC Chaos – ImPacts, strUcture, predicTability (OCCIPUT) global ocean/sea-ice ensemble simulation. It is composed of 50 members with a horizontal resolution of 1/4° and 75 geopotential levels (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>, Penduff et al., 2014). The numerical configuration is based on the version 3.5 of the NEMO model (<a href="https://www.nemo-ocean.eu/doc">Madec, 2008</a>). The 50 members were started on January 1st 1960 from a common 21-year spinup. A small stochastic perturbation is applied to the equation of state of sea water (as in <a href="https://doi.org/10.1016/j.ocemod.2013.02.004">Brankart, 2013</a>) within each member during 1960, then switched off during the rest of the simulation. This 1-year perturbation generates an ensemble spread which grows and saturates after a few months up to a few years depending on the region. The 50 members are driven through bulk formulae during the whole 1960-2015 simulation by the same realistic 6-hourly atmospheric forcing (Drakkar Forcing Set DFS5.2, Dussin et al., 2016) derived from ERA interim atmospheric reanalysis. Data is for the period 1979-2015.</p> <p>The sea level anomaly is found according to <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al (2020)</a> and converted into surface geostrophic velocity anomaly using the geostrophic relation. This velocity field is then used to calculate the eddy kinetic energy (EKE). Data is averaged over calendar month, and restricted to the latitude range 40°-60°S. A full description of this process is included in the companion paper.</p> <p>The dataset includes EKE files (eke_0??.nc), with monthy EKE saved for the period 1979-2015 for each ensemble member, and a single file (tau.nc) for the monthly-averaged wind stress over the same period.</p>
Dataset associated with paper "Topographic hotspots of Southern Ocean eddy upwelling"
<p><strong>Data repository for Yung, Morrison and Hogg (2022) <em>Topographic hotspots of Southern Ocean eddy upwelling</em>, submitted to Frontiers in Marine Science</strong></p> <p> </p> <p>This repository contains processed data, created using scripts available in the github repository <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code</a>.</p> <p> </p> <p>The data comes from the repeat year atmospheric forcing version of the ACCESS-OM2 modelling suite 0.1 degree model (see Kiss et al. (2020), http://www.cosima.org.au, model available at <a href="https://github.com/COSIMA/access-om2">https://github.com/COSIMA/access-om2</a>). The model was spun up for 270 years, and the next 10 years of output were used.</p> <p> </p> <p>Daily resolution data was used in the calculation of quantities, which results in a large amount of raw data (~4TB for Southern Ocean latitudes (35-70S), 10 years). Therefore, we only provide relevant processed data. Details of these calculations are available in the above github repository.</p> <p> </p> <p>Any quantities calculated along sea surface height contours are labelled with a letter. These are referred to in the following table.</p> <p> </p> <p>| Letter | SSH |</p> <p>| :---: | :---: |</p> <p>| A | -0.1m |</p> <p>| B | -0.2m |</p> <p>| C | -0.3m |</p> <p>| D | -0.4m |</p> <p>| E | -0.5m |</p> <p>| F | -0.6m |</p> <p>| G | -0.7m |</p> <p>| H | -0.8m |</p> <p>| I | -0.9m |</p> <p>| J | -1.0m |</p> <p>| K | -1.1m |</p> <p>| L | -1.2m |</p> <p>| M | -1.3m |</p> <p>| N | -1.4m |</p> <p>| O | -1.5m |</p> <p>| P | -0.15m|</p> <p>| Q | -0.25m|</p> <p>| R | -0.35m|</p> <p>| S | -0.45m|</p> <p>| T | -0.55m|</p> <p>| U | -0.65m|</p> <p>| V | -0.75m|</p> <p>| W | -0.85m|</p> <p>| X | -0.95m|</p> <p>| Y | -1.05m|</p> <p>| Z | -1.15m|</p> <p>| Z1 | -1.25m|</p> <p>| Z2 | -1.35m|</p> <p>| Z3 | -1.45m|</p> <p> </p> <p>There are two versions of the along-contour coordinates for each contour. The latlon named files are more useful for analysis, the other is used while computing transport across contours. These are made using <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb</a>.</p> <p> </p> <p>Distance along contour files contain the cumulative distance along the contour in 10^3 km from 80E (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb</a>). Dimensions are contour index, counting from 80E. There are also segment length files of each part of the contour.