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283 results for “Ocean Model”
The Impact of a Pressurized Regional Sea or Global Ocean on Stresses on Enceladus: Numerical models
<p>Comsol Multiphysics models solving the stress field in Enceladus' ice shell when a regional sea of global ocean is pressurized.</p> <p>Model parameters are included as part of the file name according to the template EnceladusT<em>t</em>D<em>d</em><em>Label</em>.mph with</p> <ul> <li><em>t</em> is the ice shell thickness</li> <li><em>d</em> is the thickness of the south polar sea or indentation</li> <li><em>Label </em>indicates model configuration <ul> <li><em>Fixed</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a no-slip boundary condition</li> <li><em>Roller</em>: The base of the ice shell (outside the south polar sea) is in contact with the core with a free-slip boundary condition</li> <li><em>Ocean</em>: The base of the ice shell is floating with a constant pressure condition; there is a single indentation at the South pole</li> <li><em>North</em>: The base of the ice shell is floating with a constant pressure condition; there are indentations at both poles, with the north pole indentation having half the thickness of the South pole indentation</li> </ul> </li> </ul> <p>There are two solved datasets in each model. The first uses a default value of the ocean angle (40°). The second results from a parameter sweep in which the sea angle varies systematically in increments of 2°.</p>
Model Data for "Increased Ocean Heat Convergence into the High Latitudes with CO2-Doubling Enhances Polar-Amplified Warming."
<p>This is the data repository for the following published study:</p> <p>Singh HA, Rasch PJ, and Rose BEJ. "Increased Ocean Heat Convergence into the High Latitudes with CO<sub>2</sub>-Doubling Enhances Polar-Amplified Warming", Geophysical Research Letters, Oct 2017, doi: 10.1002/2017GL074561.</p> <p>Please see 'README.txt' for further details on the data files included.</p>
Optimally interpolated dissolved oxygen based on the World Ocean Database 2018 and CMIP6 models
<p>Optimal interpolation of observed and modeled dissolved oxygen data from WOD18 and CMIP6. Technical details are provided in the publication (Ito et al., 2023). </p><p>Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of global O2 loss in optimally interpolated historical ocean observations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-72, in review, 2023.</p>
Evaluation of Upper Tropospheric Geopotential Height Anomalies over the Tropical and Subtropical Oceans in CMIP6 Models Using GNSS Radio Occultation Observations
<p>The set-up of CESM2-CAM6 sensitivity experiments for winter season (Dec-Jan-Feb: DJF), with prognostic falling ice radiative effects on (SON) and off (NOS), is an updated two-moment stratiform cloud scheme (MG2, Gettelman & Morrison, 2015) in the CESM2 atmospheric component of CAM6. CESM2-CAM6 participated in CMIP6. Both the NOS and SON simulations were configured following the same approach as the CMIP6 "historical" run spanning from 1980 to 2014.</p> <p> </p> <p>The data are:</p> <p> </p> <p>TS: skin temperature (K)</p> <p>TAUX: zonal surface wind stress</p> <p>TAUY: meridinal surface wind stress</p> <p>DTCOND: moist condensation heating rate</p> <p>QRL: long wave heating rate</p> <p>OMEGA: vertical motion</p> <p>Z3: geopotential height</p> <p> </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>
Biogeochemical river inputs for global ocean models (RivR2O)
