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53 results for “Climate model output”
Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)
<p>The dataset contains monthly output of a COSMO-CLM² (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described and evaluated in the following paper: </p> <p>Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM² over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862)</p> <p>Details of the model simulation:<br> - COSMO-CLM version 5.0_clm6<br> - Community Land Model version 4.5<br> - Horizontal resolution: 0.25°x0.25°<br> - Vertical resolution: 40 levels<br> - Time period: 1987-2016 (excluding 4 years of spin-up)<br> - Driving model: ERA-Interim<br> </p> <p>The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps.<br> - AEVAP_S: Surface evaporation [kg m-2] (summed value for each month)<br> - ALB: Surface albedo [-] (only for austral summer months)<br> - ALHFL_S: Surface latent heat flux [W m-2]<br> - ALWD_S: Downward longwave radiation at the surface [W m-2]<br> - ALWU_S: Upward longwave radiation at the surface [W m-2]<br> - ASHFL_S: Surface sensible heat flux [W m-2]<br> - ASOB_S: Surface net downward shortwave radiation [W m-2]<br> - ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2]<br> - ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2]<br> - ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2]<br> - ATHB_S: Surface net downward longwave radiation at the surface [W m-2]<br> - P: Pressure at 40 vertical levels [Pa]<br> - QV: Specific humidity at 40 vertical levels [kg kg-1]<br> - RH2M: Relative humidity at 2 meter [%]<br> - SNOW_GSP: Surface snowfall amount [kg m-2] (summed value for each month)<br> - T2M: Temperature at 2 meter [K]<br> - T: Temperature at 40 vertical levels [K]<br> - WS10M: Wind speed at 10 meter [m s-1]<br> - WS: Wind speed at 40 vertical levels [m s-1]</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Early Cretaceous climate model output from the Kiel Climate Model (from Steinig et al. 2024)
<h1>README</h1> <p>This directory contains climate model output data for 36 different Early Cretaceous (Aptian/Albian) simulations performed with the Kiel Climate Model (KCM; ECHAM5/NEMO). Each zip file contains climatological mean values (monthly mean for the atmosphere; annual mean for the ocean) averaged over the last 100 model years of each simulation in netCDF format. A detailed description of the model setup, boundary conditions and integration strategy is given in the associated publication ("Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis" in Climate of the Past; <a href="https://doi.org/10.5194/egusphere-2023-2732">https://doi.org/10.5194/egusphere-2023-2732</a>). Differences in the paleogeographies are limited to the South Atlantic and Southern Ocean regions, but the output is available globally.</p> <h2>Available Models</h2> <p>Table 1: Overview of available model simulations sorted by the four main boundary condition differences discussed in the associated paper:</p> <table> <tbody> <tr> <th>Model ID</th> <th>Opening Stage</th> <th>CO2</th> <th>Drake Passage Depth</th> <th>Walvis Ridge Depth</th> </tr> </tbody> <tbody> <tr> <td>DC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>C51</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C53</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C54</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C61</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C63</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C64</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>WC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WI1</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI2</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI3</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI4</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI5</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI6</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> </tbody> </table> <h2>Available Variables</h2> <p>Each model zip file contains a <code>means</code> directory with the climatological mean output files for the atmosphere and ocean.</p> <h3>Atmosphere</h3> <p>Files starting with <code><Model ID>_mm_*</code> contain the monthly mean climatologies of 2D atmosphere variables. Each file represent a single variable for the following list of variables:</p> <p>Table 2: Overview of available atmospheric variables. