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204 results for “Global simulations”
TCV-X21-GENEX: influence of collisions on the validation of global gyrokinetic simulations
<p>This repository contains the data that supports the findings of the study that is published in <a href="http://doi.org/10.1063/5.0144688"><em>P. Ulbl et al., Phys. Plasmas 30, </em>052507 <em>(2023)</em></a>. Three global electromagnetic gyrokinetic simulations of the <a href="https://doi.org/10.5281/zenodo.5776286">TCV-X21</a> case have been performed. A collisionless (No Coll) simulation, one with a Bhatnagar-Gross-Krook (BGK) collision operator and one with a Fokker-Planck type Lenard-Bernstein/Dougherty (LBD) collision operator.</p> <p>For getting started, please consider the README file provided in this dataset. The Jupyter notebook herein provides a low level entry on how to access the data from the netCDF file. The zip files contain sets of input parameters that are given for future reference.</p>
Simulated discharge statistics in Central and Southwestern Europe considering water use under 2K global warming
<p>We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.</p> <p> </p> <p><strong>Discharge statistics</strong> based on daily output from CWatM within 1981-2010:</p> <ul> <li><strong>Q1</strong> - 1st percentile</li> <li><strong>Q5</strong> - 5th percentile</li> <li><strong>Q10</strong> - 10th percentile</li> <li><strong>Qavg</strong> - average discharge</li> <li><strong>Q90</strong> - 90th percentile</li> <li><strong>Q95</strong> - 95th percentile</li> <li><strong>Q99</strong> - 99th percentile</li> </ul> <p><strong>Files:</strong></p> <ul> <li><strong>Qxx_reference</strong>: CWatM considering historical water use forced by RACMO-ERA5</li> <li><strong>Qxx_PGW:</strong> CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.</li> <li><strong>Qxx_PGW_adjusted_demand:</strong> We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.</li> </ul> <p>An upcoming publication will be made available and linked to this research very soon.</p> <p> </p>
ACCESS-OM2 1° resolution global repeat decade full forcing interannual simulation data for 1972-2018
<p>This data set contains the <strong>full forcing</strong> interannual simulation output from the global ocean-sea ice model ACCESS-OM2 in the 1° horizontal configuration over the period 1972-2018.</p> <p>This simulation was branched off from the repeat decade forcing spin-up and alongside the control simulation (see light blue and black lines in Fig. 1c in the publication linked below).</p> <p>The control simulation output can be found here: https://zenodo.org/record/8339578 The output here as well as in the control simulation is saved in sets of ten years (output200, output201, ...) in the ocean/ and ice/ folders as netcdf files.</p> <p>The last output folder contains the data for 2012-2018 with the last four years of this output folder (output204) are again the 1972-1975 period and should be omitted from any analysis.</p> <p>For more information on the spin-up and the model configuration, see the Methods section and Fig. 1 in Huguenin, M.F., Holmes, R.M. & England, M.H. Drivers and distribution of global ocean heat uptake over the last half century. <em>Nat Commun</em> 13, 4921 (2022). https://doi.org/10.1038/s41467-022-32540-51</p> <p> </p>
Cothran, R. D., F. Radarian, and R. A. Relyea. 2011. Altering aquatic food webs with a global insecticide: Arthropod-amphibian links in mesocosms that simulate wetland communities. Journal of the North American Benthological Society 30:893-912.
Pesticides play a critical role in maximizing yields of economically important crops and minimizing the human health threats of disease-carrying pests, but they often have collateral effects on nontarget species. We used a mesocosm study to address how the most commonly used insecticide in the USA, malathion, applied at low, ecologically relevant concentrations (20 and 110 mg/L) affects species interactions in aquatic communities. Unlike many community ecotoxicology studies, our study assessed how malathion affects both consumptive and nonconsumptive effects of predators. We also considered how the vertical distribution of predator cues and malathion (caused by potential stratification) affects species interactions. We found no evidence for vertical stratification of malathion, a result suggesting that exposure to the pesticide was uniform throughout the water column. Malathion was lethal to some primary consumers (cladocerans) at both concentrations and to top predators (dragonflies) at the highest concentration (110 mg/L). These lethal effects initiated density-mediated indirect effects in both cases. Malathion also may have decreased dragonfly foraging efficiency, resulting in increased tadpole survival (trait-mediated indirect effect), which decreased the resources used by tadpoles (periphyton). Collectively, our results show that malathion alters species interactions. However, we suggest that the degree to which pesticides affect aquatic communities will depend strongly on the species composition of communities. Therefore, the community-level consequences of pesticide exposure are likely to vary across the ecological landscape.
