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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>
ACCESS-AM2 model output for 2017-2018 MARCUS and 2018-2019 CAMMPCAN RSV Aurora Australis voyages
<p>The dataset includes model output from the ACCESS-AM2 model corresponding to the MARCUS (Measurements of Aerosols, Radiation and Clouds over the Southern Oceans) 2017-2018 voyages and the CAMMPCAN (Chemical and Mesoscale Mechanisms of Polar Cell Aerosol Nucleation) 2018-2019 voyages. The MARCUS voyages included a limited number of CAMMPCAN instruments while the CAMMPCAN voyages included the full suite of instruments. </p> <p>The model version used was ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) run with CMIP6 AMIP configuration, nudged with ERA5 reanalysis and full chemistry switched on. The model was configured with a horizontal resolution of 1.25◦ latitude and 1.875◦ longitude and 85 vertical levels. ACCESS-AM2 uses the UK Met Office’s Unified Model Global Atmosphere (UM10.6 GA7.1) as the atmosphere module, the Community Atmosphere Biosphere Land Exchange model version 2.5 (CABLE2.5) as the land-surface module and the Global Model of Aerosol Processes (GLOMAP-mode) as the aerosol module. More information on the ACCESS-AM2 model can be found at <a href="https://doi.org/10.1071/ES19033">https://doi.org/10.1071/ES19033</a>.</p> <p>The data is at daily means spanning 29-10-2017 to 26-03-2018 (149 days) for MARCUS, and 25-10-2018 to 24-03-2019 (151 days) for CAMMPCAN.</p> <p>Files included in this upload include:</p> <ul> <li>aa1718_cg893_track.nc: aerosol, chemistry, and meteorology model data for the MARCUS voyages</li> <li>cg893_daily_mean_MARCUS_size_distributions.nc: calculated aerosol size distribution model data for the MARCUS voyages </li> <li>aa1819_cg893_track.nc: aerosol, chemistry, and meteorology model data for the CAMMPCAN voyages</li> <li>cg893_daily_mean_CC_size_distributions.nc: calculated aerosol size distribution model data for the CAMMPCAN voyages</li> </ul> <p>File names refer to Aurora Australis, voyage years (either 2017-2018 or 2018-2019), followed by the model run and data type.</p> <p>An overview of the variable field names, variable long names, height profile availability and units available in the dataset is provided in VariablesOverview.xlsx. </p> <p>A Jupyter Notebook is also included and contains scripts that can be used to create figures for preliminary analysis using the model data.</p> <p>Additional information:</p> <ul> <li>MARCUS details: <a href="https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf">https://asr.science.energy.gov/meetings/stm/presentations/2017/473.pdf</a></li> <li>CAMMPCAN details: <a href="https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation">https://findanexpert.unimelb.edu.au/project/102792-cammpcan-%E2%80%93-chemical-and-mesoscale-mechanisms-of-polar-cell-aerosol-nucleation</a></li> <li>MARCUS Observations: <a href="https://doi.org/10.26179/5e54ab5e5d56f">https://doi.org/10.26179/5e54ab5e5d56f</a></li> <li>CAMMPCAN Observations: <a href="https://doi.org/10.26179/5e546f452145d">https://doi.org/10.26179/5e546f452145d</a> </li> </ul> <p>The GitHub repository containing the code used to produce these datasets can be found here: <a href="https://github.com/llamprey/aurora_voyages">https://github.com/llamprey/aurora_voyages</a></p> <p> </p> <p>Versions:</p> <p>1.0.0: Initial Version.</p> <p>1.1.0: Fixed bug where CN and CCN fields were incorrectly calculated.</p> <p>1.2.0: Updated aerosol size distribution files</p>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
The Time Variable Ionospheric Electric Field (TiVIE) Model Outputs v 1.0
<p>These are the outputs for the TiVIE model v 1.0 produced by Maria-Theresia Walach, Lancaster University for the publication Walach, M.-T., and Grocott, A. (submitted 2024). </p>
Outputs of the next generation sea ice model (neXtSIM) for winter 2006 - 2007 saved for comparison with RGPS.
