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955 results for “Ocean data”
Data for "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"
<p>Processed model data for article "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"</p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Data from: Intense upper ocean mixing due to large aggregations of spawning fish
<p>This dataset includes data collected during the cruise REMEDIOS-TL in the Ría de Pontevedra (NW Iberia) at station P2 (42.357°N, 8.773°W) from 29 June to 18 July 2018 onboard of the Research Vessel Ramón Margalef belonging to the Spanish Institude of Oceanography. The REMEDIOS project is funded by the Spanish Ministry of Economy and Inno-445vation under the research project REMEDIOS (CTM2016-75451-C2-1-R) and leaded by Beatriz Mouriño Carballido.</p> <p>The archived data are described in a manuscript entitled "Intense upper ocean mixing due to large aggregations of spawning fish" by Fernández Castro et al. published in Nature Geoscience:</p> <p>Fernández Castro, B., Peña, M., Nogueira, E. <em>et al.</em> Intense upper ocean mixing due to large aggregations of spawning fish. <em>Nat. Geosci.</em> <strong>15, </strong>287–292 (2022). https://doi.org/10.1038/s41561-022-00916-3</p> <p>The manuscript presents evidence that night-time aggregations of anchovies produce intense ocean turbulence and mixing. All the data needed to support the conclusions of the article are included in this dataset.</p> <p>The dataset includes:</p> <p>- Microstructure profiles collected with a MSS Sea&Sun profiler during the three intensive samplings of the cruise (I01, I02, I03)</p> <p>- Ocean currents measured with a bottom moored RD Instruments acoustic Doppler profiler (ADCP, 300Khz) for the duration of the cruise</p> <p>- Acoustic backscatter from a ship-borne echosounder Simrad EK80 for the frequencies 18, 38, 70, 120 and 200 KHz and the three intensive samplings of the cruise (I01, I02, I03)</p> <p>- European anchovy (Engraulis encrasicolus) egg counts from plankton hauls samplings.</p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"
<p>This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".</p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Data from: Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago
<p>This archive contains data produced in a study of the vegetation trajectories of Ogasawara Islands in 77 years related to following article:</p> <p>Ohashi, H., Kato, H., Murao, M., Kato, H., Kawakami, K., Kurokawa, H., Oguro, M., Kimura, F., Niiyama, K., Matsui, T., and Shibata, M. (2024) Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago. <em>Applied Vegetation Science</em>, 27 (1), e12767. <a href="https://doi.org/10.1111/avsc.12767">https://doi.org/10.1111/avsc.12767</a></p> <p> </p> <p><strong>Archive contents</strong><br>The archive contents are organized into five parts, each stored as a .zip compressed file.</p> <p><strong>X1_tif_original_vegmap_scan_georeference</strong></p> <p>Scanned and georeferenced original vegetation maps in GeoTiff format, which was drawn in 1935, scanned at 300 dpi. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>kitanoshima_isl_WGS84.tif<br>mukojima_isl_WGS84.tif<br>yomejima_isl_WGS84.tif<br>ototojima_isl_WGS84.tif<br>anijima_isl_WGS84.tif<br>nishijima_isl_WGS84.tif<br>chichijima_isl_WGS84.tif<br>hahajima_isl_WGS84.tif<br>mukohjima_isl_WGS84.tif<br>kitaiwoto_isl_WGS84.tif<br>iwoto_isl_WGS84.tif</em></p> <p> </p> <p><strong>X2_shp_vegmap</strong></p> <p>Shapefile of the geospatial polygon data of vegetation map of Ogasawara Islands surveyed in 1935, and stored as a .zip compressed file. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.dbf<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.prj<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shp<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shx<br>attribute_ForSect_code_en.csv<br>attribute_Veg_name_en.csv<br>metadata_vegmap_shp_ogasawara1935_en.csv</em></p> <p>Following files includes Japanese character (which may corrupt in non-Japanese environment):</p> <p><em>attribute_ForSect_jp.csv<br>attribute_Veg_name_jp.csv<br>metadata_vegmap_shp_ogasawara1935_jp.csv</em></p> <p> </p> <p><strong>X3_tif_vegmap_converted_from_shp</strong></p> <p>Rasterized data of polygon data of vegetation map for analysis. Coordinate reference system was set at JGD2000 / Japan Plane Rectangular CS XIV (EPSG: 2456)</p> <p>This directory includes:</p> <p><em>vegmap_1935.zip (compressed “vegmap_1935.tif (0.7GB)”)<br>vegnap_1979.zip (compressed “vegmap_1979.tif (1.5GB)”)<br>vegmap_2011.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>islcode_raster.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>attribute_integratedveg_ecoltype.csv<br>attribute_vegid_1935.csv<br>attribute_vegid_1979.csv<br>attribute_vegid_2011.csv</em></p> <p> </p> <p><strong>X4_scanned_image_vegdata</strong></p> <p>Scanned images of original vegetation data in 1935.</p> <p>The directory includes:<br><em>vegetation_survey_sheet_1.pdf<br>vegetation_survey_sheet_2.pdf</em><br><em>vegetation_survey_sheet_3.pdf</em></p> <p> </p> <p><strong>X5_digitized_vegdata</strong></p> <p>Digitized vegetation data.</p> <p>The directory includes:<br><em>plot_species_abundance_matrix_v0.csv<br>plotinfo_v0.csv<br>attribute_Species_en_v0.csv</em></p> <p>Following file includes Japanese character (which may corrupt in non-Japanese environment)<br><em>attribute_Species_jp_v0.csv</em><br> </p> <p><strong>X6_code_for_analysis</strong></p> <p>Tentative.</p> <p> </p> <p>このアーカイブには、小笠原諸島の77年間の植生の変遷(1935年、1979年、2012年)に関するデータが含まれています。</p> <p> </p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
Supplementary Data for "The history of Cenozoic carbonate flux in the Atlantic Ocean constrained by multiple regional carbonate compensation depth reconstructions"
<p>The files on this site accompany the paper:</p> <p>Dutkiewicz, A. And Müller, R.D., in review, The history of Cenozoic carbonate flux in the Atlantic Ocean constrained by multiple regional carbonate compensation depth reconstructions, Geochemistry, Geophysics, Geosystems.</p> <p>There are two zipped file archives:</p> <p>1) backtracked_sites.zip</p> <p>This archive contains two directories of backtrack site files, one for the North Atlantic and one for the South Atlantic.</p> <p>Each directory contains a set of files listing, by site:</p> <p>age(Ma), compacted_depth (observed)(mbsf), compacted_thickness (observed)(m), decompacted_thickness(m), decompacted_density(g/cm3), water_depth(m), tectonic_subsidence (since formation of crust)(m), decompacted_depth(mbsf) dynamic_topography(m) lithology</p> <p>The lithology classification follows the lithology classes defined in Muller et al. (2018).</p> <p>A second set of files contains:</p> <p>age(Ma), depth(mbsf), paleowaterdepth(m), dry_bulk_density(g/cm3), DLSR(m/my), carbonate(weight_%) CAR(mg/cm2/kyr)</p> <p>DLSR=decompacted linear sedimentation rate<br> CAR=carbonate accumulation rate</p> <p>2) regional_Cenozoic_carbonate_thickness_grids.zip</p> <p>This archive contains 3 folders with grids for modelled Cenozoic carbonate thicknesses for the South Atlantic, central North Atlantic and northern North Atlantic. They can be viewed with netcdf viewers like panoply, or plotted using the Generic Mapping Tools. The workflow for creating these grids can be found on GitHub:</p> <p>https://github.com/EarthByte/CarbonateSedimentThickness</p> <p><br> This site also contains a spreadsheet entitled "Dutkiewicz_Muller_G3_2022_model_data_summary.xlsx"</p> <p>It contains our model outputs including regional decompacted carbonate sediment volumes and thicknesses, depositional areas, carbonate carbon fluxes and carbonate compensation depths for the northern and central North Atlantic and South Atlantic.</p> <p>A video entitled "compacted_carb_thick_atlantic_66-0Ma.mp4" shows the Cenozoic evolution of carbonate sediment thickness in the Atlantic Ocean.</p> <p><br> References:</p> <p>Spasojevic, S., & Gurnis, M. (2012). Sea level and vertical motion of continents from dynamic earth models since the Late Cretaceous. AAPG bulletin, 96(11), 2037-2064. https://doi.org/10.1306/03261211121</p> <p>Müller, R. D., Cannon, J., Williams, S. and Dutkiewicz, A., 2018, PyBacktrack 1.0: A Tool for Reconstructing Paleobathymetry on Oceanic and Continental Crust, Geochemistry, Geophysics, Geosystems, 19, 1898-1909, https://doi.org/10.1029/2017GC007313.</p> <p><br> </p>
