Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

617

datasets available to search

ShareScore release 0.7.1

Dataset results

617 results for “Climate models”

Learn how ShareScore rates datasets ↗

edi60/100

Modeling Impacts of Climate Change on Mangroves Worldwide 2012-2080

Given the multitude of ecosystem services provided by mangroves, it is important to understand their potential responses to global climate change. Extensive reviews of the literature and manipulative experiments suggest that mangroves will be impacted by climate change, but few studies have tested these predictions over large scales using statistical models. We provide the first example of applying species and community distribution models (SDMs and CDMs, respectively) to coastal mangroves worldwide. Species projected to shift their ranges polewards by at least 2 degrees of latitude consistently experience a decrease in the amount of suitable coastal area available to them. Central America and the Caribbean are forecast to lose more mangrove species than other parts of the world. We found that the extent and grain size, at which continuous CDM outputs are examined, independent of the grain size at which the models operate, can dramatically influence the number of pseudo-absences needed for optimal parameterization. The SDMs and CDMs presented here provide a first approximation of how mangroves will respond to climate change given simple correlative relationships between occurrence records and environmental data. Additional, precise georeferenced data on mangrove localities and concerted efforts to collect data on ecological processes across large-scale climatic gradients will enable future research to improve upon these correlative models.

openCC0Dec 2023View details →

zenodo56/100

Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data

Abstract: This data set contains model output data from the regional ocean modelling system (ROMS) that was set up for the Southern Ocean (south of 24 °S; Haumann, 2016) to analyze the effects of changing surface freshwater and momentum fluxes on Southern Ocean water-mass changes over the period 1980 to 2011. All data is provided in NetCDF format. The data set contains 3 sets 40-year long model spin-up, control, and 3 perturbation simulations each. The sets differ by their model mean state to assess the effect of surface salinity biases on the results. One set of simulations are reference simulations where salinity is restored to the observed surface salinity during model spin-up, and the other two sets are simulations in which the restoring surface salinity has been altered by plus and minus 0.1 PSU, respectively. The control and perturbation simulations are 40-year extensions of the respective model spin-up simulations. In the perturbation simulations either the surface freshwater fluxes or momentum fluxes are instantaneously perturbed after the spin-up simulation to reflect the observation-derived changes in these surface fluxes and then held constant (at the perturbed level) for 40 years. They consist of sea-ice freshwater flux, glacial meltwater flux, and atmosphere-ocean momentum flux changes. Details on the model setup, forcing, and simulations can be obtained from the related research article by Haumann et al. (2020; https://doi.org/10.1029/2019AV000132). Contacts: F. Alexander Haumann, ORCID: 0000-0002-8218-977X. Email: alexander.haumann@gmail.com License: When using this data users must cite the original research article by Haumann et al. (2020) published in AGU Advances (https://doi.org/10.1029/2019AV000132). This model output data set is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/ Citations: Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice Induced Southern Ocean Subsurface Warming and Surface Cooling in a Warming Climate. AGU Advances, 1, e2019AV000132. https://doi.org/10.1029/2019AV000132 Haumann, F. A., Gruber, N., Münnich, M. (2020): Sea-ice induced Southern Ocean subsurface warming and surface cooling in a warming climate: ROMS model data. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3709154 Data processing: The model data provided here has been processed for analysis and, due to storage constraints, only variables and averages used in the related research article (Haumann et al., 2020) are published here. Years refer to model years since initiation, i.e. the start of the spin-up simulation, and reach a maximum of 80 at the end of the control and perturbation experiments. Averages are either annual means over an indicated period ('yearly'), overall means over an indicated period ('mean'), or monthly climatologies over an indicated period ('clim'). 'Yearly' and 'Clim' values are provided either for surface fields or vertically integrated fields ('surf'). 'Mean' values are provided on the native vertical model grid as 3D fields. 'Yearly' zonal mean ('zonalmean') values have been first interpolated to from the native vertical model grid to a regular vertical z-level grid and then zonally averaged. All data has been stored in NetCDF format and compressed using level 1 deflation (https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression). Full monthly mean model output from these simulations and the forcing fields can be obtained from the corresponding author upon request (alexander.haumann@gmail.com). Standards: Data files have been formatted as Network Common Data Form (NetCDF; https://www.unidata.ucar.edu/software/netcdf/) in Hierarchical Data Format, version 5 (https://portal.hdfgroup.org/display/knowledge/HDF5+Documentation) Contents: Data - ROMS_SO_d025_grd.nc, ROMS grid file (Note: one row of ghost points on either side of the grid), compressed netcdf-file - ROMS_SO_d025_ref_spinup, ROMS output from reference spin-up simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_ref_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_ref_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_ref_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file - ROMS_SO_d025_minus01_spinup, ROMS output from minus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_minus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_minus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_minus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file - ROMS_SO_d025_plus01_spinup, ROMS output from plus 0.1 PSU sensitivity spin-up simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_spinup_clim.31-40.