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67 results for “NEMO”
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Northwest Europe NEMO-ERSEM ocean model hindcast and climate projection under RCP8.5
<p>Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in </p> <p>Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400.</p> <p>Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario.</p> <p>The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in</p> <p>Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878.</p> <p>The dataset consists of </p> <ul> <li>Hindcast simulation data</li> </ul> <ol> <li>AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields.</li> <li>AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields.</li> <li>AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES.</li> </ol> <ul> <li>Climate projection data</li> </ul> <ol> <li>AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields.</li> <li>AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields.</li> <li>AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents.</li> <li>AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents.</li> <li>AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES.</li> <li>AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.</li> </ol>
Seafloor organic carbon flux output from the NEMO-MEDUSA model
<p>This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990.</p> <p>This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA.</p> <p>The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK).</p> <p>In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation.</p> <p>References:</p> <p>Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104.</p> <p>Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646.</p> <p>Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355.</p> <p>Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460.</p> <p>Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619.</p> <p>Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201.</p> <p>Yool, A., Popova, E.E. and Anderson, T.R. (2013). MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies. Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.</p>
Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea
<p>Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a></p> <p>The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form:</p> <p><strong>climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc</strong>, where :</p> <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> <p>Grid information in:</p> <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> <p>where:</p> <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul> <p> </p> <p><strong>Acknowledgments:</strong> This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.</p>
NEMO: A Tool to Support Feature Models with Numerical Features and Arithmetic Constraints
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><a href="https://youtu.be/V-ONW8PftwM">https://youtu.be/V-ONW8PftwM</a></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><a href="https://youtu.be/V-ONW8PftwM">https://doi.org/10.1007/978-3-031-08129-3_4</a></p> <p>Real-world <em>Software Product Lines</em> (SPLs) need <em>Numerical Feature Models</em> (NFMs) whose features not only have boolean values satisfying boolean constraints, but also have numeric attributes satisfying arithmetic constraints. A key operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting.</p> <p>Nemo (<strong>N</strong>umbers, f<strong>e</strong>atures, <strong>mo</strong>dels) supports NFMs by <em>bit-blasting</em>, the technique that encodes arithmetic as boolean clauses. Nemo translates NFMs to propositional formulas whose products can be counted efficiently by #SAT solvers, enabling near-optimal products to be found. We evaluate Nemo with a diverse set of real-world NFMs, complex arithmetic constraints, and counting experiments in this paper.</p>
Dataset and code of NEMO-PISCES-APECOSM
<p>This repository makes available the model output as well as the code used in the manuscript entitled "Evaluating the potential impacts of the diurnal vertical migration by marine organisms on marine biogeochemistry" by Aumont et al. Additional files are required to run the model that can be obtained from the official NEMO website or from Olivier Aumont. The output files are in netcdf formats and the variables are documented as metadata in the files. Any supplementary informations can be obtained by contacting Olivier Aumont. Furthermore, the data files used in the figures are provided as ASCII files. data_migrant_pub.csv includes the proportion of migratory organisms and active_flux.txt corresponds to the ratio of active flux over passive flux.</p>
NEMO v4.2 eORCA1 data with RIS, FRIS and LCIS explicit
