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82 results for “Climate model simulation”
Antarctic time series of temperature, precipitation, and stable isotopes in precipitation from the ECHAM5/MPI-OM-wiso past1000 climate model simulation
<p>This data set contains time series of two-metre air temperature (tas), surface temperature (ts), total precipitation (pr), oxygen-18 isotopic composition in precipitation (oxy), and deuterium isotopic composition in precipitation (dtr) from the past-millennium (800-1999 CE) simulation of the fully coupled ECHAM5/MPI-OM-wiso atmosphere-ocean general circulation model equipped with stable isotope diagnostics (Sjolte et al., 2018, Werner et al., 2016) used in the publication of Münch et al. (2021).</p> <p>The data here are provided for the Antarctic region, i.e., all model grid cells south of 60° S. The model's atmospheric component was run with a T31 spectral resolution (3.75° x 3.75°) and with 19 vertical levels, resulting in a total of N = 768 model grid cells covered by this data set. Note, however, that all time series off the continent of Antarctica have been set to NA values, so that the effectively available number of model grid cells is N<sub>eff</sub> = 442.</p> <p>Time series are provided at the original monthly resolution of the model output and on annual resolution obtained from the monthly resolution data. At annual resolution, the temperature and isotopic composition data are available as normal time averages and as precipitation-weighted time averages. In addition to the time series, the spatial field of time-invariant means is supplied, also as normal and precipitation-weighted time averages.</p> <p>Data are available as netcdf files and as R data files. In addition, processing code (bash and R scripts) are provided to reproduce the processing from monthly to annnual and time-invariant resolution and to read the data into the R data format. To process the R data, you will need the CRAN packages "ncdf4" and "lubridate", and the package "pfields" available on GitHub (see References).</p>
Monthly climate variables of isotope-enabled climate model simulations over the last millennium (850-1849CE) version 2
<p>Here we provide climate fields in monthly resolution for five isotope-enabled model: ECHAM5-wiso (Sjolte et al. 2018, Werner et al. 2016), GISS-E2-R (Lewis and Legrande 2015, Colose et al 2016), iCESM (Brady et al. 2019, Stevenson et al 2019), iHadCM3 (Bühler et al. 2021, Tindall et al. 2009), and isoGSM (Yoshimura et al. 2008) in supplement to Buehler et al. (2021, submitted to Clim. Past. Discuss.). The model simulations were performed with different sets of boundary conditions as described in Bühler et al. (2021, submitted to Clim. Past. Discuss.) in line with the PMIP3 protocoll (Schmidt et al. 2012). We provide output for surface temperature (in K), total precipitation amount (in mm month^-1), evaporation (in mm month^-1), latent heat (in W m^-2), and oxygen isotope ratios of precipitation (in permil).<br> Additionally, we provide simulation output extracted at cave locations within the SISAL v.2. database (https://researchdata.reading.ac.uk/256/, Comas-Bru et al. (2020)). We include output for sites that pass the resolution and dating screening, meaning that have at least 2 radiometric dates (or are lamina-counted) and provide 36 oxygen isotope ratio measurements within the last millennium.</p> <p>For version 2, we updated the damaged ECHAM5 precipitation file and the time axis to the iCESM precipitation and tsurf files.</p>
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
Intital simulation of Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model
<p>This dataset is from a series of “forward projection” interactive stratospheric aerosol simulations of the Jan 2022 Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model. The model experiments predict how the cloud will disperse through 2022, and apply the UM-UKCA model at GA4 (Walters et al., 2014), with GLOMAP v8.2, as applied for the “MajorVolc” datasets for Agung, El Chichon and Pinatubo (Dhomse et al., 2020), those runs aligned with the Historical Eruption SO2 emissions Assessment experiment within ISA-MIP (Timmreck et al., 2018).</p> <p>The “standard” Hunga-Tonga GA4 UM-UKCA experiment emits 0.4Tg of SO2 at 29-31km, within a 24-hour period, matching the detrainment duration specified for the ISA-MIP HErSEA experiment protocol. Following the stronger than expected mid-visible backscatter ratios (BSR) measured by CALIOP satellite-borne lidar, and from ground-based lidar from Reunion Island (very high BSR values > 200), we also ran UM-UKCA simulations with “scaled-up Hunga-Tonga SO2 emission”, at 0.8, 1.2 and 1.6 Tg of SO2 emitted.</p> <p>Unexpectedly strong stratospheric AOD observed from the OMPS satellite months after the eruption further strengthens the motivation for these simulations.