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_plus01_spinup_mean.31-40.nc, 3D averaged fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_plus01_spinup_yearly.31-40.surf.nc, 2D surface or vertically integrated annual mean fields for years 31 to 40, netcdf-file   -- ROMS_SO_d025_plus01_spinup_yearly.zlev.zonalmean.31-40.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 31 to 40, netcdf-file - ROMS_SO_d025_ref_ctrl, ROMS output from reference control simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_ctrl, ROMS output from minus 0.1 PSU sensitivity control simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_ctrl, ROMS output from plus 0.1 PSU sensitivity control simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_ctrl_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_ctrl_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_ref_seaice, ROMS output from reference sea-ice freshwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_seaice, ROMS output from minus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_seaice, ROMS output from plus 0.1 PSU sensitivity sea-ice freshwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_seaice_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_seaice_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_seaice_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_seaice_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_seaice_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_seaice_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_ref_glacial, ROMS output from reference glacial meltwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_glacial, ROMS output from minus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_glacial, ROMS output from plus 0.1 PSU sensitivity glacial meltwater flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_glacial_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_glacial_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_glacial_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_glacial_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_glacial_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_glacial_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_ref_momentum, ROMS output from reference atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_ref_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_ref_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_ref_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_ref_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_minus01_momentum, ROMS output from minus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_minus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_minus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_minus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_minus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file - ROMS_SO_d025_plus01_momentum, ROMS output from plus 0.1 PSU sensitivity atmopshere-ocean momentum flux perturbation simulation, 4 netcdf-files:   -- ROMS_SO_d025_plus01_momentum_clim.46-55.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_momentum_clim.61-80.surf.nc, 2D surface or vertically integrated climatological monthly mean fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_momentum_mean.46-55.nc, 3D averaged fields for years 46 to 55, netcdf-file   -- ROMS_SO_d025_plus01_momentum_mean.61-80.nc, 3D averaged fields for years 61 to 80, netcdf-file   -- ROMS_SO_d025_plus01_momentum_yearly.41-80.surf.nc, 2D surface or vertically integrated annual mean fields for years 41 to 80, netcdf-file   -- ROMS_SO_d025_plus01_momentum_yearly.zlev.zonalmean.41-80.nc, vertically interpolated (z-level) and zonally averaged annual mean fields for years 41 to 80, netcdf-file   File headers - ROMS_SO_d025_grd:     angle [radians]: angle between xi axis and east     f [second-1]: Coriolis parameter at RHO-points     h [meter]: Final bathymetry at RHO-points     hraw [meter]: Working bathymetry at RHO-points     lat_rho [degree_north]: latitude of RHO-points     lat_psi [degree_north]: latitude of PSI-points     lat_u [degree_north]: latitude of U-points     lat_v [degree_north]: latitude of V-points     lon_rho [degree_east]: longitude of RHO-points     lon_psi [degree_east]: longitude of PSI-points     lon_u [degree_east]: longitude of U-points     lon_v [degree_east]: longitude of V-points     mask_rho [-]: mask on RHO-points     mask_u [-]: mask on U-points     mask_v [-]: mask on V-points     pm [meter-1]: curvilinear coordinate metric in XI     pn [meter-1]: curvilinear coordinate metric in ETA     spherical [char]: Grid type logical switch - ROMS_SO_d025_*_clim.*.surf.nc, ROMS_SO_d025_*_yearly.*.surf.nc:     time [days since 0000-01-01 00:00:00]: time     lon [degree_east]: longitude     lat [degree_north]: latitude     zeta [meter]: averaged free-surface elevation     temp [degree_celsius]: averaged potential temperature     salt [psu]: averaged salinity     rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3     mld [meter]: averaged mixed layer depth     n2 [seconds-2]: averaged buoyancy frequency (upper 100 m)     n2temp [seconds-2]: averaged buoyancy frequency due to temperature (upper 100 m)     n2salt [seconds-2]: averaged buoyancy frequency due to salinity (upper 100 m)     heat_100 [10^18 J]: averaged ocean heat content (upper 100 m)     heat_100_2000 [10^18 J]: averaged ocean heat content (between 100 m and 2000 m)" ; - ROMS_SO_d025_*_mean.*.nc:     time [days since 0000-01-01 00:00:00]: time     lon [degree_east]: longitude     lat [degree_north]: latitude     zeta [meter]: averaged free-surface elevation     temp [degree_celsius]: averaged potential temperature     salt [psu]: averaged salinity     rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3 - ROMS_SO_d025_*_yearly.zlev.zonalmean.*.nc:     time [days since 0000-01-01 00:00:00]: time     lat [degree_north]: latitude of RHO-points     lat_v [degree_north]: latitude of V-points     depth [meter]: depth of levels     temp [degree_celsius]: averaged potential temperature     salt [psu]: averaged salinity     rho [kilogram meter-3]: averaged density anomaly from model reference density of 1027 kilogram meter-3     moc [10^6 meter^3 second^-1]: averaged meridional overturning circulation on V-points Missing values: missing values are filled as '-9.99e+20'. Metadata - README.txt, metadata, text format References: Haumann, F. A. (2016): Southern Ocean response to recent changes in surface freshwater fluxes. Doctoral Thesis. ETH Zurich. doi:10.3929/ethz-b-000166276. Acknowledgments: This work was supported by ETH Research Grant CH2-01 11-1 and by the SNSF grant numbers P2EZP2_175162 and P400P2_186681, as well as NSF’s SOCCOM Project under NSF Award No. PLR-1425989.

