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81 results for “ocean simulation”
Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations: video supplements
<p>These are video supplements cited in the manuscript "Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations" that has been submitted to Geoscientific Model Development (GMD; MS No.: gmd-2018-192).</p> <p>The animations (with a 10yr running window) show the development of temperature biases in the deep ocean over a period of 100 years in two pre-industrial configurations with the AWI Climate Model: AWI-CM-LR and AWI-CM-HR. Meridional biases along the 30.5°W transect through the Atlantic Ocean (S3 and S4; animated version of Fig.8 for LR and HR) and maps of along-isopycnal biases (sigma_1=31.8) are shown (S1 and S2). Time axes have been added compared to version 1.</p>
Data & figures: Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation
<p>These are the model data, key figures, and Python code generated during the project titled “Comparison between Large-Scale Observed and Simulated Antarctic Sea-Ice Variability Response to Changes in Atmospheric and Oceanic Circulation." This project was undertaken during a 3-month research scholarship at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, funded by the Helmholtz Visiting Researcher Grant, a program promoted by the Helmholtz Information and Data Science Academy (HIDA). Statistical methods pertain to the coupling of sea surface temperature and Antarctic sea-ice interactions. These methods can be applied to observations, reanalysis, and earth system model data</p>
Large Eddy Simulation of the Southern Ocean
The data set contains seven Large Eddy Simulations (LES) at the Southern Ocean Flux Site for studies of deep turbulent ocean boundary layers, with and without surface wave effects, and with both idealized and observed forcing by wind, surface buoyancy flux and Stokes drift profiles. There are 20 days of hourly statistics computed every half-hour of turbulence quantities; namely, the vertical fluxes of buoyancy (temperature) and momentum, buoyancy and velocity variances, and the turbulent kinetic energy, its production terms and its dissipation.
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>
Simulation checkpoint for a local ocean domain and its diel cycle
<p>This is for use with the <a href="https://github.com/CliMA/Oceananigans.jl#readme">Oceananigans.jl</a> via <a href="http:// https://github.com/gaelforget/ClimateModels.jl#readme">ClimateModels.jl</a> (v0.3.7 and above).</p> <p>A six day spin-up was carried out (with <a href="https://github.com/CliMA/Oceananigans.jl#readme">Oceananigans.jl</a> v0.95.5) and the resulting checkpoint is what's stored here.</p> <p>See the ClimateModels.jl documentation for user direction on how to run the Oceananigans.jl notebook.</p>
Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output
<p>This repository contains large-eddy simulation output from the CFD model <em>Diablo</em>. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript <em>Regimes and transitions in the basal melting of Antarctic ice shelves</em> submitted to the <em>Journal of Physical Oceanography</em> (December 2021).</p>
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Abiotic radiocarbon simulated in NEMO ocean model
<p>Following the Ocean Model Intercomparison Project (Orr et al., 1999, 2017), abiotic radiocarbon is simulated using the global ocean circulation model Nucleus for European Modelling of the Ocean (NEMO) version 3.6 (Madec, 2014), which is coupled to the Los Alamos sea-ice model (CICE, Rae et al., 2015). </p> <p>Simulation one, "Hist", was forced with inter-annually varying JRA-55-do atmospheric reanalysis data (version 1.5, Tsujino et al., 2018), which is available for the period 1958 through 2020 at 3-hour intervals, which is cycled over 1850 to 2020; and with historical atmospheric CO2 and ∆14C boundary conditions.</p> <p>The other simulation (”Hist-NYF”) was forced with the JRA-NY atmospheric data in every year, which is a one year chunk (01-May-1990 to 30-April-1991) from JRA-55-do (version 1.3) data when major climate modes such as the North Atlantic Oscillation, Southern Oscillation and Southern Annular Mode were largely neutral. </p> <p>The "Fix" simulation ran with fixed atmospheric CO2 and ∆14 C boundary conditions, using 1850 values and inter-annually varying JRA-55-do atmospheric reanalysis data.</p>
Data for "Simulated impact of ocean alkalinity enhancement on atmospheric CO 2 removal in the Bering Sea"
<p>This is accompanying data for a submission to AGU Earth's Future by lead author Hongjie Wang and corresponding author Brendan Carter. This submission primarily contains processed model simulation output. The original model output was generated by Kelly Kearney. Unfortunately, the original model output is requires too much memory to submit in its entirety, so this submission is intended to grant interested readers access to the worked-up fields used to produce the figures and values in the manuscript. Individuals interested in higher-resolution model output are encouraged to contact Kelly Kearney to discuss possible transfer solutions.</p> <p> </p> <p>A data hosting solution for the full simulation is being explored by the Bering10k team, and this description will be updated if a publicly accessible alternative to these processed fields becomes available.</p>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
An evaluation of cloud-precipitation structures in mixed-phase stratocumuli over the southern ocean in kilometer-scale ICON simulations during CAPRICORN