Ocean ambient noise data on the Chukchi Plateau from August 2018 to October 2019
<p>The noise spectrum level under different sea ice conditions, and monthly and hourly ambient noise level on the Chukchi Plateau from August 1st, 2018 to October 31st, 2019.</p>
Data associated with "The Role of Magma Oceans in Maintaining Surface Water on Rocky Planets Orbiting M-Dwarfs"
<p>The data included here involves all simulations outlined in the manuscript "The Role of Magma Oceans in Maintaining Surface Water on Rocky Planets Orbiting M-Dwarfs", submitted to MNRAS. Files are divided between models with and without a basal magma ocean, as well as different host stars, different magma ocean saturation limits, and different fringe/sensitivity analysis checks. Files are further sub-divided based on our studied parameter space: each is for a single orbital distance within the habitable zone, and a single initial water inventory. "MO_only" files correspond to the concurrent surface magma ocean/runaway greenhouse phase, while "combined_MO_cycling" files correspond to the deep-water cycling period.</p>
CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>
Data used in "Storms regulate Southern Ocean summer warming"
<p>The data included in this repository was used to generate the figures in the submitted manuscript "Storms regulate Southern Ocean summer warming" by du Plessis and co-authors.</p> <p><strong>Abstract: </strong>"Sea surface temperature (SST) in the Southern Ocean (SO) is the fingerprint of ocean heat uptake and critical for air-sea interactions. However, SO SST is biased warm in climate models, reflecting our limited understanding of the mechanisms that set its magnitude and variability. An important factor driving SST variability is synoptic-scale weather systems, such as storms, yet their impacts are difficult to directly observe. Using in-situ observations from underwater and surface robotic vehicles in the subpolar SO, we show evidence that storms regulate the summer evolution of SST through altering the mixed layer effective heat capacity and entraining colder water from below. Through these mechanisms, we determine that interannual variations in SO SST reflect changes in storm intensity and prevalence, which, in turn, are driven by the Southern Annular Mode. Our results demonstrate a causal link between storm forcing and lower frequency SST variability, which has implications for addressing SST biases in climate models."</p> <h3><strong>Datasets</strong></h3> <p>The observations in this study were made as a part of the SOSCEx-STORM experiment, which fits into the larger observational programme the Southern Ocean Seasonal Cycle Experiment (Swart et al. 2012). SOSCEx-STORM undertook a twinned deployment of a Wave Glider and a profiling Slocum glider which were piloted in conjunction with each other. The platforms were deployed and retrieved from the R/V Agulhas II at 54°S, 0°E, south of the Polar Front, and sampled together between 20 December 2018 and 8 March 2019. </p> <p><strong>Slocum glider data<br></strong>The glider was equipped with a continuously pumped Seabird Slocum Glider CTD, which was processed with the GEOMAR MATLAB toolbox and vertically gridded to 1 m depth intervals. </p> <p>Relevant data name: <code>slocum_grid_processed.nc</code></p> <p><em>Slocum glider Microstructure data:</em><br>The Webb Teledyne G2 Slocum glider was equipped with a Rockland Scientific Microstructure Profiler (MicroRider). The MicroRider was equipped with two piezo-electric accelerometers and two air-foil shear probes oriented orthogonally. Microstructure data was only collected during the glider climbs to prolong battery life and obtain dissipation estimates as close to the surface as possible. See Nicholson et al. (2022) for details of the MicroRider processing. The mixing layer depth (XLD) was estimated as in Brainnerd and Gregg et al. (1995).