<p><strong>PROJECT OPEN</strong></p> <p>https://www.katofthesea.com/project-open</p> <p><strong>OBJECTIVES</strong></p> <p>As a first step towards better representing AABW source waters in NEMO global ocean 1° (eORCA1), we explicitly simulate circulation beneath Filchner-Ronne (FRIS), Larsen C (LCIS), and Ross (RIS) ice shelves. These ice shelves were chosen due to their role in the formation and setting of properties of the parent waters of AABW.</p> <p><strong>METHODOLOGY</strong></p> <ul> <li> <p>NEMO version 4.2 Beta</p> </li> </ul> <p>Forced with 2 cycles of CORE forcing for a reference “Closed” cavity run and an “Open” run with FRIS, LCIS and RIS represented.</p> <ul> <li> <p>Inform initial conditions using an idealized regional configuration of each ice shelf.</p> </li> </ul> <p><strong>PROVIDED HERE:</strong></p> <p><strong>NAMELISTS: </strong></p> <p>- NEMO reference namelist (namelist_ref; also available with NEMO code), OPEN configuration (namelist_core_ia_cfg) namelist and sea ice namelists (namelist_ice_ref and namelist_ice_cfg). </p> <p>Unless stated otherwise in the “cfg” namelist, the simulation uses the namelist choices provided in the “ref” namelist. The namelist_core_ia_cfg is a namelist specific to a global ocean configuration (ORCA2, ORCA1, ORCA025 etc) forced by interannual core winds. For more information on all the namelist parameters included in these namelists, please refer to the NEMO reference manual available on Zenodo (10.5281/zenodo.6334656). </p> <p><strong>DOMAIN FILES AND INITIAL CONDITIONS: </strong></p> <p>- Initial conditions where data inside the cavity is informed using an idealized regional configuration with World Ocean Atlas (WOA; <a href="https://www.nodc.noaa.gov/OC5/woa18/woa18data.html">https://www.nodc.noaa.gov/OC5/woa18/woa18data.html</a>) restoring at the boundaries. The data in the cavities is merged with WOA with a smoothing spline applied along the ice shelf front.</p> <p>- A domain file containing bathymetry and ice shelf draft with FRIS, LCIS and RIS open along with accompanying mesh mask file.</p> <p>- The adapted eddy viscosity files where viscosity varies south of 65 degrees South according to grid cell size.</p> <p>- A freshwater flux file where the melt rate from all cavities except FRIS, LCIS and RIS is prescribed. Inside the "Open" cavities, the melt paramaterization is turned off and the location of melt is moved to the grounding line in case in the future we would like to include the flux from grounding line rives. </p> <p><strong>MODEL OUTPUT:</strong></p> <p>The data uploaded in this zip file is split into the output from the "Closed" and "Open" runs and is listed under eORCA1_output. Here the temperature and salinity fields used for comparison with WOA are provided (1981-2009), along with specific sections extracted using the PAGO functions (<a href="https://www.whoi.edu/science/PO/pago/">https://www.whoi.edu/science/PO/pago/</a>) along FRIS and RIS for certain dates that correspond to CTD sections in the area. To facilitate easy plotting of the example scripts provided, we have also extracted the thermohaline and velocity fields for FRIS cavity and adjacent continental shelf. The user can use these for a first try with the scripts provided and then move on to extracting their areas and dates of interest from the regional 1981-2009 files. </p> <p><strong>EXAMPLE SCRIPS:</strong></p> <p>To get the user familiar with the provided data, we have provided example scripts for extracting the data (open_eORCA_FRIS), plotting bathymetry and T-S (plot_bathy_T_S) and plotting the meridional overturning function and barotropic stream function in the cavity (plot_MOC_BSF_melt). In this same script the melt rate pattern is plotted and the net melt over various time periods corresponding to observational studies is calculated using calculating_net_melt.</p> <p>Additionally, a script plotting temperature and salinity across FRIS ice shelf front for the period February-March 1995 is provided and can be adapted and used to plot the other sections of data provided in this zip. </p> <p><strong>ACKNOWLEDGEMENTS AND FINANCIAL SUPPORT</strong></p> <p>Katherine Hutchinson received financial support of the European Union’s Horizon 2020 research and innovation programme Marie Skłodowska-Curie grant agreement No 898058 (Project OPEN). Nicolas Jourdain received support from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 101003536 (ESM2025). Pierre Mathiot acknowledges support from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820575 (TiPACCs). This work was performed using HPC resources from GENCI–IDRIS (Grant 2021- A0100107451) and from the IPSL Mesocentre ESPRI.</p> <p>Project OPEN is a participant in the h2020 open access data pilot.</p>
NEMO 4.0.7 ORCA1L75 - CNR ISMAR configuration files
<p>The dataset contains all static, ancillary, dynamic and forcing files to run tests with the ORCA1 configuration of NEMO (v4.0.7) for testing the CNR ISMAR setup (2010-2019)</p>
NEMO ORCA05 FOCI Test Data
<p>This is nine years of monthly NEMO data that is very similar to data written by the Ocean component of FOCI from GEOMAR.</p> <p>Feel free to use it for development and testing but be aware that these data are in no way suitable for scientific use.</p> <p>Data prepared by Jan Harlaß and Willi Rath (2020)</p>
NEMO ORCA05 Test Data
