cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5065/3wez-dd73
title: GeoMIP SSP5 run data

description: Stratospheric Aerosol Geoengineering experiments have been produced by CESM2 (WACCM6). Two experiments have been performed following the CMIP6 WACCM6 SSP5-34-OS experiment as a baseline scenario with stratospheric sulfur injections to limit global warming to 1.5C or 2.0C above 1850-1900 conditions, called Geo SSP5-34-OS 1.5 and Geo SSP5-34-OS 2.0, respectively. A third experiment has been performed that follows CMIP6 WACCM6 SSP5-85 as a baseline and to limit global warming to 1.5 degrees C, called Geo SSP5-85 1.5. Sulfur injections were applied at four predefined latitudes, 30N, 15N, 15S, and 30S, to reach three surface temperature targets: global mean temperature, and inter-hemispheric and pole-to-equator temperature gradients using a feedback algorithm. All experiments have two ensemble members. More details can be found in the reference paper [https://www.earth-syst-dynam-discuss.net/esd-2019-76/].
DOI: 10.5065/psgz-0z88
title: ICAR Downscaled Climate Data over the Western United States

description: This dataset contains quasi-dynamically downscaled climate data from 8 global climate models from the CMIP5 archive. The data were downscaled using the Intermediate Complexity Atmospheric Research (ICAR Gutmann et al., 2016) model after bias correcting the GCM three dimensional atmospheric data to match the ERA-interim reanalysis climatology (Dee et al., 2011). ICAR was configured with the Thompson microphysics, a simple PBL parameterization based on YSU, the RRTMG longwave radiation and an empirical shortwave radiation scheme, the Noah-MP land surface model, the WRF-Lake model, the BMJ cumulus parameterization, and used linear mountain wave theory for upper level wind structures combined with an iterative scheme to remove vertical motion at the model top. The output from ICAR was adjusted to match the climatological statistics of the Livneh et al. (2015) observational dataset.
DOI: 10.5065/9dby-w142
title: CESM1 CAM5 Decadal Prediction Large Ensemble

description: The CESM Decadal Prediction Large Ensemble (DPLE) is a set of simulations carried out at NCAR to support research into near-term Earth System prediction. The DPLE comprises 62 distinct ensembles, one for each of 62 initialization times (November 1 of 1954, 1955, ..., 2014, 2015). For each start date, a 40-member ensemble was generated by randomly perturbing the atmospheric initial condition at the round-off level. The simulations were integrated forward for 122 months after initialization. Observation-based ocean and sea ice initial conditions for the 1954-2015 period were obtained from a reanalysis-forced simulation of the CESM ocean and sea ice models. The initial conditions for the atmosphere and land models were obtained from CESM Large Ensemble (LENS) simulations at corresponding historical times. Full field initialization was used for all component models, and so drift adjustment prior to analysis is generally recommended.
DOI: 10.5065/x2xa-xv33
title: En-GARD Downscaled Climate Data over the Colorado River Basin

description: Daily precipitation and temperature data from 18 Global Climate Models (GCM) in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) that were downscaled using an analog regression approach in En-GARD (Gutmann et al. 2022) over the Colorado River Basin from 1950-2099. En-GARD is a statistical downscaling method designed to use information about upper level atmospheric processes (e.g. 500 mb winds) in addition to processes observed at the surface (e.g. precipitation and temperature). Each GCM was downscaled using training data from ERA-Interim reanalysis (Dee et al. 2011) and observations from the Livneh meteorological dataset (Livneh et al. 2015). Daily GCM precipitation and temperature were downscaled independently for each monthly basis (+/- 15 days for training) and on a grid-cell by grid cell basis. The GCM and ERA-Interim data were bilinearly interpolated to the Livneh 1/16 degree grid for input. Input data (Precipitation/Temperature, 500 mb zonal and meridional wind speeds) were quantile mapped to the corresponding ERA-Interim data and the closest 200 analog days, or days in which the input data matched the large-scale surface and upper atmospheric features, were selected independently for each day to be downscaled and used to train a multivariate linear regression to predict the Livneh data from those analog days. For precipitation, occurrence is modeled separately from magnitude by using a logistic regression with the same analog days to predict the probability of precipitation. To preserve realistic spatiotemporal variability, the residual term from the regression model is saved, and this residual is used to condition a stochastic sampling of the probability distribution for the prediction. Each output variable from En-GARD was quantile mapped to the Livneh meteorological data on a monthly basis to be used as input for a hydrological model that was calibrated using the Livneh meteorological data. More description of the En-GARD methodology can be found in Gutmann et al. (2022).
DOI: 10.5065/1exc-0a26
title: International Space Science Institute Observing System Simulation Experiment