</p> <p> </p> <p>vh_eddy files contain the eddy transport across the contours, averaged over 10 years. These are calculated by taking the time mean of the residual transport (<span class="math-tex">\(\overline{vh}\)</span>), e.g. SO_L_vol_trans_across_contour_binned.nc, and subtracting the mean transport <span class="math-tex">\(\overline{v}\overline{h}\)</span>, calculated from the time mean isopycnal thicknesses along contours (e.g. SO_L_dzu_across_contour_binned) and the time mean velocity, <span class="math-tex">\(v = vh/h\)</span> (calculated in <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb</a>). The full files are provided for the contour L (SSH=-1.2 m) as is provided in the paper manuscript Fig. 6. These eddy transports have dimensions of sigma1 and contour index (the two extra SO_L files have time too).</p> <p> </p> <p>vh_eddy_interp files contain the interpolated eddy transports at hotspots, for the density range 1032.2kg/m^3 <= sigma_1 <= 1032.5kg/m^3. Calculation method provided at <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb</a>.</p> <p> </p> <p>We also provide files that summarise the transport in density and SSH space for hotspots and the circumpolar total. See <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb</a> for calculation details. The exact names and specifications are provided in the README.</p> <p> </p> <p>We provide 10 year mean files of the energy conversion and energy terms over the Southern Ocean latitude range. These are made by binning daily transports and layer thicknesses into sigma 1 bins over the Southern Ocean (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb</a>) and then calculating energy terms (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb</a>, <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb</a>)</p> <p> </p> <p> </p> <p>The files named with contour_energies contain the EKE, Form stress and Reynolds stress averaged over 10 years but extracted along the same contours as eddy transport. <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb</a></p> <p> </p> <p>We also provide 10 year averaged density binned transport, layer thickness and densities.</p> <p> </p> <p>Please refer to the README file for additional details.</p>
Dataset for "IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean"
<p>Dataset for</p> <p>Sperlich, P., Brailsford, G. W., Moss, R. C., McGregor, J., Martin, R. J., Nichol, S., Mikaloff-Fletcher, S., Bukosa, B., Mandic, M., Schipper, I., Krummel, P. and Griffiths, A. D.: IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean, Atmos. Meas. Tech., https://doi.org/10.5194/amt-15-1-2022, 2022.</p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data and k-means analysis used for the study described in Fiddes et al. 2022 '<em>Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?' .</em> </p> <p>Included files: </p> <ul> <li>modis_cluster_centres_2015-2019.nc - kmeans derived cluster centres for MODIS</li> <li>modis_cluster_labels_2015-2019.nc - kmeans derived cluster labels for MODIS </li> <li>bx400_cluster_labels_2015-2019.nc - kmeans fitted cluster label for model </li> <li>COSP_vars_bx400_2015-2019.nc - model data for analysis </li> </ul> <p>The code that performs the analysis/generates this data and has instructions for where to download MODIS data can be found here: https://github.com/sfiddes/code_for_publications_2022/tree/main/ACCESS_cloud_radiation_eval</p>
Files from barotropic and baroclinic idealized model runs of the Southern Indian Ocean
<p>These data files correspond to two idealized model runs of the Southern Indian Ocean using the Regional Ocean Modelling System (ROMS) as a framework. Both simulations are forced with monthly mean QuikSCAT winds and are run at a 1/3 degree resolution. </p> <p>The barotropic model is single layer with realistic ETOPO2 bathymetry, a two arc minute ocean-floor elevation data-set smoothed to a resolution of 55.2 km. The file corresponding to this simulation is named: roms_avg_barotropic.