<p>The repository contains ICON simulated outputs (Cntrl_ICON.nc.gz, No_Gr_ICON.nc.gz, No_Ice_ICON.nc.gz, 100m_Vert_ICON.nc.gz, and 50m_Vert_ICON.nc.gz) and two km mean and ensemble forward simulated results (Cntrl_PAMTRA.gz, No_Gr_PAMTRA.gz, No_Ice_PAMTRA.gz, 100m_Vert_PAMTRA.gz, and 50m_Vert_PAMTRA.gz) for the two day period (00:00 UTC on March 26, 2016 to 00:00 UTC on March 28, 2016). The prefix in the filenames corresponds to the experiments described in the manuscript.</p>
Data from: Connections between the Southern Ocean and the Eastern tropical Pacific in unforced and forced climate model simulations
<p>The sea surface temperature (SST) over the eastern tropical Pacific significantly influences global-mean climate feedback and may be driven in part by the SST over the Southern Ocean. Previous studies demonstrated a teleconnection from the Southern Ocean to the eastern tropical Pacific by perturbing the Southern Ocean climate. We investigate if this teleconnection holds in a fully coupled, freely running climate system using CMIP6 models. We assess the relationship between the Southern Ocean (SO) and the eastern tropical Pacific (SEP) by calculating correlations between SO and SEP SST timeseries within each model and regressions between mean SO and SEP SSTs across models. We show robust, positive SO-SEP relationships in an unforced climate using pre-industrial SSTs, in a forced climate using SST anomalies between pre-industrial and quadrupled CO<sub>2</sub> simulations, and in the SST pattern of the forced response relative to the global-mean SST anomaly. The strength of SO-SEP correlations is positively related to the stratocumulus cloud feedback off the west coast of South America, and negatively related to ocean heat uptake in the same region. As both shortwave cloud feedback and ocean heat uptake are underestimated in climate models, understanding their effects on SO-SEP teleconnections and their interactions is crucial for determining the strength of SO-SEP teleconnection in the real world and its trustworthiness in climate model projection.</p>
AMDADOS simulation results and scaling for IEEE Oceans paper
<p>Presents inputs datasets for the AMDADOS paper presented at IEEE Oceans 2018 (<a href="https://doi.org/10.5281/zenodo.1346168">10.5281/zenodo.1346168</a>) to enable replication of experiments</p>
Small orbital forcing changes alter the ocean circulation of Earth's next future supercontinent: Simulations
<p>This dataset contains the simulations from the manuscript "Small orbital forcing changes alter the ocean circulation of Earth's next future supercontinent".</p> <p>From the methods section of the manuscript:<br>The simulations are carried out using the fully-coupled ocean-atmosphere general circulation model ROCKE-3D version Planet 1.0 (R3D1) (Way et al., 2017). The model is run at a horizontal resolution of 4 x 5 degrees and simulations were integrated for 5000 and 7000 years for PD and future forcing, respectively, to ensure they were in equilibrium. The topography and bathymetry is the one used by Way et al. (2021) in their simulation 2 (Aurica). This includes a fully dynamic ocean 4252 m deep and land elevations close to present day mean topography and elevations between 1 and 858 m.<br>Due to high uncertainties in its future evolution, atmospheric composition is kept constant at year 1850 levels, following the setup in Way et al. (2021): the atmosphere is dominated by nitrogen with 21% oxygen, 285 ppmv CO2, 0.3 ppmv N2O and 0.79 ppmv CH4, and no aerosols or ozone are included.<br>Two simulations are presented: PD_Aurica which which has present-day day-length (24 hrs) and insolation (1361 Wm-2) and F_Aurica where we apply orbital forcing consistent with 250 Ma into the future - day length is increased by 0.5 hrs and insolation is increased by 2.6 Wm-2 (see Way et al., 2021). </p> <p>PD_Aurica corresponds to the output files with the following names: ANN4500-4999.*F_AURICA_RAND_PD_03.nc<br>F_Aurica corresponds to the output files with the following names: ANN6500-6999.*F_AURICA_RAND_PD_01.nc</p> <p>The diagnostic files including AGC, AIJ, AIJK and AIJL in the filename contain atmospheric diagnostics and outputs, whereas those that include OIJ, OIJL, OJL and OTJ contain ocean diagnostics and outputs.</p> <p><span><span><span><a href="https://science.gsfc.nasa.gov/author/533569006/35432">Way M. J.</a></span></span><span>, </span></span><span><span><span><a href="https://science.gsfc.nasa.gov/author/830545035/35432">I. Aleinov</a></span></span><span>, </span></span><span><span>D. S. Amundsen</span><span>, </span></span><span><span><a title="See all authors for this publication">et al.</a> </span></span> <span>2017.</span> <span> "Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) 1.0: A General Circulation Model for Simulating the Climates of Rocky Planets." </span> <em>The Astrophysical Journal Supplement Series</em> <strong>231</strong> <span><strong>(1)</strong>: </span> <span>12</span> <span> [<a href="http://dx.doi.org/10.3847/1538-4365/aa7a06">10.3847/1538-4365/aa7a06</a>]</span></p> <p><span><span><span><a href="https://science.gsfc.nasa.gov/author/533569006/42520">Way M. J.</a></span></span><span>, </span></span><span><span>H. S. Davies</span><span>, </span></span><span><span>J. C. Duarte</span><span>, </span></span><span><span><a title="See all authors for this publication">et al.</a> </span></span> <span>2021.</span> <span> "The Climates of Earth’s Next Supercontinent: Effects of Tectonics, Rotation Rate, and Insolation." </span> <em>Geochemistry, Geophysics, Geosystems</em> <strong>22</strong> <span><strong>(8)</strong>: </span> <span> [<a href="http://dx.doi.org/10.1029/2021gc009983">10.1029/2021gc009983</a>]</span></p> <p> </p> <p> </p>
FESOM output supporting: Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?