description: This dataset is comprised of WRF output, created to study orographically generated gravity waves observed by satellite-borne radiometers in the stratosphere. The intent is for the OGWs in the output to be used to evaluate satellite-methods for computing vertical fluxes of horizontal momentum, to be compared with waves in ECMWF and UKMO UM models, and to be validated against stratospheric satellite observations. This dataset covers the Patagonia and Antarctic Peninsula regions. The overall objective is to provide constraints for OGW drag parameterizations.
DOI: 10.5065/dh0r-sh30
title: CESM Pacific Pacemaker Data

description: CESM Pacific Pacemaker Data
DOI: 10.5065/dbzq-1k04
title: MESACLIP: Nominal 1-degree CESM (low-resolution) simulations corresponding to high-resolution experiments

description: Over the past six years, our research team consisting of scientists at Texas A&M University (TAMU) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) has made major breakthroughs in advancing high-resolution global climate modeling and prediction. We have completed several thousand years of climate simulations at a tropical cyclone (TC) permitting and ocean-eddy-rich resolution (hereafter simply referred to as CESM-HR) as part of our NSF-funded project entitled "Understanding the Role of MESoscale Atmosphere-Ocean Interactions in Seasonal-to-Decadal CLImate Prediction (MESACLIP)". Among others, we completed a 500-year preindustrial control (PI-CTRL) simulation forced by a perpetual climate forcing that corresponds to the year 1850 conditions and a 10-member ensemble of historical and future transient climate simulations.The CESM-HR configuration is based on an earlier CESM version, CESM1.3, with many additional modifications and improvements. CESM-HR uses a 0.25 degree grid in the atmosphere and land components and a 0.1 degree grid in the ocean and sea-ice components. The primary reason for using an older model version, instead of the latest CESM2, is that CESM2 does not support a high-resolution version per the decision by the CESM Scientific Steering Committee. The component models within CESM1.3 are the Community Atmosphere Model version 5 (CAM5; Neale et al., 2012), the Parallel Ocean Program version 2 (POP2; Danabasoglu et al., 2012; Smith et al., 2010), the Community Ice Code version 4 (CICE4; Hunke & Lipscomb, 2008), and the Community Land Model version 4 (CLM4; Lawrence et al., 2011).Here we release the nominal 1 degree low-resolution (LR) equivalent simulations based on the same CESM1.3 code base and using the same CAM5 Spectral Element (SE) dycore used in CESM-HR to permit an as-clean-as-possible comparison of the respective LR and HR simulations. CESM LR uses a nominal 1 degree grid in all its components. Citation: The two papers linked below are the most appropriate references for these simulations. To cite the dataset, use Chang et al. (2025). We ask that you also cite the dataset itself using the reference Castruccio et al. [https://rda.ucar.edu/datasets/d651030/citation/] (2024) in any documents or publications using these data. Chang et al. (2020) describes the initial CESM-HR simulations, including the 500-year pre-industrial control simulation and the first 250-year historical and future climate simulation from 1850 to 2100. It also introduces the corresponding CESM LR experiments. We would also appreciate receiving a copy of the relevant publications. This will help us to justify keeping the data freely available online in the future. Thank you!
DOI: 10.5065/rhvt-yp56
title: Atlantic Pacemaker Simulations

