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212 results for “Climate Simulation”
Climate Forced Hydropower Simulations Using NASA NEX-GDDP
<p>* This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset. </p> <p>This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099"). </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B. "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".</p>
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
Data and scripts for "Simulating AMOC tipping driven by internal climate variability with a rare event algorithm."
<p>This dataset contains supplementary material for the paper Simulating AMOC tipping driven by internal climate variability with a rare event algorithm." (M.Cini, G. Zappa, F. Ragone, S. Corti, 2023) submitted to <i>npj Climate and Atmospheric Science. </i> Preprint is available at https://www.researchsquare.com/article/rs-3215995/latest.<br>Here we uploaded most relevant data and scripts concerning this study. Feel free to contact us for other resources.<br><br>This datasets contains:</p><p>1) The output of one 125-years ensemble simulation performed with the Algorithm.</p><p>2) Time series of the AMOC index for all the simulations performed.<br>3) All data-analysis related scripts. These scripts have been used to plot all the figures in the paper.</p><p>The script for the rare event algorithm is already available in the Supplementary material for "Rare event algorithm study of extreme warm summers and heatwaves over Europe" zenodo repository, available at https://zenodo.org/records/4763283.<br><br><strong>Simulation Architecture and model setup</strong></p><p>All the simulations have been performed with an intermediate complexity coupled climate model, composed by the Planet Simulator (PlaSim) and the Large Scale Geostrophic Ocean (LSG). All simulations are performed at stationary greenhouse gases forcing. More information about model setup and scope of the simulations can be found in the paper.<br><br>We performed 10 100-member ensemble simulations. First 125 years of the simulation are performed with the algorithm on (k=3). Then, simulations have been restarted at year 120 with the algorithm off (k=0) up to year 400. This last 380 years of simulation have been performed with only 20 members.<br><br><strong>y2480_k3_ntraj100</strong></p><p>y2480_k3_ntraj100.tar.gz contains the output of one ensemble simulation (y2480, i.e. the one that starts at year 2480 of the control run simulation) performed with the algorithm, i.e. contains data of 125 years simulation of 100 members.<br>Data is organized in blocks for each years. Every block contains the 4 NetCDF light file for each member and 4 files with full-size output that represent the mean state as the average of the 100 members. The 4 different file name accounts for the 4 different module output of the model: "data" for the atmosphere, "ice" for sea ice, "ocean" for the slab ocean layer in PlaSIM, lsg for LSG dynamical ocean.</p><p> </p><p><strong>Time Series</strong></p><p>Time series of the AMOC index are contained in 10 files representing the 10 different simulations performed. In each file are present 125 .txt files, one for each year of the simulation with the algorithm on, with the annual average AMOC indices of the 100 members, and 380 .txt files, one for each year of the simulation with the algorithm off, with the annual average AMOC indices of the 20 members.</p><p> </p><p><strong>Response Analysis REA</strong></p><p>Response Analysis REA contains the script that generates Fig.2, Fig. S3, Fig. S5, Fig. S6 and Fig. S7 of the paper. In general it provides tools for data analysis of the climate response to an AMOC slowdown. Be aware that data of the lsg module needs different processing. </p><p> </p><p><strong>AMOC Evolution REA</strong></p><p>AMOC Evolution REA contains the script that generates Fig.1, Fig. 4, Fig. 5, Fig. 6 and Fig. S2 of the paper. In general it provides tools for analysis of time series and scatter plots of the AMOC evolution. <br><br> </p><p><strong>Causes REA</strong></p><p>Causes REA contains the script that generates Fig.3, Fig. S4 of the paper. . In general it provides tools for analysis of driving elements of the AMOC decline. More information about these methods can be found in the "Triggering mechanisms" section of the paper. Be aware that data of the lsg module needs different processing. </p>
Phanerozoic global climatic fields simulated using the FOAM ocean-atmosphere general circulation model