<p>This is two years of 5daily NEMO data that is very similar to model hindcasts produced at GEOMAR.</p> <p>Feel free to use it for development and testing but be aware that these data are in no way suitable for scientific use.</p> <p>Data prepared by Jan Harlaß and Willi Rath (2020)</p> <ul> <li>NEMO version: release-3.6.x (revision 6721)</li> <li>Components: OPA_SRC LIM_SRC_2</li> <li>Grid: orca, zps (z-coordinate with partial steps)</li> <li>Resolution: 05</li> <li>Horizontal Gridpoints: 722 x 511</li> <li>Vertical Levels: 46</li> </ul>
Cyclone tracks from 1901 to 2010 in dynamically downscaled ERA-20C reanalysis (COSMO-CLM+NEMO)
<p>The database contains two files: one with all cyclone trajectories from 1901 to 2010, and another one only with the so-called Vb-cyclones that propagate from the Mediterranean Sea north-eastward to Central Europe.</p> <p>We detected the cyclone trajectories with the method of Wernli and Schwierz (2006) and Sprenger et al. (2017) and classified all cyclone trajectories that crossed the 47°N latitude between 12°E and 22°E as Vb-cyclones following Hofstätter and Blöschl (2019). The cyclone tracking was based on mean sea level pressure data of dynamically downscaled ERA-20C reanalysis. The downscaling was performed over Europe [including MED-CORDEX (Somot et al. 2018) and EURO-CORDEX (Giorgi et al. 2009)] from 1901 to 2010 with an interactively coupled high-resolution atmosphere-ocean model (COSMO-CLM+NEMO) by Cristina Primo. More details on the data basis can be found in Primo et al. (2019) and Krug et al. (2020).</p> <p> </p> <p>Giorgi, F., Jones, C. & Asrar, G. Addressing climate information needs at the regional level: the CORDEX framework.<em> WMO Bulletin</em> <strong>58</strong>, 175–183 (2009).</p> <p>Hofstätter, M. & Blöschl, G. Vb Cyclones Synchronized With the Arctic-/North Atlantic Oscillation. <em>J. Geophys. Res. Atmos.</em> <strong>124</strong>, 3259–3278 (2019).</p> <p>Krug, A., Primo, C., Fischer, S., Schumann, A. & Ahrens, B. On the temporal variability of widespread rain-on-snow floods. <em>Meteorol. Zeitschrift</em> <strong>29</strong>, 147–163 (2020).</p> <p>Primo, C., Kelemen, F. D., Feldmann, H., Akhtar, N. & Ahrens, B. A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance. <em>Geosci. Model Dev.</em> <strong>12</strong>, 5077–5095 (2019).</p> <p>Somot, S. <em>et al.</em> Editorial for the Med-CORDEX special issue. <em>Clim. Dyn.</em> <strong>51</strong>, 771–777 (2018). doi: 10.1007/s00382-018-4325-x</p> <p>Sprenger, M. <em>et al.</em> Global climatologies of Eulerian and Lagrangian flow features based on ERA-Interim. <em>Bull. Am. Meteorol. Soc.</em> (2017). doi:10.1175/BAMS-D-15-00299.1</p> <p>Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. <em>J. Atmos. Sci.</em> <strong>63</strong>, 2486–2507 (2006).</p>
Compiled dataset and posterior results for NEMo analyses on Acacia tolerance to aridity and salinity
<p>The dataset contains the compiled data for the aridity and salinity conditions at <em>Acacia</em> species' presence and absence locations, factors on sampling bias, as well as <em>Acacia</em> phylogeny. The data file is the input to the NEMo analyses. The dataset also contains the result files from the analyses.</p>
Tracking-NEMO-movies_subset
<p>This is a subset of the image data used to perform MSD analysis on NEMO dots, for the publiction: </p> <ul> <li><a href="http://jcb.rupress.org/content/204/2/231.long">"TNF and IL-1 exhibit distinct ubiqui-tin requirements for inducing NEMO-IKK supramolecular structures", Tarantino et al. (2014)</a>.</li> </ul> <p>This dataset is used in educational resources to introduce single-particle tracking and mean-square displacement analysis.</p> <p> </p>
Carbon-latitude analysis of the ocean carbon cycle computed from NEMO-Medusa
<p>NetCDF files containing carbon-latitude stream functions computed from the coupled physical-biogeochemical model NEMO-Medusa. The stream functions have been computed for the last years of a historical run and the first years of the RCP 8.5 scenario run (2000-2009). The dataset includes a climatology of the most important tracers, the computed meridional carbon transports in the ocean, and volume distribution in the carbon-latitude space.</p>
NEMO-FABM-ERSEM-MIZER climate change simulation data
<p>This dataset corresponds to data produced from running NEMO-FABM-ERSEM MIZER on the AMM7 domain as part of the COMFORT project and used in the Deliverable 3.3 (see related identifiers). A scientific manuscript is in preparation. The climate simulation was run from 1981-2100 forced by atmospheric and lateral boundary condition from the IPSL-CMR5a-MR (RCP 8.5) climate simulation (Dufresne et al., 2013; WCRP, 2016) . Data reported here are from 1990-2100, unless otherwise stated. </p> <p>Data has been post-processed to convert it to depth independent (2D), monthly averaged variables. Consequently all data are either depth-integrated, mean across depth (temperature, salinity) or surface data (nutrients).</p> <p>For access to the raw data or more details, please contact Helen Powley (hpo@pml.ac.uk).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Damaged mitochondria recruit the effector NEMO to activate NF-κB signaling