description: A 10-member ensemble of CESM1 (1 degree spatial resolution) simulations in which time-evolving SST anomalies in the North Atlantic (5N-55N, with a linearly tapering buffer zone that extends to EQ and 60N, the Atlantic basin) are nudged to observations (NOAA Extended Reconstruction Sea Surface Temperature version 3: ERSSTv3b) during 1920-2013. In this way, the observed evolution of North Atlantic SSTs are maintained in each simulation, with the rest of the model's coupled climate system free to evolve. Note that only the SST anomalies, not the total SST, are nudged to observations, maintaining the model's mean state, including any model biases. All external forcings are identical to the LENS, aside from stratospheric ozone.A second 10-member ensemble, identical to the first but with all forcings fixed at 1920 levels, is also available.Further details about these simulations can be found on the CESM CVCWG CESM1 North Atlantic Pacemaker Ensembles [https://www.cesm.ucar.edu/working-groups/climate/simulations/cesm1-atlantic-pacemaker] website.
DOI: 10.5065/d61z42t0
title: High-Resolution Climate Simulations Over Alaska - A Community Dataset, Version 2

description: Weather and climate variability strongly influence the people, infrastructure and economy of Alaska. However, the sparse observational network in Alaska limits understanding of meteorological variability, particularly precipitation processes that influence the hydrologic cycle. Here a new 4-km resolution dataset for Alaska is made available to the community for investigating recent historical climate variability, and potential changes in the future. The dataset, generated with the Weather Research and Forecasting model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The dataset may be particularly useful for applications that require high temporal frequency weather fields, such as driving hydrologic or glacier models. The historical fields, which span 14 years (September 2002-August 2016), are available to the community at hourly resolution for near surface (2-dimensional) fields and 6 hourly for the 3-dimensional atmosphere.
DOI: 10.5065/d6j101d1
title: CESM1 Large Ensemble Community Project

description: The CESM Large Ensemble Project is a publicly available set of climate model simulations intended for advancing understanding of internal climate variability and climate change. All simulations are performed with the nominal 1-degree latitude/longitude version of the Community Earth System Model version 1 (CESM1) with CAM5.2 as its atmospheric component. The Large Ensemble Project includes a 40-member ensemble of fully-coupled CESM1 simulations for the period 1920-2100. Each member is subject to the same radiative forcing scenario (historical up to 2005 and RCP8.5 thereafter), but begins from a slightly different initial atmospheric state (created by randomly perturbing temperatures at the level of round-off error). The Large Ensemble Project also includes a set of multi-century control simulations with the atmosphere, slab-ocean, and fully-coupled versions of CESM1 under pre-industrial (1850) radiative forcing conditions (2600 years, 900 years and 1800 years in length, respectively). Details of these model simulations may be found in Kay et al. (2015).In addition to the simulations above the CESM1 Single Forcing experiments are also available in this archive. The CESM1 Single Forcing Large Ensemble Project is a set of climate model simulations that are useful for addressing the individual roles of anthropogenic aerosols, greenhouse gases and land-use / land-cover in historical and future climate change. These simulations use the same model, forcing configuration and initialization protocol as the CESM1 Large Ensemble Project, but keep either industrial aerosols (AER), biomass burning aerosols (BMB), greenhouse gases (GHG) or land-use / land-cover (LULC) conditions fixed at 1920 while all other external anthropogenic and natural forcing factors evolve following historical and future (RCP8.5) scenarios. There are 3 sets of ensembles: XGHG (20 members, 1920-2080), XAER (20 members, 1920-2080), and XBMB (15 members, 1920-2029). All members are branched from the first member of the all forcing CESM1 Large Ensemble on January 1, 1920 by applying a small (order of 10-14 K) random noise perturbation to their initial atmospheric temperature fields. The impact of the withheld forcing factor can be deduced by subtracting the ensemble-mean of each X ensemble from the ensemble-mean of the original all forcing CESM1 Large Ensemble. Details of these three ensembles are provided in Deser et al. (2020). A 3 member ensemble (named AAER) that is complementary to the XAER has also been performed as discussed in Simpson et al. (2023). These simulations begin in 1850 and evolve under ONLY time varying industrial aerosols with the other forcings held fixed at 1850's values.
DOI: 10.15468/dl.e98tqw
title: Occurrence Download

description: A dataset containing 50242471 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poaceae"
 ]
}

 The dataset includes 50242471 records from 9072 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012571-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xcjp7b
title: Occurrence Download

description: A dataset containing 84238 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Megaloptera)",
 "BasisOfRecord is one of (Human Observation, Observation, Occurrence evidence, Material citation, Living Specimen)",
 "CoordinateUncertaintyInMeters is less than or equal to 10000",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 84238 records from 244 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007745-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ym6tu2
title: Occurrence Download

description: A dataset containing 111 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "or" : [
 "TaxonKey is Peperomia delicatula Hench.",
 "TaxonKey is Peperomia delicatula Henschen"
 ]
 }
 ]
}