<p>These files contain the output of Phanerozoic global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. All simulations have been conducted using identical boundary conditions; pCO2: 2240 ppm, solar luminosity: 1368 W m-2, vegetation: rocky desert, orbital configuration: null eccentricity and minimum obliquity. Only the continental configuration was varied from one time slice to the other (sensitivity test to the continental configuration), using the reconstructions of Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/).</p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All file names use the following pattern: "[age]rd_1368W_EccN_[model_component]_2240ppm.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos', 'ocean' or 'coupl' (atmospheric and oceanic components, plus coupler).</p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
Dataset on Alternaria disease on rocket under simulated climate change conditions
<p>This dataset is related to disease severity caused by the Alternaria spp. isolates tested in different temperature and CO2 combinations on cultivated rocket and published in https://doi.org/10.3920/WMJ2016.2108<em> </em>(Figure 1) and in https://doi.org/10.1007/s42161-018-0125-8,</p>
Climate and Crop variables of tomato greenhouse simulation
<p>Climate and growth variables of a simulated tomato greenhouse are shown.</p> <p>Description of columns:</p> <p>----------------------------------</p> <p>'DateTime' : Time Stamp [dd-MM-yyyy hh:mm:ss]</p> <p>'Cppm' : outdoor Concentration of CO2 [ppm]</p> <p>'Wind' : wind velocity [m/s]</p> <p>'HR' : exterior relative humidity [%]</p> <p>'Rad' : exterior radiation [W/m^2]</p> <p>'Temp' : outdoor temperature [K]</p> <p>'Temp__Tcover' : temperature of cover [K]</p> <p>'Temp__Tair' : temperature of air [K]</p> <p>'Temp__Tfloor' : temperature of floor [K]</p> <p>'Temp__Tsoil' : temperature of soil [K]</p> <p>'QT__QT' : heat loss of crop by evapotranspiration [W]</p> <p>'QS__R_int' : Indoor Radiation [W/m^2]</p> <p>'Gas__C_w' : Absolute Humidity [kg/m^3]</p> <p>'Gas__C_c' : CO2 Concentration [kg/m^3]</p> <p>'Gas__rho_i' : Air density [kg/m^3]</p> <p>'Gas__C_c_ppm' : CO2 Concentration [ppm]</p> <p>'Gas__HRInt' : Indoor relative humidity [%]</p> <p>'R' : Ratio of ventilation [1/s]</p> <p>'Windows__value' : percent open window [%]</p> <p>'Screen__value' : percent open screen [%]</p> <p>'Carbon__Cbuff' : dry carbon in buffer by square meter of cultivation [kg/m^2]</p> <p>'Carbon__Cfruit' : dry carbon in fruit by square meter of cultivation [kg/m^2]</p> <p>'Carbon__Cleaf' : dry carbon in leaf by square meter of cultivation [kg/m^2]</p> <p>'Carbon__Cstem' : dry carbon in stem by square meter of cultivation [kg/m^2]</p> <p>'Tsum' : Acumulative temperature [ºC day]</p> <p>'C_Total' : total dry carbon by square meter of cultivation [kg/m^2]</p> <p>'WC' : water capacity of crop [kg/m^2]</p> <p>'LAI' : leaf area index [-]</p> <p>'CC' : CO2 flux from crop to air</p> <p>'VPD' : Vapor pressure deficit [Pa]</p> <p>'Water__WaterFlows__WaterUptake' : Water uptake of crop by square meter of cultivation [kg/(sm^2)]</p> <p><br> </p>
Data from: A novel laboratory method to simulate climatic stress with successful application to experiments with medically relevant ticks
<p>Ticks are the most important vectors of zoonotic disease-causing pathogens in North America and Europe. Many tick species are expanding their geographic range. Although correlational evidence suggests that climate change is driving the range expansion of ticks, experimental evidence is necessary to develop a mechanistic understanding of ticks' response to a range of climatic conditions. Previous experiments used simulated microclimates, but these protocols require hazardous salts or expensive laboratory equipment to manipulate humidity. We developed a novel, safe, stable, convenient, and economical method to isolate individual ticks and manipulate their microclimates. The protocol involves placing individual ticks in plastic tubes, and placing six tubes along with a commercial two-way humidity control pack in an airtight container. We successfully used this method to investigate how humidity affects survival and host-seeking (questing) behavior of three tick species: the lone star tick (Amblyomma americanum), American dog tick (Dermacentor variabilis), and black-legged tick (Ixodes scapularis). We placed 72 adult females of each species individually into plastic tubes and separated them into three experimental relative humidity (RH) treatments representing distinct climates: 32% RH, 58% RH, and 84% RH. We assessed the survival and questing behavior of each tick for 30 days. In all three species, survivorship significantly declined in drier conditions. Questing height was negatively associated with RH in Amblyomma, positively associated with RH in Dermacentor, and not associated with RH in Ixodes. The frequency of questing behavior increased significantly with drier conditions for Dermacentor but not for Amblyomma or Ixodes. This report demonstrates an effective method for assessing the viability and host-seeking behavior of tick vectors of zoonotic diseases under different climatic conditions.</p>
Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results
<p>The Great Oxidation Event was a period during which Earth's atmospheric oxygen (O<sub>2</sub>) concentrations increased from ~10<sup>−5</sup> times its present atmospheric level (PAL) to near modern levels, marking the start of the Proterozoic geological eon 2.4 billion years ago. Using WACCM6, an Earth System Model, we simulate the atmosphere of Earth-analogue exoplanets with O<sub>2</sub> mixing ratios between 0.1% and 150% PAL. Using these simulations, we calculate the reflection/emission spectra over multiple orbits using the Planetary Spectrum Generator. We highlight how observer angle, albedo, chemistry, and clouds affect the simulated observations. We show that inter-annual climate variations, as well as short-term variations due to clouds, can be observed in our simulated atmospheres with a telescope concept such as LUVOIR or HabEx. Annual variability and seasonal variability can change the planet's reflected flux (including the reflected flux of key spectral features such as O<sub>2</sub> and H<sub>2</sub>O) by up to factors of 5 and 20, respectively, for the same planetary phase. This variability is best observed with a high-throughput coronagraph. For example, HabEx (4 m) with a starshade performs up to a factor of two times better than a LUVOIR B (6 m) style telescope. The variability and signal-to-noise ratio of some spectral features depends non-linearly on atmospheric O<sub>2</sub> concentration. This is caused by temperature and chemical column depth variations, as well as generally increased liquid and ice cloud content for atmospheres with O<sub>2</sub> concentrations of <1% PAL.</p>
Phanerozoic global climatic fields simulated using the mixed-layer general circulation model FOAM
<p>These files contain the output of Phanerozoic global climate simulations conducted using the “slab” mixed-layer ocean-atmosphere general circulation model FOAM. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. Boundary conditions were adapted to best match each time slice. Continental reconstructions were taken from Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). We defined pCO2 after the proxy data compilation of Foster et al. (doi:10.1038/ncomms14845) when available and Krause et al. (dot:10.1038/s41467-018-06383-y) for older time slices. Solar luminosity followed Gough et al. (doi:10.1007/BF00151270). Continental vegetation was set to Modern-like latitudinal bands between 0 Ma and 100 Ma (included), tropical evergreen, broad-leaved forest between 120 Ma and 360 Ma (included), tundra between 380 Ma and 440 Ma (included) and rocky desert afterwards. The orbital configuration was set to null eccentricity and minimum obliquity. </p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All model output file names use the following pattern: « [age]ebP2_solCgough1981_EccN_pCO2FosterKr_[model_component] _slab.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos' or 'coupl' (atmospheric component or coupler). For each time slice, the topography-bathymetry data used in FOAM is also provided (« Topobathy_[age]eb_postslarti_cor.nc »).</p>
Data and scripts for figures in Walton & Huntingford, "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation", submitted for publication to Environmental Research Letters. Scripts used for plotting and analysis are also included.</p>
Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations
<p>This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model<br>trained via DOmain RAndomization". The goal is to provide training data for occupancy detection.<br><br>The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper.<br><br>When using the dataset, please cite:</p> <blockquote> <p><em>Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan.</em></p> </blockquote> <h2>Dataset Structure</h2> <p>The following files are provided:<br><br> 1. dataset_office_rooms.h5 (provided as zip file)<br> 2. dataset_meeting_rooms.h5 (provided as zip file)<br> 3. simulated_occupancy_office_rooms.csv<br> 4. simulated_occupancy_meeting_rooms.csv</p> <p>Please use an archiving tool such as 7zip to unzip the hdf5 files.