<h2><strong>1. General Description</strong></h2> <p>The global biogeochemical riverine export dataset (RivR2O) uploaded here is a synthesis product for yearly means of preindustrial C, N and P exports to the ocean and their historical evolutions, which are ready-to-use for global ocean models. They will serve as biogeochemical river inputs in the River-2-Ocean Model Intercomparison Study (R2OMIP). The files cover >10000 global catchments which can be read as lists with coordinates, or as gridded netcdf files (0.25°X0.25°). They cover the compounds DIC, DOC, POC, DIP and DIN. The assumed pre-industrial era is assumed to be pre-1900, whereas historical data will cover 1901-2020. </p> <p>Please site the dataset as: </p> <p>Lacroix, F., Liu, M., Ma, M., Resplandy, L., Beusen, A., Hauck, J., Lennartz, S., Li, Y., Tian, H., & Regnier, P. (2024). Biogeochemical river inputs for global ocean models (RivR2O) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13799103" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13799103</a></p> <h3><strong>1.1. Preindustrial inputs and their transformations</strong></h3> <p>The files are for preindustrial river inputs can be downloaded as netcdf (<strong>r2o_riverinputs_preindustrial.nc</strong>), or as catchment lists (DIC,DOC,POC,DIN: <strong>riverexports_list_CN.csv</strong> , DIP: <strong>riverexports_list_P.csv</strong>) with given coordinates. They quantify yearly means for every catchment without a significant anthropogenic perturbation. They were constructed in the following ways:</p> <p><strong>DI</strong><strong>C, DOC, POC</strong></p> <p>Preindustrial DIC, DOC and POC were obtained by subtracting the estimated anthropogenic perturbations for every catchment, which were determined for the 1901-2020 time period by Tian et al. (2023), from the synthesis of present-day exports by Liu et al. (2024). We further accounted for a net DOC source in the tropics (+0.07 Pg C yr-1), and a source in the Southern Hemisphere (+0.01 Pg C yr-1) from estuaries and coastal vegetated ecosystems (including submerged) based on Regnier et al. (2022). Note that in the study, Northern Hemisphere lateral transfers of DOC due to estuaries and coastal vegetation are estimated to approximately zero. A fraction of POC was also removed from the dataset due to models misrepresenting burial on shelf and the remaining fraction (recycled POC) should be added to the semi-refractory DOC pool (see protocol). DIC inputs from groundwater discharge (0.016 Pg C yr-1) were distributed globally homogeneously at every river mouth. Globally, this then amounts to a total of 0.51 Pg C yr-1 of DIC, 0.35 Pg C yr-1 of DOC and 0.095 Pg C yr-1 of POC of available C export to the ocean over the preindustrial time period. </p> <p><strong>DIN </strong></p> <p>The DIN product averages over three river N exports models (ORCHIDEE-NLAT: Ma et al., in review; DLEM: Yang et al., 2015; Tian, pers. Com., IMAGE-GNM: Beusen et al., 2015, 2016) for every catchment. The resulting preindustrial DIN load to the ocean is 11 Tg N yr-1. In addition, labile DON is accounted here as DIN (9 Tg N yr-1) based on the ratio C:N of 2583:103 from labile DOC given above (See R2O MIP protocol). This in total amounts to 20 Tg DIN yr-1 inputs to the ocean in the dataset.</p> <p><strong>DIP</strong></p> <p>The DIP product averages catchment estimates from IMAGE-GNM (Beusen et al., 2016) and Lacroix et al. (2020). The resulting preindustrial DIP load to the ocean is 2.28 Tg P yr-1. In addition, we account for labile DOP as DIP here (0.19 Tg P yr-1) based on the C:P ratio of 2583:1 (See R2O MIP protocol). This in total amounts to 2.47 Tg DIP yr-1 inputs to the ocean in the dataset.