</p> <p>Disspitation data name: <code>slocum_eps.nc</code><br>Mixing layer depth data name:<em> </em><code>slocum_xld.nc</code></p> <p><em>Slocum glider SST data:</em> Initial data processing removed temperature data from the upper 2 m during the glider climb phase, and so to obtain an SST value from the Slocum glider temperature profiles, we calculated the median value between 0.5 m and 10 m depth for each dive. </p> <p>Slocum SST data name: <code>slocum_sst_median_10m.nc</code></p> <p><strong>Wave Glider data<br></strong>The Liquid Robotics SV3 Wave Glider was fitted with an Airmar WX-200 Ultrasonic Weather Station mounted on a mast at 0.7 m above sea level, providing wind speed measurements at a rate of 1 Hz, averaged into 1-hour bins. The wind measurements were corrected to a height of 10 m above sea level. Note that the Airmar WX-200 weather station of the Wave Glider was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data. </p> <p>Wave Glider data name: <code>WG_era5_1h_processed_28Aug2022.nc</code></p> <p><strong>NOAA OI SST and sea ice<br></strong>Monthly SST data was obtained from the NOAA optimum interpolation (OI) SST V2 product, which uses both in-situ and satellite data from November 1981 to January 202329. Data is provided by the National Centers for Environmental Prediction and made available on a 1◦ grid. All SST data where co-located sea ice concentration was above 0 has been removed from this analysis. NOAA OI SST and sea ice were obtained from<a href="https://psl.noaa.gov/data/gridded/data.noaa"> https://psl.noaa.gov/data/gridded/data.noaa</a><strong>.<br></strong></p> <p>Datasets: <code>sst.mnmean.nc</code>, <code>icec.mnmean.nc</code>, <code>lsmask.nc</code></p> <p><strong>Storm tracking dataset</strong><br>To track storm trajectories, we used storm tracks contained in monthly files for the Southern Ocean identified and used in the JGR-Oceans publication:</p> <p>Lodise, J., Merrifield, S. T., Collins, C., Rogowski, P., Behrens, & J., Terrill,E, (In Review). Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights from Satellite Radar Altimeter. Journal of Geophysical Research: Oceans. <a href="https://doi.org/10.1029/2022JC018925" rel="nofollow">https://doi.org/10.1029/2022JC018925</a></p> <p>Data can be accessed at <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker</a> </p> <p>All Southern Ocean storm locations can be found at: <code>ec_centers_1981_2020.nc</code></p> <p><strong>Storm radius datasets</strong></p> <p>ERA5 data of air-sea heat flux, 10 m wind speed for all hourly instances where a storm center was within 1000 km of the gliders (Figs. 2 and 3, Extended Data Figs. 2 and 4) </p> <p>Datasets of <code>combined_storms_{variable}_no_ice.nc</code> are data of air-sea heat flux and 10 m wind speed for all instances for 1000 km x 1000 km box around each storm center during summer months from 1981-2020 (> 570,000) used in Figs. 4 and 5. These data are considerably large (combined total >40 GB). Please contact me at marcel.du.plessis@gu.se to find a suitable way to share the data.</p> <p>The data was processed as follows:</p> <p>1. Download storm centers from <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main</a></p> <p>2. Run process-lodise-storm-centers.ipynb to save all the cyclone center data as '<code>ec_centers_1981_2020.nc</code>'</p> <p>3. <em>Run filter-cyclone-centers.ipynb</em>:<br> - cut all cyclone centers south of 40S<br> - only choose cyclone centers in DJF<br> - calculates minimum distance to land for each storm<br> - saves a dataset called '<code>ec_centers_1981_2020_with_min_dist_to_land.nc</code>' <br> - contains the variable distance to land for all filtered cyclones<br> - we do this step because it takes about an hour to run<br> - remove cyclones within 500 km from land<br> - remove cyclones less than 24 hours<br> - we are left with 11005 storms<br> - data saved as 'ec_centers_1981_2020_500km_from_land_filtered_24hours'</p> <p>4. Run storm_processing_cutouts.ipynb (this took several