</p> <p>Several hypotheses for the high AOD from Hunga-Tonga have been suggested:<br> 1) an unusual amount of (or influence from) co-emitted ultra-fine ash particles<br> 2) “in-plume oxidised sulphate” already converted from SO2 at the time of detrainment<br> (e.g. via aqueous-phase oxidation within water droplets within the eruptive plume).<br> 3) co-emitted marine aerosol (e.g. sea-salt aerosol) from seawater vaporized in the plume<br> </p> <p>There are 4 types of netcdf files, Stratospheric AOD (saod), Effective Radius (reff), Extinction (ext) and sulphate aerosol surface area density (sad).</p> <p><br> <br> For e.g. <br> saod550_HT_0pt4Tg_T2Mz-20220101-20230831.nc contains<br> Stratospheric aerosol optical depth (sAOD) at 550nm (2D-monthly dataset vs latitude and time) with 0.4 Tg SO2 injection Jan2022 to August 2023<br> Whereas other files<br> reff_HT_0pt4Tg_T2Mz_20220101-20230831.nc,<br> sad_HT_0pt4Tg_T2Mz_20220101-20230831.nc<br> ext550_HT_0pt4Tg_T2Mz-20220101-20230831.nc</p> <p>contain particle effective radius (reff), aerosol surface area density, aerosol extinction as 3D-monthly fields (altitude, latitude , time) from the same simulation.<br> Other saod and extinction files are also available at 870 and 1020 nm.</p> <p> </p> <p>Note that these are preliminary simulations, hence we do not expect good match with the observations. We plan to perform additional UM-UKCA simulations, comparing to the satellite and ground-based lidar measurements, and to in-situ balloon observations from Reunion Island rapid response campaign & upcoming high-altitude balloon sampling flights in Brazil.</p> <p> </p> <p>References :<br> Dhomse SS, Mann GW, Antuña Marrero JC, Shallcross SE, Chipperfield MP, Carslaw KS, Marshall L, Abraham NL, Johnson CE. 2020. Evaluating the simulated radiative forcings, aerosol properties, and stratospheric warmings from the 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo volcanic aerosol clouds. Atmospheric Chemistry and Physics. 20(21), pp. 13627-13654</p> <p><br> Timmreck, C., Mann, G. W., Aquila, V., Hommel, R., Lee, L. A., Schmidt, A., Brühl, C., Carn, S., Chin, M., Dhomse, S. S., Diehl, T., English, J. M., Mills, M. J., Neely, R., Sheng, J., Toohey, M., and Weisenstein, D.: The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design, Geosci. Model Dev., 11, 25812608, https://doi.org/10.5194/gmd-11-2581-2018, 2018.</p> <p> </p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</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>
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 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-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>
Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period
<p>The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points.</p> <p>Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033.</p> <p><strong>Project: </strong> Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM)</p> <p><strong>Funding: </strong>Federal Ministry of Education and Research (BMBF), grant 03F0887A</p>
Challenges simulating the AMOC in climate models
<p>This is a data set for the article:</p> <p>Jackson, Laura C, Helene T. Hewitt, Diego Bruciaferri, Daley Calvert, Tim Graham, Catherine Guiavarc’h, Matthew B. Menary, Adrian L. New, Malcolm Roberts and David Storkey, 2023: Challenges simulating the AMOC in climate models, submitted to Philosophical Transactions of the Royal Society A.</p> <p>Fig 1 calculated correlations between AMOC strength or trend in 1% CO2 experiment (values given in CMIP6_AMOC.txt) and sea surface salinity fields in the controls (fields for each model given in CMIP6_SSS.nc)</p> <p>We also include data of the AMOC streamfunction (*amoc.nc), March mixed layer depth (MarchMLD.nc) and temperature and salinity fields (TS.nc) for the mean of years 20-30 of models HadGEM3-GC3-1ML, MM and MH. These models are documented here <a href="https://doi.org/10.5194%2fgmd-2019-148">doi:10.5194/gmd-2019-148</a>. This data was used for plotting figures 2-4. </p> <p>Data for Fig 5 is in HadGEM3-GC3-1MM-noGM-av-20-40_MarchMLD.nc and HadGEM3-GC3-1MM-withGM-av-20-40_MarchMLD.nc</p> <p>Data for Fig 6 is in HadGEM3-GC1_control_20390301_20780330_MLD.nc and HadGEM3-GC1_relax_20390301_20780330_MLD.nc</p> <p>Data for Fig 7 is included in <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F7764695&data=05%7C01%7Claura.jackson%40metoffice.gov.uk%7Cceeee8cf4c9d4401060e08db2bcc93f2%7C17f1816120d7474687fd50fe3e3b6619%7C0%7C0%7C638151929867577211%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nkL88P4lDCDB5E4qPxvL6%2B6pu6nU5kDtCofZg5%2FAoDs%3D&reserved=0">https://zenodo.org/record/7764695</a> </p> <p> </p>
Holocene climate change in southern Oman deciphered by speleothem records and climate model simulations
<p>8 ka BP (thousand year, before present, 8K) and pre-industrial period (PI) paleoclimate equilibrium experiment, including precipitation (pr), near-surface relative humidity (hurs), evaporation (evspsbl) and 850hPa wind (u850 & v850).</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</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
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.