</p> <p>The baroclinic model is a 1 and a half layer model where the value of the pycnocline depth and the reduced gravity parameter is set at the initialization stage. Two simulations are presented, the first where 'relaistic' initialization parameters of H=800m and g'= 0.0134 m/s(^2), and the second where the density gradient between the active and passive layers is reduced to a g' of 0.0076 m/s(^2). The two data sets corresponding to these simulations are titled: roms_avg_800_0134 and roms_avg_800_0076</p> <p>Below find a list of variable names and descriptions:</p> <p>zeta=anomaly in thickness of active layer<br> ubar= mean zonal velocity of active layer<br> vbar= mean meridional velocity of active layerh=depth of bathymetry in barotropic model; pycnocline depth in baroclinic model<br> coast=coastline<br> lon_rho=longitude corresponding the density coordinates<br> lat_rho=latitude corresponding the density coordinates<br> lon_u=longitude corresponding the zonal velocities<br> lat_u=latitude corresponding the zonal velocities<br> lon_v=longitude corresponding the density velocities<br> lat_v=longitude corresponding the meridional velocities<br> time=days since model simualtion started</p>
Interannual iceberg meltwater fluxes over the Southern Ocean
<p><strong>Monthly Iceberg Meltwater Fluxes over the Southern Ocean (1972-2017) :</strong></p> <p>Here is an update of the iceberg meltwater climatology initially provided by Merino et al (2016). This flux is still derived from NEMO's Lagrangian iceberg module developed by Marsh et al. (2015) and updated by Merino et al. (2016). It is run within a global ORCA025 ocean simulation running from 1958 to 2017, forced by the Drakkar Forcing Set (DFS-5.2; Dussin et al 2016). The 1958-1972 period is used to spin up the model, and the meltwater fluxes are provided over 1972-2017. The iceberg calving fluxes are constant, but their meltwater fluxes varies seasonally and interannually. Ice-shelf meltwater fluxes are reconstructed from glaciological observations (Merino et al. 2018), and here vary linearly from 1990 to 2010 (constant before and after). </p> <p><strong>Known caveats:</strong></p> <ul> <li>The ocean grid is the old "ORCA025" grid, which does not extend southward of 70°S, i.e. iceberg do not follow the southernmost ice shelf edges (e.g. Ronne ice shelf).</li> </ul> <p><strong>References:</strong></p> <ul> <li>Dussin, Raphael, Bernard Barnier, Laurent Brodeau, and Jean Marc Molines (2016). Drakkar Forcing Set DFS5.</li> <li>Marsh, R., Ivchenko, V. O., Skliris, N., Alderson, S., Bigg, G. R., Madec, G., and others (2015). NEMO-ICB (v1. 0): interactive icebergs in the NEMO ocean model globally configured at eddy-permitting resolution. <em>Geoscientific Model Development</em>, <em>8</em>(5), 1547-1562.</li> <li>Merino, N., Le Sommer, J., Durand, G., Jourdain, N. C., Madec, G., Mathiot, P. and Tournadre, J. (2016). Antarctic icebergs melt over the Southern Ocean: Climatology and impact on sea ice. <em>Ocean Modelling</em>, <em>104</em>, 99-110.</li> <li>Merino, N., Jourdain, N. C., Le Sommer, J., Goosse, H., Mathiot, P. and Durand, G. (2018). Impact of increasing antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. <em>Ocean Modelling</em>, <em>121</em>, 76-89.</li> </ul>
Water Body Checklists 2019: Southern Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Southern Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Water Body Checklists: Southern Ocean Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Southern Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>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.</p>
Brilliantia kiribatiensis, a new genus and species of Cladophorales (Chlorophyta) from the remote coral reefs of the Southern Line Islands, Pacific Ocean
<p>Data associated with the study "<em>Brilliantia kiribatiensis</em>, a new genus and species of Cladophorales (Chlorophyta) from the remote coral reefs of the Southern Line Islands, Pacific Ocean".</p> <p><strong>ITS.fasta, ITS_bmge.fasta, LSU.fasta, LSU_bmge.fasta, SSU.fasta, SSU_bmge.fasta: </strong>SSU rDNA, LSU rDNA and rDNA ITS sequences used in phylogenetic analyses. Sequences of Brilliantia kiribatiensis were added to updated phylogenetic datasets used previously (Leliaert et al. 2007a, Leliaert et al. 2009b), aligned in MAFFT v7.215 (Katoh and Standley 2013), and stripped of hypervariable sites in BMGE v1.1 (Criscuolo and Gribaldo 2010) by using the -h 0.4 -g 0.35 parameters. Alignments were visually checked and concatenated in Seaview v4.4.2.