days)<br> - loads <code>ec_centers_1981_2020_500km_from_land_filtered_24hours.nc</code><br> - runs through each summer, <em>processes storm_localization.py </em><br> - saves data as <code>storms_{variable}_{year}.nc</code><br> - e.g. <code>storms_winds_1981.nc</code> - winds for all 1000 km radius cyclones in DJF 1981/82</p> <p>5. <em>combine_storm_years.ipyn</em>b <br> - creates datasets for each variable that has storms for all year called <code>combined_storms_{variable}.nc</code></p> <p>6. <em>remove_sea_ice_from_storms.ipnyb </em><br> - makes data nan where sea ice is present in each cyclone<br> - creates datasets called <code>combined_storms_{variable}_no_ice.nc</code></p> <p>7. <em>seasonal-means-storms.ipynb</em><br> - calculates the mean for all storms for each year <br> - saves them one dataset: <code>combined_storms_{variable}_seasonal_means.nc'</code></p> <p><strong>EN4 mixed layer depths</strong><br>We use the EN4 database of quality controlled temperature and salinity profiles from 2004 to 2022 to produce our MLD for the interannual analysis (Good et al. 2013). We use the profiles that contain the Cheng et al. (2014) XBT corrections and Gouretski and Cheng (2020) MBT corrections. We limit the data intake to 2004 as this marks the beginning of the Argo period. All under-ice profiles are removed. We calculate the MLD for each individual profile using the density threshold of de Boyer Montegut et al. (2004) where the density value first exceeds the 10 m reference value by 0.03 kg m-3. We then determine the median MLD value for each month within 3 x 3 degree grid cells, then obtain a mean value for each DJF season per 3 x 3 degree grid cell. </p> <p>Relevant data name: <code>en4_monthly_mixed_layer_depth_median.nc</code></p> <p><strong>Southern Ocean Fronts<br></strong>Position of the Subantarctic Front and Polar Front are from: </p> <div>Sokolov, S. and Rintoul, S.R., 2009. Circumpolar structure and distribution of the Antarctic Circumpolar Current fronts: 1. Mean circumpolar paths. <em>Journal of Geophysical Research: Oceans</em>, <em>114</em>(C11).</div> <div> </div> <div>Relevant data name:<em> </em><code>ACCfronts.csv</code><strong> </strong></div> <div> </div> <div><strong>ERA5<br></strong>The ERA5 data provided was by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>.</div> <div> </div> <div>The various datasets used in this study are described below:<strong><br></strong><br>Wind speed, air temperture, dew point temperature for the observational period: <code>ds_era5_vars.nc</code><br>Fluxes for the observational period: <code>ds_era5_flux.nc</code><br>Wind speed, air temperture, dew point temperature, fluxes for the case study day in Figure 3: <code>era5_case_study.nc</code><br>Mean winds and fluxes for each DJF period between 1981 and 2022.: <code>mean_summer_winds_fluxes_1981_2023.nc</code></div> <div>Monthly-mean 10 m wind speed and mean sea level pressure during SOSCEx-Storm: <code>201812_month_avg_wind_mslp.nc</code> and <code>20190102_month_avg_wind_mslp.nc</code></div> <div> </div> <div><strong>Cloud Top Pressure<br></strong>The MODIS Level-2 Cloud product was obtained from <a href="http://dx.doi.org/10.5067/MODIS/MYD06_L2.061">http://dx.doi.org/10.5067/MODIS/MYD06_L2.061</a> (Fig. 3).<strong><br></strong></div> <div> <p>Processed dataset: <code>modis_ctt_ctp.nc</code></p> <p><strong>Southern Annular Mode<br></strong>The SAM is the principal mode of variability in the atmospheric circulation of the Southern Hemisphere mid-and-high latitudes. We use the Marshall SAM Index from station-based observations of the zonal pressure difference between the latitudes of 40◦S and 65◦S.</p> <p>SAM Index was retrieved from <a href="https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based">https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based</a>.</p> <p>SAM dataset: <code>ds_sam.nc</code></p> <p> </p> </div>
Data of Figs. 6 and 8 in Legendre (2024, Jigsaw puzzle of the interwoven biologically-driven ocean carbon pumps. Progress in Oceanography)