<p>Failure to clear damaged mitochondria via mitophagy disrupts physiological function and may initiate damage signaling via inflammatory cascades, though how these pathways intersect remains unclear. We discovered that NF-κB essential regulator NEMO is recruited to damaged mitochondria in a Parkin-dependent manner in a time-course similar to recruitment of the structurally-related mitophagy adaptor, OPTN. Upon recruitment, NEMO partitions into phase-separated condensates distinct from OPTN, but colocalizing with p62/SQSTM1. NEMO recruitment in turn recruits the active catalytic IKK component phospho-IKKb, initiating NF-κB signaling and the upregulation of inflammatory cytokines. Consistent with a potential neuroinflammatory role, NEMO is recruited to mitochondria in primary astrocytes upon oxidative stress. These findings suggest that damaged, ubiquitinated mitochondria serve as an intracellular platform to initiate innate immune signaling, promoting the formation of activated IKK complexes sufficient to activate NF-kB signaling. We propose that mitophagy and NF-κB signaling are initiated as parallel pathways in response to mitochondrial stress. </p>
Synthetic in-situ T/S data over 1993-2018 from a NEMO-based simulation of the IMHOTEP project
<p>"Synthetic observations" of in-situ Temperature and Salinity profiles as a function of depth have been extracted online during the production of the global, NEMO-based experiment ** IMHOTEP-GAIc**, at every single time and location (in x,y,z dimensions) where a true in-situ profile exists in the ENACT-4 database (Good et al 2013) over the simulation period: 1980-2018. This global ocean/sea-ice/iceberg simulation uses the NEMO model, and has a horizontal resolution of 1/4°. The atmospheric forcing applied at the surface is based on the JRA reanalysis (Kobayashi et al., 2015) and varies over the full range of time-scales from 6 hours to multi-decadal. The freshwater runoff forcing applied to the experiment is fully-variable (daily to multi-decadal) based on the ISBA-CTRIP hydrographic reanalysis for rivers (Decharme et al., 2019) and from altimeter data and regional GCM simulations for the liquid and solid discharges from the Greenland ice-sheet (Mouginot et al 2019). These runoffs are only climatological around Antarctica. The synthetic in-situ T/S dataset from the model is available over the period 1993-2018.</p><p>See the README file for more information. And online documentation is also available here: https://doc-imhotep.readthedocs.io/en/latest/6-Synthetic-Obs.html</p>
Compiled dataset and posterior results for NEMo analyses on Acacia tolerance to aridity and salinity
Open the record for dataset details and reuse information.
NEMO Reference configurations inputs
<p>This page refers to input archives for running the configurations included in NEMO 4.2 (beta version) and earlier. Subsequent releases have been served from an alternative site: <a href="https://gws-access.jasmin.ac.uk/public/nemo/sette_inputs">SETTE input files delivered through JASMIN</a>. Users are advised to download from that site for versions 4.2.0 onwards. The <a href="https://sites.nemo-ocean.io/user-guide/sette.html#obtaining-configuration-input-files">NEMO User Guide</a> contains a detailed section on running SETTE, including further information on obtaining the input files. </p> <p>This site is retained to provide a DOI for citing these datasets but remember to state clearly the exact version used.</p> <p>Input archives for running the configurations included in <strong>NEMO 4.2 (beta version) -not suitable for releases 4.2.0 and later</strong>:</p> <ul> <li>AGRIF_DEMO: <em>AGRIF_DEMO_v4.0.tar</em> and <em>ORCA2_ICE_v4.0.tar</em></li> <li>AMM12: <em>AMM12_v4.0.tar</em></li> <li>C1D_PAPA: <em>INPUTS_C1D_PAPA_v4.0.tar</em></li> <li>ISOMIP+: ISOMIP+_v4.0.tar, input for the new ISOMIP+ test case on ice shelf module and the coupling with an ice sheet model</li> <li>ORCA2_ICE_PISCES: <em>ORCA2_ICE_v4.2.tar</em>, <em>INPUTS_PISCES_v4.2.tar and ORCA2_ABL_4.2.tar</em></li> <li>ORCA2_OFF_PISCES: <em>ORCA2_OFF_v4.0.tar</em> and <em>INPUTS_PISCES_v4.0.tar</em></li> <li>ORCA2_OFF_TRC: <em>ORCA2_OFF_v4.0.tar</em></li> <li>ORCA2_SAS_ICE: <em>ORCA2_ICE_v4.0.tar</em> and <em>INPUTS_SAS_v4.0.tar</em></li> <li>SPITZ12: <em>SPITZ12_v4.0.tar</em></li> <li>WED025: WED025_v4.2.tar, input for the new WED025 reference configuration, now replacing SPITZ12 to demonstrate ice shelf module, SI3, bdy with tide and sea ice capabilities<br> </li> </ul>
NEMO, HIDRA and Tide Gauge Datasets for HIDRA Machine Learning Algorithm Verification
<p>Supporting sea level datasets for paper:</p> <p>"HIDRA 1.0: Deep-Learning-Based Ensemble Sea Level Forecastingin the Northern Adriatic"</p> <p>by Lojze Žust, Anja Fettich, Matej Kristan, and Matjaž Ličer</p>
ScienceDex guides
Understand access before you commit
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.