</p> <p><strong>concatenated_SSU_ITS_LSU.fasta</strong>: concatenated alignment with following partitions: SSU: 1-1797, ITS1+5.8S+ITS2: 1798-3335, LSU: 3336-3926.</p> <p><strong>Table_S1_sequence_sources.xls: </strong>GenBank accessions, sample isolate codes and sites of collection for sequences included in the concatenated phylogenetic data set.</p> <p><strong>Table S2. </strong>Percent cover of different algal groups in 1 m2 photoquadrats. Algal groups are identified to genus level for fleshy macroalgae or functional group for turf algae, branched red algae, crustose coralline algae, and cyanobacteria.</p> <p><strong>Table SX.</strong> Morphological measurements of <em>Brilliantia kiribatiensis</em>.</p>
Seaglider (SG537) dataset collected during the ROAM-MIZ field campaign in the Southern Ocean
<p>This dataset is a part of the Robotic Observations And Modelling in the Marginal Ice Zone (ROAM-MIZ, <a href="http://www.roammiz.com">www.roammiz.com</a>) project, and contains the temperature and salinity profiles from a Seaglider (SG537), which was deployed at the Prime Meridian in the northeastern Weddell Sea (0.00W and 55S) from 18th October 2019 to 18th February 2020 and obtained a total of five repeated crossings of the Southern Boundary.</p>
Aerosol particles observed onboard the research vessel Mirai over the Southern Ocean in the austral summer of 2017
<p>We have compiled a dataset of field observations to measure aerosol particle size distributions and to collect the aerosols for the following laboratory analyses to quantify the chemical composition and ice nucleating properties of aerosols over the Southern Ocean in the austral summer of 2017 as a part of the research cruise of Japanese research vessel (R/V) Mirai (Cruise number of MR16-09 leg3). The particle size distributions (PSDs) of the submicron aerosols (14–737 nm in the electrical mobility diameter) were measured using a scanning mobility particle sizer, SMPS, which is composed of a differential mobility analyzer, DMA (model 3081, TSI Inc., Minnesota, USA) and a condensation particle counter, CPC (model 3010, TSI Inc.). Since a custom-made inlet system was installed in front of the SMPS, the PSDs of total and non-volatile aerosols upon heating at the 300°C were alternatively measured every 5 min. The PSDs of the coarse fluorescent and non-fluorescent particles (700–3000 nm in the optical diameter) were measured using a waveband integrated bioaerosol sensor, WIBS (type 4A, Droplet Measurement Technologies Ltd., Colorado, USA). Hourly averaged PSDs for the diameter range of 14–3000 nm were analyzed in the associated paper in order to relate the wave breaking state derived from the hourly observations of significant wave height on the R/V. Chemical compositions were derived from the collected samples with the following techniques at the laboratory, ion chromatography for water soluble ions (chloride, nitrate, sulfate, ammonium, sodium, potassium, magnesium, calcium ions), thermal optical transmittance technique for carbonaceous aerosols (organic and elemental carbons), and inductively coupled plasma mass spectrometry for aluminum (Al). Ice nucleating properties of the aerosol particles were analyzed using a droplet freezing method (Cryogenic Refrigerator Applied to Freezing Test, CRAFT) at National Institute of Polar Research (Tobo, 2016 <a href="https://doi.org/10.1038/srep32930">https://doi.org/10.1038/srep32930</a>). All the data indicating the concentrations were reported at standard temperature and pressure (0°C and 1 atm).</p> <p>We prepared five files (comma-separated values) in total, which are hourly aerosol concentrations measured using the SMPS and WIBS, Particle size distributions measured using the SMPS, Particle size distributions measured using the WIBS, Aerosol chemical compositions, and Ice nucleating particle concentrations during the research cruise of MR16-09 leg3. Each file includes the header part to describe the aerosol data including the date and time in UTC, and the positions of the R/V.</p> <p>The associated paper discusses some aspects of data treatment and questions regarding to the methods employed in this study.</p>
Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean
<p>This dataset contains processed model data used in</p> <p>Tesdal, J.-E., A. MacGilchrist, G., Beadling, R. L., Griffies, S. M., Krasting, J. P., & Durack, P. J. (2023). Revisiting interior water mass responses to surface forcing changes and the subsequent effects on overturning in the Southern Ocean. Journal of Geophysical Research: Oceans, 128, e2022JC019105. <a href="https://doi.org/10.1029/2022JC019105">https://doi.org/10.1029/2022JC019105</a>.</p> <p>The above publication uses two coupled climate models (AOGCMs), GFDL-CM4 and GFDL-ESM4, to assess the impact of perturbations in wind stress and Antarctic ice sheet melting on the Southern Ocean meridional overturning circulation (SO MOC) and associated water mass transformations (WMT).</p> <p>The attached archive includes netCDF files to recreate all figures and tables in <a href="https://doi.org/10.1029/2022JC019105">Tesdal et al. (2023)</a>, including overturning streamfunction (moc), volume storage change (dVdt), surface water mass transformation (swmt), meridional volume transports (mvt) zonal mean potential density referenced to 2000 dbar (sigma2) and mixed layer depth (mld). These variables are derived from preindustrial control (piControl) and idealized perturbation runs of Antarctic melting (Antwater), wind stress (Stress), as well as the combination (Antwater-Stress) using the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) protocol.</p> <p>The FAFMIP protocol (<a href="https://doi.org/10.5194/gmd-9-3993-2016">Gregory et al., 2016</a>) involves adding perturbations to the surface fluxes that are computed within the atmosphere-ocean general circulation model (AOGCM) from the state of the system (<a href="https://doi.org/10.1029/2005JC003421">Lowe and Gregory, 2006</a>; <a href="https://doi.org/10.1088/1748-9326/9/3/034004">Bouttes and Gregory, 2014</a>). The perturbations in this dataset were technically added as a flux adjustment similar to that formerly used in AOGCMs (<a href="https://doi.org/10.1007/BF01053472">Sausen et al., 1988</a>).</p> <p>The data files contain processed model output and do not include any raw model output. Model data from the piControl runs of CM4 and ESM4 are available at the Earth System Grid Federation archive (<a href="https://esgf-node.llnl.gov/projects/cmip6">https://esgf-node.llnl.gov/projects/cmip6</a>). The forcing fields (perturbations) used in the perturbation experiments can be found at <a href="https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans">https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans</a>. Python scripts and Jupyter notebooks to reproduce the tables and figures can be accessed at <a href="https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans">https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans</a>.</p> <p><strong>Contents</strong>:</p> <ul> <li>Overturning streamfunction (moc)</li> <li>Volume storage change (dVdt)</li> <li>Surface water mass transformation (swmt)</li> <li>Meridional volume transports (mvt) </li> <li>Zonal-mean potential density referenced to 2000 dbar (sigma2)</li> <li>Mixed layer depth (mld) </li> <li>Antarctic shelf mask</li> <li>Static grid files</li> </ul> <p><strong>Models</strong>:</p> <ul> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>Preindustrial control (piControl)</li> <li>Experiment with a 0.1 Sv freshwater perturbation entering at the Antarctic coast (Antwater)</li> <li>Experiment with zonal and meridional wind stress perturbations (Stress)</li> <li>Experiment with combined perturbation of both Antarctic melting and wind stress (Antwater-Stress)</li> </ul> <p><strong>NetCDF file name structure</strong>:<br> <model>_<simulation>_<member_id>_<domain>_<time_period>_<variable>.nc</p> <ul> <li>model: CM4, ESM4</li> <li>simulation: control, antwater, stress, antwaterstress</li> <li>member_id (only for antwater, stress, antwaterstress): 251, 290, 332 (CM4), 101, 151, 201 (ESM4)</li> <li>domain: global, so</li> <li>time_period: yyyy-yyyy (first year to last year)</li> <li>variable: e.g., moc_rho2_online_lores, dVdt_rho2_online_lores, swmt_sigma2_005, sigma2_jmd95_zmean</li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.