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Global Storm Tide Modeling on Unstructured Meshes with ADCIRC v55 - Simulation Results and Model Setup
<p>Simulation results and model setup for the paper entitled "Global Storm Tide Modeling with ADCIRC v55: Unstructured Mesh Design and Performance". Simulations conducted using <a href="http://adcirc.org/">ADCIRC</a> (pre-release Version 55) on unstructured triangular meshes of the global Earth's ocean. </p> <p>Contains:</p> <ol> <li>Global Tide Harmonics: Simulated harmonic constituents of global astronomical tide on various mesh designs (*.53.nc).</li> <li>Local Storm Tide: ADCIRC model setup and simulation results from storm tides forced by Hurricane Katrina and Super Typhoon Haiyan on meshes with different local refinements (1.5 km, 500 m, 150 m) in the storm landfall region. <ul> <li>ADCIRC model input files (*.13, *.14, *.15, *.22, *.221, *.222, fort.rotm)</li> <li>Global maximum storm tide elevations (*_maxele.63.nc)</li> <li>Global 3-hourly storm tide elevation time series (*.63.nc)</li> <li>Storm tide elevation and velocity time series (20-min intervals) at selected stations (*.61.nc, *.62.nc)</li> </ul> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-GLOBAL.zip) used to produce the results archived here.</li> </ol> <p>See the README files for further details.</p>
Coarse-grained Near-global Aqua-planet Simulation with Computed Dynamical Tendencies
<p>This dataset includes the coarse-grained 3D state of the near-global CRM simulations (NG-Aqua). The simulation is run at a 4km resolution using the System for Atmospheric Modeling (SAM)</p> <p>A dataset derived from the same simulation is included at the <a href="https://dx.doi.org/10.5281/zenodo.1226370">10.5281/zenodo.1226370</a>. This current posting supplements this dataset with the dynamical tendencies for total water and liquid-ice potential temperature, respectively given by FQT and FSLI. These are computed by initializing SAM run at a 160km resolution with the coarse-grained fields from NG-Aqua; evolving the state forward for 10 30 second time steps; saving the output; and finally computing the difference with the initial condition.</p> <p>This netCDF dataset is split into several part files for more robust uploading/downloading. To download this data, download each "part" file, and combine them with the "cat" linux command:</p> <pre><code>cat noBlur.nc.part?? > noBlur.nc</code></pre> <p>If using this with the uwnet code repository, you should then move this file to "data/processed/training/noBlur.nc", creating that folder if necessary.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
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>
Are terrestrial biosphere models fit for simulating the global land carbon sink?