opencc-by-4.0Dec 2019View details →

zenodo52/100

Northwest Europe NEMO-ERSEM ocean model hindcast and climate projection under RCP8.5

Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in  Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400. Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario. The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878. The dataset consists of   <ul> <li>Hindcast simulation data</li> </ul> <ol> <li>AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields.</li> <li>AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields.</li> <li>AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES.</li> </ol> <ul> <li>Climate projection data</li> </ul> <ol> <li>AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields.</li> <li>AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields.</li> <li>AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents.</li> <li>AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents.</li> <li>AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES.</li> <li>AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.</li> </ol>

opencc-by-4.0Jul 2020View details →

zenodo48/100

Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years

The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.

opencc-by-4.0Sep 2013View details →

zenodo48/100

Antarctic time series of temperature, precipitation, and stable isotopes in precipitation from the ECHAM5/MPI-OM-wiso past1000 climate model simulation

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). 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 Neff = 442. 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. 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).

opencc-by-4.0Aug 2020View details →

zenodo48/100

Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050

******************* Please view the README.txt or README.md file for detailed documentation of data. ******************** Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050 Date of release: 25/11/2020 Identifier: 10.5281/zenodo.4275759 Permalink: http://dx.doi.org/10.5281/zenodo.4275759 Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, Under Review, 2020. Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files. Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi   Dates of data: 2030, 2050 Type of data: Outdoor Temperature Geographic location: Jyväskylä Time resolution: hourly, full year Format: All data is stored in .csv files Number of files: 1 .zip --> 50 files + README.txt + README.md This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files   Notifications: Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data). Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate). Contains modified Ruosteenoja et al. [2016] information. Other data and information sources are described in README.txt, README.md, references and on the associated publication.

opencc-by-4.0Nov 2020View details →

zenodo48/100

A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19

Overview This dataset is the repository for the following paper submitted to Data in Brief: Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023). The Data in Brief article contains the supplement information and is the related data paper to: Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023). Description/abstract The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.  Folder structure The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:  “code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data. “MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023. “mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”). “yield_productivity” contains .csv files of yield information for all countries listed above. “population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods). “GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data. “built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.  Code structure 1_MODIS_NDVI_hdf_file_extraction.R  This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent. 2_MERGE_MODIS_tiles.R In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.).  Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged"). 3_CROP_MODIS_merged_tiles.R Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.  The repository provides the already clipped and merged NDVI datasets. 4_TREND_analysis_NDVI.R Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November).  December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3. To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.  5_BUILT_UP_change_raster.R Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.  6_POPULATION_numbers_plot.R For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.  7_YIELD_plot.R In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.  8_GLDAS_read_extract_trend The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).  Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area. From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year). From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.  (9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis. ___ Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf ___ Acknowledgements I would like to thank three anonymous reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia at the University of Basel under grant number TMPFP2_217358.   __ All data underlying the results of this article are publicly available on the internet: GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023);  Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023); FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023); Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023); Population development:  FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023);  the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023);  Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023); Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023); MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023). Competing interests statement: The author declares no conflict of interest. The author has no relevant financial or non-financial interests to disclose. Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text. Ethical approval: This article does not contain any studies with human participants performed by any of the authors. Informed consent: This article does not contain any studies with human participants performed by any of the authors.

opencc-by-4.0Dec 2023View details →

zenodo48/100

Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"

The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description.    <h2> </h2>

opencc-by-4.0Sep 2024View details →

zenodo48/100

Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets

Sydney morphology and land surface dataset This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models. The dataset is provided in netCDF4 and GeoTiff formats. Associated manuscript: <blockquote> <a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a> </blockquote> Citation for the open dataset:  - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022. Citation for the associated manuscript: - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022. Location of associated processing code:  - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a> Acknowledgments We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for  providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets,  © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/.   This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science  (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023).