<p>AWI-CM1 and FESOM1.4 simulation results used in the manuscript "Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?".</p>
Datasets for "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlabprogram to simulate the displacement of particles in the ocean."
<p>The data, run results and analysis scripts used to produce all of the results presented in the paper "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlab program to simulate the displacement of particles in the ocean."</p>
Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: Static 2D models and effects of paleo-seawater chemistry
<p>DePaolo et al. Gcubed 2022 data files</p> <p><strong>Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: </strong></p> <p><strong>Static 2D models and effects of paleo-seawater chemistry </strong></p> <p> </p> <p>In this folder are input and output files for v3.68 of TOUGHREACT that contain all of the files illustrated in the manuscript plus many more. Also included is v3 TOUGHREACT reference manual, which gives more information on all of the input and output files.</p> <p>In each folder there are a sequence of run folders, each containing input files (flow.inp, solute.inp, chemical.inp, MESH, GENER, plus a thermodynamic database with filename like “tkslth06acp3isi9.dat.” Also included are raw tecplot files (flowvector.tec, flowdata.tec, rct_sfarea.tec, rctn_rate.tec, min_SI.tec, minerals.tec, aqconc.tec) and other output files (all “.out” files). In some cases the .tec files, which are combined files with output for both fractures and matrix, have been separated into separate fracture and matrix files with names like “flowvector_frc.tec,” “flowvector_mtx.tec,” aqconc_frc.tec,” “aqconc_mtx.tec” to allow plotting of fracture and matrix properties separately.</p> <p>Some folders also contain .tiff or .png files that are 2D color contour plots as shown in the manuscript. All of these plots were made with Paraview (<a href="https://www.paraview.org/">https://www.paraview.org</a>) which is open-source.</p> <p>Each folder labeled like “Modern SW fastcpx Sr8…” contains several subfolders each labeled with the model year at which the run ends, like 2000, 2600, 2700, 2800, … which correspond to the warmup steps described in the manuscript:</p> <p>The typical procedure used to achieve the results reported here is (with some minor variations):</p> <ol> <li>Run the simulation for 2000 model years with 50% of the final heating from below and minimal chemical reactions. RSA for primary minerals in both matrix and fractures are set to 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup>cm<sup>2</sup>/g for secondary minerals, which yields chemical reaction rates about 500 times slower than for a more realistic system.</li> <li>Run for an additional 600 model years with the full heating from below and RSA’s at 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup> cm<sup>2</sup>/g. This step yields a steady state temperature and flow field with the full heating from below. Less time is needed than for the first phase because the fluid flow velocities are higher with higher heating rates.</li> <li>Run an additional 100 years; RSA’s increased to 10<sup>-5</sup> cm<sup>2</sup>/g and 2 x 10<sup>-5</sup> cm<sup>2</sup>/g</li> <li>Run 100 years; RSA’s at 10<sup>-4</sup> cm<sup>2</sup>/g and 2 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 years; RSA’s at 2 x 10<sup>-4</sup> cm<sup>2</sup>/g and 4 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 3 x 10<sup>-4</sup> cm<sup>2</sup>/g and 5 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 4 x 10<sup>-4</sup> cm<sup>2</sup>/g and 8 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 additional years*</li> </ol> <p>After step 8 the system has been running for 3100 model years, but only 150 years with full reactions, which is long enough to get close to quasi-steady state fluid chemistry (there is no true steady state for chemistry because the rock mineralogy is changing with time). For each of the steps marked with an asterisk, an alternative procedure is to use high RSA’s for fracture minerals, up to 50 times higher. </p> <p>In some folders there are additional subfolders extending in model time up to 3400 years.</p>
Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution (data)
<p>Data for "Variability of Eddy Kinetic Energy in the Eurasian Basin of the Arctic Ocean inferred from a Model Simulation at 1-km Resolution"</p>
Data accompanying "Diurnal variability of the upper ocean simulated by a climate model"
<p>Data used for creating figures in the draft article "Diurnal variability of the upper ocean simulated by a climate model". This includes:</p> <ul> <li>Multi-year, monthly mean diurnal cycle metrics at all model grid points.</li> <li>Monthly mean diurnal cycle data for individual years at selected locations.</li> </ul> <p>Code used to create these data files, and to create the plots, is in a Github repository (https://github.com/JackReevesEyre/cfs-analysis-gaea/). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.7846095).</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
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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.