</p> <h3><strong>1.2. Anthropogenic Perturbation (1901-2024)</strong></h3> <p>The river input files from 1901 can be downloaded as a zip file (<a href="https://zenodo.org/api/records/14266183/draft/files/r2o_river_inputs_1901_2024.zip/content" target="_blank" rel="noopener noreferrer">r2o_river_inputs_1901_2024.zip</a>), which contains a netcdf files for every year of the time series (1901-2024) as rivr2o_riverinputs_{year}.nc. E.g. for 1901 -> rivr2o_riverinputs_{year}.nc </p> <p><strong>DI</strong><strong>C, DOC, POC</strong></p> <p>Preindustrial DIC, DOC and POC were obtained by interpolating linearly the estimated anthropogenic perturbations for every catchment, which were determined for the 1901-2024 time period by Tian et al. (2023), to the present-day exports by Liu et al. (2024). Based on Regnier et al. (2022), we assumed no lateral transfers of DOC due to estuaries and coastal vegetation for the present day. The same fraction of POC was also removed from the dataset due to models misrepresenting burial on shelf and the remaining fraction (recycled POC) should be added to the semi-refractory DOC pool (see protocol). DIC inputs from groundwater discharge (0.016 Pg C yr-1) were distributed globally homogeneously at every river mouth. Globally, this then amounts to a total of 0.53 Pg C yr-1 of DIC, 0.30 Pg C yr-1 of DOC and 0.12 Pg C yr-1 of POC of available C export to the ocean over the 2011-2020 period.</p> <p><strong>DIN </strong></p> <p>The DIN product averages over three river N exports models (ORCHIDEE-NLAT: Ma et al., in review; DLEM: Yang et al., 2015; Tian, pers. Com., IMAGE-GNM: Beusen et al., 2015, 2016) for every catchment. The total amounts to 30.03 Tg DIN yr-1 inputs to the ocean in the dataset for the 2011-2020 average (including inputs from labile DON).</p> <p><strong>DIP</strong></p> <p>The DIP product averages catchment estimates from IMAGE-GNM (Beusen et al., 2016) and Lacroix et al. (2020). This in total amounts to 4.92 Tg DIP yr-1 inputs to the ocean in the dataset.</p> <h2><strong>2. Use for modelers within the </strong><strong>R2O MIP </strong></h2> <p>We only briefly describe most important information on how to apply the river input data here and refer to the official R2O MIP protocol for more detail on our general simulation guidelines.</p> <ul> <li>We firstly recommend the addition of a terrestrial dissolved organic carbon pools in the ocean models: tDOC semi-labile (DOC_sl). Their only source should be that of the terrestrial inputs given here, it should be degraded with a first order constant of k_sl = 1 / 1.5yr (based on Hansell et al., 2012). The other tDOC compound given in the dataset, tDOC labile (tdoc_l), is assumed to be rapidly degraded and should therefore be added to the ocean model DIC pool.</li> <li>The inputs should be added to the closest ocean model grid points where the ocean model has freshwater inputs. Note that the inputs are given as 10^6 C/N/P per year, and this should be taken into account in the addition of the inputs at the model timestep. We recommend scaling the inputs to the seasonality of the freshwater inputs.</li> <li>The inputs from the riverine files should be added to the corresponding pool based on the following table:</li> <li> <table> <tbody> <tr> <td> <p>River Input</p> <p>(as named in <a href="../api/records/13684982/draft/files/rivr2o_riverinputs_preindustrial.nc/content" target="_blank" rel="noopener noreferrer">rivr2o_riverinputs_preindustrial.nc</a>)</p> </td> <td> <p>Global Load (preindustrial)</p> </td> <td> <p>Global Load </p> <p>(2011-2020 Mean)</p> </td> <td> <p>Ocean Model Pool</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>DIC -></p> </td> <td> <p>0.51 Pg C yr-1</p> </td> <td> <p>0.53 Pg C yr-1</p> </td> <td> <p>DIC & Alkalinity (see protocol)</p> </td> </tr> <tr> <td> <p>DOC_l -></p> </td> <td> <p>0.19 Pg C yr-1</p> </td> <td> <p>0.21 Pg C yr-1</p> </td> <td> <p>DIC</p> </td> </tr> <tr> <td> <p>DOC_sl -></p> </td> <td> <p>0.16 Pg C yr-1</p> </td> <td> <p>0.09 