<p>This repository contains the data and code required for reproducing the results presented in the paper "Are terrestrial biosphere models fit for simulating the global land carbon sink?" by Seiler et al., 2021. The study evaluates an ensemble of terrestrial biosphere models (<a href="https://sites.exeter.ac.uk/trendy/">TRENDY</a>; v9; S3 simulations) against a wide range of reference data using the Automated Model Benchmarking R package (AMBER; version 1.1.1). The only requirement for reproducing our results is access to a Linux machine with <a href="https://docs.conda.io">conda</a>, an open-source package management system and environment management system, installed. Follow the steps described in the <em>readme</em> file to install AMBER and run the analysis. The repository also contains all output produced by our analysis. </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>
Near-inertial wave energetics modulated by background flows in a global model simulation
<p>This data set contains data from a forward global HYCOM simulation (EXPT 19.2) with realistic atmospheric forcing. This is a 4-km simulation with 41 layers. All data is on the native tri-polar grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>More details and context of this data can be found in the article: <a href="https://doi.org/10.1175/JPO-D-21-0130.1">https://doi.org/10.1175/JPO-D-21-0130.1</a>. Please cite this article along with any use of this data.<br> Raja, K. J., Buijsman, M. C., Shriver, J. F., Arbic, B. K., & Siyanbola, O. (2022). Near-Inertial Wave Energetics Modulated by Background Flows in a Global Model Simulation, <em>Journal of Physical Oceanography</em>, <em>52</em>(5), 823-840.</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
FLEXPART–ERA-Interim simulations with 3 million parcels globally (1979–2019)
<p><strong>Description</strong><br> This data set contains FLEXPART simulations driven with the reanalysis ERA-Interim from 1 February 1979 to 30 August 2019. The simulations cover the entire globe (90°S to 90°N and 180°E to 179°W) with 3 million homogeneously distributed air parcels representing the entire mass in the atmosphere. </p> <p><strong>Software</strong><br> The following software versions were used: </p> <ul> <li><strong>flex_extract v.7.1.2-dev</strong> (<a href="https://www.flexpart.eu/browser/flex_extract.git/?rev=3ca4c3ef0fa2a11ccf5a1c69e336b0893e2cb6a3">this version</a>); see Tipka et al. (2020) for details. </li> <li><strong>FLEXPART v10.4</strong> (<a href="https://www.flexpart.eu/browser/flexpart.git/?rev=3d7eebf7c4909f59db5ec32c524f88fb846e9fe5">this version</a>); see Pisso et al. (2019) for details. </li> </ul> <p><strong>Forcing data</strong><br> FLEXPART was driven with global reanalysis from ERA-Interim (Dee et al., 2011). The ERA-Interim reanalysis was downloaded and pre-processed using flex_extract v.7.1.2 (Tipka et al., 2020) using 6-hourly reanalyses at 00, 06, 12, and 18 UTC supplemented with 3- and 9-hourly forecasts (03 UTC and 09 UTC are taken from the forecasts initialised at 00UTC; and 15 UTC and 21 UTC are taken from the forecasts at 12 UTC). The settings are detailed in <em>CONTROL_EI_1deg.global</em>. The output of flex_extract were <em>EIgYYYYMMDDHH </em>files for each time step from 1 February 1979 to 30 August 2019 on a global 1° grid and contain the following variables: u, v, etadot, t, q, qc, sp, sshf, ewss, nsss, ssr, lsp, cp, sd, msl, tcc, 10u, 10v, 2t, 2d, z, lsm, sdor, cvl, cvh, sr; where the first 6 variables are available on 60 model layers. <br> <br> <strong>Simulations</strong><br> Simulations with FLEXPART v10.4 were performed in forward mode and subdivided into annual chunks; i.e. simulations for each year were started on December 1st 00 UTC of the previous year and run through to January 5th 18 UTC of the following year in order to run all years in parallel. For example, simulations for the year 2000 were started on December 1st 1999 at 00 UTC and run through to January 5th 2001 18 UTC. The overlap in December guarantees a coherent evaluation of each year with trajectories expanding up to 30 days into the past. <br> <br> Table 1 shows the main settings of the FLEXPART simulations as set in options/COMMAND and options/RELEASES.