<p>NeXtSIM was run from 1 December 2006 to 15 April 2007 with the following parameters:</p> <p><code>[mesh]</code><br><code>filename=small_arctic_10km.msh</code></p> <p><code>[simul]</code><br><code>duration=150</code><br><code>time_init=2006-11-15</code><br><code>timestep=900</code></p> <p><code>[dynamics]</code><br><code>compression_factor=13800</code><br><code>C_lab=2675000</code><br><code>nu0=0.301</code><br><code>tan_phi=0.624</code><br><code>substeps=90</code><br><code>time_relaxation_damage=15</code><br><code>use_temperature_dependent_healing=true</code></p> <p><code>[output]</code><br><code>exporter_path=/cluster/work/users/akorosov/music/sa10free_mat00</code><br><code>output_per_day=4</code><br><code>variables=M_VT</code><br><code>variables=Concentration</code><br><code>variables=Thickness</code></p> <p><code>[setup]</code><br><code>atmosphere-type=era5</code><br><code>ice-type=topaz_osisaf_icesat</code><br><code>ocean-type=topaz</code><br><code>bathymetry-type=etopo</code><br><code>dynamics-type=bbm</code></p> <p><code>[thermo]</code><br><code>diffusivity_sss=0</code><br><code>diffusivity_sst=0</code><br><code>h_young_max=0.3</code><br><code>newice_type=1</code><br><code>hnull=0.5</code></p> <p><code>[debugging]</code><br><code>check_fields_fast=false</code></p> <p>The outputs (binary snapshots at every 3 hours) were then merged with RGPS data from the same period using this notebook:</p> <p>https://github.com/nansencenter/music_nextsim_tuning_paper/blob/main/02_process_nextsim.ipynb</p> <p> </p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from 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>
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>
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/12-degree</p> <p>- REGRID versions are regridded from ORCA0083 grid to a regular 1/12-degree grid</p> <p>- Simulation performed as part of core NOC activities</p> <p>- Forced under version 5.2 of the DRAKKAR observation-based reanalysis dataset (DFS)</p> <p>- Physical simulation described in Kelly, S. J., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2020). They came from the Pacific: How changing Arctic currents could contribute to an ecological regime shift in the Atlantic Ocean. Earth's Future, 8, e2019EF001394. https://doi.org/10.1029/2019EF001394</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in August 2021</p> <p>- Seafloor fields of model properties for the periods 2006-2015</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- The output has been regridded to a regular 1/12-degree grid</p>
Model output from Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier
<p>This model output dataset accompanies the draft paper 'Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier'.</p>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</p>
Output files of the 2DV-model for the 79NG fjord
<p>This archive contains the output files of the 2D-vertical GETM runs presented by Reinert <em>et al.</em> (2023).</p> <p>The NetCDF files contain the steady state of the simulation, except for the file “default_initial_state.nc”. See the README file for further details.</p> <p><strong>Reference:</strong></p> <p>Reinert, M., Lorenz, M., Klingbeil, K., Büchmann, B., & Burchard, H. (2023). High-Resolution Simulations of the Plume Dynamics in an Idealized 79°N Glacier Cavity Using Adaptive Vertical Coordinates. <em>Journal of Advances in Modeling Earth Systems,</em> 15(10), e2023MS003721. <a title="Link to the paper describing this dataset" href="https://doi.org/10.1029/2023MS003721" target="_blank" rel="noopener">DOI: 10.1029/2023MS003721</a></p>
Model outputs for the article "Modelling the evolution of Arctic multiyear sea ice over 2000–2018"
<p><em>icemod_monthly.tar.gz </em>contains the gridded monthly averaged quantities used in the manuscript "Modelling the evolution of Arctic multiyear sea ice over 2000-2018" for each year between 2000 and 2018.</p> <p>Multiyear ice variables are conc_myi (concentration of multiyear ice in a grid cell) and thick_myi (cell average thickness of multiyear ice in a grid cell, in metres), along with source and sink terms (units per day) for multiyear concentration (dci_mlt_myi, dci_ridge_myi and dci_rplnt_myi, for melt, ridging and replenishment) and volume (dvi_mlt_myi and dvi_rplnt_myi, for melt and replenishment).</p> <p><em>transports_monthly_sections.zip </em>contains the transports of multiyear ice through the sections defining each region in Figure 8 of the paper. MYIsiaXport indicates multiyear ice area transport, while myiXport indicates multiyear ice volume transport.</p> <p>In case information is missing, do not hesitate to contact heather.regan@nersc.no, guillaume.boutin@nersc.no, or einar.olason@nersc.no.</p>
Met Office Unified Model outputs for simulations of Proxima Centauri b and TRAPPIST-1e
<p>Five datasets generated by the Met Office Unified Model configured for exoplanets Proxima Centauri b and TRAPPIST-1e. </p> <p>1. Control Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere using observed planetary data (radius, stellar constant) as model parameters</p> <p>2. Warm Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>3. Control Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere using observed planetary data</p> <p>4. Warm Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>5. Dry Trap: A simulation of TRAPPIST-1e with a dry atmosphere</p> <p>The data is used in Cohen et al. (2023). ""Traveling planetary-scale waves cause cloud variability on tidally locked aquaplanets." Submitted to The Planetary Science Journal.</p> <p>Abstract:</p> <p>"Cloud cover at the planetary limb of water-rich Earth-like planets is likely to weaken chemical<br> signatures in transmission spectra, impeding attempts to characterize these atmospheres. However,<br> based on observations of Earth and solar system worlds, exoplanets with atmospheres should have both<br> short-term weather and long-term climate variability, implying that cloud cover may be less during<br> some observing periods. We identify and describe a mechanism driving periodic clear sky events at<br> the terminators in simulations of tidally locked Earth-like planets. A feedback between dayside cloud<br> radiative effects, incoming stellar radiation and heating, and the dynamical state of the atmosphere,<br> especially the zonal wavenumber-1 Rossby wave identified in past work on tidally locked planets, leads<br> to oscillations in Rossby wave phase speeds and in the position of Rossby gyres and results in advection<br> of clouds to or away from the planet’s eastern terminator. We study this oscillation in simulations of<br> Proxima Centauri b, TRAPPIST 1-e, and rapidly rotating versions of these worlds located at the inner<br> edge of their stars’ habitable zones. We simulate time series of the transit depths of the 1.4 μm water<br> feature and 2.7 μm carbon dioxide feature. The impact of atmospheric variability on the transmission<br> spectra is sensitive to the structure of the dayside cloud cover and the location of the Rossby gyres,<br> but none of our simulations have variability significant enough to be detectable with current methods."</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>
Model output for five Hmsc models of alpine grassland communities
<p>Model output from five joint species distribution models made with Hmsc in R. One 'global' model with all data, and one model for each of four sites Skjellingahaugen, Gudmedalen, Låvisdalen, and Ulvehaugen.<br> <br> Omegas are species co-occurrence estimates.</p> <p>Models defined by EL, OO, data formatted by EL, model fit by OO.</p> <p>Scripts for model fitting and presenting output are not published here but follow the generic Hmsc pipeline as published in Ovaskainen & Abrego (2020). Joint Species Distribution Modelling With Applications in R. Cambridge university press. DOI: <a href="https://doi.org/10.1017/9781108591720">https://doi.org/10.1017/9781108591720</a></p> <p> </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.