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234 results for “Global Ocean”
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>
Insights into the Major Processes Driving the Global Distribution of Copper in the Ocean from a Global Model
<p>Annual output netcdf output file of the NEMO/PISCES Cu model on the ORCA2 grid. Reference simulation described and discussed in Richon, C. and Tagliabue, A. Insights into the Major Processes Driving the Global Distribution of Copper in the Ocean from a Global Model, Global Biogeochemical Cycles, 2019</p>
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
TSSCXG-17: Global Gridded Dataset of Surface Ocean pCO2 and Air-Sea CO2 Flux (1993-2020)
<p>This dataset presents a global gridded reconstruction of the partial pressure of CO2 (pCO2) in the surface ocean and the corresponding air-sea CO2 flux, covering the period from 1993 to 2020. Developed to enhance understanding of climate change and the global carbon cycle, this dataset addresses gaps in oceanic carbon flux data through innovative machine learning techniques. The reconstruction process integrates in situ observations, satellite data, and reanalysis products, employing a three-step algorithm involving dimensionality reduction, clustering, and regression.</p>
Wave Parameters - North Atlantic Ocean - Period 2091-2100 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: ACCESS
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <p>ACCESS (Australian Community Climate and Earth System Simulator)</p> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021
<p>This archive is to accompany the article:</p> <p>A derivative-free optimisation method for global ocean biogeochemical models,<br> Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala.</p> <p>The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610</p> <p>The original source code of MOPS were from the Supplement of Kriest et al. (2017).<br> The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm.</p> <p>In this archive:</p> <p>Supplement/Configurations/OxfordMOPS_Configs contains:<br> - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with<br> the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.)<br> - RunCode (runscripts to run the MOPS model with the TMM)<br> - TWIN_Configs (JSON files required by each optimisation experiment carried out).</p> <p>Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out.</p> <p>Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.</p>
Wave Parameters - North Atlantic Ocean - Period 2036-2045 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10.</li> <li>northernmost latitude = 42.</li> <li>westernmost longitude = -70.</li> <li>easternmost longitude = -5.</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
Wave Parameters - North Atlantic Ocean - Period 2081-2099 - RCP8.5 - MODEL: Wavewatch III - Global Driver: HadGEM
<p><strong>Wave Model:</strong></p> <ul> <li>WAVEWATCH_III - version number 5.16</li> </ul> <p><strong>Global driver: </strong></p> <ul> <li>HadGEM (Hadley Centre Global Environmental Model)</li> </ul> <p><strong>Variables:</strong></p> <ul> <li>Significant Wave Height</li> <li>Mean period, peak frequency</li> <li>Mean wave direction</li> <li>0.25° x 0.25° horizontal resolution - 3h time resolution</li> </ul> <p><strong>Region: </strong></p> <ul> <li>southernmost latitude = 10°</li> <li>northernmost latitude = 42°</li> <li>westernmost longitude = -70°</li> <li>easternmost longitude = -5°</li> </ul> <p><strong>360-day calendar</strong></p> <p><strong>NetCDF format</strong></p>
High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change (Dupont et al., GBC)
<p>Files used to make the analysis in the paper "High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change" (Dupont et al., accepted in GBC)</p> <p>- HTL_LTL_figures.ipynb is the python notebook in which are computed the different terms to make the figures of the paper </p> <p>- histrcp85.1-PISAPE-N-OW** and piCtrl2-PISAPE-N-OW** files contain the raw outputs of the one way (OW) simulation</p> <p>- histrcp85.1-PISAPE-N-TW* and piCtrl2-PISAPE-N-TW* files contain the raw outputs of the two way (TW) simulation</p> <p>- all files ending with *rmp_f.nc/ *regrid.nc/ *f20.nc are regridded files to make maps used in the paper. More details can be found in the python noteboook (briefly, dAT_* = change in active export, dDIC_*= change in dissolved inorganic carbon, dEPC200_* = change in carbon export at 200m depth, OW/TW_<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">dBMape</a>* = OW/TW change in small high trophic levels biomass, OW/<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">TW_dBM</a>meszo* = OW/TW change in mesozooplankton biomass)</p> <p>- <a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/egestt2_2.nc">egestt2_2.nc</a>, excrett2_2.nc and graztt2_2.nc are the outputs of egestion, excretion and grazing terms used to compute the active export (AT). </p>
Data for: Increasing hypoxia on global coral reefs under ocean warming
<p><span class="s1">Ocean deoxygenation is predicted to threaten marine ecosystems globally. However, current and future oxygen concentrations and the occurrence of hypoxic events on coral reefs remain underexplored. Here, using autonomous sensor data to explore oxygen variability and hypoxia exposure at 32 representative reef sites, we reveal that hypoxia is already pervasive on many reefs. 84% of reefs experienced weak to moderate (≤153 to ≤92 μmol O<sub>2</sub> kg<sup>-1</sup>) hypoxia and 13% experienced severe (≤61 μmol O<sub>2</sub> kg<sup>-1</sup>) hypoxia. Under different climate change scenarios based on 4 Shared Socioeconomic Pathways (SSPs), we show that projected ocean warming and deoxygenation will increase the duration, intensity, and severity of hypoxia, with more than 94% and 31% of reefs experiencing weak to moderate and severe hypoxia, respectively, by 2100 under SSP5-8.5. This projected oxygen loss could have negative consequences for coral reef taxa due to the key role of oxygen in organism functioning and fitness.</span></p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
Global continental discharge estimates from ocean mass balance
<p>These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. </p> <p>The ocean mass balance is obtained from these components:</p> <p>dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. </p> <p>E-P: Here, two methods are used:</p> <p>1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P</p> <p>2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products.</p> <p>The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename. </p>
Remote versus local impacts of energy backscatter on the North Atlantic SST biases in a global ocean model
<p>The data and scripts used to generate the figures in the manuscript "Remote versus local impacts of energy backscatter on the North Atlantic SST biases in a global ocean model".</p>
Data for: Increasing hypoxia on global coral reefs under ocean warming
Open the record for dataset details and reuse information.