<p><span>Figure 6. Depth variations of carbon-pump components considering only gravitational POC export (i.e., Forg calculated using eqs. 10 and 18 and eq 1 of Martin et al., 1987, with b = 0.86): carbon fluxes at different depths z, and cumulative carbon fluxes from zexp to z.</span></p> <p><span>Figure 8. Cumulative carbon-pump fluxes from Fig. 6 are added together showing that [Forg(z) + ∑FseqDIC + ∑FupDIC] = [Forg(z) + ∑FwcDIC] = Fexp. The different curves are added together (a) from left to right, and (b) from the centre to the left and the right.</span></p>
Supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation
<p>These are supplement data for the article "The impact of OTU sequence similarity threshold on diatom-based bioassessment: A case study of the rivers of Mayotte (France, Indian Ocean)", in preparation</p> <p>The folowing files are available:</p> <ul> <li>Supplement 1. Map of Mayotte with the sampling sites and the rivers.</li> <li>Supplement 2. <em>rbcL</em> primers, reaction mixture, and conditions used for the PCR of the 312-bp <em>rbcL</em> fragment. The information provided is for a single reaction with a final volume of 25µL.</li> <li>Supplement 3. The 20 fastq files containing the demultiplexed DNA reads.</li> <li>Supplement 4. Number of sequence reads for each sample before and after the trimming procedure.</li> <li>Supplement 5. The 20 OTU lists, corresponding to the 20 SSTs, including the number of DNA reads within the 90 samples and their assigned taxonomy.</li> <li>Supplement 6. Sampling site description with sample codes, names of rivers, year, number of raw DNA reads and GPS coordinates.</li> <li>Supplement 7. Values and summary statistics for the environmental variables.</li> <li>Supplement 8. The script used in Mothur for the bioinformatic analysis from trimming to the used OTU lists.</li> </ul> <p> </p>
Data in support of manuscript submitted to JGR-Oceans on dredging and estuarine dynamics in the Hudson River
<p>Data used to create the figures shown in a manuscript submitted to JGR-Oceans (special issue for the Physics of Estuaries and Coastal Seas meeting, 2018) titled "Response to channel deepening of the salinity intrusion, estuarine circulation, and stratification in an urbanized estuary" (as Matlab .mat files). Also included is a matlab script (makeFigs_salChange_upload.m) that loads the data files and creates the figures, as well as figure files (*.png). </p>
Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'
<p>Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.</p>
CESM input data for running CESM historic with CAM6 and CLM5 (no ocean) in docker container
<p>CESM docker container for HIST_CAM60_CLM50%BGC_CICE%PRES_DOCN%DOM_MOSART_CISM2%NOEVOLVE_SWAV compset and resolution f19_g17 using <a href="https://bioconda.github.io/recipes/cesm/README.html">bioconda cesm docker</a> as a base image.</p>
Codes and source data for "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies"
<p>This repository contains the MATLAB (R2022b) codes (*.m files) and the figure source data (.mat files) for the paper "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies" (He et al., 2024). </p> <p>For installation of MatLab, please refer to: https://au.mathworks.com/products/matlab.html</p> <p>For queries about this repository and its contents, please contact Dr. Qingyou He (qyhe@scsio.ac.cn).</p> <p>%% Fig1.m: For plotting main Fig.1.<br>%% Fig2.m: For plotting main Fig.2.<br>%% Fig3.m: For plotting main Fig.3.<br>%% Fig4.m: For plotting main Fig.4.<br>%% Fig5.m: For plotting main Fig.5.<br>%% Fig6.m: For plotting main Fig.6.</p> <p>%% Data Fig1.mat: For main Fig.1.<br>%% Data Fig2.mat: For main Fig.2.<br>%% Data Fig3.mat: For main Fig.3.<br>%% Data Fig4.mat: For main Fig.4.<br>%% Data Fig5.mat: For main Fig.5.<br>%% Data Fig6.mat: For main Fig.6.</p> <p><br>References:</p> <p><span>He, Q., W. Zhan, M. Feng, Y. Gong, S. Cai, and H. Zhan (2024), Common occurrences of subsurface heatwaves and cold spells in ocean eddies, <em>Nature</em>, <em>634</em>, 1111–1117, doi:10.1038/s41586-024-08051-2.</span></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.