</p> <table align="left"> <caption><strong>Table 1: Main simulation settings</strong> (set in options/COMMAND; flags not listed here are set to 'off' [0] and in options/RELEASES; note that dates have been omitted for comprehension).</caption> <thead> <tr> <th scope="col">options/COMMAND</th> </tr> </thead> <tbody> <tr> <th scope="row">LDIRECT</th> <td>1</td> <td> <p>Simulation direction in time; 1 (forward)</p> </td> </tr> <tr> <th scope="row"> <p>LOUTSTEP</p> </th> <td>10800</td> <td> <p> Interval of model output (s)</p> </td> </tr> <tr> <th scope="row"> <p>LOUTSAMPLE</p> </th> <td>900</td> <td> <p>Interval of output sampling (s), higher stat. accuracy with shorter intervals</p> </td> </tr> <tr> <th scope="row"> <p>LSYNCTIME</p> </th> <td>900</td> <td> <p> All processes are synchronized to this time interval (s)</p> </td> </tr> <tr> <th scope="row">CTL</th> <td>2</td> <td> <p>CTL>1, ABL time step = (Lagrangian timescale (TL))/CTL, uses LSYNCTIME if CTL<0</p> </td> </tr> <tr> <th scope="row"> <p>IFINE</p> </th> <td>4</td> <td> <p>Reduction for time step in vertical transport, used only if CTL>1</p> </td> </tr> <tr> <th scope="row">IOUT</th> <td>1</td> <td> <p>Output type: [1]mass 2]pptv 3]1&2 4]plume 5]1&4, +8 for NetCDF output</p> </td> </tr> <tr> <th scope="row"> <p>IPOUT</p> </th> <td>1</td> <td> <p>Particle position output: 0] no 1] every output 2] only at end 3] time averaged</p> </td> </tr> <tr> <th scope="row"> <p>LSUBGRID</p> </th> <td>1</td> <td> <p>Increase of ABL heights due to sub-grid scale orographic variations; 1] on</p> </td> </tr> <tr> <th scope="row"> <p>LCONVECTION</p> </th> <td>1</td> <td> <p>Switch for convection parameterization; 1] on</p> </td> </tr> <tr> <th scope="row"> <p>MDOMAINFILL</p> </th> <td>1</td> <td> <p>Switch for domain-filling, if limited-area particles generated at boundary</p> </td> </tr> <tr> <th scope="row">options/RELEASES</th> </tr> <tr> <th scope="row"> <p>NSPEC</p> </th> <td>1</td> <td> <p>Total number of species</p> </td> </tr> <tr> <th scope="row"> <p>SPECNUM_REL</p> </th> <td>1</td> <td> <p>Species numbers in directory SPECIES</p> </td> </tr> <tr> <th scope="row"> <p>LON1</p> </th> <td>-179.00</td> <td> <p>Left longitude of release box -180 < LON1 <180</p> </td> </tr> <tr> <th scope="row"> <p>LON2</p> </th> <td>180.00</td> <td> <p>Right longitude of release box, same as LON1</p> </td> </tr> <tr> <th scope="row">LAT1</th> <td>-90.00</td> <td> <p>Lower latitude of release box, -90 < LAT1 < 90</p> </td> </tr> <tr> <th scope="row">LAT2</th> <td>90.00</td> <td> <p>Upper latitude of release box same format as LAT1</p> </td> </tr> <tr> <th scope="row">Z1</th> <td>0.00</td> <td> <p>Lower height of release box meters/hPa above reference level</p> </td> </tr> <tr> <th scope="row">Z2</th> <td>10000.00</td> <td> <p>Upper height of release box meters/hPa above reference level</p> </td> </tr> <tr> <th scope="row">ZKIND</th> <td>1</td> <td> <p>Reference level 1=above ground, 2=above sea level, 3 for pressure in hPa</p> </td> </tr> <tr> <th scope="row"> <p>PARTS</p> </th> <td> <p>3000000</p> </td> <td> <p>Total number of particles to be released</p> </td> </tr> <tr> <th scope="row">COMMENT</th> <td> <p>"globalRELEASE"</p> </td> <td> <p>Comment, written in the outputfile </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Stored outputs</strong><br> Parcel positions and characteristics were stored at each 3-hourly time step in binary files names as <em>partposit_YYYYMMDDHH0000. </em>These files contain the following variables for each of the 3 million parcels:</p> <ul> <li>parcel id (a unique parcel identity number)</li> <li>longitude</li> <li>latitude</li> <li>height</li> <li>time step</li> <li>orography</li> <li>potential vorticity</li> <li>specific humidity</li> <li>density</li> <li>height of the surrounding boundary layer</li> <li>height of the surrounding troposphere</li> <li>temperature</li> <li>mass.