Pg C yr-1</p> </td> <td> <p>DOC_sl (new ocean model pool) and associated DON and DOP</p> </td> </tr> <tr> <td> <p>POC -></p> </td> <td> <p>0.095 Pg C yr-1</p> </td> <td> <p>0.12 Pg C yr-1</p> </td> <td> <p>marine DOC and associated nutrients (DON, DOP, see protocol)</p> </td> </tr> <tr> <td> <p>DIP -></p> </td> <td> <p>2.47 Tg P yr-1</p> </td> <td> <p>4.92 Tg P yr-1</p> </td> <td> <p>DIP / Phosphate</p> </td> </tr> <tr> <td> <p>DIN -></p> </td> <td> <p>20 Tg N yr-1</p> </td> <td> <p>30.03 Tg N yr-1</p> </td> <td> <p>DIN / Nitrate</p> </td> </tr> </tbody> </table> </li> </ul> <h2> </h2> <h2><strong>3. References</strong></h2> <p>Beusen, A. H. W., L. P. H. Van Beek, A. F. Bouwman, J. M. Mogollón, and J. J. Middelburg. Coupling Global Models for Hydrology and Nutrient Loading to Simulate Nitrogen and Phosphorus Retention in Surface Water-description of IMAGE–GNM and Analysis of Performance. Geoscientific Model Development, 8, no. 12 (2015): 4045–67. <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5194%2Fgmd-8-4045-2015&data=05%7C02%7CPierre.Regnier%40ulb.be%7Cbc3e3fa0c09249529ae508dccffc9a65%7C30a5145e75bd4212bb028ff9c0ea4ae9%7C0%7C0%7C638613931025683992%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=%2FAYnNpSwHioFv4igr0eRpwUW8HuFBDkY%2B9OmS8NpYZU%3D&reserved=0%22%20\o%20%22URL%20d%E2%80%99origine%C2%A0:%20https://doi.org/10.5194/gmd-8-4045-2015%20%20Cliquez%20pour%20suivre%20le%20lien." target="_blank" rel="noreferrer noopener">https://doi.org/10.5194/gmd-8-4045-2015</a>. </p> <p>Beusen, A. H. W., Bouwman, A. F., Van Beek, L. P. H., Mogollón, J. M., and Middelburg, J. J.: Global riverine N and P transport to ocean increased during the 20th century despite increased retention along the aquatic continuum, Biogeosciences, 13, 2441–2451, https://doi.org/10.5194/bg-13-2441-2016, 2016.</p> <p>Hansell, D. A., C. A. Carlson, and R. Schlitzer (2012), Net removal of major marine dissolved organic carbon fractions in the subsurface ocean, <em>Global Biogeochem. Cycles</em>, 26, GB1016, doi:<a title="Link to external resource: 10.1029/2011GB004069" href="https://doi.org/10.1029/2011GB004069" target="_blank" rel="noopener">10.1029/2011GB004069</a>.</p> <p>Lacroix, F., Ilyina, T., and Hartmann, J.: Oceanic CO<sub>2</sub> outgassing and biological production hotspots induced by pre-industrial river loads of nutrients and carbon in a global modeling approach, Biogeosciences, 17, 55–88, https://doi.org/10.5194/bg-17-55-2020, 2020.</p> <p>Liu et al. (2024). Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment, Nature Geoscience, <a href="https://www.nature.com/articles/s41561-024-01524-z" target="_blank" rel="noopener">https://www.nature.com/articles/s41561-024-01524-z</a></p> <p>Ma, M., Zhang, H., Lauerwald, R., Ciais, P., and Regnier, P.: Estimating lateral nitrogen transfer through the global river network using a land surface model, Earth Syst. Dynam. Discuss. [preprint], <a href="https://doi.org/10.5194/esd-2024-29" target="_blank" rel="noopener">https://doi.org/10.5194/esd-2024-29</a>, in review, 2024.</p> <p>Regnier, P., Resplandy, L., Najjar, R.G. <em>et al.</em> The land-to-ocean loops of the global carbon cycle. <em>Nature</em> <strong>603</strong>, 401–410 (2022). https://doi.org/10.1038/s41586-021-04339-9</p> <p>Tian, H., Yao, Y., Li, Y., Shi, H., Pan, S., Najjar, R. G., et al. (2023). Increased terrestrial carbon export and CO<sub>2</sub> evasion from global inland waters since the preindustrial era. <em>Global Biogeochemical Cycles</em>, 37, e2023GB007776. <a href="https://doi.org/10.1029/2023GB007776">https://doi.org/10.1029/2023GB007776</a></p> <p>Yang, Qichun, Hanqin Tian, Marjorie A. M. Friedrichs, Charles S. Hopkinson, Chaoqun Lu, and Raymond G. Najjar.: Increased Nitrogen Export from Eastern North America to the Atlantic Ocean Due to Climatic and Anthropogenic Changes during 1901–2008. Biogeosciences,120, no. 6 (2015): 1046–68. <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1002%2F2014JG002763&data=05%7C02%7CPierre.Regnier%40ulb.be%7Cbc3e3fa0c09249529ae508dccffc9a65%7C30a5145e75bd4212bb028ff9c0ea4ae9%7C0%7C0%7C638613931025698138%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=cWTUjbft9Qkg5NOABA0WvTEQ9%2B8kl9GP78JOQJ9K074%3D&reserved=0%22%20\o%20%22URL%20d%E2%80%99origine%C2%A0:%20https://doi.org/10.1002/2014JG002763%20%20Cliquez%20pour%20suivre%20le%20lien." target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/2014JG002763</a>. </p> <p> </p> <h2><strong>4. Version Log</strong></h2> <p>v1 -> pre-industrial river inputs with coastal vegetation and burial transformations</p> <p>v2 -> Groundwater DIC discharge was added.</p> <p>v3-> Bugfixes for groundwater discharge and blue carbon inputs.</p> <p>v4 -> Corrected index with list <strong>riverexports_list_CN.csv </strong>for DIN inputs</p> <p>v5 -> corrected tDOC splits according to R2O-MIP protocol</p> <p>v8 -> Added submerged coastal vegetation fluxes to tDOC_semilabile</p> <p>v9 -> labile DON and labile DOP are added to the DIP and DON pools (based on C:N:P ratio of 2583:106:1)</p> <p>v10 -> slight correction in the labile DOM C:N:P ratio (C:N:P = 2583:103:1)</p> <p>v11 -> correction of labile DOM C:N:P ratio in list files</p> <p>v12 -> Addition of anthropogenic time series for 1901-2024</p> <p>v13 -> Corrected unit mistake in historical timeseries for DIN (10^3 magnitude too large)</p>
Phanerozoic global climatic fields simulated using the FOAM ocean-atmosphere general circulation model
<p>These files contain the output of Phanerozoic global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. All simulations have been conducted using identical boundary conditions; pCO2: 2240 ppm, solar luminosity: 1368 W m-2, vegetation: rocky desert, orbital configuration: null eccentricity and minimum obliquity. Only the continental configuration was varied from one time slice to the other (sensitivity test to the continental configuration), using the reconstructions of Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/).</p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All file names use the following pattern: "[age]rd_1368W_EccN_[model_component]_2240ppm.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos', 'ocean' or 'coupl' (atmospheric and oceanic components, plus coupler).</p>
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</p>
Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"
<p>Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript.</p> <p>The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review.</p> <p>Please consult the README file for information on the specifications of the files.</p> <p>These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.</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>
CMT precipitation dataset for numerical models of the ocean
<p>Total and liquid precipitation datasets created with the method described in <strong>Bias and trend correction of precipitation datasets to force ocean models</strong> (<em>Dussin, JTECH, in revision)</em>.</p>
Observed Indian Ocean Warming Trend Applied to the IPSL-CM6A-LR model
<p>This experiment applies the observed warming trend of the tropical Indian Ocean (0.15 deg C per decade; Hu and Fedorov, 2019) to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where here we apply an observed warming rate rather than a constant (as in Ferster et al., 2021).</p> <p>This experiment relates to the submitted article entitled <em>Variations in tropical Indian Ocean SST drive multi-decadal AMOC variability in models and observations</em>.</p> <p>*The datasets are created from the IPSL-CM6A-LR output files</p> <p>**This version serves as a preliminary version of the dataset for publication purposes, additional output is available.</p>