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>aclcov</td> <td>total cloud cover</td> </tr> <tr> <td>evap</td> <td>evaporation</td> </tr> <tr> <td>precip</td> <td>total precipitation</td> </tr> <tr> <td>slp</td> <td>mean sea level pressure</td> </tr> <tr> <td>tau_x</td> <td>zonal wind stress</td> </tr> <tr> <td>tau_y</td> <td>meridional wind stress</td> </tr> <tr> <td>temp2</td> <td>2m air temperature</td> </tr> <tr> <td>tsw</td> <td>surface temperature of water</td> </tr> <tr> <td>uwnd</td> <td>10m u-velocity (winds)</td> </tr> <tr> <td>vwnd</td> <td>10m v-velocity (winds)</td> </tr> </tbody> </table> <h3>Ocean</h3> <p>Ocean variables are split across 4 individual files following the staggered grid of the NEMO ocean model. Naming structure follows <code><Model ID>.mean_grid_{T|U|V|W}.nc</code></p> <p>Table 3: Overview of available ocean variables on the T-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>iowaflup</td> <td>Ice=>ocean net freshwater</td> </tr> <tr> <td>sobowlin</td> <td>Bowl Index</td> </tr> <tr> <td>sohefldo</td> <td>Net Downward Heat Flux</td> </tr> <tr> <td>soicealb</td> <td>Ice Albedo</td> </tr> <tr> <td>soicecov</td> <td>Ice Cover</td> </tr> <tr> <td>soicetem</td> <td>Ice Surface Temperature</td> </tr> <tr> <td>somixhgt</td> <td>Turbocline Depth</td> </tr> <tr> <td>somxl010</td> <td>Mixed Layer Depth 0.01</td> </tr> <tr> <td>sorunoff</td> <td>Runoffs</td> </tr> <tr> <td>sosalflx</td> <td>Surface Salt Flux</td> </tr> <tr> <td>sosaline</td> <td>Sea Surface Salinity</td> </tr> <tr> <td>soshfldo</td> <td>Shortwave Radiation</td> </tr> <tr> <td>sosheig</td> <td>Sea Surface Height</td> </tr> <tr> <td>sosstsst</td> <td>Sea Surface temperature</td> </tr> <tr> <td>sowaflcd</td> <td>concentration/dilution water flux</td> </tr> <tr> <td>sowaflep</td> <td>atmos=>ocean net freshwater</td> </tr> <tr> <td>sowaflsp</td> <td>solid precipitation from atm</td> </tr> <tr> <td>sowafltp</td> <td>total PE flux from atm</td> </tr> <tr> <td>sowaflup</td> <td>Net Upward Water Flux</td> </tr> <tr> <td>vosaline</td> <td>Salinity</td> </tr> <tr> <td>votemper</td> <td>Temperature</td> </tr> </tbody> </table> <p> </p> <p>Table 4: Overview of available ocean variables on the U-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sozotaux</td> <td>Wind Stress along i-axis</td> </tr> <tr> <td>vozocrtx</td> <td>Zonal Current</td> </tr> <tr> <td>vozoeivu</td> <td>Zonal EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 5: Overview of available ocean variables on the V-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sometauy</td> <td>Wind Stress along j-axis</td> </tr> <tr> <td>vomecrty</td> <td>Meridional Current</td> </tr> <tr> <td>vomeeivv</td> <td>Meridional EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 6: Overview of available ocean variables on the W-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>soleaeiw</td> <td>eddy induced vel. coeff. at w-point</td> </tr> <tr> <td>soleahtw</td> <td>lateral eddy diffusivity</td> </tr> <tr> <td>voddmavs</td> <td>Salt Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeavm</td> <td>Vertical Eddy Viscosity</td> </tr> <tr> <td>votkeavt</td> <td>Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeevd</td> <td>Enhanced Vertical Diffusivity</td> </tr> <tr> <td>votkeevm</td> <td>Enhanced Vertical Viscosity</td> </tr> <tr> <td>voverctz</td> <td>Vertical Velocity</td> </tr> <tr> <td>voveeviw</td> <td>Vertical EIV Velocity</td> </tr> </tbody> </table> <p> </p> <p>Publication: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.: Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2732, 2023.</p> <p>Source: This dataset is available via <a href="https://doi.org/10.5281/zenodo.11386835">Zenodo DOI: 10.5281/zenodo.11386835</a>.</p> <p>Authors: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.</p> <p>License: This work is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0 </a>.</p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></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>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"
<p>Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript. </p>
Historical climate model output of ECHAM5-wiso from 1871-2011 at T106 resolution
<p>Historical climate model simulation of the isotope-enabled ECHAM5-wiso model from the years 1871 to 2011 at T106 (1 degree) resolution. The model code was provided by Martin Werner of AWI. The simulations were designed and run by Nathan Steiger on the Yellowstone supercomputer. The boundary conditions were interpolated HadISST fields. The simulations also included updated fractionation factors (an option within the ECHAM5-wiso Fortran code). All variables here are at monthly resolution in netcdf format. Please <a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">contact</a> Nathan Steiger if you have any questions about the simulation. In addition to the data citation, please also cite the following reference for where the data were first published: Steiger, N.J., E.J. Steig, S.G. Dee, G.H. Roe, and G.J. Hakim, (2017): <em>Climate reconstruction using data assimilation of water-isotope ratios from ice cores.</em> Journal of Geophysical Research: Atmospheres, doi:10.1002/2016JD026011.</p> <p>Standard variables include: ECHAM5 T106 orography, 2 m temperature, surface pressure, mean sea level pressure, vertically integrated water vapor, total precipitation, evaporation, soil moisture, relative humidity, specific humidity, atmospheric stream function at 200 hPa, geopotential height at 500 hPa, windspeed at 10 m, and u-velocity wind at 200 hPa. Moisture variables are given at the surface (lowest atmospheric level).</p> <p>Isotope variables include: d18O and dD of total precipitation, d18O and dD of evaporation, d18O and dD of snow fall, d18O and dD of seasonal snow cover, d18O and dD of snow on glaciers, d18O and dD of soil moisture, and specific humidity of water isotopes.</p>
IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations: Boundary conditions and outputs.