</li> </ul> <p>One <em>partposit_YYYYMMDDHH0000 </em>file is 172 MB. All files for a month are compressed in monthly archives (as <em>fp-eraint-global_v104_rYYYY_partposit_YYYYMM.tar.gz</em>)<em> </em>and sum up to 393 GB for each annual simulation. The output of the entire simulation spanning 1979–2019 sums up to 17 TB. <br> <br> <strong>Metadata </strong><br> The metadata of each annual simulation is stored in a compressed<em><strong> </strong>fp-eraint-global_v104_rYYYY_meta.tar.gz<strong> </strong></em>archive. Metadata archives contain all namelists (all files from the <em>options</em> folder), invariant simulation outputs (such as <em>header</em>), and job outputs. For easy reference, the atmospheric mass at the time of initialisation is stored in a separate text file <em>atm_mass.txt</em>). The average atmospheric mass is 5.09205935e18 kg; each parcel thus represents approximately 1.697353e12 kg of air. <br> <br> <strong>Post-processing</strong><br> The simulations were setup and their outputs were stored in a way so that post-processing with the Heat- And MoiSture Tracking framEwoRk (HAMSTER, Keune et al., 2022) is possible without further changes. HAMSTER constructs multi-day backward trajectories for a region of interest and facilitates the identification of, e.g., precipitation origins. <br> <br> <strong>Contact and data access</strong><br> Only metadata is uploaded to this repository due to space constraints; access to the full data set can be obtained by contacting the corresponding author Jessica Keune via jessica.keune[at]ugent.be. <br> <br> <strong>References</strong><br> Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, et al. (2011). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>137</em>(656), 553-597.<br> Keune, J., Schumacher, D. L., & Miralles, D. G. (2022). A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. <em>Geoscientific Model Development</em>, <em>15</em>(5), 1875-1898.<br> Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., et al. (2019). The Lagrangian particle dispersion model FLEXPART version 10.4. <em>Geoscientific Model Development</em>, <em>12</em>(12), 4955-4997.<br> Tipka, A., Haimberger, L., & Seibert, P. (2020). Flex_extract v7. 1.2–a software package to retrieve and prepare ECMWF data for use in FLEXPART. <em>Geoscientific Model Development</em>, <em>13</em>(11), 5277-5310.</p>
Global sea surface dimethyl sulfide dataset simulated by artificial neural network
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 2005 to 2014 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes; (3) the simulated global monthly sea surface concentrations of DMS ranging from 2005 to 2100 by ANN model and CMIP6 ensemble and the calculated Kt and sea-to-air fluxes; (4) the yearly mean DMS concentration of each grid in different sensitivity experiments exploring the roles different variables play in driving DMS future changes. The input variables of this ANN model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The future projections (2015-2100) are subjected into two Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L<sup>–1</sup>, m s<sup>–1</sup>, and μmol S m<sup>–2</sup> d <sup>–1</sup>, respectively.</p> <p>Compared with the previous version (v1.0), this version is based on an updated ANN model after adjusting the data match-up between satellite and in-situ chlorophyll <em>a</em> for ANN training. In addition, the historical simulation based on CMIP6 only covers the time period from 2005 to 2014, which was from 1850 to 2014 for v1.0.</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>
Global soil moisture simulated by SoilClim and mHM models at 0.5° resolution for the 1980–2022 period
<p>This deposit contains two .zip archives (SoilClim_AWR_2m_1980_2022.zip and mHM_SM_2m_1980_2022.zip), each containing 1570 GeoTIFF files. </p> <p>The file SoilClim_AWR_2m_1980_2022.zip contains 10-day simulations of relative available water (AWR), where 100% represents the full field capacity and 0% represents the wilting point, produced the SoilClim water balance model for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p> <p>The file mHM_SM_2m_1980_2022.zip contains 10-day simulations of soil moisture (SM), produced the mesoscale Hydrologic Model (mHM) for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.