<br>Both hdf5 files contain two datasets with the following keys:<br><br> 1. "<em>data</em>": contains the simulated indoor climate and occupancy data<br> 2. "metadata": contains the metadata that were used for each simulation</p> <p>The csv files contain the time series of occupancy that were used for the simulations.<br><br></p> <h2>Data</h2> <p><em>Data</em> includes the following fields:</p> <p><em>Datetime:</em> day of the year (may be relevant due to seasonal differences) and time of the day<br><em>Zone Air CO2 Concentration:</em> CO2 level in ppm<br><em>Zone Mean Air Temperature:</em> temperature in °C<br><em>Zone Air Relative Humidity: </em>relative humidity in %<br><em>Occupancy: </em>level of occupancy relative to the maximum capacity of the room (in the range [0-1])<br><em>Ventilation:</em> fraction of window opening in the range [0.01, 1]<br><em>SimID:</em> foreign key to reference the room properties the simulation was based on<br><em>BinaryOccupancy:</em> 0 or 1 denoting absence or presence (for binary classification)</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th><em>Datetime</em></th> <th><em>Zone Air CO2 Concentration</em></th> <th><em>Zone Mean Air Temperature</em></th> <th><em>Zone Air Relative Humidity</em></th> <th><em>Occupancy</em></th> <th><em>Ventilation</em></th> <th><em>simID</em></th> <th><em>BinaryOccupancy</em></th> </tr> <tr> <td> <p>10/09 11:21:00</p> </td> <td> <p>1084.5624647371608</p> </td> <td> <p>24.545635909907148</p> </td> <td> <p>41.18393114737054</p> </td> <td> <p>0.7</p> </td> <td> <p>0.0</p> </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> <p><em>Metadata</em> includes the following fields. <br>Underscores denote that the field was not selected during randomization but calculated from the other values.</p> <p>width: room width in m<br>length: room length in m<br>height: hoom height in m<br>infiltration: infiltration per exterior area in m³/m²s<br>outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500])<br>orientation: angle between the room's facade orientation and the north direction in degrees<br>maxOccupants: room occupation limit, i.e. the maximum number of occupants<br>_floorArea: floor area in m² (calculated from room dimensions)<br>_volume: room volume in m³ (calculated from room dimensions)<br>_exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions)<br>_winToFloorRatio: ratio between total window area and floor area (calculated from room model)<br>firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value<br>simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table> <p> </p> <h2>Occupancy Data</h2> <p>The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days.</p> <p><em>Datetime, </em><em>Date, </em><em>Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution)</em><br><em>Occupants: number of present occupants</em><br><em>Occupancy: binary occupancy state (0=unoccupied, 1=occupied)</em><br><em>WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated)</em><br><em>maxOccupants: maximum number of occupants considered for the simulated sequence</em><br><em>WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1]<br><br></em></p> <p>Example row:</p> <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table> <p> </p> <p> </p>
CESM1.2 simulation data for "Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks"
<p>CESM1.2 simulation data for Early Eocene</p> <p><strong>Citations:</strong></p> <p>Zhu, J., Poulsen, C. J., & Tierney, J. E. (2019). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. <em>Science Advances</em>, 5(9), eaax1874. <a href="https://doi.org/10.1126/sciadv.aax1874">https://doi.org/10.1126/sciadv.aax1874</a></p> <p>Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164" rel="nofollow">https://doi.org/10.1016/j.epsl.2020.116164</a></p> <p> </p> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP), surface temperature (TS) and surface temperature at reference height (TREFHT) from four Eocene simulations with 1×, 3×, 6× and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>TS and TREFHT are on the atmosphere grid of 1.9 × 2.5° (latitude × longitude).</li> <li>TEMP is on the POP ocean grid (~1°; see here: http://www.cesm.ucar.edu/models/cesm1.2/pop2/).</li> <li>NEW on July 09, 2024: restart files for the Eocene simulations.</li> </ul> <p>A case folder is available on GitHub: <a href="https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne">https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne</a></p> <p> </p>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</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>
Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations
<p>This dataset contains NetCDF files necessary to replicate results from the 2024 paper "<em>Impact of Grid Resolution on Wave-mean Flow Interactions with High Resolution Mars Global Climate Model Simulations</em>"</p> <p>The dataset contains NetCDF files with 1 year of zonally-averaged NASA Ames Mars Global Climate Model (MGCM) fields with 5-sol binning for each of the simulations presented in the paper: </p> <ul> <li>a "low-resolution" simulation with no parameterization for gravity waves</li> <li>a "high-resolution" simulation with no parameterization for gravity waves</li> <li>a "low-resolution" simulation with parameterizations for orographic and non-orographic gravity waves</li> </ul> <p>Also included are:</p> <ul> <li>a file describing the coordinates for the MGCM's vertical grids used in the study </li> <li> atmospheric fields not provided in the other NetCDF files and necessary to replicate figures 3 and supplemental figure FS2 from the paper.</li> <li>a README.txt detailing the content of each file in the dataset</li> </ul>
Climate Forced Hydropower Simulations for the African Continent Using NASA NEX GDDP
<p>This dataset includes the results of simulations of future hydropower usable capacity for power plants across the five African power pools. These include 87 power plants in 27 different countries. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. We include a PDF file "Description of Data.pdf" that describes all the information included in the dataset. </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C, & Nijssen, Bart. "Power pools for the win: Assessing climate resilience of hydropower resources in African power pools".</p>
Climate-ecosystem modelling made easy: the Land Sites Platform - simulation results
<p>Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform" (Keetz & Lieungh et al., accepted; publication details to be added).</p> <p>The repository includes model input data for the BOR1 site, and model output for two cases (simulations) executed for that site. Case "bor1-1000y-allpfts" was run with default settings, whereas case "bor1-1000y-grasspfts" was run with C3 grass and Arctic C3 grass as the only plant functional types.<br> Concatenated versions of the model output (made by combining monthly history files) for each case are stored in the top folder as NetCDF files (.nc). The full case folders, including monthly output under /archive/lnd/hist, are stored under "cases/" as zipped directories.<br> The "data/" folder contains model input data for the site, and is identical for the two cases.</p> <p>Jupyter notebooks to analyse the data and create the plots in Figure 4 of the article are stored under "notebooks/technical_paper_results/" in the NorESM-LSP GitHub repository, which is accessible in the same version as the submitted manuscript here: 10.5281/zenodo.7310652</p> <p>Description: Model data to reproduce the experiments and figures presented in "Climate-ecosystem modelling made easy: the Land Sites Platform".<br> Coverage: Geographical coordinates 9.07876, 61.0355. Forcing data from the closest 0.5-degree model grid cell.<br> Format: NetCDF (.nc), zipped directories (.zip), shell scripts (.sh), XML files (.xml), text files (.txt), and others<br> Language: English<br> Relation: 10.5281/zenodo.7310652, <br> Source: GSWP3 for input data (Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N. (2006) GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface. Bulletin of the American Meteorological Society, 87(10), 1381–98.)</p>
Simulations for pre-industrial climate using EC-Earth3-LR model — selected data for a study on AMOC
<p>A long-term control simulation of pre-industrial period (1850 CE) climates were performed by the EC-Earth3-LR climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations.</p> <p>In total, a 2000-year long control simulation was made, which has pre-industrial orbital boundary conditions, initialized by a pre-run steady restart file (the output of approximately 500-year pre-industrial control simulation). This dataset is used to investigate internal climate variability without external forcing changes under pre-industrial climate conditions.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2022).</p> <p><strong>Model configuration</strong><br> Time periods: Pre-Industrial (2000-year time slice)<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for standard oceanographic and meteorological variables.</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </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.