Global variation in zooplankton niche divergence across ocean basins
Open the record for dataset details and reuse information.
Dataset for Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study
<p>Gridded dataset of gelatinous zooplankton (GZ) biomass (mg C m<sup>-3</sup>) and numeric density (individuals m<sup>-3</sup>), time-averaged, in a 1-degree grid. Data are separated by phyla: Cnidaria, Ctenophora, and Chordata (pelagic tunicates). Original data compiled as part of the Jellyfish Database Initiative Project (JeDI; Condon et al. 2015, doi:10.1575/1912/7191) and converted to carbon biomass units for Lucas et al. 2014.</p> <p>Cnidarian additions to this dataset include records from the northern California Current (Brodeur et al., 2014) and Gulf of Mexico (Robinson et al., 2015). Chordata additions include salps from the Bermuda Atlantic Time Series (BATS; Stone & Steinberg, 2014), Western Antarctic Peninsula (WAP; Steinberg et al., 2015), and Southern Ocean, from KRILLBASE (Atkinson et al., 2017). Note that we excluded the KRILLBASE records from the WAP region that to prevent double-counting. See Methods in Luo et al. (2020) for details on biometric conversions to carbon biomass.</p> <p>Data were averaged by time (season, then year), and then within each 1-degree grid cell.</p> <p> </p> <p> </p> <p>Code for the model using this dataset is available at: <a href="https://github.com/jessluo/gz_biogeochem_pub">https://github.com/jessluo/gz_biogeochem_pub</a></p> <p> </p> <p><strong>Luo, Jessica Y.</strong>, Condon, R. H., Stock, C. A., Duarte, C. M., Lucas, C. H., Pitt, K. A., & Cowen, R. K. (2020). Gelatinous zooplankton‐mediated carbon flows in the global oceans: A data‐driven modeling study. <em>Global Biogeochemical Cycles</em>, 34, e2020GB006704. <a href="https://doi.org/10.1029/2020GB006704">https://doi.org/10.1029/2020GB006704</a></p>
Effect of temperature on extracellular enzymatic activities in the global ocean
<p>Supplementary data for:</p> <p>Ayo B, Abad N, Artolozaga I, Azua I, Baña Z, Unanue M, Gasol JM, Duarte CM & Iriberri J.</p> <p>Imbalanced nutrient recycling in a warmer ocean driven by differential response of extracellular enzymatic activities.</p> <p>Accepted for publication in Global Change Biology.</p>
Global oceans self-consistent spatial discretization mesh
<p>Global oceans<br> =============</p> <p>An unstructured mesh spatial discretisation of the global oceans.</p> <p>This is stored in an unstructured VTU file defined by the visualisation toolkit VTK [2].</p> <p>A state PVSM file for Paraview [3] is also provided to reproduce visualisations shown in [1]. Note that Paraview requires absolute pathnames, so it may be necessary to edit file references to the VTU file in this state file.</p> <p>Files<br> -----</p> <p>- GlobalOceans.vtu<br> - GlobalOceans.pvsm</p> <p>Author<br> ------</p> <p>- Dr Adam S. Candy <a.s.candy@tudelft.nl>, <candy@cantab.net><br> - Technische Universiteit Delft<br> - Imperial College London</p> <p>References<br> ----------</p> <p>[1] Candy, A.S., 2016. A consistent approach to unstructured mesh generation for geophysical models. In review. Preprint available at https://arxiv.org/abs/1703.08491.<br> [2] The Visualization Toolkit (VTK), version 5.10.1. URL: http://www.vtk.org.<br> [3] Paraview, version 4.3.1. https://www.paraview.org.</p>
Stronger oceanic CO2 sink in eddy-resolving simulations of global warming: simulations outputs
<p>This repository contains 1) the air-sea CO2 flux and the Dissolved Inorganic Carbon (DIC) distribution in idealized simulations run at different resolutions 2) the terms of the DIC budget for each simulation integrated temporally on the all simulation and integrated spatially on different boxes of the domain. These data are used in the article "Stronger oceanic CO$_2$ sink in eddy-resolving simulations of global warming" published in Geophysical Research Letters for producing Figs. 2, 3, 4. Refer to this paper for details about the data.</p>
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.