 The dataset includes 111 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012041-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uzds0a
title: Occurrence Download

description: A dataset containing 841721 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Museums Victoria provider for OZCAM"
 ]
}

 The dataset includes 841721 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010924-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2du8zn
title: Occurrence Download

description: A dataset containing 287975 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Limosa limosa (Linnaeus, 1758))",
 "HasCoordinate is true",
 "DecimalLatitude is greater than or equal to 29.93667",
 "DecimalLatitude is less than or equal to 51.91193",
 "DecimalLongitude is greater than or equal to -14.67522",
 "DecimalLongitude is less than or equal to 39.79752",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)",
 "Year is greater than or equal to 1970",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 287975 records from 353 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041692-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wb25sn
title: Occurrence Download

description: A dataset containing 12207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((6.675 52.225,6.725 52.225,6.725 52.275,6.675 52.275,6.675 52.225))",
 "DatasetKey is Observation.org, Nature data from around the World",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 12207 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0307793-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3s72xj
title: Occurrence Download

description: A dataset containing 775 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Literature Occurrence"
 ]
 },
 "TaxonKey is Mauremys sinensis (Gray, 1834)"
 ]
}

 The dataset includes 775 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0216762-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wk3zax
title: Occurrence Download

description: A dataset containing 2261 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Fossil",
 "BasisOfRecord is Living Specimen"
 ]
 },
 {
 "or" : [
 "Country is Ecuador",
 "Country is Peru",
 "Country is Brazil",
 "Country is Chile",
 "Country is Colombia"
 ]
 },
 {
 "or" : [
 "PublishingOrg is 90fd6680-349f-11d8-aa2d-b8a03c50a862",
 "PublishingOrg is 7b8aff00-a9f8-11d8-944b-b8a03c50a862",
 "PublishingOrg is a3c228d0-3110-11db-abb8-b8a03c50a862",
 "PublishingOrg is 4c415e40-1e21-11de-9e40-a0d6ecebb8bf",
 "PublishingOrg is 98e934b0-5f31-11de-b67e-b8a03c50a862",
 "PublishingOrg is 396d5f30-dea9-11db-8ab4-b8a03c50a862",
 "PublishingOrg is b6f05cf0-43ce-11d9-8439-b8a03c50a862",
 "PublishingOrg is 8e1a97a0-3ca8-11d9-8439-b8a03c50a862",
 "PublishingOrg is 061b4f20-f241-11da-a328-b8a03c50a862",
 "PublishingOrg is 2cd829bb-b713-433d-99cf-64bef11e5b3e"
 ]
 },
 "TaxonKey is Tropaeolum L."
 ]
}

 The dataset includes 2261 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010921-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bzadkc
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mazama americana gualea J.A.Allen, 1915"
 ]
}

 The dataset includes 17 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0244635-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r95gzy
title: Occurrence Download

description: A dataset containing 116 species occurrences available in GBIF matching the query:
{
 "CatalogNumber" : [
 "is 217880"
 ]
}

 The dataset includes 116 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0314387-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbrdht
title: Occurrence Download

description: A dataset containing 96 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Michigan Museum of Paleontology - Vertebrates",
 "TaxonKey is Dimetrodon Cope, 1878"
 ]
}

 The dataset includes 96 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000402-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jmbtz4
title: Occurrence Download

description: A dataset containing 513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "Year 1978-2018",
 "HasCoordinate is true",
 "TaxonKey is Acer rubrum L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 513 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010921-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jaj5d8
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Spain",
 "Country is Portugal"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Arthothelium crozalsianum B.de Lesd., 1924"
 ]
}

 The dataset includes 68 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000340-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z5cdk6
title: Occurrence Download

description: A dataset containing 13000 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Mozambique",
 "TaxonKey is Fabaceae"
 ]
}

 The dataset includes 13000 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0307810-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4xu358
title: Occurrence Download

description: A dataset containing 8797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abaeis nicippe (Cramer, 1779)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8797 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0196635-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.