opencc-by-4.0May 2022View details →

zenodo48/100

Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)

The dataset contains monthly output of a COSMO-CLM² (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described and evaluated in the following paper:  Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM² over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862) Details of the model simulation: - COSMO-CLM version 5.0_clm6 - Community Land Model version 4.5 - Horizontal resolution: 0.25°x0.25° - Vertical resolution: 40 levels - Time period: 1987-2016 (excluding 4 years of spin-up) - Driving model: ERA-Interim   The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps. - AEVAP_S: Surface evaporation [kg m-2] (summed value for each month) - ALB: Surface albedo [-] (only for austral summer months) - ALHFL_S: Surface latent heat flux [W m-2] - ALWD_S: Downward longwave radiation at the surface [W m-2] - ALWU_S: Upward longwave radiation at the surface [W m-2] - ASHFL_S: Surface sensible heat flux [W m-2] - ASOB_S: Surface net downward shortwave radiation [W m-2] - ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2] - ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2] - ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2] - ATHB_S: Surface net downward longwave radiation at the surface [W m-2] - P: Pressure at 40 vertical levels [Pa] - QV: Specific humidity at 40 vertical levels [kg kg-1] - RH2M: Relative humidity at 2 meter [%] - SNOW_GSP: Surface snowfall amount [kg m-2] (summed value for each month) - T2M: Temperature at 2 meter [K] - T: Temperature at 40 vertical levels [K] - WS10M: Wind speed at 10 meter [m s-1] - WS: Wind speed at 40 vertical levels [m s-1]

opencc-by-4.0Jan 2019View details →

zenodo48/100

Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought

This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive

opencc-by-4.0Oct 2024View details →

zenodo48/100

Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.

We reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here.

opencc-by-4.0Oct 2024View details →

zenodo48/100

Monthly climate variables of isotope-enabled climate model simulations over the last millennium (850-1849CE) version 2

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). 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. For version 2, we updated the damaged ECHAM5 precipitation file and the time axis to the iCESM precipitation and tsurf files.

opencc-by-4.0Jan 2023View details →

zenodo48/100

Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"

This repository contains the data to produce figures for the paper: "Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018." Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)

opencc-by-4.0Jul 2023View details →

zenodo48/100

Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"

This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>). These model outputs are presented and discussed in the article "The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>. All files uploaded were generated from simulations run by the authors. For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me. Note: This version (v1.1) is the same version as v1.0 but with the correct README file.

opencc-by-4.0Sep 2023View details →

zenodo48/100

Coupled climate-glacier modelling of the last glaciation in the Alps: modelling data

This dataset contains key distributed 2D variables resulting from the modelling of the Alpine Ice Field over the last glacial cycle from Jouvet and al. (2023, 10.1017/jog.2023.74), including basal surface topography, ice thickness, pressure-adjusted basal temperature, basal and surface ice flow speeds. The results are given on a raster grid in UTM system of coordinate with a spatial resolution of 2 km and a temporal resolution of 100 year. The data are compiled in netCDF. As explained in the paper, the model was designed to match LGM evidence. Modelled results related to intermediate states and to the Holocene must be interpreted with caution considering the relative coarse resolution (2 km). Small ice caps aside the main Alpine Icefield (except the Jura) were excluded.

opencc-by-4.0Aug 2023View details →

zenodo44/100

JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.

The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that (1) gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute (2) theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes We deliver (1) data collection and (2) numerical codes in the following files attached: (1) Data collection: <ul> <li>A PDF file named JUMP-zonal-jets-data-collection-GRL.pdf that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li>JUMP-JetsInTheLab.zip</li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li>uvData-SRS-istep-312000-nstep-50-niz-12.nc</li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter <ul> <li>uvData-JupObs-istep-0-nstep-4-niz-1.nc</li> <li>StatisticalData-JupObs.nc</li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li>IPV-QGPV-Jupiter-Saturn.zip</li> </ul> </li> </ul> (2) Numerical codes: <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul>   The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver (1) velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and (2) guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.   Acknowledgments This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.

opencc-by-4.0Feb 2020View details →

zenodo44/100

AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations

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. The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019). References Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481 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

opencc-by-4.0Sep 2020View details →

zenodo44/100

Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"

This dataset results from an experiment on young oil palm plants (Elaeis guineensis) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), i.e., a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%). Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.

opencc-by-4.0Jul 2024View details →

zenodo44/100

Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS

This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization. This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834

opencc-by-4.0Aug 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record