<p>Inputs, boundary conditions, and ouputs files of the experiments described in Sepulchre et al. manuscript "<em>IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations</em>" submitted for publication to Geoscientific Model Development:</p> <p><a href="https://www.geosci-model-dev-discuss.net/gmd-2019-332"><strong>https://www.geosci-model-dev-discuss.net/gmd-2019-332</strong></a></p> <p>The 90Ma_IPSLCM5A2_inputs.tar tarball contains the input and boundary files used to run the 3,000-year Cretaceous experiment.</p> <p>The output_files.tar tarball contains the netcdf output files of the preindustrial, historical and Cretaceous simulations analyzed in the manuscript. Diagnoses are presented through a Jupyter notebook that can be retrieved and played interactively <strong><a href="https://doi.org/10.5281/zenodo.3549652"><strong>here</strong></a>.</strong></p> <p><strong> </strong></p>
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
FOCI model output used in the study by Ivanciu et al. - Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean
<p>This dataset comprises the output from simulations with the coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020) used in the analysis presented in the study by Ivanciu et al., 2021 “Twenty-first century Southern Hemisphere impacts of ozone recovery and climate change from the stratosphere to the ocean”. Four ensembles of three simulations each were performed: FixODS (II012, II014, II016), FixGHG (II013, II015, II017), INTERACT_O3 (SW128, II010, II011) and PRESC_O3 (JH027, II037, JH039). A detailed description of the simulations can be found in the above-mentioned manuscript.</p>
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model - model outputs
<p>This dataset contains the model outputs used in the figures in the paper entitled "Implementing the iCORAL (version 1.0) coral reef CaCO<sub>3</sub> production module in the iLOVECLIM climate model" submitted to GMD. For the description of the model and simulations we refer to this article.</p> <p>Provided files:</p> <ul> <li>Surface values of temperature (temp), salinity (salt), phosphate (opo4) and aragonite saturation state (omega) for:</li> </ul> <p>The modern period (mean of 2000-2010): <strong>temp_modern.nc</strong>, <strong>salt_modern.nc</strong>, <strong>opo4_modern.nc</strong>, <strong>omega_modern.nc</strong></p> <p>The pre-industrial (PI, mean of last 100 years of the simulation): <strong>temp_PI.nc</strong>, <strong>salt_PI.nc</strong>, <strong>opo4_PI.nc</strong>, <strong>omega_PI.nc</strong></p> <ul> <li>Coral location for:</li> </ul> <p>Imin=50 μE/m2/s: <strong>coral_location_Imin50.nc</strong></p> <p>Imin=300 μE/m2/s: <strong>coral_location_Imin300.nc</strong></p> <p>The values indicate:</p> <p>4 = presence of corals in the model simulation (coral area less or equal to 5% of the grid cell area) but not in observations</p> <p>3 = presence of corals in both model and observational data</p> <p>2 = presence of corals in observational data but not in the model simulation</p> <p>1 = presence of corals in the model simulation (coral area more than 5% of the grid cell area) but not in observations</p> <ul> <li>Global coral reef area (10<sup>3</sup> km<sup>2</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_area_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>min</sub></em> (the minimum light intensity necessary for reef growth, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Imin.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>I<sub>k</sub></em> (the saturating light intensity, µE m<sup>-2</sup> s<sup>-1</sup>): <strong>Total_prod_vs_Ik.txt</strong></li> <li>Global coral reef carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and <em>g<sub>max</sub></em> (the maximum production growth): <strong>Total_prod_vs_gmax.txt</strong></li> <li>Global carbonate production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>) and global coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_prod_vs_total_area.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and global production (Pg CaCO<sub>3</sub> yr<sup>-1</sup>): <strong>Total_production_vs_rmse_Perry.txt</strong></li> <li>Root mean square error (RMSE, kg CaCO<sub>3</sub> m<sup>-2</sup> yr<sup>-1</sup>) between the simulations and the observational data of regional production (Perry et al., 2018) and coral reef area (10<sup>3</sup> km<sup>2</sup>):<strong> Total_area_vs_rmse_Perry.txt</strong></li> </ul>
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)
<p>Outputs used in:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are:</p> <ul> <li>MAPI-2w: 2-way coupling, consideration <em>online</em> of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the <em>offline</em> correction (Franco <em>et al.</em>, 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w: 0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> <p>MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM. </p> <p>PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments. </p> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p><em>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</em></p> <p>Reference</p> <p><em>Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012.</em></p> <p><em>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.</em></p>
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
<p>Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.</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.