Output from model simulations of a turbulent ice shelf-ocean boundary current
<p><em>This dataset contains output from 2 LES and 19 MITgcm simulations of an idealised configuration of the ice shelf-ocean boundary current. Core fields are provided such as velocity and density and these are given as ice-plane averaged. The output was generated to make an inter-model comparison of the representation of dynamical processes at the ice shelf-ocean boundary. The two LES configurations differ in their sub-grid-scale parameterisation. The MITgcm simulations investigate the sensitivity to various parameter changes including: resolution, diffusivity coefficients, advection scheme and melt-parameterisation. All configurations are outlined in Patmore et al. (2022).</em></p>
Daily climatology of 3D ocean currents, SST and SSH based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 daily average ROMS output files (one for each year) to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p> <p>zeta = "time-averaged free-surface" [meter]</p>
AdriE ocean climate model ensemble for the Adriatic Sea - monthly fields
<p>This dataset contains the monthly-averaged fields of the key physical oceanographic quantities from the AdriE ocean model ensemble. The model runs were carried out by using the ROMS modelling system (Haidvogel et al., 2008) forced by the SMHI-RCA4 Regional Climate Model (Samuelsson et al., 2021), in turn driven by different General Circulation Models. The period is 1987-2099 in the severe RCP8.5 scenario for the climate simulations, whereas the evaluation runs span the period 1987-2010. Each file contains the results for one run.</p> <p>The model implementation and its validation are fully described in a manuscript recently submitted to Ocean Science (Bonaldo, D., Carniel, S., Colucci, R. R., Denamiel, C., Pranic, P., Raicich, F., Ricchi, A., Sangelantoni, L., Vilibic, I., and Vitelletti, M. L.: AdriE: a high-resolution ocean model ensemble for the Adriatic Sea under severe climate change conditions, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1468, 2024).</p>
Ocean-only simulation outputs based on the IPSL-CM5A-LR and IPSL-CM5A-MR models
<p><br> Name of the simulations<br> piControl2 : COUPLED-LR<br> piControlMR3 : COUPLED-MR<br> OR2L2E : CLIM-LR<br> OR2L2G : CLIM-MR<br> OR2L2M : TOTAL-LR member 1<br> OR2L2M2 : TOTAL-LR member 2<br> OR2L2M3 : TOTAL-LR member 3<br> OR2L2M4 : TOTAL-LR member 4<br> OR2L2M5 : TOTAL-LR member 5<br> OR2L2R : TOTAL-MR member 1<br> OR2L2R2 : TOTAL-MR member 2<br> OR2L2R3 : TOTAL-MR member 3<br> OR2L2R4 : TOTAL-MR member 4<br> OR2L2R5 : TOTAL-MR member 5<br> OR2L2Q : RANDOM-LR<br> OR2L2S : RANDOM-MR<br> piControl2_perm : like COUPLED-LR but with random permutation identical to those used to generate RANDOM-LR<br> piControlMR3_perm : like COUPLED-MR but with random permutation identical to those used to generate RANDOM-MR</p> <p>Variables :<br> zomsfatl : Atlantic meridional overturning streamfunction<br> thetao_700 : averaged potential temperature between 0m and 700m<br> dens : 3D seawater density<br> EOF-NA_SLP : PC1 of the North Atlantic monthly SLP</p> <p> </p>
Ocean model fields shown in paper titled "E3SMv0-HiLAT: A Modifed Climate System Model Targeted for the Study of High Latitudes"
<p>These files contain ocean model climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).</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>
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.